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11612 lines
549 KiB
Python
11612 lines
549 KiB
Python
"""
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tool to extract table form data from alto xml data
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"""
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import gc
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import math
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import os
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import random
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import sys
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import time
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import warnings
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from multiprocessing import Process, Queue, cpu_count
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from sys import getsizeof
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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stderr = sys.stderr
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sys.stderr = open(os.devnull, "w")
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from keras import backend as K
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from keras.models import load_model
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sys.stderr = stderr
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import tensorflow as tf
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tf.get_logger().setLevel("ERROR")
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warnings.filterwarnings("ignore")
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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from shapely import geometry
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from lxml import etree as ET
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from matplotlib import pyplot, transforms
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import matplotlib.patches as mpatches
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import imutils
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from .utils import filter_contours_area_of_image_tables
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class eynollah:
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def __init__(
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self,
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image_dir,
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f_name,
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dir_out,
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dir_models,
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dir_of_cropped_images=None,
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dir_of_layout=None,
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dir_of_deskewed=None,
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dir_of_all=None,
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allow_enhancement="false",
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curved_line="false",
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full_layout="false",
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allow_scaling="false",
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headers_off="false"
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):
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self.image_dir = image_dir # XXX This does not seem to be a directory as the name suggests, but a file
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self.dir_out = dir_out
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self.f_name = f_name
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self.dir_of_cropped_images = dir_of_cropped_images
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self.allow_enhancement = allow_enhancement
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self.curved_line = curved_line
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self.full_layout = full_layout
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self.allow_scaling = allow_scaling
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self.dir_of_layout = dir_of_layout
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self.headers_off = headers_off
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self.dir_of_deskewed = dir_of_deskewed
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self.dir_of_all = dir_of_all
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if self.f_name is None:
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try:
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self.f_name = image_dir.split("/")[len(image_dir.split("/")) - 1]
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self.f_name = self.f_name.split(".")[0]
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except:
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self.f_name = self.f_name.split(".")[0]
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self.dir_models = dir_models
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self.kernel = np.ones((5, 5), np.uint8)
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self.model_dir_of_enhancemnet = dir_models + "/model_enhancement.h5"
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self.model_dir_of_col_classifier = dir_models + "/model_scale_classifier.h5"
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self.model_region_dir_p = dir_models + "/model_main_covid19_lr5-5_scale_1_1_great.h5" # dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_great.h5'
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# self.model_region_dir_p_ens = dir_models +'/model_ensemble_s.h5'#'/model_main_covid19_lr5-5_scale_1_1_great.h5'#'/model_main_scale_1_1und_1_2_corona_great.h5'
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self.model_region_dir_p2 = dir_models + "/model_main_home_corona3_rot.h5"
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self.model_region_dir_fully_np = dir_models + "/model_no_patches_class0_30eopch.h5"
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self.model_region_dir_fully = dir_models + "/model_3up_new_good_no_augmentation.h5" # "model_3col_p_soft_10_less_aug_binarization_only.h5"
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self.model_page_dir = dir_models + "/model_page_mixed_best.h5"
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self.model_region_dir_p_ens = dir_models + "/model_ensemble_s.h5" # dir_models +'/model_main_covid_19_many_scalin_down_lr5-5_the_best.h5' #dir_models +'/model_ensemble_s.h5'
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###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5'
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self.model_textline_dir = dir_models + "/model_textline_newspapers.h5" #'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5'
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def find_polygons_size_filter(self, contours, median_area, scaler_up=1.2, scaler_down=0.8):
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found_polygons_early = list()
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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area = polygon.area
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# Check that polygon has area greater than minimal area
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if area >= median_area * scaler_down and area <= median_area * scaler_up:
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found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
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return found_polygons_early
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def filter_contours_area_of_image(self, image, contours, hirarchy, max_area, min_area):
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found_polygons_early = list()
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jv = 0
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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area = polygon.area
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] == -1: # and hirarchy[0][jv][3]==-1 :
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found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
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jv += 1
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return found_polygons_early
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def filter_contours_area_of_image_interiors(self, image, contours, hirarchy, max_area, min_area):
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found_polygons_early = list()
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jv = 0
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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area = polygon.area
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1:
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# print(c[0][0][1])
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found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
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jv += 1
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return found_polygons_early
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def resize_image(self, img_in, input_height, input_width):
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return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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def resize_ann(self, seg_in, input_height, input_width):
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return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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def rotatedRectWithMaxArea(self, w, h, angle):
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if w <= 0 or h <= 0:
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return 0, 0
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width_is_longer = w >= h
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side_long, side_short = (w, h) if width_is_longer else (h, w)
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# since the solutions for angle, -angle and 180-angle are all the same,
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# if suffices to look at the first quadrant and the absolute values of sin,cos:
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sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
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if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
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# half constrained case: two crop corners touch the longer side,
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# the other two corners are on the mid-line parallel to the longer line
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x = 0.5 * side_short
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wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
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else:
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# fully constrained case: crop touches all 4 sides
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cos_2a = cos_a * cos_a - sin_a * sin_a
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wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
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return wr, hr
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def get_one_hot(self, seg, input_height, input_width, n_classes):
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seg = seg[:, :, 0]
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seg_f = np.zeros((input_height, input_width, n_classes))
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for j in range(n_classes):
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seg_f[:, :, j] = (seg == j).astype(int)
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return seg_f
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def color_images(self, seg, n_classes):
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ann_u = range(n_classes)
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if len(np.shape(seg)) == 3:
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seg = seg[:, :, 0]
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seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(np.uint8)
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colors = sns.color_palette("hls", n_classes)
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for c in ann_u:
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c = int(c)
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segl = seg == c
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seg_img[:, :, 0] = segl * c
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seg_img[:, :, 1] = segl * c
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seg_img[:, :, 2] = segl * c
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return seg_img
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def color_images_diva(self, seg, n_classes):
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ann_u = range(n_classes)
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if len(np.shape(seg)) == 3:
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seg = seg[:, :, 0]
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seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
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# colors=sns.color_palette("hls", n_classes)
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colors = [[1, 0, 0], [8, 0, 0], [2, 0, 0], [4, 0, 0]]
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for c in ann_u:
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c = int(c)
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segl = seg == c
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seg_img[:, :, 0][seg == c] = colors[c][0] # segl*(colors[c][0])
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seg_img[:, :, 1][seg == c] = colors[c][1] # seg_img[:,:,1]=segl*(colors[c][1])
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seg_img[:, :, 2][seg == c] = colors[c][2] # seg_img[:,:,2]=segl*(colors[c][2])
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return seg_img
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def rotate_image(self, img_patch, slope):
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(h, w) = img_patch.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, slope, 1.0)
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return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
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def rotyate_image_different(self, img, slope):
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# img = cv2.imread('images/input.jpg')
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num_rows, num_cols = img.shape[:2]
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rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
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img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
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return img_rotation
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def cleaning_probs(self, probs: np.ndarray, sigma: float) -> np.ndarray:
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# Smooth
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if sigma > 0.0:
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return cv2.GaussianBlur(probs, (int(3 * sigma) * 2 + 1, int(3 * sigma) * 2 + 1), sigma)
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elif sigma == 0.0:
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return cv2.fastNlMeansDenoising((probs * 255).astype(np.uint8), h=20) / 255
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else: # Negative sigma, do not do anything
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return probs
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def crop_image_inside_box(self, box, img_org_copy):
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image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
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return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
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def otsu_copy(self, img):
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img_r = np.zeros(img.shape)
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img1 = img[:, :, 0]
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img2 = img[:, :, 1]
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img3 = img[:, :, 2]
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# print(img.min())
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# print(img[:,:,0].min())
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# blur = cv2.GaussianBlur(img,(5,5))
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# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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return img_r
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def otsu_copy_binary(self, img):
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img_r = np.zeros((img.shape[0], img.shape[1], 3))
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img1 = img[:, :, 0]
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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img_r = img_r / float(np.max(img_r)) * 255
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return img_r
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def predict_enhancement(self, img):
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model_enhancement, session_enhancemnet = self.start_new_session_and_model(self.model_dir_of_enhancemnet)
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img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1]
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img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2]
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# n_classes = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[3]
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if img.shape[0] < img_height_model:
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img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST)
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if img.shape[1] < img_width_model:
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img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST)
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margin = True
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if margin:
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kernel = np.ones((5, 5), np.uint8)
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margin = int(0 * img_width_model)
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width_mid = img_width_model - 2 * margin
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height_mid = img_height_model - 2 * margin
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img = img / float(255.0)
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img_h = img.shape[0]
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img_w = img.shape[1]
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prediction_true = np.zeros((img_h, img_w, 3))
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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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nxf = int(nxf)
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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else:
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nyf = int(nyf)
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for i in range(nxf):
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for j in range(nyf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + img_width_model
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + img_width_model
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if j == 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + img_height_model
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elif j > 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + img_height_model
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - img_width_model
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - img_height_model
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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seg = label_p_pred[0, :, :, :]
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seg = seg * 255
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if i == 0 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg
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elif i == 0 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
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elif i == 0 and j != 0 and j != nyf - 1:
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seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j != 0 and j != nyf - 1:
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
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elif i != 0 and i != nxf - 1 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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elif i != 0 and i != nxf - 1 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg
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else:
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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prediction_true = prediction_true.astype(int)
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|
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del model_enhancement
|
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del session_enhancemnet
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|
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return prediction_true
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|
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def check_dpi(self):
|
|
self.dpi = os.popen('identify -format "%x " ' + self.image_dir).read()
|
|
self.dpi = int(float(self.dpi))
|
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|
|
def resize_image_with_column_classifier(self, is_image_enhanced):
|
|
self.check_dpi()
|
|
img = cv2.imread(self.image_dir)
|
|
img = img.astype(np.uint8)
|
|
|
|
_, page_coord = self.early_page_for_num_of_column_classification()
|
|
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
|
|
|
|
img_1ch = cv2.imread(self.image_dir, 0)
|
|
|
|
width_early = img_1ch.shape[1]
|
|
|
|
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
|
|
# plt.imshow(img_1ch)
|
|
# plt.show()
|
|
img_1ch = img_1ch / 255.0
|
|
|
|
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
|
|
|
|
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
|
|
img_in[0, :, :, 0] = img_1ch[:, :]
|
|
img_in[0, :, :, 1] = img_1ch[:, :]
|
|
img_in[0, :, :, 2] = img_1ch[:, :]
|
|
|
|
label_p_pred = model_num_classifier.predict(img_in)
|
|
num_col = np.argmax(label_p_pred[0]) + 1
|
|
|
|
print(num_col, label_p_pred, "num_col_classifier")
|
|
|
|
session_col_classifier.close()
|
|
del model_num_classifier
|
|
del session_col_classifier
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
# sys.exit()
|
|
if num_col == 1 and width_early < 1100:
|
|
img_w_new = 2000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
|
|
|
|
elif num_col == 1 and width_early >= 2500:
|
|
img_w_new = 2000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
|
|
elif num_col == 1 and width_early >= 1100 and width_early < 2500:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 2 and width_early < 2000:
|
|
img_w_new = 2400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
|
|
|
|
elif num_col == 2 and width_early >= 3500:
|
|
img_w_new = 2400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
|
|
|
|
elif num_col == 2 and width_early >= 2000 and width_early < 3500:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 3 and width_early < 2000:
|
|
img_w_new = 3000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
|
|
|
|
elif num_col == 3 and width_early >= 4000:
|
|
img_w_new = 3000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
|
|
|
|
elif num_col == 3 and width_early >= 2000 and width_early < 4000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 4 and width_early < 2500:
|
|
img_w_new = 4000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
|
|
|
|
elif num_col == 4 and width_early >= 5000:
|
|
img_w_new = 4000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
|
|
|
|
elif num_col == 4 and width_early >= 2500 and width_early < 5000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 5 and width_early < 3700:
|
|
img_w_new = 5000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
|
|
|
|
elif num_col == 5 and width_early >= 7000:
|
|
img_w_new = 5000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
|
|
|
|
elif num_col == 5 and width_early >= 3700 and width_early < 7000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 6 and width_early < 4500:
|
|
img_w_new = 6500 # 5400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 6500)
|
|
|
|
else:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early:
|
|
img_new = np.copy(img)
|
|
num_column_is_classified = False
|
|
else:
|
|
img_new = self.resize_image(img, img_h_new, img_w_new)
|
|
num_column_is_classified = True
|
|
|
|
if img_new.shape[1] > img.shape[1]:
|
|
img_new = self.predict_enhancement(img_new)
|
|
is_image_enhanced = True
|
|
|
|
return img, img_new, is_image_enhanced
|
|
|
|
def resize_and_enhance_image_with_column_classifier(self, is_image_enhanced):
|
|
self.check_dpi()
|
|
img = cv2.imread(self.image_dir)
|
|
|
|
img = img.astype(np.uint8)
|
|
|
|
_, page_coord = self.early_page_for_num_of_column_classification()
|
|
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
|
|
|
|
img_1ch = cv2.imread(self.image_dir, 0)
|
|
img_1ch = img_1ch.astype(np.uint8)
|
|
|
|
width_early = img_1ch.shape[1]
|
|
|
|
img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
|
|
# plt.imshow(img_1ch)
|
|
# plt.show()
|
|
img_1ch = img_1ch / 255.0
|
|
|
|
img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
|
|
|
|
img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
|
|
img_in[0, :, :, 0] = img_1ch[:, :]
|
|
img_in[0, :, :, 1] = img_1ch[:, :]
|
|
img_in[0, :, :, 2] = img_1ch[:, :]
|
|
|
|
# plt.imshow(img_in[0,:,:,:])
|
|
# plt.show()
|
|
|
|
label_p_pred = model_num_classifier.predict(img_in)
|
|
num_col = np.argmax(label_p_pred[0]) + 1
|
|
|
|
print(num_col, label_p_pred, "num_col_classifier")
|
|
|
|
session_col_classifier.close()
|
|
del model_num_classifier
|
|
del session_col_classifier
|
|
del img_in
|
|
del img_1ch
|
|
del page_coord
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
print(self.dpi)
|
|
|
|
if self.dpi < 298:
|
|
|
|
# sys.exit()
|
|
if num_col == 1 and width_early < 1100:
|
|
img_w_new = 2000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
|
|
|
|
elif num_col == 1 and width_early >= 2500:
|
|
img_w_new = 2000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2000)
|
|
elif num_col == 1 and width_early >= 1100 and width_early < 2500:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 2 and width_early < 2000:
|
|
img_w_new = 2400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
|
|
|
|
elif num_col == 2 and width_early >= 3500:
|
|
img_w_new = 2400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 2400)
|
|
|
|
elif num_col == 2 and width_early >= 2000 and width_early < 3500:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 3 and width_early < 2000:
|
|
img_w_new = 3000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
|
|
|
|
elif num_col == 3 and width_early >= 4000:
|
|
img_w_new = 3000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 3000)
|
|
|
|
elif num_col == 3 and width_early >= 2000 and width_early < 4000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 4 and width_early < 2500:
|
|
img_w_new = 4000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
|
|
|
|
elif num_col == 4 and width_early >= 5000:
|
|
img_w_new = 4000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 4000)
|
|
|
|
elif num_col == 4 and width_early >= 2500 and width_early < 5000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 5 and width_early < 3700:
|
|
img_w_new = 5000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
|
|
|
|
elif num_col == 5 and width_early >= 7000:
|
|
img_w_new = 5000
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 5000)
|
|
|
|
elif num_col == 5 and width_early >= 3700 and width_early < 7000:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
elif num_col == 6 and width_early < 4500:
|
|
img_w_new = 6500 # 5400
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * 6500)
|
|
|
|
else:
|
|
img_w_new = width_early
|
|
img_h_new = int(img.shape[0] / float(img.shape[1]) * width_early)
|
|
|
|
if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early:
|
|
img_new = np.copy(img)
|
|
num_column_is_classified = False
|
|
else:
|
|
img_new = self.resize_image(img, img_h_new, img_w_new)
|
|
num_column_is_classified = True
|
|
|
|
# img_new=self.resize_image(img,img_h_new,img_w_new)
|
|
image_res = self.predict_enhancement(img_new)
|
|
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
|
|
# self.image=self.image.astype(np.uint16)
|
|
|
|
# self.scale_x=1
|
|
# self.scale_y=1
|
|
# self.height_org = self.image.shape[0]
|
|
# self.width_org = self.image.shape[1]
|
|
is_image_enhanced = True
|
|
else:
|
|
"""
|
|
if img.shape[0]<=2530 and img.shape[0]>=img.shape[1]:
|
|
img_h_new=3000
|
|
img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
|
|
img_new=self.resize_image(img,img_h_new,img_w_new)
|
|
image_res=self.predict_enhancement(img_new)
|
|
#cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
|
|
#self.image=self.image.astype(np.uint16)
|
|
##self.scale_x=1
|
|
##self.scale_y=1
|
|
##self.height_org = self.image.shape[0]
|
|
##self.width_org = self.image.shape[1]
|
|
is_image_enhanced=True
|
|
else:
|
|
is_image_enhanced=False
|
|
image_res=np.copy(img)
|
|
|
|
"""
|
|
is_image_enhanced = False
|
|
num_column_is_classified = True
|
|
image_res = np.copy(img)
|
|
|
|
return is_image_enhanced, img, image_res, num_col, num_column_is_classified
|
|
|
|
def resize_and_enhance_image(self, is_image_enhanced):
|
|
self.check_dpi()
|
|
img = cv2.imread(self.image_dir)
|
|
img = img.astype(np.uint8)
|
|
# sys.exit()
|
|
|
|
print(self.dpi)
|
|
|
|
if self.dpi < 298:
|
|
if img.shape[0] < 1000:
|
|
img_h_new = int(img.shape[0] * 3)
|
|
img_w_new = int(img.shape[1] * 3)
|
|
if img_h_new < 2800:
|
|
img_h_new = 3000
|
|
img_w_new = int(img.shape[1] / float(img.shape[0]) * 3000)
|
|
elif img.shape[0] >= 1000 and img.shape[0] < 2000:
|
|
img_h_new = int(img.shape[0] * 2)
|
|
img_w_new = int(img.shape[1] * 2)
|
|
if img_h_new < 2800:
|
|
img_h_new = 3000
|
|
img_w_new = int(img.shape[1] / float(img.shape[0]) * 3000)
|
|
else:
|
|
img_h_new = int(img.shape[0] * 1.5)
|
|
img_w_new = int(img.shape[1] * 1.5)
|
|
img_new = self.resize_image(img, img_h_new, img_w_new)
|
|
image_res = self.predict_enhancement(img_new)
|
|
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
|
|
# self.image=self.image.astype(np.uint16)
|
|
|
|
# self.scale_x=1
|
|
# self.scale_y=1
|
|
# self.height_org = self.image.shape[0]
|
|
# self.width_org = self.image.shape[1]
|
|
is_image_enhanced = True
|
|
else:
|
|
is_image_enhanced = False
|
|
image_res = np.copy(img)
|
|
|
|
return is_image_enhanced, img, image_res
|
|
|
|
def resize_and_enhance_image_new(self, is_image_enhanced):
|
|
# self.check_dpi()
|
|
img = cv2.imread(self.image_dir)
|
|
img = img.astype(np.uint8)
|
|
# sys.exit()
|
|
|
|
image_res = np.copy(img)
|
|
|
|
return is_image_enhanced, img, image_res
|
|
|
|
def get_image_and_scales(self, img_org, img_res, scale):
|
|
self.image = np.copy(img_res)
|
|
self.image_org = np.copy(img_org)
|
|
self.height_org = self.image.shape[0]
|
|
self.width_org = self.image.shape[1]
|
|
|
|
self.img_hight_int = int(self.image.shape[0] * scale)
|
|
self.img_width_int = int(self.image.shape[1] * scale)
|
|
self.scale_y = self.img_hight_int / float(self.image.shape[0])
|
|
self.scale_x = self.img_width_int / float(self.image.shape[1])
|
|
|
|
self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int)
|
|
del img_res
|
|
del img_org
|
|
|
|
def get_image_and_scales_after_enhancing(self, img_org, img_res):
|
|
|
|
# self.image = cv2.imread(self.image_dir)
|
|
|
|
self.image = np.copy(img_res)
|
|
self.image = self.image.astype(np.uint8)
|
|
self.image_org = np.copy(img_org)
|
|
self.height_org = self.image_org.shape[0]
|
|
self.width_org = self.image_org.shape[1]
|
|
|
|
self.scale_y = img_res.shape[0] / float(self.image_org.shape[0])
|
|
self.scale_x = img_res.shape[1] / float(self.image_org.shape[1])
|
|
|
|
del img_org
|
|
del img_res
|
|
|
|
def get_image_and_scales_deskewd(self, img_deskewd):
|
|
|
|
self.image = img_deskewd
|
|
self.image_org = np.copy(self.image)
|
|
self.height_org = self.image.shape[0]
|
|
self.width_org = self.image.shape[1]
|
|
|
|
self.img_hight_int = int(self.image.shape[0] * 1)
|
|
self.img_width_int = int(self.image.shape[1] * 1)
|
|
self.scale_y = self.img_hight_int / float(self.image.shape[0])
|
|
self.scale_x = self.img_width_int / float(self.image.shape[1])
|
|
|
|
self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int)
|
|
|
|
def start_new_session_and_model(self, model_dir):
|
|
config = tf.ConfigProto()
|
|
config.gpu_options.allow_growth = True
|
|
|
|
session = tf.InteractiveSession()
|
|
model = load_model(model_dir, compile=False)
|
|
|
|
return model, session
|
|
|
|
def return_bonding_box_of_contours(self, cnts):
|
|
boxes_tot = []
|
|
for i in range(len(cnts)):
|
|
x, y, w, h = cv2.boundingRect(cnts[i])
|
|
|
|
box = [x, y, w, h]
|
|
boxes_tot.append(box)
|
|
return boxes_tot
|
|
|
|
def find_features_of_lines(self, contours_main):
|
|
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
|
|
slope_lines = []
|
|
|
|
for kk in range(len(contours_main)):
|
|
[vx, vy, x, y] = cv2.fitLine(contours_main[kk], cv2.DIST_L2, 0, 0.01, 0.01)
|
|
slope_lines.append(((vy / vx) / np.pi * 180)[0])
|
|
|
|
slope_lines_org = slope_lines
|
|
slope_lines = np.array(slope_lines)
|
|
slope_lines[(slope_lines < 10) & (slope_lines > -10)] = 0
|
|
|
|
slope_lines[(slope_lines < -200) | (slope_lines > 200)] = 1
|
|
slope_lines[(slope_lines != 0) & (slope_lines != 1)] = 2
|
|
|
|
dis_x = np.abs(x_max_main - x_min_main)
|
|
return slope_lines, dis_x, x_min_main, x_max_main, np.array(cy_main), np.array(slope_lines_org), y_min_main, y_max_main, np.array(cx_main)
|
|
|
|
def return_parent_contours(self, contours, hierarchy):
|
|
contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1]
|
|
return contours_parent
|
|
|
|
def isNaN(self, num):
|
|
return num != num
|
|
|
|
def early_deskewing_slope_calculation_based_on_lines(self, region_pre_p):
|
|
# lines are labels by 6 in this model
|
|
seperators_closeup = ((region_pre_p[:, :, :] == 6)) * 1
|
|
|
|
seperators_closeup = seperators_closeup.astype(np.uint8)
|
|
imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = self.find_features_of_lines(contours_lines)
|
|
|
|
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
|
|
args = np.array(range(len(slope_lines)))
|
|
len_x = seperators_closeup.shape[1] / 4.0
|
|
|
|
args_hor = args[slope_lines == 0]
|
|
dist_x_hor = dist_x[slope_lines == 0]
|
|
x_min_main_hor = x_min_main[slope_lines == 0]
|
|
x_max_main_hor = x_max_main[slope_lines == 0]
|
|
cy_main_hor = cy_main[slope_lines == 0]
|
|
|
|
args_hor = args_hor[dist_x_hor >= len_x / 2.0]
|
|
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x / 2.0]
|
|
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x / 2.0]
|
|
cy_main_hor = cy_main_hor[dist_x_hor >= len_x / 2.0]
|
|
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x / 2.0]
|
|
|
|
slope_lines_org_hor = slope_lines_org_hor[np.abs(slope_lines_org_hor) < 1.2]
|
|
slope_mean_hor = np.mean(slope_lines_org_hor)
|
|
|
|
if np.abs(slope_mean_hor) > 1.2:
|
|
slope_mean_hor = 0
|
|
|
|
# deskewed_new=rotate_image(image_regions_eraly_p[:,:,:],slope_mean_hor)
|
|
|
|
args_ver = args[slope_lines == 1]
|
|
y_min_main_ver = y_min_main[slope_lines == 1]
|
|
y_max_main_ver = y_max_main[slope_lines == 1]
|
|
x_min_main_ver = x_min_main[slope_lines == 1]
|
|
x_max_main_ver = x_max_main[slope_lines == 1]
|
|
cx_main_ver = cx_main[slope_lines == 1]
|
|
dist_y_ver = y_max_main_ver - y_min_main_ver
|
|
len_y = seperators_closeup.shape[0] / 3.0
|
|
|
|
return slope_mean_hor, cx_main_ver, dist_y_ver
|
|
|
|
def return_contours_of_interested_region(self, region_pre_p, pixel, min_area=0.0002):
|
|
|
|
# pixels of images are identified by 5
|
|
if len(region_pre_p.shape) == 3:
|
|
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
|
else:
|
|
cnts_images = (region_pre_p[:, :] == pixel) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_area)
|
|
|
|
return contours_imgs
|
|
|
|
def return_contours_of_interested_region_by_min_size(self, region_pre_p, pixel, min_size=0.00003):
|
|
|
|
# pixels of images are identified by 5
|
|
if len(region_pre_p.shape) == 3:
|
|
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
|
else:
|
|
cnts_images = (region_pre_p[:, :] == pixel) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_size)
|
|
|
|
return contours_imgs
|
|
|
|
def return_contours_of_interested_textline(self, region_pre_p, pixel):
|
|
|
|
# pixels of images are identified by 5
|
|
if len(region_pre_p.shape) == 3:
|
|
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
|
else:
|
|
cnts_images = (region_pre_p[:, :] == pixel) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003)
|
|
return contours_imgs
|
|
|
|
def find_images_contours_and_replace_table_and_graphic_pixels_by_image(self, region_pre_p):
|
|
|
|
# pixels of images are identified by 5
|
|
cnts_images = (region_pre_p[:, :, 0] == 5) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
|
|
# print(len(contours_imgs),'contours_imgs')
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
|
|
|
|
# print(len(contours_imgs),'contours_imgs')
|
|
|
|
boxes_imgs = self.return_bonding_box_of_contours(contours_imgs)
|
|
|
|
for i in range(len(boxes_imgs)):
|
|
x1 = int(boxes_imgs[i][0])
|
|
x2 = int(boxes_imgs[i][0] + boxes_imgs[i][2])
|
|
y1 = int(boxes_imgs[i][1])
|
|
y2 = int(boxes_imgs[i][1] + boxes_imgs[i][3])
|
|
region_pre_p[y1:y2, x1:x2, 0][region_pre_p[y1:y2, x1:x2, 0] == 8] = 5
|
|
region_pre_p[y1:y2, x1:x2, 0][region_pre_p[y1:y2, x1:x2, 0] == 7] = 5
|
|
return region_pre_p
|
|
|
|
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
|
|
|
|
img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
|
|
img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
|
|
n_classes = model.layers[len(model.layers) - 1].output_shape[3]
|
|
|
|
if patches:
|
|
if img.shape[0] < img_height_model:
|
|
img = self.resize_image(img, img_height_model, img.shape[1])
|
|
|
|
if img.shape[1] < img_width_model:
|
|
img = self.resize_image(img, img.shape[0], img_width_model)
|
|
|
|
# print(img_height_model,img_width_model)
|
|
# margin = int(0.2 * img_width_model)
|
|
margin = int(marginal_of_patch_percent * img_height_model)
|
|
|
|
width_mid = img_width_model - 2 * margin
|
|
height_mid = img_height_model - 2 * margin
|
|
|
|
img = img / float(255.0)
|
|
# print(sys.getsizeof(img))
|
|
# print(np.max(img))
|
|
|
|
img = img.astype(np.float16)
|
|
|
|
# print(sys.getsizeof(img))
|
|
|
|
img_h = img.shape[0]
|
|
img_w = img.shape[1]
|
|
|
|
prediction_true = np.zeros((img_h, img_w, 3))
|
|
mask_true = np.zeros((img_h, img_w))
|
|
nxf = img_w / float(width_mid)
|
|
nyf = img_h / float(height_mid)
|
|
|
|
if nxf > int(nxf):
|
|
nxf = int(nxf) + 1
|
|
else:
|
|
nxf = int(nxf)
|
|
|
|
if nyf > int(nyf):
|
|
nyf = int(nyf) + 1
|
|
else:
|
|
nyf = int(nyf)
|
|
|
|
for i in range(nxf):
|
|
for j in range(nyf):
|
|
|
|
if i == 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + img_width_model
|
|
elif i > 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + img_width_model
|
|
|
|
if j == 0:
|
|
index_y_d = j * height_mid
|
|
index_y_u = index_y_d + img_height_model
|
|
elif j > 0:
|
|
index_y_d = j * height_mid
|
|
index_y_u = index_y_d + img_height_model
|
|
|
|
if index_x_u > img_w:
|
|
index_x_u = img_w
|
|
index_x_d = img_w - img_width_model
|
|
if index_y_u > img_h:
|
|
index_y_u = img_h
|
|
index_y_d = img_h - img_height_model
|
|
|
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
|
|
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
|
|
|
|
seg = np.argmax(label_p_pred, axis=3)[0]
|
|
|
|
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
|
|
|
if i == 0 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
|
seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
|
|
|
|
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
|
|
elif i == nxf - 1 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
|
|
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
|
|
|
|
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
|
|
elif i == 0 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
|
|
|
|
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
|
|
elif i == nxf - 1 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
|
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
|
|
|
|
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
|
|
elif i == 0 and j != 0 and j != nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
|
|
|
|
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
|
|
|
|
elif i == nxf - 1 and j != 0 and j != nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
|
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
|
|
|
|
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
|
|
|
|
elif i != 0 and i != nxf - 1 and j == 0:
|
|
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
|
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
|
|
|
|
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
|
|
elif i != 0 and i != nxf - 1 and j == nyf - 1:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
|
|
|
|
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
|
|
else:
|
|
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
|
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
|
|
|
|
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
|
|
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
|
|
|
|
prediction_true = prediction_true.astype(np.uint8)
|
|
del img
|
|
del mask_true
|
|
del seg_color
|
|
del seg
|
|
del img_patch
|
|
|
|
if not patches:
|
|
img_h_page = img.shape[0]
|
|
img_w_page = img.shape[1]
|
|
img = img / float(255.0)
|
|
img = self.resize_image(img, img_height_model, img_width_model)
|
|
|
|
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
|
|
|
|
seg = np.argmax(label_p_pred, axis=3)[0]
|
|
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
|
