mirror of
https://github.com/qurator-spk/eynollah.git
synced 2026-04-30 19:22:03 +02:00
make get_deskewed_masks() unconditional, call only when needed
This commit is contained in:
Robert Sachunsky
parent
52eb4c9a0a
commit
6a92f0d49c
1 changed files with 32 additions and 37 deletions
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@ -1502,13 +1502,11 @@ class Eynollah:
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label_imgs=2,
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label_seps=3,
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):
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"""detect page boundary and apply its mask/bbox, post-process column classifier result, optionally detect tables"""
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#print(text_regions_p_1.shape, 'text_regions_p_1 shape run graphics')
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#print(erosion_hurts, 'erosion_hurts')
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t_in_gr = time.time()
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image_page, page_coord, cont_page = self.extract_page(image)
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#print("inside graphics 1 ", time.time() - t_in_gr)
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if self.tables:
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table_prediction, table_confidence = self.get_tables_from_model(image_page)
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else:
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@ -1533,6 +1531,7 @@ class Eynollah:
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textline_mask_tot_ea = textline_mask_tot_ea[box]
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regions_confidence = regions_confidence[box]
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textline_confidence = textline_confidence[box]
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self.logger.debug("Cropped page is %dx%d", *text_regions_p_1.shape)
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mask_images = (text_regions_p_1 == label_imgs).astype(np.uint8)
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mask_images = cv2.erode(mask_images, KERNEL, iterations=10)
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@ -1712,18 +1711,9 @@ class Eynollah:
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text_regions_p,
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regions_without_separators,
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):
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
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textline_mask_tot_d = rotate_image(textline_mask_tot, slope_deskew)
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text_regions_p_d = rotate_image(text_regions_p, slope_deskew)
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regions_without_separators_d = rotate_image(regions_without_separators, slope_deskew)
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else:
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textline_mask_tot_d = None
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text_regions_p_d = None
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regions_without_separators_d = None
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return (
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textline_mask_tot_d,
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text_regions_p_d,
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regions_without_separators_d,
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return (rotate_image(textline_mask_tot, slope_deskew),
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rotate_image(text_regions_p, slope_deskew),
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rotate_image(regions_without_separators, slope_deskew),
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)
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def run_boxes_order(
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@ -2199,8 +2189,7 @@ class Eynollah:
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self.logger.info(f"Processing file: {writer.image_filename}")
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self.logger.info("Step 1/5: Image Enhancement")
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num_col_classifier, num_column_is_classified = \
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self.run_enhancement(image)
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num_col_classifier, num_column_is_classified = self.run_enhancement(image)
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writer.scale_x = image['scale_x']
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writer.scale_y = image['scale_y']
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@ -2263,7 +2252,6 @@ class Eynollah:
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self.logger.info("Job done in %.1fs", time.time() - t0)
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return
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#print("text region early -1 in %.1fs", time.time() - t0)
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t1 = time.time()
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self.logger.info("Step 2/5: Layout Analysis")
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@ -2274,7 +2262,8 @@ class Eynollah:
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textline_mask_tot_ea,
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regions_confidence,
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textline_confidence) = self.get_early_layout(image, num_col_classifier)
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#print("text region early -2 in %.1fs", time.time() - t0)
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t2 = time.time()
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self.logger.info("Eearly layout took %.1fs", t2 - t1)
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if num_col_classifier == 1 or num_col_classifier ==2:
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if num_col_classifier == 1:
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@ -2289,8 +2278,9 @@ class Eynollah:
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slope_deskew = self.run_deskew(textline_mask_tot_ea)
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if self.plotter:
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self.plotter.save_deskewed_image(slope_deskew, image['img'], image['name'])
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#print("text region early -2,5 in %.1fs", time.time() - t0)
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#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
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t3 = time.time()
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self.logger.info("Deskewing took %.1fs", t3 - t2)
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(num_col, num_col_classifier,
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page_coord, image_page, cont_page,
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text_regions_p, table_prediction, textline_mask_tot_ea,
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@ -2335,8 +2325,6 @@ class Eynollah:
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self.logger.info("Job done in %.1fs", time.time() - t0)
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return
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#print("text region early in %.1fs", time.time() - t0)
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t1 = time.time()
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if num_col_classifier in (1,2):
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org_h_l_m = textline_mask_tot_ea.shape[0]
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org_w_l_m = textline_mask_tot_ea.shape[1]
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@ -2364,13 +2352,14 @@ class Eynollah:
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num_col_classifier,
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table_prediction)
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(text_regions_p_d,
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textline_mask_tot_ea_d,
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regions_without_separators_d) = self.get_deskewed_masks(
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slope_deskew,
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text_regions_p,
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textline_mask_tot_ea,
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regions_without_separators)
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if self.full_layout:
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textline_mask_tot_ea_org[text_regions_p == label_drop_fl] = 0
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polygons_of_drop_capitals = return_contours_of_interested_region(text_regions_p,
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label_drop_fl,
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min_area=0.00003)
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conf_drops = get_region_confidences(polygons_of_drop_capitals, regionsfl_confidence)
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t6 = time.time()
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self.logger.info("Full layout took %.1fs", t6 - t5)
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min_area_mar = 0.00001
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marginal_mask = (text_regions_p == label_marg_fl).astype(np.uint8)
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@ -2384,16 +2373,18 @@ class Eynollah:
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conf_images = get_region_confidences(polygons_of_images, regions_confidence)
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conf_tables = get_region_confidences(polygons_of_tables, table_confidence)
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if self.full_layout:
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textline_mask_tot_ea_org[text_regions_p == label_drop_fl] = 0
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polygons_of_drop_capitals = return_contours_of_interested_region(text_regions_p,
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label_drop_fl,
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min_area=0.00003)
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conf_drops = get_region_confidences(polygons_of_drop_capitals, regionsfl_confidence)
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polygons_of_textregions = return_contours_of_interested_region(text_regions_p, label_text,
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min_area=MIN_AREA_REGION)
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if np.abs(slope_deskew) >= SLOPE_THRESHOLD and not self.reading_order_machine_based:
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(text_regions_p_d,
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textline_mask_tot_ea_d,
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regions_without_separators_d) = self.get_deskewed_masks(
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slope_deskew,
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text_regions_p,
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textline_mask_tot_ea,
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regions_without_separators)
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polygons_of_textregions_d = return_contours_of_interested_region(text_regions_p_d, label_text,
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min_area=MIN_AREA_REGION)
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if (len(polygons_of_textregions) and
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@ -2421,6 +2412,8 @@ class Eynollah:
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polygons_of_marginals = []
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conf_textregions = conf_marginals
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conf_marginals = []
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t7 = time.time()
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self.logger.info("Region contours took %.1fs", t7 - t6)
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if not self.curved_line:
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self.logger.info("Mode: Light line detection")
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@ -2466,6 +2459,8 @@ class Eynollah:
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all_found_textline_polygons,
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slopes,
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conf_textregions)
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t8 = time.time()
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self.logger.info("Line contours took %.1fs", t8 - t7)
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(polygons_of_marginals_left,
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polygons_of_marginals_right,
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