# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../.."))) os.environ["FLAGS_allocator_strategy"] = "auto_growth" import cv2 import numpy as np import time import sys import tools.infer.utility as utility from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, check_and_read from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process import json class TextDetector(object): def __init__(self, args, logger=None): if logger is None: logger = get_logger() self.args = args self.det_algorithm = args.det_algorithm self.use_onnx = args.use_onnx pre_process_list = [ { "DetResizeForTest": { "limit_side_len": args.det_limit_side_len, "limit_type": args.det_limit_type, } }, { "NormalizeImage": { "std": [0.229, 0.224, 0.225], "mean": [0.485, 0.456, 0.406], "scale": "1./255.", "order": "hwc", } }, {"ToCHWImage": None}, {"KeepKeys": {"keep_keys": ["image", "shape"]}}, ] postprocess_params = {} if self.det_algorithm == "DB": postprocess_params["name"] = "DBPostProcess" postprocess_params["thresh"] = args.det_db_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["max_candidates"] = 1000 postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio postprocess_params["use_dilation"] = args.use_dilation postprocess_params["score_mode"] = args.det_db_score_mode postprocess_params["box_type"] = args.det_box_type elif self.det_algorithm == "DB++": postprocess_params["name"] = "DBPostProcess" postprocess_params["thresh"] = args.det_db_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["max_candidates"] = 1000 postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio postprocess_params["use_dilation"] = args.use_dilation postprocess_params["score_mode"] = args.det_db_score_mode postprocess_params["box_type"] = args.det_box_type pre_process_list[1] = { "NormalizeImage": { "std": [1.0, 1.0, 1.0], "mean": [0.48109378172549, 0.45752457890196, 0.40787054090196], "scale": "1./255.", "order": "hwc", } } elif self.det_algorithm == "EAST": postprocess_params["name"] = "EASTPostProcess" postprocess_params["score_thresh"] = args.det_east_score_thresh postprocess_params["cover_thresh"] = args.det_east_cover_thresh postprocess_params["nms_thresh"] = args.det_east_nms_thresh elif self.det_algorithm == "SAST": pre_process_list[0] = { "DetResizeForTest": {"resize_long": args.det_limit_side_len} } postprocess_params["name"] = "SASTPostProcess" postprocess_params["score_thresh"] = args.det_sast_score_thresh postprocess_params["nms_thresh"] = args.det_sast_nms_thresh if args.det_box_type == "poly": postprocess_params["sample_pts_num"] = 6 postprocess_params["expand_scale"] = 1.2 postprocess_params["shrink_ratio_of_width"] = 0.2 else: postprocess_params["sample_pts_num"] = 2 postprocess_params["expand_scale"] = 1.0 postprocess_params["shrink_ratio_of_width"] = 0.3 elif self.det_algorithm == "PSE": postprocess_params["name"] = "PSEPostProcess" postprocess_params["thresh"] = args.det_pse_thresh postprocess_params["box_thresh"] = args.det_pse_box_thresh postprocess_params["min_area"] = args.det_pse_min_area postprocess_params["box_type"] = args.det_box_type postprocess_params["scale"] = args.det_pse_scale elif self.det_algorithm == "FCE": pre_process_list[0] = {"DetResizeForTest": {"rescale_img": [1080, 736]}} postprocess_params["name"] = "FCEPostProcess" postprocess_params["scales"] = args.scales postprocess_params["alpha"] = args.alpha postprocess_params["beta"] = args.beta postprocess_params["fourier_degree"] = args.fourier_degree postprocess_params["box_type"] = args.det_box_type elif self.det_algorithm == "CT": pre_process_list[0] = {"ScaleAlignedShort": {"short_size": 640}} postprocess_params["name"] = "CTPostProcess" else: logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) sys.exit(0) self.preprocess_op = create_operators(pre_process_list) self.postprocess_op = build_post_process(postprocess_params) ( self.predictor, self.input_tensor, self.output_tensors, self.config, ) = utility.create_predictor(args, "det", logger) if self.use_onnx: img_h, img_w = self.input_tensor.shape[2:] if isinstance(img_h, str) or isinstance(img_w, str): pass elif img_h is not None and img_w is not None and img_h > 0 and img_w > 0: pre_process_list[0] = { "DetResizeForTest": {"image_shape": [img_h, img_w]} } self.preprocess_op = create_operators(pre_process_list) if args.benchmark: import auto_log pid = os.getpid() gpu_id = utility.get_infer_gpuid() self.autolog = auto_log.AutoLogger( model_name="det", model_precision=args.precision, batch_size=1, data_shape="dynamic", save_path=None, # not used if logger is not None inference_config=self.config, pids=pid, process_name=None, gpu_ids=gpu_id if args.use_gpu else None, time_keys=["preprocess_time", "inference_time", "postprocess_time"], warmup=2, logger=logger, ) def order_points_clockwise(self, pts): rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0) diff = np.diff(np.array(tmp), axis=1) rect[1] = tmp[np.argmin(diff)] rect[3] = tmp[np.argmax(diff)] return rect def pad_polygons(self, polygon, max_points): padding_size = max_points - len(polygon) if padding_size == 0: return polygon last_point = polygon[-1] padding = np.repeat([last_point], padding_size, axis=0) return np.vstack([polygon, padding]) def clip_det_res(self, points, img_height, img_width): for pno in range(points.shape[0]): points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) return points def filter_tag_det_res(self, dt_boxes, image_shape): img_height, img_width = image_shape[0:2] dt_boxes_new = [] for box in dt_boxes: if type(box) is list: box = np.array(box) box = self.order_points_clockwise(box) box = self.clip_det_res(box, img_height, img_width) rect_width = int(np.linalg.norm(box[0] - box[1])) rect_height = int(np.linalg.norm(box[0] - box[3])) if rect_width <= 3 or rect_height <= 3: continue dt_boxes_new.append(box) dt_boxes = np.array(dt_boxes_new) return dt_boxes def filter_tag_det_res_only_clip(self, dt_boxes, image_shape): img_height, img_width = image_shape[0:2] dt_boxes_new = [] for box in dt_boxes: if type(box) is list: box = np.array(box) box = self.clip_det_res(box, img_height, img_width) dt_boxes_new.append(box) if len(dt_boxes_new) > 0: max_points = max(len(polygon) for polygon in dt_boxes_new) dt_boxes_new = [ self.pad_polygons(polygon, max_points) for polygon in dt_boxes_new ] dt_boxes = np.array(dt_boxes_new) return dt_boxes def predict(self, img): ori_im = img.copy() data = {"image": img} st = time.time() if self.args.benchmark: self.autolog.times.start() data = transform(data, self.preprocess_op) img, shape_list = data if img is None: return None, 0 img = np.expand_dims(img, axis=0) shape_list = np.expand_dims(shape_list, axis=0) img = img.copy() if self.args.benchmark: self.autolog.times.stamp() if self.use_onnx: input_dict = {} input_dict[self.input_tensor.name] = img outputs = self.predictor.run(self.output_tensors, input_dict) else: self.input_tensor.copy_from_cpu(img) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) if self.args.benchmark: self.autolog.times.stamp() preds = {} if self.det_algorithm == "EAST": preds["f_geo"] = outputs[0] preds["f_score"] = outputs[1] elif self.det_algorithm == "SAST": preds["f_border"] = outputs[0] preds["f_score"] = outputs[1] preds["f_tco"] = outputs[2] preds["f_tvo"] = outputs[3] elif self.det_algorithm in ["DB", "PSE", "DB++"]: preds["maps"] = outputs[0] elif self.det_algorithm == "FCE": for i, output in enumerate(outputs): preds["level_{}".format(i)] = output elif self.det_algorithm == "CT": preds["maps"] = outputs[0] preds["score"] = outputs[1] else: raise NotImplementedError post_result = self.postprocess_op(preds, shape_list) dt_boxes = post_result[0]["points"] if self.args.det_box_type == "poly": dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) else: dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) if self.args.benchmark: self.autolog.times.end(stamp=True) et = time.time() return dt_boxes, et - st def __call__(self, img, use_slice=False): # For image like poster with one side much greater than the other side, # splitting recursively and processing with overlap to enhance performance. MIN_BOUND_DISTANCE = 50 dt_boxes = np.zeros((0, 4, 2), dtype=np.float32) elapse = 0 if ( img.shape[0] / img.shape[1] > 2 and img.shape[0] > self.args.det_limit_side_len and use_slice ): start_h = 0 end_h = 0 while end_h <= img.shape[0]: end_h = start_h + img.shape[1] * 3 // 4 subimg = img[start_h:end_h, :] if