From ddeb6938e5d2a3a6796825aed9e4a6e9af4de82d Mon Sep 17 00:00:00 2001 From: Konstantin Baierer Date: Wed, 24 Feb 2021 13:55:12 +0100 Subject: [PATCH] self.kernel -> constant KERNEL --- sbb_newspapers_org_image/eynollah.py | 56 +++++++++++++--------------- 1 file changed, 26 insertions(+), 30 deletions(-) diff --git a/sbb_newspapers_org_image/eynollah.py b/sbb_newspapers_org_image/eynollah.py index 0267f12..b1028ea 100644 --- a/sbb_newspapers_org_image/eynollah.py +++ b/sbb_newspapers_org_image/eynollah.py @@ -91,6 +91,7 @@ SLOPE_THRESHOLD = 0.13 RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45: DPI_THRESHOLD = 298 MAX_SLOPE = 999 +KERNEL = np.ones((5, 5), np.uint8) class eynollah: def __init__( @@ -131,7 +132,6 @@ class eynollah: ) self.logger = getLogger('eynollah') self.dir_models = dir_models - self.kernel = np.ones((5, 5), np.uint8) self.model_dir_of_enhancemnet = dir_models + "/model_enhancement.h5" self.model_dir_of_col_classifier = dir_models + "/model_scale_classifier.h5" @@ -554,14 +554,13 @@ class eynollah: self.logger.debug("enter early_page_for_num_of_column_classification") img = self.imread() model_page, session_page = self.start_new_session_and_model(self.model_page_dir) - for ii in range(1): - img = cv2.GaussianBlur(img, (5, 5), 0) + img = cv2.GaussianBlur(img, (5, 5), 0) img_page_prediction = self.do_prediction(False, img, model_page) imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(imgray, 0, 255, 0) - thresh = cv2.dilate(thresh, self.kernel, iterations=3) + thresh = cv2.dilate(thresh, KERNEL, iterations=3) contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) cnt = contours[np.argmax(cnt_size)] @@ -576,15 +575,14 @@ class eynollah: def extract_page(self): self.logger.debug("enter extract_page") model_page, session_page = self.start_new_session_and_model(self.model_page_dir) - for ii in range(1): - img = cv2.GaussianBlur(self.image, (5, 5), 0) + img = cv2.GaussianBlur(self.image, (5, 5), 0) img_page_prediction = self.do_prediction(False, img, model_page) imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(imgray, 0, 255, 0) - thresh = cv2.dilate(thresh, self.kernel, iterations=3) + thresh = cv2.dilate(thresh, KERNEL, iterations=3) contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))]) @@ -830,7 +828,7 @@ class eynollah: all_text_region_raw = all_text_region_raw.astype(np.uint8) img_int_p = all_text_region_raw[:, :] - # img_int_p=cv2.erode(img_int_p,self.kernel,iterations = 2) + # img_int_p=cv2.erode(img_int_p,KERNEL,iterations = 2) # plt.imshow(img_int_p) # plt.show() @@ -897,9 +895,9 @@ class eynollah: mask_biggest2 = np.zeros(mask_texts_only.shape) mask_biggest2 = cv2.fillPoly(mask_biggest2, pts=[cnt_textlines_in_image[jjjj]], color=(1, 1, 1)) if num_col + 1 == 1: - mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=5) + mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=5) else: - mask_biggest2 = cv2.dilate(mask_biggest2, self.kernel, iterations=4) + mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=4) pixel_img = 1 mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par)) @@ -941,7 +939,7 @@ class eynollah: 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,self.kernel,iterations = 2) + img_int_p=cv2.erode(img_int_p,KERNEL,iterations = 2) if img_int_p.shape[0]/img_int_p.shape[1]<0.1: slopes_per_each_subprocess.append(0) @@ -1025,11 +1023,9 @@ class eynollah: boxes_sub_new = [] poly_sub = [] for mv in range(len(boxes_per_process)): - crop_img, _ = crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2)) crop_img = crop_img[:, :, 0] - crop_img = cv2.erode(crop_img, self.kernel, iterations=2) - + crop_img = cv2.erode(crop_img, KERNEL, iterations=2) try: textline_con, hierachy = return_contours_of_image(crop_img) textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierachy, max_area=1, min_area=0.0008) @@ -1194,7 +1190,7 @@ class eynollah: tree = ET.ElementTree(pcgts) tree.write(os.path.join(self.dir_out, self.image_filename_stem) + ".xml") - def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals): + def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes): self.logger.debug('enter build_pagexml_no_full_layout') # create the file structure @@ -1228,7 +1224,7 @@ class eynollah: id_indexer = len(found_polygons_text_region) + len(found_polygons_marginals) for mm in range(len(found_polygons_text_region_img)): - textregion=ET.SubElement(page, 'ImageRegion') + textregion = ET.SubElement(page, 'ImageRegion') textregion.set('id', 'r%s' % id_indexer) id_indexer += 1 coord_text = ET.SubElement(textregion, 'Coords') @@ -1243,7 +1239,7 @@ class eynollah: return pcgts - def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals): + def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes): self.logger.debug('enter build_pagexml_full_layout') # create the file structure @@ -1375,12 +1371,12 @@ class eynollah: prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3 mask_lines_only=(prediction_regions_org[:,:]==3)*1 - prediction_regions_org = cv2.erode(prediction_regions_org[:,:], self.kernel, iterations=2) + prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2) #plt.imshow(text_region2_1st_channel) #plt.show() - prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], self.kernel, iterations=2) + prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2) mask_texts_only=(prediction_regions_org[:,:]==1)*1 mask_images_only=(prediction_regions_org[:,:]==2)*1 @@ -1680,14 +1676,14 @@ class eynollah: 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_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[:, :], self.kernel, iterations=6) + 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) @@ -1739,7 +1735,7 @@ class eynollah: def run_deskew(self, textline_mask_tot_ea): sigma = 2 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_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter) slope_first = 0 if self.plotter: @@ -1763,7 +1759,7 @@ 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) + 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 @@ -1798,14 +1794,14 @@ class eynollah: 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) + 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[:, :], self.kernel, iterations=6) + 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 @@ -2065,9 +2061,9 @@ class eynollah: 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=self.kernel, iterations=1), image_page_rotated, boxes_text, text_only, num_col_classifier, scale_param, slope_deskew) + 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=self.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, 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 = 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)] @@ -2091,7 +2087,7 @@ class eynollah: polygons_of_tabels = [] pixel_img = 4 polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img) - 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 = 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=KERNEL, curved_line=self.curved_line) # print(len(contours_only_text_parent_h),len(contours_only_text_parent_h_d_ordered),'contours_only_text_parent_h') pixel_lines = 6 @@ -2114,14 +2110,14 @@ class eynollah: 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) + 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[:, :] == 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) + 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