diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 4b1b5e9..b83db98 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -78,6 +78,7 @@ from .utils.xml import order_and_id_of_texts from .plot import EynollahPlotter from .writer import EynollahXmlWriter +MIN_AREA_REGION = 0.0005 SLOPE_THRESHOLD = 0.13 RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45: DPI_THRESHOLD = 298 @@ -225,6 +226,7 @@ class Eynollah: self.model_page_dir = dir_models + "/eynollah-page-extraction_20210425" self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425" self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314" + self.model_reading_order_machine_dir = dir_models + "/model_6_reading_order_machine_based" if self.textline_light: self.model_textline_dir = dir_models + "/eynollah-textline_light_20210425" else: @@ -246,6 +248,7 @@ class Eynollah: self.model_region = self.our_load_model(self.model_region_dir_p_ens_light) self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np) self.model_region_fl = self.our_load_model(self.model_region_dir_fully) + self.model_reading_order_machine = self.our_load_model(self.model_reading_order_machine_dir) self.ls_imgs = os.listdir(self.dir_in) @@ -264,6 +267,7 @@ class Eynollah: self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np) self.model_region_fl = self.our_load_model(self.model_region_dir_fully) self.model_enhancement = self.our_load_model(self.model_dir_of_enhancement) + self.model_reading_order_machine = self.our_load_model(self.model_reading_order_machine_dir) self.ls_imgs = os.listdir(self.dir_in) @@ -1647,9 +1651,39 @@ class Eynollah: mask_images_only=(prediction_regions_org[:,:] ==2)*1 polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only) - polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + test_khat = np.zeros(prediction_regions_org.shape) + + test_khat = cv2.fillPoly(test_khat, pts = polygons_lines_xml, color=(1,1,1)) + + + #plt.imshow(test_khat[:,:]) + #plt.show() + + #for jv in range(1): + #print(jv, hir_lines_xml[0][232][3]) + #test_khat = np.zeros(prediction_regions_org.shape) + + #test_khat = cv2.fillPoly(test_khat, pts = [polygons_lines_xml[232]], color=(1,1,1)) + + + #plt.imshow(test_khat[:,:]) + #plt.show() + + + polygons_lines_xml = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + + + test_khat = np.zeros(prediction_regions_org.shape) + + test_khat = cv2.fillPoly(test_khat, pts = polygons_lines_xml, color=(1,1,1)) + + + #plt.imshow(test_khat[:,:]) + #plt.show() + #sys.exit() + polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001) polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001) @@ -1785,7 +1819,7 @@ class Eynollah: polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only) - polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + polygons_lines_xml = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001) polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001) @@ -1853,7 +1887,7 @@ class Eynollah: mask_images_only=(prediction_regions_org[:,:] ==2)*1 polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only) - polygons_lines_xml = textline_con_fil = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) + polygons_lines_xml = filter_contours_area_of_image(mask_lines_only, polygons_lines_xml, hir_lines_xml, max_area=1, min_area=0.00001) polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001) @@ -2821,13 +2855,157 @@ class Eynollah: model = load_model(model_file , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches}) return model + + def do_order_of_regions_with_machine(self,contours_only_text_parent, contours_only_text_parent_h, text_regions_p): + + #print(text_regions_p.shape) + y_len = text_regions_p.shape[0] + x_len = text_regions_p.shape[1] + + img_poly = np.zeros((y_len,x_len), dtype='uint8') + + unique_pix = np.unique(text_regions_p) + #print(unique_pix, 'unique_pix') + + #for pix in unique_pix: + #print(pix) + #plt.imshow((text_regions_p[:,:]==pix)*1 ) + #plt.show() + + img_poly[text_regions_p[:,:]==1] = 1 + img_poly[text_regions_p[:,:]==2] = 2 + img_poly[text_regions_p[:,:]==3] = 4 + img_poly[text_regions_p[:,:]==6] = 5 + + #plt.imshow(text_regions_p) + #plt.show() + + + #plt.imshow(img_poly) + #plt.show() + model_ro_machine, _ = self.start_new_session_and_model(self.model_reading_order_machine_dir) + + height1 =672#448 + width1 = 448#224 + + height2 =672#448 + width2= 448#224 + + height3 =672#448 + width3 = 448#224 + + _, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(contours_only_text_parent_h) + + + img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8') + + for j in range(len(cy_main)): + #print(j, int(y_max_main[j]), x_min_main[j], x_max_main[j] ) + img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1 + + #plt.imshow(img_header_and_sep[:,:]) + #plt.show() + + co_text_all = contours_only_text_parent + contours_only_text_parent_h + #id_all_text = id_paragraph + id_header + + #texts_corr_order_index = [index_tot_regions[tot_region_ref.index(i)] for i in id_all_text ] + #texts_corr_order_index_int = [int(x) for x in texts_corr_order_index] + + #co_text_all, texts_corr_order_index_int = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, min_area) + + labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8') + for i in range(len(co_text_all)): + img_label = np.zeros((y_len,x_len,3),dtype='uint8') + img_label=cv2.fillPoly(img_label, pts =[co_text_all[i]], color=(1,1,1)) + labels_con[:,:,i] = img_label[:,:,0] + + + img3= np.copy(img_poly) + + labels_con = resize_image(labels_con, height1, width1) + + img_header_and_sep = resize_image(img_header_and_sep, height1, width1) + + img3= resize_image (img3, height3, width3) + + img3 = img3.astype(np.uint16) + + + #plt.imshow(img3) + #plt.show() + + order_matrix = np.zeros((labels_con.shape[2], labels_con.shape[2]))-1 + + for i in range(labels_con.shape[2]): + for j in range(labels_con.shape[2]): + if j>i: + img1= np.repeat(labels_con[:,:,i][:, :, np.newaxis], 3, axis=2) + img2 = np.repeat(labels_con[:,:,j][:, :, np.newaxis], 3, axis=2) + #img1 = img1.astype(np.uint16) + #img2 = img2.astype(np.uint16) + + img2[:,:,0][img3[:,:]==5] = 2 + img2[:,:,0][img_header_and_sep[:,:]==1] = 3 + + + + img1[:,:,0][img3[:,:]==5] = 2 + img1[:,:,0][img_header_and_sep[:,:]==1] = 3 + + + #plt.imshow(labels_con[:,:,i]) + #plt.show() + + #plt.imshow(img2[:,:,0]) + #plt.show() + + + #plt.imshow(img1[:,:,0]) + #plt.show() + + #sys.exit() + input_1= np.zeros( (height1, width1,3)) + + input_1[:,:,0] = img1[:,:,0]/3. + input_1[:,:,2] = img2[:,:,0]/3. + input_1[:,:,1] = img3[:,:]/5. + + #y_pr=model.predict([img1.reshape(1,height1,width1,3) , img2.reshape(1,height2,width2,3),img3.reshape(1,height3,width3,3) ], verbose=2) + y_pr=model_ro_machine.predict(input_1.reshape(1,height1,width1,3) , verbose=0) + #print(y_pr) + + if y_pr>=0.5: + order_class = 1 + else: + order_class = 0 + + order_matrix[i,j] = y_pr#order_class + order_matrix[j,i] = 1-y_pr#int( 1 - order_class) + + + sum_mat = np.sum(order_matrix, axis=1) + index_sort = np.argsort(sum_mat) + index_sort = index_sort[::-1] + + print(index_sort) + REGION_ID_TEMPLATE = 'region_%04d' + order_of_texts = [] + id_of_texts = [] + for order, id_text in enumerate(index_sort): + order_of_texts.append(id_text) + id_of_texts.append( REGION_ID_TEMPLATE % order ) + + + return order_of_texts, id_of_texts def run(self): """ Get image and scales, then extract the page of scanned image """ self.logger.debug("enter run") - + + self.reading_order_machine_based = True#True t0_tot = time.time() @@ -2896,7 +3074,7 @@ class Eynollah: text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1 - min_con_area = 0.000005 + ###min_con_area = 0.000005 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) @@ -2906,8 +3084,8 @@ class Eynollah: areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1]) #self.logger.info('areas_cnt_text %s', areas_cnt_text) contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)] - contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area] - areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area] + contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > MIN_AREA_REGION] + areas_cnt_text_parent = [area for area in areas_cnt_text if area > MIN_AREA_REGION] index_con_parents = np.argsort(areas_cnt_text_parent) contours_only_text_parent = list(np.array(contours_only_text_parent,dtype=object)[index_con_parents]) areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents]) @@ -2983,8 +3161,8 @@ class Eynollah: 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 = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area] - areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area] + contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > MIN_AREA_REGION] + areas_cnt_text_parent = [area for area in areas_cnt_text if area > MIN_AREA_REGION] index_con_parents = np.argsort(areas_cnt_text_parent) contours_only_text_parent = list(np.array(contours_only_text_parent,dtype=object)[index_con_parents]) @@ -3086,21 +3264,33 @@ class Eynollah: self.plotter.write_images_into_directory(polygons_of_images, image_page) t_order = time.time() 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) + + if self.reading_order_machine_based: + order_text_new, id_of_texts_tot = self.do_order_of_regions_with_machine(contours_only_text_parent, contours_only_text_parent_h, text_regions_p) 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) + 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) pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, contours_tables, polygons_of_drop_capitals, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_xml) self.logger.info("Job done in %.1fs", time.time() - t0) ##return pcgts + + print(id_of_texts_tot,'id_of_texts_tot') + print(order_text_new,'order_text_new') + else: contours_only_text_parent_h = None - 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) + if self.reading_order_machine_based: + order_text_new, id_of_texts_tot = self.do_order_of_regions_with_machine(contours_only_text_parent, contours_only_text_parent_h, text_regions_p) else: - contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[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) + 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: + contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[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) + pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_textline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables) self.logger.info("Job done in %.1fs", time.time() - t0) ##return pcgts diff --git a/qurator/eynollah/utils/contour.py b/qurator/eynollah/utils/contour.py index bac8235..53b39b5 100644 --- a/qurator/eynollah/utils/contour.py +++ b/qurator/eynollah/utils/contour.py @@ -44,8 +44,8 @@ def get_text_region_boxes_by_given_contours(contours): def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area): found_polygons_early = list() - jv = 0 - for c in contours: + + for jv,c in enumerate(contours): if len(c) < 3: # A polygon cannot have less than 3 points continue @@ -53,14 +53,12 @@ def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area area = polygon.area if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hierarchy[0][jv][3] == -1: # and hierarchy[0][jv][3]==-1 : found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint)) - jv += 1 return found_polygons_early def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, min_area): found_polygons_early = list() - jv = 0 - for c in contours: + for jv,c in enumerate(contours): if len(c) < 3: # A polygon cannot have less than 3 points continue @@ -73,7 +71,6 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, m if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 : # print(c[0][0][1]) found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32)) - jv += 1 return found_polygons_early def find_new_features_of_contours(contours_main): @@ -234,8 +231,6 @@ def get_textregion_contours_in_org_image_multi2(cnts, img, slope_first): with Pool(cpu_count()) as p: cnts_org = p.starmap(loop_contour_image, [(index_l,cnts, img,slope_first) for index_l in range(len(cnts))]) - print(len(cnts_org),'lendiha') - return cnts_org def get_textregion_contours_in_org_image(cnts, img, slope_first):