diff --git a/src/eynollah/utils/__init__.py b/src/eynollah/utils/__init__.py index 152ac6e..7c06900 100644 --- a/src/eynollah/utils/__init__.py +++ b/src/eynollah/utils/__init__.py @@ -138,8 +138,7 @@ def return_x_start_end_mothers_childs_and_type_of_reading_order( min_ys=np.min(y_sep) max_ys=np.max(y_sep) - y_mains=[] - y_mains.append(min_ys) + y_mains= [min_ys] y_mains_sep_ohne_grenzen=[] for ii in range(len(new_main_sep_y)): @@ -493,8 +492,7 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl # print(forest[np.argmin(z[forest]) ] ) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) - forest = [] - forest.append(peaks_neg_fin[i + 1]) + forest = [peaks_neg_fin[i + 1]] if i == (len(peaks_neg_fin) - 1): # print(print(forest[np.argmin(z[forest]) ] )) if not isNaN(forest[np.argmin(z[forest])]): @@ -662,8 +660,7 @@ def find_num_col_only_image(regions_without_separators, multiplier=3.8): # print(forest[np.argmin(z[forest]) ] ) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) - forest = [] - forest.append(peaks_neg_fin[i + 1]) + forest = [peaks_neg_fin[i + 1]] if i == (len(peaks_neg_fin) - 1): # print(print(forest[np.argmin(z[forest]) ] )) if not isNaN(forest[np.argmin(z[forest])]): @@ -1235,8 +1232,7 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref): 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) + peaks_neg_new = [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) @@ -1404,8 +1400,7 @@ def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new( return img_p_in[:,:,0], special_separators def return_points_with_boundies(peaks_neg_fin, first_point, last_point): - peaks_neg_tot = [] - peaks_neg_tot.append(first_point) + peaks_neg_tot = [first_point] for ii in range(len(peaks_neg_fin)): peaks_neg_tot.append(peaks_neg_fin[ii]) peaks_neg_tot.append(last_point) @@ -1588,8 +1583,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, args_cy_splitter=np.argsort(cy_main_splitters) cy_main_splitters_sort=cy_main_splitters[args_cy_splitter] - splitter_y_new=[] - splitter_y_new.append(0) + splitter_y_new= [0] for i in range(len(cy_main_splitters_sort)): splitter_y_new.append( cy_main_splitters_sort[i] ) splitter_y_new.append(region_pre_p.shape[0]) @@ -1663,8 +1657,7 @@ def return_boxes_of_images_by_order_of_reading_new( num_col, peaks_neg_fin = find_num_col( regions_without_separators[int(splitter_y_new[i]):int(splitter_y_new[i+1]),:], num_col_classifier, tables, multiplier=3.) - peaks_neg_fin_early=[] - peaks_neg_fin_early.append(0) + peaks_neg_fin_early= [0] #print(peaks_neg_fin,'peaks_neg_fin') for p_n in peaks_neg_fin: peaks_neg_fin_early.append(p_n) diff --git a/src/eynollah/utils/contour.py b/src/eynollah/utils/contour.py index 0e84153..0be8879 100644 --- a/src/eynollah/utils/contour.py +++ b/src/eynollah/utils/contour.py @@ -239,8 +239,7 @@ def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) if len(cont_int)==0: - cont_int = [] - cont_int.append(contour_par) + cont_int = [contour_par] confidence_contour = 0 else: cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1]) diff --git a/src/eynollah/utils/separate_lines.py b/src/eynollah/utils/separate_lines.py index ead5cfb..c87653c 100644 --- a/src/eynollah/utils/separate_lines.py +++ b/src/eynollah/utils/separate_lines.py @@ -1174,8 +1174,7 @@ def separate_lines_new_inside_tiles(img_path, thetha): if diff_peaks[i] > cut_off: if not np.isnan(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) - forest = [] - forest.append(peaks_neg[i + 1]) + forest = [peaks_neg[i + 1]] if i == (len(peaks_neg) - 1): if not np.isnan(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) @@ -1195,8 +1194,7 @@ def separate_lines_new_inside_tiles(img_path, thetha): if diff_peaks_pos[i] > cut_off: if not np.isnan(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) - forest = [] - forest.append(peaks[i + 1]) + forest = [peaks[i + 1]] if i == (len(peaks) - 1): if not np.isnan(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) diff --git a/train/inference.py b/train/inference.py index 094c528..0e55aa8 100644 --- a/train/inference.py +++ b/train/inference.py @@ -305,8 +305,7 @@ class sbb_predict: input_1= np.zeros( (inference_bs, img_height, img_width,3)) - starting_list_of_regions = [] - starting_list_of_regions.append( list(range(labels_con.shape[2])) ) + starting_list_of_regions = [list(range(labels_con.shape[2]))] index_update = 0 index_selected = starting_list_of_regions[0] diff --git a/train/train.py b/train/train.py index e8e92af..795009a 100644 --- a/train/train.py +++ b/train/train.py @@ -365,8 +365,7 @@ def run(_config, n_classes, n_epochs, input_height, y_tot=np.zeros((testX.shape[0],n_classes)) - score_best=[] - score_best.append(0) + score_best= [0] num_rows = return_number_of_total_training_data(dir_train) weights=[]