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eynollah
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remove more dead code

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pull/19/head
Konstantin Baierer 4 years ago
parent 6398579a72
commit 2952a1ca13
3 changed files with 42 additions and 68 deletions
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73
sbb_newspapers_org_image/eynollah.py
Unescape Escape View File

@ -1,4 +1,5 @@
# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring
# pylint: disable=too-many-locals,wrong-import-position,too-many-lines
"""
tool to extract table form data from alto xml data
"""
@ -37,7 +38,6 @@ from .utils.contour import (
return_contours_of_interested_region_by_min_size,
return_contours_of_interested_textline,
return_parent_contours,
return_contours_of_interested_region_by_size,
)
from .utils.rotate import (
@ -65,7 +65,6 @@ from .utils import (
boosting_headers_by_longshot_region_segmentation,
crop_image_inside_box,
find_num_col,
otsu_copy,
otsu_copy_binary,
delete_seperator_around,
return_regions_without_seperators,
@ -77,8 +76,6 @@ from .utils import (
order_of_regions,
implent_law_head_main_not_parallel,
return_hor_spliter_by_index,
combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
return_points_with_boundies,
find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new,
)
@ -668,7 +665,7 @@ class eynollah:
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 3700 / float(img_width_h)), 3700)
else:
img = otsu_copy_binary(img)#self.otsu_copy(img)
img = otsu_copy_binary(img)
img = img.astype(np.uint8)
img= resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
@ -852,7 +849,7 @@ class eynollah:
slopes_per_each_subprocess.append(slope_for_all)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
_, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
if abs(slope_for_all) < 45:
# all_box_coord.append(crop_coor)
@ -925,11 +922,10 @@ class eynollah:
index_by_text_region_contours = []
for mv in range(len(boxes_text)):
crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated)
mask_textline=np.zeros((textline_mask_tot_ea.shape))
mask_textline=cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
denoised=None
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] ]
_, crop_coor = crop_image_inside_box(boxes_text[mv],image_page_rotated)
mask_textline = np.zeros((textline_mask_tot_ea.shape))
mask_textline = cv2.fillPoly(mask_textline,pts=[contours_per_process[mv]],color=(1,1,1))
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] ]
all_text_region_raw=all_text_region_raw.astype(np.uint8)
img_int_p=all_text_region_raw[:,:]#self.all_text_region_raw[mv]
img_int_p=cv2.erode(img_int_p,KERNEL,iterations = 2)
@ -1372,7 +1368,6 @@ class eynollah:
mask_images_only=(prediction_regions_org[:,:]==2)*1
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001)
polygons_of_only_images = return_contours_of_interested_region(mask_images_only, 1)
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001)
text_regions_p_true = np.zeros(prediction_regions_org.shape)
@ -1482,7 +1477,6 @@ class eynollah:
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
arg_text_con_h.append(jj)
break
arg_arg_text_con_h = np.argsort(arg_text_con_h)
args_contours_h = np.array(range(len(arg_text_con_h)))
order_by_con_head = np.zeros(len(arg_text_con_h))
@ -1490,7 +1484,7 @@ class eynollah:
ref_point = 0
order_of_texts_tot = []
id_of_texts_tot = []
for iij in range(len(boxes)):
for iij, _ in enumerate(boxes):
args_contours_box = args_contours[np.array(arg_text_con) == iij]
args_contours_box_h = args_contours_h[np.array(arg_text_con_h) == iij]
con_inter_box = []
@ -1521,7 +1515,7 @@ class eynollah:
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = tartib + ref_point
for jji in range(len(id_of_texts)):
for jji, _ in enumerate(id_of_texts):
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
id_of_texts_tot.append(id_of_texts[jji])
ref_point = ref_point + len(id_of_texts)
@ -1610,7 +1604,6 @@ class eynollah:
con_inter_box_h = []
for i in range(len(args_contours_box)):
con_inter_box.append(contours_only_text_parent[args_contours_box[i]])
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])
@ -1619,15 +1612,13 @@ class eynollah:
indexes_sorted_main = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 1]
indexes_by_type_main = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 1]
indexes_sorted_head = np.array(indexes_sorted)[np.array(kind_of_texts_sorted) == 2]
indexes_by_type_head = np.array(index_by_kind_sorted)[np.array(kind_of_texts_sorted) == 2]
for zahler, mtv in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler]
tartib = np.where(indexes_sorted == arg_order_v)[0][0]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = tartib + ref_point
for jji in range(len(id_of_texts)):
for jji, _ in enumerate(id_of_texts):
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
id_of_texts_tot.append(id_of_texts[jji])
ref_point = ref_point + len(id_of_texts)
@ -1661,23 +1652,17 @@ class eynollah:
if self.plotter:
self.plotter.save_page_image(image_page)
img_g3_page = img_g3[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]]
mask_images = (text_regions_p_1[:, :] == 2) * 1
mask_images = mask_images.astype(np.uint8)
mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
