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eynollah
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further untangle run

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pull/19/head
Konstantin Baierer 4 years ago
parent bf6eaafbc7
commit 9dca742694
1 changed files with 385 additions and 391 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -705,7 +705,9 @@ class eynollah:
del img del img
del imgray del imgray
K.clear_session()
gc.collect() gc.collect()
self.logger.debug("exit extract_page")
return croped_page, page_coord return croped_page, page_coord
def extract_text_regions(self, img, patches, cols): def extract_text_regions(self, img, patches, cols):
@ -2140,6 +2142,45 @@ class eynollah:
return self.do_order_of_regions_full_layout(*args, **kwargs) return self.do_order_of_regions_full_layout(*args, **kwargs)
return self.do_order_of_regions_no_full_layout(*args, **kwargs) return self.do_order_of_regions_no_full_layout(*args, **kwargs)
def run_graphics_and_columns(self, text_regions_p_1, num_column_is_classified):
img_g = cv2.imread(self.image_filename, cv2.IMREAD_GRAYSCALE)
img_g = img_g.astype(np.uint8)
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
img_g3 = img_g3.astype(np.uint8)
img_g3[:, :, 0] = img_g[:, :]
img_g3[:, :, 1] = img_g[:, :]
img_g3[:, :, 2] = img_g[:, :]
image_page, page_coord = self.extract_page()
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[:, :], self.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[:, :], self.kernel, iterations=6)
try:
num_col, peaks_neg_fin = find_num_col(img_only_regions, multiplier=6.0)
if not num_column_is_classified:
num_col_classifier = num_col + 1
except:
num_col = None
peaks_neg_fin = []
num_col_classifier = None
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines
def run_enhancement(self): def run_enhancement(self):
self.logger.info("resize and enhance image") self.logger.info("resize and enhance image")
is_image_enhanced, img_org, img_res, _, num_column_is_classified = self.resize_and_enhance_image_with_column_classifier() is_image_enhanced, img_org, img_res, _, num_column_is_classified = self.resize_and_enhance_image_with_column_classifier()
@ -2163,6 +2204,7 @@ class eynollah:
self.get_image_and_scales_after_enhancing(img_org, img_res) self.get_image_and_scales_after_enhancing(img_org, img_res)
return img_res, is_image_enhanced, num_column_is_classified return img_res, is_image_enhanced, num_column_is_classified
def run(self): def run(self):
""" """
Get image and scales, then extract the page of scanned image Get image and scales, then extract the page of scanned image
@ -2177,479 +2219,431 @@ class eynollah:
t1 = time.time() t1 = time.time()
text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced) text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced)
self.logger.info("Textregion detection took %ss ", str(time.time() - t1)) self.logger.info("Textregion detection took %ss ", str(time.time() - t1))
t1 = time.time()
img_g = cv2.imread(self.image_filename, cv2.IMREAD_GRAYSCALE) t1 = time.time()
img_g = img_g.astype(np.uint8) num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines = self.run_graphics_and_columns(text_regions_p_1, num_column_is_classified)
self.logger.info("Graphics detection took %ss ", str(time.time() - t1))
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3)) #print(num_col, "num_colnum_col")
img_g3 = img_g3.astype(np.uint8) if not num_col:
img_g3[:, :, 0] = img_g[:, :] self.logger.info("No columns detected, outputting an empty PAGE-XML")
img_g3[:, :, 1] = img_g[:, :] self.write_into_page_xml([], page_coord, self.dir_out, [], [], [], [], [], [], [], [], self.curved_line, [], [])
img_g3[:, :, 2] = img_g[:, :] self.logger.info("Job done in %ss", str(time.time() - t1))
return
patches = True
scaler_h_textline = 1 # 1.2#1.2
scaler_w_textline = 1 # 0.9#1
textline_mask_tot_ea, textline_mask_tot_long_shot = self.textline_contours(image_page, patches, scaler_h_textline, scaler_w_textline)
image_page, page_coord = self.extract_page()
# print(image_page.shape,'page')
if self.plotter:
self.plotter.save_page_image(image_page)
K.clear_session() K.clear_session()
gc.collect() gc.collect()
#print(np.unique(textline_mask_tot_ea[:, :]), "textline")
if self.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
self.logger.info("textline detection took %ss", str(time.time() - t1))
t1 = time.time()
# plt.imshow(textline_mask_tot_ea)
# plt.show()
img_g3_page = img_g3[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :] sigma = 2
del img_g3 main_page_deskew = True
del img_g slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
slope_first = 0 # return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma, plotter=self.plotter)
text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]] if self.plotter:
self.plotter.save_deskewed_image(slope_deskew)
self.logger.info("slope_deskew: %s", slope_deskew)
mask_images = (text_regions_p_1[:, :] == 2) * 1 ##plt.imshow(img_rotated)
mask_images = mask_images.astype(np.uint8) ##plt.show()
mask_images = cv2.erode(mask_images[:, :], self.kernel, iterations=10)
mask_lines = (text_regions_p_1[:, :] == 3) * 1 self.logger.info("deskewing: " + str(time.time() - t1))
mask_lines = mask_lines.astype(np.uint8) t1 = time.time()
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1 image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :]
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8) textline_mask_tot[mask_images[:, :] == 1] = 0
img_only_regions = cv2.erode(img_only_regions_with_sep[:, :], self.kernel, iterations=6)
try: pixel_img = 1
num_col, peaks_neg_fin = find_num_col(img_only_regions, multiplier=6.0) min_area = 0.00001
if not num_column_is_classified: max_area = 0.0006
num_col_classifier = num_col + 1 textline_mask_tot_small_size = return_contours_of_interested_region_by_size(textline_mask_tot, pixel_img, min_area, max_area)
except: text_regions_p_1[mask_lines[:, :] == 1] = 3
num_col = None text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
peaks_neg_fin = [] text_regions_p = np.array(text_regions_p)
#print(num_col, "num_colnum_col") if num_col_classifier == 1 or num_col_classifier == 2:
if not num_col: try:
self.logger.info("No columns detected, outputting an empty PAGE-XML") regions_without_seperators = (text_regions_p[:, :] == 1) * 1
txt_con_org = [] regions_without_seperators = regions_without_seperators.astype(np.uint8)
order_text_new = [] text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=self.kernel)
id_of_texts_tot = [] except:
all_found_texline_polygons = [] pass
all_box_coord = []
polygons_of_images = [] # plt.imshow(text_regions_p)
polygons_of_marginals = [] # plt.show()
all_found_texline_polygons_marginals = []
all_box_coord_marginals = []
slopes = []
slopes_marginals = []
self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, 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, self.curved_line, slopes, slopes_marginals)
else:
patches = True
scaler_h_textline = 1 # 1.2#1.2
scaler_w_textline = 1 # 0.9#1
textline_mask_tot_ea, textline_mask_tot_long_shot = self.textline_contours(image_page, patches, scaler_h_textline, scaler_w_textline)
if self.plotter:
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page)
self.plotter.save_plot_of_layout_main(text_regions_p, image_page)
self.logger.info("detection of marginals took %ss", str(time.time() - t1))
t1 = time.time()
if not self.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)
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
regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
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)
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)
K.clear_session() K.clear_session()
gc.collect() gc.collect()
#print(np.unique(textline_mask_tot_ea[:, :]), "textline")
if self.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
self.logger.info("textline detection took %ss", str(time.time() - t1))
t1 = time.time()
# plt.imshow(textline_mask_tot_ea)
# plt.show()
sigma = 2 self.logger.info("num_col_classifier: %s", num_col_classifier)
main_page_deskew = True
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
slope_first = 0 # return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma, plotter=self.plotter)
if self.plotter: if num_col_classifier >= 3:
self.plotter.save_deskewed_image(slope_deskew) if np.abs(slope_deskew) < SLOPE_THRESHOLD:
self.logger.info("slope_deskew: %s", slope_deskew) regions_without_seperators = regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.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[:, :], self.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
##plt.imshow(img_rotated) if np.abs(slope_deskew) < SLOPE_THRESHOLD:
##plt.show() boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
self.logger.info("deskewing: " + str(time.time() - t1)) self.logger.debug("len(boxes): %s", len(boxes))
self.logger.info("detecting boxes took %ss", str(time.time() - t1))
t1 = time.time() t1 = time.time()
img_revised_tab = text_regions_p[:, :]
pixel_img = 2
polygons_of_images = return_contours_of_interested_region(img_revised_tab, pixel_img)
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :] # plt.imshow(img_revised_tab)
textline_mask_tot[mask_images[:, :] == 1] = 0 # plt.show()
K.clear_session()
pixel_img = 1 pixel_img = 4
min_area = 0.00001 min_area_mar = 0.00001
max_area = 0.0006 polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
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)
if num_col_classifier == 1 or num_col_classifier == 2: if self.full_layout:
try: # set first model with second model
regions_without_seperators = (text_regions_p[:, :] == 1) * 1 text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
regions_without_seperators = regions_without_seperators.astype(np.uint8) text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=self.kernel) text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
except:
pass
# plt.imshow(text_regions_p) K.clear_session()
# plt.show() # gc.collect()
patches = True
image_page = image_page.astype(np.uint8)
if self.plotter: # print(type(image_page))
self.plotter.save_plot_of_layout_main_all(text_regions_p, image_page) regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
self.plotter.save_plot_of_layout_main(text_regions_p, image_page) text_regions_p[:,:][regions_fully[:,:,0]==6]=6
self.logger.info("detection of marginals took %ss", str(time.time() - t1)) regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
t1 = time.time() regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
K.clear_session()
gc.collect()
if not self.full_layout: # plt.imshow(regions_fully[:,:,0])
# plt.show()
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
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)
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
regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
pixel_lines = 3 # plt.imshow(regions_fully[:,:,0])
if np.abs(slope_deskew) < SLOPE_THRESHOLD: # plt.show()
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)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: K.clear_session()
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) gc.collect()
K.clear_session() patches = False
gc.collect() regions_fully_np, _ = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
self.logger.info("num_col_classifier: %s", num_col_classifier)
if num_col_classifier >= 3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_seperators = regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.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[:, :], self.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 # plt.imshow(regions_fully_np[:,:,0])
# plt.show()
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if num_col_classifier > 2:
boxes = return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch, num_col_classifier) regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0
else: else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier) regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p)
self.logger.debug("len(boxes): %s", len(boxes)) # plt.imshow(regions_fully_np[:,:,0])
self.logger.info("detecting boxes took %ss", str(time.time() - t1)) # plt.show()
t1 = time.time()
img_revised_tab = text_regions_p[:, :]
pixel_img = 2
polygons_of_images = return_contours_of_interested_region(img_revised_tab, pixel_img)
# plt.imshow(img_revised_tab) K.clear_session()
# plt.show() gc.collect()
K.clear_session()
pixel_img = 4 # plt.imshow(regions_fully[:,:,0])
min_area_mar = 0.00001 # plt.show()
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
if self.full_layout:
# set first model with second model
text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
K.clear_session()
# gc.collect()
patches = True
image_page = image_page.astype(np.uint8)
# print(type(image_page))
regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
text_regions_p[:,:][regions_fully[:,:,0]==6]=6
regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
K.clear_session()
gc.collect()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully) regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
# plt.imshow(regions_fully[:,:,0]) # plt.imshow(regions_fully[:,:,0])
# plt.show() # plt.show()
K.clear_session() text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4
gc.collect() text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4
patches = False
regions_fully_np, _ = self.extract_text_regions(image_page, patches, cols=num_col_classifier)
# plt.imshow(regions_fully_np[:,:,0]) #plt.imshow(text_regions_p)
# plt.show() #plt.show()
if num_col_classifier > 2: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0 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)
else:
regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p)
# plt.imshow(regions_fully_np[:,:,0]) text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
# plt.show() textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
K.clear_session() regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
gc.collect()
# plt.imshow(regions_fully[:,:,0]) K.clear_session()
# plt.show() gc.collect()
img_revised_tab = np.copy(text_regions_p[:, :])
self.logger.info("detection of full layout took %ss", str(time.time() - t1))
t1 = time.time()
pixel_img = 5
polygons_of_images = return_contours_of_interested_region(img_revised_tab, pixel_img)
regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions) # plt.imshow(img_revised_tab)
# plt.show()
# plt.imshow(regions_fully[:,:,0]) # print(img_revised_tab.shape,text_regions_p_1_n.shape)
# plt.show() # text_regions_p_1_n=resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
# print(np.unique(text_regions_p_1_n),'uni')
text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4 text_only = ((img_revised_tab[:, :] == 1)) * 1
text_regions_p[:, :][regions_fully_np[:, :, 0] == 4] = 4 if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1
#plt.imshow(text_regions_p) # print(text_only.shape,text_only_d.shape)
#plt.show() # plt.imshow(text_only)
# plt.show()
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: # plt.imshow(text_only_d)
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) # plt.show()
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1]) min_con_area = 0.000005
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1]) if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1]) contours_only_text, hir_on_text = return_contours_of_image(text_only)
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1 contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
regions_without_seperators = (text_regions_p[:, :] == 1) * 1 # ( (text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions) index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
K.clear_session() cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest])
gc.collect() cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
img_revised_tab = np.copy(text_regions_p[:, :])
self.logger.info("detection of full layout took %ss", str(time.time() - t1))
t1 = time.time()
pixel_img = 5
polygons_of_images = return_contours_of_interested_region(img_revised_tab, pixel_img)
# plt.imshow(img_revised_tab) contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
# plt.show() contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
# print(img_revised_tab.shape,text_regions_p_1_n.shape) areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
# text_regions_p_1_n=resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1]) areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
# print(np.unique(text_regions_p_1_n),'uni')
text_only = ((img_revised_tab[:, :] == 1)) * 1 contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: index_con_parents_d=np.argsort(areas_cnt_text_d)
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1 contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
##text_only_h=( (img_revised_tab[:,:,0]==2) )*1 areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
# print(text_only.shape,text_only_d.shape) cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest_d])
# plt.imshow(text_only) cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_d)
# plt.show() try:
cx_bigest_d_last5=cx_bigest_d[-5:]
cy_biggest_d_last5=cy_biggest_d[-5:]
dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))]
ind_largest=len(cx_bigest_d)-5+np.argmin(dists_d)
cx_bigest_d_big[0]=cx_bigest_d[ind_largest]
cy_biggest_d_big[0]=cy_biggest_d[ind_largest]
except:
pass
# plt.imshow(text_only_d) (h, w) = text_only.shape[:2]
# plt.show() center = (w // 2.0, h // 2.0)
M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
min_con_area = 0.000005 M_22 = np.array(M)[:2, :2]
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text, hir_on_text = return_contours_of_image(text_only)
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
index_con_parents_d=np.argsort(areas_cnt_text_d)
contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest_d])
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_d)
try:
cx_bigest_d_last5=cx_bigest_d[-5:]
cy_biggest_d_last5=cy_biggest_d[-5:]
dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))]
ind_largest=len(cx_bigest_d)-5+np.argmin(dists_d)
cx_bigest_d_big[0]=cx_bigest_d[ind_largest]
cy_biggest_d_big[0]=cy_biggest_d[ind_largest]
except:
pass
(h, w) = text_only.shape[:2]
center = (w // 2.0, h // 2.0)
M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
M_22 = np.array(M)[:2, :2]
p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big])
x_diff = p_big[0] - cx_bigest_d_big
y_diff = p_big[1] - cy_biggest_d_big
# print(p_big)
# print(cx_bigest_d_big,cy_biggest_d_big)
# print(x_diff,y_diff)
contours_only_text_parent_d_ordered = []
for i in range(len(contours_only_text_parent)):
# img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
# plt.imshow(img1[:,:,0])
# plt.show()
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
# print(p)
p[0] = p[0] - x_diff[0]
p[1] = p[1] - y_diff[0]
# print(p)
# print(cx_bigest_d)
# print(cy_biggest_d)
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))]
# print(np.argmin(dists))
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, hir_on_text = return_contours_of_image(text_only)
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))]) p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)] x_diff = p_big[0] - cx_bigest_d_big
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area] y_diff = p_big[1] - cy_biggest_d_big
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent) # print(p_big)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents]) # print(cx_bigest_d_big,cy_biggest_d_big)
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents]) # print(x_diff,y_diff)
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest]) contours_only_text_parent_d_ordered = []
cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent) for i in range(len(contours_only_text_parent)):
# print(areas_cnt_text_parent,'areas_cnt_text_parent') # img1=np.zeros((text_only.shape[0],text_only.shape[1],3))
# print(areas_cnt_text_parent_d,'areas_cnt_text_parent_d') # img1=cv2.fillPoly(img1,pts=[contours_only_text_parent[i]] ,color=(1,1,1))
# print(len(contours_only_text_parent),len(contours_only_text_parent_d),'vizzz') # plt.imshow(img1[:,:,0])
# plt.show()
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first) p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent) # print(p)
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals) p[0] = p[0] - x_diff[0]
p[1] = p[1] - y_diff[0]
# print(p)
# print(cx_bigest_d)
# print(cy_biggest_d)
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))]
# print(np.argmin(dists))
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, hir_on_text = return_contours_of_image(text_only)
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
if not self.curved_line: areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
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) areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, index_by_text_par_con_marginal = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
else: contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
scale_param = 1 contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
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=self.kernel, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew) areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
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=self.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)]
contours_text_vertical = [contours_only_text_parent[i] for i in index_of_vertical_text_contours]
K.clear_session() index_con_parents = np.argsort(areas_cnt_text_parent)
gc.collect() contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
# print(index_by_text_par_con,'index_by_text_par_con') areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
if self.full_layout: cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest])
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con]) # print(areas_cnt_text_parent,'areas_cnt_text_parent')
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) # print(areas_cnt_text_parent_d,'areas_cnt_text_parent_d')
else: # print(len(contours_only_text_parent),len(contours_only_text_parent_d),'vizzz')
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)
if self.plotter: txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
self.plotter.save_plot_of_layout(text_regions_p, image_page) boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
self.plotter.save_plot_of_layout_all(text_regions_p, image_page) boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
K.clear_session() if not self.curved_line:
gc.collect() 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, index_by_text_par_con_marginal = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
polygons_of_tabels = [] else:
pixel_img = 4 scale_param = 1
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img) 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=self.kernel, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_texline_polygons = adhere_drop_capital_region_into_cprresponding_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_texline_polygons, all_found_texline_polygons_h, kernel=self.kernel, curved_line=self.curved_line) 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=self.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)]
contours_text_vertical = [contours_only_text_parent[i] for i in index_of_vertical_text_contours]
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h') K.clear_session()
pixel_lines = 6 gc.collect()
# print(index_by_text_par_con,'index_by_text_par_con')
if not self.headers_off: if self.full_layout:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: 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, contours_only_text_parent_h) contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
else: 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)
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, contours_only_text_parent_h_d_ordered) else:
elif self.headers_off: contours_only_text_parent_d_ordered = None
if np.abs(slope_deskew) < SLOPE_THRESHOLD: 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)
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)
else:
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)
# print(peaks_neg_fin,peaks_neg_fin_d,'num_col2') if self.plotter:
# print(spliter_y_new,spliter_y_new_d,'num_col_classifier') self.plotter.save_plot_of_layout(text_regions_p, image_page)
# print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch') self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
if num_col_classifier >= 3: K.clear_session()
gc.collect()
if np.abs(slope_deskew) < SLOPE_THRESHOLD: polygons_of_tabels = []
regions_without_seperators = regions_without_seperators.astype(np.uint8) pixel_img = 4
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.kernel, iterations=6) polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
random_pixels_for_image = np.random.randn(regions_without_seperators.shape[0], regions_without_seperators.shape[1]) all_found_texline_polygons = adhere_drop_capital_region_into_cprresponding_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_texline_polygons, all_found_texline_polygons_h, kernel=self.kernel, curved_line=self.curved_line)
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[:, :] == 5)] = 1
else:
regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.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[:, :] == 5)] = 1
# print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h')
pixel_lines = 6
if not self.headers_off:
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, contours_only_text_parent_h)
else:
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, contours_only_text_parent_h_d_ordered)
elif self.headers_off:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: 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) 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)
else: else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier) 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)
if self.plotter: # print(peaks_neg_fin,peaks_neg_fin_d,'num_col2')
self.plotter.write_images_into_directory(polygons_of_images, image_page) # print(spliter_y_new,spliter_y_new_d,'num_col_classifier')
# print(matrix_of_lines_ch.shape,matrix_of_lines_ch_d.shape,'matrix_of_lines_ch')
if num_col_classifier >= 3:
if self.full_layout:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot) regions_without_seperators = regions_without_seperators.astype(np.uint8)
regions_without_seperators = cv2.erode(regions_without_seperators[:, :], self.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[:, :] == 5)] = 1
else: 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) regions_without_seperators_d = regions_without_seperators_d.astype(np.uint8)
regions_without_seperators_d = cv2.erode(regions_without_seperators_d[:, :], self.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[:, :] == 5)] = 1
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)
else:
boxes_d = return_boxes_of_images_by_order_of_reading_new(spliter_y_new_d, regions_without_seperators_d, matrix_of_lines_ch_d, num_col_classifier)
if self.plotter:
self.plotter.write_images_into_directory(polygons_of_images, image_page)
if self.full_layout:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
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_full(contours_only_text_parent, contours_only_text_parent_h, page_coord, self.dir_out, 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_full(contours_only_text_parent, contours_only_text_parent_h, page_coord, self.dir_out, 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)
else:
contours_only_text_parent_h = None
# self.logger.debug('bura galmir?')
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
#contours_only_text_parent = list(np.array(contours_only_text_parent)[index_by_text_par_con])
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
else: else:
contours_only_text_parent_h = None contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
# self.logger.debug('bura galmir?') 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)
if np.abs(slope_deskew) < SLOPE_THRESHOLD: # order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
#contours_only_text_parent = list(np.array(contours_only_text_parent)[index_by_text_par_con]) self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, 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, self.curved_line, slopes, slopes_marginals)
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
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)
# order_text_new , id_of_texts_tot=self.do_order_of_regions(contours_only_text_parent,contours_only_text_parent_h,boxes,textline_mask_tot)
self.write_into_page_xml(txt_con_org, page_coord, self.dir_out, 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, self.curved_line, slopes, slopes_marginals)
self.logger.info("Job done in %ss", str(time.time() - t1)) self.logger.info("Job done in %ss", str(time.time() - t1))

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