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eynollah
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updating 1&2 columns images + full layout

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pull/138/head^2
vahidrezanezhad 5 months ago
parent a62ae370c3
commit be144db9f8
2 changed files with 115 additions and 42 deletions
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141
qurator/eynollah/eynollah.py
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@ -1083,43 +1083,64 @@ class Eynollah:
model_region = self.model_region_fl_new if patches else self.model_region_fl_np model_region = self.model_region_fl_new if patches else self.model_region_fl_np
if not patches: if not patches:
img = otsu_copy_binary(img) if self.light_version:
pass
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
prediction_regions2 = None prediction_regions2 = None
else: else:
if cols == 1: if cols == 1:
img = otsu_copy_binary(img) if self.light_version:
pass
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 1000 / float(img_width_h)), 1000) img = resize_image(img, int(img_height_h * 1000 / float(img_width_h)), 1000)
img = img.astype(np.uint8) img = img.astype(np.uint8)
if cols == 2: if cols == 2:
img = otsu_copy_binary(img) if self.light_version:
pass
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 1300 / float(img_width_h)), 1300) img = resize_image(img, int(img_height_h * 1300 / float(img_width_h)), 1300)
img = img.astype(np.uint8) img = img.astype(np.uint8)
if cols == 3: if cols == 3:
img = otsu_copy_binary(img) if self.light_version:
pass
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 1600 / float(img_width_h)), 1600) img = resize_image(img, int(img_height_h * 1600 / float(img_width_h)), 1600)
img = img.astype(np.uint8) img = img.astype(np.uint8)
if cols == 4: if cols == 4:
img = otsu_copy_binary(img) if self.light_version:
pass
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 1900 / float(img_width_h)), 1900) img = resize_image(img, int(img_height_h * 1900 / float(img_width_h)), 1900)
img = img.astype(np.uint8) img = img.astype(np.uint8)
if cols == 5: if cols == 5:
img = otsu_copy_binary(img) if self.light_version:
pass
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 2200 / float(img_width_h)), 2200) img = resize_image(img, int(img_height_h * 2200 / float(img_width_h)), 2200)
img = img.astype(np.uint8) img = img.astype(np.uint8)
if cols >= 6: if cols >= 6:
img = otsu_copy_binary(img) if self.light_version:
pass
else:
img = otsu_copy_binary(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = resize_image(img, int(img_height_h * 2500 / float(img_width_h)), 2500) img = resize_image(img, int(img_height_h * 2500 / float(img_width_h)), 2500)
img = img.astype(np.uint8) img = img.astype(np.uint8)
@ -1611,6 +1632,7 @@ class Eynollah:
img_h = img_org.shape[0] img_h = img_org.shape[0]
img_w = img_org.shape[1] img_w = img_org.shape[1]
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w)) img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
#print(img.shape,'bin shape')
if not self.dir_in: if not self.dir_in:
prediction_textline = self.do_prediction(patches, img, model_textline) prediction_textline = self.do_prediction(patches, img, model_textline)
else: else:
@ -1664,6 +1686,7 @@ class Eynollah:
box_sub.put(boxes_sub_new) box_sub.put(boxes_sub_new)
def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier): def get_regions_light_v(self,img,is_image_enhanced, num_col_classifier):
self.logger.debug("enter get_regions_light_v") self.logger.debug("enter get_regions_light_v")
t_in = time.time()
erosion_hurts = False erosion_hurts = False
img_org = np.copy(img) img_org = np.copy(img)
img_height_h = img_org.shape[0] img_height_h = img_org.shape[0]
@ -1671,7 +1694,7 @@ class Eynollah:
#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens) #model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
#print(num_col_classifier,'num_col_classifier')
if num_col_classifier == 1: if num_col_classifier == 1:
img_w_new = 1000 img_w_new = 1000
@ -1711,10 +1734,13 @@ class Eynollah:
#img= np.copy(prediction_bin) #img= np.copy(prediction_bin)
img_bin = np.copy(prediction_bin) img_bin = np.copy(prediction_bin)
#print("inside 1 ", time.time()-t_in)
textline_mask_tot_ea = self.run_textline(img_bin) textline_mask_tot_ea = self.run_textline(img_bin)
#print("inside 2 ", time.time()-t_in)
if not self.dir_in: if not self.dir_in:
if num_col_classifier == 1 or num_col_classifier == 2: if num_col_classifier == 1 or num_col_classifier == 2:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
@ -1728,11 +1754,13 @@ class Eynollah:
else: else:
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region) prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
#print("inside 3 ", time.time()-t_in)
#plt.imshow(prediction_regions_org[:,:,0]) #plt.imshow(prediction_regions_org[:,:,0])
#plt.show() #plt.show()
prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h ) prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h ) textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
img_bin = resize_image(img_bin,img_height_h, img_width_h )
prediction_regions_org=prediction_regions_org[:,:,0] prediction_regions_org=prediction_regions_org[:,:,0]
@ -1787,8 +1815,8 @@ class Eynollah:
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 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)) text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
#print("inside 4 ", time.time()-t_in)
return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin
