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2177 lines
91 KiB
Python
2177 lines
91 KiB
Python
#! /usr/bin/env python3
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__version__ = '1.0'
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import sys
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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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from sys import getsizeof
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import random
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from tqdm import tqdm
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from keras.models import model_from_json
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from keras.models import load_model
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import math
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from shapely import geometry
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from sklearn.cluster import KMeans
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import gc
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import tensorflow as tf
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tf.get_logger().setLevel('ERROR')
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from keras import backend as K
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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import xml.etree.ElementTree as ET
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import warnings
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import click
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import time
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from multiprocessing import Process, Queue, cpu_count
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import datetime
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warnings.filterwarnings('ignore')
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##with warnings.catch_warnings():
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##warnings.simplefilter("ignore",category=RuntimeWarning)
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__doc__ = \
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"""
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tool to extract text lines from document images
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"""
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class textline_detector:
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def __init__(self, image_dir, dir_out, f_name, dir_models):
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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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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_page_dir = dir_models + '/model_page_mixed_best.h5'
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self.model_region_dir = dir_models + '/model_strukturerkennung.h5'
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self.model_textline_dir = dir_models + '/model_textline_new.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(
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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, hierarchy, 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(
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image.shape[:2]) and hierarchy[0][jv][3] == -1 : # and hierarchy[0][jv][3]==-1 :
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found_polygons_early.append(
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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, hierarchy, 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 \
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hierarchy[0][jv][3] != -1:
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# print(c[0][0][1])
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found_polygons_early.append(
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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 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 cleaning_probs(self, probs: np.ndarray, sigma: float) -> np.ndarray:
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# Smooth
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if sigma > 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.:
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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 get_image_and_scales(self):
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self.image = cv2.imread(self.image_dir)
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self.height_org = self.image.shape[0]
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self.width_org = self.image.shape[1]
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if self.image.shape[0] < 2500:
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self.img_hight_int = 2800
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self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
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else:
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self.img_hight_int = int(self.image.shape[0]*1.2)# 6500
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self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
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#self.img_hight_int = self.image.shape[0]
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#self.img_width_int = self.image.shape[1]
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self.scale_y = self.img_hight_int / float(self.image.shape[0])
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self.scale_x = self.img_width_int / float(self.image.shape[1])
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self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int)
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def start_new_session_and_model(self, model_dir):
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.InteractiveSession()
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model = load_model(model_dir, compile=False)
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return model, session
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def do_prediction(self,patches,img,model):
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img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
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img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
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n_classes = model.layers[len(model.layers) - 1].output_shape[3]
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if patches:
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margin = int(0.1 * 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.predict(
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img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i==0 and j==0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
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:] = seg_color
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elif i==nxf-1 and j==nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
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seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
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:] = seg_color
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elif i==0 and j==nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
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:] = seg_color
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elif i==nxf-1 and j==0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
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:] = seg_color
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elif i==0 and j!=0 and j!=nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
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:] = seg_color
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elif i==nxf-1 and j!=0 and j!=nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
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:] = seg_color
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elif i!=0 and i!=nxf-1 and j==0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
