# 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.append(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_gif from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process import tools.infer.benchmark_utils as benchmark_utils logger = get_logger() class TextDetector(object): def __init__(self, args): self.args = args self.det_algorithm = args.det_algorithm 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 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 self.det_sast_polygon = args.det_sast_polygon if self.det_sast_polygon: 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 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) self.det_times = utility.Timer() def order_points_clockwise(self, pts): """ reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py # sort the points based on their x-coordinates """ xSorted = pts[np.argsort(pts[:, 0]), :] # grab the left-most and right-most points from the sorted # x-roodinate points leftMost = xSorted[:2, :] rightMost = xSorted[2:, :] # now, sort the left-most coordinates according to their # y-coordinates so we can grab the top-left and bottom-left # points, respectively leftMost = leftMost[np.argsort(leftMost[:, 1]), :] (tl, bl) = leftMost rightMost = rightMost[np.argsort(rightMost[:, 1]), :] (tr, br) = rightMost rect = np.array([tl, tr, br, bl], dtype="float32") return rect 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: 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: box = self.clip_det_res(box, img_height, img_width) dt_boxes_new.append(box) dt_boxes = np.array(dt_boxes_new) return dt_boxes def __call__(self, img): ori_im = img.copy() data = {'image': img} self.det_times.total_time.start() self.det_times.preprocess_time.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() self.det_times.preprocess_time.end() self.det_times.inference_time.start() 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) self.det_times.inference_time.end() 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 == 'DB': preds['maps'] = outputs[0] else: raise NotImplementedError self.det_times.postprocess_time.start() self.predictor.try_shrink_memory() post_result = self.postprocess_op(preds, shape_list) dt_boxes = post_result[0]['points'] if self.det_algorithm == "SAST" and self.det_sast_polygon: 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) self.det_times.postprocess_time.end() self.det_times.total_time.end() self.det_times.img_num += 1 return dt_boxes, self.det_times.total_time.value() if __name__ == "__main__": args = utility.parse_args() image_file_list = get_image_file_list(args.image_dir) text_detector = TextDetector(args) count = 0 total_time = 0 draw_img_save = "./inference_results" if args.warmup: img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) for i in range(10): res = text_detector(img) cpu_mem, gpu_mem, gpu_util = 0, 0, 0 if not os.path.exists(draw_img_save): os.makedirs(draw_img_save) for image_file in image_file_list: img, flag = check_and_read_gif(image_file) if not flag: img = cv2.imread(image_file) if img is None: logger.info("error in loading image:{}".format(image_file)) continue st = time.time() dt_boxes, _ = text_detector(img) elapse = time.time() - st if count > 0: total_time += elapse count += 1 if args.benchmark: cm, gm, gu = utility.get_current_memory_mb(0) cpu_mem += cm gpu_mem += gm gpu_util += gu logger.info("Predict time of {}: {}".format(image_file, elapse)) src_im = utility.draw_text_det_res(dt_boxes, image_file) img_name_pure = os.path.split(image_file)[-1] img_path = os.path.join(draw_img_save, "det_res_{}".format(img_name_pure)) logger.info("The visualized image saved in {}".format(img_path)) # print the information about memory and time-spent if args.benchmark: mems = { 'cpu_rss_mb': cpu_mem / count, 'gpu_rss_mb': gpu_mem / count, 'gpu_util': gpu_util * 100 / count } else: mems = None logger.info("The predict time about detection module is as follows: ") det_time_dict = text_detector.det_times.report(average=True) det_model_name = args.det_model_dir if args.benchmark: # construct log information model_info = { 'model_name': args.det_model_dir.split('/')[-1], 'precision': args.precision } data_info = { 'batch_size': 1, 'shape': 'dynamic_shape', 'data_num': det_time_dict['img_num'] } perf_info = { 'preprocess_time_s': det_time_dict['preprocess_time'], 'inference_time_s': det_time_dict['inference_time'], 'postprocess_time_s': det_time_dict['postprocess_time'], 'total_time_s': det_time_dict['total_time'] } benchmark_log = benchmark_utils.PaddleInferBenchmark( text_detector.config, model_info, data_info, perf_info, mems) benchmark_log("Det")