def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier): def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier):
self.logger.debug("enter get_regions_from_xy_2models") self.logger.debug("enter get_regions_from_xy_2models")
@ -2553,7 +2581,11 @@ class Eynollah:
prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20) prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20) prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
return prediction_table_erode.astype(np.int16) return prediction_table_erode.astype(np.int16)
def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts): def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts, img_bin_light):
#print(text_regions_p_1.shape, 'text_regions_p_1 shape run graphics')
#print(erosion_hurts, 'erosion_hurts')
t_in_gr = time.time()
img_g = self.imread(grayscale=True, uint8=True) img_g = self.imread(grayscale=True, uint8=True)
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3)) img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
@ -2563,7 +2595,7 @@ class Eynollah:
img_g3[:, :, 2] = img_g[:, :] img_g3[:, :, 2] = img_g[:, :]
image_page, page_coord, cont_page = self.extract_page() image_page, page_coord, cont_page = self.extract_page()
#print("inside graphics 1 ", time.time() - t_in_gr)
if self.tables: if self.tables:
table_prediction = self.get_tables_from_model(image_page, num_col_classifier) table_prediction = self.get_tables_from_model(image_page, num_col_classifier)
else: else:
@ -2574,6 +2606,9 @@ class Eynollah:
text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
textline_mask_tot_ea = textline_mask_tot_ea[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] textline_mask_tot_ea = textline_mask_tot_ea[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
img_bin_light = img_bin_light[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
mask_images = (text_regions_p_1[:, :] == 2) * 1 mask_images = (text_regions_p_1[:, :] == 2) * 1
mask_images = mask_images.astype(np.uint8) mask_images = mask_images.astype(np.uint8)
mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10) mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
@ -2582,7 +2617,7 @@ class Eynollah:
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1 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_with_sep = img_only_regions_with_sep.astype(np.uint8)
#print("inside graphics 2 ", time.time() - t_in_gr)
if erosion_hurts: if erosion_hurts:
img_only_regions = np.copy(img_only_regions_with_sep[:,:]) img_only_regions = np.copy(img_only_regions_with_sep[:,:])
else: else:
@ -2600,8 +2635,10 @@ class Eynollah:
except Exception as why: except Exception as why:
self.logger.error(why) self.logger.error(why)
num_col = None num_col = None
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea #print("inside graphics 3 ", time.time() - t_in_gr)
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light
def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts): def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts):
t_in_gr = time.time()
img_g = self.imread(grayscale=True, uint8=True) img_g = self.imread(grayscale=True, uint8=True)
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3)) img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
@ -2629,13 +2666,11 @@ class Eynollah:
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1 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_with_sep = img_only_regions_with_sep.astype(np.uint8)
if erosion_hurts: if erosion_hurts:
img_only_regions = np.copy(img_only_regions_with_sep[:,:]) img_only_regions = np.copy(img_only_regions_with_sep[:,:])
else: else:
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6) img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6)
try: try:
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0) num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
num_col = num_col + 1 num_col = num_col + 1
@ -2682,6 +2717,7 @@ class Eynollah:
return textline_mask_tot_ea return textline_mask_tot_ea
def run_deskew(self, textline_mask_tot_ea): def run_deskew(self, textline_mask_tot_ea):
#print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew')
sigma = 2 sigma = 2
main_page_deskew = True main_page_deskew = True
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter) slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
@ -2805,7 +2841,7 @@ class Eynollah:
self.logger.debug('exit run_boxes_no_full_layout') self.logger.debug('exit run_boxes_no_full_layout')
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables
def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts): def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, img_bin_light):
self.logger.debug('enter run_boxes_full_layout') self.logger.debug('enter run_boxes_full_layout')
if self.tables: if self.tables:
@ -2900,20 +2936,23 @@ class Eynollah:
image_page = image_page.astype(np.uint8) image_page = image_page.astype(np.uint8)
regions_fully, regions_fully_only_drop = self.extract_text_regions_new(image_page, True, cols=num_col_classifier) if self.light_version:
regions_fully, regions_fully_only_drop = self.extract_text_regions_new(img_bin_light, True, cols=num_col_classifier)
else:
regions_fully, regions_fully_only_drop = self.extract_text_regions_new(image_page, True, cols=num_col_classifier)
