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0.0 0.0 0.0 52.64 MB

License: Apache License 2.0

Shell 0.25% C++ 3.11% Python 69.00% Java 1.70% Cuda 0.64% Makefile 0.13% CMake 0.54% Batchfile 0.02% Jupyter Notebook 24.61%

integrated-detection-and-pedestrian-re-identification-system_p1nw's Introduction

使用说明

1.

创建 models 文件夹将,利用 PaddleDetection 将权重导出并放入

models/
      model1/infer_cfg.yml
             model.pdiparams
             model.pdiparams.info
             model.pdmodel

2.

创建 Market-1501-v15.09.15 文件夹,用来存放 Re-IDquerybounding_box_test

3.

运行 main.py 打开使用界面

image

4.

首先界面右上角选择你自己准备的模型,并选择置信度GPU 或 CPU 加速

再添加你想要检测的视频(如果使用 CPU 多线程则线程数量对应视频数量),点击「开始检测」即可开始行人目标检测。

5.

检测完毕后,检测结果将会被存放在 output/detect

点击「开始追踪」按钮后,会启动追踪操作,追踪完毕之后的追踪结果会被存放在 output/track 中。

6.

多镜头使用说明:

添加两个及以上的视频(视频以数字命名不要有下划线)且同个人在不同视频中都有出现。

通过点击选择需要寻找的目标,根据追踪的结果,会自动将每个视频中的每个 ID 的图片单独抠出,存放在 Market-1501-v15.09.15/bounding_box_test,每个视频中的每个 ID 的一张图片会被存放在 query 文件夹中。

如下图:image

7.

选择一个 ID 进行多镜头追踪,追踪完毕后会输出在输入的视频中目标的 ID、目标的图片和在视频中出现的当前帧。 如下图:

image

8.

输出图会保存在 show.png 中。

如下图:

image

integrated-detection-and-pedestrian-re-identification-system_p1nw's People

Contributors

mr-ind1fferent avatar aieson avatar trellixvulnteam avatar

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