https://github.com/xlite-dev/yolov5face-toolkit

🍅 YOLO5Face 2021 with MNN/NCNN/TNN/ONNXRuntime
https://github.com/xlite-dev/yolov5face-toolkit

cpp yolo5-face yolo5face yolov5-face yolov5-face-landmark yolov5face yolov7-face yolov8-face

Last synced: 6 months ago
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🍅 YOLO5Face 2021 with MNN/NCNN/TNN/ONNXRuntime

• Host: GitHub
• URL: https://github.com/xlite-dev/yolov5face-toolkit
• Owner: xlite-dev
• License: gpl-3.0
• Created: 2022-01-19T15:31:29.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2023-04-21T15:11:17.000Z (almost 3 years ago)
• Last Synced: 2025-03-24T15:01:55.905Z (about 1 year ago)
• Topics: cpp, yolo5-face, yolo5face, yolov5-face, yolov5-face-landmark, yolov5face, yolov7-face, yolov8-face
• Language: C++
• Homepage:
• Size: 38.8 MB
• Stars: 56
• Watchers: 2
• Forks: 8
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# YOLOv5Face ToolKit

使用 🍅🍅 Lite.AI.ToolKit C++工具箱来跑YOLO5Face人脸检测(带关键点)的一些案例(https://github.com/DefTruth/lite.ai.toolkit) , 包含ONNXRuntime C++、MNN、TNN和NCNN版本。

![](resources/YOLO5Face.png)

如果觉得有用，不妨给个Star⭐️🌟支持一下吧~ 🙃🤪🍀

## 2. C++版本源码

YOLO5Face C++ 版本的源码包含ONNXRuntime、MNN、TNN和NCNN四个版本，源码可以在 [lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) 工具箱中找到。本项目主要介绍如何基于 [lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) 工具箱，直接使用YOLO5Face来跑人脸检测。需要说明的是，本项目是基于MacOS下编译的 [liblite.ai.toolkit.v0.1.0.dylib](https://github.com/DefTruth/yolox.lite.ai.toolkit/blob/main/lite.ai.toolkit/lib) 来实现的，对于使用MacOS的用户，可以直接下载本项目包含的*liblite.ai.toolkit.v0.1.0*动态库和其他依赖库进行使用。而非MacOS用户，则需要从[lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) 中下载源码进行编译。[lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) c++工具箱目前包含70+流行的开源模型，就不多介绍了，只是平时顺手捏的，整合了自己学习过程中接触到的一些模型，感兴趣的同学可以去看看。

* [yolo5face.cpp](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolo5face.cpp)

* [yolo5face.h](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolo5face.h)

* [mnn_yolo5face.cpp](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolo5face.cpp)

* [mnn_yolo5face.h](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolo5faceh)

* [tnn_yolo5face.cpp](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolo5face.cpp)

* [tnn_yolo5face.h](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolo5face.h)

* [ncnn_yolo5face.cpp](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ncnn/cv/ncnn_yolo5face.cpp)

* [ncnn_yolo5face.h](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ncnn/cv/ncnn_yolo5face.h)  

ONNXRuntime C++、MNN、TNN和NCNN版本的推理实现均已测试通过，欢迎白嫖~  

## 3. 模型文件

### 3.1 ONNX模型文件

可以从我提供的链接下载 ([Baidu Drive](https://pan.baidu.com/s/1elUGcx7CZkkjEoYhTMwTRQ) code: 8gin) , 也可以从本仓库下载。

|                 Class                 |      Pretrained ONNX Files      |              Rename or Converted From (Repo)              | Size  |

| :-----------------------------------: | :-----------------------------: | :-------------------------------------------------------: | :---: |  

| *lite::cv::face::detect::YOLO5Face* | yolov5face-blazeface-640x640.onnx | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 3.4Mb |

| *lite::cv::face::detect::YOLO5Face* | yolov5face-l-640x640.onnx | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 181Mb |

| *lite::cv::face::detect::YOLO5Face* | yolov5face-m-640x640.onnx | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 83Mb |

| *lite::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-320x320.onnx | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 2.5Mb |

| *lite::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-640x640.onnx | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 4.6Mb |

| *lite::cv::face::detect::YOLO5Face* | yolov5face-n-640x640.onnx | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 9.5Mb |

| *lite::cv::face::detect::YOLO5Face* | yolov5face-s-640x640.onnx | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 30Mb |