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:] = seg_color
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elif i!=0 and i!=nxf-1 and j==nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
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:] = seg_color
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else:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
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:] = seg_color
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prediction_true = prediction_true.astype(np.uint8)
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if not patches:
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img = img /float( 255.0)
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img = self.resize_image(img, img_height_model, img_width_model)
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label_p_pred = model.predict(
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img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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prediction_true = self.resize_image(seg_color, self.image.shape[0], self.image.shape[1])
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prediction_true = prediction_true.astype(np.uint8)
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return prediction_true
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def extract_page(self):
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patches=False
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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img = self.image#self.otsu_copy(self.image)
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#for ii in range(1):
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# img = cv2.GaussianBlur(img, (15, 15), 0)
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img_page_prediction=self.do_prediction(patches,img,model_page)
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imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(imgray, 0, 255, 0)
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thresh = cv2.dilate(thresh, self.kernel, iterations=6)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
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cnt = contours[np.argmax(cnt_size)]
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x, y, w, h = cv2.boundingRect(cnt)
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try:
|
|
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] ]] ) )
|
|
except:
|
|
box = [0, 0, self.image.shape[1]-1, self.image.shape[0]-1]
|
|
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 self.image
|
|
del contours
|
|
del thresh
|
|
del img
|
|
|
|
gc.collect()
|
|
return croped_page, page_coord
|
|
|
|
def extract_text_regions(self, img):
|
|
|
|
patches=True
|
|
model_region, session_region = self.start_new_session_and_model(self.model_region_dir)
|
|
img = self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
|
|
|
|
prediction_regions=self.do_prediction(patches,img,model_region)
|
|
|
|
|
|
session_region.close()
|
|
del model_region
|
|
del session_region
|
|
gc.collect()
|
|
return prediction_regions
|
|
|
|
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, hierarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
main_contours = self.filter_contours_area_of_image(thresh, contours, hierarchy, 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 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=True
|
|
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
|
|
#####img = self.otsu_copy(img)
|
|
img = img.astype(np.uint8)
|
|
|
|
prediction_textline=self.do_prediction(patches,img,model_textline)
|
|
|
|
session_textline.close()
|
|
|
|
del model_textline
|
|
del session_textline
|
|
gc.collect()
|
|
return prediction_textline[:,:,0]
|
|
|
|
def get_textlines_for_each_textregions(self, textline_mask_tot, boxes):
|
|
########textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1) ####should be changed
|
|
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(self, img_patch, contour_text_interest, 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. * 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./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_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]
|
|
|
|
|
|
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[:]
|
|
|
|
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.:
|
|
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.:
|
|
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
|
|
|
|
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(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
|
|
|
|
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. / 1.8 * dis_to_next)
|
|
elif jj == 1:
|
|
point_down = peaks[jj] + first_nonzero + int(1. / 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. / 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
|
|
|
|
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. / 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. / 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. / 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. / 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. / 1.9 * dis_to_next_up)
|
|
point_down = peaks[jj] + first_nonzero + int(1. / 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
|
|
|
|
|
|
|
|
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 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. * 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./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.:
|
|
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.:
|
|
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. / 1.8 * dis_to_next)
|
|
elif jj == 1:
|
|
point_down = peaks[jj] + first_nonzero + int(1. / 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. / 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. / 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. / 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. / 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. / 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. / 1.9 * dis_to_next_up)
|
|
point_down = peaks[jj] + first_nonzero + int(1. / 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 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 textline_contours_postprocessing(self, textline_mask, slope, contour_text_interest, box_ind):
|
|
|
|
|
|
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)##should be changed
|
|
|
|
|
|
try:
|
|
|
|
dst = self.rotate_image(textline_mask, slope)
|
|
dst = dst[:, :, 0]
|
|
dst[dst != 0] = 1
|
|
|
|
#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))
|
|
|
|
|
|
|
|
img_contour_rot = self.rotate_image(img_contour, slope)
|
|
|
|
img_contour_rot = img_contour_rot.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)
|
|
|
|
|
|
if abs(slope)>45:
|
|
_, contours_rotated_clean = self.seperate_lines_vertical(dst, contours_text_rot[ind_big_con], slope)
|
|
else:
|
|
_, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope)
|
|
|
|
|
|
except:
|
|
|
|
contours_rotated_clean = []
|
|
|
|
return contours_rotated_clean
|
|
|
|
|
|
def return_contours_of_image(self,image_box_tabels_1):
|
|
|
|
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,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 get_standard_deviation_of_summed_textline_patch_along_width(self,img_patch,sigma_,multiplier=3.8 ):
|
|
img_patch_sum_along_width=img_patch[:,:].sum(axis=1)
|
|
|
|
img_patch_sum_along_width_updown=img_patch_sum_along_width[len(img_patch_sum_along_width)::-1]
|
|
|
|
first_nonzero=(next((i for i, x in enumerate(img_patch_sum_along_width) if x), 0))
|
|
last_nonzero=(next((i for i, x in enumerate(img_patch_sum_along_width_updown) if x), 0))
|
|
|
|
last_nonzero=len(img_patch_sum_along_width)-last_nonzero
|
|
|
|
|
|
y=img_patch_sum_along_width#[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
|
|
|
|
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)]
|
|
|
|
return interest_neg_fin,np.std(z)
|
|
|
|
def return_deskew_slope(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.)
|
|
len_x_org_patch_half=int(img_patch_copy.shape[1]/2.)
|
|
len_y_org_patch_half=int(img_patch_copy.shape[0]/2.)
