factor more methods out of utils
Konstantin Baierer
4 years ago
parent
8c72f8bc03
commit
fb247dafc2
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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import numpy as np
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import cv2
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from .contour import (
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find_new_features_of_contoures,
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return_contours_of_image,
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return_parent_contours,
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)
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def adhere_drop_capital_region_into_cprresponding_textline(
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text_regions_p,
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polygons_of_drop_capitals,
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contours_only_text_parent,
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contours_only_text_parent_h,
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all_box_coord,
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all_box_coord_h,
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all_found_texline_polygons,
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all_found_texline_polygons_h,
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kernel=None,
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curved_line=False,
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):
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# print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
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# print(all_found_texline_polygons[3])
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cx_m, cy_m, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
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cx_h, cy_h, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_h)
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cx_d, cy_d, _, _, y_min_d, y_max_d, _ = find_new_features_of_contoures(polygons_of_drop_capitals)
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img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
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for j_cont in range(len(contours_only_text_parent)):
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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
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# 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))
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# plt.imshow(img_con_all[:,:,0])
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# plt.show()
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# img_con_all=cv2.dilate(img_con_all, kernel, iterations=3)
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# plt.imshow(img_con_all[:,:,0])
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# plt.show()
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# print(np.unique(img_con_all[:,:,0]))
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for i_drop in range(len(polygons_of_drop_capitals)):
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# print(i_drop,'i_drop')
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img_con_all_copy = np.copy(img_con_all)
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img_con = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
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img_con = cv2.fillPoly(img_con, pts=[polygons_of_drop_capitals[i_drop]], color=(1, 1, 1))
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# plt.imshow(img_con[:,:,0])
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# plt.show()
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##img_con=cv2.dilate(img_con, kernel, iterations=30)
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# plt.imshow(img_con[:,:,0])
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# plt.show()
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# print(np.unique(img_con[:,:,0]))
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img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
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img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
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kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
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res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
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region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
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# region_with_intersected_drop=region_with_intersected_drop/3
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region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
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# print(len(region_with_intersected_drop),'region_with_intersected_drop1')
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if len(region_with_intersected_drop) == 0:
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img_con_all_copy = np.copy(img_con_all)
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img_con = cv2.dilate(img_con, kernel, iterations=4)
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img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
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img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
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kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
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res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
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region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
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# region_with_intersected_drop=region_with_intersected_drop/3
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region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
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# print(np.unique(img_con_all_copy[:,:,0]))
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if curved_line:
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if len(region_with_intersected_drop) > 1:
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sum_pixels_of_intersection = []
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for i in range(len(region_with_intersected_drop)):
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# print((region_with_intersected_drop[i]*3+1))
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sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
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# print(sum_pixels_of_intersection)
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region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
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# print(region_final,'region_final')
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# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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try:
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cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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# print(all_box_coord[j_cont])
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# print(cx_t)
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# print(cy_t)
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# print(cx_d[i_drop])
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# print(cy_d[i_drop])
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y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
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# print(y_lines)
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y_lines[y_lines < y_min_d[i_drop]] = 0
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# print(y_lines)
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arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
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# print(arg_min)
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cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0, 0] = all_found_texline_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_texline_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]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# print(len(contours_combined),'len textlines mixed')
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areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
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contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
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# print(np.shape(contours_biggest))
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# print(contours_biggest[:])
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# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
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# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
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# contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
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all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
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except:
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# print('gordun1')
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pass
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elif len(region_with_intersected_drop) == 1:
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region_final = region_with_intersected_drop[0] - 1
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# 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)]))])
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# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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# print(all_box_coord[j_cont])
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# print(cx_t)
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# print(cy_t)
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# print(cx_d[i_drop])
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# print(cy_d[i_drop])
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y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
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y_lines[y_lines < y_min_d[i_drop]] = 0
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# print(y_lines)
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arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
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# print(arg_min)
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cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0, 0] = all_found_texline_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_texline_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]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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# plt.imshow(img_textlines)
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# plt.show()
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imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# print(len(contours_combined),'len textlines mixed')
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areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
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contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
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# print(np.shape(contours_biggest))
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# print(contours_biggest[:])
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# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
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# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
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# print(np.shape(contours_biggest),'contours_biggest')
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# print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
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##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
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all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
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# print(cx_t,'print')
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try:
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# print(all_found_texline_polygons[j_cont][0])
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cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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# print(all_box_coord[j_cont])
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# print(cx_t)
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# print(cy_t)
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# print(cx_d[i_drop])
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# print(cy_d[i_drop])
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y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
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y_lines[y_lines < y_min_d[i_drop]] = 0
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# print(y_lines)
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arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
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# print(arg_min)
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cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
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cnt_nearest[:, 0, 0] = all_found_texline_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_texline_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]], color=(255, 255, 255))
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img_textlines = img_textlines.astype(np.uint8)
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imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# print(len(contours_combined),'len textlines mixed')
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areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
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contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
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# print(np.shape(contours_biggest))
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# print(contours_biggest[:])
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contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] # -all_box_coord[int(region_final)][2]
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contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] # -all_box_coord[int(region_final)][0]
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##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
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all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
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# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
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except:
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pass
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else:
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pass
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##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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###print(all_box_coord[j_cont])
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###print(cx_t)
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###print(cy_t)
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###print(cx_d[i_drop])
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###print(cy_d[i_drop])
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##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
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##y_lines[y_lines<y_min_d[i_drop]]=0
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###print(y_lines)
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##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
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###print(arg_min)
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##cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
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##cnt_nearest[:,0,0]=all_found_texline_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_texline_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] ],color=(255,255,255))
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##img_textlines=img_textlines.astype(np.uint8)
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##plt.imshow(img_textlines)
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##plt.show()
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##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
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##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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##contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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##print(len(contours_combined),'len textlines mixed')
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##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
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##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
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###print(np.shape(contours_biggest))
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###print(contours_biggest[:])
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##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
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##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
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##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
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##all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
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else:
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if len(region_with_intersected_drop) > 1:
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sum_pixels_of_intersection = []
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for i in range(len(region_with_intersected_drop)):
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# print((region_with_intersected_drop[i]*3+1))
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sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
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# print(sum_pixels_of_intersection)
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region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
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# print(region_final,'region_final')
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# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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try:
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cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
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# print(all_box_coord[j_cont])
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# print(cx_t)
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# print(cy_t)
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# print(cx_d[i_drop])
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# print(cy_d[i_drop])
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y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
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# print(y_lines)
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y_lines[y_lines < y_min_d[i_drop]] = 0
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# print(y_lines)
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arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
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# print(arg_min)
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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 ,_, _, _ ,_,_= 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, _, _, _, _, _ = 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=return_contours_of_image(img_con)
|
||||
#####contours_new_parent=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 ,_, _, _ ,_,_= 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 filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1):
|
||||
|
||||
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
|
||||
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
||||
contours_drop_parent = 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
|
||||
|
||||
@ -0,0 +1,3 @@
|
||||
|
||||
def isNaN(num):
|
||||
return num != num
|
||||
@ -0,0 +1,7 @@
|
||||
def test_utils_import():
|
||||
import sbb_newspapers_org_image.utils
|
||||
import sbb_newspapers_org_image.utils.contour
|
||||
import sbb_newspapers_org_image.utils.drop_capitals
|
||||
import sbb_newspapers_org_image.utils.drop_capitals
|
||||
import sbb_newspapers_org_image.utils.is_nan
|
||||
import sbb_newspapers_org_image.utils.rotate
|
||||
Loading…
Reference in New Issue