# 6 is the separators lable in old full layout model # 6 is the separators lable in old full layout model
# 4 is the drop capital class in old full layout model # 4 is the drop capital class in old full layout model
# in the new full layout drop capital is 3 and separators are 5 # in the new full layout drop capital is 3 and separators are 5
text_regions_p[:,:][regions_fully[:,:,0]==5]=6 text_regions_p[:,:][regions_fully[:,:,0]==5]=6
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 3] = 4 ###regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 3] = 4
#text_regions_p[:,:][regions_fully[:,:,0]==6]=6 #text_regions_p[:,:][regions_fully[:,:,0]==6]=6
#regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p) ##regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
#regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4 ##regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
drop_capital_label_in_full_layout_model = 3
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully) regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully, drop_capital_label_in_full_layout_model)
##regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier) ##regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
##if num_col_classifier > 2: ##if num_col_classifier > 2:
##regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0 ##regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0
@ -2923,7 +2962,7 @@ class Eynollah:
###regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions) ###regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
# plt.imshow(regions_fully[:,:,0]) # plt.imshow(regions_fully[:,:,0])
# plt.show() # plt.show()
text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4 text_regions_p[:, :][regions_fully[:, :, 0] == drop_capital_label_in_full_layout_model] = 4
####text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4 ####text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
#plt.imshow(text_regions_p) #plt.imshow(text_regions_p)
#plt.show() #plt.show()
@ -3463,22 +3502,41 @@ class Eynollah:
self.ls_imgs = [1] self.ls_imgs = [1]
for img_name in self.ls_imgs: for img_name in self.ls_imgs:
print(img_name)
t0 = time.time() t0 = time.time()
if self.dir_in: if self.dir_in:
self.reset_file_name_dir(os.path.join(self.dir_in,img_name)) self.reset_file_name_dir(os.path.join(self.dir_in,img_name))
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version) img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
self.logger.info("Enhancing took %.1fs ", time.time() - t0) self.logger.info("Enhancing took %.1fs ", time.time() - t0)
#print("text region early -1 in %.1fs", time.time() - t0)
t1 = time.time() t1 = time.time()
if self.light_version: if self.light_version:
text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea = self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier) text_regions_p_1 ,erosion_hurts, polygons_lines_xml, textline_mask_tot_ea, img_bin_light = self.get_regions_light_v(img_res, is_image_enhanced, num_col_classifier)
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea) #print("text region early -2 in %.1fs", time.time() - t0)
if num_col_classifier == 1 or num_col_classifier ==2:
if num_col_classifier == 1:
img_w_new = 1000
img_h_new = int(textline_mask_tot_ea.shape[0] / float(textline_mask_tot_ea.shape[1]) * img_w_new)
elif num_col_classifier == 2:
img_w_new = 1300
img_h_new = int(textline_mask_tot_ea.shape[0] / float(textline_mask_tot_ea.shape[1]) * img_w_new)
textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new )
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew)
else:
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
#print("text region early -2,5 in %.1fs", time.time() - t0)
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t) #self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea = \ num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light = \
self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts) self.run_graphics_and_columns_light(text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts, img_bin_light)
#self.logger.info("run graphics %.1fs ", time.time() - t1t) #self.logger.info("run graphics %.1fs ", time.time() - t1t)
#print("text region early -3 in %.1fs", time.time() - t0)
textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea) textline_mask_tot_ea_org = np.copy(textline_mask_tot_ea)
#print("text region early -4 in %.1fs", time.time() - t0)
else: else:
text_regions_p_1 ,erosion_hurts, polygons_lines_xml = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier) text_regions_p_1 ,erosion_hurts, polygons_lines_xml = self.get_regions_from_xy_2models(img_res, is_image_enhanced, num_col_classifier)
self.logger.info("Textregion detection took %.1fs ", time.time() - t1) self.logger.info("Textregion detection took %.1fs ", time.time() - t1)
@ -3498,7 +3556,7 @@ class Eynollah:
continue continue
else: else:
return pcgts return pcgts
#print("text region early in %.1fs", time.time() - t0)
t1 = time.time() t1 = time.time()
if not self.light_version: if not self.light_version:
textline_mask_tot_ea = self.run_textline(image_page) textline_mask_tot_ea = self.run_textline(image_page)
@ -3513,17 +3571,20 @@ class Eynollah:
textline_mask_tot, text_regions_p, image_page_rotated = self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction) textline_mask_tot, text_regions_p, image_page_rotated = self.run_marginals(image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction)
self.logger.info("detection of marginals took %.1fs", time.time() - t1) self.logger.info("detection of marginals took %.1fs", time.time() - t1)
#print("text region early 2 marginal in %.1fs", time.time() - t0)
t1 = time.time() t1 = time.time()