|
|
|
|
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,queue_of_all_params,boxes_per_process,textline_mask_tot,contours_per_process):
|
|
|
|
slopes_per_each_subprocess = []
|
|
bounding_box_of_textregion_per_each_subprocess=[]
|
|
textlines_rectangles_per_each_subprocess=[]
|
|
contours_textregion_per_each_subprocess=[]
|
|
|
|
for mv in range(len(boxes_per_process)):
|
|
|
|
contours_textregion_per_each_subprocess.append(contours_per_process[mv])
|
|
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:
|
|
sigma_des=2
|
|
slope_corresponding_textregion=self.return_deskew_slope(crop_img,sigma_des)
|
|
except:
|
|
slope_corresponding_textregion=999
|
|
#print(slope_corresponding_textregion,'slope_corresponding_textregion')
|
|
|
|
|
|
if np.abs(slope_corresponding_textregion)>120.5 and slope_corresponding_textregion!=999:
|
|
slope_corresponding_textregion=0
|
|
elif slope_corresponding_textregion==999:
|
|
slope_corresponding_textregion=0
|
|
slopes_per_each_subprocess.append(slope_corresponding_textregion)
|
|
|
|
bounding_rectangle_of_textlines = self.textline_contours_postprocessing(crop_img
|
|
, slope_corresponding_textregion,
|
|
contours_per_process[mv], boxes_per_process[mv])
|
|
|
|
textlines_rectangles_per_each_subprocess.append(bounding_rectangle_of_textlines)
|
|
bounding_box_of_textregion_per_each_subprocess.append(boxes_per_process[mv] )
|
|
|
|
|
|
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])
|
|
|
|
def get_slopes_and_deskew(self, contours,textline_mask_tot):
|
|
num_cores =cpu_count()
|
|
queue_of_all_params = 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=(queue_of_all_params, 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=[]
|
|
all_found_text_regions=[]
|
|
self.boxes=[]
|
|
|
|
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]
|
|
|
|
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])
|
|
all_found_text_regions.append(contours_for_subprocess[j])
|
|
|
|
for i in range(num_cores):
|
|
processes[i].join()
|
|
|
|
return all_found_text_regions
|
|
|
|
|
|
def order_of_regions(self, textline_mask,contours_main):
|
|
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
|
|
|
|
|
|
|
|
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(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(self,contours,page_coord,dir_of_image,order_of_texts , id_of_texts):
|
|
|
|
found_polygons_text_region=contours
|
|
|
|
|
|
# create the file structure
|
|
data = ET.Element('PcGts')
|
|
|
|
data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15")
|
|
data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
|
|
data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15")
|
|
|
|
|
|
|
|
metadata=ET.SubElement(data,'Metadata')
|
|
|
|
author=ET.SubElement(metadata, 'Creator')
|
|
author.text = 'SBB_QURATOR'
|
|
|
|
|
|
created=ET.SubElement(metadata, 'Created')
|
|
created.text = datetime.datetime.now().isoformat()
|
|
changetime=ET.SubElement(metadata, 'LastChange')
|
|
changetime.text = datetime.datetime.now().isoformat()
|
|
|
|
|
|
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, 'Border')
|
|
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=np.argsort(order_of_texts)
|
|
for vj in args_sort:
|
|
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_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','heading')
|
|
#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(self.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')
|
|
#points = ET.SubElement(coord, 'Points')
|
|
|
|
points_co=''
|
|
for l in range(len(self.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(self.all_found_texline_polygons[mm][j][l])==2:
|
|
points_co=points_co+str( int( (self.all_found_texline_polygons[mm][j][l][0] +page_coord[2]
|
|
+self.all_box_coord[mm][2])/self.scale_x) )
|
|
points_co=points_co+','
|
|
points_co=points_co+str( int( (self.all_found_texline_polygons[mm][j][l][1] +page_coord[0]
|
|
+self.all_box_coord[mm][0])/self.scale_y) )
|
|