prediction_true = self.resize_image(seg_color, img_h_page, img_w_page)
|
|
prediction_true = prediction_true.astype(np.uint8)
|
|
|
|
del img
|
|
del seg_color
|
|
del label_p_pred
|
|
del seg
|
|
del model
|
|
gc.collect()
|
|
|
|
return prediction_true
|
|
|
|
def early_page_for_num_of_column_classification(self):
|
|
img = cv2.imread(self.image_dir)
|
|
img = img.astype(np.uint8)
|
|
patches = False
|
|
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
|
###img = self.otsu_copy(self.image)
|
|
for ii in range(1):
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
|
|
img_page_prediction = self.do_prediction(patches, img, model_page)
|
|
|
|
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
thresh = cv2.dilate(thresh, self.kernel, iterations=3)
|
|
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
|
|
|
|
cnt = contours[np.argmax(cnt_size)]
|
|
|
|
x, y, w, h = cv2.boundingRect(cnt)
|
|
|
|
box = [x, y, w, h]
|
|
|
|
croped_page, page_coord = self.crop_image_inside_box(box, img)
|
|
|
|
session_page.close()
|
|
del model_page
|
|
del session_page
|
|
del contours
|
|
del thresh
|
|
del img
|
|
del cnt_size
|
|
del cnt
|
|
del box
|
|
del x
|
|
del y
|
|
del w
|
|
del h
|
|
del imgray
|
|
del img_page_prediction
|
|
|
|
gc.collect()
|
|
return croped_page, page_coord
|
|
|
|
def extract_page(self):
|
|
patches = False
|
|
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
|
|
###img = self.otsu_copy(self.image)
|
|
for ii in range(1):
|
|
img = cv2.GaussianBlur(self.image, (5, 5), 0)
|
|
|
|
img_page_prediction = self.do_prediction(patches, img, model_page)
|
|
|
|
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
thresh = cv2.dilate(thresh, self.kernel, iterations=3)
|
|
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
|
|
|
|
cnt = contours[np.argmax(cnt_size)]
|
|
|
|
x, y, w, h = cv2.boundingRect(cnt)
|
|
|
|
if x <= 30:
|
|
w = w + x
|
|
x = 0
|
|
if (self.image.shape[1] - (x + w)) <= 30:
|
|
w = w + (self.image.shape[1] - (x + w))
|
|
|
|
if y <= 30:
|
|
h = h + y
|
|
y = 0
|
|
if (self.image.shape[0] - (y + h)) <= 30:
|
|
h = h + (self.image.shape[0] - (y + h))
|
|
|
|
box = [x, y, w, h]
|
|
|
|
croped_page, page_coord = self.crop_image_inside_box(box, self.image)
|
|
|
|
self.cont_page = []
|
|
self.cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
|
|
|
|
session_page.close()
|
|
del model_page
|
|
del session_page
|
|
del contours
|
|
del thresh
|
|
del img
|
|
del imgray
|
|
|
|
gc.collect()
|
|
return croped_page, page_coord
|
|
|
|
def extract_drop_capital_13(self, img, patches, cols):
|
|
|
|
img_height_h = img.shape[0]
|
|
img_width_h = img.shape[1]
|
|
patches = False
|
|
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
|
|
|
|
img_1 = img[: int(img.shape[0] / 3.0), :, :]
|
|
img_2 = img[int(img.shape[0] / 3.0) : int(2 * img.shape[0] / 3.0), :, :]
|
|
img_3 = img[int(2 * img.shape[0] / 3.0) :, :, :]
|
|
|
|
# img_1 = self.otsu_copy_binary(img_1)#self.otsu_copy(img)
|
|
# img_1 = img_1.astype(np.uint16)
|
|
|
|
plt.imshow(img_1)
|
|
plt.show()
|
|
# img_2 = self.otsu_copy_binary(img_2)#self.otsu_copy(img)
|
|
# img_2 = img_2.astype(np.uint16)
|
|
|
|
plt.imshow(img_2)
|
|
plt.show()
|
|
# img_3 = self.otsu_copy_binary(img_3)#self.otsu_copy(img)
|
|
# img_3 = img_3.astype(np.uint16)
|
|
|
|
plt.imshow(img_3)
|
|
plt.show()
|
|
|
|
prediction_regions_1 = self.do_prediction(patches, img_1, model_region)
|
|
|
|
plt.imshow(prediction_regions_1)
|
|
plt.show()
|
|
|
|
prediction_regions_2 = self.do_prediction(patches, img_2, model_region)
|
|
|
|
plt.imshow(prediction_regions_2)
|
|
plt.show()
|
|
prediction_regions_3 = self.do_prediction(patches, img_3, model_region)
|
|
|
|
plt.imshow(prediction_regions_3)
|
|
plt.show()
|
|
prediction_regions = np.zeros((img_height_h, img_width_h))
|
|
|
|
prediction_regions[: int(img.shape[0] / 3.0), :] = prediction_regions_1[:, :, 0]
|
|
prediction_regions[int(img.shape[0] / 3.0) : int(2 * img.shape[0] / 3.0), :] = prediction_regions_2[:, :, 0]
|
|
prediction_regions[int(2 * img.shape[0] / 3.0) :, :] = prediction_regions_3[:, :, 0]
|
|
|
|
session_region.close()
|
|
del img_1
|
|
del img_2
|
|
del img_3
|
|
del prediction_regions_1
|
|
del prediction_regions_2
|
|
del prediction_regions_3
|
|
del model_region
|
|
del session_region
|
|
del img
|
|
gc.collect()
|
|
return prediction_regions
|
|
|
|
def extract_text_regions(self, img, patches, cols):
|
|
img_height_h = img.shape[0]
|
|
img_width_h = img.shape[1]
|
|
|
|
###if patches and cols>=3 :
|
|
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
|
|
###if not patches:
|
|
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
|
|
|
|
###if patches and cols==2 :
|
|
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
|
|
|
|
###if patches and cols==1 :
|
|
###model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_2col)
|
|
|
|
###if patches and cols>=2:
|
|
|
|
###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
|
|
###img = img.astype(np.uint8)
|
|
|
|
###if patches and cols==1:
|
|
|
|
###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
|
|
###img = img.astype(np.uint8)
|
|
###img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) )
|
|
|
|
if patches:
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
|
|
if not patches:
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
|
|
|
|
if patches and cols == 1:
|
|
img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img2 = img2.astype(np.uint8)
|
|
img2 = self.resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
|
|
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
|
|
prediction_regions2 = self.resize_image(prediction_regions2, img_height_h, img_width_h)
|
|
|
|
if patches and cols == 2:
|
|
img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img2 = img2.astype(np.uint8)
|
|
img2 = self.resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
|
|
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
|
|
prediction_regions2 = self.resize_image(prediction_regions2, img_height_h, img_width_h)
|
|
elif patches and cols > 2:
|
|
img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img2 = img2.astype(np.uint8)
|
|
img2 = self.resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3))
|
|
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
|
|
prediction_regions2 = self.resize_image(prediction_regions2, img_height_h, img_width_h)
|
|
|
|
if patches and cols == 2:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
|
|
img = img.astype(np.uint8)
|
|
|
|
if img_width_h >= 2000:
|
|
img = self.resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
|
|
else:
|
|
pass # img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) )
|
|
img = img.astype(np.uint8)
|
|
|
|
if patches and cols == 1:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
|
|
img = img.astype(np.uint8)
|
|
img = self.resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
|
|
img = img.astype(np.uint8)
|
|
|
|
if patches and cols == 3:
|
|
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
# img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
|
|
|
|
if patches and cols == 4:
|
|
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
# img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
|
|
|
|
if patches and cols >= 5:
|
|
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
# img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
|
|
|
|
if not patches:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
prediction_regions2 = None
|
|
|
|
marginal_of_patch_percent = 0.1
|
|
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
|
|
prediction_regions = self.resize_image(prediction_regions, img_height_h, img_width_h)
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
del img
|
|
gc.collect()
|
|
return prediction_regions, prediction_regions2
|
|
|
|
def extract_only_text_regions(self, img, patches):
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_only_text)
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
img_org = np.copy(img)
|
|
|
|
img_h = img_org.shape[0]
|
|
img_w = img_org.shape[1]
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1))
|
|
|
|
prediction_regions1 = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions1 = self.resize_image(prediction_regions1, img_h, img_w)
|
|
|
|
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=4)
|
|
# prediction_regions1 = cv2.erode(prediction_regions1, self.kernel, iterations=7)
|
|
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=2)
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1))
|
|
|
|
prediction_regions2 = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions2 = self.resize_image(prediction_regions2, img_h, img_w)
|
|
|
|
# prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
|
|
prediction_regions2 = cv2.erode(prediction_regions2, self.kernel, iterations=2)
|
|
prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
|
|
|
|
# prediction_regions=( (prediction_regions2[:,:,0]==1) & (prediction_regions1[:,:,0]==1) )
|
|
# prediction_regions=(prediction_regions1[:,:,0]==1)
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
return prediction_regions1[:, :, 0]
|
|
|
|
def extract_binarization(self, img, patches):
|
|
|
|
model_bin, session_bin = self.start_new_session_and_model(self.model_binafrization)
|
|
|
|
img_h = img.shape[0]
|
|
img_w = img.shape[1]
|
|
|
|
img = self.resize_image(img, int(img.shape[0] * 1), int(img.shape[1] * 1))
|
|
|
|
prediction_regions = self.do_prediction(patches, img, model_bin)
|
|
|
|
res = (prediction_regions[:, :, 0] != 0) * 1
|
|
|
|
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
|
|
res[:, :][res[:, :] == 0] = 2
|
|
res = res - 1
|
|
res = res * 255
|
|
img_fin[:, :, 0] = res
|
|
img_fin[:, :, 1] = res
|
|
img_fin[:, :, 2] = res
|
|
|
|
session_bin.close()
|
|
del model_bin
|
|
del session_bin
|
|
gc.collect()
|
|
# plt.imshow(img_fin[:,:,0])
|
|
# plt.show()
|
|
return img_fin
|
|
|
|
def get_slopes_and_deskew_new(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew):
|
|
num_cores = cpu_count()
|
|
queue_of_all_params = Queue()
|
|
|
|
processes = []
|
|
nh = np.linspace(0, len(boxes), num_cores + 1)
|
|
indexes_by_text_con = np.array(range(len(contours_par)))
|
|
|
|
for i in range(num_cores):
|
|
boxes_per_process = boxes[int(nh[i]) : int(nh[i + 1])]
|
|
contours_per_process = contours[int(nh[i]) : int(nh[i + 1])]
|
|
contours_par_per_process = contours_par[int(nh[i]) : int(nh[i + 1])]
|
|
indexes_text_con_per_process = indexes_by_text_con[int(nh[i]) : int(nh[i + 1])]
|
|
|
|
processes.append(Process(target=self.do_work_of_slopes_new, args=(queue_of_all_params, boxes_per_process, textline_mask_tot, contours_per_process, contours_par_per_process, indexes_text_con_per_process, image_page_rotated, slope_deskew)))
|
|
|
|
for i in range(num_cores):
|
|
processes[i].start()
|
|
|
|
slopes = []
|
|
all_found_texline_polygons = []
|
|
all_found_text_regions = []
|
|
all_found_text_regions_par = []
|
|
boxes = []
|
|
all_box_coord = []
|
|
all_index_text_con = []
|
|
|
|
for i in range(num_cores):
|
|
list_all_par = queue_of_all_params.get(True)
|
|
|
|
slopes_for_sub_process = list_all_par[0]
|
|
polys_for_sub_process = list_all_par[1]
|
|
boxes_for_sub_process = list_all_par[2]
|
|
contours_for_subprocess = list_all_par[3]
|
|
contours_par_for_subprocess = list_all_par[4]
|
|
boxes_coord_for_subprocess = list_all_par[5]
|
|
indexes_for_subprocess = list_all_par[6]
|
|
|
|
for j in range(len(slopes_for_sub_process)):
|
|
slopes.append(slopes_for_sub_process[j])
|
|
all_found_texline_polygons.append(polys_for_sub_process[j])
|
|
boxes.append(boxes_for_sub_process[j])
|
|
all_found_text_regions.append(contours_for_subprocess[j])
|
|
all_found_text_regions_par.append(contours_par_for_subprocess[j])
|
|
all_box_coord.append(boxes_coord_for_subprocess[j])
|
|
all_index_text_con.append(indexes_for_subprocess[j])
|
|
|
|
for i in range(num_cores):
|
|
processes[i].join()
|
|
# print(slopes,'slopes')
|
|
return slopes, all_found_texline_polygons, boxes, all_found_text_regions, all_found_text_regions_par, all_box_coord, all_index_text_con
|
|
|
|
def get_slopes_and_deskew_new_curved(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, mask_texts_only, num_col, scale_par, slope_deskew):
|
|
num_cores = cpu_count()
|
|
queue_of_all_params = Queue()
|
|
|
|
processes = []
|
|
nh = np.linspace(0, len(boxes), num_cores + 1)
|
|
indexes_by_text_con = np.array(range(len(contours_par)))
|
|
|
|
for i in range(num_cores):
|
|
boxes_per_process = boxes[int(nh[i]) : int(nh[i + 1])]
|
|
contours_per_process = contours[int(nh[i]) : int(nh[i + 1])]
|
|
contours_par_per_process = contours_par[int(nh[i]) : int(nh[i + 1])]
|
|
indexes_text_con_per_process = indexes_by_text_con[int(nh[i]) : int(nh[i + 1])]
|
|
|
|
processes.append(Process(target=self.do_work_of_slopes_new_curved, args=(queue_of_all_params, boxes_per_process, textline_mask_tot, contours_per_process, contours_par_per_process, image_page_rotated, mask_texts_only, num_col, scale_par, indexes_text_con_per_process, slope_deskew)))
|
|
|
|
for i in range(num_cores):
|
|
processes[i].start()
|
|
|
|
slopes = []
|
|
all_found_texline_polygons = []
|
|
all_found_text_regions = []
|
|
all_found_text_regions_par = []
|
|
boxes = []
|
|
all_box_coord = []
|
|
all_index_text_con = []
|
|
|
|
for i in range(num_cores):
|
|
list_all_par = queue_of_all_params.get(True)
|
|
|
|
polys_for_sub_process = list_all_par[0]
|
|
boxes_for_sub_process = list_all_par[1]
|
|
contours_for_subprocess = list_all_par[2]
|
|
contours_par_for_subprocess = list_all_par[3]
|
|
boxes_coord_for_subprocess = list_all_par[4]
|
|
indexes_for_subprocess = list_all_par[5]
|
|
slopes_for_sub_process = list_all_par[6]
|
|
|
|
for j in range(len(polys_for_sub_process)):
|
|
slopes.append(slopes_for_sub_process[j])
|
|
all_found_texline_polygons.append(polys_for_sub_process[j])
|
|
boxes.append(boxes_for_sub_process[j])
|
|
all_found_text_regions.append(contours_for_subprocess[j])
|
|
all_found_text_regions_par.append(contours_par_for_subprocess[j])
|
|
all_box_coord.append(boxes_coord_for_subprocess[j])
|
|
all_index_text_con.append(indexes_for_subprocess[j])
|
|
|
|
for i in range(num_cores):
|
|
processes[i].join()
|
|
# print(slopes,'slopes')
|
|
return all_found_texline_polygons, boxes, all_found_text_regions, all_found_text_regions_par, all_box_coord, all_index_text_con, slopes
|
|
|
|
def do_work_of_slopes_new_curved(self, queue_of_all_params, boxes_text, textline_mask_tot_ea, contours_per_process, contours_par_per_process, image_page_rotated, mask_texts_only, num_col, scale_par, indexes_r_con_per_pro, slope_deskew):
|
|
slopes_per_each_subprocess = []
|
|
bounding_box_of_textregion_per_each_subprocess = []
|
|
textlines_rectangles_per_each_subprocess = []
|
|
contours_textregion_per_each_subprocess = []
|
|
contours_textregion_par_per_each_subprocess = []
|
|
all_box_coord_per_process = []
|
|
index_by_text_region_contours = []
|
|
slope_biggest = 0
|
|
|
|
textline_cnt_seperated = np.zeros(textline_mask_tot_ea.shape)
|
|
|
|
for mv in range(len(boxes_text)):
|
|
|
|
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
all_text_region_raw = all_text_region_raw.astype(np.uint8)
|
|
|
|
img_int_p = all_text_region_raw[:, :] # self.all_text_region_raw[mv]
|
|
|
|
##img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2)
|
|
|
|
# plt.imshow(img_int_p)
|
|
# plt.show()
|
|
|
|
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
|
|
|
|
slopes_per_each_subprocess.append(0)
|
|
|
|
slope_first = 0
|
|
slope_for_all = [slope_deskew][0]
|
|
|
|
else:
|
|
|
|
try:
|
|
textline_con, hierachy = self.return_contours_of_image(img_int_p)
|
|
textline_con_fil = self.filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_des = int(y_diff_mean * (4.0 / 40.0))
|
|
|
|
if sigma_des < 1:
|
|
sigma_des = 1
|
|
|
|
img_int_p[img_int_p > 0] = 1
|
|
# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
|
|
slope_for_all = self.return_deskew_slop(img_int_p, sigma_des)
|
|
|
|
if abs(slope_for_all) < 0.5:
|
|
slope_for_all = [slope_deskew][0]
|
|
# old method
|
|
# slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
|
|
# text_patch_processed=textline_contours_postprocessing(gada)
|
|
|
|
except:
|
|
slope_for_all = 999
|
|
|
|
##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)
|
|
|
|
if slope_for_all == 999:
|
|
slope_for_all = [slope_deskew][0]
|
|
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
|
|
##slope_for_all=slope_biggest
|
|
##elif slope_for_all==999:
|
|
##slope_for_all=slope_biggest
|
|
slopes_per_each_subprocess.append(slope_for_all)
|
|
|
|
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
|
|
|
crop_img, crop_coor = self.crop_image_inside_box(boxes_text[mv], image_page_rotated)
|
|
if abs(slope_for_all) < 45:
|
|
|
|
# all_box_coord.append(crop_coor)
|
|
|
|
textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
|
|
cnt_o_t_max = contours_par_per_process[mv]
|
|
|
|
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
|
|
|
|
mask_biggest = np.zeros(mask_texts_only.shape)
|
|
mask_biggest = cv2.fillPoly(mask_biggest, pts=[cnt_o_t_max], color=(1, 1, 1))
|
|
|
|
mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w]
|
|
|
|
textline_biggest_region = mask_biggest * textline_mask_tot_ea
|
|
|
|
# print(slope_for_all,'slope_for_all')
|
|
textline_rotated_seperated = self.seperate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col, slope_for_all)
|
|
|
|
# new line added
|
|
##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
|
|
textline_rotated_seperated[mask_region_in_patch_region[:, :] != 1] = 0
|
|
# till here
|
|
|
|
textline_cnt_seperated[y : y + h, x : x + w] = textline_rotated_seperated
|
|
textline_region_in_image[y : y + h, x : x + w] = textline_rotated_seperated
|
|
|
|
# plt.imshow(textline_region_in_image)
|
|
# plt.show()
|
|
|
|
# plt.imshow(textline_cnt_seperated)
|
|
# plt.show()
|
|
|
|
pixel_img = 1
|
|
cnt_textlines_in_image = self.return_contours_of_interested_textline(textline_region_in_image, pixel_img)
|
|
|
|
textlines_cnt_per_region = []
|
|
for jjjj in range(len(cnt_textlines_in_image)):
|
|
mask_biggest2 = np.zeros(mask_texts_only.shape)
|
|
mask_biggest2 = cv2.fillPoly(mask_biggest2, pts=[cnt_textlines_in_image[jjjj]], color=(1, 1, 1))
|
|
if num_col + 1 == 1:
|
|
mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=5)
|
|
else:
|
|
|
|
mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=4)
|
|
|
|
pixel_img = 1
|
|
|
|
mask_biggest2 = self.resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par))
|
|
|
|
cnt_textlines_in_image_ind = self.return_contours_of_interested_textline(mask_biggest2, pixel_img)
|
|
|
|
try:
|
|
# textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0]/scale_par)
|
|
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
|
|
except:
|
|
pass
|
|
|
|
else:
|
|
slope_first = 0
|
|
add_boxes_coor_into_textlines = True
|
|
textlines_cnt_per_region = self.textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], slope_first, add_boxes_coor_into_textlines)
|
|
add_boxes_coor_into_textlines = False
|
|
# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
|
|
|
|
# textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
|
|
# index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
|
|
|
|
textlines_rectangles_per_each_subprocess.append(textlines_cnt_per_region)
|
|
# all_found_texline_polygons.append(cnt_clean_rot)
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
|
|
|
|
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
|
|
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
|
|
all_box_coord_per_process.append(crop_coor)
|
|
|
|
queue_of_all_params.put([textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours, slopes_per_each_subprocess])
|
|
|
|
def do_work_of_slopes_new(self, queue_of_all_params, boxes_text, textline_mask_tot_ea, contours_per_process, contours_par_per_process, indexes_r_con_per_pro, image_page_rotated, slope_deskew):
|
|
|
|
slopes_per_each_subprocess = []
|
|
bounding_box_of_textregion_per_each_subprocess = []
|
|
textlines_rectangles_per_each_subprocess = []
|
|
contours_textregion_per_each_subprocess = []
|
|
contours_textregion_par_per_each_subprocess = []
|
|
all_box_coord_per_process = []
|
|
index_by_text_region_contours = []
|
|
slope_biggest = 0
|
|
|
|
for mv in range(len(boxes_text)):
|
|
|
|
crop_img, crop_coor = self.crop_image_inside_box(boxes_text[mv], image_page_rotated)
|
|
|
|
# all_box_coord.append(crop_coor)
|
|
|
|
denoised = None
|
|
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
all_text_region_raw = all_text_region_raw.astype(np.uint8)
|
|
|
|
img_int_p = all_text_region_raw[:, :] # self.all_text_region_raw[mv]
|
|
|
|
img_int_p = cv2.erode(img_int_p, self.kernel, iterations=2)
|
|
|
|
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
|
|
|
|
slopes_per_each_subprocess.append(0)
|
|
|
|
slope_for_all = [slope_deskew][0]
|
|
|
|
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
###cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
|
|
cnt_clean_rot = self.textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], 0)
|
|
|
|
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
|
|
|
|
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
|
# all_found_texline_polygons.append(cnt_clean_rot)
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
|
|
else:
|
|
|
|
try:
|
|
textline_con, hierachy = self.return_contours_of_image(img_int_p)
|
|
textline_con_fil = self.filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.00008)
|
|
|
|
y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_des = int(y_diff_mean * (4.0 / 40.0))
|
|
|
|
if sigma_des < 1:
|
|
sigma_des = 1
|
|
|
|
img_int_p[img_int_p > 0] = 1
|
|
# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
|
|
slope_for_all = self.return_deskew_slop(img_int_p, sigma_des)
|
|
|
|
if abs(slope_for_all) <= 0.5:
|
|
slope_for_all = [slope_deskew][0]
|
|
|
|
except:
|
|
slope_for_all = 999
|
|
|
|
##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)
|
|
|
|
if slope_for_all == 999:
|
|
slope_for_all = [slope_deskew][0]
|
|
##if np.abs(slope_for_all)>32.5 and slope_for_all!=999:
|
|
##slope_for_all=slope_biggest
|
|
##elif slope_for_all==999:
|
|
##slope_for_all=slope_biggest
|
|
slopes_per_each_subprocess.append(slope_for_all)
|
|
|
|
slope_first = 0
|
|
|
|
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
|
|
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
|
|
|
|
# plt.imshow(mask_only_con_region)
|
|
# plt.show()
|
|
all_text_region_raw = np.copy(textline_mask_tot_ea[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]])
|
|
mask_only_con_region = mask_only_con_region[boxes_text[mv][1] : boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0] : boxes_text[mv][0] + boxes_text[mv][2]]
|
|
|
|
##plt.imshow(textline_mask_tot_ea)
|
|
##plt.show()
|
|
##plt.imshow(all_text_region_raw)
|
|
##plt.show()
|
|
|
|
##plt.imshow(mask_only_con_region)
|
|
##plt.show()
|
|
|
|
all_text_region_raw[mask_only_con_region == 0] = 0
|
|
###cnt_clean_rot=self.textline_contours_postprocessing(all_text_region_raw,slopes[jj],contours_only_text_parent[jj],boxes_text[jj],slope_first)
|
|
cnt_clean_rot = self.textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], slope_first)
|
|
|
|
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
|
|
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
|
|
# all_found_texline_polygons.append(cnt_clean_rot)
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
|
|
|
|
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
|
|
contours_textregion_par_per_each_subprocess.append(contours_par_per_process[mv])
|
|
all_box_coord_per_process.append(crop_coor)
|
|
|
|
queue_of_all_params.put([slopes_per_each_subprocess, textlines_rectangles_per_each_subprocess, bounding_box_of_textregion_per_each_subprocess, contours_textregion_per_each_subprocess, contours_textregion_par_per_each_subprocess, all_box_coord_per_process, index_by_text_region_contours])
|
|
|
|
def get_text_region_contours_and_boxes(self, image):
|
|
rgb_class_of_texts = (1, 1, 1)
|
|
mask_texts = np.all(image == rgb_class_of_texts, axis=-1)
|
|
|
|
image = np.repeat(mask_texts[:, :, np.newaxis], 3, axis=2) * 255
|
|
image = image.astype(np.uint8)
|
|
|
|
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, self.kernel)
|
|
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, self.kernel)
|
|
|
|
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
|
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
|
|
self.boxes = []
|
|
|
|
for jj in range(len(main_contours)):
|
|
x, y, w, h = cv2.boundingRect(main_contours[jj])
|
|
self.boxes.append([x, y, w, h])
|
|
|
|
return main_contours
|
|
|
|
def boosting_headers_by_longshot_region_segmentation(self, textregion_pre_p, textregion_pre_np, img_only_text):
|
|
textregion_pre_p_org = np.copy(textregion_pre_p)
|
|
# 4 is drop capitals
|
|
headers_in_longshot = (textregion_pre_np[:, :, 0] == 2) * 1
|
|
# headers_in_longshot= ( (textregion_pre_np[:,:,0]==2) | (textregion_pre_np[:,:,0]==1) )*1
|
|
textregion_pre_p[:, :, 0][(headers_in_longshot[:, :] == 1) & (textregion_pre_p[:, :, 0] != 4)] = 2
|
|
textregion_pre_p[:, :, 0][textregion_pre_p[:, :, 0] == 1] = 0
|
|
# textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7) & (textregion_pre_p[:,:,0]!=2)]=1 # eralier it was so, but by this manner the drop capitals are alse deleted
|
|
textregion_pre_p[:, :, 0][(img_only_text[:, :] == 1) & (textregion_pre_p[:, :, 0] != 7) & (textregion_pre_p[:, :, 0] != 4) & (textregion_pre_p[:, :, 0] != 2)] = 1
|
|
return textregion_pre_p
|
|
|
|
def boosting_text_only_regions_by_header(self, textregion_pre_np, img_only_text):
|
|
result = ((img_only_text[:, :] == 1) | (textregion_pre_np[:, :, 0] == 2)) * 1
|
|
return result
|
|
|
|
def get_all_image_patches_coordination(self, image_page):
|
|
self.all_box_coord = []
|
|
for jk in range(len(self.boxes)):
|
|
_, crop_coor = self.crop_image_inside_box(self.boxes[jk], image_page)
|
|
self.all_box_coord.append(crop_coor)
|
|
|
|
def textline_contours(self, img, patches, scaler_h, scaler_w):
|
|
|
|
if patches:
|
|
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
|
|
if not patches:
|
|
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir_np)
|
|
|
|
##img = self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
|
|
img_org = np.copy(img)
|
|
img_h = img_org.shape[0]
|
|
img_w = img_org.shape[1]
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
|
|
|
|
prediction_textline = self.do_prediction(patches, img, model_textline)
|
|
|
|
prediction_textline = self.resize_image(prediction_textline, img_h, img_w)
|
|
|
|
patches = False
|
|
prediction_textline_longshot = self.do_prediction(patches, img, model_textline)
|
|
|
|
prediction_textline_longshot_true_size = self.resize_image(prediction_textline_longshot, img_h, img_w)
|
|
|
|
# scaler_w=1.5
|
|
# scaler_h=1.5
|
|
# patches=True
|
|
# img= self.resize_image(img_org, int(img_org.shape[0]*scaler_h), int(img_org.shape[1]*scaler_w))
|
|
|
|
# prediction_textline_streched=self.do_prediction(patches,img,model_textline)
|
|
|
|
# prediction_textline_streched= self.resize_image(prediction_textline_streched, img_h, img_w)
|
|
|
|
##plt.imshow(prediction_textline_streched[:,:,0])
|
|
##plt.show()
|
|
|
|
# sys.exit()
|
|
session_textline.close()
|
|
|
|
del model_textline
|
|
del session_textline
|
|
del img
|
|
del img_org
|
|
|
|
gc.collect()
|
|
return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
|
|
|
|
def get_textlines_for_each_textregions(self, textline_mask_tot, boxes):
|
|
textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1)
|
|
self.area_of_cropped = []
|
|
self.all_text_region_raw = []
|
|
for jk in range(len(boxes)):
|
|
crop_img, crop_coor = self.crop_image_inside_box(boxes[jk], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
|
|
crop_img = crop_img.astype(np.uint8)
|
|
self.all_text_region_raw.append(crop_img[:, :, 0])
|
|
self.area_of_cropped.append(crop_img.shape[0] * crop_img.shape[1])
|
|
|
|
def seperate_lines_new_inside_teils2(self, img_patch, thetha):
|
|
|
|
(h, w) = img_patch.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
|
|
x_d = M[0, 2]
|
|
y_d = M[1, 2]
|
|
|
|
thetha = thetha / 180.0 * np.pi
|
|
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
|
|
# contour_text_interest_copy = contour_text_interest.copy()
|
|
|
|
# x_cont = contour_text_interest[:, 0, 0]
|
|
# y_cont = contour_text_interest[:, 0, 1]
|
|
# x_cont = x_cont - np.min(x_cont)
|
|
# y_cont = y_cont - np.min(y_cont)
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_patch.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_patch.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
textline_patch_sum_along_width = img_patch.sum(axis=1)
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
|
|
y_padded = np.zeros(len(y) + 40)
|
|
y_padded[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
if 1 > 0:
|
|
|
|
try:
|
|
|
|
y_padded_smoothed_e = gaussian_filter1d(y_padded, 2)
|
|
y_padded_up_to_down_e = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40)
|
|
y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e
|
|
y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
|
|
|
|
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
|
|
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks_e[:]
|
|
peaks_neg_new = peaks_neg_e[:]
|
|
|
|
clusters_to_be_deleted = []
|
|
if len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
else:
|
|
peaks_new_tot = peaks_e[:]
|
|
|
|
textline_con, hierachy = self.return_contours_of_image(img_patch)
|
|
textline_con_fil = self.filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
|
|
# print(sigma_gaus,'sigma_gaus')
|
|
except:
|
|
sigma_gaus = 12
|
|
if sigma_gaus < 3:
|
|
sigma_gaus = 3
|
|
# print(sigma_gaus,'sigma')
|
|
|
|
y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus)
|
|
y_padded_up_to_down = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40)
|
|
y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down
|
|
y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(y_padded_smoothed, height=0)
|
|
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
|
|
|
|
peaks_new = peaks[:]
|
|
peaks_neg_new = peaks_neg[:]
|
|
|
|
try:
|
|
neg_peaks_max = np.max(y_padded_smoothed[peaks])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.24]
|
|
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
clusters_to_be_deleted = []
|
|
|
|
if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
|
|
|
|
if len(arg_neg_must_be_deleted) == 1:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
# plt.plot(y_padded_up_to_down_padded)
|
|
# plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_up_to_down_padded)
|
|
# plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_smoothed)
|
|
# plt.plot(peaks,y_padded_smoothed[peaks],'*')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_smoothed)
|
|
# plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
|
|
# plt.show()
|
|
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
except:
|
|
pass
|
|
|
|
else:
|
|
peaks_new_tot = peaks[:]
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
mean_value_of_peaks = np.mean(y_padded_smoothed[peaks])
|
|
std_value_of_peaks = np.std(y_padded_smoothed[peaks])
|
|
peaks_values = y_padded_smoothed[peaks]
|
|
|
|
###peaks_neg = peaks_neg - 20 - 20
|
|
###peaks = peaks - 20
|
|
peaks_neg_true = peaks_neg[:]
|
|
peaks_pos_true = peaks[:]
|
|
|
|
if len(peaks_neg_true) > 0:
|
|
peaks_neg_true = np.array(peaks_neg_true)
|
|
|
|
peaks_neg_true = peaks_neg_true - 20 - 20
|
|
|
|
# print(peaks_neg_true)
|
|
for i in range(len(peaks_neg_true)):
|
|
img_patch[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0
|
|
|
|
else:
|
|
pass
|
|
|
|
if len(peaks_pos_true) > 0:
|
|
peaks_pos_true = np.array(peaks_pos_true)
|
|
peaks_pos_true = peaks_pos_true - 20
|
|
|
|
for i in range(len(peaks_pos_true)):
|
|
##img_patch[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1
|
|
img_patch[peaks_pos_true[i] - 6 : peaks_pos_true[i] + 6, :] = 1
|
|
else:
|
|
pass
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
# img_patch = cv2.erode(img_patch,kernel,iterations = 3)
|
|
#######################img_patch = cv2.erode(img_patch,kernel,iterations = 2)
|
|
img_patch = cv2.erode(img_patch, kernel, iterations=1)
|
|
return img_patch
|
|
|
|
def seperate_lines_new_inside_teils(self, img_path, thetha):
|
|
(h, w) = img_path.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
|
|
x_d = M[0, 2]
|
|
y_d = M[1, 2]
|
|
|
|
thetha = thetha / 180.0 * np.pi
|
|
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_path.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_path.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
mada_n = img_path.sum(axis=1)
|
|
|
|
##plt.plot(mada_n)
|
|
##plt.show()
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = mada_n[:] # [first_nonzero:last_nonzero]
|
|
y_help = np.zeros(len(y) + 40)
|
|
y_help[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
if len(peaks_real) <= 2 and len(peaks_real) > 1:
|
|
sigma_gaus = 10
|
|
else:
|
|
sigma_gaus = 5
|
|
|
|
z = gaussian_filter1d(y_help, sigma_gaus)
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
zneg = np.zeros(len(zneg_rev) + 40)
|
|
zneg[20 : len(zneg_rev) + 20] = zneg_rev
|
|
zneg = gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
for nn in range(len(peaks_neg)):
|
|
if peaks_neg[nn] > len(z) - 1:
|
|
peaks_neg[nn] = len(z) - 1
|
|
if peaks_neg[nn] < 0:
|
|
peaks_neg[nn] = 0
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg))
|
|
|
|
cut_off = 20
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks_neg)):
|
|
if i == 0:
|
|
forest.append(peaks_neg[i])
|
|
if i < (len(peaks_neg) - 1):
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg[i + 1])
|
|
if i == (len(peaks_neg) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
diff_peaks_pos = np.abs(np.diff(peaks))
|
|
|
|
cut_off = 20
|
|
peaks_pos_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks)):
|
|
if i == 0:
|
|
forest.append(peaks[i])
|
|
if i < (len(peaks) - 1):
|
|
if diff_peaks_pos[i] <= cut_off:
|
|
forest.append(peaks[i + 1])
|
|
if diff_peaks_pos[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not self.isNaN(forest[np.argmax(z[forest])]):
|
|
peaks_pos_true.append(forest[np.argmax(z[forest])])
|
|
forest = []
|
|
forest.append(peaks[i + 1])
|
|
if i == (len(peaks) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not self.isNaN(forest[np.argmax(z[forest])]):
|
|
peaks_pos_true.append(forest[np.argmax(z[forest])])
|
|
|
|
# print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss')
|
|
|
|
if len(peaks_neg_true) > 0:
|
|
peaks_neg_true = np.array(peaks_neg_true)
|
|
"""
|
|
#plt.figure(figsize=(40,40))
|
|
#plt.subplot(1,2,1)
|
|
#plt.title('Textline segmentation von Textregion')
|
|
#plt.imshow(img_path)
|
|
#plt.xlabel('X')
|
|
#plt.ylabel('Y')
|
|
#plt.subplot(1,2,2)
|
|
#plt.title('Dichte entlang X')
|
|
#base = pyplot.gca().transData
|
|
#rot = transforms.Affine2D().rotate_deg(90)
|
|
#plt.plot(zneg,np.array(range(len(zneg))))
|
|
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
|
|
#plt.gca().invert_yaxis()
|
|
|
|
#plt.xlabel('Dichte')
|
|
#plt.ylabel('Y')
|
|
##plt.plot([0,len(y)], [grenze,grenze])
|
|
#plt.show()
|
|
"""
|
|
peaks_neg_true = peaks_neg_true - 20 - 20
|
|
|
|
# print(peaks_neg_true)
|
|
for i in range(len(peaks_neg_true)):
|
|
img_path[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0
|
|
|
|
else:
|
|
pass
|
|
|
|
if len(peaks_pos_true) > 0:
|
|
peaks_pos_true = np.array(peaks_pos_true)
|
|
peaks_pos_true = peaks_pos_true - 20
|
|
|
|
for i in range(len(peaks_pos_true)):
|
|
img_path[peaks_pos_true[i] - 8 : peaks_pos_true[i] + 8, :] = 1
|
|
else:
|
|
pass
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
# img_path = cv2.erode(img_path,kernel,iterations = 3)
|
|
img_path = cv2.erode(img_path, kernel, iterations=2)
|
|
return img_path
|
|
|
|
def seperate_lines_new(self, img_path, thetha, num_col):
|
|
|
|
if num_col == 1:
|
|
num_patches = int(img_path.shape[1] / 200.0)
|
|
else:
|
|
num_patches = int(img_path.shape[1] / 100.0)
|
|
# num_patches=int(img_path.shape[1]/200.)
|
|
if num_patches == 0:
|
|
num_patches = 1
|
|
(h, w) = img_path.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
|
|
x_d = M[0, 2]
|
|
y_d = M[1, 2]
|
|
|
|
thetha = thetha / 180.0 * np.pi
|
|
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_path.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_path.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
mada_n = img_path.sum(axis=1)
|
|
|
|
##plt.plot(mada_n)
|
|
##plt.show()
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = mada_n[:] # [first_nonzero:last_nonzero]
|
|
y_help = np.zeros(len(y) + 40)
|
|
y_help[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
if len(peaks_real) <= 2 and len(peaks_real) > 1:
|
|
sigma_gaus = 10
|
|
else:
|
|
sigma_gaus = 6
|
|
|
|
z = gaussian_filter1d(y_help, sigma_gaus)
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
zneg = np.zeros(len(zneg_rev) + 40)
|
|
zneg[20 : len(zneg_rev) + 20] = zneg_rev
|
|
zneg = gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
for nn in range(len(peaks_neg)):
|
|
if peaks_neg[nn] > len(z) - 1:
|
|
peaks_neg[nn] = len(z) - 1
|
|
if peaks_neg[nn] < 0:
|
|
peaks_neg[nn] = 0
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg))
|
|
cut_off = 20
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks_neg)):
|
|
if i == 0:
|
|
forest.append(peaks_neg[i])
|
|
if i < (len(peaks_neg) - 1):
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
# print(len(z),forest)
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg[i + 1])
|
|
if i == (len(peaks_neg) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
peaks_neg_true = np.array(peaks_neg_true)
|
|
|
|
"""
|
|
#plt.figure(figsize=(40,40))
|
|
#plt.subplot(1,2,1)
|
|
#plt.title('Textline segmentation von Textregion')
|
|
#plt.imshow(img_path)
|
|
#plt.xlabel('X')
|
|
#plt.ylabel('Y')
|
|
#plt.subplot(1,2,2)
|
|
#plt.title('Dichte entlang X')
|
|
#base = pyplot.gca().transData
|
|
#rot = transforms.Affine2D().rotate_deg(90)
|
|
#plt.plot(zneg,np.array(range(len(zneg))))
|
|
#plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*')
|
|
#plt.gca().invert_yaxis()
|
|
|
|
#plt.xlabel('Dichte')
|
|
#plt.ylabel('Y')
|
|
##plt.plot([0,len(y)], [grenze,grenze])
|
|
#plt.show()
|
|
"""
|
|
|
|
peaks_neg_true = peaks_neg_true - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
|
|
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
|
|
|
|
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
|
|
|
|
##plt.imshow(img_patch_ineterst)
|
|
##plt.show()
|
|
|
|
length_x = int(img_path.shape[1] / float(num_patches))
|
|
margin = int(0.04 * length_x)
|
|
|
|
width_mid = length_x - 2 * margin
|
|
|
|
nxf = img_path.shape[1] / float(width_mid)
|
|
|
|
if nxf > int(nxf):
|
|
nxf = int(nxf) + 1
|
|
else:
|
|
nxf = int(nxf)
|
|
|
|
slopes_tile_wise = []
|
|
for i in range(nxf):
|
|
if i == 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
elif i > 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
|
|
if index_x_u > img_path.shape[1]:
|
|
index_x_u = img_path.shape[1]
|
|
index_x_d = img_path.shape[1] - length_x
|
|
|
|
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
|
|
|
|
sigma = 2
|
|
try:
|
|
slope_xline = self.return_deskew_slop(img_xline, sigma)
|
|
except:
|
|
slope_xline = 0
|
|
slopes_tile_wise.append(slope_xline)
|
|
# print(slope_xline,'xlineeee')
|
|
img_line_rotated = self.rotate_image(img_xline, slope_xline)
|
|
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
|
|
|
|
"""
|
|
|
|
xline=np.linspace(0,img_path.shape[1],nx)
|
|
slopes_tile_wise=[]
|
|
|
|
for ui in range( nx-1 ):
|
|
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
|
|
|
|
|
|
##plt.imshow(img_xline)
|
|
##plt.show()
|
|
|
|
sigma=3
|
|
try:
|
|
slope_xline=self.return_deskew_slop(img_xline,sigma)
|
|
except:
|
|
slope_xline=0
|
|
slopes_tile_wise.append(slope_xline)
|
|
print(slope_xline,'xlineeee')
|
|
img_line_rotated=self.rotate_image(img_xline,slope_xline)
|
|
|
|
##plt.imshow(img_line_rotated)
|
|
##plt.show()
|
|
"""
|
|
|
|
# dis_up=peaks_neg_true[14]-peaks_neg_true[0]
|
|
# dis_down=peaks_neg_true[18]-peaks_neg_true[14]
|
|
|
|
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
|
|
|
|
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
|
|
|
|
for i in range(nxf):
|
|
if i == 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
elif i > 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
|
|
if index_x_u > img_path.shape[1]:
|
|
index_x_u = img_path.shape[1]
|
|
index_x_d = img_path.shape[1] - length_x
|
|
|
|
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
|
|
|
|
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
|
|
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
|
|
|
|
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
|
|
|
|
img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
|
|
##plt.imshow(img_xline)
|
|
##plt.show()
|
|
img_line_rotated = self.rotate_image(img_resized, slopes_tile_wise[i])
|
|
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
|
|
|
|
img_patch_seperated = self.seperate_lines_new_inside_teils(img_line_rotated, 0)
|
|
|
|
##plt.imshow(img_patch_seperated)
|
|
##plt.show()
|
|
img_patch_seperated_returned = self.rotate_image(img_patch_seperated, -slopes_tile_wise[i])
|
|
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
|
|
|
|
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
|
|
|
|
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
|
|
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
|
|
|
|
"""
|
|
for ui in range( nx-1 ):
|
|
img_xline=img_patch_ineterst[:,int(xline[ui]):int(xline[ui+1])]
|
|
|
|
|
|
img_int=np.zeros((img_xline.shape[0],img_xline.shape[1]))
|
|
img_int[:,:]=img_xline[:,:]#img_patch_org[:,:,0]
|
|
|
|
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(3) ) ))
|
|
|
|
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
|
|
##plt.imshow(img_xline)
|
|
##plt.show()
|
|
img_line_rotated=self.rotate_image(img_resized,slopes_tile_wise[ui])
|
|
|
|
|
|
#img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)
|
|
|
|
img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)
|
|
|
|
img_patch_seperated_returned=self.rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
|
|
##plt.imshow(img_patch_seperated)
|
|
##plt.show()
|
|
print(img_patch_seperated_returned.shape)
|
|
#plt.imshow(img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ])
|
|
#plt.show()
|
|
|
|
img_patch_ineterst_revised[:,int(xline[ui]):int(xline[ui+1])]=img_patch_seperated_returned[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]
|
|
|
|
|
|
"""
|
|
|
|
# print(img_patch_ineterst_revised.shape,np.unique(img_patch_ineterst_revised))
|
|
##plt.imshow(img_patch_ineterst_revised)
|
|
##plt.show()
|
|
return img_patch_ineterst_revised
|
|
|
|
def seperate_lines_new2(self, img_path, thetha, num_col, slope_region):
|
|
|
|
if num_col == 1:
|
|
num_patches = int(img_path.shape[1] / 200.0)
|
|
else:
|
|
num_patches = int(img_path.shape[1] / 140.0)