### 3.2 MNN模型文件

MNN模型文件下载地址，([Baidu Drive](https://pan.baidu.com/s/1KyO-bCYUv6qPq2M8BH_Okg) code: 9v63), 也可以从本仓库下载。

|                 Class                 |      Pretrained MNN Files      |              Rename or Converted From (Repo)              | Size  |

| :-----------------------------------: | :-----------------------------: | :-------------------------------------------------------: | :---: |

| *lite::mnn::cv::face::detect::YOLO5Face* | yolov5face-blazeface-640x640.mnn | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 3.4Mb |

| *lite::mnn::cv::face::detect::YOLO5Face* | yolov5face-l-640x640.mnn | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 181Mb |

| *lite::mnn::cv::face::detect::YOLO5Face* | yolov5face-m-640x640.mnn | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 83Mb |

| *lite::mnn::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-320x320.mnn | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 2.5Mb |

| *lite::mnn::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-640x640.mnn | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 4.6Mb |

| *lite::mnn::cv::face::detect::YOLO5Face* | yolov5face-n-640x640.mnn | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 9.5Mb |

| *lite::mnn::cv::face::detect::YOLO5Face* | yolov5face-s-640x640.mnn | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 30Mb |

### 3.3 TNN模型文件

TNN模型文件下载地址，([Baidu Drive](https://pan.baidu.com/s/1lvM2YKyUbEc5HKVtqITpcw) code: 6o6k), 也可以从本仓库下载。

|                 Class                 |      Pretrained TNN Files      |              Rename or Converted From (Repo)              | Size  |

| :-----------------------------------: | :-----------------------------: | :-------------------------------------------------------: | :---: |

| *lite::tnn::cv::face::detect::YOLO5Face* | yolov5face-blazeface-640x640.opt.tnnproto&tnnmodel | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 3.4Mb |

| *lite::tnn::cv::face::detect::YOLO5Face* | yolov5face-l-640x640.opt.tnnproto&tnnmodel | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 181Mb |

| *lite::tnn::cv::face::detect::YOLO5Face* | yolov5face-m-640x640.opt.tnnproto&tnnmodel | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 83Mb |

| *lite::tnn::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-320x320.opt.tnnproto&tnnmodel | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 2.5Mb |

| *lite::tnn::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-640x640.opt.tnnproto&tnnmodel | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 4.6Mb |

| *lite::tnn::cv::face::detect::YOLO5Face* | yolov5face-n-640x640.opt.tnnproto&tnnmodel | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 9.5Mb |

| *lite::tnn::cv::face::detect::YOLO5Face* | yolov5face-s-640x640.opt.tnnproto&tnnmodel | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 30Mb |

### 3.4 NCNN模型文件

NCNN模型文件下载地址，([Baidu Drive](https://pan.baidu.com/s/1hlnqyNsFbMseGFWscgVhgQ) code: sc7f), 也可以从本仓库下载。

|                 Class                 |      Pretrained NCNN Files      |              Rename or Converted From (Repo)              | Size  |

| :-----------------------------------: | :-----------------------------: | :-------------------------------------------------------: | :---: |

| *lite::ncnn::cv::face::detect::YOLO5Face* | yolov5face-l-640x640.opt.param&bin | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 181Mb |

| *lite::ncnn::cv::face::detect::YOLO5Face* | yolov5face-m-640x640.opt.param&bin | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 83Mb |

| *lite::ncnn::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-320x320.opt.param&bin | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 2.5Mb |

| *lite::ncnn::cv::face::detect::YOLO5Face* | yolov5face-n-0.5-640x640.opt.param&bin | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 4.6Mb |

| *lite::ncnn::cv::face::detect::YOLO5Face* | yolov5face-n-640x640.opt.param&bin | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 9.5Mb |

| *lite::ncnn::cv::face::detect::YOLO5Face* | yolov5face-s-640x640.opt.param&bin | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face)  | 30Mb |

## 4. 接口文档

在[lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) 中，YOLO5Face的实现类为：

```c++

class LITE_EXPORTS lite::cv::face::detect::YOLO5Face;

class LITE_EXPORTS lite::mnn::cv::face::detect::YOLO5Face;

class LITE_EXPORTS lite::tnn::cv::face::detect::YOLO5Face;

class LITE_EXPORTS lite::ncnn::cv::face::detect::YOLO5Face;

```  

该类型目前包含1公共接口`detect`用于进行目标检测。

```c++

public:

    /**

     * @param mat cv::Mat BGR format

     * @param detected_boxes_kps vector of BoxfWithLandmarks to catch detected boxes and landmarks.

     * @param score_threshold default 0.25f, only keep the result which >= score_threshold.

     * @param iou_threshold default 0.45f, iou threshold for NMS.

     * @param topk default 400, maximum output boxes after NMS.

     */

    void detect(const cv::Mat &mat, std::vector &detected_boxes_kps,

                float score_threshold = 0.25f, float iou_threshold = 0.45f,

                unsigned int topk = 400);