else:
|
|
points_co=points_co+str( int( ( self.all_found_texline_polygons[mm][j][l][0][0] +page_coord[2]
|
|
+self.all_box_coord[mm][2])/self.scale_x ) )
|
|
points_co=points_co+','
|
|
points_co=points_co+str( int( ( self.all_found_texline_polygons[mm][j][l][0][1] +page_coord[0]
|
|
+self.all_box_coord[mm][0])/self.scale_y) )
|
|
|
|
if l<(len(self.all_found_texline_polygons[mm][j])-1):
|
|
points_co=points_co+' '
|
|
#print(points_co)
|
|
coord.set('points',points_co)
|
|
|
|
|
|
|
|
tree = ET.ElementTree(data)
|
|
tree.write(os.path.join(self.dir_out, self.f_name) + ".xml")
|
|
|
|
|
|
def run(self):
|
|
|
|
#get image and scales, then extract the page of scanned image
|
|
t1=time.time()
|
|
self.get_image_and_scales()
|
|
image_page,page_coord=self.extract_page()
|
|
|
|
|
|
##########
|
|
K.clear_session()
|
|
gc.collect()
|
|
t2=time.time()
|
|
|
|
try:
|
|
try:
|
|
# extract text regions and corresponding contours and surrounding box
|
|
text_regions=self.extract_text_regions(image_page)
|
|
|
|
text_regions = cv2.erode(text_regions, self.kernel, iterations=3)
|
|
text_regions = cv2.dilate(text_regions, self.kernel, iterations=4)
|
|
|
|
#plt.imshow(text_regions[:,:,0])
|
|
#plt.show()
|
|
|
|
contours=self.get_text_region_contours_and_boxes(text_regions)
|
|
|
|
|
|
|
|
##########
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
|
|
except:
|
|
text_regions=None
|
|
contours=[]
|
|
|
|
|
|
t3=time.time()
|
|
|
|
|
|
if len(contours)>0:
|
|
|
|
|
|
|
|
# extracting textlines using segmentation
|
|
textline_mask_tot=self.textline_contours(image_page)
|
|
##########
|
|
K.clear_session()
|
|
gc.collect()
|
|
|
|
t4=time.time()
|
|
|
|
|
|
# calculate the slope for deskewing for each box of text region.
|
|
contours=self.get_slopes_and_deskew(contours,textline_mask_tot)
|
|
|
|
gc.collect()
|
|
t5=time.time()
|
|
|
|
|
|
# get orders of each textregion. This method by now only works for one column documents.
|
|
indexes_sorted, matrix_of_orders=self.order_of_regions(textline_mask_tot,contours)
|
|
order_of_texts, id_of_texts=self.order_and_id_of_texts(contours ,matrix_of_orders ,indexes_sorted )
|
|
|
|
|
|
##########
|
|
gc.collect()
|
|
t6=time.time()
|
|
|
|
|
|
self.get_all_image_patches_coordination(image_page)
|
|
|
|
##########
|
|
##########
|
|
gc.collect()
|
|
|
|
t7=time.time()
|
|
|
|
else:
|
|
contours=[]
|
|
order_of_texts=None
|
|
id_of_texts=None
|
|
self.write_into_page_xml(contours,page_coord,self.dir_out , order_of_texts , id_of_texts)
|
|
|
|
# Destroy the current Keras session/graph to free memory
|
|
K.clear_session()
|
|
|
|
print( "time total = "+"{0:.2f}".format(time.time()-t1) )
|
|
print( "time needed for page extraction = "+"{0:.2f}".format(t2-t1) )
|
|
print( "time needed for text region extraction and get contours = "+"{0:.2f}".format(t3-t2) )
|
|
if len(contours)>0:
|
|
print( "time needed for textlines = "+"{0:.2f}".format(t4-t3) )
|
|
print( "time needed to get slopes of regions (deskewing) = "+"{0:.2f}".format(t5-t4) )
|
|
print( "time needed to get order of regions = "+"{0:.2f}".format(t6-t5) )
|
|
print( "time needed to implement deskewing = "+"{0:.2f}".format(t7-t6) )
|
|
except:
|
|
contours=[]
|
|
order_of_texts=None
|
|
id_of_texts=None
|
|
self.write_into_page_xml(contours,page_coord,self.dir_out , order_of_texts , id_of_texts)
|
|
print( "time total = "+"{0:.2f}".format(time.time()-t1) )
|
|
|
|
|
|
|
|
|
|
@click.command()
|
|
@click.option('--image', '-i', help='image filename',
|
|
type=click.Path(exists=True, dir_okay=False), required=True)
|
|
@click.option('--out', '-o', help='directory to write output xml data',
|
|
type=click.Path(exists=True, file_okay=False), required=True)
|
|
@click.option('--model', '-m', help='directory of models',
|
|
type=click.Path(exists=True, file_okay=False), required=True)
|
|
def main(image, out, model):
|
|
x = textline_detector(image, out, None, model)
|
|
x.run()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|