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https://github.com/parker-int64/yolov5-RGBD

Qt QML based yolov5 + RGBD camera program
https://github.com/parker-int64/yolov5-RGBD

cuda cudnn depth opencv openvino qml-applications qt rgbd tensorrt yolov5

Last synced: 5 days ago
JSON representation

Qt QML based yolov5 + RGBD camera program

Host: GitHub
URL: https://github.com/parker-int64/yolov5-RGBD
Owner: parker-int64
Created: 2022-07-29T01:21:59.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-07-29T02:31:25.000Z (over 2 years ago)
Last Synced: 2024-08-02T01:25:16.429Z (4 months ago)
Topics: cuda, cudnn, depth, opencv, openvino, qml-applications, qt, rgbd, tensorrt, yolov5
Language: C++
Homepage:
Size: 284 KB
Stars: 17
Watchers: 2
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

awesome-yolo-object-detection - parker-int64/yolov5-RGBD - int64/yolov5-RGBD?style=social"/> : Qt QML based yolov5 + RGBD camera program. (Applications)
awesome-yolo-object-detection - parker-int64/yolov5-RGBD - int64/yolov5-RGBD?style=social"/> : Qt QML based yolov5 + RGBD camera program. (Applications)

README

        # Yolo RGBD Demo program

---

**项目仅做参考，部分功能未完成**

If I’d had more time, I could have finished it.

文件目录说明

```

YoloRGBD

    ├─assets    ------ 资源目录，存放图片，字体文件等

    ├─bin       ------ 默认可执行文件生成目录

    │  └─conf   ------ conf文件用于存放Debug生成的json文件

    ├─iconwin   ------ windows下icon的源文件，文件下app.rc是编译所需文件

    ├─include   ------ 头文件目录

    ├─qml       ------ qml界面文件目录

    ├─script    ------ 脚本文件目录，`nvidia_gpu_info.ps1`用于获取Nvidia GPU使用率

    ├─src       ------ 源文件目录

    ├─video     ------ 测试用视频文件目录

    ├─weights   ------ 存放yolox (Intel OpenVino), yolov5 Engine(yolov5 Nvidia tensrorRT) 的网络模型文件

    ├─qtquickcontrols2.conf    ----- qml主题文件

    └─qml.qrc   ------ qml的资源文件，里面存放了`.qml`文件路径信息， 资源文件路径信息，qml主题文件路径信息，是编译所需的源文件

```

## 编译方式

---

建议采用Qt Creator，（CMake）构建系统构建。

环境说明：

||||

|----|----|----|

|类别|版本|备注|

|OS|Windows 10 20H2|CPU: Intel i5/i7 六代以上, GPU: Nvidia GTX 1060|

|Qt|5.15.2(MSVC 2019)|Qt Creator 7.0.0|

|OpenCV|4.x|3.x到4.x需更改部分代码（将`bin`添加至环境变量中）|

|OpenVino|2021.4.1(2022.1.0也测试可行)|需要自行复制`tbb.dll/tbb_debug.dll`到可执行文件目录(或者将其添加至环境变量中)|

|Nvidia CUDA|10.3|将`bin`添加至环境变量中|

|Nvidia cuDNN|8.x|将`bin`添加至环境变量中|

|Nvidia TensorRT|8.x|将`bin`添加至环境变量中|

|OpenNI2|/|需拷贝OpenNI Lib下所有文件到可执行文件目录|

**OpenVino 仅支持Intel CPU/GPU以及部分Arm设备**，详情参见[Support lists](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Working_with_devices.html)，如需编译请访问[Get Started](https://docs.openvino.ai/latest/get_started.html)下载所需套件。

**Nvidia CUDA/cuDNN/TensorRT 仅支持Nvidia自家GPU**，如需编译请访问[Nvidia Developer](https://developer.nvidia.com/)下载所需套件。

# 运行截图

+ Home page

![HomePage](https://raw.githubusercontent.com/parker-int64/yolov5-RGBD/main/assets/homepage.png)

+ Detect page

![DetectPage](https://raw.githubusercontent.com/parker-int64/yolov5-RGBD/main/assets/detectpage.png)

# 运行方式

按照正确的顺序选择摄像头序列，像素大小，推理后端类别和模型大小，先点击`Save and Init Engine`初始化引擎，然后可以点击左边的`Start Capture`和`Start Yolo Detection`