#! /usr/bin/env python3 """ tool to extract table form data from alto xml data """ import gc import math import os import random import sys import time import warnings from multiprocessing import Process, Queue, cpu_count from sys import getsizeof import cv2 import numpy as np import matplotlib.pyplot as plt import seaborn as sns os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" stderr = sys.stderr sys.stderr = open(os.devnull, "w") from keras import backend as K from keras.models import load_model sys.stderr = stderr import tensorflow as tf tf.get_logger().setLevel("ERROR") warnings.filterwarnings("ignore") from scipy.signal import find_peaks from scipy.ndimage import gaussian_filter1d from shapely import geometry from lxml import etree as ET import click from matplotlib import pyplot, transforms import matplotlib.patches as mpatches import imutils class eynollah: def __init__(self, image_dir, f_name, dir_out, dir_models, dir_of_cropped_images=None, dir_of_layout=None, dir_of_deskewed=None, dir_of_all=None, allow_enhancement="false", curved_line="false", full_layout="false", allow_scaling="false", headers_off="false"): self.image_dir = image_dir # XXX This does not seem to be a directory as the name suggests, but a file self.dir_out = dir_out self.f_name = f_name self.dir_of_cropped_images = dir_of_cropped_images self.allow_enhancement = allow_enhancement self.curved_line = curved_line self.full_layout = full_layout self.allow_scaling = allow_scaling self.dir_of_layout = dir_of_layout self.headers_off = headers_off self.dir_of_deskewed = dir_of_deskewed self.dir_of_all = dir_of_all if self.f_name is None: try: self.f_name = image_dir.split("/")[len(image_dir.split("/")) - 1] self.f_name = self.f_name.split(".")[0] except: self.f_name = self.f_name.split(".")[0] self.dir_models = dir_models self.kernel = np.ones((5, 5), np.uint8) self.model_dir_of_enhancemnet = dir_models + "/model_enhancement.h5" self.model_dir_of_col_classifier = dir_models + "/model_scale_classifier.h5" 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' # 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' self.model_region_dir_p2 = dir_models + "/model_main_home_corona3_rot.h5" self.model_region_dir_fully_np = dir_models + "/model_no_patches_class0_30eopch.h5" self.model_region_dir_fully = dir_models + "/model_3up_new_good_no_augmentation.h5" # "model_3col_p_soft_10_less_aug_binarization_only.h5" self.model_page_dir = dir_models + "/model_page_mixed_best.h5" 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' ###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' 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' def filter_contours_area_of_image_tables(self, image, contours, hirarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) # area = cv2.contourArea(c) area = polygon.area ##print(np.prod(thresh.shape[:2])) # Check that polygon has area greater than minimal area # print(hirarchy[0][jv][3],hirarchy ) if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hirarchy[0][jv][3]==-1 : # print(c[0][0][1]) found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32)) jv += 1 return found_polygons_early def find_polygons_size_filter(self, contours, median_area, scaler_up=1.2, scaler_down=0.8): found_polygons_early = list() for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area # Check that polygon has area greater than minimal area if area >= median_area * scaler_down and area <= median_area * scaler_up: found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint)) return found_polygons_early def filter_contours_area_of_image(self, image, contours, hirarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area 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 : found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint)) jv += 1 return found_polygons_early def filter_contours_area_of_image_interiors(self, image, contours, hirarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1: # print(c[0][0][1]) found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint)) jv += 1 return found_polygons_early def resize_image(self, img_in, input_height, input_width): return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) def resize_ann(self, seg_in, input_height, input_width): return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) def rotatedRectWithMaxArea(self, w, h, angle): if w <= 0 or h <= 0: return 0, 0 width_is_longer = w >= h side_long, side_short = (w, h) if width_is_longer else (h, w) # since the solutions for angle, -angle and 180-angle are all the same, # if suffices to look at the first quadrant and the absolute values of sin,cos: sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle)) if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10: # half constrained case: two crop corners touch the longer side, # the other two corners are on the mid-line parallel to the longer line x = 0.5 * side_short wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a) else: # fully constrained case: crop touches all 4 sides cos_2a = cos_a * cos_a - sin_a * sin_a wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a return wr, hr def get_one_hot(self, seg, input_height, input_width, n_classes): seg = seg[:, :, 0] seg_f = np.zeros((input_height, input_width, n_classes)) for j in