if not self.full_layout: if not self.full_layout:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts) polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts)
if self.full_layout: if self.full_layout:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts) if not self.light_version:
img_bin_light = None
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, img_bin_light)
text_only = ((img_revised_tab[:, :] == 1)) * 1 text_only = ((img_revised_tab[:, :] == 1)) * 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1 text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
#print("text region early 2 in %.1fs", time.time() - t0)
###min_con_area = 0.000005 ###min_con_area = 0.000005
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text, hir_on_text = return_contours_of_image(text_only) contours_only_text, hir_on_text = return_contours_of_image(text_only)
@ -3625,13 +3686,16 @@ class Eynollah:
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d)) # self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
else: else:
pass pass
#print("text region early 3 in %.1fs", time.time() - t0)
if self.light_version: if self.light_version:
txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first) txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first)
else: else:
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first) txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
#print("text region early 4 in %.1fs", time.time() - t0)
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent) boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals) boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
#print("text region early 5 in %.1fs", time.time() - t0)
if not self.curved_line: if not self.curved_line:
if self.light_version: if self.light_version:
if self.textline_light: if self.textline_light:
@ -3651,7 +3715,7 @@ class Eynollah:
all_found_textline_polygons = small_textlines_to_parent_adherence2(all_found_textline_polygons, textline_mask_tot_ea, num_col_classifier) all_found_textline_polygons = small_textlines_to_parent_adherence2(all_found_textline_polygons, textline_mask_tot_ea, num_col_classifier)
all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew) all_found_textline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_textline_polygons_marginals, textline_mask_tot_ea, num_col_classifier) all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_textline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
#print("text region early 6 in %.1fs", time.time() - t0)
if self.full_layout: if self.full_layout:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con]) contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
@ -3778,7 +3842,10 @@ class Eynollah:
#print(x, y, w, h, h/float(w),'ratio') #print(x, y, w, h, h/float(w),'ratio')
h2w_ratio = h/float(w) h2w_ratio = h/float(w)
mask_poly = np.zeros(image_page.shape) mask_poly = np.zeros(image_page.shape)
img_poly_on_img = np.copy(image_page) if not self.light_version:
img_poly_on_img = np.copy(image_page)
else:
img_poly_on_img = np.copy(img_bin_light)
mask_poly = cv2.fillPoly(mask_poly, pts=[ind_poly], color=(1, 1, 1)) mask_poly = cv2.fillPoly(mask_poly, pts=[ind_poly], color=(1, 1, 1))
@ -3805,8 +3872,10 @@ class Eynollah:
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines) pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables, ocr_all_textlines)
self.logger.info("Job done in %.1fs", time.time() - t0) self.logger.info("Job done in %.1fs", time.time() - t0)
##return pcgts ##return pcgts
#print("text region early 7 in %.1fs", time.time() - t0)
self.writer.write_pagexml(pcgts) self.writer.write_pagexml(pcgts)
#self.logger.info("Job done in %.1fs", time.time() - t0) #self.logger.info("Job done in %.1fs", time.time() - t0)
#print("Job done in %.1fs", time.time() - t0)
if self.dir_in: if self.dir_in:
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot) self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)

14
qurator/eynollah/utils/__init__.py
Unescape Escape View File

@ -775,9 +775,8 @@ def put_drop_out_from_only_drop_model(layout_no_patch, layout1):
return layout_no_patch return layout_no_patch
def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch): def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch, drop_capital_label):
drop_only = (layout_in_patch[:, :, 0] == drop_capital_label) * 1
drop_only = (layout_in_patch[:, :, 0] == 4) * 1
contours_drop, hir_on_drop = return_contours_of_image(drop_only) contours_drop, hir_on_drop = return_contours_of_image(drop_only)
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop) contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
@ -786,13 +785,18 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch):
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001] 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] 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 = [] contours_drop_parent_final = []
for jj in range(len(contours_drop_parent)): for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj]) x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
layout_in_patch[y : y + h, x : x + w, 0] = 4
if ( ( areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) ) / float(w*h) ) > 0.4:
layout_in_patch[y : y + h, x : x + w, 0] = drop_capital_label
else:
layout_in_patch[y : y + h, x : x + w, 0][layout_in_patch[y : y + h, x : x + w, 0] == drop_capital_label] = drop_capital_label
return layout_in_patch return layout_in_patch

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