mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-31 01:34:17 +01:00
remove more dead code
This commit is contained in:
Konstantin Baierer
parent
6398579a72
commit
2952a1ca13
3 changed files with 42 additions and 68 deletions
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@ -1,4 +1,5 @@
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# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring
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# pylint: disable=too-many-locals,wrong-import-position,too-many-lines
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"""
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tool to extract table form data from alto xml data
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"""
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@ -37,7 +38,6 @@ from .utils.contour import (
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return_contours_of_interested_region_by_min_size,
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return_contours_of_interested_textline,
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return_parent_contours,
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return_contours_of_interested_region_by_size,
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)
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from .utils.rotate import (
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@ -65,7 +65,6 @@ from .utils import (
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boosting_headers_by_longshot_region_segmentation,
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crop_image_inside_box,
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find_num_col,
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otsu_copy,
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otsu_copy_binary,
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delete_seperator_around,
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return_regions_without_seperators,
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@ -77,8 +76,6 @@ from .utils import (
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order_of_regions,
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implent_law_head_main_not_parallel,
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return_hor_spliter_by_index,
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combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
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return_points_with_boundies,
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find_number_of_columns_in_document,
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return_boxes_of_images_by_order_of_reading_new,
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)
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@ -668,7 +665,7 @@ class eynollah:
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img = img.astype(np.uint8)
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img= resize_image(img, int(img_height_h * 3700 / float(img_width_h)), 3700)
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else:
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img = otsu_copy_binary(img)#self.otsu_copy(img)
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img = otsu_copy_binary(img)
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img = img.astype(np.uint8)
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img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
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@ -852,7 +849,7 @@ class eynollah:
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slopes_per_each_subprocess.append(slope_for_all)
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index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
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crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
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_, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
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if abs(slope_for_all) < 45:
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# all_box_coord.append(crop_coor)
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@ -925,10 +922,9 @@ class eynollah:
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index_by_text_region_contours = []
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for mv in range(len(boxes_text)):
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crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated)
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_, crop_coor = crop_image_inside_box(boxes_text[mv],image_page_rotated)
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mask_textline = np.zeros((textline_mask_tot_ea.shape))
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mask_textline = cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
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denoised=None
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all_text_region_raw = (textline_mask_tot_ea*mask_textline[:,:])[boxes_text[mv][1]:boxes_text[mv][1]+boxes_text[mv][3] , boxes_text[mv][0]:boxes_text[mv][0]+boxes_text[mv][2] ]
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all_text_region_raw=all_text_region_raw.astype(np.uint8)
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img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]
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@ -1372,7 +1368,6 @@ class eynollah:
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mask_images_only=(prediction_regions_org[:,:]==2)*1
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polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001)
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polygons_of_only_images = return_contours_of_interested_region(mask_images_only, 1)
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polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001)
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text_regions_p_true = np.zeros(prediction_regions_org.shape)
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@ -1482,7 +1477,6 @@ class eynollah:
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if cx_text_only_h[ii] >= boxes[jj][0] and cx_text_only_h[ii] < boxes[jj][1] and cy_text_only_h[ii] >= boxes[jj][2] and cy_text_only_h[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
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arg_text_con_h.append(jj)
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break
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arg_arg_text_con_h = np.argsort(arg_text_con_h)
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args_contours_h = np.array(range(len(arg_text_con_h)))
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order_by_con_head = np.zeros(len(arg_text_con_h))
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@ -1490,7 +1484,7 @@ class eynollah:
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ref_point = 0
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order_of_texts_tot = []
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id_of_texts_tot = []
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for iij in range(len(boxes)):
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for iij, _ in enumerate(boxes):
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args_contours_box = args_contours[np.array(arg_text_con) == iij]
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args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij]
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con_inter_box = []
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@ -1521,7 +1515,7 @@ class eynollah:
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tartib = np.where(indexes_sorted == arg_order_v)[0][0]
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order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point
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for jji in range(len(id_of_texts)):
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for jji, _ in enumerate(id_of_texts):
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order_of_texts_tot.append(order_of_texts[jji] + ref_point)