range(n_classes): seg_f[:, :, j] = (seg == j).astype(int) return seg_f def color_images(self, seg, n_classes): ann_u = range(n_classes) if len(np.shape(seg)) == 3: seg = seg[:, :, 0] seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(np.uint8) colors = sns.color_palette("hls", n_classes) for c in ann_u: c = int(c) segl = seg == c seg_img[:, :, 0] = segl * c seg_img[:, :, 1] = segl * c seg_img[:, :, 2] = segl * c return seg_img def color_images_diva(self, seg, n_classes): ann_u = range(n_classes) if len(np.shape(seg)) == 3: seg = seg[:, :, 0] seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float) # colors=sns.color_palette("hls", n_classes) colors = [[1, 0, 0], [8, 0, 0], [2, 0, 0], [4, 0, 0]] for c in ann_u: c = int(c) segl = seg == c seg_img[:, :, 0][seg == c] = colors[c][0] # segl*(colors[c][0]) seg_img[:, :, 1][seg == c] = colors[c][1] # seg_img[:,:,1]=segl*(colors[c][1]) seg_img[:, :, 2][seg == c] = colors[c][2] # seg_img[:,:,2]=segl*(colors[c][2]) return seg_img def rotate_image(self, img_patch, slope): (h, w) = img_patch.shape[:2] center = (w // 2, h // 2) M = cv2.getRotationMatrix2D(center, slope, 1.0) return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE) def rotyate_image_different(self, img, slope): # img = cv2.imread('images/input.jpg') num_rows, num_cols = img.shape[:2] rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1) img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows)) return img_rotation def cleaning_probs(self, probs: np.ndarray, sigma: float) -> np.ndarray: # Smooth if sigma > 0.0: return cv2.GaussianBlur(probs, (int(3 * sigma) * 2 + 1, int(3 * sigma) * 2 + 1), sigma) elif sigma == 0.0: return cv2.fastNlMeansDenoising((probs * 255).astype(np.uint8), h=20) / 255 else: # Negative sigma, do not do anything return probs def crop_image_inside_box(self, box, img_org_copy): image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]] return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]] def otsu_copy(self, img): img_r = np.zeros(img.shape) img1 = img[:, :, 0] img2 = img[:, :, 1] img3 = img[:, :, 2] # print(img.min()) # print(img[:,:,0].min()) # blur = cv2.GaussianBlur(img,(5,5)) # ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) img_r[:, :, 0] = threshold1 img_r[:, :, 1] = threshold1 img_r[:, :, 2] = threshold1 return img_r def otsu_copy_binary(self, img): img_r = np.zeros((img.shape[0], img.shape[1], 3)) img1 = img[:, :, 0] retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) img_r[:, :, 0] = threshold1 img_r[:, :, 1] = threshold1 img_r[:, :, 2] = threshold1 img_r = img_r / float(np.max(img_r)) * 255 return img_r def predict_enhancement(self, img): model_enhancement, session_enhancemnet = self.start_new_session_and_model(self.model_dir_of_enhancemnet) img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1] img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2] # n_classes = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[3] if img.shape[0] < img_height_model: img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST) if img.shape[1] < img_width_model: img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST) margin = True if margin: kernel = np.ones((5, 5), np.uint8) margin = int(0 * img_width_model) width_mid = img_width_model - 2 * margin height_mid = img_height_model - 2 * margin img = img / float(255.0) 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_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) seg = label_p_pred[0, :, :, :] seg = seg * 255 if i == 0 and j == 0: seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin] prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg elif i == nxf - 1 and j == nyf - 1: seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0] prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg elif i == 0 and j == nyf - 1: seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg elif i == nxf - 1 and j == 0: seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0] prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg elif i == 0 and j != 0 and j != nyf - 1: seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg elif i == nxf - 1 and j != 0 and j != nyf - 1: seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0] prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg elif i != 0 and i != nxf - 1 and j == 0: seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin] prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg elif i != 0 and i != nxf - 1 and j == nyf - 1: seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg else: seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin] prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg prediction_true = prediction_true.astype(int) del model_enhancement del session_enhancemnet return prediction_true def check_dpi(self): self.dpi = os.popen('identify -format "%x " ' + self.image_dir).read() self.dpi = int(float(self.dpi)) 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 = self.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 = self.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 = self.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 = self.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 = self.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 = self.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_negfirst_nonzero) & (peaks_neg.