```

`detect`接口的输入参数说明：

* mat: cv::Mat类型，BGR格式。

* detected_boxes_kps: BoxfWithLandmarks向量，包含被检测到的框box(Boxf)，box中包含x1,y1,x2,y2,label,score等成员; 以及landmarks(landmarks)人脸关键点(5个)，其中包含了points，代表关键点，是一个cv::point2f向量(vector); 

* score_threshold：分类得分（质量得分）阈值，默认0.25，小于该阈值的框将被丢弃。

* iou_threshold：NMS中的iou阈值，默认0.3。

* topk：默认400，只保留前k个检测到的结果。

## 5. 使用案例

这里测试使用的是yolov5face-n-640x640.onnx(yolov5n-face)nano版本的模型，你可以尝试使用其他版本的模型。

### 5.1 ONNXRuntime版本

```c++

#include "lite/lite.h"

static void test_default()

{

    std::string onnx_path = "../hub/onnx/cv/yolov5face-n-640x640.onnx"; // yolov5n-face

    std::string test_img_path = "../resources/4.jpg";

    std::string save_img_path = "../logs/4.jpg";

    

    auto *yolov5face = new lite::cv::face::detect::YOLO5Face(onnx_path);

    

    std::vector detected_boxes;

    cv::Mat img_bgr = cv::imread(test_img_path);

    yolov5face->detect(img_bgr, detected_boxes);

    

    lite::utils::draw_boxes_with_landmarks_inplace(img_bgr, detected_boxes);

    

    cv::imwrite(save_img_path, img_bgr);

    

    std::cout << "Default Version Done! Detected Face Num: " << detected_boxes.size() << std::endl;

    

    delete yolov5face;

}

```  

### 5.2 MNN版本

```c++

#include "lite/lite.h"

static void test_mnn()

{

#ifdef ENABLE_MNN

    std::string mnn_path = "../hub/mnn/cv/yolov5face-n-640x640.mnn"; // yolov5n-face

    std::string test_img_path = "../resources/12.jpg";

    std::string save_img_path = "../logs/12.jpg";

    

    auto *yolov5face = new lite::mnn::cv::face::detect::YOLO5Face(mnn_path);

    

    std::vector detected_boxes;

    cv::Mat img_bgr = cv::imread(test_img_path);

    yolov5face->detect(img_bgr, detected_boxes);

    

    lite::utils::draw_boxes_with_landmarks_inplace(img_bgr, detected_boxes);

    

    cv::imwrite(save_img_path, img_bgr);

    

    std::cout << "MNN Version Done! Detected Face Num: " << detected_boxes.size() << std::endl;

    

    delete yolov5face;

#endif

}

```  

### 5.3 TNN版本

```c++

#include "lite/lite.h"

static void test_tnn()

{

#ifdef ENABLE_TNN

    std::string proto_path = "../hub/tnn/cv/yolov5face-n-640x640.opt.tnnproto"; // yolov5n-face

    std::string model_path = "../hub/tnn/cv/yolov5face-n-640x640.opt.tnnmodel";

    std::string test_img_path = "../resources/9.jpg";

    std::string save_img_path = "../logs/9.jpg";

    

    auto *yolov5face = new lite::tnn::cv::face::detect::YOLO5Face(proto_path, model_path);

    

    std::vector detected_boxes;

    cv::Mat img_bgr = cv::imread(test_img_path);

    yolov5face->detect(img_bgr, detected_boxes);

    

    lite::utils::draw_boxes_with_landmarks_inplace(img_bgr, detected_boxes);

    

    cv::imwrite(save_img_path, img_bgr);

    

    std::cout << "TNN Version Done! Detected Face Num: " << detected_boxes.size() << std::endl;

    

    delete yolov5face;

#endif

}

```  

### 5.4 NCNN版本

```c++

#include "lite/lite.h"

static void test_ncnn()

{

#ifdef ENABLE_NCNN

    std::string param_path = "../hub/ncnn/cv/yolov5face-n-640x640.opt.param"; // yolov5n-face

    std::string bin_path = "../hub/ncnn/cv/yolov5face-n-640x640.opt.bin";

    std::string test_img_path = "../resources/1.jpg";

    std::string save_img_path = "../logs/1.jpg";

    

    auto *yolov5face = new lite::ncnn::cv::face::detect::YOLO5Face(param_path, bin_path, 1, 640, 640);

    

    std::vector detected_boxes;

    cv::Mat img_bgr = cv::imread(test_img_path);

    yolov5face->detect(img_bgr, detected_boxes);

    

    lite::utils::draw_boxes_with_landmarks_inplace(img_bgr, detected_boxes);

    

    cv::imwrite(save_img_path, img_bgr);

    

    std::cout << "NCNN Version Done! Detected Face Num: " << detected_boxes.size() << std::endl;

    

    delete yolov5face;

#endif

}

