qurator-spk/eynollah
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remove commented out code

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kba 2024-08-23 21:35:43 +02:00
parent ac2958edb1
commit 59dbffea59
1 changed files with 2 additions and 16 deletions
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18
qurator/eynollah/eynollah.py
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@ -2715,16 +2715,16 @@ class Eynollah():
"""
self.logger.debug("enter run")
t0_tot = time.time()
if not self.batch_processing_mode:
# FIXME: why?
self.ls_imgs = [1]
for img_name in self.ls_imgs:
t0 = time.time()
if self.batch_processing_mode:
self.reset_file_name_dir(join(self.dirs.dir_in,img_name))
self.reset_file_name_dir(join(self.dirs.dir_in, img_name))
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)
@ -2848,10 +2848,6 @@ class Eynollah():
p[1] = p[1] - y_diff[0]
dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))]
contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
# img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
# plt.imshow(img2[:,:,0])
# plt.show()
else:
contours_only_text_parent_d_ordered = []
contours_only_text_parent_d = []
@ -2879,9 +2875,6 @@ class Eynollah():
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent)
#self.logger.debug('areas_cnt_text_parent %s', areas_cnt_text_parent)
# self.logger.debug('areas_cnt_text_parent_d %s', areas_cnt_text_parent_d)
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
else:
pass
if self.light_version:
@ -2962,13 +2955,6 @@ class Eynollah():
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)
#print(boxes_d,'boxes_d')
#img_once = np.zeros((textline_mask_tot_d.shape[0],textline_mask_tot_d.shape[1]))
#for box_i in boxes_d:
#img_once[int(box_i[2]):int(box_i[3]),int(box_i[0]):int(box_i[1]) ] =1
#plt.imshow(img_once)
#plt.show()
#print(np.unique(img_once),'img_once')
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)
t_order = time.time()