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eynollah
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machine based reading order & works for not full layout case

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pull/138/head^2
vahidrezanezhad 1 year ago
parent 59c0d90e5a
commit 941d87328a
1 changed files with 43 additions and 41 deletions
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qurator/eynollah/eynollah.py
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@ -2881,16 +2881,17 @@ class Eynollah:
height3 =672#448
width3 = 448#224
_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h)
img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')
for j in range(len(cy_main)):
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
co_text_all = contours_only_text_parent + contours_only_text_parent_h
if contours_only_text_parent_h:
_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h)
for j in range(len(cy_main)):
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
co_text_all = contours_only_text_parent + contours_only_text_parent_h
else:
co_text_all = contours_only_text_parent
labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8')
@ -2984,7 +2985,7 @@ class Eynollah:
return order_of_texts, id_of_texts
def update_list_and_return_first_biger_than_one_length(self,index_element_to_be_updated, innner_index_pr_pos, pr_list, pos_list,list_inp):
def update_list_and_return_first_with_length_bigger_than_one(self,index_element_to_be_updated, innner_index_pr_pos, pr_list, pos_list,list_inp):
list_inp.pop(index_element_to_be_updated)
if len(pr_list)>0:
list_inp.insert(index_element_to_be_updated, pr_list)
@ -3030,16 +3031,17 @@ class Eynollah:
height3 =672#448
width3 = 448#224
_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h)
img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')
for j in range(len(cy_main)):
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
co_text_all = contours_only_text_parent + contours_only_text_parent_h
if contours_only_text_parent_h:
_, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h)
for j in range(len(cy_main)):
img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1
co_text_all = contours_only_text_parent + contours_only_text_parent_h
else:
co_text_all = contours_only_text_parent
labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8')
@ -3118,7 +3120,7 @@ class Eynollah:
tot_counter = tot_counter+1
starting_list_of_regions, index_update = self.update_list_and_return_first_biger_than_one_length(index_update, i, pr_list, post_list,starting_list_of_regions)
starting_list_of_regions, index_update = self.update_list_and_return_first_with_length_bigger_than_one(index_update, i, pr_list, post_list,starting_list_of_regions)
index_sort = [i[0] for i in starting_list_of_regions ]
@ -3138,7 +3140,7 @@ class Eynollah:
"""
self.logger.debug("enter run")
self.reading_order_machine_based = True#True
self.reading_order_machine_based = True#False#True#True
t0_tot = time.time()
@ -3359,32 +3361,32 @@ class Eynollah:
all_found_textline_polygons = adhere_drop_capital_region_into_corresponding_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_textline_polygons, all_found_textline_polygons_h, kernel=KERNEL, curved_line=self.curved_line)
pixel_lines = 6
if not self.reading_order_machine_based:
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h)
else:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h_d_ordered)
elif self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
else:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
if not self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h)
else:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines, contours_only_text_parent_h_d_ordered)
elif self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, _, matrix_of_lines_ch, splitter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
else:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
if num_col_classifier >= 3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
if num_col_classifier >= 3:
else:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
if not self.reading_order_machine_based:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables, self.right2left)
else:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, regions_without_separators, matrix_of_lines_ch, num_col_classifier, erosion_hurts, self.tables, self.right2left)
else:
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables, self.right2left)
boxes_d, peaks_neg_tot_tables_d = return_boxes_of_images_by_order_of_reading_new(splitter_y_new_d, regions_without_separators_d, matrix_of_lines_ch_d, num_col_classifier, erosion_hurts, self.tables, self.right2left)
#print(boxes_d,'boxes_d')
#img_once = np.zeros((textline_mask_tot_d.shape[0],textline_mask_tot_d.shape[1]))

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