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id_of_texts_tot.append(id_of_texts[jji])
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ref_point = ref_point + len(id_of_texts)
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@ -1610,7 +1604,6 @@ class eynollah:
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con_inter_box_h = []
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for i in range(len(args_contours_box)):
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con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
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indexes_sorted, matrix_of_orders, kind_of_texts_sorted, index_by_kind_sorted = order_of_regions(textline_mask_tot[int(boxes[iij][2]) : int(boxes[iij][3]), int(boxes[iij][0]) : int(boxes[iij][1])], con_inter_box, con_inter_box_h, boxes[iij][2])
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@ -1619,15 +1612,13 @@ class eynollah:
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indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
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indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
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indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
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indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
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for zahler, mtv in enumerate(args_contours_box):
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arg_order_v = indexes_sorted_main[zahler]
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tartib = np.where(indexes_sorted == arg_order_v)[0][0]
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order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
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for jji in range(len(id_of_texts)):
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for jji, _ in enumerate(id_of_texts):
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order_of_texts_tot.append(order_of_texts[jji] + ref_point)
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id_of_texts_tot.append(id_of_texts[jji])
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ref_point = ref_point + len(id_of_texts)
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@ -1661,23 +1652,17 @@ class eynollah:
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if self.plotter:
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self.plotter.save_page_image(image_page)
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img_g3_page = img_g3[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :]
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text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
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mask_images = (text_regions_p_1[:, :] == 2) * 1
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mask_images = mask_images.astype(np.uint8)
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mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
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mask_lines = (text_regions_p_1[:, :] == 3) * 1
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mask_lines = mask_lines.astype(np.uint8)
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img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
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img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
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img_only_regions = cv2.erode(img_only_regions_with_sep[:, :], KERNEL, iterations=6)
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try:
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num_col, peaks_neg_fin = find_num_col(img_only_regions, multiplier=6.0)
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num_col, _ = find_num_col(img_only_regions, multiplier=6.0)
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num_col = num_col + 1
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if not num_column_is_classified:
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num_col_classifier = num_col + 1
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@ -1741,7 +1726,6 @@ class eynollah:
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pixel_img = 1
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min_area = 0.00001
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max_area = 0.0006
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textline_mask_tot_small_size = return_contours_of_interested_region_by_size(textline_mask_tot, pixel_img, min_area, max_area)
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text_regions_p_1[mask_lines[:, :] == 1] = 3
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text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
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text_regions_p = np.array(text_regions_p)
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@ -1753,7 +1737,6 @@ class eynollah:
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text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=KERNEL)
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except Exception as e:
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self.logger.error("exception %s", e)
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pass
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if self.plotter:
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self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page)
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@ -1763,7 +1746,7 @@ class eynollah:
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def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier):
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self.logger.debug('enter run_boxes_no_full_layout')
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
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_, textline_mask_tot_d, text_regions_p_1_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
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text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
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textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
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regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
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@ -1774,10 +1757,10 @@ class eynollah:
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regions_without_seperators_d = None
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pixel_lines = 3
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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_, _, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, seperators_closeup_n_d = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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_, _, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
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K.clear_session()
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self.logger.info("num_col_classifier: %s", num_col_classifier)
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@ -1786,18 +1769,9 @@ class eynollah:
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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regions_without_seperators = regions_without_seperators.astype(np.uint8)
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regions_without_seperators = cv2.erode(regions_without_seperators[:, :], KERNEL, iterations=6)
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#random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
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#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
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#random_pixels_for_image[random_pixels_for_image != 0] = 1
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#regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1
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else:
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regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
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regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], KERNEL, iterations=6)
