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ba501fd963
* add override mark * delete some * recovery * recovery * add tracking * add tracking py_bind and example * add pptracking * add pptracking * iomanip head file * add opencv_video lib * add python libs package Signed-off-by: ChaoII <849453582@qq.com> * complete comments Signed-off-by: ChaoII <849453582@qq.com> * add jdeTracker_ member variable Signed-off-by: ChaoII <849453582@qq.com> * add 'FASTDEPLOY_DECL' macro Signed-off-by: ChaoII <849453582@qq.com> * remove kwargs params Signed-off-by: ChaoII <849453582@qq.com> * [Doc]update pptracking docs * delete 'ENABLE_PADDLE_FRONTEND' switch * add pptracking unit test * update pptracking unit test Signed-off-by: ChaoII <849453582@qq.com> * modify test video file path and remove trt test * update unit test model url * remove 'FASTDEPLOY_DECL' macro Signed-off-by: ChaoII <849453582@qq.com> * fix build python packages about pptracking on win32 Signed-off-by: ChaoII <849453582@qq.com> Signed-off-by: ChaoII <849453582@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com>
90 lines
3.6 KiB
Python
90 lines
3.6 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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import numpy as np
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import pickle
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def test_pptracking_cpu():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/pptracking.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "pptracking/fairmot_hrnetv2_w18_dlafpn_30e_576x320"
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# use default backend
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runtime_option = fd.RuntimeOption()
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "infer_cfg.yml")
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model = fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=runtime_option)
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cap = cv2.VideoCapture("./person.mp4")
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frame_id = 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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# compare diff
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expect = pickle.load(open("pptracking/frame" + str(frame_id) + ".pkl", "rb"))
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diff_boxes = np.fabs(np.array(expect["boxes"]) - np.array(result.boxes))
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diff_scores = np.fabs(np.array(expect["scores"]) - np.array(result.scores))
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diff = max(diff_boxes.max(), diff_scores.max())
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thres = 1e-05
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assert diff < thres, "The label diff is %f, which is bigger than %f" % (diff, thres)
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frame_id = frame_id + 1
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cv2.waitKey(30)
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if frame_id >= 10:
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cap.release()
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cv2.destroyAllWindows()
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break
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def test_pptracking_gpu():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/pptracking.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "pptracking/fairmot_hrnetv2_w18_dlafpn_30e_576x320"
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runtime_option = fd.RuntimeOption()
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runtime_option.use_gpu()
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# Not supported trt backend, up to now
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# runtime_option.use_trt_backend()
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "infer_cfg.yml")
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model = fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=runtime_option)
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cap = cv2.VideoCapture("./person.mp4")
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frame_id = 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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# compare diff
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expect = pickle.load(open("pptracking/frame" + str(frame_id) + ".pkl", "rb"))
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diff_boxes = np.fabs(np.array(expect["boxes"]) - np.array(result.boxes))
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diff_scores = np.fabs(np.array(expect["scores"]) - np.array(result.scores))
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diff = max(diff_boxes.max(), diff_scores.max())
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thres = 1e-05
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assert diff < thres, "The label diff is %f, which is bigger than %f" % (diff, thres)
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frame_id = frame_id + 1
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cv2.waitKey(30)
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if frame_id >= 10:
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cap.release()
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cv2.destroyAllWindows()
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break
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