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| # 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 tools.infer.utility as utility | |
| from ppocr.data import create_operators, transform | |
| from ppocr.postprocess import build_post_process | |
| from ppocr.utils.logging import get_logger | |
| from ppocr.utils.utility import get_image_file_list, check_and_read | |
| from ppstructure.utility import parse_args | |
| from picodet_postprocess import PicoDetPostProcess | |
| logger = get_logger() | |
| class LayoutPredictor(object): | |
| def __init__(self, args): | |
| pre_process_list = [{ | |
| 'Resize': { | |
| 'size': [800, 608] | |
| } | |
| }, { | |
| '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'] | |
| } | |
| }] | |
| postprocess_params = { | |
| 'name': 'PicoDetPostProcess', | |
| "layout_dict_path": args.layout_dict_path, | |
| "score_threshold": args.layout_score_threshold, | |
| "nms_threshold": args.layout_nms_threshold, | |
| } | |
| 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, 'layout', logger) | |
| def __call__(self, img): | |
| ori_im = img.copy() | |
| data = {'image': img} | |
| data = transform(data, self.preprocess_op) | |
| img = data[0] | |
| if img is None: | |
| return None, 0 | |
| img = np.expand_dims(img, axis=0) | |
| img = img.copy() | |
| preds, elapse = 0, 1 | |
| starttime = time.time() | |
| self.input_tensor.copy_from_cpu(img) | |
| self.predictor.run() | |
| np_score_list, np_boxes_list = [], [] | |
| output_names = self.predictor.get_output_names() | |
| num_outs = int(len(output_names) / 2) | |
| for out_idx in range(num_outs): | |
| np_score_list.append( | |
| self.predictor.get_output_handle(output_names[out_idx]) | |
| .copy_to_cpu()) | |
| np_boxes_list.append( | |
| self.predictor.get_output_handle(output_names[ | |
| out_idx + num_outs]).copy_to_cpu()) | |
| preds = dict(boxes=np_score_list, boxes_num=np_boxes_list) | |
| post_preds = self.postprocess_op(ori_im, img, preds) | |
| elapse = time.time() - starttime | |
| return post_preds, elapse | |
| def main(args): | |
| image_file_list = get_image_file_list(args.image_dir) | |
| layout_predictor = LayoutPredictor(args) | |
| count = 0 | |
| total_time = 0 | |
| repeats = 50 | |
| for image_file in image_file_list: | |
| img, flag, _ = check_and_read(image_file) | |
| if not flag: | |
| img = cv2.imread(image_file) | |
| if img is None: | |
| logger.info("error in loading image:{}".format(image_file)) | |
| continue | |
| layout_res, elapse = layout_predictor(img) | |
| logger.info("result: {}".format(layout_res)) | |
| if count > 0: | |
| total_time += elapse | |
| count += 1 | |
| logger.info("Predict time of {}: {}".format(image_file, elapse)) | |
| if __name__ == "__main__": | |
| main(parse_args()) | |