|
|
# num_patches=int(img_path.shape[1]/200.)
|
|
if num_patches == 0:
|
|
num_patches = 1
|
|
|
|
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
|
|
|
|
# plt.imshow(img_patch_ineterst)
|
|
# plt.show()
|
|
|
|
length_x = int(img_path.shape[1] / float(num_patches))
|
|
# margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
|
|
margin = int(0.04 * length_x)
|
|
# print(margin,'margin')
|
|
# if margin<=4:
|
|
# margin = int(0.08 * length_x)
|
|
|
|
# margin=0
|
|
|
|
width_mid = length_x - 2 * margin
|
|
|
|
nxf = img_path.shape[1] / float(width_mid)
|
|
|
|
if nxf > int(nxf):
|
|
nxf = int(nxf) + 1
|
|
else:
|
|
nxf = int(nxf)
|
|
|
|
slopes_tile_wise = []
|
|
for i in range(nxf):
|
|
if i == 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
elif i > 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
|
|
if index_x_u > img_path.shape[1]:
|
|
index_x_u = img_path.shape[1]
|
|
index_x_d = img_path.shape[1] - length_x
|
|
|
|
# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
|
|
|
|
sigma = 2
|
|
try:
|
|
slope_xline = self.return_deskew_slop(img_xline, sigma)
|
|
except:
|
|
slope_xline = 0
|
|
|
|
if abs(slope_region) < 25 and abs(slope_xline) > 25:
|
|
slope_xline = [slope_region][0]
|
|
# if abs(slope_region)>70 and abs(slope_xline)<25:
|
|
# slope_xline=[slope_region][0]
|
|
slopes_tile_wise.append(slope_xline)
|
|
# print(slope_xline,'xlineeee')
|
|
img_line_rotated = self.rotate_image(img_xline, slope_xline)
|
|
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
|
|
|
|
# print(slopes_tile_wise,'slopes_tile_wise')
|
|
img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
|
|
|
|
img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
|
|
|
|
for i in range(nxf):
|
|
if i == 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
elif i > 0:
|
|
index_x_d = i * width_mid
|
|
index_x_u = index_x_d + length_x
|
|
|
|
if index_x_u > img_path.shape[1]:
|
|
index_x_u = img_path.shape[1]
|
|
index_x_d = img_path.shape[1] - length_x
|
|
|
|
img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
|
|
|
|
img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
|
|
img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
|
|
|
|
img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
|
|
|
|
img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
|
|
# plt.imshow(img_xline)
|
|
# plt.show()
|
|
img_line_rotated = self.rotate_image(img_resized, slopes_tile_wise[i])
|
|
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
|
|
|
|
img_patch_seperated = self.seperate_lines_new_inside_teils2(img_line_rotated, 0)
|
|
|
|
img_patch_seperated_returned = self.rotate_image(img_patch_seperated, -slopes_tile_wise[i])
|
|
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
|
|
|
|
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
|
|
|
|
img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
|
|
img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
|
|
|
|
# plt.imshow(img_patch_ineterst_revised)
|
|
# plt.show()
|
|
return img_patch_ineterst_revised
|
|
|
|
def seperate_lines(self, img_patch, contour_text_interest, thetha, x_help, y_help):
|
|
|
|
(h, w) = img_patch.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
|
|
x_d = M[0, 2]
|
|
y_d = M[1, 2]
|
|
|
|
thetha = thetha / 180.0 * np.pi
|
|
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
|
|
contour_text_interest_copy = contour_text_interest.copy()
|
|
|
|
x_cont = contour_text_interest[:, 0, 0]
|
|
y_cont = contour_text_interest[:, 0, 1]
|
|
x_cont = x_cont - np.min(x_cont)
|
|
y_cont = y_cont - np.min(y_cont)
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_patch.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_patch.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
textline_patch_sum_along_width = img_patch.sum(axis=1)
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
|
|
y_padded = np.zeros(len(y) + 40)
|
|
y_padded[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
if 1 > 0:
|
|
|
|
try:
|
|
|
|
y_padded_smoothed_e = gaussian_filter1d(y_padded, 2)
|
|
y_padded_up_to_down_e = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40)
|
|
y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e
|
|
y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
|
|
|
|
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
|
|
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks_e[:]
|
|
peaks_neg_new = peaks_neg_e[:]
|
|
|
|
clusters_to_be_deleted = []
|
|
if len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
else:
|
|
peaks_new_tot = peaks_e[:]
|
|
|
|
textline_con, hierachy = self.return_contours_of_image(img_patch)
|
|
textline_con_fil = self.filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
|
|
# print(sigma_gaus,'sigma_gaus')
|
|
except:
|
|
sigma_gaus = 12
|
|
if sigma_gaus < 3:
|
|
sigma_gaus = 3
|
|
# print(sigma_gaus,'sigma')
|
|
|
|
y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus)
|
|
y_padded_up_to_down = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40)
|
|
y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down
|
|
y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(y_padded_smoothed, height=0)
|
|
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
|
|
|
|
try:
|
|
neg_peaks_max = np.max(y_padded_smoothed[peaks])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42]
|
|
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
except:
|
|
arg_neg_must_be_deleted = []
|
|
arg_diff_cluster = []
|
|
|
|
try:
|
|
peaks_new = peaks[:]
|
|
peaks_neg_new = peaks_neg[:]
|
|
clusters_to_be_deleted = []
|
|
|
|
if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
|
|
|
|
if len(arg_neg_must_be_deleted) == 1:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
##plt.plot(y_padded_up_to_down_padded)
|
|
##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
|
|
##plt.show()
|
|
|
|
##plt.plot(y_padded_up_to_down_padded)
|
|
##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*')
|
|
##plt.show()
|
|
|
|
##plt.plot(y_padded_smoothed)
|
|
##plt.plot(peaks,y_padded_smoothed[peaks],'*')
|
|
##plt.show()
|
|
|
|
##plt.plot(y_padded_smoothed)
|
|
##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*')
|
|
##plt.show()
|
|
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
else:
|
|
peaks_new_tot = peaks[:]
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
except:
|
|
pass
|
|
|
|
mean_value_of_peaks = np.mean(y_padded_smoothed[peaks])
|
|
std_value_of_peaks = np.std(y_padded_smoothed[peaks])
|
|
peaks_values = y_padded_smoothed[peaks]
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
for jj in range(len(peaks_neg)):
|
|
if peaks_neg[jj] > len(x) - 1:
|
|
peaks_neg[jj] = len(x) - 1
|
|
|
|
for jj in range(len(peaks)):
|
|
if peaks[jj] > len(x) - 1:
|
|
peaks[jj] = len(x) - 1
|
|
|
|
textline_boxes = []
|
|
textline_boxes_rot = []
|
|
|
|
if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == (len(peaks) - 1):
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
else:
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
|
|
if point_down_narrow >= img_patch.shape[0]:
|
|
point_down_narrow = img_patch.shape[0] - 2
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
elif len(peaks) < 1:
|
|
pass
|
|
|
|
elif len(peaks) == 1:
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
# x_min = x_min_cont
|
|
# x_max = x_max_cont
|
|
|
|
y_min = y_min_cont
|
|
y_max = y_max_cont
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(y_min)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(y_min)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(y_max)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(y_max)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]]))
|
|
|
|
elif len(peaks) == 2:
|
|
dis_to_next = np.abs(peaks[1] - peaks[0])
|
|
for jj in range(len(peaks)):
|
|
if jj == 0:
|
|
point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
elif jj == 1:
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside)
|
|
x_max = np.max(xvinside)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
else:
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == 0:
|
|
dis_to_next = peaks[jj + 1] - peaks[jj]
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next)
|
|
elif jj == len(peaks) - 1:
|
|
dis_to_next = peaks[jj] - peaks[jj - 1]
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.7 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
else:
|
|
dis_to_next_down = peaks[jj + 1] - peaks[jj]
|
|
dis_to_next_up = peaks[jj] - peaks[jj - 1]
|
|
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next_up)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)])
|
|
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
|
|
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
|
|
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
x_min_rot1 = x_min_rot1 - x_help
|
|
x_max_rot2 = x_max_rot2 - x_help
|
|
x_max_rot3 = x_max_rot3 - x_help
|
|
x_min_rot4 = x_min_rot4 - x_help
|
|
|
|
point_up_rot1 = point_up_rot1 - y_help
|
|
point_up_rot2 = point_up_rot2 - y_help
|
|
point_down_rot3 = point_down_rot3 - y_help
|
|
point_down_rot4 = point_down_rot4 - y_help
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
return peaks, textline_boxes_rot
|
|
|
|
def return_rotated_contours(self, slope, img_patch):
|
|
dst = self.rotate_image(img_patch, slope)
|
|
dst = dst.astype(np.uint8)
|
|
dst = dst[:, :, 0]
|
|
dst[dst != 0] = 1
|
|
|
|
imgray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
|
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
|
|
contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
return contours
|
|
|
|
def seperate_lines_vertical_cont(self, img_patch, contour_text_interest, thetha, box_ind, add_boxes_coor_into_textlines):
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
pixel = 255
|
|
min_area = 0
|
|
max_area = 1
|
|
|
|
if len(img_patch.shape) == 3:
|
|
cnts_images = (img_patch[:, :, 0] == pixel) * 1
|
|
else:
|
|
cnts_images = (img_patch[:, :] == pixel) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
|
|
|
|
cont_final = []
|
|
###print(add_boxes_coor_into_textlines,'ikki')
|
|
for i in range(len(contours_imgs)):
|
|
img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3))
|
|
img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255))
|
|
|
|
img_contour = img_contour.astype(np.uint8)
|
|
|
|
img_contour = cv2.dilate(img_contour, kernel, iterations=4)
|
|
imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY)
|
|
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
|
|
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
|
|
##0]
|
|
##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
|
|
##if add_boxes_coor_into_textlines:
|
|
##print(np.shape(contours_text_rot[0]),'sjppo')
|
|
##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0]
|
|
##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1]
|
|
cont_final.append(contours_text_rot[0])
|
|
|
|
##print(cont_final,'nadizzzz')
|
|
return None, cont_final
|
|
|
|
def seperate_lines_vertical(self, img_patch, contour_text_interest, thetha):
|
|
|
|
thetha = thetha + 90
|
|
|
|
(h, w) = img_patch.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
M = cv2.getRotationMatrix2D(center, -thetha, 1.0)
|
|
x_d = M[0, 2]
|
|
y_d = M[1, 2]
|
|
|
|
thetha = thetha / 180.0 * np.pi
|
|
rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]])
|
|
contour_text_interest_copy = contour_text_interest.copy()
|
|
|
|
x_cont = contour_text_interest[:, 0, 0]
|
|
y_cont = contour_text_interest[:, 0, 1]
|
|
x_cont = x_cont - np.min(x_cont)
|
|
y_cont = y_cont - np.min(y_cont)
|
|
|
|
x_min_cont = 0
|
|
x_max_cont = img_patch.shape[1]
|
|
y_min_cont = 0
|
|
y_max_cont = img_patch.shape[0]
|
|
|
|
xv = np.linspace(x_min_cont, x_max_cont, 1000)
|
|
|
|
textline_patch_sum_along_width = img_patch.sum(axis=0)
|
|
|
|
first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None))
|
|
|
|
y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero]
|
|
y_padded = np.zeros(len(y) + 40)
|
|
y_padded[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
if 1 > 0:
|
|
|
|
try:
|
|
|
|
y_padded_smoothed_e = gaussian_filter1d(y_padded, 2)
|
|
y_padded_up_to_down_e = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40)
|
|
y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e
|
|
y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2)
|
|
|
|
peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0)
|
|
peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0)
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3]
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks_e[:]
|
|
peaks_neg_new = peaks_neg_e[:]
|
|
|
|
clusters_to_be_deleted = []
|
|
if len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
else:
|
|
peaks_new_tot = peaks_e[:]
|
|
|
|
textline_con, hierachy = self.return_contours_of_image(img_patch)
|
|
textline_con_fil = self.filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
|
|
# print(sigma_gaus,'sigma_gaus')
|
|
except:
|
|
sigma_gaus = 12
|
|
if sigma_gaus < 3:
|
|
sigma_gaus = 3
|
|
# print(sigma_gaus,'sigma')
|
|
|
|
y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus)
|
|
y_padded_up_to_down = -y_padded + np.max(y_padded)
|
|
y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40)
|
|
y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down
|
|
y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(y_padded_smoothed, height=0)
|
|
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
|
|
|
|
# plt.plot(y_padded_up_to_down_padded)
|
|
# plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
|
|
# plt.title('negs')
|
|
# plt.show()
|
|
|
|
# plt.plot(y_padded_smoothed)
|
|
# plt.plot(peaks,y_padded_smoothed[peaks],'*')
|
|
# plt.title('poss')
|
|
# plt.show()
|
|
|
|
neg_peaks_max = np.max(y_padded_up_to_down_padded[peaks_neg])
|
|
|
|
arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42]
|
|
|
|
diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted)
|
|
|
|
arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted)))
|
|
arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1]
|
|
|
|
peaks_new = peaks[:]
|
|
peaks_neg_new = peaks_neg[:]
|
|
clusters_to_be_deleted = []
|
|
|
|
if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0:
|
|
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1])
|
|
for i in range(len(arg_diff_cluster) - 1):
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1])
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :])
|
|
elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted[:])
|
|
|
|
if len(arg_neg_must_be_deleted) == 1:
|
|
clusters_to_be_deleted.append(arg_neg_must_be_deleted)
|
|
|
|
if len(clusters_to_be_deleted) > 0:
|
|
peaks_new_extra = []
|
|
for m in range(len(clusters_to_be_deleted)):
|
|
min_cluster = np.min(peaks[clusters_to_be_deleted[m]])
|
|
max_cluster = np.max(peaks[clusters_to_be_deleted[m]])
|
|
peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0))
|
|
for m1 in range(len(clusters_to_be_deleted[m])):
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]]
|
|
peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]]
|
|
|
|
peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]]
|
|
peaks_new_tot = []
|
|
for i1 in peaks_new:
|
|
peaks_new_tot.append(i1)
|
|
for i1 in peaks_new_extra:
|
|
peaks_new_tot.append(i1)
|
|
peaks_new_tot = np.sort(peaks_new_tot)
|
|
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
else:
|
|
peaks_new_tot = peaks[:]
|
|
peaks = peaks_new_tot[:]
|
|
peaks_neg = peaks_neg_new[:]
|
|
|
|
mean_value_of_peaks = np.mean(y_padded_smoothed[peaks])
|
|
std_value_of_peaks = np.std(y_padded_smoothed[peaks])
|
|
peaks_values = y_padded_smoothed[peaks]
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
for jj in range(len(peaks_neg)):
|
|
if peaks_neg[jj] > len(x) - 1:
|
|
peaks_neg[jj] = len(x) - 1
|
|
|
|
for jj in range(len(peaks)):
|
|
if peaks[jj] > len(x) - 1:
|
|
peaks[jj] = len(x) - 1
|
|
|
|
textline_boxes = []
|
|
textline_boxes_rot = []
|
|
|
|
if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3:
|
|
# print('11')
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == (len(peaks) - 1):
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
else:
|
|
dis_to_next_up = abs(peaks[jj] - peaks_neg[jj])
|
|
dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1])
|
|
|
|
if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0:
|
|
point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
else:
|
|
point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
|
|
point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0)
|
|
|
|
point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2)
|
|
|
|
if point_down_narrow >= img_patch.shape[0]:
|
|
point_down_narrow = img_patch.shape[0] - 2
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
elif len(peaks) < 1:
|
|
pass
|
|
|
|
elif len(peaks) == 1:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
|
|
y_min = y_min_cont
|
|
y_max = y_max_cont
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]]))
|
|
|
|
elif len(peaks) == 2:
|
|
dis_to_next = np.abs(peaks[1] - peaks[0])
|
|
for jj in range(len(peaks)):
|
|
if jj == 0:
|
|
point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
elif jj == 1:
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside)
|
|
x_max = np.max(xvinside)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
else:
|
|
for jj in range(len(peaks)):
|
|
|
|
if jj == 0:
|
|
dis_to_next = peaks[jj + 1] - peaks[jj]
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
if point_up < 0:
|
|
point_up = 1
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next)
|
|
elif jj == len(peaks) - 1:
|
|
dis_to_next = peaks[jj] - peaks[jj - 1]
|
|
# point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.7 * dis_to_next)
|
|
if point_down >= img_patch.shape[0]:
|
|
point_down = img_patch.shape[0] - 2
|
|
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
|
|
else:
|
|
dis_to_next_down = peaks[jj + 1] - peaks[jj]
|
|
dis_to_next_up = peaks[jj] - peaks[jj - 1]
|
|
|
|
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next_up)
|
|
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down)
|
|
|
|
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))]
|
|
distances = np.array(distances)
|
|
|
|
xvinside = xv[distances >= 0]
|
|
|
|
if len(xvinside) == 0:
|
|
x_min = x_min_cont
|
|
x_max = x_max_cont
|
|
else:
|
|
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
|
|
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
|
|
|
|
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
|
|
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
|
|
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
|
|
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
|
|
|
|
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
|
|
x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d
|
|
x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d
|
|
x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d
|
|
|
|
if x_min_rot1 < 0:
|
|
x_min_rot1 = 0
|
|
if x_min_rot4 < 0:
|
|
x_min_rot4 = 0
|
|
if point_up_rot1 < 0:
|
|
point_up_rot1 = 0
|
|
if point_up_rot2 < 0:
|
|
point_up_rot2 = 0
|
|
|
|
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
|
|
|
|
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
|
|
|
|
return peaks, textline_boxes_rot
|
|
|
|
def textline_contours_postprocessing(self, textline_mask, slope, contour_text_interest, box_ind, slope_first, add_boxes_coor_into_textlines=False):
|
|
|
|
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
|
|
textline_mask = textline_mask.astype(np.uint8)
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel)
|
|
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel)
|
|
textline_mask = cv2.erode(textline_mask, kernel, iterations=2)
|
|
# textline_mask = cv2.erode(textline_mask, kernel, iterations=1)
|
|
|
|
# print(textline_mask.shape[0]/float(textline_mask.shape[1]),'miz')
|
|
try:
|
|
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
|
|
# plt.imshow(textline_mask)
|
|
# plt.show()
|
|
|
|
# if abs(slope)>1:
|
|
# x_help=30
|
|
# y_help=2
|
|
# else:
|
|
# x_help=2
|
|
# y_help=2
|
|
|
|
x_help = 30
|
|
y_help = 2
|
|
|
|
textline_mask_help = np.zeros((textline_mask.shape[0] + int(2 * y_help), textline_mask.shape[1] + int(2 * x_help), 3))
|
|
textline_mask_help[y_help : y_help + textline_mask.shape[0], x_help : x_help + textline_mask.shape[1], :] = np.copy(textline_mask[:, :, :])
|
|
|
|
dst = self.rotate_image(textline_mask_help, slope)
|
|
dst = dst[:, :, 0]
|
|
dst[dst != 0] = 1
|
|
|
|
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
|
|
# plt.imshow(dst)
|
|
# plt.show()
|
|
|
|
contour_text_copy = contour_text_interest.copy()
|
|
|
|
contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[0]
|
|
contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
|
|
|
|
img_contour = np.zeros((box_ind[3], box_ind[2], 3))
|
|
img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=(255, 255, 255))
|
|
|
|
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
|
|
# plt.imshow(img_contour)
|
|
# plt.show()
|
|
|
|
img_contour_help = np.zeros((img_contour.shape[0] + int(2 * y_help), img_contour.shape[1] + int(2 * x_help), 3))
|
|
|
|
img_contour_help[y_help : y_help + img_contour.shape[0], x_help : x_help + img_contour.shape[1], :] = np.copy(img_contour[:, :, :])
|
|
|
|
img_contour_rot = self.rotate_image(img_contour_help, slope)
|
|
|
|
# plt.imshow(img_contour_rot_help)
|
|
# plt.show()
|
|
|
|
# plt.imshow(dst_help)
|
|
# plt.show()
|
|
|
|
# if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3:
|
|
# plt.imshow(img_contour_rot_help)
|
|
# plt.show()
|
|
|
|
# plt.imshow(dst_help)
|
|
# plt.show()
|
|
|
|
img_contour_rot = img_contour_rot.astype(np.uint8)
|
|
# dst_help = dst_help.astype(np.uint8)
|
|
imgrayrot = cv2.cvtColor(img_contour_rot, cv2.COLOR_BGR2GRAY)
|
|
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
|
|
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))]
|
|
ind_big_con = np.argmax(len_con_text_rot)
|
|
|
|
# print('juzaa')
|
|
if abs(slope) > 45:
|
|
# print(add_boxes_coor_into_textlines,'avval')
|
|
_, contours_rotated_clean = self.seperate_lines_vertical_cont(textline_mask, contours_text_rot[ind_big_con], box_ind, slope, add_boxes_coor_into_textlines=add_boxes_coor_into_textlines)
|
|
else:
|
|
_, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope, x_help, y_help)
|
|
|
|
except:
|
|
|
|
contours_rotated_clean = []
|
|
|
|
return contours_rotated_clean
|
|
|
|
def textline_contours_to_get_slope_correctly(self, textline_mask, img_patch, contour_interest):
|
|
|
|
slope_new = 0 # deskew_images(img_patch)
|
|
|
|
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
|
|
|
|
textline_mask = textline_mask.astype(np.uint8)
|
|
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, self.kernel)
|
|
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, self.kernel)
|
|
textline_mask = cv2.erode(textline_mask, self.kernel, iterations=1)
|
|
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, self.kernel)
|
|
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, self.kernel)
|
|
|
|
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.003)
|
|
|
|
textline_maskt = textline_mask[:, :, 0]
|
|
textline_maskt[textline_maskt != 0] = 1
|
|
|
|
peaks_point, _ = self.seperate_lines(textline_maskt, contour_interest, slope_new)
|
|
|
|
mean_dis = np.mean(np.diff(peaks_point))
|
|
|
|
len_x = thresh.shape[1]
|
|
|
|
slope_lines = []
|
|
contours_slope_new = []
|
|
|
|
for kk in range(len(main_contours)):
|
|
|
|
if len(main_contours[kk].shape) == 2:
|
|
xminh = np.min(main_contours[kk][:, 0])
|
|
xmaxh = np.max(main_contours[kk][:, 0])
|
|
|
|
yminh = np.min(main_contours[kk][:, 1])
|
|
ymaxh = np.max(main_contours[kk][:, 1])
|
|
elif len(main_contours[kk].shape) == 3:
|
|
xminh = np.min(main_contours[kk][:, 0, 0])
|
|
xmaxh = np.max(main_contours[kk][:, 0, 0])
|
|
|
|
yminh = np.min(main_contours[kk][:, 0, 1])
|
|
ymaxh = np.max(main_contours[kk][:, 0, 1])
|
|
|
|
if ymaxh - yminh <= mean_dis and (xmaxh - xminh) >= 0.3 * len_x: # xminh>=0.05*len_x and xminh<=0.4*len_x and xmaxh<=0.95*len_x and xmaxh>=0.6*len_x:
|
|
contours_slope_new.append(main_contours[kk])
|
|
|
|
rows, cols = thresh.shape[:2]
|
|
[vx, vy, x, y] = cv2.fitLine(main_contours[kk], cv2.DIST_L2, 0, 0.01, 0.01)
|
|
|
|
slope_lines.append((vy / vx) / np.pi * 180)
|
|
|
|
if len(slope_lines) >= 2:
|
|
|
|
slope = np.mean(slope_lines) # slope_true/np.pi*180
|
|
else:
|
|
slope = 999
|
|
|
|
else:
|
|
slope = 0
|
|
|
|
return slope
|
|
|
|
def return_contours_of_image(self, image):
|
|
|
|
if len(image.shape) == 2:
|
|
image = np.repeat(image[:, :, np.newaxis], 3, axis=2)
|
|
image = image.astype(np.uint8)
|
|
else:
|
|
image = image.astype(np.uint8)
|
|
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
return contours, hierachy
|
|
|
|
def find_contours_mean_y_diff(self, contours_main):
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
return np.mean(np.diff(np.sort(np.array(cy_main))))
|
|
|
|
def isNaN(self, num):
|
|
return num != num
|
|
|
|
def find_num_col_olddd(self, regions_without_seperators, sigma_, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=1)
|
|
|
|
meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
|
|
|
|
first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
|
|
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
|
|
|
last_nonzero = len(regions_without_seperators_0) - last_nonzero
|
|
|
|
y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
|
|
|
|
y_help = np.zeros(len(y) + 20)
|
|
|
|
y_help[10 : len(y) + 10] = y
|
|
|
|
x = np.array(range(len(y)))
|
|
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 20)
|
|
|
|
zneg[10 : len(zneg_rev) + 10] = zneg_rev
|
|
|
|
z = gaussian_filter1d(y, sigma_)
|
|
zneg = gaussian_filter1d(zneg, sigma_)
|
|
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
peaks_neg = peaks_neg - 10 - 10
|
|
|
|
last_nonzero = last_nonzero - 0 # 100
|
|
first_nonzero = first_nonzero + 0 # +100
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
|
|
|
|
peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])]
|
|
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
interest_neg = z[peaks_neg]
|
|
|
|
if interest_neg[0] < 0.1:
|
|
interest_neg = interest_neg[1:]
|
|
if interest_neg[len(interest_neg) - 1] < 0.1:
|
|
interest_neg = interest_neg[: len(interest_neg) - 1]
|
|
|
|
min_peaks_pos = np.min(interest_pos)
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
|
|
grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
|
|
|
interest_neg_fin = interest_neg # [(interest_neg<grenze)]
|
|
peaks_neg_fin = peaks_neg # [(interest_neg<grenze)]
|
|
interest_neg_fin = interest_neg # [(interest_neg<grenze)]
|
|
|
|
num_col = (len(interest_neg_fin)) + 1
|
|
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 30]
|
|
|
|
return interest_neg_fin
|
|
|
|
def find_num_col_deskew(self, regions_without_seperators, sigma_, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=1)
|
|
|
|
meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
|
|
|
|
first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
|
|
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
|
|
|
last_nonzero = len(regions_without_seperators_0) - last_nonzero
|
|
|
|
y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
|
|
|
|
y_help = np.zeros(len(y) + 20)
|
|
|
|
y_help[10 : len(y) + 10] = y
|
|
|
|
x = np.array(range(len(y)))
|
|
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 20)
|
|
|
|
zneg[10 : len(zneg_rev) + 10] = zneg_rev
|
|
|
|
z = gaussian_filter1d(y, sigma_)
|
|
zneg = gaussian_filter1d(zneg, sigma_)
|
|
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
peaks_neg = peaks_neg - 10 - 10
|
|
|
|
# print(np.std(z),'np.std(z)np.std(z)np.std(z)')
|
|
|
|
##plt.plot(z)
|
|
##plt.show()
|
|
|
|
##plt.imshow(regions_without_seperators)
|
|
##plt.show()
|
|
"""
|
|
last_nonzero=last_nonzero-0#100
|
|
first_nonzero=first_nonzero+0#+100
|
|
|
|
peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
|
|
|
|
peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
|
|
"""
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
interest_neg = z[peaks_neg]
|
|
|
|
min_peaks_pos = np.mean(interest_pos)
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
|
|
# print(interest_pos)
|
|
grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
|
|
|
interest_neg_fin = interest_neg[(interest_neg < grenze)]
|
|
peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
|
|
interest_neg_fin = interest_neg[(interest_neg < grenze)]
|
|
|
|
"""
|
|
if interest_neg[0]<0.1:
|
|
interest_neg=interest_neg[1:]
|
|
if interest_neg[len(interest_neg)-1]<0.1:
|
|
interest_neg=interest_neg[:len(interest_neg)-1]
|
|
|
|
|
|
|
|
min_peaks_pos=np.min(interest_pos)
|
|
min_peaks_neg=0#np.min(interest_neg)
|
|
|
|
|
|
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
|
|
grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
|
"""
|
|
# interest_neg_fin=interest_neg#[(interest_neg<grenze)]
|
|
# peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
|
|
# interest_neg_fin=interest_neg#[(interest_neg<grenze)]
|
|
|
|
num_col = (len(interest_neg_fin)) + 1
|
|
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 30]
|
|
|
|
# print(len(interest_neg_fin),np.mean(interest_neg_fin))
|
|
return interest_neg_fin, np.std(z)
|
|
|
|
def return_deskew_slop(self, img_patch_org, sigma_des, main_page=False):
|
|
|
|
if main_page and self.dir_of_all is not None:
|
|
|
|
plt.figure(figsize=(70, 40))
|
|
plt.rcParams["font.size"] = "50"
|
|
plt.subplot(1, 2, 1)
|
|
plt.imshow(img_patch_org)
|
|
plt.subplot(1, 2, 2)
|
|
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3), np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))), linewidth=8)
|
|
plt.xlabel("Density of textline prediction in direction of X axis", fontsize=60)
|
|
plt.ylabel("Height", fontsize=60)
|
|
plt.yticks([0, len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
|
|
plt.gca().invert_yaxis()
|
|
|
|
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_density_of_textline.png"))
|
|
# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
|
|
|
|
# img_patch_org=self.resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
|
|
|
|
# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
|
|
|
|
img_int = np.zeros((img_patch_org.shape[0], img_patch_org.shape[1]))
|
|
img_int[:, :] = img_patch_org[:, :] # img_patch_org[:,:,0]
|
|
|
|
img_resized = np.zeros((int(img_int.shape[0] * (1.8)), int(img_int.shape[1] * (2.6))))
|
|
|
|
img_resized[int(img_int.shape[0] * (0.4)) : int(img_int.shape[0] * (0.4)) + img_int.shape[0], int(img_int.shape[1] * (0.8)) : int(img_int.shape[1] * (0.8)) + img_int.shape[1]] = img_int[:, :]
|
|
|
|
if main_page and img_patch_org.shape[1] > img_patch_org.shape[0]:
|
|
|
|
# plt.imshow(img_resized)
|
|
# plt.show()
|
|
angels = np.array(
|
|
[
|
|
-45,
|
|
0,
|
|
45,
|
|
90,
|
|
]
|
|
) # np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
# plt.imshow(img_rot)
|
|
# plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
|
|
# neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
|
# print(var_spectrum,'var_spectrum')
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(rot,var_spectrum,'var_spectrum')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
angels = np.linspace(ang_int - 22.5, ang_int + 22.5, 100)
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
##plt.imshow(img_rot)
|
|
##plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(indexer,'indexer')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
elif main_page and img_patch_org.shape[1] <= img_patch_org.shape[0]:
|
|
|
|
# plt.imshow(img_resized)
|
|
# plt.show()
|
|
angels = np.linspace(-12, 12, 100) # np.array([0 , 45 , 90 , -45])
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
# plt.imshow(img_rot)
|
|
# plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
|
|
# neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
|
# print(var_spectrum,'var_spectrum')
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(rot,var_spectrum,'var_spectrum')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
if self.dir_of_all is not None:
|
|
print("galdi?")
|
|
plt.figure(figsize=(60, 30))
|
|
plt.rcParams["font.size"] = "50"
|
|
plt.plot(angels, np.array(var_res), "-o", markersize=25, linewidth=4)
|
|
plt.xlabel("angle", fontsize=50)
|
|
plt.ylabel("variance of sum of rotated textline in direction of x axis", fontsize=50)
|
|
|
|
plt.plot(angels[np.argmax(var_res)], var_res[np.argmax(np.array(var_res))], "*", markersize=50, label="Angle of deskewing=" + str("{:.2f}".format(angels[np.argmax(var_res)])) + r"$\degree$")
|
|
plt.legend(loc="best")
|
|
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_rotation_angle.png"))
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
early_slope_edge = 11
|
|
if abs(ang_int) > early_slope_edge and ang_int < 0:
|
|
|
|
angels = np.linspace(-90, -12, 100)
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
##plt.imshow(img_rot)
|
|
##plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(indexer,'indexer')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
elif abs(ang_int) > early_slope_edge and ang_int > 0:
|
|
|
|
angels = np.linspace(90, 12, 100)
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
##plt.imshow(img_rot)
|
|
##plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(indexer,'indexer')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
else:
|
|
|
|
angels = np.linspace(-25, 25, 60)
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
# plt.imshow(img_rot)
|
|
# plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
|
|
# neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
|
# print(var_spectrum,'var_spectrum')
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(rot,var_spectrum,'var_spectrum')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
# print(ang_int,'ang_int')
|
|
|
|
early_slope_edge = 22
|
|
if abs(ang_int) > early_slope_edge and ang_int < 0:
|
|
|
|
angels = np.linspace(-90, -25, 60)
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
##plt.imshow(img_rot)
|
|
##plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(indexer,'indexer')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
elif abs(ang_int) > early_slope_edge and ang_int > 0:
|
|
|
|
angels = np.linspace(90, 25, 60)
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
indexer = 0
|
|
for rot in angels:
|
|
img_rot = self.rotate_image(img_resized, rot)
|
|
##plt.imshow(img_rot)
|
|
##plt.show()
|
|
img_rot[img_rot != 0] = 1
|
|
# res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
|
try:
|
|
neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
|
|
# print(indexer,'indexer')
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
|
|
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
return ang_int
|
|
|
|
def return_deskew_slope_new(self, img_patch, sigma_des):
|
|
max_x_y = max(img_patch.shape[0], img_patch.shape[1])
|
|
|
|
##img_patch=self.resize_image(img_patch,max_x_y,max_x_y)
|
|
|
|
img_patch_copy = np.zeros((img_patch.shape[0], img_patch.shape[1]))
|
|
img_patch_copy[:, :] = img_patch[:, :] # img_patch_org[:,:,0]
|
|
|
|
img_patch_padded = np.zeros((int(max_x_y * (1.4)), int(max_x_y * (1.4))))
|
|
|
|
img_patch_padded_center_p = int(img_patch_padded.shape[0] / 2.0)
|
|
len_x_org_patch_half = int(img_patch_copy.shape[1] / 2.0)
|
|
len_y_org_patch_half = int(img_patch_copy.shape[0] / 2.0)
|
|
|
|
img_patch_padded[img_patch_padded_center_p - len_y_org_patch_half : img_patch_padded_center_p - len_y_org_patch_half + img_patch_copy.shape[0], img_patch_padded_center_p - len_x_org_patch_half : img_patch_padded_center_p - len_x_org_patch_half + img_patch_copy.shape[1]] = img_patch_copy[:, :]
|
|
# img_patch_padded[ int( img_patch_copy.shape[0]*(.1)):int( img_patch_copy.shape[0]*(.1))+img_patch_copy.shape[0] , int( img_patch_copy.shape[1]*(.8)):int( img_patch_copy.shape[1]*(.8))+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
|
|
angles = np.linspace(-25, 25, 80)
|
|
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
# plt.imshow(img_patch)
|
|
# plt.show()
|
|
indexer = 0
|
|
for rot in angles:
|
|
# print(rot,'rot')
|
|
img_rotated = self.rotate_image(img_patch_padded, rot)
|
|
img_rotated[img_rotated != 0] = 1
|
|
|
|
# plt.imshow(img_rotated)
|
|
# plt.show()
|
|
|
|
try:
|
|
neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3)
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
# print(var_res)
|
|
|
|
ang_int = angles[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
if abs(ang_int) > 15:
|
|
angles = np.linspace(-90, -50, 30)
|
|
res = []
|
|
num_of_peaks = []
|
|
index_cor = []
|
|
var_res = []
|
|
|
|
# plt.imshow(img_patch)
|
|
# plt.show()
|
|
indexer = 0
|
|
for rot in angles:
|
|
# print(rot,'rot')
|
|
img_rotated = self.rotate_image(img_patch_padded, rot)
|
|
img_rotated[img_rotated != 0] = 1
|
|
|
|
# plt.imshow(img_rotated)
|
|
# plt.show()
|
|
|
|
try:
|
|
neg_peaks, var_spectrum = self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated, sigma_des, 20.3)
|
|
res_me = np.mean(neg_peaks)
|
|
if res_me == 0:
|
|
res_me = 1000000000000000000000
|
|
else:
|
|
pass
|
|
|
|
res_num = len(neg_peaks)
|
|
except:
|
|
res_me = 1000000000000000000000
|
|
res_num = 0
|
|
var_spectrum = 0
|
|
if self.isNaN(res_me):
|
|
pass
|
|
else:
|
|
res.append(res_me)
|
|
var_res.append(var_spectrum)
|
|
num_of_peaks.append(res_num)
|
|
index_cor.append(indexer)
|
|
indexer = indexer + 1
|
|
|
|
try:
|
|
var_res = np.array(var_res)
|
|
# print(var_res)
|
|
|
|
ang_int = angles[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
|
except:
|
|
ang_int = 0
|
|
|
|
return ang_int
|
|
|
|
def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
|
|
slope_biggest = 0
|
|
slopes_sub = []
|
|
boxes_sub_new = []
|
|
poly_sub = []
|
|
for mv in range(len(boxes_per_process)):
|
|
|
|
crop_img, _ = self.crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
|
|
crop_img = crop_img[:, :, 0]
|
|
crop_img = cv2.erode(crop_img, self.kernel, iterations=2)
|
|
|
|
try:
|
|
textline_con, hierachy = self.return_contours_of_image(crop_img)
|
|
textline_con_fil = self.filter_contours_area_of_image(crop_img, textline_con, hierachy, max_area=1, min_area=0.0008)
|
|
y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil)
|
|
|
|
sigma_des = int(y_diff_mean * (4.0 / 40.0))
|
|
|
|
if sigma_des < 1:
|
|
sigma_des = 1
|
|
|
|
crop_img[crop_img > 0] = 1
|
|
slope_corresponding_textregion = self.return_deskew_slop(crop_img, sigma_des)
|
|
|
|
except:
|
|
slope_corresponding_textregion = 999
|
|
|
|
if slope_corresponding_textregion == 999:
|
|
slope_corresponding_textregion = slope_biggest
|
|
##if np.abs(slope_corresponding_textregion)>12.5 and slope_corresponding_textregion!=999:
|
|
##slope_corresponding_textregion=slope_biggest
|
|
##elif slope_corresponding_textregion==999:
|
|
##slope_corresponding_textregion=slope_biggest
|
|
slopes_sub.append(slope_corresponding_textregion)
|
|
|
|
cnt_clean_rot = self.textline_contours_postprocessing(crop_img, slope_corresponding_textregion, contours_per_process[mv], boxes_per_process[mv])
|
|
|
|
poly_sub.append(cnt_clean_rot)
|
|
boxes_sub_new.append(boxes_per_process[mv])
|
|
|
|
q.put(slopes_sub)
|
|
poly.put(poly_sub)
|
|
box_sub.put(boxes_sub_new)
|
|
|
|
def get_slopes_and_deskew(self, contours, textline_mask_tot):
|
|
|
|
slope_biggest = 0 # self.return_deskew_slop(img_int_p,sigma_des)
|
|
|
|
num_cores = cpu_count()
|
|
q = Queue()
|
|
poly = Queue()
|
|
box_sub = Queue()
|
|
|
|
processes = []
|
|
nh = np.linspace(0, len(self.boxes), num_cores + 1)
|
|
|
|
for i in range(num_cores):
|
|
boxes_per_process = self.boxes[int(nh[i]) : int(nh[i + 1])]
|
|
contours_per_process = contours[int(nh[i]) : int(nh[i + 1])]
|
|
processes.append(Process(target=self.do_work_of_slopes, args=(q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process)))
|
|
|
|
for i in range(num_cores):
|
|
processes[i].start()
|
|
|
|
self.slopes = []
|
|
self.all_found_texline_polygons = []
|
|
self.boxes = []
|
|
|
|
for i in range(num_cores):
|
|
slopes_for_sub_process = q.get(True)
|
|
boxes_for_sub_process = box_sub.get(True)
|
|
polys_for_sub_process = poly.get(True)
|
|
|
|
for j in range(len(slopes_for_sub_process)):
|
|
self.slopes.append(slopes_for_sub_process[j])
|
|
self.all_found_texline_polygons.append(polys_for_sub_process[j])
|
|
self.boxes.append(boxes_for_sub_process[j])
|
|
|
|
for i in range(num_cores):
|
|
processes[i].join()
|
|
|
|
def order_of_regions_old(self, textline_mask, contours_main):
|
|
mada_n = textline_mask.sum(axis=1)
|
|
y = mada_n[:]
|
|
|
|
y_help = np.zeros(len(y) + 40)
|
|
y_help[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
sigma_gaus = 8
|
|
|
|
z = gaussian_filter1d(y_help, sigma_gaus)
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 40)
|
|
zneg[20 : len(zneg_rev) + 20] = zneg_rev
|
|
zneg = gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
if contours_main != None:
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
|
|
if contours_main != None:
|
|
indexer_main = np.array(range(len(contours_main)))
|
|
|
|
if contours_main != None:
|
|
len_main = len(contours_main)
|
|
else:
|
|
len_main = 0
|
|
|
|
matrix_of_orders = np.zeros((len_main, 5))
|
|
|
|
matrix_of_orders[:, 0] = np.array(range(len_main))
|
|
|
|
matrix_of_orders[:len_main, 1] = 1
|
|
matrix_of_orders[len_main:, 1] = 2
|
|
|
|
matrix_of_orders[:len_main, 2] = cx_main
|
|
matrix_of_orders[:len_main, 3] = cy_main
|
|
|
|
matrix_of_orders[:len_main, 4] = np.array(range(len_main))
|
|
|
|
peaks_neg_new = []
|
|
peaks_neg_new.append(0)
|
|
for iii in range(len(peaks_neg)):
|
|
peaks_neg_new.append(peaks_neg[iii])
|
|
peaks_neg_new.append(textline_mask.shape[0])
|
|
|
|
final_indexers_sorted = []
|
|
for i in range(len(peaks_neg_new) - 1):
|
|
top = peaks_neg_new[i]
|
|
down = peaks_neg_new[i + 1]
|
|
|
|
indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
|
|
sorted_inside = np.argsort(cxs_in)
|
|
|
|
ind_in_int = indexes_in[sorted_inside]
|
|
|
|
for j in range(len(ind_in_int)):
|
|
final_indexers_sorted.append(int(ind_in_int[j]))
|
|
|
|
return final_indexers_sorted, matrix_of_orders
|
|
|
|
def order_and_id_of_texts_old(self, found_polygons_text_region, matrix_of_orders, indexes_sorted):
|
|
id_of_texts = []
|
|
order_of_texts = []
|
|
index_b = 0
|
|
for mm in range(len(found_polygons_text_region)):
|
|
id_of_texts.append("r" + str(index_b))
|
|
index_matrix = matrix_of_orders[:, 0][(matrix_of_orders[:, 1] == 1) & (matrix_of_orders[:, 4] == mm)]
|
|
order_of_texts.append(np.where(indexes_sorted == index_matrix)[0][0])
|
|
|
|
index_b += 1
|
|
|
|
order_of_texts
|
|
return order_of_texts, id_of_texts
|
|
|
|
def write_into_page_xml_only_textlines(self, contours, page_coord, all_found_texline_polygons, all_box_coord, dir_of_image):
|
|
|
|
found_polygons_text_region = contours
|
|
|
|
# create the file structure
|
|
data = ET.Element("PcGts")
|
|
|
|
data.set("xmlns", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
|
|
data.set("xmlns:xsi", "http://www.w3.org/2001/XMLSchema-instance")
|
|
data.set("xsi:schemaLocation", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
|
|
|
|
metadata = ET.SubElement(data, "Metadata")
|
|
|
|
author = ET.SubElement(metadata, "Creator")
|
|
author.text = "SBB_QURATOR"
|
|
|
|
created = ET.SubElement(metadata, "Created")
|
|
created.text = "2019-06-17T18:15:12"
|
|
|
|
changetime = ET.SubElement(metadata, "LastChange")
|
|
changetime.text = "2019-06-17T18:15:12"
|
|
|
|
page = ET.SubElement(data, "Page")
|
|
|
|
page.set("imageFilename", self.image_dir)
|
|
page.set("imageHeight", str(self.height_org))
|
|
page.set("imageWidth", str(self.width_org))
|
|
page.set("type", "content")
|
|
page.set("readingDirection", "left-to-right")
|
|
page.set("textLineOrder", "top-to-bottom")
|
|
|
|
page_print_sub = ET.SubElement(page, "PrintSpace")
|
|
coord_page = ET.SubElement(page_print_sub, "Coords")
|
|
points_page_print = ""
|
|
|
|
for lmm in range(len(self.cont_page[0])):
|
|
if len(self.cont_page[0][lmm]) == 2:
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0]) / self.scale_x))
|
|
points_page_print = points_page_print + ","
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][1]) / self.scale_y))
|
|
else:
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
|
|
points_page_print = points_page_print + ","
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][1]) / self.scale_y))
|
|
|
|
if lmm < (len(self.cont_page[0]) - 1):
|
|
points_page_print = points_page_print + " "
|
|
coord_page.set("points", points_page_print)
|
|
|
|
if len(contours) > 0:
|
|
|
|
id_indexer = 0
|
|
id_indexer_l = 0
|
|
|
|
for mm in range(len(found_polygons_text_region)):
|
|
textregion = ET.SubElement(page, "TextRegion")
|
|
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
textregion.set("type", "paragraph")
|
|
# if mm==0:
|
|
# textregion.set('type','header')
|
|
# else:
|
|
# textregion.set('type','paragraph')
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_text_region[mm])):
|
|
if len(found_polygons_text_region[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_text_region[mm]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord_text.set("points", points_co)
|
|
|
|
for j in range(len(all_found_texline_polygons[mm])):
|
|
|
|
textline = ET.SubElement(textregion, "TextLine")
|
|
|
|
textline.set("id", "l" + str(id_indexer_l))
|
|
|
|
id_indexer_l += 1
|
|
|
|
coord = ET.SubElement(textline, "Coords")
|
|
|
|
texteq = ET.SubElement(textline, "TextEquiv")
|
|
|
|
uni = ET.SubElement(texteq, "Unicode")
|
|
uni.text = " "
|
|
|
|
# points = ET.SubElement(coord, 'Points')
|
|
|
|
points_co = ""
|
|
for l in range(len(all_found_texline_polygons[mm][j])):
|
|
# point = ET.SubElement(coord, 'Point')
|
|
|
|
# point.set('x',str(found_polygons[j][l][0]))
|
|
# point.set('y',str(found_polygons[j][l][1]))
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < (len(all_found_texline_polygons[mm][j]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord.set("points", points_co)
|
|
|
|
texteqreg = ET.SubElement(textregion, "TextEquiv")
|
|
|
|
unireg = ET.SubElement(texteqreg, "Unicode")
|
|
unireg.text = " "
|
|
|
|
# print(dir_of_image)
|
|
print(self.f_name)
|
|
# print(os.path.join(dir_of_image, self.f_name) + ".xml")
|
|
tree = ET.ElementTree(data)
|
|
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
|
|
|
|
def write_into_page_xml_full(self, contours, contours_h, page_coord, dir_of_image, order_of_texts, id_of_texts, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals):
|
|
|
|
found_polygons_text_region = contours
|
|
found_polygons_text_region_h = contours_h
|
|
|
|
# create the file structure
|
|
data = ET.Element("PcGts")
|
|
|
|
data.set("xmlns", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
|
|
data.set("xmlns:xsi", "http://www.w3.org/2001/XMLSchema-instance")
|
|
data.set("xsi:schemaLocation", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
|
|
|
|
metadata = ET.SubElement(data, "Metadata")
|
|
|
|
author = ET.SubElement(metadata, "Creator")
|
|
author.text = "SBB_QURATOR"
|
|
|
|
created = ET.SubElement(metadata, "Created")
|
|
created.text = "2019-06-17T18:15:12"
|
|
|
|
changetime = ET.SubElement(metadata, "LastChange")
|
|
changetime.text = "2019-06-17T18:15:12"
|
|
|
|
page = ET.SubElement(data, "Page")
|
|
|
|
page.set("imageFilename", self.image_dir)
|
|
page.set("imageHeight", str(self.height_org))
|
|
page.set("imageWidth", str(self.width_org))
|
|
page.set("type", "content")
|
|
page.set("readingDirection", "left-to-right")
|
|
