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https://github.com/PaddlePaddle/FastDeploy.git
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ChaoII
c7ec14de95
* [Model] add vsr serials models Signed-off-by: ChaoII <849453582@qq.com> * [Model] add vsr serials models Signed-off-by: ChaoII <849453582@qq.com> * fix build problem Signed-off-by: ChaoII <849453582@qq.com> * fix code style Signed-off-by: ChaoII <849453582@qq.com> * modify according to review suggestions Signed-off-by: ChaoII <849453582@qq.com> * modify vsr trt example Signed-off-by: ChaoII <849453582@qq.com> * update sr directory * fix BindPPSR * add doxygen comment * add sr unit test * update model file url Signed-off-by: ChaoII <849453582@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com>
84 lines
2.5 KiB
Python
84 lines
2.5 KiB
Python
# Copyright (c) 2022 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 fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", required=True, help="Path of model.")
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parser.add_argument(
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"--video", type=str, required=True, help="Path of test video file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "infer_cfg.yml")
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model = fd.vision.tracking.PPTracking(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 初始化轨迹记录器
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recorder = fd.vision.tracking.TrailRecorder()
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# 绑定记录器 注意：每次预测时，往trail_recorder里面插入数据，随着预测次数的增加，内存会不断地增长，
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# 可以通过unbind_recorder()方法来解除绑定
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model.bind_recorder(recorder)
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# 预测图片分割结果
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cap = cv2.VideoCapture(args.video)
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# count = 0
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while True:
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_, frame = cap.read()
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if frame is None:
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break
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result = model.predict(frame)
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# count += 1
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# if count == 10:
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# model.unbind_recorder()
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img = fd.vision.vis_mot(frame, result, 0.0, recorder)
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cv2.imshow("video", img)
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if cv2.waitKey(30) == ord("q"):
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break
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model.unbind_recorder()
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cap.release()
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cv2.destroyAllWindows()
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