```  

* 输出结果为:

  

![](resources/YOLO5Face.png)

## 6. 编译运行

在MacOS下可以直接编译运行本项目，无需下载其他依赖库。其他系统则需要从[lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) 中下载源码先编译*lite.ai.toolkit.v0.1.0*动态库。

```shell

git clone --depth=1 https://github.com/DefTruth/YOLO5Face.lite.ai.toolkit.git

cd YOLO5Face.lite.ai.toolkit 

sh ./build.sh

```  

* CMakeLists.txt设置

```cmake

cmake_minimum_required(VERSION 3.17)

project(YOLO5Face.lite.ai.toolkit)

set(CMAKE_CXX_STANDARD 11)

# setting up lite.ai.toolkit

set(LITE_AI_DIR ${CMAKE_SOURCE_DIR}/lite.ai.toolkit)

set(LITE_AI_INCLUDE_DIR ${LITE_AI_DIR}/include)

set(LITE_AI_LIBRARY_DIR ${LITE_AI_DIR}/lib)

include_directories(${LITE_AI_INCLUDE_DIR})

link_directories(${LITE_AI_LIBRARY_DIR})

set(OpenCV_LIBS

        opencv_highgui

        opencv_core

        opencv_imgcodecs

        opencv_imgproc

        opencv_video

        opencv_videoio

        )

# add your executable

set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/examples/build)

add_executable(lite_yolo5face examples/test_lite_yolo5face.cpp)

target_link_libraries(lite_yolo5face

        lite.ai.toolkit

        onnxruntime

        MNN  # need, if built lite.ai.toolkit with ENABLE_MNN=ON,  default OFF

        ncnn # need, if built lite.ai.toolkit with ENABLE_NCNN=ON, default OFF

        TNN  # need, if built lite.ai.toolkit with ENABLE_TNN=ON,  default OFF

        ${OpenCV_LIBS})  # link lite.ai.toolkit & other libs.

```

* building && testing information:

```shell

[ 50%] Building CXX object CMakeFiles/lite_yolo5face.dir/examples/test_lite_yolo5face.cpp.o

[100%] Linking CXX executable lite_yolo5face

[100%] Built target lite_yolo5face

Testing Start ...

LITEORT_DEBUG LogId: ../hub/onnx/cv/yolov5face-n-640x640.onnx

=============== Input-Dims ==============

input_node_dims: 1

input_node_dims: 3

input_node_dims: 640

input_node_dims: 640

=============== Output-Dims ==============

Output: 0 Name: output Dim: 0 :1

Output: 0 Name: output Dim: 1 :25200

Output: 0 Name: output Dim: 2 :16

========================================

generate_bboxes_kps num: 2824

Default Version Done! Detected Face Num: 326

LITEORT_DEBUG LogId: ../hub/onnx/cv/yolov5face-n-640x640.onnx

=============== Input-Dims ==============

input_node_dims: 1

input_node_dims: 3

input_node_dims: 640

input_node_dims: 640

=============== Output-Dims ==============

Output: 0 Name: output Dim: 0 :1

Output: 0 Name: output Dim: 1 :25200

Output: 0 Name: output Dim: 2 :16

========================================

generate_bboxes_kps num: 253

ONNXRuntime Version Done! Detected Face Num: 16

LITEMNN_DEBUG LogId: ../hub/mnn/cv/yolov5face-n-640x640.mnn

=============== Input-Dims ==============

        **Tensor shape**: 1, 3, 640, 640, 

Dimension Type: (CAFFE/PyTorch/ONNX)NCHW

=============== Output-Dims ==============

getSessionOutputAll done!

Output: output:         **Tensor shape**: 1, 25200, 16, 

========================================

generate_bboxes_kps num: 71

MNN Version Done! Detected Face Num: 5

LITENCNN_DEBUG LogId: ../hub/ncnn/cv/yolov5face-n-640x640.opt.param

generate_bboxes_kps num: 34

NCNN Version Done! Detected Face Num: 2

LITETNN_DEBUG LogId: ../hub/tnn/cv/yolov5face-n-640x640.opt.tnnproto

=============== Input-Dims ==============

input: [1 3 640 640 ]

Input Data Format: NCHW

=============== Output-Dims ==============

output: [1 25200 16 ]

========================================

generate_bboxes_kps num: 98

TNN Version Done! Detected Face Num: 7

Testing Successful !

```  

![](resources/10.jpg)