page.set("textLineOrder", "top-to-bottom")
|
|
|
|
page_print_sub = ET.SubElement(page, "PrintSpace")
|
|
coord_page = ET.SubElement(page_print_sub, "Coords")
|
|
points_page_print = ""
|
|
|
|
for lmm in range(len(self.cont_page[0])):
|
|
if len(self.cont_page[0][lmm]) == 2:
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0]) / self.scale_x))
|
|
points_page_print = points_page_print + ","
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][1]) / self.scale_y))
|
|
else:
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
|
|
points_page_print = points_page_print + ","
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][1]) / self.scale_y))
|
|
|
|
if lmm < (len(self.cont_page[0]) - 1):
|
|
points_page_print = points_page_print + " "
|
|
coord_page.set("points", points_page_print)
|
|
|
|
if len(contours) > 0:
|
|
region_order = ET.SubElement(page, "ReadingOrder")
|
|
region_order_sub = ET.SubElement(region_order, "OrderedGroup")
|
|
|
|
region_order_sub.set("id", "ro357564684568544579089")
|
|
|
|
# args_sort=order_of_texts
|
|
for vj in order_of_texts:
|
|
name = "coord_text_" + str(vj)
|
|
name = ET.SubElement(region_order_sub, "RegionRefIndexed")
|
|
name.set("index", str(order_of_texts[vj]))
|
|
name.set("regionRef", id_of_texts[vj])
|
|
|
|
id_of_marginalia = []
|
|
indexer_region = len(contours) + len(contours_h)
|
|
for vm in range(len(found_polygons_marginals)):
|
|
id_of_marginalia.append("r" + str(indexer_region))
|
|
|
|
name = "coord_text_" + str(indexer_region)
|
|
name = ET.SubElement(region_order_sub, "RegionRefIndexed")
|
|
name.set("index", str(indexer_region))
|
|
name.set("regionRef", "r" + str(indexer_region))
|
|
indexer_region += 1
|
|
|
|
id_indexer = 0
|
|
id_indexer_l = 0
|
|
|
|
for mm in range(len(found_polygons_text_region)):
|
|
textregion = ET.SubElement(page, "TextRegion")
|
|
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
textregion.set("type", "paragraph")
|
|
# if mm==0:
|
|
# textregion.set('type','header')
|
|
# else:
|
|
# textregion.set('type','paragraph')
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_text_region[mm])):
|
|
if len(found_polygons_text_region[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_text_region[mm]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord_text.set("points", points_co)
|
|
|
|
for j in range(len(all_found_texline_polygons[mm])):
|
|
|
|
textline = ET.SubElement(textregion, "TextLine")
|
|
|
|
textline.set("id", "l" + str(id_indexer_l))
|
|
|
|
id_indexer_l += 1
|
|
|
|
coord = ET.SubElement(textline, "Coords")
|
|
|
|
texteq = ET.SubElement(textline, "TextEquiv")
|
|
|
|
uni = ET.SubElement(texteq, "Unicode")
|
|
uni.text = " "
|
|
|
|
# points = ET.SubElement(coord, 'Points')
|
|
|
|
points_co = ""
|
|
for l in range(len(all_found_texline_polygons[mm][j])):
|
|
# point = ET.SubElement(coord, 'Point')
|
|
|
|
if curved_line is None or curved_line == "false" or curved_line == "False" or curved_line == "FALSE":
|
|
# point.set('x',str(found_polygons[j][l][0]))
|
|
# point.set('y',str(found_polygons[j][l][1]))
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if (curved_line == "true" or curved_line == "True" or curved_line == "TRUE") and np.abs(slopes[mm]) <= 45:
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
elif (curved_line == "true" or curved_line == "True" or curved_line == "TRUE") and np.abs(slopes[mm]) > 45:
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < (len(all_found_texline_polygons[mm][j]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord.set("points", points_co)
|
|
|
|
texteqreg = ET.SubElement(textregion, "TextEquiv")
|
|
|
|
unireg = ET.SubElement(texteqreg, "Unicode")
|
|
unireg.text = " "
|
|
|
|
print(len(contours_h))
|
|
if len(contours_h) > 0:
|
|
for mm in range(len(found_polygons_text_region_h)):
|
|
textregion = ET.SubElement(page, "TextRegion")
|
|
try:
|
|
id_indexer = id_indexer
|
|
id_indexer_l = id_indexer_l
|
|
except:
|
|
id_indexer = 0
|
|
id_indexer_l = 0
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
textregion.set("type", "header")
|
|
# if mm==0:
|
|
# textregion.set('type','header')
|
|
# else:
|
|
# textregion.set('type','paragraph')
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_text_region_h[mm])):
|
|
|
|
if len(found_polygons_text_region_h[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region_h[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_text_region_h[mm]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord_text.set("points", points_co)
|
|
|
|
for j in range(len(all_found_texline_polygons_h[mm])):
|
|
|
|
textline = ET.SubElement(textregion, "TextLine")
|
|
|
|
textline.set("id", "l" + str(id_indexer_l))
|
|
|
|
id_indexer_l += 1
|
|
|
|
coord = ET.SubElement(textline, "Coords")
|
|
|
|
texteq = ET.SubElement(textline, "TextEquiv")
|
|
|
|
uni = ET.SubElement(texteq, "Unicode")
|
|
uni.text = " "
|
|
|
|
# points = ET.SubElement(coord, 'Points')
|
|
|
|
points_co = ""
|
|
for l in range(len(all_found_texline_polygons_h[mm][j])):
|
|
# point = ET.SubElement(coord, 'Point')
|
|
|
|
if curved_line is None or curved_line == "false" or curved_line == "False" or curved_line == "FALSE":
|
|
# point.set('x',str(found_polygons[j][l][0]))
|
|
# point.set('y',str(found_polygons[j][l][1]))
|
|
if len(all_found_texline_polygons_h[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0] + all_box_coord_h[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][1] + all_box_coord_h[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][0] + all_box_coord_h[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][1] + all_box_coord_h[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if curved_line == "true" or curved_line == "True" or curved_line == "TRUE":
|
|
if len(all_found_texline_polygons_h[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_h[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < (len(all_found_texline_polygons_h[mm][j]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord.set("points", points_co)
|
|
|
|
texteqreg = ET.SubElement(textregion, "TextEquiv")
|
|
|
|
unireg = ET.SubElement(texteqreg, "Unicode")
|
|
unireg.text = " "
|
|
|
|
if len(found_polygons_drop_capitals) > 0:
|
|
id_indexer = len(contours_h) + len(contours) + len(found_polygons_marginals)
|
|
for mm in range(len(found_polygons_drop_capitals)):
|
|
textregion = ET.SubElement(page, "TextRegion")
|
|
|
|
# id_indexer_l=id_indexer_l
|
|
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
textregion.set("type", "drop-capital")
|
|
# if mm==0:
|
|
# textregion.set('type','header')
|
|
# else:
|
|
# textregion.set('type','paragraph')
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_drop_capitals[mm])):
|
|
|
|
if len(found_polygons_drop_capitals[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_drop_capitals[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_drop_capitals[mm]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord_text.set("points", points_co)
|
|
|
|
##for j in range(len(all_found_texline_polygons_h[mm])):
|
|
|
|
##textline=ET.SubElement(textregion, 'TextLine')
|
|
|
|
##textline.set('id','l'+str(id_indexer_l))
|
|
|
|
##id_indexer_l+=1
|
|
|
|
##coord = ET.SubElement(textline, 'Coords')
|
|
|
|
##texteq=ET.SubElement(textline, 'TextEquiv')
|
|
|
|
##uni=ET.SubElement(texteq, 'Unicode')
|
|
##uni.text = ' '
|
|
|
|
###points = ET.SubElement(coord, 'Points')
|
|
|
|
##points_co=''
|
|
##for l in range(len(all_found_texline_polygons_h[mm][j])):
|
|
###point = ET.SubElement(coord, 'Point')
|
|
|
|
##if curved_line is None or curved_line=='false' or curved_line=='False' or curved_line=='FALSE' :
|
|
###point.set('x',str(found_polygons[j][l][0]))
|
|
###point.set('y',str(found_polygons[j][l][1]))
|
|
##if len(all_found_texline_polygons_h[mm][j][l])==2:
|
|
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
|
|
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x) )
|
|
##points_co=points_co+','
|
|
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
|
|
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
|
|
##else:
|
|
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
|
|
##+all_box_coord_h[mm][2]+page_coord[2])/self.scale_x ) )
|
|
##points_co=points_co+','
|
|
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
|
|
##+all_box_coord_h[mm][0]+page_coord[0])/self.scale_y) )
|
|
|
|
##if curved_line=='true' or curved_line=='True' or curved_line=='TRUE' :
|
|
##if len(all_found_texline_polygons_h[mm][j][l])==2:
|
|
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0]
|
|
##+page_coord[2])/self.scale_x) )
|
|
##points_co=points_co+','
|
|
##points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1]
|
|
##+page_coord[0])/self.scale_y) )
|
|
##else:
|
|
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0]
|
|
##+page_coord[2])/self.scale_x ) )
|
|
##points_co=points_co+','
|
|
##points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1]
|
|
##+page_coord[0])/self.scale_y) )
|
|
|
|
##if l<(len(all_found_texline_polygons_h[mm][j])-1):
|
|
##points_co=points_co+' '
|
|
###print(points_co)
|
|
####coord.set('points',points_co)
|
|
|
|
texteqreg = ET.SubElement(textregion, "TextEquiv")
|
|
|
|
unireg = ET.SubElement(texteqreg, "Unicode")
|
|
unireg.text = " "
|
|
|
|
try:
|
|
|
|
try:
|
|
###id_indexer=id_indexer
|
|
id_indexer_l = id_indexer_l
|
|
except:
|
|
###id_indexer=0
|
|
id_indexer_l = 0
|
|
for mm in range(len(found_polygons_marginals)):
|
|
textregion = ET.SubElement(page, "TextRegion")
|
|
|
|
textregion.set("id", id_of_marginalia[mm])
|
|
|
|
textregion.set("type", "marginalia")
|
|
# if mm==0:
|
|
# textregion.set('type','header')
|
|
# else:
|
|
# textregion.set('type','paragraph')
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_marginals[mm])):
|
|
if len(found_polygons_marginals[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_marginals[mm]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord_text.set("points", points_co)
|
|
|
|
for j in range(len(all_found_texline_polygons_marginals[mm])):
|
|
|
|
textline = ET.SubElement(textregion, "TextLine")
|
|
|
|
textline.set("id", "l" + str(id_indexer_l))
|
|
|
|
id_indexer_l += 1
|
|
|
|
coord = ET.SubElement(textline, "Coords")
|
|
|
|
texteq = ET.SubElement(textline, "TextEquiv")
|
|
|
|
uni = ET.SubElement(texteq, "Unicode")
|
|
uni.text = " "
|
|
|
|
# points = ET.SubElement(coord, 'Points')
|
|
|
|
points_co = ""
|
|
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
|
|
# point = ET.SubElement(coord, 'Point')
|
|
|
|
if curved_line is None or curved_line == "false" or curved_line == "False" or curved_line == "FALSE":
|
|
# point.set('x',str(found_polygons[j][l][0]))
|
|
# point.set('y',str(found_polygons[j][l][1]))
|
|
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if curved_line == "true" or curved_line == "True" or curved_line == "TRUE":
|
|
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < (len(all_found_texline_polygons_marginals[mm][j]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord.set("points", points_co)
|
|
|
|
texteqreg = ET.SubElement(textregion, "TextEquiv")
|
|
|
|
unireg = ET.SubElement(texteqreg, "Unicode")
|
|
unireg.text = " "
|
|
except:
|
|
pass
|
|
|
|
try:
|
|
id_indexer = len(contours_h) + len(contours) + len(found_polygons_marginals) + len(found_polygons_drop_capitals)
|
|
for mm in range(len(found_polygons_text_region_img)):
|
|
textregion = ET.SubElement(page, "ImageRegion")
|
|
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_text_region_img[mm])):
|
|
|
|
if len(found_polygons_text_region_img[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_text_region_img[mm]) - 1):
|
|
points_co = points_co + " "
|
|
|
|
coord_text.set("points", points_co)
|
|
except:
|
|
pass
|
|
|
|
try:
|
|
for mm in range(len(found_polygons_tables)):
|
|
textregion = ET.SubElement(page, "TableRegion")
|
|
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_tables[mm])):
|
|
|
|
if len(found_polygons_tables[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_tables[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_tables[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_tables[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_tables[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_tables[mm]) - 1):
|
|
points_co = points_co + " "
|
|
|
|
coord_text.set("points", points_co)
|
|
except:
|
|
pass
|
|
|
|
print(dir_of_image)
|
|
print(self.f_name)
|
|
print(os.path.join(dir_of_image, self.f_name) + ".xml")
|
|
tree = ET.ElementTree(data)
|
|
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
|
|
|
|
def write_into_page_xml(self, contours, page_coord, dir_of_image, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, curved_line, slopes, slopes_marginals):
|
|
|
|
found_polygons_text_region = contours
|
|
##found_polygons_text_region_h=contours_h
|
|
|
|
# create the file structure
|
|
data = ET.Element("PcGts")
|
|
|
|
data.set("xmlns", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
|
|
data.set("xmlns:xsi", "http://www.w3.org/2001/XMLSchema-instance")
|
|
data.set("xsi:schemaLocation", "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
|
|
|
|
metadata = ET.SubElement(data, "Metadata")
|
|
|
|
author = ET.SubElement(metadata, "Creator")
|
|
author.text = "SBB_QURATOR"
|
|
|
|
created = ET.SubElement(metadata, "Created")
|
|
created.text = "2019-06-17T18:15:12"
|
|
|
|
changetime = ET.SubElement(metadata, "LastChange")
|
|
changetime.text = "2019-06-17T18:15:12"
|
|
|
|
page = ET.SubElement(data, "Page")
|
|
|
|
page.set("imageFilename", self.image_dir)
|
|
page.set("imageHeight", str(self.height_org))
|
|
page.set("imageWidth", str(self.width_org))
|
|
page.set("type", "content")
|
|
page.set("readingDirection", "left-to-right")
|
|
page.set("textLineOrder", "top-to-bottom")
|
|
|
|
page_print_sub = ET.SubElement(page, "PrintSpace")
|
|
coord_page = ET.SubElement(page_print_sub, "Coords")
|
|
points_page_print = ""
|
|
|
|
for lmm in range(len(self.cont_page[0])):
|
|
if len(self.cont_page[0][lmm]) == 2:
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0]) / self.scale_x))
|
|
points_page_print = points_page_print + ","
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][1]) / self.scale_y))
|
|
else:
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][0]) / self.scale_x))
|
|
points_page_print = points_page_print + ","
|
|
points_page_print = points_page_print + str(int((self.cont_page[0][lmm][0][1]) / self.scale_y))
|
|
|
|
if lmm < (len(self.cont_page[0]) - 1):
|
|
points_page_print = points_page_print + " "
|
|
coord_page.set("points", points_page_print)
|
|
|
|
if len(contours) > 0:
|
|
region_order = ET.SubElement(page, "ReadingOrder")
|
|
region_order_sub = ET.SubElement(region_order, "OrderedGroup")
|
|
|
|
region_order_sub.set("id", "ro357564684568544579089")
|
|
|
|
indexer_region = 0
|
|
|
|
for vj in order_of_texts:
|
|
name = "coord_text_" + str(vj)
|
|
name = ET.SubElement(region_order_sub, "RegionRefIndexed")
|
|
|
|
name.set("index", str(indexer_region))
|
|
name.set("regionRef", id_of_texts[vj])
|
|
indexer_region += 1
|
|
|
|
id_of_marginalia = []
|
|
for vm in range(len(found_polygons_marginals)):
|
|
id_of_marginalia.append("r" + str(indexer_region))
|
|
|
|
name = "coord_text_" + str(indexer_region)
|
|
name = ET.SubElement(region_order_sub, "RegionRefIndexed")
|
|
name.set("index", str(indexer_region))
|
|
name.set("regionRef", "r" + str(indexer_region))
|
|
indexer_region += 1
|
|
|
|
id_indexer = 0
|
|
id_indexer_l = 0
|
|
|
|
for mm in range(len(found_polygons_text_region)):
|
|
textregion = ET.SubElement(page, "TextRegion")
|
|
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
textregion.set("type", "paragraph")
|
|
# if mm==0:
|
|
# textregion.set('type','header')
|
|
# else:
|
|
# textregion.set('type','paragraph')
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_text_region[mm])):
|
|
if len(found_polygons_text_region[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_text_region[mm]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord_text.set("points", points_co)
|
|
|
|
for j in range(len(all_found_texline_polygons[mm])):
|
|
|
|
textline = ET.SubElement(textregion, "TextLine")
|
|
|
|
textline.set("id", "l" + str(id_indexer_l))
|
|
|
|
id_indexer_l += 1
|
|
|
|
coord = ET.SubElement(textline, "Coords")
|
|
|
|
texteq = ET.SubElement(textline, "TextEquiv")
|
|
|
|
uni = ET.SubElement(texteq, "Unicode")
|
|
uni.text = " "
|
|
|
|
# points = ET.SubElement(coord, 'Points')
|
|
|
|
points_co = ""
|
|
for l in range(len(all_found_texline_polygons[mm][j])):
|
|
# point = ET.SubElement(coord, 'Point')
|
|
|
|
if curved_line is None or curved_line == "false" or curved_line == "False" or curved_line == "FALSE":
|
|
# point.set('x',str(found_polygons[j][l][0]))
|
|
# point.set('y',str(found_polygons[j][l][1]))
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if (curved_line == "true" or curved_line == "True" or curved_line == "TRUE") and abs(slopes[mm]) <= 45:
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
elif (curved_line == "true" or curved_line == "True" or curved_line == "TRUE") and abs(slopes[mm]) > 45:
|
|
if len(all_found_texline_polygons[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + all_box_coord[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < (len(all_found_texline_polygons[mm][j]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord.set("points", points_co)
|
|
|
|
texteqreg = ET.SubElement(textregion, "TextEquiv")
|
|
|
|
unireg = ET.SubElement(texteqreg, "Unicode")
|
|
unireg.text = " "
|
|
|
|
###print(len(contours_h))
|
|
###if len(contours_h)>0:
|
|
###for mm in range(len(found_polygons_text_region_h)):
|
|
###textregion=ET.SubElement(page, 'TextRegion')
|
|
###try:
|
|
###id_indexer=id_indexer
|
|
###id_indexer_l=id_indexer_l
|
|
###except:
|
|
###id_indexer=0
|
|
###id_indexer_l=0
|
|
###textregion.set('id','r'+str(id_indexer))
|
|
###id_indexer+=1
|
|
|
|
###textregion.set('type','header')
|
|
####if mm==0:
|
|
#### textregion.set('type','header')
|
|
####else:
|
|
#### textregion.set('type','paragraph')
|
|
###coord_text = ET.SubElement(textregion, 'Coords')
|
|
|
|
###points_co=''
|
|
###for lmm in range(len(found_polygons_text_region_h[mm])):
|
|
|
|
###if len(found_polygons_text_region_h[mm][lmm])==2:
|
|
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][0] +page_coord[2])/self.scale_x ) )
|
|
###points_co=points_co+','
|
|
###points_co=points_co+str( int( (found_polygons_text_region_h[mm][lmm][1] +page_coord[0])/self.scale_y ) )
|
|
###else:
|
|
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
|
|
###points_co=points_co+','
|
|
###points_co=points_co+str( int((found_polygons_text_region_h[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
|
|
|
|
###if lmm<(len(found_polygons_text_region_h[mm])-1):
|
|
###points_co=points_co+' '
|
|
####print(points_co)
|
|
###coord_text.set('points',points_co)
|
|
|
|
###for j in range(len(all_found_texline_polygons_h[mm])):
|
|
|
|
###textline=ET.SubElement(textregion, 'TextLine')
|
|
|
|
###textline.set('id','l'+str(id_indexer_l))
|
|
|
|
###id_indexer_l+=1
|
|
|
|
###coord = ET.SubElement(textline, 'Coords')
|
|
|
|
###texteq=ET.SubElement(textline, 'TextEquiv')
|
|
|
|
###uni=ET.SubElement(texteq, 'Unicode')
|
|
###uni.text = ' '
|
|
|
|
####points = ET.SubElement(coord, 'Points')
|
|
|
|
###points_co=''
|
|
###for l in range(len(all_found_texline_polygons_h[mm][j])):
|
|
####point = ET.SubElement(coord, 'Point')
|
|
|
|
####point.set('x',str(found_polygons[j][l][0]))
|
|
####point.set('y',str(found_polygons[j][l][1]))
|
|
###if len(all_found_texline_polygons_h[mm][j][l])==2:
|
|
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][0] +page_coord[2]
|
|
###+all_box_coord_h[mm][2])/self.scale_x) )
|
|
###points_co=points_co+','
|
|
###points_co=points_co+str( int( (all_found_texline_polygons_h[mm][j][l][1] +page_coord[0]
|
|
###+all_box_coord_h[mm][0])/self.scale_y) )
|
|
###else:
|
|
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][0] +page_coord[2]
|
|
###+all_box_coord_h[mm][2])/self.scale_x ) )
|
|
###points_co=points_co+','
|
|
###points_co=points_co+str( int( ( all_found_texline_polygons_h[mm][j][l][0][1] +page_coord[0]
|
|
###+all_box_coord_h[mm][0])/self.scale_y) )
|
|
|
|
###if l<(len(all_found_texline_polygons_h[mm][j])-1):
|
|
###points_co=points_co+' '
|
|
####print(points_co)
|
|
###coord.set('points',points_co)
|
|
|
|
###texteqreg=ET.SubElement(textregion, 'TextEquiv')
|
|
|
|
###unireg=ET.SubElement(texteqreg, 'Unicode')
|
|
###unireg.text = ' '
|
|
try:
|
|
# id_indexer_l=0
|
|
|
|
try:
|
|
###id_indexer=id_indexer
|
|
id_indexer_l = id_indexer_l
|
|
except:
|
|
###id_indexer=0
|
|
id_indexer_l = 0
|
|
|
|
for mm in range(len(found_polygons_marginals)):
|
|
textregion = ET.SubElement(page, "TextRegion")
|
|
|
|
textregion.set("id", id_of_marginalia[mm])
|
|
|
|
textregion.set("type", "marginalia")
|
|
# if mm==0:
|
|
# textregion.set('type','header')
|
|
# else:
|
|
# textregion.set('type','paragraph')
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_marginals[mm])):
|
|
if len(found_polygons_marginals[mm][lmm]) == 2:
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_marginals[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_marginals[mm]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord_text.set("points", points_co)
|
|
|
|
for j in range(len(all_found_texline_polygons_marginals[mm])):
|
|
|
|
textline = ET.SubElement(textregion, "TextLine")
|
|
|
|
textline.set("id", "l" + str(id_indexer_l))
|
|
|
|
id_indexer_l += 1
|
|
|
|
coord = ET.SubElement(textline, "Coords")
|
|
|
|
texteq = ET.SubElement(textline, "TextEquiv")
|
|
|
|
uni = ET.SubElement(texteq, "Unicode")
|
|
uni.text = " "
|
|
|
|
# points = ET.SubElement(coord, 'Points')
|
|
|
|
points_co = ""
|
|
for l in range(len(all_found_texline_polygons_marginals[mm][j])):
|
|
# point = ET.SubElement(coord, 'Point')
|
|
|
|
if curved_line is None or curved_line == "false" or curved_line == "False" or curved_line == "FALSE":
|
|
# point.set('x',str(found_polygons[j][l][0]))
|
|
# point.set('y',str(found_polygons[j][l][1]))
|
|
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + all_box_coord_marginals[mm][2] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + all_box_coord_marginals[mm][0] + page_coord[0]) / self.scale_y))
|
|
|
|
if curved_line == "true" or curved_line == "True" or curved_line == "TRUE":
|
|
if len(all_found_texline_polygons_marginals[mm][j][l]) == 2:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][1] + page_coord[0]) / self.scale_y))
|
|
else:
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((all_found_texline_polygons_marginals[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
|
|
|
|
if l < (len(all_found_texline_polygons_marginals[mm][j]) - 1):
|
|
points_co = points_co + " "
|
|
# print(points_co)
|
|
coord.set("points", points_co)
|
|
except:
|
|
pass
|
|
|
|
try:
|
|
|
|
for mm in range(len(found_polygons_text_region_img)):
|
|
textregion = ET.SubElement(page, "ImageRegion")
|
|
|
|
textregion.set("id", "r" + str(id_indexer))
|
|
id_indexer += 1
|
|
|
|
coord_text = ET.SubElement(textregion, "Coords")
|
|
points_co = ""
|
|
for lmm in range(len(found_polygons_text_region_img[mm])):
|
|
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm, 0, 0] + page_coord[2]) / self.scale_x))
|
|
points_co = points_co + ","
|
|
points_co = points_co + str(int((found_polygons_text_region_img[mm][lmm, 0, 1] + page_coord[0]) / self.scale_y))
|
|
|
|
if lmm < (len(found_polygons_text_region_img[mm]) - 1):
|
|
points_co = points_co + " "
|
|
|
|
coord_text.set("points", points_co)
|
|
###for mm in range(len(found_polygons_text_region_img)):
|
|
###textregion=ET.SubElement(page, 'ImageRegion')
|
|
|
|
###textregion.set('id','r'+str(id_indexer))
|
|
###id_indexer+=1
|
|
|
|
###coord_text = ET.SubElement(textregion, 'Coords')
|
|
###print(found_polygons_text_region_img[mm])
|
|
###points_co=''
|
|
###for lmm in range(len(found_polygons_text_region_img[mm])):
|
|
###print(len(found_polygons_text_region_img[mm][lmm]))
|
|
|
|
###if len(found_polygons_text_region_img[mm][lmm])==2:
|
|
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][0]+page_coord[2] )/self.scale_x ) )
|
|
###points_co=points_co+','
|
|
###points_co=points_co+str( int( (found_polygons_text_region_img[mm][lmm][1]+page_coord[0] )/self.scale_y ) )
|
|
###else:
|
|
###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][0]+page_coord[2] )/self.scale_x) )
|
|
###points_co=points_co+','
|
|
###points_co=points_co+str( int((found_polygons_text_region_img[mm][lmm][0][1]+page_coord[0] )/self.scale_y) )
|
|
|
|
###if lmm<(len(found_polygons_text_region_img[mm])-1):
|
|
###points_co=points_co+' '
|
|
|
|
###coord_text.set('points',points_co)
|
|
except:
|
|
pass
|
|
|
|
####try:
|
|
####for mm in range(len(found_polygons_tables)):
|
|
####textregion=ET.SubElement(page, 'TableRegion')
|
|
|
|
####textregion.set('id','r'+str(id_indexer))
|
|
####id_indexer+=1
|
|
|
|
####coord_text = ET.SubElement(textregion, 'Coords')
|
|
|
|
####points_co=''
|
|
####for lmm in range(len(found_polygons_tables[mm])):
|
|
|
|
####if len(found_polygons_tables[mm][lmm])==2:
|
|
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][0] +page_coord[2])/self.scale_x ) )
|
|
####points_co=points_co+','
|
|
####points_co=points_co+str( int( (found_polygons_tables[mm][lmm][1] +page_coord[0])/self.scale_y ) )
|
|
####else:
|
|
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
|
|
####points_co=points_co+','
|
|
####points_co=points_co+str( int((found_polygons_tables[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
|
|
|
|
####if lmm<(len(found_polygons_tables[mm])-1):
|
|
####points_co=points_co+' '
|
|
|
|
####coord_text.set('points',points_co)
|
|
####except:
|
|
####pass
|
|
"""
|
|
|
|
try:
|
|
for mm in range(len(found_polygons_drop_capitals)):
|
|
textregion=ET.SubElement(page, 'DropCapitals')
|
|
|
|
textregion.set('id','r'+str(id_indexer))
|
|
id_indexer+=1
|
|
|
|
|
|
coord_text = ET.SubElement(textregion, 'Coords')
|
|
|
|
points_co=''
|
|
for lmm in range(len(found_polygons_drop_capitals[mm])):
|
|
|
|
if len(found_polygons_drop_capitals[mm][lmm])==2:
|
|
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][0] +page_coord[2])/self.scale_x ) )
|
|
points_co=points_co+','
|
|
points_co=points_co+str( int( (found_polygons_drop_capitals[mm][lmm][1] +page_coord[0])/self.scale_y ) )
|
|
else:
|
|
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][0] +page_coord[2])/self.scale_x) )
|
|
points_co=points_co+','
|
|
points_co=points_co+str( int((found_polygons_drop_capitals[mm][lmm][0][1] +page_coord[0])/self.scale_y) )
|
|
|
|
if lmm<(len(found_polygons_drop_capitals[mm])-1):
|
|
points_co=points_co+' '
|
|
|
|
|
|
coord_text.set('points',points_co)
|
|
except:
|
|
pass
|
|
"""
|
|
|
|
# print(dir_of_image)
|
|
print(self.f_name)
|
|
# print(os.path.join(dir_of_image, self.f_name) + ".xml")
|
|
tree = ET.ElementTree(data)
|
|
tree.write(os.path.join(dir_of_image, self.f_name) + ".xml")
|
|
# cv2.imwrite(os.path.join(dir_of_image, self.f_name) + ".tif",self.image_org)
|
|
|
|
def deskew_region_prediction(self, regions_prediction, slope):
|
|
image_regions_deskewd = np.zeros(regions_prediction[:, :].shape)
|
|
for ind in np.unique(regions_prediction[:, :]):
|
|
interest_reg = (regions_prediction[:, :] == ind) * 1
|
|
interest_reg = interest_reg.astype(np.uint8)
|
|
deskewed_new = self.rotate_image(interest_reg, slope)
|
|
deskewed_new = deskewed_new[:, :]
|
|
deskewed_new[deskewed_new != 0] = ind
|
|
|
|
image_regions_deskewd = image_regions_deskewd + deskewed_new
|
|
return image_regions_deskewd
|
|
|
|
def deskew_erarly(self, textline_mask):
|
|
textline_mask_org = np.copy(textline_mask)
|
|
# print(textline_mask.shape,np.unique(textline_mask),'hizzzzz')
|
|
# slope_new=0#deskew_images(img_patch)
|
|
|
|
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
|
|
|
|
textline_mask = textline_mask.astype(np.uint8)
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
|
|
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(hirarchy)
|
|
|
|
commenst_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=0.01, min_area=0.003)
|
|
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.003)
|
|
interior_contours = self.filter_contours_area_of_image_interiors(thresh, contours, hirarchy, max_area=1, min_area=0)
|
|
|
|
img_comm = np.zeros(thresh.shape)
|
|
img_comm_in = cv2.fillPoly(img_comm, pts=main_contours, color=(255, 255, 255))
|
|
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
|
|
|
|
img_comm_in = np.repeat(img_comm_in[:, :, np.newaxis], 3, axis=2)
|
|
img_comm_in = img_comm_in.astype(np.uint8)
|
|
|
|
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
|
|
##imgray = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
|
|
|
|
##mask = cv2.inRange(imgray, lower_blue, upper_blue)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
# print(np.unique(mask))
|
|
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
##plt.imshow(thresh)
|
|
##plt.show()
|
|
|
|
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
areas = [cv2.contourArea(contours[jj]) for jj in range(len(contours))]
|
|
|
|
median_area = np.mean(areas)
|
|
contours_slope = contours # self.find_polugons_size_filter(contours,median_area=median_area,scaler_up=100,scaler_down=0.5)
|
|
|
|
if len(contours_slope) > 0:
|
|
for jv in range(len(contours_slope)):
|
|
new_poly = list(contours_slope[jv])
|
|
if jv == 0:
|
|
merged_all = new_poly
|
|
else:
|
|
merged_all = merged_all + new_poly
|
|
|
|
merge = np.array(merged_all)
|
|
|
|
img_in = np.zeros(textline_mask.shape)
|
|
img_p_in = cv2.fillPoly(img_in, pts=[merge], color=(255, 255, 255))
|
|
|
|
##plt.imshow(img_p_in)
|
|
##plt.show()
|
|
|
|
rect = cv2.minAreaRect(merge)
|
|
|
|
box = cv2.boxPoints(rect)
|
|
|
|
box = np.int0(box)
|
|
|
|
indexes = [0, 1, 2, 3]
|
|
x_list = box[:, 0]
|
|
y_list = box[:, 1]
|
|
|
|
index_y_sort = np.argsort(y_list)
|
|
|
|
index_upper_left = index_y_sort[np.argmin(x_list[index_y_sort[0:2]])]
|
|
index_upper_right = index_y_sort[np.argmax(x_list[index_y_sort[0:2]])]
|
|
|
|
index_lower_left = index_y_sort[np.argmin(x_list[index_y_sort[2:]]) + 2]
|
|
index_lower_right = index_y_sort[np.argmax(x_list[index_y_sort[2:]]) + 2]
|
|
|
|
alpha1 = float(box[index_upper_right][1] - box[index_upper_left][1]) / (float(box[index_upper_right][0] - box[index_upper_left][0]))
|
|
alpha2 = float(box[index_lower_right][1] - box[index_lower_left][1]) / (float(box[index_lower_right][0] - box[index_lower_left][0]))
|
|
|
|
slope_true = (alpha1 + alpha2) / 2.0
|
|
|
|
# slope=0#slope_true/np.pi*180
|
|
|
|
# if abs(slope)>=1:
|
|
# slope=0
|
|
|
|
# dst=self.rotate_image(textline_mask,slope_true)
|
|
# dst=dst[:,:,0]
|
|
# dst[dst!=0]=1
|
|
image_regions_deskewd = np.zeros(textline_mask_org[:, :].shape)
|
|
for ind in np.unique(textline_mask_org[:, :]):
|
|
interest_reg = (textline_mask_org[:, :] == ind) * 1
|
|
interest_reg = interest_reg.astype(np.uint8)
|
|
deskewed_new = self.rotate_image(interest_reg, slope_true)
|
|
deskewed_new = deskewed_new[:, :]
|
|
deskewed_new[deskewed_new != 0] = ind
|
|
|
|
image_regions_deskewd = image_regions_deskewd + deskewed_new
|
|
return image_regions_deskewd, slope_true
|
|
|
|
def return_regions_without_seperators(self, regions_pre):
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0)) * 1
|
|
# regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1
|
|
|
|
regions_without_seperators = regions_without_seperators.astype(np.uint8)
|
|
|
|
regions_without_seperators = cv2.erode(regions_without_seperators, kernel, iterations=6)
|
|
|
|
return regions_without_seperators
|
|
|
|
def return_regions_without_seperators_new(self, regions_pre, regions_only_text):
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0) & (regions_pre[:, :] != 1) & (regions_pre[:, :] != 2)) * 1
|
|
|
|
# plt.imshow(regions_without_seperators)
|
|
# plt.show()
|
|
|
|
regions_without_seperators_n = ((regions_without_seperators[:, :] == 1) | (regions_only_text[:, :] == 1)) * 1
|
|
|
|
# regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1
|
|
|
|
regions_without_seperators_n = regions_without_seperators_n.astype(np.uint8)
|
|
|
|
regions_without_seperators_n = cv2.erode(regions_without_seperators_n, kernel, iterations=6)
|
|
|
|
return regions_without_seperators_n
|
|
|
|
def image_change_background_pixels_to_zero(self, image_page):
|
|
image_back_zero = np.zeros((image_page.shape[0], image_page.shape[1]))
|
|
image_back_zero[:, :] = image_page[:, :, 0]
|
|
image_back_zero[:, :][image_back_zero[:, :] == 0] = -255
|
|
image_back_zero[:, :][image_back_zero[:, :] == 255] = 0
|
|
image_back_zero[:, :][image_back_zero[:, :] == -255] = 255
|
|
return image_back_zero
|
|
|
|
def find_num_col_only_image(self, regions_without_seperators, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
|
|
|
|
##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
|
|
sigma_ = 15
|
|
|
|
meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
|
|
|
|
first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
|
|
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
|
|
|
last_nonzero = len(regions_without_seperators_0) - last_nonzero
|
|
|
|
y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
|
|
|
|
y_help = np.zeros(len(y) + 20)
|
|
|
|
y_help[10 : len(y) + 10] = y
|
|
|
|
x = np.array(range(len(y)))
|
|
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 20)
|
|
|
|
zneg[10 : len(zneg_rev) + 10] = zneg_rev
|
|
|
|
z = gaussian_filter1d(y, sigma_)
|
|
zneg = gaussian_filter1d(zneg, sigma_)
|
|
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
peaks_neg = peaks_neg - 10 - 10
|
|
|
|
peaks_neg_org = np.copy(peaks_neg)
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
|
|
|
|
peaks = peaks[(peaks > 0.09 * regions_without_seperators.shape[1]) & (peaks < 0.91 * regions_without_seperators.shape[1])]
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > 500) & (peaks_neg < (regions_without_seperators.shape[1] - 500))]
|
|
# print(peaks)
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
interest_neg = z[peaks_neg]
|
|
min_peaks_pos = np.mean(interest_pos) # np.min(interest_pos)
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
# $print(min_peaks_pos)
|
|
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
|
|
# print(interest_pos)
|
|
grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
|
|
|
interest_neg_fin = interest_neg[(interest_neg < grenze)]
|
|
peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
|
|
|
|
num_col = (len(interest_neg_fin)) + 1
|
|
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or (peaks_neg_fin[0] < p_m and peaks_neg_fin[1] < p_m) or (peaks_neg_fin[0] > p_m and peaks_neg_fin[1] > p_m):
|
|
num_col = 1
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
|
|
num_col = 1
|
|
else:
|
|
pass
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
|
|
cut_off = 400
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
for i in range(len(peaks_neg_fin)):
|
|
if i == 0:
|
|
forest.append(peaks_neg_fin[i])
|
|
if i < (len(peaks_neg_fin) - 1):
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if i == (len(peaks_neg_fin) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
num_col = (len(peaks_neg_true)) + 1
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_quarter = int(len(y) / 4.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
p_u_quarter = len(y) - p_quarter
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and peaks_neg_true[1] < p_m) or (peaks_neg_true[0] > p_m and peaks_neg_true[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
|
|
peaks_neg_true = [peaks_neg_true[0]]
|
|
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
|
|
peaks_neg_true = [peaks_neg_true[1]]
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
|
|
if num_col == 4:
|
|
if len(np.array(peaks_neg_true)[np.array(peaks_neg_true) < p_g_l]) == 2 or len(np.array(peaks_neg_true)[np.array(peaks_neg_true) > (len(y) - p_g_l)]) == 2:
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
else:
|
|
pass
|
|
|
|
# no deeper hill around found hills
|
|
|
|
peaks_fin_true = []
|
|
for i in range(len(peaks_neg_true)):
|
|
hill_main = peaks_neg_true[i]
|
|
# deep_depth=z[peaks_neg]
|
|
hills_around = peaks_neg_org[((peaks_neg_org > hill_main) & (peaks_neg_org <= hill_main + 400)) | ((peaks_neg_org < hill_main) & (peaks_neg_org >= hill_main - 400))]
|
|
deep_depth_around = z[hills_around]
|
|
|
|
# print(hill_main,z[hill_main],hills_around,deep_depth_around,'manoooo')
|
|
try:
|
|
if np.min(deep_depth_around) < z[hill_main]:
|
|
pass
|
|
else:
|
|
peaks_fin_true.append(hill_main)
|
|
except:
|
|
pass
|
|
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
|
|
|
|
if len(diff_peaks_annormal) > 0:
|
|
arg_help = np.array(range(len(diff_peaks)))
|
|
arg_help_ann = arg_help[diff_peaks < 360]
|
|
|
|
peaks_neg_fin_new = []
|
|
|
|
for ii in range(len(peaks_neg_fin)):
|
|
if ii in arg_help_ann:
|
|
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
|
|
if arg_min == 0:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
|
|
|
|
elif (ii - 1) in arg_help_ann:
|
|
pass
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new = peaks_neg_fin
|
|
|
|
# sometime pages with one columns gives also some negative peaks. delete those peaks
|
|
param = z[peaks_neg_true] / float(min_peaks_pos) * 100
|
|
|
|
if len(param[param <= 41]) == 0:
|
|
peaks_neg_true = []
|
|
|
|
return len(peaks_fin_true), peaks_fin_true
|
|
|
|
def return_hor_spliter_by_index_for_without_verticals(self, peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
|
|
# print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
|
|
arg_min_hor_sort = np.argsort(x_min_hor_some)
|
|
x_min_hor_some_sort = np.sort(x_min_hor_some)
|
|
x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
|
|
|
|
arg_minmax = np.array(range(len(peaks_neg_fin_t)))
|
|
indexer_lines = []
|
|
indexes_to_delete = []
|
|
indexer_lines_deletions_len = []
|
|
indexr_uniq_ind = []
|
|
for i in range(len(x_min_hor_some_sort)):
|
|
min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
|
|
|
|
max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
|
|
|
|
min_h[0] = min_h[0] # +20
|
|
max_h[len(max_h) - 1] = max_h[len(max_h) - 1] - 20
|
|
|
|
min_h_neg = arg_minmax[(min_h < 0)]
|
|
min_h_neg_n = min_h[min_h < 0]
|
|
|
|
try:
|
|
min_h_neg = [min_h_neg[np.argmax(min_h_neg_n)]]
|
|
except:
|
|
min_h_neg = []
|
|
|
|
max_h_neg = arg_minmax[(max_h > 0)]
|
|
max_h_neg_n = max_h[max_h > 0]
|
|
|
|
if len(max_h_neg_n) > 0:
|
|
max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]]
|
|
else:
|
|
max_h_neg = []
|
|
|
|
if len(min_h_neg) > 0 and len(max_h_neg) > 0:
|
|
deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
|
|
unique_delets_int = []
|
|
# print(deletions,len(deletions),'delii')
|
|
if len(deletions) > 0:
|
|
|
|
for j in range(len(deletions)):
|
|
indexes_to_delete.append(deletions[j])
|
|
# print(deletions,indexes_to_delete,'badiii')
|
|
unique_delets = np.unique(indexes_to_delete)
|
|
# print(min_h_neg[0],unique_delets)
|
|
unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
|
|
|
|
indexer_lines_deletions_len.append(len(deletions))
|
|
indexr_uniq_ind.append([deletions])
|
|
|
|
else:
|
|
indexer_lines_deletions_len.append(0)
|
|
indexr_uniq_ind.append(-999)
|
|
|
|
index_line_true = min_h_neg[0] - len(unique_delets_int)
|
|
# print(index_line_true)
|
|
if index_line_true > 0 and min_h_neg[0] >= 2:
|
|
index_line_true = index_line_true
|
|
else:
|
|
index_line_true = min_h_neg[0]
|
|
|
|
indexer_lines.append(index_line_true)
|
|
|
|
if len(unique_delets_int) > 0:
|
|
for dd in range(len(unique_delets_int)):
|
|
indexes_to_delete.append(unique_delets_int[dd])
|
|
else:
|
|
indexer_lines.append(-999)
|
|
indexer_lines_deletions_len.append(-999)
|
|
indexr_uniq_ind.append(-999)
|
|
|
|
peaks_true = []
|
|
for m in range(len(peaks_neg_fin_t)):
|
|
if m in indexes_to_delete:
|
|
pass
|
|
else:
|
|
peaks_true.append(peaks_neg_fin_t[m])
|
|
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
|
|
|
|
def find_num_col_by_vertical_lines(self, regions_without_seperators, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :, 0].sum(axis=0)
|
|
|
|
##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
|
|
sigma_ = 35 # 70#35
|
|
|
|
z = gaussian_filter1d(regions_without_seperators_0, sigma_)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
# print(peaks,'peaksnew')
|
|
return peaks
|
|
|
|
def find_num_col(self, regions_without_seperators, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
|
|
|
|
##plt.plot(regions_without_seperators_0)
|
|
##plt.show()
|
|
|
|
sigma_ = 35 # 70#35
|
|
|
|
meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
|
|
|
|
first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
|
|
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
|
|
|
# print(last_nonzero)
|
|
# print(isNaN(last_nonzero))
|
|
# last_nonzero=0#halalikh
|
|
last_nonzero = len(regions_without_seperators_0) - last_nonzero
|
|
|
|
y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
|
|
|
|
y_help = np.zeros(len(y) + 20)
|
|
|
|
y_help[10 : len(y) + 10] = y
|
|
|
|
x = np.array(range(len(y)))
|
|
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 20)
|
|
|
|
zneg[10 : len(zneg_rev) + 10] = zneg_rev
|
|
|
|
z = gaussian_filter1d(y, sigma_)
|
|
zneg = gaussian_filter1d(zneg, sigma_)
|
|
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
peaks_neg = peaks_neg - 10 - 10
|
|
|
|
last_nonzero = last_nonzero - 100
|
|
first_nonzero = first_nonzero + 200
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
|
|
|
|
peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])]
|
|
peaks_neg = peaks_neg[(peaks_neg > 370) & (peaks_neg < (regions_without_seperators.shape[1] - 370))]
|
|
|
|
# print(peaks)
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
# plt.plot(z)
|
|
# plt.show()
|
|
interest_neg = z[peaks_neg]
|
|
|
|
min_peaks_pos = np.min(interest_pos)
|
|
max_peaks_pos = np.max(interest_pos)
|
|
|
|
if max_peaks_pos / min_peaks_pos >= 35:
|
|
min_peaks_pos = np.mean(interest_pos)
|
|
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
# print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax')
|
|
# $print(min_peaks_pos)
|
|
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
|
|
# print(interest_pos)
|
|
grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
|
|
|
# print(interest_neg,'interest_neg')
|
|
# print(grenze,'grenze')
|
|
# print(min_peaks_pos,'min_peaks_pos')
|
|
# print(dis_talaei,'dis_talaei')
|
|
# print(peaks_neg,'peaks_neg')
|
|
|
|
interest_neg_fin = interest_neg[(interest_neg < grenze)]
|
|
peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
|
|
# interest_neg_fin=interest_neg[(interest_neg<grenze)]
|
|
|
|
num_col = (len(interest_neg_fin)) + 1
|
|
|
|
# print(peaks_neg_fin,'peaks_neg_fin')
|
|
# print(num_col,'diz')
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_g_l = int(len(y) / 4.0)
|
|
p_g_u = len(y) - int(len(y) / 4.0)
|
|
|
|
if num_col == 3:
|
|
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or ((peaks_neg_fin[0] + 200) < p_m and peaks_neg_fin[1] < p_m) or ((peaks_neg_fin[0] - 200) > p_m and peaks_neg_fin[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_fin = []
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_fin = []
|
|
else:
|
|
pass
|
|
|
|
##print(len(peaks_neg_fin))
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
|
|
cut_off = 400
|
|
peaks_neg_true = []
|
|
forest = []
|
|
|
|
# print(len(peaks_neg_fin),'len_')
|
|
|
|
for i in range(len(peaks_neg_fin)):
|
|
if i == 0:
|
|
forest.append(peaks_neg_fin[i])
|
|
if i < (len(peaks_neg_fin) - 1):
|
|
if diff_peaks[i] <= cut_off:
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if diff_peaks[i] > cut_off:
|
|
# print(forest[np.argmin(z[forest]) ] )
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
forest = []
|
|
forest.append(peaks_neg_fin[i + 1])
|
|
if i == (len(peaks_neg_fin) - 1):
|
|
# print(print(forest[np.argmin(z[forest]) ] ))
|
|
if not self.isNaN(forest[np.argmin(z[forest])]):
|
|
peaks_neg_true.append(forest[np.argmin(z[forest])])
|
|
|
|
num_col = (len(peaks_neg_true)) + 1
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_quarter = int(len(y) / 5.0)
|
|
p_g_l = int(len(y) / 4.0)
|
|
p_g_u = len(y) - int(len(y) / 4.0)
|
|
|
|
p_u_quarter = len(y) - p_quarter
|
|
|
|
##print(num_col,'early')
|
|
if num_col == 3:
|
|
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and (peaks_neg_true[1] + 200) < p_m) or ((peaks_neg_true[0] - 200) > p_m and peaks_neg_true[1] > p_m):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
|
|
peaks_neg_true = [peaks_neg_true[0]]
|
|
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
|
|
peaks_neg_true = [peaks_neg_true[1]]
|
|
else:
|
|
pass
|
|
|
|
if num_col == 2:
|
|
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
|
|
num_col = 1
|
|
peaks_neg_true = []
|
|
else:
|
|
pass
|
|
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
|
|
|
|
if len(diff_peaks_annormal) > 0:
|
|
arg_help = np.array(range(len(diff_peaks)))
|
|
arg_help_ann = arg_help[diff_peaks < 360]
|
|
|
|
peaks_neg_fin_new = []
|
|
|
|
for ii in range(len(peaks_neg_fin)):
|
|
if ii in arg_help_ann:
|
|
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
|
|
if arg_min == 0:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
|
|
|
|
elif (ii - 1) in arg_help_ann:
|
|
pass
|
|
else:
|
|
peaks_neg_fin_new.append(peaks_neg_fin[ii])
|
|
else:
|
|
peaks_neg_fin_new = peaks_neg_fin
|
|
|
|
# plt.plot(gaussian_filter1d(y, sigma_))
|
|
# plt.plot(peaks_neg_true,z[peaks_neg_true],'*')
|
|
# plt.plot([0,len(y)], [grenze,grenze])
|
|
# plt.show()
|
|
|
|
##print(len(peaks_neg_true))
|
|
return len(peaks_neg_true), peaks_neg_true
|
|
|
|
def find_new_features_of_contoures(self, contours_main):
|
|
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
try:
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))])
|
|
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] for j in range(len(contours_main))])
|
|
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
except:
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))])
|
|
|
|
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))])
|
|
|
|
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))])
|
|
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))])
|
|
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))])
|
|
|
|
# dis_x=np.abs(x_max_main-x_min_main)
|
|
|
|
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
|
|
|
|
def return_points_with_boundies(self, peaks_neg_fin, first_point, last_point):
|
|
peaks_neg_tot = []
|
|
peaks_neg_tot.append(first_point)
|
|
for ii in range(len(peaks_neg_fin)):
|
|
peaks_neg_tot.append(peaks_neg_fin[ii])
|
|
peaks_neg_tot.append(last_point)
|
|
return peaks_neg_tot
|
|
|
|
def contours_in_same_horizon(self, cy_main_hor):
|
|
X1 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
|
|
X2 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
|
|
|
|
X1[0::1, :] = cy_main_hor[:]
|
|
X2 = X1.T
|
|
|
|
X_dif = np.abs(X2 - X1)
|
|
args_help = np.array(range(len(cy_main_hor)))
|
|
all_args = []
|
|
for i in range(len(cy_main_hor)):
|
|
list_h = list(args_help[X_dif[i, :] <= 20])
|
|
list_h.append(i)
|
|
if len(list_h) > 1:
|
|
all_args.append(list(set(list_h)))
|
|
return np.unique(all_args)
|
|
|
|
def return_boxes_of_images_by_order_of_reading_without_seperators(self, spliter_y_new, image_p_rev, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
|
|
|
|
boxes = []
