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https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 12:19:54 +02:00
few minor fixes
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parent
b4d168cae3
commit
62e8d78e73
3 changed files with 19 additions and 41 deletions
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@ -1,27 +1,22 @@
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"""
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Tool to load model and binarize a given image.
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Load model and binarize a given image.
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"""
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import os
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import sys
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from glob import glob
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from os import environ, devnull
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from os.path import join
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from warnings import catch_warnings, simplefilter
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import os
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import numpy as np
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from PIL import Image
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import cv2
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environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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stderr = sys.stderr
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sys.stderr = open(devnull, 'w')
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from tensorflow.python.keras import backend as tensorflow_backend
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sys.stderr = stderr
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import logging
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@ -112,15 +112,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
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# print(arg_min)
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cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
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0] # +all_box_coord[int(region_final)][2]
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cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
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1] # +all_box_coord[int(region_final)][0]
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cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
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cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
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img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
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img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
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color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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@ -180,15 +177,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
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# print(arg_min)
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cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
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0] # +all_box_coord[int(region_final)][2]
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cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
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1] # +all_box_coord[int(region_final)][0]
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cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
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cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
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img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
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img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
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color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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@ -238,15 +232,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
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# print(arg_min)
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cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
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0] # +all_box_coord[int(region_final)][2]
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cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
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1] # +all_box_coord[int(region_final)][0]
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cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
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cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
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img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
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img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
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color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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contours_combined = return_contours_of_interested_region(img_textlines, 255, 0)
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@ -356,15 +347,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
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# print(arg_min)
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cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + \
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all_box_coord[int(region_final)][2]
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cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + \
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all_box_coord[int(region_final)][0]
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cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
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cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
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img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
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img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
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color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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contours_combined = return_contours_of_interested_region(img_textlines, 255, 0)
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@ -424,15 +412,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
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# print(arg_min)
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cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + \
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all_box_coord[int(region_final)][2]
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cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + \
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all_box_coord[int(region_final)][0]
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cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
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cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
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img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
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img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
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color=(255, 255, 255))
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img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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contours_combined = return_contours_of_interested_region(img_textlines, 255, 0)
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@ -126,8 +126,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
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polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
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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 = find_new_features_of_contours(
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polygons_of_marginals)
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(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) = (find_new_features_of_contours(polygons_of_marginals))
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text_regions[(text_regions[:, :] == 4)] = 1
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@ -155,8 +154,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
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polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
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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 = find_new_features_of_contours(
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polygons_of_marginals)
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(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) = (find_new_features_of_contours(polygons_of_marginals))
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text_regions[(text_regions[:, :] == 4)] = 1
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