mask_lines = (text_regions_p_1[:, :] == 3) * 1
mask_lines = mask_lines.astype(np.uint8)
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 = cv2.erode(img_only_regions_with_sep[:, :], KERNEL, iterations=6)
try:
num_col, peaks_neg_fin = find_num_col(img_only_regions, multiplier=6.0)
num_col, _ = find_num_col(img_only_regions, multiplier=6.0)
num_col = num_col + 1
if not num_column_is_classified:
num_col_classifier = num_col + 1
@ -1741,7 +1726,6 @@ class eynollah:
pixel_img = 1
min_area = 0.00001
max_area = 0.0006
textline_mask_tot_small_size = return_contours_of_interested_region_by_size(textline_mask_tot, pixel_img, min_area, max_area)
text_regions_p_1[mask_lines[:, :] == 1] = 3
text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
text_regions_p = np.array(text_regions_p)
@ -1753,7 +1737,6 @@ class eynollah:
text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=KERNEL)
except Exception as e:
self.logger.error("exception %s", e)
pass
if self.plotter:
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page)
@ -1763,7 +1746,7 @@ class eynollah:
def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier):
self.logger.debug('enter run_boxes_no_full_layout')
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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)
_, textline_mask_tot_d, text_regions_p_1_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
@ -1774,10 +1757,10 @@ class eynollah:
regions_without_seperators_d = None
pixel_lines = 3
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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)
_, _, 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)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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)
_, _, 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)
K.clear_session()
self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -1786,18 +1769,9 @@ class eynollah:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_seperators = regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], KERNEL, iterations=6)
#random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1])
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
#random_pixels_for_image[random_pixels_for_image != 0] = 1
#regions_without_seperators[(random_pixels_for_image[:, :] == 1) & (text_regions_p[:, :] == 2)] = 1
else:
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], KERNEL, iterations=6)
#random_pixels_for_image = np.random.randn(regions_without_seperators_d.shape[0], regions_without_seperators_d.shape[1])
#random_pixels_for_image[random_pixels_for_image < -0.5] = 0
#random_pixels_for_image[random_pixels_for_image != 0] = 1
#regions_without_seperators_d[(random_pixels_for_image[:, :] == 1) & (text_regions_p_1_n[:, :] == 2)] = 1
t1 = time.time()
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
@ -1862,7 +1836,7 @@ class eynollah:
#plt.show()
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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)
_, 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)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
@ -1902,7 +1876,7 @@ class eynollah:
if not num_col:
self.logger.info("No columns detected, outputting an empty PAGE-XML")
self.write_into_page_xml(self.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], []))
self.write_into_page_xml(self.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], []))
self.logger.info("Job done in %ss", str(time.time() - t1))
return
@ -2048,15 +2022,14 @@ class eynollah:
if not self.curved_line:
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)
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)
_, 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)
else:
scale_param = 1
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)
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
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)
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)
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
index_of_vertical_text_contours = np.array(range(len(slopes)))[(abs(np.array(slopes)) > 60)]
K.clear_session()
# print(index_by_text_par_con,'index_by_text_par_con')
@ -2067,7 +2040,7 @@ class eynollah:
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)
else:
contours_only_text_parent_d_ordered = None
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)
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)
if self.plotter:
self.plotter.save_plot_of_layout(text_regions_p, image_page)
@ -2090,9 +2063,9 @@ class eynollah:
_, _, 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)
elif self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
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)
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)
else:
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)
_, _, 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')
# print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
@ -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))

19
sbb_newspapers_org_image/unused.py
Unescape Escape View File

@ -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

18
sbb_newspapers_org_image/utils/__init__.py
Unescape Escape View File

@ -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]

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