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 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 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 = self.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 = self.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 = self.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 = self.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 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]) 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) else: contours_only_text_parent_d_ordered = None 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) ###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) # text_regions_p=self.return_region_segmentation_after_implementing_not_head_maintext_parallel(text_regions_p,boxes) # if you want to save the layout result just uncommet following plot if self.dir_of_layout is not None: self.save_plot_of_layout(text_regions_p, image_page) if self.dir_of_all is not None: self.save_plot_of_layout_all(text_regions_p, image_page) K.clear_session() gc.collect() ##print('Job done in: '+str(time.time()-t1)) polygons_of_tabels = [] pixel_img = 4 polygons_of_drop_capitals = self.return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img) # polygons_of_drop_capitals=[] 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) # print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h') pixel_lines = 6 if self.headers_off is None or self.headers_off == "false" or self.headers_off == "False" or self.headers_off == "FALSE": 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, contours_only_text_parent_h) else: 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) elif self.headers_off == "true" or self.headers_off == "True" or self.headers_off == "TRUE": 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) else: 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) # print(peaks_neg_fin,peaks_neg_fin_d,'num_col2') # print(spliter_y_new,spliter_y_new_d,'num_col_classifier') # print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch') 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[:, :] == 5)] = 1 else: 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[:, :] == 5)] = 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(slopes) if self.dir_of_cropped_images is not None: self.write_images_into_directory(polygons_of_images, self.dir_of_cropped_images, image_page) if self.full_layout == "true" or self.full_layout == "True" or self.full_layout == "TRUE": if np.abs(slope_deskew) < 0.13: 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) else: 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) 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) else: contours_only_text_parent_h = None # print('bura galmir?') if np.abs(slope_deskew) < 0.13: contours_only_text_parent = list(np.array(contours_only_text_parent)[index_by_text_par_con]) 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) else: contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con]) 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) # 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) 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) except: 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) print("Job done in: " + str(time.time() - t1)) @click.command() @click.option("--image", "-i", help="image filename", type=click.Path(exists=True, dir_okay=False)) @click.option("--out", "-o", help="directory to write output xml data", type=click.Path(exists=True, file_okay=False)) @click.option("--model", "-m", help="directory of models", type=click.Path(exists=True, file_okay=False)) @click.option("--save_images", "-si", help="if a directory is given, images in documents will be cropped and saved there", type=click.Path(exists=True, file_okay=False)) @click.option("--save_layout", "-sl", help="if a directory is given, plot of layout will be saved there", type=click.Path(exists=True, file_okay=False)) @click.option("--save_deskewed", "-sd", help="if a directory is given, deskewed image will be saved there", type=click.Path(exists=True, file_okay=False)) @click.option("--save_all", "-sa", help="if a directory is given, all plots needed for documentation will be saved there", type=click.Path(exists=True, file_okay=False)) @click.option("--allow_enhancement", "-ae", help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory") @click.option("--curved_line", "-cl", help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectabgle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.") @click.option("--full_layout", "-fl", help="if this parameter set to true, this tool will try to return all elements of layout.") @click.option("--allow_scaling", "-as", help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection") @click.option("--headers_off", "-ho", help="if this parameter set to true, this tool would ignore headers role in reading order") def main(image, out, model, save_images, save_layout, save_deskewed, save_all, allow_enhancement, curved_line, full_layout, allow_scaling, headers_off): x = eynollah(image, None, out, model, save_images, save_layout, save_deskewed, save_all, allow_enhancement, curved_line, full_layout, allow_scaling, headers_off) x.run() if __name__ == "__main__": main()