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#random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
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#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
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#random_pixels_for_image[random_pixels_for_image != 0] = 1
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#regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 2)] = 1
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t1 = time.time()
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
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@ -1862,7 +1836,7 @@ class eynollah:
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#plt.show()
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
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_, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
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text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
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textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
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@ -1902,7 +1876,7 @@ class eynollah:
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if not num_col:
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self.logger.info("No columns detected, outputting an empty PAGE-XML")
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self.write_into_page_xml(self.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], []))
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self.write_into_page_xml(self.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], []))
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self.logger.info("Job done in %ss", str(time.time() - t1))
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return
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@ -2048,15 +2022,14 @@ class eynollah:
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if not self.curved_line:
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slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
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slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
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_, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
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else:
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scale_param = 1
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all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con, slopes = self.get_slopes_and_deskew_new_curved(txt_con_org, contours_only_text_parent, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew)
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all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
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all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal, 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)
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all_found_texline_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)
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all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
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index_of_vertical_text_contours = np.array(range(len(slopes)))[(abs(np.array(slopes)) > 60)]
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K.clear_session()
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# print(index_by_text_par_con,'index_by_text_par_con')
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@ -2067,7 +2040,7 @@ class eynollah:
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text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, _, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
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else:
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contours_only_text_parent_d_ordered = None
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text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
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text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, _, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
|
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if self.plotter:
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self.plotter.save_plot_of_layout(text_regions_p, image_page)
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|
|
@ -2090,9 +2063,9 @@ class eynollah:
|
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_, _, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines, contours_only_text_parent_h_d_ordered)
|
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elif self.headers_off:
|
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if np.abs(slope_deskew) < SLOPE_THRESHOLD:
|
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num_col, peaks_neg_fin, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
|
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num_col, _, matrix_of_lines_ch, spliter_y_new, _ = find_number_of_columns_in_document(np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
|
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else:
|
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num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
|
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_, _, matrix_of_lines_ch_d, spliter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, pixel_lines)
|
||||
|
||||
# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
|
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# print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
|
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|
|
@ -2128,7 +2101,7 @@ class eynollah:
|
|||
else:
|
||||
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered, boxes_d, textline_mask_tot_d)
|
||||
|
||||
self.write_into_page_xml(self.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals))
|
||||
self.write_into_page_xml(self.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes))
|
||||
|
||||
else:
|
||||
contours_only_text_parent_h = None
|
||||
|
|
@ -2137,6 +2110,6 @@ class eynollah:
|
|||
else:
|
||||
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
|
||||
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)
|
||||
self.write_into_page_xml(self.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals))
|
||||
self.write_into_page_xml(self.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes))
|
||||
|
||||
self.logger.info("Job done in %ss", str(time.time() - t1))
|
||||
|
|
|
|||
|
|
@ -3166,3 +3166,22 @@ def return_hor_spliter_by_index_for_without_verticals(peaks_neg_fin_t, x_min_hor
|
|||
else:
|
||||
peaks_true.append(peaks_neg_fin_t[m])
|
||||
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
|
||||
|
||||
def otsu_copy(img):
|
||||
img_r = np.zeros(img.shape)
|
||||
img1 = img[:, :, 0]
|
||||
img2 = img[:, :, 1]
|
||||
img3 = img[:, :, 2]
|
||||
# print(img.min())
|
||||
# print(img[:,:,0].min())
|
||||
# blur = cv2.GaussianBlur(img,(5,5))
|
||||
# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
|
||||
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
|
||||
img_r[:, :, 0] = threshold1
|
||||
img_r[:, :, 1] = threshold1
|
||||
img_r[:, :, 2] = threshold1
|
||||
return img_r
|
||||
|
||||
|
|
|
|||
|
|
@ -299,24 +299,6 @@ def crop_image_inside_box(box, img_org_copy):
|
|||
image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
|
||||
return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
|
||||
|
||||
def otsu_copy(img):
|
||||
img_r = np.zeros(img.shape)
|
||||
img1 = img[:, :, 0]
|
||||
img2 = img[:, :, 1]
|
||||
img3 = img[:, :, 2]
|
||||
# print(img.min())
|
||||
# print(img[:,:,0].min())
|
||||
# blur = cv2.GaussianBlur(img,(5,5))
|
||||
# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
|
||||
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
|
||||
img_r[:, :, 0] = threshold1
|
||||
img_r[:, :, 1] = threshold1
|
||||
img_r[:, :, 2] = threshold1
|
||||
return img_r
|
||||
|
||||
def otsu_copy_binary(img):
|
||||
img_r = np.zeros((img.shape[0], img.shape[1], 3))
|
||||
img1 = img[:, :, 0]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue