From a14462db22dba86c3d15c7f6bc335d7b666622e3 Mon Sep 17 00:00:00 2001 From: Konstantin Baierer Date: Fri, 5 Feb 2021 14:58:49 +0100 Subject: [PATCH] remove else-pass, reset t1 after measuring --- sbb_newspapers_org_image/eynollah.py | 25 ++++++++----------------- 1 file changed, 8 insertions(+), 17 deletions(-) diff --git a/sbb_newspapers_org_image/eynollah.py b/sbb_newspapers_org_image/eynollah.py index bb8b12d..1b8eda0 100644 --- a/sbb_newspapers_org_image/eynollah.py +++ b/sbb_newspapers_org_image/eynollah.py @@ -749,8 +749,6 @@ class eynollah: img = img.astype(np.uint8) if img_width_h >= 2000: img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)) - else: - pass # img= resize_image(img, int(img_height_h*1), int(img_width_h*1) ) img = img.astype(np.uint8) if patches and cols == 1: @@ -1808,9 +1806,7 @@ class eynollah: rate_two_models=text_sume_second/float(text_sume_early)*100 self.logger.info("ratio_of_two_models: %s", rate_two_models) - if is_image_enhanced and rate_two_models<95.50:#98.45: - pass - else: + if not(is_image_enhanced and rate_two_models<95.50):#98.45: prediction_regions_org=np.copy(prediction_regions_org_copy) ##prediction_regions_org[mask_lines2[:,:]==1]=3 @@ -2178,11 +2174,13 @@ class eynollah: img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced) self.get_image_and_scales_after_enhancing(img_org, img_res) self.logger.info("Enhancing took %ss ", str(time.time() - t1)) + t1 = time.time() text_regions_p_1 = self.get_regions_from_xy_2models(img_res, is_image_enhanced) K.clear_session() gc.collect() self.logger.info("Textregion detection took %ss " + str(time.time() - t1)) + t1 = time.time() img_g = cv2.imread(self.image_filename, cv2.IMREAD_GRAYSCALE) img_g = img_g.astype(np.uint8) @@ -2251,6 +2249,7 @@ class eynollah: 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() # sys.exit() @@ -2269,6 +2268,7 @@ class eynollah: ##plt.show() ##sys.exit() self.logger.info("deskewing: " + str(time.time() - t1)) + t1 = time.time() image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :] textline_mask_tot[mask_images[:, :] == 1] = 0 @@ -2285,9 +2285,7 @@ class eynollah: try: regions_without_seperators = (text_regions_p[:, :] == 1) * 1 regions_without_seperators = regions_without_seperators.astype(np.uint8) - text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=self.kernel) - except: pass @@ -2299,6 +2297,7 @@ class eynollah: 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: @@ -2344,6 +2343,7 @@ class eynollah: self.logger.debug("len(boxes): %s", len(boxes)) self.logger.info("detecting boxes took %ss", str(time.time() - t1)) + 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) @@ -2431,6 +2431,7 @@ class eynollah: 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) @@ -2588,8 +2589,6 @@ class eynollah: K.clear_session() gc.collect() - ##print('Job done in: '+str(time.time()-t1)) - polygons_of_tabels = [] pixel_img = 4 polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img) @@ -2618,25 +2617,17 @@ 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[:, :], 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: - 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 - else: - pass 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)