|
|
|
|
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
|
|
# holes in the text and also finding spliter which covers more than one columns.
|
|
for i in range(len(spliter_y_new) - 1):
|
|
# print(spliter_y_new[i],spliter_y_new[i+1])
|
|
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
|
|
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
|
|
|
|
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
|
|
|
|
# check to see is there any vertical seperator to find holes.
|
|
if np.abs(spliter_y_new[i + 1] - spliter_y_new[i]) > 1.0 / 3.0 * regions_without_seperators.shape[0]: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
|
|
|
|
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
|
|
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.plot(org_img_dichte)
|
|
##plt.show()
|
|
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
|
|
|
|
num_col, peaks_neg_fin = self.find_num_col_only_image(image_p_rev[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=2.4)
|
|
|
|
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
|
|
x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)]
|
|
x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)]
|
|
cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)]
|
|
arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)]
|
|
|
|
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
|
|
|
|
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
|
|
|
|
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
|
|
|
|
start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0]
|
|
arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
# arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
|
|
# lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
|
|
# lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
|
|
|
|
# print(len(arg_min_hor_sort),len(arg_org_hor_some_sort),'vizzzzzz')
|
|
|
|
vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1
|
|
for kkk1 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
# print(lines_indexes_deleted,'hiii')
|
|
index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1])
|
|
|
|
for kkk2 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
|
|
vahid_subset[kkk1, kkk2] = kkk1
|
|
else:
|
|
pass
|
|
# print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
|
|
|
|
# check the len of matrix if it has no length means that there is no spliter at all
|
|
|
|
if len(vahid_subset > 0):
|
|
# print('hihoo')
|
|
|
|
# find parenets args
|
|
line_int = np.zeros(vahid_subset.shape[0])
|
|
|
|
childs_id = []
|
|
arg_child = []
|
|
for li in range(vahid_subset.shape[0]):
|
|
if np.all(vahid_subset[:, li] == -1):
|
|
line_int[li] = -1
|
|
else:
|
|
line_int[li] = 1
|
|
|
|
# childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
|
|
# helpi=[]
|
|
# for nad in range(len(childs_args_in)):
|
|
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
|
|
|
|
arg_child.append(arg_min_hor_sort_with_subset[li])
|
|
|
|
arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
# arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
# arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
|
|
# arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
|
|
start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1]
|
|
|
|
cy_hor_some_sort = cy_hor_some[arg_parent]
|
|
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
if tj in np.unique(start_index_of_hor_parent):
|
|
cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj]
|
|
cy_help_sort = np.sort(cy_help)
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mj in range(len(cy_help_sort)):
|
|
newest_y_spliter.append(cy_help_sort[mj])
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
else:
|
|
line_int = []
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
# if line_int is all -1 means that big spliters have no child and we can easily go through
|
|
if np.all(np.array(line_int) == -1):
|
|
for j in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
# print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
|
|
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
|
|
##plt.show()
|
|
|
|
# print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
|
|
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
|
|
pass
|
|
|
|
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
# num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.4)
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for j in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
if j in start_index_of_hor_parent:
|
|
|
|
x_min_ch = x_min_hor_some[arg_child]
|
|
x_max_ch = x_max_hor_some[arg_child]
|
|
cy_hor_some_sort_child = cy_hor_some[arg_child]
|
|
cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child)
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])]
|
|
|
|
if len(cy_child_in) > 0:
|
|
###num_col_ch, peaks_neg_ch=find_num_col( regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
|
|
|
|
num_col_ch, peaks_neg_ch = self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.3)
|
|
|
|
peaks_neg_ch = peaks_neg_ch[:] + newest_peaks[j]
|
|
|
|
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
|
|
|
|
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch)
|
|
|
|
newest_y_spliter_ch_tot = []
|
|
|
|
for tjj in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_new = []
|
|
newest_y_spliter_new.append(newest_y_spliter[n])
|
|
if tjj in np.unique(ss_in_ch):
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mjj in range(len(cy_child_in)):
|
|
newest_y_spliter_new.append(cy_child_in[mjj])
|
|
newest_y_spliter_new.append(newest_y_spliter[n + 1])
|
|
|
|
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
|
|
|
|
for jn in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_h = newest_y_spliter_ch_tot[jn]
|
|
|
|
for nd in range(len(newest_y_spliter_h) - 1):
|
|
|
|
matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
|
|
# num_col_sub_ch, peaks_neg_fin_sub_ch=find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]):int(newest_y_spliter_h[nd+1]),nst_p_ch[jn]:nst_p_ch[jn+1]],multiplier=2.3)
|
|
|
|
num_col_sub_ch, peaks_neg_fin_sub_ch = self.find_num_col_only_image(image_p_rev[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=2.3)
|
|
# print(peaks_neg_fin_sub_ch,'gada kutullllllll')
|
|
else:
|
|
peaks_neg_fin_sub_ch = []
|
|
|
|
peaks_sub_ch = []
|
|
peaks_sub_ch.append(nst_p_ch[jn])
|
|
|
|
for kjj in range(len(peaks_neg_fin_sub_ch)):
|
|
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn])
|
|
|
|
peaks_sub_ch.append(nst_p_ch[jn + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for khh in range(len(peaks_sub_ch) - 1):
|
|
boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]])
|
|
|
|
else:
|
|
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
###num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=2.3)
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.3)
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
|
|
pass
|
|
|
|
# plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
###num_col_sub, peaks_neg_fin_sub=find_num_col(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]],multiplier=5.0)
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col_only_image(image_p_rev[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=2.3)
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
return boxes
|
|
|
|
def return_boxes_of_images_by_order_of_reading_without_seperators_2cols(self, spliter_y_new, image_p_rev, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
|
|
|
|
boxes = []
|
|
|
|
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
|
|
# holes in the text and also finding spliter which covers more than one columns.
|
|
for i in range(len(spliter_y_new) - 1):
|
|
# print(spliter_y_new[i],spliter_y_new[i+1])
|
|
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
|
|
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
|
|
|
|
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
|
|
|
|
# check to see is there any vertical seperator to find holes.
|
|
if np.abs(spliter_y_new[i + 1] - spliter_y_new[i]) > 1.0 / 3.0 * regions_without_seperators.shape[0]: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
|
|
|
|
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
|
|
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.plot(org_img_dichte)
|
|
##plt.show()
|
|
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
|
|
|
|
try:
|
|
num_col, peaks_neg_fin = self.find_num_col_only_image(image_p_rev[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=2.4)
|
|
except:
|
|
peaks_neg_fin = []
|
|
num_col = 0
|
|
|
|
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
|
|
|
|
for kh in range(len(peaks_neg_tot) - 1):
|
|
boxes.append([peaks_neg_tot[kh], peaks_neg_tot[kh + 1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
else:
|
|
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
|
|
return boxes
|
|
|
|
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(self, regions_pre_p):
|
|
seperators_closeup = ((regions_pre_p[:, :] == 6)) * 1
|
|
|
|
seperators_closeup = seperators_closeup.astype(np.uint8)
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1)
|
|
seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1)
|
|
|
|
seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1)
|
|
seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1)
|
|
|
|
if len(seperators_closeup.shape) == 2:
|
|
seperators_closeup_n = np.zeros((seperators_closeup.shape[0], seperators_closeup.shape[1], 3))
|
|
seperators_closeup_n[:, :, 0] = seperators_closeup
|
|
seperators_closeup_n[:, :, 1] = seperators_closeup
|
|
seperators_closeup_n[:, :, 2] = seperators_closeup
|
|
else:
|
|
seperators_closeup_n = seperators_closeup[:, :, :]
|
|
# seperators_closeup=seperators_closeup.astype(np.uint8)
|
|
seperators_closeup_n = seperators_closeup_n.astype(np.uint8)
|
|
imgray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_lines, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = self.find_features_of_lines(contours_lines)
|
|
|
|
dist_y = np.abs(y_max_main - y_min_main)
|
|
|
|
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
|
|
args = np.array(range(len(slope_lines)))
|
|
len_x = seperators_closeup.shape[1] * 0
|
|
len_y = seperators_closeup.shape[0] * 0.01
|
|
|
|
args_hor = args[slope_lines == 0]
|
|
dist_x_hor = dist_x[slope_lines == 0]
|
|
dist_y_hor = dist_y[slope_lines == 0]
|
|
x_min_main_hor = x_min_main[slope_lines == 0]
|
|
x_max_main_hor = x_max_main[slope_lines == 0]
|
|
cy_main_hor = cy_main[slope_lines == 0]
|
|
y_min_main_hor = y_min_main[slope_lines == 0]
|
|
y_max_main_hor = y_max_main[slope_lines == 0]
|
|
|
|
args_hor = args_hor[dist_x_hor >= len_x]
|
|
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x]
|
|
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x]
|
|
cy_main_hor = cy_main_hor[dist_x_hor >= len_x]
|
|
y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x]
|
|
y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x]
|
|
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x]
|
|
dist_y_hor = dist_y_hor[dist_x_hor >= len_x]
|
|
dist_x_hor = dist_x_hor[dist_x_hor >= len_x]
|
|
|
|
args_ver = args[slope_lines == 1]
|
|
dist_y_ver = dist_y[slope_lines == 1]
|
|
dist_x_ver = dist_x[slope_lines == 1]
|
|
x_min_main_ver = x_min_main[slope_lines == 1]
|
|
x_max_main_ver = x_max_main[slope_lines == 1]
|
|
y_min_main_ver = y_min_main[slope_lines == 1]
|
|
y_max_main_ver = y_max_main[slope_lines == 1]
|
|
cx_main_ver = cx_main[slope_lines == 1]
|
|
|
|
args_ver = args_ver[dist_y_ver >= len_y]
|
|
x_max_main_ver = x_max_main_ver[dist_y_ver >= len_y]
|
|
x_min_main_ver = x_min_main_ver[dist_y_ver >= len_y]
|
|
cx_main_ver = cx_main_ver[dist_y_ver >= len_y]
|
|
y_min_main_ver = y_min_main_ver[dist_y_ver >= len_y]
|
|
y_max_main_ver = y_max_main_ver[dist_y_ver >= len_y]
|
|
dist_x_ver = dist_x_ver[dist_y_ver >= len_y]
|
|
dist_y_ver = dist_y_ver[dist_y_ver >= len_y]
|
|
|
|
img_p_in_ver = np.zeros(seperators_closeup_n[:, :, 2].shape)
|
|
for jv in range(len(args_ver)):
|
|
img_p_in_ver = cv2.fillPoly(img_p_in_ver, pts=[contours_lines[args_ver[jv]]], color=(1, 1, 1))
|
|
|
|
img_in_hor = np.zeros(seperators_closeup_n[:, :, 2].shape)
|
|
for jv in range(len(args_hor)):
|
|
img_p_in_hor = cv2.fillPoly(img_in_hor, pts=[contours_lines[args_hor[jv]]], color=(1, 1, 1))
|
|
|
|
all_args_uniq = self.contours_in_same_horizon(cy_main_hor)
|
|
# print(all_args_uniq,'all_args_uniq')
|
|
if len(all_args_uniq) > 0:
|
|
if type(all_args_uniq[0]) is list:
|
|
contours_new = []
|
|
for dd in range(len(all_args_uniq)):
|
|
merged_all = None
|
|
some_args = args_hor[all_args_uniq[dd]]
|
|
some_cy = cy_main_hor[all_args_uniq[dd]]
|
|
some_x_min = x_min_main_hor[all_args_uniq[dd]]
|
|
some_x_max = x_max_main_hor[all_args_uniq[dd]]
|
|
|
|
img_in = np.zeros(seperators_closeup_n[:, :, 2].shape)
|
|
for jv in range(len(some_args)):
|
|
|
|
img_p_in = cv2.fillPoly(img_p_in_hor, pts=[contours_lines[some_args[jv]]], color=(1, 1, 1))
|
|
img_p_in[int(np.mean(some_cy)) - 5 : int(np.mean(some_cy)) + 5, int(np.min(some_x_min)) : int(np.max(some_x_max))] = 1
|
|
|
|
else:
|
|
img_p_in = seperators_closeup
|
|
else:
|
|
img_p_in = seperators_closeup
|
|
|
|
sep_ver_hor = img_p_in + img_p_in_ver
|
|
sep_ver_hor_cross = (sep_ver_hor == 2) * 1
|
|
|
|
sep_ver_hor_cross = np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
|
|
sep_ver_hor_cross = sep_ver_hor_cross.astype(np.uint8)
|
|
imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
cx_cross, cy_cross, _, _, _, _, _ = self.find_new_features_of_contoures(contours_cross)
|
|
|
|
for ii in range(len(cx_cross)):
|
|
sep_ver_hor[int(cy_cross[ii]) - 15 : int(cy_cross[ii]) + 15, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40] = 0
|
|
sep_ver_hor[int(cy_cross[ii]) - 15 : int(cy_cross[ii]) + 15, int(cx_cross[ii]) - 40 : int(cx_cross[ii]) - 4] = 0
|
|
|
|
img_p_in[:, :] = sep_ver_hor[:, :]
|
|
|
|
if len(img_p_in.shape) == 2:
|
|
seperators_closeup_n = np.zeros((img_p_in.shape[0], img_p_in.shape[1], 3))
|
|
seperators_closeup_n[:, :, 0] = img_p_in
|
|
seperators_closeup_n[:, :, 1] = img_p_in
|
|
seperators_closeup_n[:, :, 2] = img_p_in
|
|
else:
|
|
seperators_closeup_n = img_p_in[:, :, :]
|
|
# seperators_closeup=seperators_closeup.astype(np.uint8)
|
|
seperators_closeup_n = seperators_closeup_n.astype(np.uint8)
|
|
imgray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = self.find_features_of_lines(contours_lines)
|
|
|
|
dist_y = np.abs(y_max_main - y_min_main)
|
|
|
|
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
|
|
args = np.array(range(len(slope_lines)))
|
|
len_x = seperators_closeup.shape[1] * 0.04
|
|
len_y = seperators_closeup.shape[0] * 0.08
|
|
|
|
args_hor = args[slope_lines == 0]
|
|
dist_x_hor = dist_x[slope_lines == 0]
|
|
dist_y_hor = dist_y[slope_lines == 0]
|
|
x_min_main_hor = x_min_main[slope_lines == 0]
|
|
x_max_main_hor = x_max_main[slope_lines == 0]
|
|
cy_main_hor = cy_main[slope_lines == 0]
|
|
y_min_main_hor = y_min_main[slope_lines == 0]
|
|
y_max_main_hor = y_max_main[slope_lines == 0]
|
|
|
|
args_hor = args_hor[dist_x_hor >= len_x]
|
|
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x]
|
|
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x]
|
|
cy_main_hor = cy_main_hor[dist_x_hor >= len_x]
|
|
y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x]
|
|
y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x]
|
|
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x]
|
|
dist_y_hor = dist_y_hor[dist_x_hor >= len_x]
|
|
dist_x_hor = dist_x_hor[dist_x_hor >= len_x]
|
|
|
|
args_ver = args[slope_lines == 1]
|
|
dist_y_ver = dist_y[slope_lines == 1]
|
|
dist_x_ver = dist_x[slope_lines == 1]
|
|
x_min_main_ver = x_min_main[slope_lines == 1]
|
|
x_max_main_ver = x_max_main[slope_lines == 1]
|
|
y_min_main_ver = y_min_main[slope_lines == 1]
|
|
y_max_main_ver = y_max_main[slope_lines == 1]
|
|
cx_main_ver = cx_main[slope_lines == 1]
|
|
|
|
args_ver = args_ver[dist_y_ver >= len_y]
|
|
x_max_main_ver = x_max_main_ver[dist_y_ver >= len_y]
|
|
x_min_main_ver = x_min_main_ver[dist_y_ver >= len_y]
|
|
cx_main_ver = cx_main_ver[dist_y_ver >= len_y]
|
|
y_min_main_ver = y_min_main_ver[dist_y_ver >= len_y]
|
|
y_max_main_ver = y_max_main_ver[dist_y_ver >= len_y]
|
|
dist_x_ver = dist_x_ver[dist_y_ver >= len_y]
|
|
dist_y_ver = dist_y_ver[dist_y_ver >= len_y]
|
|
|
|
matrix_of_lines_ch = np.zeros((len(cy_main_hor) + len(cx_main_ver), 10))
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 0] = args_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 0] = args_ver
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 1] = cx_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 2] = x_min_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 2] = x_min_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 3] = x_max_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 3] = x_max_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 4] = dist_x_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 4] = dist_x_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 5] = cy_main_hor
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 6] = y_min_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 6] = y_min_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 7] = y_max_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 7] = y_max_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 8] = dist_y_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 8] = dist_y_ver
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 9] = 1
|
|
|
|
return matrix_of_lines_ch, seperators_closeup_n
|
|
|
|
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(self, img_p_in_ver, img_in_hor):
|
|
|
|
# plt.imshow(img_in_hor)
|
|
# plt.show()
|
|
|
|
# img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2)
|
|
img_p_in_ver = img_p_in_ver.astype(np.uint8)
|
|
img_p_in_ver = np.repeat(img_p_in_ver[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(img_p_in_ver, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines_ver, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines_ver, dist_x_ver, x_min_main_ver, x_max_main_ver, cy_main_ver, slope_lines_org_ver, y_min_main_ver, y_max_main_ver, cx_main_ver = self.find_features_of_lines(contours_lines_ver)
|
|
|
|
for i in range(len(x_min_main_ver)):
|
|
img_p_in_ver[int(y_min_main_ver[i]) : int(y_min_main_ver[i]) + 30, int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0
|
|
img_p_in_ver[int(y_max_main_ver[i]) - 30 : int(y_max_main_ver[i]), int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0
|
|
|
|
# plt.imshow(img_p_in_ver[:,:,0])
|
|
# plt.show()
|
|
img_in_hor = img_in_hor.astype(np.uint8)
|
|
img_in_hor = np.repeat(img_in_hor[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(img_in_hor, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines_hor, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines_hor, dist_x_hor, x_min_main_hor, x_max_main_hor, cy_main_hor, slope_lines_org_hor, y_min_main_hor, y_max_main_hor, cx_main_hor = self.find_features_of_lines(contours_lines_hor)
|
|
|
|
args_hor = np.array(range(len(slope_lines_hor)))
|
|
all_args_uniq = self.contours_in_same_horizon(cy_main_hor)
|
|
# print(all_args_uniq,'all_args_uniq')
|
|
if len(all_args_uniq) > 0:
|
|
if type(all_args_uniq[0]) is list:
|
|
special_seperators = []
|
|
contours_new = []
|
|
for dd in range(len(all_args_uniq)):
|
|
merged_all = None
|
|
some_args = args_hor[all_args_uniq[dd]]
|
|
some_cy = cy_main_hor[all_args_uniq[dd]]
|
|
some_x_min = x_min_main_hor[all_args_uniq[dd]]
|
|
some_x_max = x_max_main_hor[all_args_uniq[dd]]
|
|
|
|
# img_in=np.zeros(seperators_closeup_n[:,:,2].shape)
|
|
for jv in range(len(some_args)):
|
|
|
|
img_p_in = cv2.fillPoly(img_in_hor, pts=[contours_lines_hor[some_args[jv]]], color=(1, 1, 1))
|
|
img_p_in[int(np.mean(some_cy)) - 5 : int(np.mean(some_cy)) + 5, int(np.min(some_x_min)) : int(np.max(some_x_max))] = 1
|
|
|
|
sum_dis = dist_x_hor[some_args].sum()
|
|
diff_max_min_uniques = np.max(x_max_main_hor[some_args]) - np.min(x_min_main_hor[some_args])
|
|
|
|
# print( sum_dis/float(diff_max_min_uniques) ,diff_max_min_uniques/float(img_p_in_ver.shape[1]),dist_x_hor[some_args].sum(),diff_max_min_uniques,np.mean( dist_x_hor[some_args]),np.std( dist_x_hor[some_args]) )
|
|
|
|
if diff_max_min_uniques > sum_dis and ((sum_dis / float(diff_max_min_uniques)) > 0.85) and ((diff_max_min_uniques / float(img_p_in_ver.shape[1])) > 0.85) and np.std(dist_x_hor[some_args]) < (0.55 * np.mean(dist_x_hor[some_args])):
|
|
# print(dist_x_hor[some_args],dist_x_hor[some_args].sum(),np.min(x_min_main_hor[some_args]) ,np.max(x_max_main_hor[some_args]),'jalibdi')
|
|
# print(np.mean( dist_x_hor[some_args] ),np.std( dist_x_hor[some_args] ),np.var( dist_x_hor[some_args] ),'jalibdiha')
|
|
special_seperators.append(np.mean(cy_main_hor[some_args]))
|
|
|
|
else:
|
|
img_p_in = img_in_hor
|
|
special_seperators = []
|
|
else:
|
|
img_p_in = img_in_hor
|
|
special_seperators = []
|
|
|
|
img_p_in_ver[:, :, 0][img_p_in_ver[:, :, 0] == 255] = 1
|
|
# print(img_p_in_ver.shape,np.unique(img_p_in_ver[:,:,0]))
|
|
|
|
# plt.imshow(img_p_in[:,:,0])
|
|
# plt.show()
|
|
|
|
# plt.imshow(img_p_in_ver[:,:,0])
|
|
# plt.show()
|
|
sep_ver_hor = img_p_in + img_p_in_ver
|
|
# print(sep_ver_hor.shape,np.unique(sep_ver_hor[:,:,0]),'sep_ver_horsep_ver_horsep_ver_hor')
|
|
# plt.imshow(sep_ver_hor[:,:,0])
|
|
# plt.show()
|
|
|
|
sep_ver_hor_cross = (sep_ver_hor[:, :, 0] == 2) * 1
|
|
|
|
sep_ver_hor_cross = np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2)
|
|
sep_ver_hor_cross = sep_ver_hor_cross.astype(np.uint8)
|
|
imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
cx_cross, cy_cross, _, _, _, _, _ = self.find_new_features_of_contoures(contours_cross)
|
|
|
|
for ii in range(len(cx_cross)):
|
|
img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40, 0] = 0
|
|
img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) - 40 : int(cx_cross[ii]) - 4, 0] = 0
|
|
|
|
# plt.imshow(img_p_in[:,:,0])
|
|
# plt.show()
|
|
|
|
return img_p_in[:, :, 0], special_seperators
|
|
|
|
def return_boxes_of_images_by_order_of_reading(self, spliter_y_new, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
|
|
boxes = []
|
|
|
|
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
|
|
# holes in the text and also finding spliter which covers more than one columns.
|
|
for i in range(len(spliter_y_new) - 1):
|
|
# print(spliter_y_new[i],spliter_y_new[i+1])
|
|
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
|
|
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
|
|
|
|
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
|
|
|
|
# check to see is there any vertical seperator to find holes.
|
|
if len(matrix_new[:, 9][matrix_new[:, 9] == 1]) > 0 and np.max(matrix_new[:, 8][matrix_new[:, 9] == 1]) >= 0.1 * (np.abs(spliter_y_new[i + 1] - spliter_y_new[i])):
|
|
|
|
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
|
|
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.plot(org_img_dichte)
|
|
##plt.show()
|
|
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
|
|
|
|
num_col, peaks_neg_fin = self.find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0)
|
|
|
|
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
|
|
x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)]
|
|
x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)]
|
|
cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)]
|
|
arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)]
|
|
|
|
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
|
|
|
|
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = self.return_hor_spliter_by_index(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
|
|
|
|
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
|
|
|
|
start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0]
|
|
arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
# arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
|
|
# lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
|
|
# lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
|
|
|
|
vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1
|
|
for kkk1 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1])
|
|
|
|
for kkk2 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
|
|
vahid_subset[kkk1, kkk2] = kkk1
|
|
else:
|
|
pass
|
|
# print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
|
|
|
|
# print(vahid_subset,'zartt222')
|
|
|
|
# check the len of matrix if it has no length means that there is no spliter at all
|
|
|
|
if len(vahid_subset > 0):
|
|
# print('hihoo')
|
|
|
|
# find parenets args
|
|
line_int = np.zeros(vahid_subset.shape[0])
|
|
|
|
childs_id = []
|
|
arg_child = []
|
|
for li in range(vahid_subset.shape[0]):
|
|
# print(vahid_subset[:,li])
|
|
if np.all(vahid_subset[:, li] == -1):
|
|
line_int[li] = -1
|
|
else:
|
|
line_int[li] = 1
|
|
|
|
# childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
|
|
# helpi=[]
|
|
# for nad in range(len(childs_args_in)):
|
|
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
|
|
|
|
arg_child.append(arg_min_hor_sort_with_subset[li])
|
|
|
|
# line_int=vahid_subset[0,:]
|
|
|
|
# print(arg_child,line_int[0],'zartt33333')
|
|
arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
# arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
# arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
|
|
# arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
|
|
start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1]
|
|
|
|
cy_hor_some_sort = cy_hor_some[arg_parent]
|
|
|
|
# print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt')
|
|
|
|
# args_indexes=np.array(range(len(start_index_of_hor) ))
|
|
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
if tj in np.unique(start_index_of_hor_parent):
|
|
##print(cy_hor_some_sort)
|
|
cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj]
|
|
cy_help_sort = np.sort(cy_help)
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mj in range(len(cy_help_sort)):
|
|
newest_y_spliter.append(cy_help_sort[mj])
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
else:
|
|
line_int = []
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
# if line_int is all -1 means that big spliters have no child and we can easily go through
|
|
if np.all(np.array(line_int) == -1):
|
|
for j in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
# print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
|
|
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
|
|
##plt.show()
|
|
|
|
# print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
|
|
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
|
|
pass
|
|
|
|
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if len(matrix_new_new[:, 9][matrix_new_new[:, 9] == 1]) > 0 and np.max(matrix_new_new[:, 8][matrix_new_new[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter[n + 1] - newest_y_spliter[n])):
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for j in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
if j in start_index_of_hor_parent:
|
|
|
|
x_min_ch = x_min_hor_some[arg_child]
|
|
x_max_ch = x_max_hor_some[arg_child]
|
|
cy_hor_some_sort_child = cy_hor_some[arg_child]
|
|
cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child)
|
|
|
|
# print(cy_hor_some_sort_child,'ychilds')
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])]
|
|
|
|
if len(cy_child_in) > 0:
|
|
num_col_ch, peaks_neg_ch = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
|
|
# print(peaks_neg_ch,'mizzzz')
|
|
# peaks_neg_ch=[]
|
|
# for djh in range(len(peaks_neg_ch)):
|
|
# peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] )
|
|
|
|
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
|
|
|
|
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = self.return_hor_spliter_by_index(peaks_neg_ch_tot, x_min_ch, x_max_ch)
|
|
|
|
newest_y_spliter_ch_tot = []
|
|
|
|
for tjj in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_new = []
|
|
newest_y_spliter_new.append(newest_y_spliter[n])
|
|
if tjj in np.unique(ss_in_ch):
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mjj in range(len(cy_child_in)):
|
|
newest_y_spliter_new.append(cy_child_in[mjj])
|
|
newest_y_spliter_new.append(newest_y_spliter[n + 1])
|
|
|
|
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
|
|
|
|
for jn in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_h = newest_y_spliter_ch_tot[jn]
|
|
|
|
for nd in range(len(newest_y_spliter_h) - 1):
|
|
|
|
matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if len(matrix_new_new2[:, 9][matrix_new_new2[:, 9] == 1]) > 0 and np.max(matrix_new_new2[:, 8][matrix_new_new2[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter_h[nd + 1] - newest_y_spliter_h[nd])):
|
|
num_col_sub_ch, peaks_neg_fin_sub_ch = self.find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=5.0)
|
|
|
|
else:
|
|
peaks_neg_fin_sub_ch = []
|
|
|
|
peaks_sub_ch = []
|
|
peaks_sub_ch.append(nst_p_ch[jn])
|
|
|
|
for kjj in range(len(peaks_neg_fin_sub_ch)):
|
|
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn])
|
|
|
|
peaks_sub_ch.append(nst_p_ch[jn + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for khh in range(len(peaks_sub_ch) - 1):
|
|
boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]])
|
|
|
|
else:
|
|
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if len(matrix_new_new[:, 9][matrix_new_new[:, 9] == 1]) > 0 and np.max(matrix_new_new[:, 8][matrix_new_new[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter[n + 1] - newest_y_spliter[n])):
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
# plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if len(matrix_new_new[:, 9][matrix_new_new[:, 9] == 1]) > 0 and np.max(matrix_new_new[:, 8][matrix_new_new[:, 9] == 1]) >= 0.2 * (np.abs(newest_y_spliter[n + 1] - newest_y_spliter[n])):
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
|
|
return boxes
|
|
|
|
def return_boxes_of_images_by_order_of_reading_new(self, spliter_y_new, regions_without_seperators, matrix_of_lines_ch):
|
|
boxes = []
|
|
|
|
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
|
|
# holes in the text and also finding spliter which covers more than one columns.
|
|
for i in range(len(spliter_y_new) - 1):
|
|
# print(spliter_y_new[i],spliter_y_new[i+1])
|
|
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
|
|
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
|
|
|
|
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
|
|
|
|
# check to see is there any vertical seperator to find holes.
|
|
if 1 > 0: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
|
|
|
|
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
|
|
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.plot(org_img_dichte)
|
|
##plt.show()
|
|
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
|
|
|
|
# print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'firssst')
|
|
|
|
# plt.imshow(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:])
|
|
# plt.show()
|
|
try:
|
|
num_col, peaks_neg_fin = self.find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin = []
|
|
|
|
# print(peaks_neg_fin,'peaks_neg_fin')
|
|
# num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0)
|
|
x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)]
|
|
x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)]
|
|
cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)]
|
|
arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)]
|
|
|
|
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, regions_without_seperators[:, :].shape[1])
|
|
|
|
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
|
|
|
|
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
|
|
|
|
start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0]
|
|
arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0]
|
|
|
|
# arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0]
|
|
# lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0]
|
|
# lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0]
|
|
|
|
vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1
|
|
for kkk1 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1])
|
|
|
|
for kkk2 in range(len(start_index_of_hor_with_subset)):
|
|
|
|
if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]):
|
|
vahid_subset[kkk1, kkk2] = kkk1
|
|
else:
|
|
pass
|
|
# print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0]))
|
|
|
|
# check the len of matrix if it has no length means that there is no spliter at all
|
|
|
|
if len(vahid_subset > 0):
|
|
# print('hihoo')
|
|
|
|
# find parenets args
|
|
line_int = np.zeros(vahid_subset.shape[0])
|
|
|
|
childs_id = []
|
|
arg_child = []
|
|
for li in range(vahid_subset.shape[0]):
|
|
# print(vahid_subset[:,li])
|
|
if np.all(vahid_subset[:, li] == -1):
|
|
line_int[li] = -1
|
|
else:
|
|
line_int[li] = 1
|
|
|
|
# childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1]
|
|
# helpi=[]
|
|
# for nad in range(len(childs_args_in)):
|
|
# helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]])
|
|
|
|
arg_child.append(arg_min_hor_sort_with_subset[li])
|
|
|
|
# line_int=vahid_subset[0,:]
|
|
|
|
arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1]
|
|
# arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
# arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1]
|
|
|
|
# arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1]
|
|
start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1]
|
|
|
|
cy_hor_some_sort = cy_hor_some[arg_parent]
|
|
|
|
# print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt')
|
|
|
|
# args_indexes=np.array(range(len(start_index_of_hor) ))
|
|
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
if tj in np.unique(start_index_of_hor_parent):
|
|
# print(cy_hor_some_sort)
|
|
cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj]
|
|
cy_help_sort = np.sort(cy_help)
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mj in range(len(cy_help_sort)):
|
|
newest_y_spliter.append(cy_help_sort[mj])
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
else:
|
|
line_int = []
|
|
newest_y_spliter_tot = []
|
|
|
|
for tj in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = []
|
|
newest_y_spliter.append(spliter_y_new[i])
|
|
|
|
newest_y_spliter.append(spliter_y_new[i + 1])
|
|
|
|
newest_y_spliter_tot.append(newest_y_spliter)
|
|
|
|
# if line_int is all -1 means that big spliters have no child and we can easily go through
|
|
if np.all(np.array(line_int) == -1):
|
|
for j in range(len(newest_peaks) - 1):
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
# print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa')
|
|
##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]])
|
|
##plt.show()
|
|
|
|
# print(matrix_new[:,0][ (matrix_new[:,9]==1 )])
|
|
for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]:
|
|
pass
|
|
|
|
###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
# print( int(newest_y_spliter[n]),int(newest_y_spliter[n+1]),newest_peaks[j],newest_peaks[j+1] )
|
|
try:
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin_sub = []
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for j in range(len(newest_peaks) - 1):
|
|
|
|
newest_y_spliter = newest_y_spliter_tot[j]
|
|
|
|
if j in start_index_of_hor_parent:
|
|
|
|
x_min_ch = x_min_hor_some[arg_child]
|
|
x_max_ch = x_max_hor_some[arg_child]
|
|
cy_hor_some_sort_child = cy_hor_some[arg_child]
|
|
cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child)
|
|
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])]
|
|
|
|
if len(cy_child_in) > 0:
|
|
try:
|
|
num_col_ch, peaks_neg_ch = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_ch = []
|
|
# print(peaks_neg_ch,'mizzzz')
|
|
# peaks_neg_ch=[]
|
|
# for djh in range(len(peaks_neg_ch)):
|
|
# peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] )
|
|
|
|
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
|
|
|
|
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch)
|
|
|
|
newest_y_spliter_ch_tot = []
|
|
|
|
for tjj in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_new = []
|
|
newest_y_spliter_new.append(newest_y_spliter[n])
|
|
if tjj in np.unique(ss_in_ch):
|
|
|
|
# print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha')
|
|
for mjj in range(len(cy_child_in)):
|
|
newest_y_spliter_new.append(cy_child_in[mjj])
|
|
newest_y_spliter_new.append(newest_y_spliter[n + 1])
|
|
|
|
newest_y_spliter_ch_tot.append(newest_y_spliter_new)
|
|
|
|
for jn in range(len(nst_p_ch) - 1):
|
|
newest_y_spliter_h = newest_y_spliter_ch_tot[jn]
|
|
|
|
for nd in range(len(newest_y_spliter_h) - 1):
|
|
|
|
matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )):
|
|
try:
|
|
num_col_sub_ch, peaks_neg_fin_sub_ch = self.find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin_sub_ch = []
|
|
|
|
else:
|
|
peaks_neg_fin_sub_ch = []
|
|
|
|
peaks_sub_ch = []
|
|
peaks_sub_ch.append(nst_p_ch[jn])
|
|
|
|
for kjj in range(len(peaks_neg_fin_sub_ch)):
|
|
peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn])
|
|
|
|
peaks_sub_ch.append(nst_p_ch[jn + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for khh in range(len(peaks_sub_ch) - 1):
|
|
boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]])
|
|
|
|
else:
|
|
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
try:
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0)
|
|
except:
|
|
peaks_neg_fin_sub = []
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
for n in range(len(newest_y_spliter) - 1):
|
|
|
|
# plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)])
|
|
# print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1])
|
|
matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])]
|
|
# print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada')
|
|
if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )):
|
|
try:
|
|
num_col_sub, peaks_neg_fin_sub = self.find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0)
|
|
except:
|
|
peaks_neg_fin_sub = []
|
|
else:
|
|
peaks_neg_fin_sub = []
|
|
|
|
peaks_sub = []
|
|
peaks_sub.append(newest_peaks[j])
|
|
|
|
for kj in range(len(peaks_neg_fin_sub)):
|
|
peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j])
|
|
|
|
peaks_sub.append(newest_peaks[j + 1])
|
|
|
|
# peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1])
|
|
|
|
for kh in range(len(peaks_sub) - 1):
|
|
boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]])
|
|
|
|
else:
|
|
boxes.append([0, regions_without_seperators[:, :].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
|
|
return boxes
|
|
|
|
def return_boxes_of_images_by_order_of_reading_2cols(self, spliter_y_new, regions_without_seperators, matrix_of_lines_ch, seperators_closeup_n):
|
|
boxes = []
|
|
|
|
# here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \
|
|
# holes in the text and also finding spliter which covers more than one columns.
|
|
for i in range(len(spliter_y_new) - 1):
|
|
# print(spliter_y_new[i],spliter_y_new[i+1])
|
|
matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])]
|
|
# print(len( matrix_new[:,9][matrix_new[:,9]==1] ))
|
|
|
|
# print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa')
|
|
|
|
# check to see is there any vertical seperator to find holes.
|
|
if 1 > 0: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )):
|
|
# print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'burayaaaa galimiirrrrrrrrrrrrrrrrrrrrrrrrrrr')
|
|
# org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30)
|
|
# org_img_dichte=org_img_dichte-np.min(org_img_dichte)
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.plot(org_img_dichte)
|
|
##plt.show()
|
|
###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.)
