mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-09 20:29:55 +02:00
Merge branch 'main' into eynollah_light
This commit is contained in:
vahidrezanezhad
GitHub
commit
1621532092
10 changed files with 64 additions and 38 deletions
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@ -1 +0,0 @@
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__import__("pkg_resources").declare_namespace(__name__)
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@ -29,6 +29,7 @@ from qurator.eynollah.eynollah import Eynollah
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"-m",
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help="directory of models",
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type=click.Path(exists=True, file_okay=False),
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required=True,
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)
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@click.option(
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"--save_images",
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@ -393,7 +393,6 @@ class Eynollah:
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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prediction_true = prediction_true.astype(int)
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return prediction_true
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def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
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@ -495,12 +494,11 @@ class Eynollah:
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label_p_pred = model_num_classifier.predict(img_in, verbose=0)
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else:
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label_p_pred = self.model_classifier.predict(img_in, verbose=0)
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num_col = np.argmax(label_p_pred[0]) + 1
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self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
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img_new, _ = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
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if img_new.shape[1] > img.shape[1]:
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@ -527,7 +525,7 @@ class Eynollah:
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prediction_bin = prediction_bin*255
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prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
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prediction_bin = prediction_bin.astype(np.uint8)
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img= np.copy(prediction_bin)
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img_bin = np.copy(prediction_bin)
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@ -535,6 +533,7 @@ class Eynollah:
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img = self.imread()
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img_bin = None
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t1 = time.time()
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_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
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if not self.dir_in:
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model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
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@ -578,7 +577,6 @@ class Eynollah:
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image_res = np.copy(img)
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is_image_enhanced = False
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self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
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return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin
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@ -655,10 +653,6 @@ class Eynollah:
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except:
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self.logger.warning("no GPU device available")
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# try:
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# model = load_model(model_dir, compile=False)
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# except:
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# model = load_model(model_dir , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
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if model_dir.endswith('.h5') and Path(model_dir[:-3]).exists():
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# prefer SavedModel over HDF5 format if it exists
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model_dir = model_dir[:-3]
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@ -672,6 +666,7 @@ class Eynollah:
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model = load_model(model_dir , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
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self.models[model_dir] = model
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return model, None
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def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
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@ -808,6 +803,7 @@ class Eynollah:
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label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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prediction_true = resize_image(seg_color, img_h_page, img_w_page)
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@ -858,7 +854,8 @@ class Eynollah:
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index_y_d = img_h - img_height_model
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
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verbose=0)
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seg = np.argmax(label_p_pred, axis=3)[0]
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@ -957,8 +954,6 @@ class Eynollah:
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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prediction_true = prediction_true.astype(np.uint8)
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##del model
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##gc.collect()
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return prediction_true
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def extract_page(self):
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@ -1044,7 +1039,6 @@ class Eynollah:
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croped_page, page_coord = crop_image_inside_box(box, img)
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return croped_page, page_coord
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def extract_text_regions(self, img, patches, cols):
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self.logger.debug("enter extract_text_regions")
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img_height_h = img.shape[0]
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@ -1138,7 +1132,6 @@ class Eynollah:
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marginal_of_patch_percent = 0.1
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prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
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prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
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self.logger.debug("exit extract_text_regions")
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return prediction_regions, prediction_regions2
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@ -1541,6 +1534,7 @@ class Eynollah:
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else:
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return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
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def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
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self.logger.debug('enter do_work_of_slopes')
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slope_biggest = 0
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@ -1713,6 +1707,7 @@ class Eynollah:
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if not self.dir_in:
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model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
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img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
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if self.dir_in:
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@ -1761,8 +1756,6 @@ class Eynollah:
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prediction_bin = prediction_bin*255
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prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
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if not self.dir_in:
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model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
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@ -1781,7 +1774,6 @@ class Eynollah:
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mask_lines_only=(prediction_regions_org[:,:]==3)*1
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mask_texts_only=(prediction_regions_org[:,:]==1)*1
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mask_images_only=(prediction_regions_org[:,:]==2)*1
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@ -1799,7 +1791,6 @@ class Eynollah:
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text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
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return text_regions_p_true, erosion_hurts, polygons_lines_xml
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except:
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@ -1819,7 +1810,6 @@ class Eynollah:
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prediction_bin = prediction_bin*255
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prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
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if not self.dir_in:
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@ -1840,7 +1830,6 @@ class Eynollah:
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prediction_regions_org=prediction_regions_org[:,:,0]
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#mask_lines_only=(prediction_regions_org[:,:]==3)*1
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#img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
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#prediction_regions_org = self.do_prediction(True, img, model_region)
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@ -2367,9 +2356,8 @@ class Eynollah:
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img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
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img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
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prediction_ext = self.do_prediction(patches, img_new, model_region)
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pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
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pre_updown = cv2.flip(pre_updown, -1)
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@ -2390,9 +2378,8 @@ class Eynollah:
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img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
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img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
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prediction_ext = self.do_prediction(patches, img_new, model_region)
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pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
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pre_updown = cv2.flip(pre_updown, -1)
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@ -2405,12 +2392,10 @@ class Eynollah:
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else:
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prediction_table = np.zeros(img.shape)
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img_w_half = int(img.shape[1]/2.)
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pre1 = self.do_prediction(patches, img[:,0:img_w_half,:], model_region)
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pre2 = self.do_prediction(patches, img[:,img_w_half:,:], model_region)
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pre_full = self.do_prediction(patches, img[:,:,:], model_region)
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pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), model_region)
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pre_updown = cv2.flip(pre_updown, -1)
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@ -2432,8 +2417,6 @@ class Eynollah:
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prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
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prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
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return prediction_table_erode.astype(np.int16)
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def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts):
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img_g = self.imread(grayscale=True, uint8=True)
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@ -2558,6 +2541,7 @@ class Eynollah:
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textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
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if self.textline_light:
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textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16)
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if self.plotter:
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self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
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return textline_mask_tot_ea
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@ -2787,7 +2771,6 @@ class Eynollah:
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regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
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regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
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regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
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if num_col_classifier > 2:
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regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0
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@ -2836,6 +2819,7 @@ class Eynollah:
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"""
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self.logger.debug("enter run")
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t0_tot = time.time()
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if not self.dir_in:
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@ -3023,6 +3007,7 @@ class Eynollah:
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else:
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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)
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slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
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else:
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scale_param = 1
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@ -1,5 +1,5 @@
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{
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"version": "0.0.11",
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"version": "0.2.0",
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"git_url": "https://github.com/qurator-spk/eynollah",
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"tools": {
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"ocrd-eynollah-segment": {
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@ -49,7 +49,17 @@
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"default": false,
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"description": "ignore the special role of headings during reading order detection"
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}
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}
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},
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"resources": [
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{
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"description": "models for eynollah (TensorFlow format)",
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"url": "https://qurator-data.de/eynollah/2021-04-25/SavedModel.tar.gz",
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"name": "default",
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"size": 1483106598,
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"type": "archive",
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"path_in_archive": "default"
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}
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]
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}
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}
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}
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@ -20,7 +20,7 @@ def contours_in_same_horizon(cy_main_hor):
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list_h.append(i)
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if len(list_h) > 1:
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all_args.append(list(set(list_h)))
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return np.unique(all_args)
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return np.unique(np.array(all_args, dtype=object))
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def find_contours_mean_y_diff(contours_main):
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M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
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