diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index ff35d6f..f183dee 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -89,7 +89,7 @@ from .utils.xml import order_and_id_of_texts from .plot import EynollahPlotter from .writer import EynollahXmlWriter -MIN_AREA_REGION = 0.0005 +MIN_AREA_REGION = 0.00001 SLOPE_THRESHOLD = 0.13 RATIO_OF_TWO_MODEL_THRESHOLD = 95.50 #98.45: DPI_THRESHOLD = 298 @@ -182,6 +182,7 @@ class Eynollah: logger=None, pcgts=None, ): + self.light_version = light_version if not dir_in: if image_pil: self._imgs = self._cache_images(image_pil=image_pil) @@ -209,7 +210,6 @@ class Eynollah: self.input_binary = input_binary self.allow_scaling = allow_scaling self.headers_off = headers_off - self.light_version = light_version self.ignore_page_extraction = ignore_page_extraction self.ocr = do_ocr self.pcgts = pcgts @@ -828,7 +828,64 @@ class Eynollah: batch_indexer = batch_indexer + 1 if batch_indexer == n_batch_inference: + label_p_pred = model.predict(img_patch,verbose=0) + + seg = np.argmax(label_p_pred, axis=3) + + indexer_inside_batch = 0 + for i_batch, j_batch in zip(list_i_s, list_j_s): + seg_in = seg[indexer_inside_batch,:,:] + seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2) + + index_y_u_in = list_y_u[indexer_inside_batch] + index_y_d_in = list_y_d[indexer_inside_batch] + + index_x_u_in = list_x_u[indexer_inside_batch] + index_x_d_in = list_x_d[indexer_inside_batch] + + if i_batch == 0 and j_batch == 0: + seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] + prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color + elif i_batch == nxf - 1 and j_batch == nyf - 1: + seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :] + prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color + elif i_batch == 0 and j_batch == nyf - 1: + seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :] + prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color + elif i_batch == nxf - 1 and j_batch == 0: + seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] + prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color + elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: + seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] + prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color + elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: + seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] + prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color + elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: + seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] + prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color + elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: + seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :] + prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color + else: + seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] + prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color + + indexer_inside_batch = indexer_inside_batch +1 + + list_i_s = [] + list_j_s = [] + list_x_u = [] + list_x_d = [] + list_y_u = [] + list_y_d = [] + + batch_indexer = 0 + + img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) + + elif i==(nxf-1) and j==(nyf-1): label_p_pred = model.predict(img_patch,verbose=0) seg = np.argmax(label_p_pred, axis=3) @@ -885,6 +942,7 @@ class Eynollah: batch_indexer = 0 img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) + prediction_true = prediction_true.astype(np.uint8) #del model #gc.collect() @@ -1789,9 +1847,9 @@ class Eynollah: t_bin = time.time() if not self.dir_in: model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization) - prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=10) + prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5) else: - prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=10) + prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5) #print("inside bin ", time.time()-t_bin) prediction_bin=prediction_bin[:,:,0] @@ -1808,7 +1866,6 @@ class Eynollah: textline_mask_tot_ea = self.run_textline(img_bin) - #print("inside 2 ", time.time()-t_in) #print(img_resized.shape, num_col_classifier, "num_col_classifier") @@ -1839,6 +1896,10 @@ class Eynollah: mask_texts_only = (prediction_regions_org[:,:] ==1)*1 + mask_texts_only = mask_texts_only.astype('uint8') + + mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=3) + mask_images_only=(prediction_regions_org[:,:] ==2)*1 polygons_lines_xml, hir_lines_xml = return_contours_of_image(mask_lines_only)