|
|
|
|
try:
|
|
num_col, peaks_neg_fin = self.find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0)
|
|
|
|
except:
|
|
peaks_neg_fin = []
|
|
num_col = 0
|
|
|
|
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
|
|
|
|
for kh in range(len(peaks_neg_tot) - 1):
|
|
boxes.append([peaks_neg_tot[kh], peaks_neg_tot[kh + 1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
|
|
else:
|
|
boxes.append([0, seperators_closeup_n[:, :, 0].shape[1], spliter_y_new[i], spliter_y_new[i + 1]])
|
|
|
|
return boxes
|
|
|
|
def return_hor_spliter_by_index(self, peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
|
|
|
|
arg_min_hor_sort = np.argsort(x_min_hor_some)
|
|
x_min_hor_some_sort = np.sort(x_min_hor_some)
|
|
x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
|
|
|
|
arg_minmax = np.array(range(len(peaks_neg_fin_t)))
|
|
indexer_lines = []
|
|
indexes_to_delete = []
|
|
indexer_lines_deletions_len = []
|
|
indexr_uniq_ind = []
|
|
for i in range(len(x_min_hor_some_sort)):
|
|
min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
|
|
max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
|
|
|
|
min_h[0] = min_h[0] # +20
|
|
max_h[len(max_h) - 1] = max_h[len(max_h) - 1] ##-20
|
|
|
|
min_h_neg = arg_minmax[(min_h < 0) & (np.abs(min_h) < 360)]
|
|
max_h_neg = arg_minmax[(max_h >= 0) & (np.abs(max_h) < 360)]
|
|
|
|
if len(min_h_neg) > 0 and len(max_h_neg) > 0:
|
|
deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
|
|
unique_delets_int = []
|
|
# print(deletions,len(deletions),'delii')
|
|
if len(deletions) > 0:
|
|
# print(deletions,len(deletions),'delii2')
|
|
|
|
for j in range(len(deletions)):
|
|
indexes_to_delete.append(deletions[j])
|
|
# print(deletions,indexes_to_delete,'badiii')
|
|
unique_delets = np.unique(indexes_to_delete)
|
|
# print(min_h_neg[0],unique_delets)
|
|
unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
|
|
|
|
indexer_lines_deletions_len.append(len(deletions))
|
|
indexr_uniq_ind.append([deletions])
|
|
|
|
else:
|
|
indexer_lines_deletions_len.append(0)
|
|
indexr_uniq_ind.append(-999)
|
|
|
|
index_line_true = min_h_neg[0] - len(unique_delets_int)
|
|
# print(index_line_true)
|
|
if index_line_true > 0 and min_h_neg[0] >= 2:
|
|
index_line_true = index_line_true
|
|
else:
|
|
index_line_true = min_h_neg[0]
|
|
|
|
indexer_lines.append(index_line_true)
|
|
|
|
if len(unique_delets_int) > 0:
|
|
for dd in range(len(unique_delets_int)):
|
|
indexes_to_delete.append(unique_delets_int[dd])
|
|
else:
|
|
indexer_lines.append(-999)
|
|
indexer_lines_deletions_len.append(-999)
|
|
indexr_uniq_ind.append(-999)
|
|
|
|
peaks_true = []
|
|
for m in range(len(peaks_neg_fin_t)):
|
|
if m in indexes_to_delete:
|
|
pass
|
|
else:
|
|
peaks_true.append(peaks_neg_fin_t[m])
|
|
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
|
|
|
|
def return_region_segmentation_after_implementing_not_head_maintext_parallel(self, image_regions_eraly_p, boxes):
|
|
image_revised = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1]))
|
|
for i in range(len(boxes)):
|
|
|
|
image_box = image_regions_eraly_p[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1])]
|
|
image_box = np.array(image_box)
|
|
# plt.imshow(image_box)
|
|
# plt.show()
|
|
|
|
# print(int(boxes[i][2]),int(boxes[i][3]),int(boxes[i][0]),int(boxes[i][1]),'addaa')
|
|
image_box = self.implent_law_head_main_not_parallel(image_box)
|
|
image_box = self.implent_law_head_main_not_parallel(image_box)
|
|
image_box = self.implent_law_head_main_not_parallel(image_box)
|
|
|
|
image_revised[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1])] = image_box[:, :]
|
|
return image_revised
|
|
|
|
def tear_main_texts_on_the_boundaries_of_boxes(self, img_revised_tab, boxes):
|
|
for i in range(len(boxes)):
|
|
img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 0][img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 0] == 1] = 0
|
|
img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 1][img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 1] == 1] = 0
|
|
img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 2][img_revised_tab[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][1] - 10) : int(boxes[i][1]), 2] == 1] = 0
|
|
return img_revised_tab
|
|
|
|
def implent_law_head_main_not_parallel(self, text_regions):
|
|
# print(text_regions.shape)
|
|
text_indexes = [1, 2] # 1: main text , 2: header , 3: comments
|
|
|
|
for t_i in text_indexes:
|
|
textline_mask = text_regions[:, :] == t_i
|
|
textline_mask = textline_mask * 255.0
|
|
|
|
textline_mask = textline_mask.astype(np.uint8)
|
|
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2)
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
# print(type(textline_mask),np.unique(textline_mask),textline_mask.shape)
|
|
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
if t_i == 1:
|
|
contours_main, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
# print(type(contours_main))
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
# print(contours_main[0],np.shape(contours_main[0]),contours_main[0][:,0,0])
|
|
elif t_i == 2:
|
|
contours_header, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
# print(type(contours_header))
|
|
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
|
|
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
|
|
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
|
|
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
|
|
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
|
|
args = np.array(range(1, len(cy_header) + 1))
|
|
args_main = np.array(range(1, len(cy_main) + 1))
|
|
for jj in range(len(contours_main)):
|
|
headers_in_main = [(cy_header > y_min_main[jj]) & ((cy_header < y_max_main[jj]))]
|
|
mains_in_main = [(cy_main > y_min_main[jj]) & ((cy_main < y_max_main[jj]))]
|
|
args_log = args * headers_in_main
|
|
res = args_log[args_log > 0]
|
|
res_true = res - 1
|
|
|
|
args_log_main = args_main * mains_in_main
|
|
res_main = args_log_main[args_log_main > 0]
|
|
res_true_main = res_main - 1
|
|
|
|
if len(res_true) > 0:
|
|
sum_header = np.sum(areas_header[res_true])
|
|
sum_main = np.sum(areas_main[res_true_main])
|
|
if sum_main > sum_header:
|
|
cnt_int = [contours_header[j] for j in res_true]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
|
|
else:
|
|
cnt_int = [contours_main[j] for j in res_true_main]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
|
|
|
|
for jj in range(len(contours_header)):
|
|
main_in_header = [(cy_main > y_min_header[jj]) & ((cy_main < y_max_header[jj]))]
|
|
header_in_header = [(cy_header > y_min_header[jj]) & ((cy_header < y_max_header[jj]))]
|
|
args_log = args_main * main_in_header
|
|
res = args_log[args_log > 0]
|
|
res_true = res - 1
|
|
|
|
args_log_header = args * header_in_header
|
|
res_header = args_log_header[args_log_header > 0]
|
|
res_true_header = res_header - 1
|
|
|
|
if len(res_true) > 0:
|
|
|
|
sum_header = np.sum(areas_header[res_true_header])
|
|
sum_main = np.sum(areas_main[res_true])
|
|
|
|
if sum_main > sum_header:
|
|
|
|
cnt_int = [contours_header[j] for j in res_true_header]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
|
|
else:
|
|
cnt_int = [contours_main[j] for j in res_true]
|
|
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
|
|
|
|
return text_regions
|
|
|
|
def delete_seperator_around(self, spliter_y, peaks_neg, image_by_region):
|
|
# format of subboxes box=[x1, x2 , y1, y2]
|
|
|
|
if len(image_by_region.shape) == 3:
|
|
for i in range(len(spliter_y) - 1):
|
|
for j in range(1, len(peaks_neg[i]) - 1):
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 6] = 0
|
|
image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 6] = 0
|
|
image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 6] = 0
|
|
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 7] = 0
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 7] = 0
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 7] = 0
|
|
else:
|
|
for i in range(len(spliter_y) - 1):
|
|
for j in range(1, len(peaks_neg[i]) - 1):
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 6] = 0
|
|
|
|
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 7] = 0
|
|
return image_by_region
|
|
|
|
def find_features_of_contoures(self, contours_main):
|
|
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
|
|
return y_min_main, y_max_main
|
|
|
|
def add_tables_heuristic_to_layout(self, image_regions_eraly_p, boxes, slope_mean_hor, spliter_y, peaks_neg_tot, image_revised):
|
|
|
|
image_revised_1 = self.delete_seperator_around(spliter_y, peaks_neg_tot, image_revised)
|
|
img_comm_e = np.zeros(image_revised_1.shape)
|
|
img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
|
|
|
|
for indiv in np.unique(image_revised_1):
|
|
|
|
# print(indiv,'indd')
|
|
image_col = (image_revised_1 == indiv) * 255
|
|
img_comm_in = np.repeat(image_col[:, :, np.newaxis], 3, axis=2)
|
|
img_comm_in = img_comm_in.astype(np.uint8)
|
|
|
|
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
|
|
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.0001)
|
|
|
|
img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=(indiv, indiv, indiv))
|
|
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
|
|
|
|
# img_comm=np.repeat(img_comm[:, :, np.newaxis], 3, axis=2)
|
|
img_comm = img_comm.astype(np.uint8)
|
|
|
|
if not self.isNaN(slope_mean_hor):
|
|
image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1], 3))
|
|
for i in range(len(boxes)):
|
|
|
|
image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
|
|
|
|
image_box_tabels_1 = (image_box[:, :, 0] == 7) * 1
|
|
|
|
contours_tab, _ = self.return_contours_of_image(image_box_tabels_1)
|
|
|
|
contours_tab = filter_contours_area_of_image_tables(image_box_tabels_1, contours_tab, _, 1, 0.001)
|
|
|
|
image_box_tabels_1 = (image_box[:, :, 0] == 6) * 1
|
|
|
|
image_box_tabels_and_m_text = ((image_box[:, :, 0] == 7) | (image_box[:, :, 0] == 1)) * 1
|
|
image_box_tabels_and_m_text = image_box_tabels_and_m_text.astype(np.uint8)
|
|
|
|
image_box_tabels_1 = image_box_tabels_1.astype(np.uint8)
|
|
image_box_tabels_1 = cv2.dilate(image_box_tabels_1, self.kernel, iterations=5)
|
|
|
|
contours_table_m_text, _ = self.return_contours_of_image(image_box_tabels_and_m_text)
|
|
|
|
image_box_tabels = np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
|
|
|
|
image_box_tabels = image_box_tabels.astype(np.uint8)
|
|
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
y_min_main_line, y_max_main_line = self.find_features_of_contoures(contours_line)
|
|
# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
|
|
y_min_main_tab, y_max_main_tab = self.find_features_of_contoures(contours_tab)
|
|
|
|
cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = self.find_new_features_of_contoures(contours_table_m_text)
|
|
cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = self.find_new_features_of_contoures(contours_tab)
|
|
|
|
if len(y_min_main_tab) > 0:
|
|
y_down_tabs = []
|
|
y_up_tabs = []
|
|
|
|
for i_t in range(len(y_min_main_tab)):
|
|
y_down_tab = []
|
|
y_up_tab = []
|
|
for i_l in range(len(y_min_main_line)):
|
|
if y_min_main_tab[i_t] > y_min_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l] and y_min_main_tab[i_t] > y_max_main_line[i_l] and y_max_main_tab[i_t] > y_min_main_line[i_l]:
|
|
pass
|
|
elif y_min_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_max_main_line[i_l] and y_max_main_tab[i_t] < y_min_main_line[i_l] and y_min_main_tab[i_t] < y_min_main_line[i_l]:
|
|
pass
|
|
elif np.abs(y_max_main_line[i_l] - y_min_main_line[i_l]) < 100:
|
|
pass
|
|
|
|
else:
|
|
y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t]]))
|
|
y_down_tab.append(np.max([y_max_main_line[i_l], y_max_main_tab[i_t]]))
|
|
|
|
if len(y_up_tab) == 0:
|
|
for v_n in range(len(cx_tab_m_text)):
|
|
if cx_tabl[i_t] <= x_max_tab_m_text[v_n] and cx_tabl[i_t] >= x_min_tab_m_text[v_n] and cy_tabl[i_t] <= y_max_tab_m_text[v_n] and cy_tabl[i_t] >= y_min_tab_m_text[v_n] and cx_tabl[i_t] != cx_tab_m_text[v_n] and cy_tabl[i_t] != cy_tab_m_text[v_n]:
|
|
y_up_tabs.append(y_min_tab_m_text[v_n])
|
|
y_down_tabs.append(y_max_tab_m_text[v_n])
|
|
# y_up_tabs.append(y_min_main_tab[i_t])
|
|
# y_down_tabs.append(y_max_main_tab[i_t])
|
|
else:
|
|
y_up_tabs.append(np.min(y_up_tab))
|
|
y_down_tabs.append(np.max(y_down_tab))
|
|
|
|
else:
|
|
y_down_tabs = []
|
|
y_up_tabs = []
|
|
pass
|
|
|
|
for ii in range(len(y_up_tabs)):
|
|
image_box[y_up_tabs[ii] : y_down_tabs[ii], :, 0] = 7
|
|
|
|
image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
|
|
|
|
else:
|
|
for i in range(len(boxes)):
|
|
|
|
image_box = img_comm[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :]
|
|
image_revised_last[int(boxes[i][2]) : int(boxes[i][3]), int(boxes[i][0]) : int(boxes[i][1]), :] = image_box[:, :, :]
|
|
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.imshow(image_box[:,:,0])
|
|
##plt.show()
|
|
return image_revised_last
|
|
|
|
def find_features_of_contours(self, contours_main):
|
|
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
|
|
return y_min_main, y_max_main, areas_main
|
|
|
|
def remove_headers_and_mains_intersection(self, seperators_closeup_n, img_revised_tab, boxes):
|
|
for ind in range(len(boxes)):
|
|
asp = np.zeros((img_revised_tab[:, :, 0].shape[0], seperators_closeup_n[:, :, 0].shape[1]))
|
|
asp[int(boxes[ind][2]) : int(boxes[ind][3]), int(boxes[ind][0]) : int(boxes[ind][1])] = img_revised_tab[int(boxes[ind][2]) : int(boxes[ind][3]), int(boxes[ind][0]) : int(boxes[ind][1]), 0]
|
|
|
|
head_patch_con = (asp[:, :] == 2) * 1
|
|
main_patch_con = (asp[:, :] == 1) * 1
|
|
# print(head_patch_con)
|
|
head_patch_con = head_patch_con.astype(np.uint8)
|
|
main_patch_con = main_patch_con.astype(np.uint8)
|
|
|
|
head_patch_con = np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
|
|
main_patch_con = np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
|
|
|
|
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_head_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
contours_head_patch_con = self.return_parent_contours(contours_head_patch_con, hiearchy)
|
|
|
|
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
contours_main_patch_con = self.return_parent_contours(contours_main_patch_con, hiearchy)
|
|
|
|
y_patch_head_min, y_patch_head_max, _ = self.find_features_of_contours(contours_head_patch_con)
|
|
y_patch_main_min, y_patch_main_max, _ = self.find_features_of_contours(contours_main_patch_con)
|
|
|
|
for i in range(len(y_patch_head_min)):
|
|
for j in range(len(y_patch_main_min)):
|
|
if y_patch_head_max[i] > y_patch_main_min[j] and y_patch_head_min[i] < y_patch_main_min[j]:
|
|
y_down = y_patch_head_max[i]
|
|
y_up = y_patch_main_min[j]
|
|
|
|
patch_intersection = np.zeros(asp.shape)
|
|
patch_intersection[y_up:y_down, :] = asp[y_up:y_down, :]
|
|
|
|
head_patch_con = (patch_intersection[:, :] == 2) * 1
|
|
main_patch_con = (patch_intersection[:, :] == 1) * 1
|
|
head_patch_con = head_patch_con.astype(np.uint8)
|
|
main_patch_con = main_patch_con.astype(np.uint8)
|
|
|
|
head_patch_con = np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
|
|
main_patch_con = np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
|
|
|
|
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_head_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
contours_head_patch_con = self.return_parent_contours(contours_head_patch_con, hiearchy)
|
|
|
|
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
contours_main_patch_con = self.return_parent_contours(contours_main_patch_con, hiearchy)
|
|
|
|
_, _, areas_head = self.find_features_of_contours(contours_head_patch_con)
|
|
_, _, areas_main = self.find_features_of_contours(contours_main_patch_con)
|
|
|
|
if np.sum(areas_head) > np.sum(areas_main):
|
|
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2
|
|
else:
|
|
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 2] = 1
|
|
|
|
elif y_patch_head_min[i] < y_patch_main_max[j] and y_patch_head_max[i] > y_patch_main_max[j]:
|
|
y_down = y_patch_main_max[j]
|
|
y_up = y_patch_head_min[i]
|
|
|
|
patch_intersection = np.zeros(asp.shape)
|
|
patch_intersection[y_up:y_down, :] = asp[y_up:y_down, :]
|
|
|
|
head_patch_con = (patch_intersection[:, :] == 2) * 1
|
|
main_patch_con = (patch_intersection[:, :] == 1) * 1
|
|
head_patch_con = head_patch_con.astype(np.uint8)
|
|
main_patch_con = main_patch_con.astype(np.uint8)
|
|
|
|
head_patch_con = np.repeat(head_patch_con[:, :, np.newaxis], 3, axis=2)
|
|
main_patch_con = np.repeat(main_patch_con[:, :, np.newaxis], 3, axis=2)
|
|
|
|
imgray = cv2.cvtColor(head_patch_con, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_head_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
contours_head_patch_con = self.return_parent_contours(contours_head_patch_con, hiearchy)
|
|
|
|
imgray = cv2.cvtColor(main_patch_con, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
contours_main_patch_con = self.return_parent_contours(contours_main_patch_con, hiearchy)
|
|
|
|
_, _, areas_head = self.find_features_of_contours(contours_head_patch_con)
|
|
_, _, areas_main = self.find_features_of_contours(contours_main_patch_con)
|
|
|
|
if np.sum(areas_head) > np.sum(areas_main):
|
|
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2
|
|
else:
|
|
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 2] = 1
|
|
|
|
# print(np.unique(patch_intersection) )
|
|
##plt.figure(figsize=(20,20))
|
|
##plt.imshow(patch_intersection)
|
|
##plt.show()
|
|
else:
|
|
pass
|
|
|
|
return img_revised_tab
|
|
|
|
def order_of_regions(self, textline_mask, contours_main, contours_header, y_ref):
|
|
|
|
##plt.imshow(textline_mask)
|
|
##plt.show()
|
|
"""
|
|
print(len(contours_main),'contours_main')
|
|
mada_n=textline_mask.sum(axis=1)
|
|
y=mada_n[:]
|
|
|
|
y_help=np.zeros(len(y)+40)
|
|
y_help[20:len(y)+20]=y
|
|
x=np.array( range(len(y)) )
|
|
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
##plt.imshow(textline_mask[:,:])
|
|
##plt.show()
|
|
|
|
|
|
sigma_gaus=8
|
|
|
|
z= gaussian_filter1d(y_help, sigma_gaus)
|
|
zneg_rev=-y_help+np.max(y_help)
|
|
|
|
zneg=np.zeros(len(zneg_rev)+40)
|
|
zneg[20:len(zneg_rev)+20]=zneg_rev
|
|
zneg= gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
peaks_neg=peaks_neg-20-20
|
|
peaks=peaks-20
|
|
"""
|
|
|
|
textline_sum_along_width = textline_mask.sum(axis=1)
|
|
|
|
y = textline_sum_along_width[:]
|
|
y_padded = np.zeros(len(y) + 40)
|
|
y_padded[20 : len(y) + 20] = y
|
|
x = np.array(range(len(y)))
|
|
|
|
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
|
|
|
|
sigma_gaus = 8
|
|
|
|
z = gaussian_filter1d(y_padded, sigma_gaus)
|
|
zneg_rev = -y_padded + np.max(y_padded)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 40)
|
|
zneg[20 : len(zneg_rev) + 20] = zneg_rev
|
|
zneg = gaussian_filter1d(zneg, sigma_gaus)
|
|
|
|
peaks, _ = find_peaks(z, height=0)
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
|
|
peaks_neg = peaks_neg - 20 - 20
|
|
peaks = peaks - 20
|
|
|
|
##plt.plot(z)
|
|
##plt.show()
|
|
|
|
if contours_main != None:
|
|
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
|
|
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
|
|
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
|
|
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
|
|
|
|
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
|
|
|
|
if len(contours_header) != None:
|
|
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
|
|
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
|
|
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
|
|
|
|
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
|
|
|
|
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
|
|
# print(cy_main,'mainy')
|
|
|
|
peaks_neg_new = []
|
|
|
|
peaks_neg_new.append(0 + y_ref)
|
|
for iii in range(len(peaks_neg)):
|
|
peaks_neg_new.append(peaks_neg[iii] + y_ref)
|
|
|
|
peaks_neg_new.append(textline_mask.shape[0] + y_ref)
|
|
|
|
if len(cy_main) > 0 and np.max(cy_main) > np.max(peaks_neg_new):
|
|
cy_main = np.array(cy_main) * (np.max(peaks_neg_new) / np.max(cy_main)) - 10
|
|
|
|
if contours_main != None:
|
|
indexer_main = np.array(range(len(contours_main)))
|
|
|
|
if contours_main != None:
|
|
len_main = len(contours_main)
|
|
else:
|
|
len_main = 0
|
|
|
|
matrix_of_orders = np.zeros((len(contours_main) + len(contours_header), 5))
|
|
|
|
matrix_of_orders[:, 0] = np.array(range(len(contours_main) + len(contours_header)))
|
|
|
|
matrix_of_orders[: len(contours_main), 1] = 1
|
|
matrix_of_orders[len(contours_main) :, 1] = 2
|
|
|
|
matrix_of_orders[: len(contours_main), 2] = cx_main
|
|
matrix_of_orders[len(contours_main) :, 2] = cx_header
|
|
|
|
matrix_of_orders[: len(contours_main), 3] = cy_main
|
|
matrix_of_orders[len(contours_main) :, 3] = cy_header
|
|
|
|
matrix_of_orders[: len(contours_main), 4] = np.array(range(len(contours_main)))
|
|
matrix_of_orders[len(contours_main) :, 4] = np.array(range(len(contours_header)))
|
|
|
|
# print(peaks_neg_new,'peaks_neg_new')
|
|
|
|
# print(matrix_of_orders,'matrix_of_orders')
|
|
# print(peaks_neg_new,np.max(peaks_neg_new))
|
|
final_indexers_sorted = []
|
|
final_types = []
|
|
final_index_type = []
|
|
for i in range(len(peaks_neg_new) - 1):
|
|
top = peaks_neg_new[i]
|
|
down = peaks_neg_new[i + 1]
|
|
|
|
# print(top,down,'topdown')
|
|
|
|
indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))]
|
|
|
|
# print(top,down)
|
|
# print(cys_in,'cyyyins')
|
|
# print(indexes_in,'indexes')
|
|
sorted_inside = np.argsort(cxs_in)
|
|
|
|
ind_in_int = indexes_in[sorted_inside]
|
|
ind_in_type = types_of_text[sorted_inside]
|
|
ind_ind_type = index_types_of_text[sorted_inside]
|
|
|
|
for j in range(len(ind_in_int)):
|
|
final_indexers_sorted.append(int(ind_in_int[j]))
|
|
final_types.append(int(ind_in_type[j]))
|
|
final_index_type.append(int(ind_ind_type[j]))
|
|
|
|
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
|
|
|
|
# print(peaks_neg_new,'peaks')
|
|
# print(final_indexers_sorted,'indexsorted')
|
|
# print(final_types,'types')
|
|
# print(final_index_type,'final_index_type')
|
|
|
|
return final_indexers_sorted, matrix_of_orders, final_types, final_index_type
|
|
|
|
def order_and_id_of_texts(self, found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point):
|
|
indexes_sorted = np.array(indexes_sorted)
|
|
index_of_types = np.array(index_of_types)
|
|
kind_of_texts = np.array(kind_of_texts)
|
|
|
|
id_of_texts = []
|
|
order_of_texts = []
|
|
|
|
index_of_types_1 = index_of_types[kind_of_texts == 1]
|
|
indexes_sorted_1 = indexes_sorted[kind_of_texts == 1]
|
|
|
|
index_of_types_2 = index_of_types[kind_of_texts == 2]
|
|
indexes_sorted_2 = indexes_sorted[kind_of_texts == 2]
|
|
|
|
##print(index_of_types,'index_of_types')
|
|
##print(kind_of_texts,'kind_of_texts')
|
|
##print(len(found_polygons_text_region),'found_polygons_text_region')
|
|
##print(index_of_types_1,'index_of_types_1')
|
|
##print(indexes_sorted_1,'indexes_sorted_1')
|
|
index_b = 0 + ref_point
|
|
for mm in range(len(found_polygons_text_region)):
|
|
|
|
id_of_texts.append("r" + str(index_b))
|
|
interest = indexes_sorted_1[indexes_sorted_1 == index_of_types_1[mm]]
|
|
|
|
if len(interest) > 0:
|
|
order_of_texts.append(interest[0])
|
|
index_b += 1
|
|
else:
|
|
pass
|
|
|
|
for mm in range(len(found_polygons_text_region_h)):
|
|
id_of_texts.append("r" + str(index_b))
|
|
interest = indexes_sorted_2[index_of_types_2[mm]]
|
|
order_of_texts.append(interest)
|
|
index_b += 1
|
|
|
|
return order_of_texts, id_of_texts
|
|
|
|
def get_text_region_boxes_by_given_contours(self, contours):
|
|
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
boxes = []
|
|
contours_new = []
|
|
for jj in range(len(contours)):
|
|
x, y, w, h = cv2.boundingRect(contours[jj])
|
|
|
|
boxes.append([x, y, w, h])
|
|
contours_new.append(contours[jj])
|
|
|
|
del contours
|
|
return boxes, contours_new
|
|
|
|
def return_teilwiese_deskewed_lines(self, text_regions_p, textline_rotated):
|
|
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
textline_rotated = cv2.erode(textline_rotated, kernel, iterations=1)
|
|
|
|
textline_rotated_new = np.zeros(textline_rotated.shape)
|
|
rgb_m = 1
|
|
rgb_h = 2
|
|
|
|
cnt_m, boxes_m = self.return_contours_of_interested_region_and_bounding_box(text_regions_p, rgb_m)
|
|
cnt_h, boxes_h = self.return_contours_of_interested_region_and_bounding_box(text_regions_p, rgb_h)
|
|
|
|
areas_cnt_m = np.array([cv2.contourArea(cnt_m[j]) for j in range(len(cnt_m))])
|
|
|
|
argmax = np.argmax(areas_cnt_m)
|
|
|
|
# plt.imshow(textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2]])
|
|
# plt.show()
|
|
|
|
for argmax in range(len(boxes_m)):
|
|
|
|
textline_text_region = textline_rotated[boxes_m[argmax][1] : boxes_m[argmax][1] + boxes_m[argmax][3], boxes_m[argmax][0] : boxes_m[argmax][0] + boxes_m[argmax][2]]
|
|
|
|
textline_text_region_revised = self.seperate_lines_new(textline_text_region, 0)
|
|
# except:
|
|
# textline_text_region_revised=textline_rotated[ boxes_m[argmax][1]:boxes_m[argmax][1]+boxes_m[argmax][3] ,boxes_m[argmax][0]:boxes_m[argmax][0]+boxes_m[argmax][2] ]
|
|
textline_rotated_new[boxes_m[argmax][1] : boxes_m[argmax][1] + boxes_m[argmax][3], boxes_m[argmax][0] : boxes_m[argmax][0] + boxes_m[argmax][2]] = textline_text_region_revised[:, :]
|
|
|
|
# textline_rotated_new[textline_rotated_new>0]=1
|
|
# textline_rotated_new[textline_rotated_new<0]=0
|
|
# plt.imshow(textline_rotated_new)
|
|
# plt.show()
|
|
|
|
def return_contours_of_interested_region_and_bounding_box(self, region_pre_p, pixel):
|
|
|
|
# pixels of images are identified by 5
|
|
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
|
|
|
|
boxes = []
|
|
|
|
for jj in range(len(contours_imgs)):
|
|
x, y, w, h = cv2.boundingRect(contours_imgs[jj])
|
|
boxes.append([int(x), int(y), int(w), int(h)])
|
|
return contours_imgs, boxes
|
|
|
|
def find_number_of_columns_in_document(self, region_pre_p, num_col_classifier, pixel_lines, contours_h=None):
|
|
|
|
seperators_closeup = ((region_pre_p[:, :, :] == pixel_lines)) * 1
|
|
|
|
seperators_closeup[0:110, :, :] = 0
|
|
seperators_closeup[seperators_closeup.shape[0] - 150 :, :, :] = 0
|
|
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
seperators_closeup = seperators_closeup.astype(np.uint8)
|
|
seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1)
|
|
seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1)
|
|
|
|
##plt.imshow(seperators_closeup[:,:,0])
|
|
##plt.show()
|
|
seperators_closeup_new = np.zeros((seperators_closeup.shape[0], seperators_closeup.shape[1]))
|
|
|
|
##_,seperators_closeup_n=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(region_pre_p[:,:,0])
|
|
seperators_closeup_n = np.copy(seperators_closeup)
|
|
|
|
seperators_closeup_n = seperators_closeup_n.astype(np.uint8)
|
|
##plt.imshow(seperators_closeup_n[:,:,0])
|
|
##plt.show()
|
|
|
|
seperators_closeup_n_binary = np.zeros((seperators_closeup_n.shape[0], seperators_closeup_n.shape[1]))
|
|
seperators_closeup_n_binary[:, :] = seperators_closeup_n[:, :, 0]
|
|
|
|
seperators_closeup_n_binary[:, :][seperators_closeup_n_binary[:, :] != 0] = 1
|
|
# seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==0]=255
|
|
# seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==-255]=0
|
|
|
|
# seperators_closeup_n_binary=(seperators_closeup_n_binary[:,:]==2)*1
|
|
|
|
# gray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY)
|
|
|
|
# print(np.unique(seperators_closeup_n_binary))
|
|
|
|
##plt.imshow(seperators_closeup_n_binary)
|
|
##plt.show()
|
|
|
|
# print( np.unique(gray),np.unique(seperators_closeup_n[:,:,1]) )
|
|
|
|
gray = cv2.bitwise_not(seperators_closeup_n_binary)
|
|
gray = gray.astype(np.uint8)
|
|
|
|
##plt.imshow(gray)
|
|
##plt.show()
|
|
bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, -2)
|
|
##plt.imshow(bw[:,:])
|
|
##plt.show()
|
|
|
|
horizontal = np.copy(bw)
|
|
vertical = np.copy(bw)
|
|
|
|
cols = horizontal.shape[1]
|
|
horizontal_size = cols // 30
|
|
# Create structure element for extracting horizontal lines through morphology operations
|
|
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
|
|
# Apply morphology operations
|
|
horizontal = cv2.erode(horizontal, horizontalStructure)
|
|
horizontal = cv2.dilate(horizontal, horizontalStructure)
|
|
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
horizontal = cv2.dilate(horizontal, kernel, iterations=2)
|
|
horizontal = cv2.erode(horizontal, kernel, iterations=2)
|
|
# plt.imshow(horizontal)
|
|
# plt.show()
|
|
|
|
rows = vertical.shape[0]
|
|
verticalsize = rows // 30
|
|
# Create structure element for extracting vertical lines through morphology operations
|
|
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
|
|
# Apply morphology operations
|
|
vertical = cv2.erode(vertical, verticalStructure)
|
|
vertical = cv2.dilate(vertical, verticalStructure)
|
|
|
|
vertical = cv2.dilate(vertical, kernel, iterations=1)
|
|
# Show extracted vertical lines
|
|
|
|
horizontal, special_seperators = self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical, horizontal)
|
|
|
|
##plt.imshow(vertical)
|
|
##plt.show()
|
|
# print(vertical.shape,np.unique(vertical),'verticalvertical')
|
|
seperators_closeup_new[:, :][vertical[:, :] != 0] = 1
|
|
seperators_closeup_new[:, :][horizontal[:, :] != 0] = 1
|
|
|
|
##plt.imshow(seperators_closeup_new)
|
|
##plt.show()
|
|
##seperators_closeup_n
|
|
vertical = np.repeat(vertical[:, :, np.newaxis], 3, axis=2)
|
|
vertical = vertical.astype(np.uint8)
|
|
|
|
##plt.plot(vertical[:,:,0].sum(axis=0))
|
|
##plt.show()
|
|
|
|
# plt.plot(vertical[:,:,0].sum(axis=1))
|
|
# plt.show()
|
|
|
|
imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line_vers, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = self.find_features_of_lines(contours_line_vers)
|
|
# print(slope_lines,'vertical')
|
|
args = np.array(range(len(slope_lines)))
|
|
args_ver = args[slope_lines == 1]
|
|
dist_x_ver = dist_x[slope_lines == 1]
|
|
y_min_main_ver = y_min_main[slope_lines == 1]
|
|
y_max_main_ver = y_max_main[slope_lines == 1]
|
|
x_min_main_ver = x_min_main[slope_lines == 1]
|
|
x_max_main_ver = x_max_main[slope_lines == 1]
|
|
cx_main_ver = cx_main[slope_lines == 1]
|
|
dist_y_ver = y_max_main_ver - y_min_main_ver
|
|
len_y = seperators_closeup.shape[0] / 3.0
|
|
|
|
# plt.imshow(horizontal)
|
|
# plt.show()
|
|
|
|
horizontal = np.repeat(horizontal[:, :, np.newaxis], 3, axis=2)
|
|
horizontal = horizontal.astype(np.uint8)
|
|
imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_line_hors, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = self.find_features_of_lines(contours_line_hors)
|
|
|
|
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
|
|
args = np.array(range(len(slope_lines)))
|
|
len_x = seperators_closeup.shape[1] / 5.0
|
|
|
|
dist_y = np.abs(y_max_main - y_min_main)
|
|
|
|
args_hor = args[slope_lines == 0]
|
|
dist_x_hor = dist_x[slope_lines == 0]
|
|
y_min_main_hor = y_min_main[slope_lines == 0]
|
|
y_max_main_hor = y_max_main[slope_lines == 0]
|
|
x_min_main_hor = x_min_main[slope_lines == 0]
|
|
x_max_main_hor = x_max_main[slope_lines == 0]
|
|
dist_y_hor = dist_y[slope_lines == 0]
|
|
cy_main_hor = cy_main[slope_lines == 0]
|
|
|
|
args_hor = args_hor[dist_x_hor >= len_x / 2.0]
|
|
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x / 2.0]
|
|
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x / 2.0]
|
|
cy_main_hor = cy_main_hor[dist_x_hor >= len_x / 2.0]
|
|
y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x / 2.0]
|
|
y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x / 2.0]
|
|
dist_y_hor = dist_y_hor[dist_x_hor >= len_x / 2.0]
|
|
|
|
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x / 2.0]
|
|
dist_x_hor = dist_x_hor[dist_x_hor >= len_x / 2.0]
|
|
|
|
matrix_of_lines_ch = np.zeros((len(cy_main_hor) + len(cx_main_ver), 10))
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 0] = args_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 0] = args_ver
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 1] = cx_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 2] = x_min_main_hor + 50 # x_min_main_hor+150
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 2] = x_min_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 3] = x_max_main_hor - 50 # x_max_main_hor-150
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 3] = x_max_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 4] = dist_x_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 4] = dist_x_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 5] = cy_main_hor
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 6] = y_min_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 6] = y_min_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 7] = y_max_main_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 7] = y_max_main_ver
|
|
|
|
matrix_of_lines_ch[: len(cy_main_hor), 8] = dist_y_hor
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 8] = dist_y_ver
|
|
|
|
matrix_of_lines_ch[len(cy_main_hor) :, 9] = 1
|
|
|
|
if contours_h is not None:
|
|
slope_lines_head, dist_x_head, x_min_main_head, x_max_main_head, cy_main_head, slope_lines_org_head, y_min_main_head, y_max_main_head, cx_main_head = self.find_features_of_lines(contours_h)
|
|
matrix_l_n = np.zeros((matrix_of_lines_ch.shape[0] + len(cy_main_head), matrix_of_lines_ch.shape[1]))
|
|
matrix_l_n[: matrix_of_lines_ch.shape[0], :] = np.copy(matrix_of_lines_ch[:, :])
|
|
args_head = np.array(range(len(cy_main_head))) + len(cy_main_hor)
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 0] = args_head
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 2] = x_min_main_head + 30
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 3] = x_max_main_head - 30
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 4] = dist_x_head
|
|
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 5] = y_min_main_head - 3 - 8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 6] = y_min_main_head - 5 - 8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 7] = y_min_main_head + 1 - 8
|
|
matrix_l_n[matrix_of_lines_ch.shape[0] :, 8] = 4
|
|
|
|
matrix_of_lines_ch = np.copy(matrix_l_n)
|
|
|
|
# print(matrix_of_lines_ch)
|
|
|
|
"""
|
|
|
|
|
|
|
|
seperators_closeup=seperators_closeup.astype(np.uint8)
|
|
imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=self.find_features_of_lines(contours_lines)
|
|
|
|
slope_lines_org_hor=slope_lines_org[slope_lines==0]
|
|
args=np.array( range(len(slope_lines) ))
|
|
len_x=seperators_closeup.shape[1]/4.0
|
|
|
|
args_hor=args[slope_lines==0]
|
|
dist_x_hor=dist_x[slope_lines==0]
|
|
x_min_main_hor=x_min_main[slope_lines==0]
|
|
x_max_main_hor=x_max_main[slope_lines==0]
|
|
cy_main_hor=cy_main[slope_lines==0]
|
|
|
|
args_hor=args_hor[dist_x_hor>=len_x/2.0]
|
|
x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0]
|
|
x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0]
|
|
cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0]
|
|
slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0]
|
|
|
|
|
|
slope_lines_org_hor=slope_lines_org_hor[np.abs(slope_lines_org_hor)<1.2]
|
|
slope_mean_hor=np.mean(slope_lines_org_hor)
|
|
|
|
|
|
|
|
args_ver=args[slope_lines==1]
|
|
y_min_main_ver=y_min_main[slope_lines==1]
|
|
y_max_main_ver=y_max_main[slope_lines==1]
|
|
x_min_main_ver=x_min_main[slope_lines==1]
|
|
x_max_main_ver=x_max_main[slope_lines==1]
|
|
cx_main_ver=cx_main[slope_lines==1]
|
|
dist_y_ver=y_max_main_ver-y_min_main_ver
|
|
len_y=seperators_closeup.shape[0]/3.0
|
|
|
|
|
|
|
|
print(matrix_of_lines_ch[:,8][matrix_of_lines_ch[:,9]==0],'khatlarrrr')
|
|
args_main_spliters=matrix_of_lines_ch[:,0][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
|
|
|
|
cy_main_spliters=matrix_of_lines_ch[:,5][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))]
|
|
"""
|
|
|
|
cy_main_spliters = cy_main_hor[(x_min_main_hor <= 0.16 * region_pre_p.shape[1]) & (x_max_main_hor >= 0.84 * region_pre_p.shape[1])]
|
|
|
|
cy_main_spliters = np.array(list(cy_main_spliters) + list(special_seperators))
|
|
|
|
if contours_h is not None:
|
|
try:
|
|
cy_main_spliters_head = cy_main_head[(x_min_main_head <= 0.16 * region_pre_p.shape[1]) & (x_max_main_head >= 0.84 * region_pre_p.shape[1])]
|
|
cy_main_spliters = np.array(list(cy_main_spliters) + list(cy_main_spliters_head))
|
|
except:
|
|
pass
|
|
args_cy_spliter = np.argsort(cy_main_spliters)
|
|
|
|
cy_main_spliters_sort = cy_main_spliters[args_cy_spliter]
|
|
|
|
spliter_y_new = []
|
|
spliter_y_new.append(0)
|
|
for i in range(len(cy_main_spliters_sort)):
|
|
spliter_y_new.append(cy_main_spliters_sort[i])
|
|
|
|
spliter_y_new.append(region_pre_p.shape[0])
|
|
|
|
spliter_y_new_diff = np.diff(spliter_y_new) / float(region_pre_p.shape[0]) * 100
|
|
|
|
args_big_parts = np.array(range(len(spliter_y_new_diff)))[spliter_y_new_diff > 22]
|
|
|
|
regions_without_seperators = self.return_regions_without_seperators(region_pre_p)
|
|
|
|
##print(args_big_parts,'args_big_parts')
|
|
# image_page_otsu=self.otsu_copy(image_page_deskewd)
|
|
# print(np.unique(image_page_otsu[:,:,0]))
|
|
# image_page_background_zero=self.image_change_background_pixels_to_zero(image_page_otsu)
|
|
|
|
length_y_threshold = regions_without_seperators.shape[0] / 4.0
|
|
|
|
num_col_fin = 0
|
|
peaks_neg_fin_fin = []
|
|
|
|
for iteils in args_big_parts:
|
|
|
|
regions_without_seperators_teil = regions_without_seperators[int(spliter_y_new[iteils]) : int(spliter_y_new[iteils + 1]), :, 0]
|
|
# image_page_background_zero_teil=image_page_background_zero[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:]
|
|
|
|
# print(regions_without_seperators_teil.shape)
|
|
##plt.imshow(regions_without_seperators_teil)
|
|
##plt.show()
|
|
|
|
# num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators_teil,multiplier=6.0)
|
|
|
|
# regions_without_seperators_teil=cv2.erode(regions_without_seperators_teil,kernel,iterations = 3)
|
|
#
|
|
num_col, peaks_neg_fin = self.find_num_col(regions_without_seperators_teil, multiplier=7.0)
|
|
|
|
if num_col > num_col_fin:
|
|
num_col_fin = num_col
|
|
peaks_neg_fin_fin = peaks_neg_fin
|
|
"""
|
|
#print(length_y_vertical_lines,length_y_threshold,'x_center_of_ver_linesx_center_of_ver_linesx_center_of_ver_lines')
|
|
if len(cx_main_ver)>0 and len( dist_y_ver[dist_y_ver>=length_y_threshold] ) >=1:
|
|
num_col, peaks_neg_fin=self.find_num_col(regions_without_seperators_teil,multiplier=6.0)
|
|
else:
|
|
#plt.imshow(image_page_background_zero_teil)
|
|
#plt.show()
|
|
#num_col, peaks_neg_fin=self.find_num_col_only_image(image_page_background_zero,multiplier=2.4)#2.3)
|
|
num_col, peaks_neg_fin=self.find_num_col_only_image(image_page_background_zero_teil,multiplier=3.4)#2.3)
|
|
|
|
print(num_col,'birda')
|
|
if num_col>0:
|
|
pass
|
|
elif num_col==0:
|
|
print(num_col,'birda2222')
|
|
num_col_regions, peaks_neg_fin_regions=self.find_num_col(regions_without_seperators_teil,multiplier=10.0)
|
|
if num_col_regions==0:
|
|
pass
|
|
else:
|
|
|
|
num_col=num_col_regions
|
|
peaks_neg_fin=peaks_neg_fin_regions[:]
|
|
"""
|
|
|
|
# print(num_col+1,'num colmsssssssss')
|
|
|
|
if len(args_big_parts) == 1 and (len(peaks_neg_fin_fin) + 1) < num_col_classifier:
|
|
peaks_neg_fin = self.find_num_col_by_vertical_lines(vertical)
|
|
peaks_neg_fin = peaks_neg_fin[peaks_neg_fin >= 500]
|
|
peaks_neg_fin = peaks_neg_fin[peaks_neg_fin <= (vertical.shape[1] - 500)]
|
|
peaks_neg_fin_fin = peaks_neg_fin[:]
|
|
|
|
# print(peaks_neg_fin_fin,'peaks_neg_fin_fintaza')
|
|
|
|
return num_col_fin, peaks_neg_fin_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n
|
|
|
|
def return_contours_of_interested_region_by_size(self, region_pre_p, pixel, min_area, max_area):
|
|
|
|
# pixels of images are identified by 5
|
|
if len(region_pre_p.shape) == 3:
|
|
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
|
else:
|
|
cnts_images = (region_pre_p[:, :] == pixel) * 1
|
|
cnts_images = cnts_images.astype(np.uint8)
|
|
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
|
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
contours_imgs = self.return_parent_contours(contours_imgs, hiearchy)
|
|
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
|
|
|
|
img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3))
|
|
img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1))
|
|
return img_ret[:, :, 0]
|
|
|
|
def get_regions_from_xy_neu(self, img):
|
|
img_org = np.copy(img)
|
|
|
|
img_height_h = img_org.shape[0]
|
|
img_width_h = img_org.shape[1]
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p)
|
|
|
|
gaussian_filter = False
|
|
patches = True
|
|
binary = True
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_org = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_org = self.resize_image(prediction_regions_org, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(prediction_regions_org[:,:,0])
|
|
# plt.show()
|
|
# sys.exit()
|
|
prediction_regions_org = prediction_regions_org[:, :, 0]
|
|
|
|
gaussian_filter = False
|
|
patches = False
|
|
binary = False
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_orgt = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_orgt = self.resize_image(prediction_regions_orgt, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(prediction_regions_orgt[:,:,0])
|
|
# plt.show()
|
|
# sys.exit()
|
|
prediction_regions_orgt = prediction_regions_orgt[:, :, 0]
|
|
|
|
mask_texts_longshot = (prediction_regions_orgt[:, :] == 1) * 1
|
|
|
|
mask_texts_longshot = np.uint8(mask_texts_longshot)
|
|
# mask_texts_longshot = cv2.dilate(mask_texts_longshot[:,:], self.kernel, iterations=2)
|
|
|
|
pixel_img = 1
|
|
polygons_of_only_texts_longshot = self.return_contours_of_interested_region(mask_texts_longshot, pixel_img)
|
|
|
|
longshot_true = np.zeros(mask_texts_longshot.shape)
|
|
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
|
|
|
|
longshot_true = cv2.fillPoly(longshot_true, pts=polygons_of_only_texts_longshot, color=(1, 1, 1))
|
|
|
|
# plt.imshow(longshot_true)
|
|
# plt.show()
|
|
|
|
gaussian_filter = False
|
|
patches = False
|
|
binary = False
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
one_third_upper_ny = int(img.shape[0] / 3.0)
|
|
|
|
img = img[0:one_third_upper_ny, :, :]
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_longshot_one_third = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_longshot_one_third = self.resize_image(prediction_regions_longshot_one_third, one_third_upper_ny, img_width_h)
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
img = img[one_third_upper_ny : int(2 * one_third_upper_ny), :, :]
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_longshot_one_third_middle = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_longshot_one_third_middle = self.resize_image(prediction_regions_longshot_one_third_middle, one_third_upper_ny, img_width_h)
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
img = img[int(2 * one_third_upper_ny) :, :, :]
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_longshot_one_third_down = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_longshot_one_third_down = self.resize_image(prediction_regions_longshot_one_third_down, img_height_h - int(2 * one_third_upper_ny), img_width_h)
|
|
|
|
# plt.imshow(prediction_regions_org[:,:,0])
|
|
# plt.show()
|
|
# sys.exit()
|
|
prediction_regions_longshot = np.zeros((img_height_h, img_width_h))
|
|
|
|
# prediction_regions_longshot=prediction_regions_longshot[:,:,0]
|
|
|
|
# prediction_regions_longshot[0:one_third_upper_ny,:]=prediction_regions_longshot_one_third[:,:,0]
|
|
# prediction_regions_longshot[one_third_upper_ny:int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_middle[:,:,0]
|
|
# prediction_regions_longshot[int(2*one_third_upper_ny):,:]=prediction_regions_longshot_one_third_down[:,:,0]
|
|
|
|
prediction_regions_longshot = longshot_true[:, :]
|
|
# plt.imshow(prediction_regions_longshot)
|
|
# plt.show()
|
|
|
|
gaussian_filter = False
|
|
patches = True
|
|
binary = False
|
|
|
|
ratio_x = 1 # 1.1
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
# img= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
|
|
prediction_regions = self.do_prediction(patches, img, model_region)
|
|
text_region1 = self.resize_image(prediction_regions, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(text_region1[:,:,0])
|
|
# plt.show()
|
|
ratio_x = 1
|
|
ratio_y = 1.2 # 1.3
|
|
binary = False
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
|
|
prediction_regions = self.do_prediction(patches, img, model_region)
|
|
text_region2 = self.resize_image(prediction_regions, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(text_region2[:,:,0])
|
|
# plt.show()
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
# text_region1=text_region1[:,:,0]
|
|
# text_region2=text_region2[:,:,0]
|
|
|
|
# text_region1[(text_region1[:,:]==2) & (text_region2[:,:]==1)]=1
|
|
|
|
mask_zeros_from_1 = (text_region2[:, :, 0] == 0) * 1
|
|
# mask_text_from_1=(text_region1[:,:,0]==1)*1
|
|
|
|
mask_img_text_region1 = (text_region1[:, :, 0] == 2) * 1
|
|
text_region2_1st_channel = text_region1[:, :, 0]
|
|
|
|
text_region2_1st_channel[mask_zeros_from_1 == 1] = 0
|
|
|
|
##text_region2_1st_channel[mask_img_text_region1[:,:]==1]=2
|
|
# text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
|
|
|
|
mask_lines1 = (text_region1[:, :, 0] == 3) * 1
|
|
mask_lines2 = (text_region2[:, :, 0] == 3) * 1
|
|
|
|
mask_lines2[mask_lines1[:, :] == 1] = 1
|
|
|
|
# plt.imshow(text_region2_1st_channel)
|
|
# plt.show()
|
|
|
|
text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:, :], self.kernel, iterations=4)
|
|
|
|
# plt.imshow(text_region2_1st_channel)
|
|
# plt.show()
|
|
|
|
text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:, :], self.kernel, iterations=4)
|
|
|
|
text_region2_1st_channel[mask_lines2[:, :] == 1] = 3
|
|
|
|
# text_region2_1st_channel[ (prediction_regions_org[:,:]==1) & (text_region2_1st_channel[:,:]==2)]=1
|
|
|
|
# only in the case of model 3
|
