From a65caa4d25323b87c91fe02a8e0077652f71c849 Mon Sep 17 00:00:00 2001 From: Konstantin Baierer Date: Tue, 23 Feb 2021 17:11:32 +0100 Subject: [PATCH] :art: unncesssary if True --- sbb_newspapers_org_image/eynollah.py | 171 +++++++++++++-------------- 1 file changed, 79 insertions(+), 92 deletions(-) diff --git a/sbb_newspapers_org_image/eynollah.py b/sbb_newspapers_org_image/eynollah.py index 41538b4..5798c63 100644 --- a/sbb_newspapers_org_image/eynollah.py +++ b/sbb_newspapers_org_image/eynollah.py @@ -171,93 +171,81 @@ class eynollah: if img.shape[1] < img_width_model: img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST) + margin = int(0 * img_width_model) + width_mid = img_width_model - 2 * margin + height_mid = img_height_model - 2 * margin + img = img / float(255.0) + + img_h = img.shape[0] + img_w = img.shape[1] + + prediction_true = np.zeros((img_h, img_w, 3)) + mask_true = np.zeros((img_h, img_w)) + nxf = img_w / float(width_mid) + nyf = img_h / float(height_mid) + + nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) + nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) + + for i in range(nxf): + for j in range(nyf): + if i == 0: + index_x_d = i * width_mid + index_x_u = index_x_d + img_width_model + else: + index_x_d = i * width_mid + index_x_u = index_x_d + img_width_model + if j == 0: + index_y_d = j * height_mid + index_y_u = index_y_d + img_height_model + else: + index_y_d = j * height_mid + index_y_u = index_y_d + img_height_model + + if index_x_u > img_w: + index_x_u = img_w + index_x_d = img_w - img_width_model + if index_y_u > img_h: + index_y_u = img_h + index_y_d = img_h - img_height_model + + img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] + label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) + + seg = label_p_pred[0, :, :, :] + seg = seg * 255 + + if i == 0 and j == 0: + seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin] + prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg + elif i == nxf - 1 and j == nyf - 1: + seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0] + prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg + elif i == 0 and j == nyf - 1: + seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin] + prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg + elif i == nxf - 1 and j == 0: + seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0] + prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg + elif i == 0 and j != 0 and j != nyf - 1: + seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin] + prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg + elif i == nxf - 1 and j != 0 and j != nyf - 1: + seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0] + prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg + elif i != 0 and i != nxf - 1 and j == 0: + seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin] + prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg + elif i != 0 and i != nxf - 1 and j == nyf - 1: + seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin] + prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg + else: + seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin] + prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg - margin = True - - if margin: - kernel = np.ones((5, 5), np.uint8) - - margin = int(0 * img_width_model) - - width_mid = img_width_model - 2 * margin - height_mid = img_height_model - 2 * margin - - img = img / float(255.0) - - img_h = img.shape[0] - img_w = img.shape[1] - - prediction_true = np.zeros((img_h, img_w, 3)) - mask_true = np.zeros((img_h, img_w)) - nxf = img_w / float(width_mid) - nyf = img_h / float(height_mid) - - nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) - nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) - - for i in range(nxf): - for j in range(nyf): - if i == 0: - index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model - else: - index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model - - if j == 0: - index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model - else: - index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model - - if index_x_u > img_w: - index_x_u = img_w - index_x_d = img_w - img_width_model - if index_y_u > img_h: - index_y_u = img_h - index_y_d = img_h - img_height_model - - img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) - - seg = label_p_pred[0, :, :, :] - seg = seg * 255 - - if i == 0 and j == 0: - seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin] - prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg - elif i == nxf - 1 and j == nyf - 1: - seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0] - prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg - elif i == 0 and j == nyf - 1: - seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg - elif i == nxf - 1 and j == 0: - seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0] - prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg - elif i == 0 and j != 0 and j != nyf - 1: - seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg - elif i == nxf - 1 and j != 0 and j != nyf - 1: - seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0] - prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg - elif i != 0 and i != nxf - 1 and j == 0: - seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin] - prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg - elif i != 0 and i != nxf - 1 and j == nyf - 1: - seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg - else: - seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin] - prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg - - prediction_true = prediction_true.astype(int) - - del model_enhancement - del session_enhancemnet + prediction_true = prediction_true.astype(int) - return prediction_true + return prediction_true def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred): self.logger.debug("enter calculate_width_height_by_columns") @@ -1252,7 +1240,6 @@ class eynollah: id_indexer_l = 0 if len(found_polygons_text_region) > 0: self.xml_reading_order(page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals) - for mm in range(len(found_polygons_text_region)): textregion = ET.SubElement(page, 'TextRegion') textregion.set('id', 'r%s' % id_indexer) @@ -1282,9 +1269,9 @@ class eynollah: points_co += ',' points_co += str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y)) else: - points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x)) - points_co = points_co + ',' - points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y)) + points_co += str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x)) + points_co += ',' + points_co += str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y)) elif curved_line and abs(slopes[mm]) > 45: if len(all_found_texline_polygons[mm][j][l]) == 2: points_co += str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x)) @@ -1298,7 +1285,6 @@ class eynollah: if l < len(all_found_texline_polygons[mm][j]) - 1: points_co += ' ' coord.set('points', points_co) - add_textequiv(textregion) for mm in range(len(found_polygons_marginals)): @@ -2002,12 +1988,13 @@ class eynollah: text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page) text_regions_p = np.array(text_regions_p) - if num_col_classifier == 1 or num_col_classifier == 2: + if num_col_classifier in (1, 2): 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) - except: + except Exception as e: + self.logger.error("exception %s", e) pass if self.plotter: