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144 lines
4.6 KiB
Python
144 lines
4.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))
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os.environ["FLAGS_allocator_strategy"] = "auto_growth"
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import cv2
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import numpy as np
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import time
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import tools.infer.utility as utility
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from ppocr.data import create_operators, transform
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from ppocr.postprocess import build_post_process
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from ppocr.utils.logging import get_logger
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from ppocr.utils.utility import get_image_file_list, check_and_read
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from ppstructure.utility import parse_args
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from picodet_postprocess import PicoDetPostProcess
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logger = get_logger()
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class LayoutPredictor(object):
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def __init__(self, args):
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pre_process_list = [
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{"Resize": {"size": [800, 608]}},
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{
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"NormalizeImage": {
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"std": [0.229, 0.224, 0.225],
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"mean": [0.485, 0.456, 0.406],
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"scale": "1./255.",
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"order": "hwc",
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}
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},
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{"ToCHWImage": None},
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{"KeepKeys": {"keep_keys": ["image"]}},
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]
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postprocess_params = {
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"name": "PicoDetPostProcess",
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"layout_dict_path": args.layout_dict_path,
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"score_threshold": args.layout_score_threshold,
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"nms_threshold": args.layout_nms_threshold,
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}
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self.preprocess_op = create_operators(pre_process_list)
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self.postprocess_op = build_post_process(postprocess_params)
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(
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self.predictor,
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self.input_tensor,
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self.output_tensors,
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self.config,
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) = utility.create_predictor(args, "layout", logger)
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self.use_onnx = args.use_onnx
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def __call__(self, img):
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ori_im = img.copy()
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data = {"image": img}
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data = transform(data, self.preprocess_op)
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img = data[0]
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if img is None:
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return None, 0
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img = np.expand_dims(img, axis=0)
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img = img.copy()
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preds, elapse = 0, 1
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starttime = time.time()
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np_score_list, np_boxes_list = [], []
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if self.use_onnx:
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input_dict = {}
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input_dict[self.input_tensor.name] = img
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outputs = self.predictor.run(self.output_tensors, input_dict)
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num_outs = int(len(outputs) / 2)
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for out_idx in range(num_outs):
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np_score_list.append(outputs[out_idx])
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np_boxes_list.append(outputs[out_idx + num_outs])
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else:
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self.input_tensor.copy_from_cpu(img)
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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num_outs = int(len(output_names) / 2)
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for out_idx in range(num_outs):
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np_score_list.append(
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self.predictor.get_output_handle(
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output_names[out_idx]
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).copy_to_cpu()
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)
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np_boxes_list.append(
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self.predictor.get_output_handle(
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output_names[out_idx + num_outs]
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).copy_to_cpu()
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)
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preds = dict(boxes=np_score_list, boxes_num=np_boxes_list)
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post_preds = self.postprocess_op(ori_im, img, preds)
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elapse = time.time() - starttime
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return post_preds, elapse
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def main(args):
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image_file_list = get_image_file_list(args.image_dir)
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layout_predictor = LayoutPredictor(args)
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count = 0
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total_time = 0
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repeats = 50
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for image_file in image_file_list:
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img, flag, _ = check_and_read(image_file)
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if not flag:
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img = cv2.imread(image_file)
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if img is None:
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logger.info("error in loading image:{}".format(image_file))
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continue
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layout_res, elapse = layout_predictor(img)
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logger.info("result: {}".format(layout_res))
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if count > 0:
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total_time += elapse
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count += 1
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logger.info("Predict time of {}: {}".format(image_file, elapse))
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if __name__ == "__main__":
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main(parse_args())
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