len(subimg) == 0: break sub_dt_boxes, sub_elapse = self.predict(subimg) offset = start_h # To prevent text blocks from being cut off, roll back a certain buffer area. if ( len(sub_dt_boxes) == 0 or img.shape[1] - max([x[-1][1] for x in sub_dt_boxes]) > MIN_BOUND_DISTANCE ): start_h = end_h else: sorted_indices = np.argsort(sub_dt_boxes[:, 2, 1]) sub_dt_boxes = sub_dt_boxes[sorted_indices] bottom_line = ( 0 if len(sub_dt_boxes) <= 1 else int(np.max(sub_dt_boxes[:-1, 2, 1])) ) if bottom_line > 0: start_h += bottom_line sub_dt_boxes = sub_dt_boxes[ sub_dt_boxes[:, 2, 1] <= bottom_line ] else: start_h = end_h if len(sub_dt_boxes) > 0: if dt_boxes.shape[0] == 0: dt_boxes = sub_dt_boxes + np.array( [0, offset], dtype=np.float32 ) else: dt_boxes = np.append( dt_boxes, sub_dt_boxes + np.array([0, offset], dtype=np.float32), axis=0, ) elapse += sub_elapse elif ( img.shape[1] / img.shape[0] > 3 and img.shape[1] > self.args.det_limit_side_len * 3 and use_slice ): start_w = 0 end_w = 0 while end_w <= img.shape[1]: end_w = start_w + img.shape[0] * 3 // 4 subimg = img[:, start_w:end_w] if len(subimg) == 0: break sub_dt_boxes, sub_elapse = self.predict(subimg) offset = start_w if ( len(sub_dt_boxes) == 0 or img.shape[0] - max([x[-1][0] for x in sub_dt_boxes]) > MIN_BOUND_DISTANCE ): start_w = end_w else: sorted_indices = np.argsort(sub_dt_boxes[:, 2, 0]) sub_dt_boxes = sub_dt_boxes[sorted_indices] right_line = ( 0 if len(sub_dt_boxes) <= 1 else int(np.max(sub_dt_boxes[:-1, 1, 0])) ) if right_line > 0: start_w += right_line sub_dt_boxes = sub_dt_boxes[sub_dt_boxes[:, 1, 0] <= right_line] else: start_w = end_w if len(sub_dt_boxes) > 0: if dt_boxes.shape[0] == 0: dt_boxes = sub_dt_boxes + np.array( [offset, 0], dtype=np.float32 ) else: dt_boxes = np.append( dt_boxes, sub_dt_boxes + np.array([offset, 0], dtype=np.float32), axis=0, ) elapse += sub_elapse else: dt_boxes, elapse = self.predict(img) return dt_boxes, elapse if __name__ == "__main__": args = utility.parse_args() image_file_list = get_image_file_list(args.image_dir) total_time = 0 draw_img_save_dir = args.draw_img_save_dir os.makedirs(draw_img_save_dir, exist_ok=True) # logger log_file = args.save_log_path if os.path.isdir(args.save_log_path) or ( not os.path.exists(args.save_log_path) and args.save_log_path.endswith("/") ): log_file = os.path.join(log_file, "benchmark_detection.log") logger = get_logger(log_file=log_file) # create text detector text_detector = TextDetector(args, logger) if args.warmup: img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) for i in range(2): res = text_detector(img) save_results = [] for idx, image_file in enumerate(image_file_list): img, flag_gif, flag_pdf = check_and_read(image_file) if not flag_gif and not flag_pdf: img = cv2.imread(image_file) if not flag_pdf: if img is None: logger.debug("error in loading image:{}".format(image_file)) continue imgs = [img] else: page_num = args.page_num if page_num > len(img) or page_num == 0: page_num = len(img) imgs = img[:page_num] for index, img in enumerate(imgs): st = time.time() dt_boxes, _ = text_detector(img) elapse = time.time() - st total_time += elapse if len(imgs) > 1: save_pred = ( os.path.basename(image_file) + "_" + str(index) + "\t" + str(json.dumps([x.tolist() for x in dt_boxes])) + "\n" ) else: save_pred = ( os.path.basename(image_file) + "\t" + str(json.dumps([x.tolist() for x in dt_boxes])) + "\n" ) save_results.append(save_pred) logger.info(save_pred) if len(imgs) > 1: logger.info( "{}_{} The predict time of {}: {}".format( idx, index, image_file, elapse ) ) else: logger.info( "{} The predict time of {}: {}".format(idx, image_file, elapse) ) src_im = utility.draw_text_det_res(dt_boxes, img) if flag_gif: save_file = image_file[:-3] + "png" elif flag_pdf: save_file = image_file.replace(".pdf", "_" + str(index) + ".png") else: save_file = image_file img_path = os.path.join( draw_img_save_dir, "det_res_{}".format(os.path.basename(save_file)) ) cv2.imwrite(img_path, src_im) logger.info("The visualized image saved in {}".format(img_path)) with open(os.path.join(draw_img_save_dir, "det_results.txt"), "w") as f: f.writelines(save_results) f.close() if args.benchmark: text_detector.autolog.report()