|
|
|
text_region2_1st_channel[(prediction_regions_longshot[:, :] == 1) & (text_region2_1st_channel[:, :] == 2)] = 1
|
|
|
|
text_region2_1st_channel[(prediction_regions_org[:, :] == 2) & (text_region2_1st_channel[:, :] == 0)] = 2
|
|
|
|
# text_region2_1st_channel[prediction_regions_org[:,:]==0]=0
|
|
|
|
# plt.imshow(text_region2_1st_channel)
|
|
# plt.show()
|
|
|
|
# text_region2_1st_channel[:,:400]=0
|
|
|
|
mask_texts_only = (text_region2_1st_channel[:, :] == 1) * 1
|
|
|
|
mask_images_only = (text_region2_1st_channel[:, :] == 2) * 1
|
|
|
|
mask_lines_only = (text_region2_1st_channel[:, :] == 3) * 1
|
|
|
|
pixel_img = 1
|
|
polygons_of_only_texts = self.return_contours_of_interested_region(mask_texts_only, pixel_img)
|
|
|
|
polygons_of_only_images = self.return_contours_of_interested_region(mask_images_only, pixel_img)
|
|
|
|
polygons_of_only_lines = self.return_contours_of_interested_region(mask_lines_only, pixel_img)
|
|
|
|
text_regions_p_true = np.zeros(text_region2_1st_channel.shape)
|
|
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_lines, color=(3, 3, 3))
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_images, color=(2, 2, 2))
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1, 1, 1))
|
|
|
|
##print(np.unique(text_regions_p_true))
|
|
|
|
# text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
|
|
# text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
|
|
|
|
return text_regions_p_true # text_region2_1st_channel
|
|
|
|
def get_regions_from_xy(self, img):
|
|
img_org = np.copy(img)
|
|
|
|
img_height_h = img_org.shape[0]
|
|
img_width_h = img_org.shape[1]
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p)
|
|
|
|
gaussian_filter = False
|
|
patches = True
|
|
binary = True
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_org = self.do_prediction(patches, img, model_region)
|
|
|
|
###plt.imshow(prediction_regions_org[:,:,0])
|
|
###plt.show()
|
|
##sys.exit()
|
|
prediction_regions_org = prediction_regions_org[:, :, 0]
|
|
|
|
gaussian_filter = False
|
|
patches = True
|
|
binary = False
|
|
|
|
ratio_x = 1.1
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
# img= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
|
|
prediction_regions = self.do_prediction(patches, img, model_region)
|
|
text_region1 = self.resize_image(prediction_regions, img_height_h, img_width_h)
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1.1
|
|
binary = False
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
|
|
prediction_regions = self.do_prediction(patches, img, model_region)
|
|
text_region2 = self.resize_image(prediction_regions, img_height_h, img_width_h)
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
mask_zeros_from_1 = (text_region1[:, :, 0] == 0) * 1
|
|
# mask_text_from_1=(text_region1[:,:,0]==1)*1
|
|
|
|
mask_img_text_region1 = (text_region1[:, :, 0] == 2) * 1
|
|
text_region2_1st_channel = text_region2[:, :, 0]
|
|
|
|
text_region2_1st_channel[mask_zeros_from_1 == 1] = 0
|
|
|
|
text_region2_1st_channel[mask_img_text_region1[:, :] == 1] = 2
|
|
# text_region2_1st_channel[(mask_text_from_1==1) & (text_region2_1st_channel==2)]=1
|
|
|
|
mask_lines1 = (text_region1[:, :, 0] == 3) * 1
|
|
mask_lines2 = (text_region2[:, :, 0] == 3) * 1
|
|
|
|
mask_lines2[mask_lines1[:, :] == 1] = 1
|
|
|
|
##plt.imshow(text_region2_1st_channel)
|
|
##plt.show()
|
|
|
|
text_region2_1st_channel = cv2.erode(text_region2_1st_channel[:, :], self.kernel, iterations=5)
|
|
|
|
##plt.imshow(text_region2_1st_channel)
|
|
##plt.show()
|
|
|
|
text_region2_1st_channel = cv2.dilate(text_region2_1st_channel[:, :], self.kernel, iterations=5)
|
|
|
|
text_region2_1st_channel[mask_lines2[:, :] == 1] = 3
|
|
|
|
text_region2_1st_channel[(prediction_regions_org[:, :] == 1) & (text_region2_1st_channel[:, :] == 2)] = 1
|
|
text_region2_1st_channel[prediction_regions_org[:, :] == 3] = 3
|
|
|
|
##plt.imshow(text_region2_1st_channel)
|
|
##plt.show()
|
|
return text_region2_1st_channel
|
|
|
|
def rotation_image_new(self, img, thetha):
|
|
rotated = imutils.rotate(img, thetha)
|
|
return self.rotate_max_area_new(img, rotated, thetha)
|
|
|
|
def rotate_max_area_new(self, image, rotated, angle):
|
|
wr, hr = self.rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
|
|
h, w, _ = rotated.shape
|
|
y1 = h // 2 - int(hr / 2)
|
|
y2 = y1 + int(hr)
|
|
x1 = w // 2 - int(wr / 2)
|
|
x2 = x1 + int(wr)
|
|
return rotated[y1:y2, x1:x2]
|
|
|
|
def rotation_not_90_func(self, img, textline, text_regions_p_1, thetha):
|
|
rotated = imutils.rotate(img, thetha)
|
|
rotated_textline = imutils.rotate(textline, thetha)
|
|
rotated_layout = imutils.rotate(text_regions_p_1, thetha)
|
|
return self.rotate_max_area(img, rotated, rotated_textline, rotated_layout, thetha)
|
|
|
|
def rotate_max_area(self, image, rotated, rotated_textline, rotated_layout, angle):
|
|
wr, hr = self.rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
|
|
h, w, _ = rotated.shape
|
|
y1 = h // 2 - int(hr / 2)
|
|
y2 = y1 + int(hr)
|
|
x1 = w // 2 - int(wr / 2)
|
|
x2 = x1 + int(wr)
|
|
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2]
|
|
|
|
def rotation_not_90_func_full_layout(self, img, textline, text_regions_p_1, text_regions_p_fully, thetha):
|
|
rotated = imutils.rotate(img, thetha)
|
|
rotated_textline = imutils.rotate(textline, thetha)
|
|
rotated_layout = imutils.rotate(text_regions_p_1, thetha)
|
|
rotated_layout_full = imutils.rotate(text_regions_p_fully, thetha)
|
|
return self.rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha)
|
|
|
|
def rotate_max_area_full_layout(self, image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle):
|
|
wr, hr = self.rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
|
|
h, w, _ = rotated.shape
|
|
y1 = h // 2 - int(hr / 2)
|
|
y2 = y1 + int(hr)
|
|
x1 = w // 2 - int(wr / 2)
|
|
x2 = x1 + int(wr)
|
|
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_layout_full[y1:y2, x1:x2]
|
|
|
|
def get_regions_from_xy_2models_ens(self, img):
|
|
img_org = np.copy(img)
|
|
|
|
img_height_h = img_org.shape[0]
|
|
img_width_h = img_org.shape[1]
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
|
|
|
gaussian_filter = False
|
|
patches = False
|
|
binary = False
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
prediction_regions_long = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_long = self.resize_image(prediction_regions_long, img_height_h, img_width_h)
|
|
|
|
gaussian_filter = False
|
|
patches = True
|
|
binary = False
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1.2
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_org_y = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_org_y = self.resize_image(prediction_regions_org_y, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(prediction_regions_org[:,:,0])
|
|
# plt.show()
|
|
# sys.exit()
|
|
prediction_regions_org_y = prediction_regions_org_y[:, :, 0]
|
|
|
|
mask_zeros_y = (prediction_regions_org_y[:, :] == 0) * 1
|
|
|
|
ratio_x = 1.2
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_org = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_org = self.resize_image(prediction_regions_org, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(prediction_regions_org[:,:,0])
|
|
# plt.show()
|
|
# sys.exit()
|
|
prediction_regions_org = prediction_regions_org[:, :, 0]
|
|
|
|
prediction_regions_org[(prediction_regions_org[:, :] == 1) & (mask_zeros_y[:, :] == 1)] = 0
|
|
|
|
prediction_regions_org[(prediction_regions_long[:, :, 0] == 1) & (prediction_regions_org[:, :] == 2)] = 1
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
|
|
return prediction_regions_org
|
|
|
|
def get_regions_from_xy_2models(self, img, is_image_enhanced):
|
|
img_org = np.copy(img)
|
|
|
|
img_height_h = img_org.shape[0]
|
|
img_width_h = img_org.shape[1]
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
|
|
|
|
gaussian_filter = False
|
|
patches = True
|
|
binary = False
|
|
|
|
ratio_y = 1.3
|
|
ratio_x = 1
|
|
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_org_y = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_org_y = self.resize_image(prediction_regions_org_y, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(prediction_regions_org_y[:,:,0])
|
|
# plt.show()
|
|
# sys.exit()
|
|
prediction_regions_org_y = prediction_regions_org_y[:, :, 0]
|
|
|
|
mask_zeros_y = (prediction_regions_org_y[:, :] == 0) * 1
|
|
|
|
if is_image_enhanced:
|
|
ratio_x = 1.2
|
|
else:
|
|
ratio_x = 1
|
|
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_org = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_org = self.resize_image(prediction_regions_org, img_height_h, img_width_h)
|
|
|
|
##plt.imshow(prediction_regions_org[:,:,0])
|
|
##plt.show()
|
|
##sys.exit()
|
|
prediction_regions_org = prediction_regions_org[:, :, 0]
|
|
|
|
prediction_regions_org[(prediction_regions_org[:, :] == 1) & (mask_zeros_y[:, :] == 1)] = 0
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
###K.clear_session()
|
|
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
|
|
|
|
gaussian_filter = False
|
|
patches = True
|
|
binary = False
|
|
|
|
ratio_x = 1
|
|
ratio_y = 1
|
|
median_blur = False
|
|
|
|
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
|
|
|
if binary:
|
|
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
|
img = img.astype(np.uint16)
|
|
|
|
if median_blur:
|
|
img = cv2.medianBlur(img, 5)
|
|
if gaussian_filter:
|
|
img = cv2.GaussianBlur(img, (5, 5), 0)
|
|
img = img.astype(np.uint16)
|
|
prediction_regions_org2 = self.do_prediction(patches, img, model_region)
|
|
|
|
prediction_regions_org2 = self.resize_image(prediction_regions_org2, img_height_h, img_width_h)
|
|
|
|
# plt.imshow(prediction_regions_org2[:,:,0])
|
|
# plt.show()
|
|
# sys.exit()
|
|
##prediction_regions_org=prediction_regions_org[:,:,0]
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
###K.clear_session()
|
|
|
|
mask_zeros2 = (prediction_regions_org2[:, :, 0] == 0) * 1
|
|
mask_lines2 = (prediction_regions_org2[:, :, 0] == 3) * 1
|
|
|
|
text_sume_early = ((prediction_regions_org[:, :] == 1) * 1).sum()
|
|
|
|
prediction_regions_org_copy = np.copy(prediction_regions_org)
|
|
|
|
prediction_regions_org_copy[(prediction_regions_org_copy[:, :] == 1) & (mask_zeros2[:, :] == 1)] = 0
|
|
|
|
text_sume_second = ((prediction_regions_org_copy[:, :] == 1) * 1).sum()
|
|
|
|
rate_two_models = text_sume_second / float(text_sume_early) * 100
|
|
|
|
print(rate_two_models, "ratio_of_two_models")
|
|
if is_image_enhanced and rate_two_models < 95.50: # 98.45:
|
|
pass
|
|
else:
|
|
prediction_regions_org = np.copy(prediction_regions_org_copy)
|
|
|
|
##prediction_regions_org[mask_lines2[:,:]==1]=3
|
|
prediction_regions_org[(mask_lines2[:, :] == 1) & (prediction_regions_org[:, :] == 0)] = 3
|
|
|
|
del mask_lines2
|
|
del mask_zeros2
|
|
del prediction_regions_org2
|
|
|
|
# if is_image_enhanced:
|
|
# pass
|
|
# else:
|
|
# model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
|
|
|
|
# gaussian_filter=False
|
|
# patches=True
|
|
# binary=False
|
|
|
|
# ratio_x=1
|
|
# ratio_y=1
|
|
# median_blur=False
|
|
|
|
# img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
|
|
|
# if binary:
|
|
# img = self.otsu_copy_binary(img)#self.otsu_copy(img)
|
|
# img = img.astype(np.uint16)
|
|
|
|
# if median_blur:
|
|
# img=cv2.medianBlur(img,5)
|
|
# if gaussian_filter:
|
|
# img= cv2.GaussianBlur(img,(5,5),0)
|
|
# img = img.astype(np.uint16)
|
|
# prediction_regions_org2=self.do_prediction(patches,img,model_region)
|
|
|
|
# prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h )
|
|
|
|
##plt.imshow(prediction_regions_org2[:,:,0])
|
|
##plt.show()
|
|
##sys.exit()
|
|
###prediction_regions_org=prediction_regions_org[:,:,0]
|
|
|
|
# session_region.close()
|
|
# del model_region
|
|
# del session_region
|
|
# gc.collect()
|
|
####K.clear_session()
|
|
|
|
# mask_zeros2=(prediction_regions_org2[:,:,0]==0)*1
|
|
# mask_lines2=(prediction_regions_org2[:,:,0]==3)*1
|
|
|
|
# text_sume_early=( (prediction_regions_org[:,:]==1)*1 ).sum()
|
|
|
|
# prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros2[:,:]==1)]=0
|
|
|
|
###prediction_regions_org[mask_lines2[:,:]==1]=3
|
|
# prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
|
|
|
|
# text_sume_second=( (prediction_regions_org[:,:]==1)*1 ).sum()
|
|
|
|
# print(text_sume_second/float(text_sume_early)*100,'twomodelsratio')
|
|
|
|
# del mask_lines2
|
|
# del mask_zeros2
|
|
# del prediction_regions_org2
|
|
|
|
mask_lines_only = (prediction_regions_org[:, :] == 3) * 1
|
|
|
|
prediction_regions_org = cv2.erode(prediction_regions_org[:, :], self.kernel, iterations=2)
|
|
|
|
# plt.imshow(text_region2_1st_channel)
|
|
# plt.show()
|
|
|
|
prediction_regions_org = cv2.dilate(prediction_regions_org[:, :], self.kernel, iterations=2)
|
|
|
|
mask_texts_only = (prediction_regions_org[:, :] == 1) * 1
|
|
|
|
mask_images_only = (prediction_regions_org[:, :] == 2) * 1
|
|
|
|
pixel_img = 1
|
|
min_area_text = 0.00001
|
|
polygons_of_only_texts = self.return_contours_of_interested_region(mask_texts_only, pixel_img, min_area_text)
|
|
|
|
polygons_of_only_images = self.return_contours_of_interested_region(mask_images_only, pixel_img)
|
|
|
|
polygons_of_only_lines = self.return_contours_of_interested_region(mask_lines_only, pixel_img, min_area_text)
|
|
|
|
text_regions_p_true = np.zeros(prediction_regions_org.shape)
|
|
# text_regions_p_true[:,:]=text_regions_p_1[:,:]
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_lines, color=(3, 3, 3))
|
|
|
|
##text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_images, color=(2,2,2))
|
|
text_regions_p_true[:, :][mask_images_only[:, :] == 1] = 2
|
|
|
|
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts=polygons_of_only_texts, color=(1, 1, 1))
|
|
|
|
##print(np.unique(text_regions_p_true))
|
|
|
|
# text_regions_p_true_3d=np.repeat(text_regions_p_1[:, :, np.newaxis], 3, axis=2)
|
|
# text_regions_p_true_3d=text_regions_p_true_3d.astype(np.uint8)
|
|
|
|
del polygons_of_only_texts
|
|
del polygons_of_only_images
|
|
del polygons_of_only_lines
|
|
del mask_images_only
|
|
del prediction_regions_org
|
|
del img
|
|
del mask_zeros_y
|
|
|
|
del prediction_regions_org_y
|
|
del img_org
|
|
gc.collect()
|
|
|
|
return text_regions_p_true
|
|
|
|
def get_textregion_contours_in_org_image(self, cnts, img, slope_first):
|
|
|
|
cnts_org = []
|
|
# print(cnts,'cnts')
|
|
for i in range(len(cnts)):
|
|
img_copy = np.zeros(img.shape)
|
|
img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
|
|
|
|
# plt.imshow(img_copy)
|
|
# plt.show()
|
|
|
|
# print(img.shape,'img')
|
|
img_copy = self.rotation_image_new(img_copy, -slope_first)
|
|
##print(img_copy.shape,'img_copy')
|
|
# plt.imshow(img_copy)
|
|
# plt.show()
|
|
|
|
img_copy = img_copy.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
|
|
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
|
|
# print(np.shape(cont_int[0]))
|
|
cnts_org.append(cont_int[0])
|
|
|
|
# print(cnts_org,'cnts_org')
|
|
|
|
# sys.exit()
|
|
self.y_shift = np.abs(img_copy.shape[0] - img.shape[0])
|
|
self.x_shift = np.abs(img_copy.shape[1] - img.shape[1])
|
|
return cnts_org
|
|
|
|
def write_images_into_directory(self, img_contoures, dir_of_cropped_imgs, image_page):
|
|
index = 0
|
|
for cont_ind in img_contoures:
|
|
# cont_ind[:,0,0]=cont_ind[:,0,0]/self.scale_x
|
|
# cont_ind[:,0,1]=cont_ind[:,0,1]/self.scale_y
|
|
|
|
x, y, w, h = cv2.boundingRect(cont_ind)
|
|
box = [x, y, w, h]
|
|
croped_page, page_coord = self.crop_image_inside_box(box, image_page)
|
|
|
|
croped_page = self.resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x))
|
|
|
|
path = os.path.join(dir_of_cropped_imgs, self.f_name + "_" + str(index) + ".jpg")
|
|
cv2.imwrite(path, croped_page)
|
|
index += 1
|
|
|
|
def get_marginals(self, text_with_lines, text_regions, num_col, slope_deskew):
|
|
mask_marginals = np.zeros((text_with_lines.shape[0], text_with_lines.shape[1]))
|
|
mask_marginals = mask_marginals.astype(np.uint8)
|
|
|
|
text_with_lines = text_with_lines.astype(np.uint8)
|
|
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
|
|
|
|
text_with_lines_eroded = cv2.erode(text_with_lines, self.kernel, iterations=5)
|
|
|
|
if text_with_lines.shape[0] <= 1500:
|
|
pass
|
|
elif text_with_lines.shape[0] > 1500 and text_with_lines.shape[0] <= 1800:
|
|
text_with_lines = self.resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.5), text_with_lines.shape[1])
|
|
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=5)
|
|
text_with_lines = self.resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
|
|
else:
|
|
text_with_lines = self.resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.8), text_with_lines.shape[1])
|
|
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=7)
|
|
text_with_lines = self.resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
|
|
|
|
text_with_lines_y = text_with_lines.sum(axis=0)
|
|
text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0)
|
|
|
|
thickness_along_y_percent = text_with_lines_y_eroded.max() / (float(text_with_lines.shape[0])) * 100
|
|
|
|
# print(thickness_along_y_percent,'thickness_along_y_percent')
|
|
|
|
if thickness_along_y_percent < 30:
|
|
min_textline_thickness = 8
|
|
elif thickness_along_y_percent >= 30 and thickness_along_y_percent < 50:
|
|
min_textline_thickness = 20
|
|
else:
|
|
min_textline_thickness = 40
|
|
|
|
if thickness_along_y_percent >= 14:
|
|
|
|
text_with_lines_y_rev = -1 * text_with_lines_y[:]
|
|
# print(text_with_lines_y)
|
|
# print(text_with_lines_y_rev)
|
|
|
|
# plt.plot(text_with_lines_y)
|
|
# plt.show()
|
|
|
|
text_with_lines_y_rev = text_with_lines_y_rev - np.min(text_with_lines_y_rev)
|
|
|
|
# plt.plot(text_with_lines_y_rev)
|
|
# plt.show()
|
|
sigma_gaus = 1
|
|
region_sum_0 = gaussian_filter1d(text_with_lines_y, sigma_gaus)
|
|
|
|
region_sum_0_rev = gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
|
|
|
|
# plt.plot(region_sum_0_rev)
|
|
# plt.show()
|
|
region_sum_0_updown = region_sum_0[len(region_sum_0) :: -1]
|
|
|
|
first_nonzero = next((i for i, x in enumerate(region_sum_0) if x), None)
|
|
last_nonzero = next((i for i, x in enumerate(region_sum_0_updown) if x), None)
|
|
|
|
last_nonzero = len(region_sum_0) - last_nonzero
|
|
|
|
##img_sum_0_smooth_rev=-region_sum_0
|
|
|
|
mid_point = (last_nonzero + first_nonzero) / 2.0
|
|
|
|
one_third_right = (last_nonzero - mid_point) / 3.0
|
|
one_third_left = (mid_point - first_nonzero) / 3.0
|
|
|
|
# img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
|
|
|
|
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
|
|
|
|
peaks = np.array(peaks)
|
|
|
|
# print(region_sum_0[peaks])
|
|
##plt.plot(region_sum_0)
|
|
##plt.plot(peaks,region_sum_0[peaks],'*')
|
|
##plt.show()
|
|
# print(first_nonzero,last_nonzero,peaks)
|
|
peaks = peaks[(peaks > first_nonzero) & ((peaks < last_nonzero))]
|
|
|
|
# print(first_nonzero,last_nonzero,peaks)
|
|
|
|
# print(region_sum_0[peaks]<10)
|
|
####peaks=peaks[region_sum_0[peaks]<25 ]
|
|
|
|
# print(region_sum_0[peaks])
|
|
peaks = peaks[region_sum_0[peaks] < min_textline_thickness]
|
|
# print(peaks)
|
|
# print(first_nonzero,last_nonzero,one_third_right,one_third_left)
|
|
|
|
if num_col == 1:
|
|
peaks_right = peaks[peaks > mid_point]
|
|
peaks_left = peaks[peaks < mid_point]
|
|
if num_col == 2:
|
|
peaks_right = peaks[peaks > (mid_point + one_third_right)]
|
|
peaks_left = peaks[peaks < (mid_point - one_third_left)]
|
|
|
|
try:
|
|
point_right = np.min(peaks_right)
|
|
except:
|
|
point_right = last_nonzero
|
|
|
|
try:
|
|
point_left = np.max(peaks_left)
|
|
except:
|
|
point_left = first_nonzero
|
|
|
|
# print(point_left,point_right)
|
|
# print(text_regions.shape)
|
|
if point_right >= mask_marginals.shape[1]:
|
|
point_right = mask_marginals.shape[1] - 1
|
|
|
|
try:
|
|
mask_marginals[:, point_left:point_right] = 1
|
|
except:
|
|
mask_marginals[:, :] = 1
|
|
|
|
# print(mask_marginals.shape,point_left,point_right,'nadosh')
|
|
mask_marginals_rotated = self.rotate_image(mask_marginals, -slope_deskew)
|
|
|
|
# print(mask_marginals_rotated.shape,'nadosh')
|
|
mask_marginals_rotated_sum = mask_marginals_rotated.sum(axis=0)
|
|
|
|
mask_marginals_rotated_sum[mask_marginals_rotated_sum != 0] = 1
|
|
index_x = np.array(range(len(mask_marginals_rotated_sum))) + 1
|
|
|
|
index_x_interest = index_x[mask_marginals_rotated_sum == 1]
|
|
|
|
min_point_of_left_marginal = np.min(index_x_interest) - 16
|
|
max_point_of_right_marginal = np.max(index_x_interest) + 16
|
|
|
|
if min_point_of_left_marginal < 0:
|
|
min_point_of_left_marginal = 0
|
|
if max_point_of_right_marginal >= text_regions.shape[1]:
|
|
max_point_of_right_marginal = text_regions.shape[1] - 1
|
|
|
|
# print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
|
|
# print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
|
|
# plt.imshow(mask_marginals)
|
|
# plt.show()
|
|
|
|
# plt.imshow(mask_marginals_rotated)
|
|
# plt.show()
|
|
|
|
text_regions[(mask_marginals_rotated[:, :] != 1) & (text_regions[:, :] == 1)] = 4
|
|
|
|
pixel_img = 4
|
|
min_area_text = 0.00001
|
|
polygons_of_marginals = self.return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
|
|
|
|
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = self.find_new_features_of_contoures(polygons_of_marginals)
|
|
|
|
text_regions[(text_regions[:, :] == 4)] = 1
|
|
|
|
marginlas_should_be_main_text = []
|
|
|
|
x_min_marginals_left = []
|
|
x_min_marginals_right = []
|
|
|
|
for i in range(len(cx_text_only)):
|
|
|
|
x_width_mar = abs(x_min_text_only[i] - x_max_text_only[i])
|
|
y_height_mar = abs(y_min_text_only[i] - y_max_text_only[i])
|
|
# print(x_width_mar,y_height_mar,'y_height_mar')
|
|
if x_width_mar > 16 and y_height_mar / x_width_mar < 10:
|
|
marginlas_should_be_main_text.append(polygons_of_marginals[i])
|
|
if x_min_text_only[i] < (mid_point - one_third_left):
|
|
x_min_marginals_left_new = x_min_text_only[i]
|
|
if len(x_min_marginals_left) == 0:
|
|
x_min_marginals_left.append(x_min_marginals_left_new)
|
|
else:
|
|
x_min_marginals_left[0] = min(x_min_marginals_left[0], x_min_marginals_left_new)
|
|
else:
|
|
x_min_marginals_right_new = x_min_text_only[i]
|
|
if len(x_min_marginals_right) == 0:
|
|
x_min_marginals_right.append(x_min_marginals_right_new)
|
|
else:
|
|
x_min_marginals_right[0] = min(x_min_marginals_right[0], x_min_marginals_right_new)
|
|
|
|
if len(x_min_marginals_left) == 0:
|
|
x_min_marginals_left = [0]
|
|
if len(x_min_marginals_right) == 0:
|
|
x_min_marginals_right = [text_regions.shape[1] - 1]
|
|
|
|
# print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
|
|
|
|
# print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
|
|
text_regions = cv2.fillPoly(text_regions, pts=marginlas_should_be_main_text, color=(4, 4))
|
|
|
|
# print(np.unique(text_regions))
|
|
|
|
# text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
|
|
# text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
|
|
|
|
text_regions[:, : int(min_point_of_left_marginal)][text_regions[:, : int(min_point_of_left_marginal)] == 1] = 0
|
|
text_regions[:, int(max_point_of_right_marginal) :][text_regions[:, int(max_point_of_right_marginal) :] == 1] = 0
|
|
|
|
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
|
|
|
|
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
|
|
# plt.plot(region_sum_0)
|
|
# plt.plot(peaks,region_sum_0[peaks],'*')
|
|
# plt.show()
|
|
|
|
# plt.imshow(text_regions)
|
|
# plt.show()
|
|
|
|
# sys.exit()
|
|
else:
|
|
pass
|
|
return text_regions
|
|
|
|
def do_work_of_textline_seperation(self, queue_of_all_params, polygons_per_process, index_polygons_per_process, con_par_org, textline_mask_tot, mask_texts_only, num_col, scale_par, boxes_text):
|
|
|
|
textregions_cnt_tot_per_process = []
|
|
textlines_cnt_tot_per_process = []
|
|
index_polygons_per_process_per_process = []
|
|
polygons_per_par_process_per_process = []
|
|
textline_cnt_seperated = np.zeros(textline_mask_tot.shape)
|
|
for iiii in range(len(polygons_per_process)):
|
|
# crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)
|
|
# arg_max=np.argmax(areas_cnt_only_text)
|
|
textregions_cnt_tot_per_process.append(polygons_per_process[iiii] / scale_par)
|
|
textline_region_in_image = np.zeros(textline_mask_tot.shape)
|
|
cnt_o_t_max = polygons_per_process[iiii]
|
|
|
|
x, y, w, h = cv2.boundingRect(cnt_o_t_max)
|
|
|
|
mask_biggest = np.zeros(mask_texts_only.shape)
|
|
mask_biggest = cv2.fillPoly(mask_biggest, pts=[cnt_o_t_max], color=(1, 1, 1))
|
|
|
|
mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w]
|
|
|
|
textline_biggest_region = mask_biggest * textline_mask_tot
|
|
|
|
textline_rotated_seperated = self.seperate_lines_new2(textline_biggest_region[y : y + h, x : x + w], 0, num_col)
|
|
|
|
# new line added
|
|
##print(np.shape(textline_rotated_seperated),np.shape(mask_biggest))
|
|
textline_rotated_seperated[mask_region_in_patch_region[:, :] != 1] = 0
|
|
# till here
|
|
|
|
textline_cnt_seperated[y : y + h, x : x + w] = textline_rotated_seperated
|
|
textline_region_in_image[y : y + h, x : x + w] = textline_rotated_seperated
|
|
|
|
# plt.imshow(textline_region_in_image)
|
|
# plt.show()
|
|
|
|
# plt.imshow(textline_cnt_seperated)
|
|
# plt.show()
|
|
|
|
pixel_img = 1
|
|
cnt_textlines_in_image = self.return_contours_of_interested_textline(textline_region_in_image, pixel_img)
|
|
|
|
textlines_cnt_per_region = []
|
|
for jjjj in range(len(cnt_textlines_in_image)):
|
|
mask_biggest2 = np.zeros(mask_texts_only.shape)
|
|
mask_biggest2 = cv2.fillPoly(mask_biggest2, pts=[cnt_textlines_in_image[jjjj]], color=(1, 1, 1))
|
|
if num_col + 1 == 1:
|
|
mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=5)
|
|
else:
|
|
|
|
mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=4)
|
|
|
|
pixel_img = 1
|
|
cnt_textlines_in_image_ind = self.return_contours_of_interested_textline(mask_biggest2, pixel_img)
|
|
|
|
try:
|
|
textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0] / scale_par)
|
|
except:
|
|
pass
|
|
# print(len(cnt_textlines_in_image_ind))
|
|
|
|
# plt.imshow(mask_biggest2)
|
|
# plt.show()
|
|
textlines_cnt_tot_per_process.append(textlines_cnt_per_region)
|
|
index_polygons_per_process_per_process.append(index_polygons_per_process[iiii])
|
|
polygons_per_par_process_per_process.append(con_par_org[iiii])
|
|
|
|
queue_of_all_params.put([index_polygons_per_process_per_process, polygons_per_par_process_per_process, textregions_cnt_tot_per_process, textlines_cnt_tot_per_process])
|
|
|
|
def small_textlines_to_parent_adherence2(self, textlines_con, textline_iamge, num_col):
|
|
# print(textlines_con)
|
|
# textlines_con=textlines_con.astype(np.uint32)
|
|
|
|
textlines_con_changed = []
|
|
for m1 in range(len(textlines_con)):
|
|
|
|
# textlines_tot=textlines_con[m1]
|
|
# textlines_tot=textlines_tot.astype()
|
|
textlines_tot = []
|
|
textlines_tot_org_form = []
|
|
# print(textlines_tot)
|
|
|
|
for nn in range(len(textlines_con[m1])):
|
|
textlines_tot.append(np.array(textlines_con[m1][nn], dtype=np.int32))
|
|
textlines_tot_org_form.append(textlines_con[m1][nn])
|
|
|
|
##img_text_all=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1]))
|
|
##img_text_all=cv2.fillPoly(img_text_all, pts =textlines_tot , color=(1,1,1))
|
|
|
|
##plt.imshow(img_text_all)
|
|
##plt.show()
|
|
areas_cnt_text = np.array([cv2.contourArea(textlines_tot[j]) for j in range(len(textlines_tot))])
|
|
areas_cnt_text = areas_cnt_text / float(textline_iamge.shape[0] * textline_iamge.shape[1])
|
|
indexes_textlines = np.array(range(len(textlines_tot)))
|
|
|
|
# print(areas_cnt_text,np.min(areas_cnt_text),np.max(areas_cnt_text))
|
|
if num_col == 0:
|
|
min_area = 0.0004
|
|
elif num_col == 1:
|
|
min_area = 0.0003
|
|
else:
|
|
min_area = 0.0001
|
|
indexes_textlines_small = indexes_textlines[areas_cnt_text < min_area]
|
|
|
|
# print(indexes_textlines)
|
|
|
|
textlines_small = []
|
|
textlines_small_org_form = []
|
|
for i in indexes_textlines_small:
|
|
textlines_small.append(textlines_tot[i])
|
|
textlines_small_org_form.append(textlines_tot_org_form[i])
|
|
|
|
textlines_big = []
|
|
textlines_big_org_form = []
|
|
for i in list(set(indexes_textlines) - set(indexes_textlines_small)):
|
|
textlines_big.append(textlines_tot[i])
|
|
textlines_big_org_form.append(textlines_tot_org_form[i])
|
|
|
|
img_textline_s = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_textline_s = cv2.fillPoly(img_textline_s, pts=textlines_small, color=(1, 1, 1))
|
|
|
|
img_textline_b = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_textline_b = cv2.fillPoly(img_textline_b, pts=textlines_big, color=(1, 1, 1))
|
|
|
|
sum_small_big_all = img_textline_s + img_textline_b
|
|
sum_small_big_all2 = (sum_small_big_all[:, :] == 2) * 1
|
|
|
|
sum_intersection_sb = sum_small_big_all2.sum(axis=1).sum()
|
|
|
|
if sum_intersection_sb > 0:
|
|
|
|
dis_small_from_bigs_tot = []
|
|
for z1 in range(len(textlines_small)):
|
|
# print(len(textlines_small),'small')
|
|
intersections = []
|
|
for z2 in range(len(textlines_big)):
|
|
img_text = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_text = cv2.fillPoly(img_text, pts=[textlines_small[z1]], color=(1, 1, 1))
|
|
|
|
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1]))
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z2]], color=(1, 1, 1))
|
|
|
|
sum_small_big = img_text2 + img_text
|
|
sum_small_big_2 = (sum_small_big[:, :] == 2) * 1
|
|
|
|
sum_intersection = sum_small_big_2.sum(axis=1).sum()
|
|
|
|
# print(sum_intersection)
|
|
|
|
intersections.append(sum_intersection)
|
|
|
|
if len(np.array(intersections)[np.array(intersections) > 0]) == 0:
|
|
intersections = []
|
|
|
|
try:
|
|
dis_small_from_bigs_tot.append(np.argmax(intersections))
|
|
except:
|
|
dis_small_from_bigs_tot.append(-1)
|
|
|
|
smalls_list = np.array(dis_small_from_bigs_tot)[np.array(dis_small_from_bigs_tot) >= 0]
|
|
|
|
# index_small_textlines_rest=list( set(indexes_textlines_small)-set(smalls_list) )
|
|
|
|
textlines_big_with_change = []
|
|
textlines_big_with_change_con = []
|
|
textlines_small_with_change = []
|
|
|
|
for z in list(set(smalls_list)):
|
|
index_small_textlines = list(np.where(np.array(dis_small_from_bigs_tot) == z)[0])
|
|
# print(z,index_small_textlines)
|
|
|
|
img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1], 3))
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z]], color=(255, 255, 255))
|
|
|
|
textlines_big_with_change.append(z)
|
|
|
|
for k in index_small_textlines:
|
|
img_text2 = cv2.fillPoly(img_text2, pts=[textlines_small[k]], color=(255, 255, 255))
|
|
textlines_small_with_change.append(k)
|
|
|
|
img_text2 = img_text2.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_text2, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
cont, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(cont[0],type(cont))
|
|
|
|
textlines_big_with_change_con.append(cont)
|
|
textlines_big_org_form[z] = cont[0]
|
|
|
|
# plt.imshow(img_text2)
|
|
# plt.show()
|
|
|
|
# print(textlines_big_with_change,'textlines_big_with_change')
|
|
# print(textlines_small_with_change,'textlines_small_with_change')
|
|
# print(textlines_big)
|
|
textlines_con_changed.append(textlines_big_org_form)
|
|
|
|
else:
|
|
textlines_con_changed.append(textlines_big_org_form)
|
|
return textlines_con_changed
|
|
|
|
def check_any_text_region_in_model_one_is_main_or_header(self, regions_model_1, regions_model_full, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered):
|
|
text_only = (regions_model_1[:, :] == 1) * 1
|
|
contours_only_text, hir_on_text = self.return_contours_of_image(text_only)
|
|
|
|
"""
|
|
contours_only_text_parent=self.return_parent_contours( contours_only_text,hir_on_text)
|
|
|
|
areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
|
areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1])
|
|
|
|
###areas_cnt_text_h=np.array([cv2.contourArea(contours_only_text_parent_h[j]) for j in range(len(contours_only_text_parent_h))])
|
|
###areas_cnt_text_h=areas_cnt_text_h/float(text_only_h.shape[0]*text_only_h.shape[1])
|
|
|
|
###contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.0002]
|
|
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001]
|
|
"""
|
|
|
|
cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin = self.find_new_features_of_contoures(contours_only_text_parent)
|
|
|
|
length_con = x_max_main - x_min_main
|
|
height_con = y_max_main - y_min_main
|
|
|
|
all_found_texline_polygons_main = []
|
|
all_found_texline_polygons_head = []
|
|
|
|
all_box_coord_main = []
|
|
all_box_coord_head = []
|
|
|
|
slopes_main = []
|
|
slopes_head = []
|
|
|
|
contours_only_text_parent_main = []
|
|
contours_only_text_parent_head = []
|
|
|
|
contours_only_text_parent_main_d = []
|
|
contours_only_text_parent_head_d = []
|
|
|
|
for ii in range(len(contours_only_text_parent)):
|
|
con = contours_only_text_parent[ii]
|
|
img = np.zeros((regions_model_1.shape[0], regions_model_1.shape[1], 3))
|
|
img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255))
|
|
|
|
all_pixels = ((img[:, :, 0] == 255) * 1).sum()
|
|
|
|
pixels_header = (((img[:, :, 0] == 255) & (regions_model_full[:, :, 0] == 2)) * 1).sum()
|
|
pixels_main = all_pixels - pixels_header
|
|
|
|
if (pixels_header >= pixels_main) and ((length_con[ii] / float(height_con[ii])) >= 1.3):
|
|
regions_model_1[:, :][(regions_model_1[:, :] == 1) & (img[:, :, 0] == 255)] = 2
|
|
contours_only_text_parent_head.append(con)
|
|
if contours_only_text_parent_d_ordered is not None:
|
|
contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii])
|
|
all_box_coord_head.append(all_box_coord[ii])
|
|
slopes_head.append(slopes[ii])
|
|
all_found_texline_polygons_head.append(all_found_texline_polygons[ii])
|
|
else:
|
|
regions_model_1[:, :][(regions_model_1[:, :] == 1) & (img[:, :, 0] == 255)] = 1
|
|
contours_only_text_parent_main.append(con)
|
|
if contours_only_text_parent_d_ordered is not None:
|
|
contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii])
|
|
all_box_coord_main.append(all_box_coord[ii])
|
|
slopes_main.append(slopes[ii])
|
|
all_found_texline_polygons_main.append(all_found_texline_polygons[ii])
|
|
|
|
# print(all_pixels,pixels_main,pixels_header)
|
|
|
|
# plt.imshow(img[:,:,0])
|
|
# plt.show()
|
|
return regions_model_1, contours_only_text_parent_main, contours_only_text_parent_head, all_box_coord_main, all_box_coord_head, all_found_texline_polygons_main, all_found_texline_polygons_head, slopes_main, slopes_head, contours_only_text_parent_main_d, contours_only_text_parent_head_d
|
|
|
|
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(self, layout_in_patch):
|
|
|
|
drop_only = (layout_in_patch[:, :, 0] == 4) * 1
|
|
contours_drop, hir_on_drop = self.return_contours_of_image(drop_only)
|
|
contours_drop_parent = self.return_parent_contours(contours_drop, hir_on_drop)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
|
|
|
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
|
|
|
|
contours_drop_parent_final = []
|
|
|
|
for jj in range(len(contours_drop_parent)):
|
|
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
|
layout_in_patch[y : y + h, x : x + w, 0] = 4
|
|
|
|
return layout_in_patch
|
|
|
|
def filter_small_drop_capitals_from_no_patch_layout(self, layout_no_patch, layout1):
|
|
|
|
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
|
|
contours_drop, hir_on_drop = self.return_contours_of_image(drop_only)
|
|
contours_drop_parent = self.return_parent_contours(contours_drop, hir_on_drop)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
|
|
|
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.001]
|
|
|
|
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
|
|
|
|
contours_drop_parent_final = []
|
|
|
|
for jj in range(len(contours_drop_parent)):
|
|
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
|
# boxes.append([int(x), int(y), int(w), int(h)])
|
|
|
|
iou_of_box_and_contoure = float(drop_only.shape[0] * drop_only.shape[1]) * areas_cnt_text[jj] / float(w * h) * 100
|
|
height_to_weight_ratio = h / float(w)
|
|
weigh_to_height_ratio = w / float(h)
|
|
|
|
if iou_of_box_and_contoure > 60 and weigh_to_height_ratio < 1.2 and height_to_weight_ratio < 2:
|
|
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
|
|
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
|
|
|
|
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
|
|
contours_drop_parent_final.append(contours_drop_parent[jj])
|
|
|
|
layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0
|
|
|
|
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
|
|
|
|
return layout_no_patch
|
|
|
|
def put_drop_out_from_only_drop_model(self, layout_no_patch, layout1):
|
|
|
|
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
|
|
contours_drop, hir_on_drop = self.return_contours_of_image(drop_only)
|
|
contours_drop_parent = self.return_parent_contours(contours_drop, hir_on_drop)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
|
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
|
|
|
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.00001]
|
|
|
|
contours_drop_parent_final = []
|
|
|
|
for jj in range(len(contours_drop_parent)):
|
|
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
|
# boxes.append([int(x), int(y), int(w), int(h)])
|
|
|
|
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
|
|
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
|
|
|
|
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
|
|
contours_drop_parent_final.append(contours_drop_parent[jj])
|
|
|
|
layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0
|
|
|
|
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
|
|
|
|
return layout_no_patch
|
|
|
|
def do_order_of_regions(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
|
|
|
|
if self.full_layout == "true" or self.full_layout == "True" or self.full_layout == "TRUE":
|
|
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = self.find_new_features_of_contoures(contours_only_text_parent)
|
|
cx_text_only_h, cy_text_only_h, x_min_text_only_h, _, _, _, y_cor_x_min_main_h = self.find_new_features_of_contoures(contours_only_text_parent_h)
|
|
|
|
try:
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
|
|
arg_text_con.append(jj)
|
|
break
|
|
arg_arg_text_con = np.argsort(arg_text_con)
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
|
|
arg_text_con_h = []
|
|
for ii in range(len(cx_text_only_h)):
|
|
for jj in range(len(boxes)):
|
|
if (x_min_text_only_h[ii] + 80) >= boxes[jj][0] and (x_min_text_only_h[ii] + 80) < boxes[jj][1] and y_cor_x_min_main_h[ii] >= boxes[jj][2] and y_cor_x_min_main_h[ii] < boxes[jj][3]:
|
|
arg_text_con_h.append(jj)
|
|
break
|
|
arg_arg_text_con = np.argsort(arg_text_con_h)
|
|
args_contours_h = np.array(range(len(arg_text_con_h)))
|
|
|
|
order_by_con_head = np.zeros(len(arg_text_con_h))
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij in range(len(boxes)):
|
|
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij]
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
|
|
for i in range(len(args_contours_box)):
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
|
|
for i in range(len(args_contours_box_h)):
|
|
con_inter_box_h.append(contours_only_text_parent_h[args_contours_box_h[i]])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = self.order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = self.order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box_h:
|
|
arg_order_v = indexes_sorted_head[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
# print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
|
|
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
for jji in range(len(id_of_texts)):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point = ref_point + len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
for tj1 in range(len(contours_only_text_parent_h)):
|
|
order_of_texts_tot.append(int(order_by_con_head[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
|
|
except:
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
|
|
arg_text_con.append(jj)
|
|
break
|
|
arg_arg_text_con = np.argsort(arg_text_con)
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
############################# head
|
|
|
|
arg_text_con_h = []
|
|
for ii in range(len(cx_text_only_h)):
|
|
for jj in range(len(boxes)):
|
|
if cx_text_only_h[ii] >= boxes[jj][0] and cx_text_only_h[ii] < boxes[jj][1] and cy_text_only_h[ii] >= boxes[jj][2] and cy_text_only_h[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
|
|
arg_text_con_h.append(jj)
|
|
break
|
|
arg_arg_text_con_h = np.argsort(arg_text_con_h)
|
|
args_contours_h = np.array(range(len(arg_text_con_h)))
|
|
|
|
order_by_con_head = np.zeros(len(arg_text_con_h))
|
|
#####
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij in range(len(boxes)):
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij]
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
|
|
for i in range(len(args_contours_box)):
|
|
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
for i in range(len(args_contours_box_h)):
|
|
|
|
con_inter_box_h.append(contours_only_text_parent_h[args_contours_box_h[i]])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = self.order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = self.order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box_h:
|
|
arg_order_v = indexes_sorted_head[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
# print(indexes_sorted,np.where(indexes_sorted==arg_order_v ),arg_order_v,tartib,'inshgalla')
|
|
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
for jji in range(len(id_of_texts)):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point = ref_point + len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
for tj1 in range(len(contours_only_text_parent_h)):
|
|
order_of_texts_tot.append(int(order_by_con_head[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
return order_text_new, id_of_texts_tot
|
|
|
|
else:
|
|
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = self.find_new_features_of_contoures(contours_only_text_parent)
|
|
|
|
try:
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
|
|
arg_text_con.append(jj)
|
|
break
|
|
arg_arg_text_con = np.argsort(arg_text_con)
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij in range(len(boxes)):
|
|
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
|
|
for i in range(len(args_contours_box)):
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = self.order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = self.order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
for jji in range(len(id_of_texts)):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point = ref_point + len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
|
|
except:
|
|
arg_text_con = []
|
|
for ii in range(len(cx_text_only)):
|
|
for jj in range(len(boxes)):
|
|
if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
|
|
arg_text_con.append(jj)
|
|
break
|
|
arg_arg_text_con = np.argsort(arg_text_con)
|
|
args_contours = np.array(range(len(arg_text_con)))
|
|
|
|
order_by_con_main = np.zeros(len(arg_text_con))
|
|
|
|
ref_point = 0
|
|
order_of_texts_tot = []
|
|
id_of_texts_tot = []
|
|
for iij in range(len(boxes)):
|
|
args_contours_box = args_contours[np.array(arg_text_con) == iij]
|
|
con_inter_box = []
|
|
con_inter_box_h = []
|
|
|
|
for i in range(len(args_contours_box)):
|
|
|
|
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
|
|
|
|
indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = self.order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
|
|
|
|
order_of_texts, id_of_texts = self.order_and_id_of_texts(con_inter_box, con_inter_box_h, matrix_of_orders, indexes_sorted, index_by_kind_sorted, kind_of_texts_sorted, ref_point)
|
|
|
|
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
|
|
indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
|
|
|
|
zahler = 0
|
|
for mtv in args_contours_box:
|
|
arg_order_v = indexes_sorted_main[zahler]
|
|
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
|
|
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
|
|
zahler = zahler + 1
|
|
|
|
for jji in range(len(id_of_texts)):
|
|
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
|
|
id_of_texts_tot.append(id_of_texts[jji])
|
|
ref_point = ref_point + len(id_of_texts)
|
|
|
|
order_of_texts_tot = []
|
|
for tj1 in range(len(contours_only_text_parent)):
|
|
order_of_texts_tot.append(int(order_by_con_main[tj1]))
|
|
|
|
order_text_new = []
|
|
for iii in range(len(order_of_texts_tot)):
|
|
tartib_new = np.where(np.array(order_of_texts_tot) == iii)[0][0]
|
|
order_text_new.append(tartib_new)
|
|
|
|
return order_text_new, id_of_texts_tot
|
|
|
|
def adhere_drop_capital_region_into_cprresponding_textline(self, text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h):
|
|
# print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
|
|
# print(all_found_texline_polygons[3])
|
|
cx_m, cy_m, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent)
|
|
cx_h, cy_h, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent_h)
|
|
cx_d, cy_d, _, _, y_min_d, y_max_d, _ = self.find_new_features_of_contoures(polygons_of_drop_capitals)
|
|
|
|
img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
|
for j_cont in range(len(contours_only_text_parent)):
|
|
img_con_all[all_box_coord[j_cont][0] : all_box_coord[j_cont][1], all_box_coord[j_cont][2] : all_box_coord[j_cont][3], 0] = (j_cont + 1) * 3
|
|
# img_con_all=cv2.fillPoly(img_con_all,pts=[contours_only_text_parent[j_cont]],color=((j_cont+1)*3,(j_cont+1)*3,(j_cont+1)*3))
|
|
|
|
# plt.imshow(img_con_all[:,:,0])
|
|
# plt.show()
|
|
# img_con_all=cv2.dilate(img_con_all, self.kernel, iterations=3)
|
|
|
|
# plt.imshow(img_con_all[:,:,0])
|
|
# plt.show()
|
|
# print(np.unique(img_con_all[:,:,0]))
|
|
for i_drop in range(len(polygons_of_drop_capitals)):
|
|
# print(i_drop,'i_drop')
|
|
img_con_all_copy = np.copy(img_con_all)
|
|
img_con = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
|
img_con = cv2.fillPoly(img_con, pts=[polygons_of_drop_capitals[i_drop]], color=(1, 1, 1))
|
|
|
|
# plt.imshow(img_con[:,:,0])
|
|
# plt.show()
|
|
##img_con=cv2.dilate(img_con, self.kernel, iterations=30)
|
|
|
|
# plt.imshow(img_con[:,:,0])
|
|
# plt.show()
|
|
|
|
# print(np.unique(img_con[:,:,0]))
|
|
|
|
img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
|
|
|
|
img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
|
|
|
|
kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
|
|
res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
|
|
region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
|
|
# region_with_intersected_drop=region_with_intersected_drop/3
|
|
region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
|
|
|
|
# print(len(region_with_intersected_drop),'region_with_intersected_drop1')
|
|
if len(region_with_intersected_drop) == 0:
|
|
img_con_all_copy = np.copy(img_con_all)
|
|
img_con = cv2.dilate(img_con, self.kernel, iterations=4)
|
|
|
|
img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
|
|
|
|
img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
|
|
|
|
kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
|
|
res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
|
|
region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
|
|
# region_with_intersected_drop=region_with_intersected_drop/3
|
|
region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
|
|
# print(np.unique(img_con_all_copy[:,:,0]))
|
|
if self.curved_line == "True" or self.curved_line == "true":
|
|
|
|
if len(region_with_intersected_drop) > 1:
|
|
sum_pixels_of_intersection = []
|
|
for i in range(len(region_with_intersected_drop)):
|
|
# print((region_with_intersected_drop[i]*3+1))
|
|
sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
|
|
# print(sum_pixels_of_intersection)
|
|
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
|
|
|
|
# print(region_final,'region_final')
|
|
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
try:
|
|
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
# print(all_box_coord[j_cont])
|
|
# print(cx_t)
|
|
# print(cy_t)
|
|
# print(cx_d[i_drop])
|
|
# print(cy_d[i_drop])
|
|
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
|
|
|
|
# print(y_lines)
|
|
|
|
y_lines[y_lines < y_min_d[i_drop]] = 0
|
|
# print(y_lines)
|
|
|
|
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
|
# print(arg_min)
|
|
|
|
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
|
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
|
|
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
|
|
|
|
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
|
|
|
img_textlines = img_textlines.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(len(contours_combined),'len textlines mixed')
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
|
|
|
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
|
|
|
# print(np.shape(contours_biggest))
|
|
# print(contours_biggest[:])
|
|
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
|
|
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
|
|
|
|
# contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
|
|
|
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
|
|
|
except:
|
|
# print('gordun1')
|
|
pass
|
|
elif len(region_with_intersected_drop) == 1:
|
|
region_final = region_with_intersected_drop[0] - 1
|
|
|
|
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
|
|
|
|
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
|
|
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
# print(all_box_coord[j_cont])
|
|
# print(cx_t)
|
|
# print(cy_t)
|
|
# print(cx_d[i_drop])
|
|
# print(cy_d[i_drop])
|
|
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
|
|
|
|
y_lines[y_lines < y_min_d[i_drop]] = 0
|
|
# print(y_lines)
|
|
|
|
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
|
# print(arg_min)
|
|
|
|
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
|
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
|
|
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
|
|
|
|
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
|
|
|
img_textlines = img_textlines.astype(np.uint8)
|
|
|
|
# plt.imshow(img_textlines)
|
|
# plt.show()
|
|
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(len(contours_combined),'len textlines mixed')
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
|
|
|
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
|
|
|
# print(np.shape(contours_biggest))
|
|
# print(contours_biggest[:])
|
|
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
|
|
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
|
|
# print(np.shape(contours_biggest),'contours_biggest')
|
|
# print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
|
|
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
|
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
|
|
|
# print(cx_t,'print')
|
|
try:
|
|
# print(all_found_texline_polygons[j_cont][0])
|
|
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
# print(all_box_coord[j_cont])
|
|
# print(cx_t)
|
|
# print(cy_t)
|
|
# print(cx_d[i_drop])
|
|
# print(cy_d[i_drop])
|
|
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
|
|
|
|
y_lines[y_lines < y_min_d[i_drop]] = 0
|
|
# print(y_lines)
|
|
|
|
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
|
# print(arg_min)
|
|
|
|
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
|
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
|
|
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
|
|
|
|
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
|
|
|
img_textlines = img_textlines.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(len(contours_combined),'len textlines mixed')
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
|
|
|
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
|
|
|
# print(np.shape(contours_biggest))
|
|
# print(contours_biggest[:])
|
|
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] # -all_box_coord[int(region_final)][2]
|
|
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] # -all_box_coord[int(region_final)][0]
|
|
|
|
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
|
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
|
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
|
|
|
|
except:
|
|
pass
|
|
else:
|
|
pass
|
|
|
|
##cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
###print(all_box_coord[j_cont])
|
|
###print(cx_t)
|
|
###print(cy_t)
|
|
###print(cx_d[i_drop])
|
|
###print(cy_d[i_drop])
|
|
##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
|
|
|
|
##y_lines[y_lines<y_min_d[i_drop]]=0
|
|
###print(y_lines)
|
|
|
|
##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
|
|
###print(arg_min)
|
|
|
|
##cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
|
##cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
|
|
##cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
|
|
|
|
##img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
|
|
##img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
|
|
##img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
|
|
|
|
##img_textlines=img_textlines.astype(np.uint8)
|
|
|
|
##plt.imshow(img_textlines)
|
|
##plt.show()
|
|
##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
|
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
##contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
##print(len(contours_combined),'len textlines mixed')
|
|
##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
|
|
|
##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
|
|
|
|
###print(np.shape(contours_biggest))
|
|
###print(contours_biggest[:])
|
|
##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
|
|
##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
|
|
|
|
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
|
##all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
|
|
|
|
else:
|
|
if len(region_with_intersected_drop) > 1:
|
|
sum_pixels_of_intersection = []
|
|
for i in range(len(region_with_intersected_drop)):
|
|
# print((region_with_intersected_drop[i]*3+1))
|
|
sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
|
|
# print(sum_pixels_of_intersection)
|
|
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
|
|
|
|
# print(region_final,'region_final')
|
|
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
try:
|
|
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
# print(all_box_coord[j_cont])
|
|
# print(cx_t)
|
|
# print(cy_t)
|
|
# print(cx_d[i_drop])
|
|
# print(cy_d[i_drop])
|
|
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
|
|
|
|
# print(y_lines)
|
|
|
|
y_lines[y_lines < y_min_d[i_drop]] = 0
|
|
# print(y_lines)
|
|
|
|
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
|
# print(arg_min)
|
|
|
|
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
|
cnt_nearest[:, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
|
|
cnt_nearest[:, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
|
|
|
|
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
|
|
|
img_textlines = img_textlines.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(len(contours_combined),'len textlines mixed')
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
|
|
|
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
|
|
|
# print(np.shape(contours_biggest))
|
|
# print(contours_biggest[:])
|
|
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] - all_box_coord[int(region_final)][2]
|
|
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] - all_box_coord[int(region_final)][0]
|
|
|
|
contours_biggest = contours_biggest.reshape(np.shape(contours_biggest)[0], np.shape(contours_biggest)[2])
|
|
|
|
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
|
|
|
except:
|
|
# print('gordun1')
|
|
pass
|
|
elif len(region_with_intersected_drop) == 1:
|
|
region_final = region_with_intersected_drop[0] - 1
|
|
|
|
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
|
|
|
|
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
|
|
# print(cx_t,'print')
|
|
try:
|
|
# print(all_found_texline_polygons[j_cont][0])
|
|
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
|
# print(all_box_coord[j_cont])
|
|
# print(cx_t)
|
|
# print(cy_t)
|
|
# print(cx_d[i_drop])
|
|
# print(cy_d[i_drop])
|
|
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
|
|
|
|
y_lines[y_lines < y_min_d[i_drop]] = 0
|
|
# print(y_lines)
|
|
|
|
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
|
# print(arg_min)
|
|
|
|
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
|
cnt_nearest[:, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
|
|
cnt_nearest[:, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
|
|
|
|
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
|
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
|
|
|
img_textlines = img_textlines.astype(np.uint8)
|
|
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(len(contours_combined),'len textlines mixed')
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
|
|
|
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
|
|
|
# print(np.shape(contours_biggest))
|
|
# print(contours_biggest[:])
|
|
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] - all_box_coord[int(region_final)][2]
|
|
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] - all_box_coord[int(region_final)][0]
|
|
|
|
contours_biggest = contours_biggest.reshape(np.shape(contours_biggest)[0], np.shape(contours_biggest)[2])
|
|
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
|
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
|
|
|
|
except:
|
|
pass
|
|
else:
|
|
pass
|
|
|
|
#####for i_drop in range(len(polygons_of_drop_capitals)):
|
|
#####for j_cont in range(len(contours_only_text_parent)):
|
|
#####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
|
|
#####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
|
|
#####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))
|
|
|
|
#####img_con=img_con.astype(np.uint8)
|
|
######imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
|
|
######ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
######contours_new,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
#####contours_new,hir_new=self.return_contours_of_image(img_con)
|
|
#####contours_new_parent=self.return_parent_contours( contours_new,hir_new)
|
|
######plt.imshow(img_con)
|
|
######plt.show()
|
|
#####try:
|
|
#####if len(contours_new_parent)==1:
|
|
######print(all_found_texline_polygons[j_cont][0])
|
|
#####cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[j_cont])
|
|
######print(all_box_coord[j_cont])
|
|
######print(cx_t)
|
|
######print(cy_t)
|
|
######print(cx_d[i_drop])
|
|
######print(cy_d[i_drop])
|
|
#####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)
|
|
|
|
######print(y_lines)
|
|
|
|
#####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
|
|
######print(arg_min)
|
|
|
|
#####cnt_nearest=np.copy(all_found_texline_polygons[j_cont][arg_min])
|
|
#####cnt_nearest[:,0]=all_found_texline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
|
|
#####cnt_nearest[:,1]=all_found_texline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]
|
|
|
|
#####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
|
|
#####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
|
|
#####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
|
|
|
|
#####img_textlines=img_textlines.astype(np.uint8)
|
|
#####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
|
#####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
#####contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
#####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
|
|
|
#####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
|
|
|
|
######print(np.shape(contours_biggest))
|
|
######print(contours_biggest[:])
|
|
#####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
|
|
#####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]
|
|
|
|
#####all_found_texline_polygons[j_cont][arg_min]=contours_biggest
|
|
######print(contours_biggest)
|
|
######plt.imshow(img_textlines[:,:,0])
|
|
######plt.show()
|
|
#####else:
|
|
#####pass
|
|
#####except:
|
|
#####pass
|
|
return all_found_texline_polygons
|
|
|
|
def save_plot_of_layout_main(self, text_regions_p, image_page):
|
|
values = np.unique(text_regions_p[:, :])
|
|
|
|
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
|
|
|
pixels = ["Background", "Main text", "Images", "Seperators", "Marginalia"]
|
|
values_indexes = [0, 1, 2, 3, 4]
|
|
plt.figure(figsize=(40, 40))
|
|
plt.rcParams["font.size"] = "40"
|
|
|
|
im = plt.imshow(text_regions_p[:, :])
|
|
colors = [im.cmap(im.norm(value)) for value in values]
|
|
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
|
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
|
|
plt.savefig(os.path.join(self.dir_of_layout, self.f_name + "_layout_main.png"))
|
|
|
|
def save_plot_of_layout_main_all(self, text_regions_p, image_page):
|
|
values = np.unique(text_regions_p[:, :])
|
|
|
|
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
|
|
|
pixels = ["Background", "Main text", "Images", "Seperators", "Marginalia"]
|
|
values_indexes = [0, 1, 2, 3, 4]
|
|
|
|
plt.figure(figsize=(70, 40))
|
|
plt.rcParams["font.size"] = "40"
|
|
plt.subplot(1, 2, 1)
|
|
plt.imshow(image_page)
|
|
plt.subplot(1, 2, 2)
|
|
im = plt.imshow(text_regions_p[:, :])
|
|
colors = [im.cmap(im.norm(value)) for value in values]
|
|
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
|
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
|
|
|
|
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_layout_main_and_page.png"))
|
|
|
|
def save_plot_of_layout(self, text_regions_p, image_page):
|
|
values = np.unique(text_regions_p[:, :])
|
|
|
|
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
|
|
|
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capitals", "Images", "Seperators"]
|
|
values_indexes = [0, 1, 2, 8, 4, 5, 6]
|
|
plt.figure(figsize=(40, 40))
|
|
plt.rcParams["font.size"] = "40"
|
|
im = plt.imshow(text_regions_p[:, :])
|
|
colors = [im.cmap(im.norm(value)) for value in values]
|
|
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
|
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
|
|
plt.savefig(os.path.join(self.dir_of_layout, self.f_name + "_layout.png"))
|
|
|
|
def save_plot_of_layout_all(self, text_regions_p, image_page):
|
|
values = np.unique(text_regions_p[:, :])
|
|
|
|
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
|
|
|
|
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capitals", "Images", "Seperators"]
|
|
values_indexes = [0, 1, 2, 8, 4, 5, 6]
|
|
|
|
plt.figure(figsize=(70, 40))
|
|
plt.rcParams["font.size"] = "40"
|
|
plt.subplot(1, 2, 1)
|
|
plt.imshow(image_page)
|
|
plt.subplot(1, 2, 2)
|
|
im = plt.imshow(text_regions_p[:, :])
|
|
colors = [im.cmap(im.norm(value)) for value in values]
|
|
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
|
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
|
|
|
|
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_layout_and_page.png"))
|
|
|
|
def save_deskewed_image(self, slope_deskew):
|
|
img_rotated = self.rotyate_image_different(self.image_org, slope_deskew)
|
|
|
|
if self.dir_of_all is not None:
|
|
cv2.imwrite(os.path.join(self.dir_of_all, self.f_name + "_org.png"), self.image_org)
|
|
|
|
cv2.imwrite(os.path.join(self.dir_of_deskewed, self.f_name + "_deskewed.png"), img_rotated)
|
|
del img_rotated
|
|
|
|
def run(self):
|
|
is_image_enhanced = False
|
|
# get image and sclaes, then extract the page of scanned image
|
|
t1 = time.time()
|
|
|
|
##########
|
|
|
|
###is_image_enhanced,img_org,img_res=self.resize_and_enhance_image(is_image_enhanced)
|
|
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified = self.resize_and_enhance_image_with_column_classifier(is_image_enhanced)
|
|
|
|
print(is_image_enhanced, "is_image_enhanced")
|
|
K.clear_session()
|
|
scale = 1
|
|
if (self.allow_enhancement == "true" or self.allow_enhancement == "True" or self.allow_enhancement == "TRUE") and is_image_enhanced:
|
|
cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif", img_res)
|
|
img_res = img_res.astype(np.uint8)
|
|
self.get_image_and_scales(img_org, img_res, scale)
|
|
|
|
if (self.allow_enhancement == "False" or self.allow_enhancement == "false" or self.allow_enhancement == "FALSE" or self.allow_enhancement == None) and is_image_enhanced:
|
|
self.get_image_and_scales_after_enhancing(img_org, img_res)
|
|
|
|
if (self.allow_enhancement == "true" or self.allow_enhancement == "True" or self.allow_enhancement == "TRUE") and not is_image_enhanced:
|
|
self.get_image_and_scales(img_org, img_res, scale)
|
|
|
|
if (self.allow_enhancement == "False" or self.allow_enhancement == "false" or self.allow_enhancement == "FALSE" or self.allow_enhancement == None) and not is_image_enhanced:
|
|
self.get_image_and_scales(img_org, img_res, scale)
|
|
|
|
if (self.allow_scaling == "true" or self.allow_scaling == "True" or self.allow_scaling == "TRUE") and not is_image_enhanced:
|
|
img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced)
|
|
self.get_image_and_scales_after_enhancing(img_org, img_res)
|
|
|
|
# print(self.scale_x)
|
|
|
|
print("enhancing: " + str(time.time() - t1))
|
|
text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced)
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
print("textregion: " + str(time.time() - t1))
|
|
|
|
img_g = cv2.imread(self.image_dir, 0)
|
|
img_g = img_g.astype(np.uint8)
|
|
|
|
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
|
|
|
|
img_g3 = img_g3.astype(np.uint8)
|
|
|
|
img_g3[:, :, 0] = img_g[:, :]
|
|
img_g3[:, :, 1] = img_g[:, :]
|
|
img_g3[:, :, 2] = img_g[:, :]
|
|
|
|
###self.produce_groundtruth_for_textline()
|
|
image_page, page_coord = self.extract_page()
|
|
|
|
# print(image_page.shape,'page')
|
|
|
|
if self.dir_of_all is not None:
|
|
cv2.imwrite(os.path.join(self.dir_of_all, self.f_name + "_page.png"), image_page)
|
|
##########
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
img_g3_page = img_g3[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :]
|
|
del img_g3
|
|
del img_g
|
|
|
|
text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
|
|
mask_images = (text_regions_p_1[:, :] == 2) * 1
|
|
mask_lines = (text_regions_p_1[:, :] == 3) * 1
|
|
|
|
mask_images = mask_images.astype(np.uint8)
|
|
mask_lines = mask_lines.astype(np.uint8)
|
|
|
|
mask_images = cv2.erode(mask_images[:, :], self.kernel, iterations=10)
|
|
|
|
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
|
|
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
|
|
img_only_regions = cv2.erode(img_only_regions_with_sep[:, :], self.kernel, iterations=6)
|
|
|
|
try:
|
|
num_col, peaks_neg_fin = self.find_num_col(img_only_regions, multiplier=6.0)
|
|
if not num_column_is_classified:
|
|
num_col_classifier = num_col + 1
|
|
except:
|
|
num_col = None
|
|
peaks_neg_fin = []
|
|
|
|
print(num_col, "num_colnum_col")
|
|
if num_col is None:
|
|
txt_con_org = []
|
|
order_text_new = []
|
|
id_of_texts_tot = []
|
|
all_found_texline_polygons = []
|
|
all_box_coord = []
|
|
polygons_of_images = []
|
|
polygons_of_marginals = []
|
|
all_found_texline_polygons_marginals = []
|
|
all_box_coord_marginals = []
|
|
slopes = []
|
|
slopes_marginals = []
|
|
self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, self.curved_line, slopes, slopes_marginals)
|
|
else:
|
|
# pass
|
|
try:
|
|
patches = True
|
|
scaler_h_textline = 1 # 1.2#1.2
|
|
scaler_w_textline = 1 # 0.9#1
|
|
textline_mask_tot_ea, textline_mask_tot_long_shot = self.textline_contours(image_page, patches, scaler_h_textline, scaler_w_textline)
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
print(np.unique(textline_mask_tot_ea[:, :]), "textline")
|
|
|
|
if self.dir_of_all is not None:
|
|
|
|
values = np.unique(textline_mask_tot_ea[:, :])
|
|
pixels = ["Background", "Textlines"]
|
|
values_indexes = [0, 1]
|
|
plt.figure(figsize=(70, 40))
|
|
plt.rcParams["font.size"] = "40"
|
|
plt.subplot(1, 2, 1)
|
|
plt.imshow(image_page)
|
|
plt.subplot(1, 2, 2)
|
|
im = plt.imshow(textline_mask_tot_ea[:, :])
|
|
colors = [im.cmap(im.norm(value)) for value in values]
|
|
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
|
|
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
|
|
|
|
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_textline_and_page.png"))
|
|
print("textline: " + str(time.time() - t1))
|
|
# plt.imshow(textline_mask_tot_ea)
|
|
# plt.show()
|
|
# sys.exit()
|
|
|
|
sigma = 2
|
|
main_page_deskew = True
|
|
slope_deskew = self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2), sigma, main_page_deskew)
|
|
slope_first = 0 # self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma)
|
|
|
|
if self.dir_of_deskewed is not None:
|
|
self.save_deskewed_image(slope_deskew)
|
|
# img_rotated=self.rotyate_image_different(self.image_org,slope_deskew)
|
|
print(slope_deskew, "slope_deskew")
|
|
|
|
##plt.imshow(img_rotated)
|
|
##plt.show()
|
|
##sys.exit()
|
|
print("deskewing: " + str(time.time() - t1))
|
|
|
|
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :] # self.rotation_not_90_func(image_page,textline_mask_tot_ea,slope_first)
|
|
textline_mask_tot[mask_images[:, :] == 1] = 0
|
|
|
|
pixel_img = 1
|
|
min_area = 0.00001
|
|
max_area = 0.0006
|
|
textline_mask_tot_small_size = self.return_contours_of_interested_region_by_size(textline_mask_tot, pixel_img, min_area, max_area)
|
|
|
|
# text_regions_p_1[(textline_mask_tot[:,:]==1) & (text_regions_p_1[:,:]==2)]=1
|
|
|
|
text_regions_p_1[mask_lines[:, :] == 1] = 3
|
|
|
|
##text_regions_p_1[textline_mask_tot_small_size[:,:]==1]=1
|
|
|
|
text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
|
|
|
|
text_regions_p = np.array(text_regions_p)
|
|
|
|
if num_col_classifier == 1 or num_col_classifier == 2:
|
|
|
|
try:
|
|
regions_without_seperators = (text_regions_p[:, :] == 1) * 1
|
|
regions_without_seperators = regions_without_seperators.astype(np.uint8)
|
|
|
|
text_regions_p = self.get_marginals(self.rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew)
|
|
|
|
except:
|
|
pass
|
|
else:
|
|
pass
|
|
|
|
# plt.imshow(text_regions_p)
|
|
# plt.show()
|
|
|
|
if self.dir_of_all is not None:
|
|
self.save_plot_of_layout_main_all(text_regions_p, image_page)
|
|
if self.dir_of_layout is not None:
|
|
self.save_plot_of_layout_main(text_regions_p, image_page)
|
|
|
|
print("marginals: " + str(time.time() - t1))
|
|
|
|
if self.full_layout is None or self.full_layout == "False" or self.full_layout == "false" or self.full_layout == "FALSE":
|
|
|
|
if np.abs(slope_deskew) >= 0.13:
|
|
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = self.rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
|
|
|
|
text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
textline_mask_tot_d = self.resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
|
|
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
|
|
|
|
regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
|
|
|
|
pixel_lines = 3
|
|
if np.abs(slope_deskew) < 0.13:
|
|
num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
|
|
|
|
if np.abs(slope_deskew) >= 0.13:
|
|
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
# print(peaks_neg_fin,num_col,'num_col2')
|
|
|
|
print(num_col_classifier, "num_col_classifier")
|
|
|
|
if num_col_classifier >= 3:
|
|
if np.abs(slope_deskew) < 0.13:
|
|
regions_without_seperators = regions_without_seperators.astype(np.uint8)
|
|
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6)
|
|
|
|
random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
|
|
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
|
|
random_pixels_for_image[random_pixels_for_image != 0] = 1
|
|
|
|
regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1
|
|
|
|
if np.abs(slope_deskew) >= 0.13:
|
|
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
|
|
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6)
|
|
|
|
random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
|
|
random_pixels_for_image[random_pixels_for_image < -0.5] = 0
|
|
random_pixels_for_image[random_pixels_for_image != 0] = 1
|
|
|
|
regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 2)] = 1
|
|
else:
|
|
pass
|
|
|
|
if np.abs(slope_deskew) < 0.13:
|
|
boxes = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch)
|
|
else:
|
|
boxes_d = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d)
|
|
|
|
# print(len(boxes),'boxes')
|
|
|
|
# sys.exit()
|
|
|
|
print("boxes in: " + str(time.time() - t1))
|
|
img_revised_tab = text_regions_p[:, :]
|
|
|
|
# plt.imshow(img_revised_tab)
|
|
# plt.show()
|
|
K.clear_session()
|
|
|
|
pixel_img = 4
|
|
min_area_mar = 0.00001
|
|
polygons_of_marginals = self.return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
|
|
|
if self.full_layout == "true" or self.full_layout == "True" or self.full_layout == "TRUE":
|
|
# set first model with second model
|
|
text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
|
|
text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
|
|
text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
|
|
|
|
K.clear_session()
|
|
# gc.collect()
|
|
|
|
patches = True
|
|
|
|
image_page = image_page.astype(np.uint8)
|
|
|
|
# print(type(image_page))
|
|
regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
|
|
|
|
regions_fully_only_drop = self.put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
|
|
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
|
|
regions_fully = self.putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
|
|
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
patches = False
|
|
regions_fully_np, _ = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
|
|
|
|
# plt.imshow(regions_fully_np[:,:,0])
|
|
# plt.show()
|
|
|
|
if num_col_classifier > 2:
|
|
regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0
|
|
else:
|
|
regions_fully_np = self.filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p)
|
|
|
|
# regions_fully_np=self.filter_small_drop_capitals_from_no_patch_layout(regions_fully_np,text_regions_p)
|
|
# plt.imshow(regions_fully_np[:,:,0])
|
|
# plt.show()
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
|
|
regions_fully = self.boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
|
|
|
|
# plt.imshow(regions_fully[:,:,0])
|
|
# plt.show()
|
|
|
|
text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4
|
|
##text_regions_p[:,:][(regions_fully[:,:,0]==7) & (text_regions_p[:,:]!=0)]=7
|
|
|
|
text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
|
|
|
|
# plt.imshow(text_regions_p)
|
|
# plt.show()
|
|
|
|
if np.abs(slope_deskew) >= 0.13:
|
|
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = self.rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
|
|
|
|
text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
textline_mask_tot_d = self.resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
regions_fully_n = self.resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
|
|
|
|
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
|
|
|
|
regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
img_revised_tab = np.copy(text_regions_p[:, :])
|
|
|
|
print("full layout in: " + str(time.time() - t1))
|
|
|
|
# sys.exit()
|
|
|
|
pixel_img = 5
|
|
polygons_of_images = self.return_contours_of_interested_region(img_revised_tab, pixel_img)
|
|
|
|
# plt.imshow(img_revised_tab)
|
|
# plt.show()
|
|
|
|
# print(img_revised_tab.shape,text_regions_p_1_n.shape)
|
|
|
|
# text_regions_p_1_n=self.resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
|
|
|
|
# print(np.unique(text_regions_p_1_n),'uni')
|
|
text_only = ((img_revised_tab[:, :] == 1)) * 1
|
|
if np.abs(slope_deskew) >= 0.13:
|
|
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
|
|
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1
|
|
|
|
# print(text_only.shape,text_only_d.shape)
|
|
# plt.imshow(text_only)
|
|
# plt.show()
|
|
|
|
# plt.imshow(text_only_d)
|
|
# plt.show()
|
|
|
|
min_con_area = 0.000005
|
|
|
|
if np.abs(slope_deskew) >= 0.13:
|
|
|
|
contours_only_text, hir_on_text = self.return_contours_of_image(text_only)
|
|
contours_only_text_parent = self.return_parent_contours(contours_only_text, hir_on_text)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
|
|
|
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
|
|
|
|
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
|
|
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
|
|
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
|
|
|
|
index_con_parents = np.argsort(areas_cnt_text_parent)
|
|
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
|
|
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
|
|
|
|
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = self.find_new_features_of_contoures([contours_biggest])
|
|
cx_bigest, cy_biggest, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent)
|
|
|
|
contours_only_text_d, hir_on_text_d = self.return_contours_of_image(text_only_d)
|
|
contours_only_text_parent_d = self.return_parent_contours(contours_only_text_d, hir_on_text_d)
|
|
|
|
areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
|
|
|
|
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
|
|
|
|
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
|
|
|
|
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = self.find_new_features_of_contoures([contours_biggest_d])
|
|
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent_d)
|
|
|
|
(h, w) = text_only.shape[:2]
|
|
center = (w // 2.0, h // 2.0)
|
|
M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
|
|
|
|
M_22 = np.array(M)[:2, :2]
|
|
|
|
p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big])
|
|
|
|
x_diff = p_big[0] - cx_bigest_d_big
|
|
y_diff = p_big[1] - cy_biggest_d_big
|
|
|
|
# print(p_big)
|
|
# print(cx_bigest_d_big,cy_biggest_d_big)
|
|
# print(x_diff,y_diff)
|
|
|
|
contours_only_text_parent_d_ordered = []
|
|
for i in range(len(contours_only_text_parent)):
|
|
# img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
|
|
# img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
|
|
|
|
# plt.imshow(img1[:,:,0])
|
|
# plt.show()
|
|
|
|
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
|
|
# print(p)
|
|
p[0] = p[0] - x_diff[0]
|
|
p[1] = p[1] - y_diff[0]
|
|
# print(p)
|
|
# print(cx_bigest_d)
|
|
# print(cy_biggest_d)
|
|
dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))]
|
|
# print(np.argmin(dists))
|
|
contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
|
|
|
|
# img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
|
|
# img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
|
|
|
|
# plt.imshow(img2[:,:,0])
|
|
# plt.show()
|
|
|
|
else:
|
|
contours_only_text, hir_on_text = self.return_contours_of_image(text_only)
|
|
contours_only_text_parent = self.return_parent_contours(contours_only_text, hir_on_text)
|
|
|
|
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
|
|
|
|
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
|
|
|
|
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
|
|
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
|
|
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
|
|
|
|
index_con_parents = np.argsort(areas_cnt_text_parent)
|
|
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
|
|
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
|
|
|
|
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = self.find_new_features_of_contoures([contours_biggest])
|
|
cx_bigest, cy_biggest, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent)
|
|
# print(areas_cnt_text_parent,'areas_cnt_text_parent')
|
|
|
|
###index_con_parents_d=np.argsort(areas_cnt_text_parent_d)
|
|
##contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d])
|
|
###areas_cnt_text_parent_d=list(np.array(areas_cnt_text_parent_d)[index_con_parents_d])
|
|
|
|
##print(areas_cnt_text_parent_d,'areas_cnt_text_parent_d')
|
|
|
|
# print(len(contours_only_text_parent),len(contours_only_text_parent_d),'vizzz')
|
|
|
|
txt_con_org = self.get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
|
|
|
|
###boxes_text,_=self.get_text_region_boxes_by_given_contours(contours_only_text_parent)
|
|
boxes_text, _ = self.get_text_region_boxes_by_given_contours(contours_only_text_parent)
|
|
boxes_marginals, _ = self.get_text_region_boxes_by_given_contours(polygons_of_marginals)
|
|
####boxes_text_h,_=self.get_text_region_boxes_by_given_contours(text_only_h,contours_only_text_parent_h,image_page)
|
|
|
|
if self.curved_line is None or self.curved_line == "false" or self.curved_line == "False" or self.curved_line == "FALSE":
|
|
slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
|
|
|
|
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
|
|
|
|
if self.curved_line == "true" or self.curved_line == "True" or self.curved_line == "TRUE":
|
|
scale_param = 1
|
|
all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_curved(txt_con_org, contours_only_text_parent, cv2.erode(textline_mask_tot_ea, kernel=self.kernel, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew)
|
|
|
|
# all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
|
|
all_found_texline_polygons = self.small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
|
|
|
|
# slopes=list(np.zeros(len(contours_only_text_parent)))
|
|
|
|
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=self.kernel, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
|
|
|
|
# all_found_texline_polygons,boxes_text,txt_con_org,contours_only_text_parent,all_box_coord=self.get_slopes_and_deskew_new_curved(txt_con_org,contours_only_text_parent,textline_mask_tot_ea,image_page_rotated,boxes_text,text_only,num_col,scale_param)
|
|
all_found_texline_polygons_marginals = self.small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
|
|
|
|
index_of_vertical_text_contours = np.array(range(len(slopes)))[(abs(np.array(slopes)) > 60)]
|
|
|
|
contours_text_vertical = [contours_only_text_parent[i] for i in index_of_vertical_text_contours]
|
|
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
# contours_only_text_parent_d_ordered=list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
|
|
###print(index_by_text_par_con,'index_by_text_par_con')
|
|
|
|
if self.full_layout == "true" or self.full_layout == "True" or self.full_layout == "TRUE":
|
|
##for iii in range(len(contours_only_text_parent)):
|
|
##img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
|
|
##img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[iii]] ,color=(1,1,1))
|
|
|
|
##plt.imshow(img1[:,:,0])
|
|
##plt.show()
|
|
|
|
##img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
|
|
##img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d_ordered[iii]] ,color=(1,1,1))
|
|
|
|
##plt.imshow(img2[:,:,0])
|
|
##plt.show()
|
|
|
|
if np.abs(slope_deskew) >= 0.13:
|
|
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
|
|
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|
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
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else:
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contours_only_text_parent_d_ordered = None
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text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
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###text_regions_p,contours_only_text_parent,contours_only_text_parent_h,all_box_coord,all_box_coord_h,all_found_texline_polygons,all_found_texline_polygons_h=self.check_any_text_region_in_model_one_is_main_or_header(text_regions_p,regions_fully,contours_only_text_parent,all_box_coord,all_found_texline_polygons)
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# text_regions_p=self.return_region_segmentation_after_implementing_not_head_maintext_parallel(text_regions_p,boxes)
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# if you want to save the layout result just uncommet following plot
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if self.dir_of_layout is not None:
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self.save_plot_of_layout(text_regions_p, image_page)
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if self.dir_of_all is not None:
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self.save_plot_of_layout_all(text_regions_p, image_page)
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K.clear_session()
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gc.collect()
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##print('Job done in: '+str(time.time()-t1))
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polygons_of_tabels = []
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pixel_img = 4
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polygons_of_drop_capitals = self.return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
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# polygons_of_drop_capitals=[]
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all_found_texline_polygons = self.adhere_drop_capital_region_into_cprresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h)
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# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
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pixel_lines = 6
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if self.headers_off is None or self.headers_off == "false" or self.headers_off == "False" or self.headers_off == "FALSE":
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if np.abs(slope_deskew) < 0.13:
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num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h)
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else:
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered)
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elif self.headers_off == "true" or self.headers_off == "True" or self.headers_off == "TRUE":
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if np.abs(slope_deskew) < 0.13:
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num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = self.find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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else:
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = self.find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
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# print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
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# print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
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if num_col_classifier >= 3:
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if np.abs(slope_deskew) < 0.13:
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regions_without_seperators = regions_without_seperators.astype(np.uint8)
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regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6)
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random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
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random_pixels_for_image[random_pixels_for_image < -0.5] = 0
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random_pixels_for_image[random_pixels_for_image != 0] = 1
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regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 5)] = 1
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else:
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regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
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regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.kernel, iterations=6)
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random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
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random_pixels_for_image[random_pixels_for_image < -0.5] = 0
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random_pixels_for_image[random_pixels_for_image != 0] = 1
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regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 5)] = 1
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else:
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pass
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if np.abs(slope_deskew) < 0.13:
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boxes = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch)
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else:
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boxes_d = self.return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d)
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# print(slopes)
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if self.dir_of_cropped_images is not None:
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self.write_images_into_directory(polygons_of_images, self.dir_of_cropped_images, image_page)
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if self.full_layout == "true" or self.full_layout == "True" or self.full_layout == "TRUE":
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if np.abs(slope_deskew) < 0.13:
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order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
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else:
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order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d)
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self.write_into_page_xml_full(contours_only_text_parent, contours_only_text_parent_h, page_coord, self.dir_out, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals)
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else:
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contours_only_text_parent_h = None
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# print('bura galmir?')
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if np.abs(slope_deskew) < 0.13:
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contours_only_text_parent = list(np.array(contours_only_text_parent)[index_by_text_par_con])
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order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
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else:
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contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
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order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)
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# order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
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self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, self.curved_line, slopes, slopes_marginals)
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except:
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txt_con_org = []
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order_text_new = []
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id_of_texts_tot = []
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all_found_texline_polygons = []
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all_box_coord = []
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polygons_of_images = []
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polygons_of_marginals = []
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all_found_texline_polygons_marginals = []
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all_box_coord_marginals = []
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slopes = []
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slopes_marginals = []
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self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, self.curved_line, slopes, slopes_marginals)
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print("Job done in: " + str(time.time() - t1))
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