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

Super fast accurate face detector ! SCRFD(CVPR 2021) with MNN/TNN/NCNN/ONNXRuntime C++.
https://github.com/xlite-dev/scrfd-toolkit
mnn ncnn onnxruntime scrfd tnn
Last synced: 6 days ago
JSON representation
Super fast accurate face detector ! SCRFD(CVPR 2021) with MNN/TNN/NCNN/ONNXRuntime C++.
Host: GitHub
URL: https://github.com/xlite-dev/scrfd-toolkit
Owner: xlite-dev
License: gpl-3.0
Created: 2022-01-08T07:47:33.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2022-01-12T16:30:54.000Z (over 3 years ago)
Last Synced: 2025-07-10T19:57:24.936Z (3 months ago)
Topics: mnn, ncnn, onnxruntime, scrfd, tnn
Language: C++
Homepage:
Size: 32.5 MB
Stars: 17
Watchers: 2
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

          # scrfd.lite.ai.toolkit

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

![](resources/scrfd.png)

Star⭐️🌟什么的，就随缘吧~ 🙃🤪🍀

## 2. C++版本源码

SCRFD 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SCRFD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。

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

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

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

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

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

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

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

* [ncnn_scrfd.h](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ncnn/cv/ncnn_scrfd.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) , 也可以从本直接仓库下载。

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

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

| *lite::cv::face::detect::SCRFD* | scrfd_500m_shape160x160.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_500m_shape320x320.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_500m_shape640x640.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape160x160.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape320x320.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape640x640.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::cv::face::detect::SCRFD* | scrfd_1g_shape160x160.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_1g_shape320x320.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_1g_shape640x640.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_2.5g_shape160x160.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_2.5g_shape320x320.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_2.5g_shape640x640.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape160x160.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape320x320.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape640x640.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::cv::face::detect::SCRFD* | scrfd_10g_shape640x640.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_10g_shape1280x1280.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape640x640.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

| *lite::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape1280x1280.onnx | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

### 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), 也可以从本直接仓库下载。

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

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

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_500m_shape160x160.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_500m_shape320x320.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_500m_shape640x640.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape160x160.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape320x320.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape640x640.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_1g_shape160x160.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_1g_shape320x320.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_1g_shape640x640.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape160x160.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape320x320.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape640x640.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape160x160.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape320x320.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape640x640.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_10g_shape640x640.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_10g_shape1280x1280.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape640x640.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

| *lite::mnn::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape1280x1280.mnn | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

### 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), 也可以从本直接仓库下载。

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

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

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_500m_shape160x160.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_500m_shape320x320.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_500m_shape640x640.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape160x160.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape320x320.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape640x640.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_1g_shape160x160.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_1g_shape320x320.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_1g_shape640x640.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape160x160.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape320x320.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape640x640.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape160x160.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape320x320.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape640x640.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_10g_shape640x640.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_10g_shape1280x1280.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape640x640.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

| *lite::tnn::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape1280x1280.opt.tnnproto&tnnmodel | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

### 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), 也可以从本直接仓库下载。

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

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

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_500m_shape160x160.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_500m_shape320x320.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_500m_shape640x640.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape160x160.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape320x320.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_500m_bnkps_shape640x640.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.5Mb |  

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_1g_shape160x160.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_1g_shape320x320.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_1g_shape640x640.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 2.7Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape160x160.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape320x320.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_2.5g_shape640x640.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape160x160.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape320x320.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_2.5g_bnkps_shape640x640.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 3.3Mb |  

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_10g_shape640x640.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_10g_shape1280x1280.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape640x640.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

| *lite::ncnn::cv::face::detect::SCRFD* | scrfd_10g_bnkps_shape1280x1280.opt.param&bin | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd)  | 16.9Mb |  

## 4. 接口文档

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

```c++

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

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

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

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

```  

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scrfd_2.5g_bnkps_shape640x640.onnx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/scrfd_2.5g_bnkps_shape640x640.onnx";

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

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

    

    auto *scrfd = new lite::cv::face::detect::SCRFD(onnx_path);

    

    std::vector detected_boxes;

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

    scrfd->detect(img_bgr, detected_boxes, 0.3f);

    

    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 scrfd;

}

```  

### 5.2 MNN版本

```c++

#include "lite/lite.h"

static void test_mnn()

{

#ifdef ENABLE_MNN

    std::string mnn_path = "../hub/mnn/cv/scrfd_2.5g_bnkps_shape640x640.mnn";

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

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

    

    auto *scrfd = new lite::mnn::cv::face::detect::SCRFD(mnn_path);

    

    std::vector detected_boxes;

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

    scrfd->detect(img_bgr, detected_boxes, 0.3f);

    

    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 scrfd;

#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/scrfd_2.5g_bnkps_shape640x640.opt.tnnproto";

    std::string model_path = "../hub/tnn/cv/scrfd_2.5g_bnkps_shape640x640.opt.tnnmodel";

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

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

    

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

    

    std::vector detected_boxes;

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

    scrfd->detect(img_bgr, detected_boxes, 0.3f);

    

    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 scrfd;

#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/scrfd_2.5g_bnkps_shape640x640.opt.param";

    std::string bin_path = "../hub/ncnn/cv/scrfd_2.5g_bnkps_shape640x640.opt.bin";

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

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

    

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

    

    std::vector detected_boxes;

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

    scrfd->detect(img_bgr, detected_boxes, 0.3f);

    

    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 scrfd;

#endif

}

```  

* 输出结果为:

  

![](resources/scrfd.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/scrfd.lite.ai.toolkit.git

cd scrfd.lite.ai.toolkit 

sh ./build.sh

```  

* CMakeLists.txt设置

```cmake

cmake_minimum_required(VERSION 3.17)

project(scrfd.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_scrfd examples/test_lite_scrfd.cpp)

target_link_libraries(lite_scrfd

        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_scrfd.dir/examples/test_lite_scrfd.cpp.o

[100%] Linking CXX executable lite_scrfd

[100%] Built target lite_scrfd

Testing Start ...

LITEORT_DEBUG LogId: ../hub/onnx/cv/scrfd_2.5g_bnkps_shape640x640.onnx

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

input_node_dims: 1

input_node_dims: 3

input_node_dims: 640

input_node_dims: 640

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

Output: 0 Name: score_8 Dim: 0 :1

Output: 0 Name: score_8 Dim: 1 :12800

Output: 0 Name: score_8 Dim: 2 :1

Output: 1 Name: score_16 Dim: 0 :1

Output: 1 Name: score_16 Dim: 1 :3200

Output: 1 Name: score_16 Dim: 2 :1

Output: 2 Name: score_32 Dim: 0 :1

Output: 2 Name: score_32 Dim: 1 :800

Output: 2 Name: score_32 Dim: 2 :1

Output: 3 Name: bbox_8 Dim: 0 :1

Output: 3 Name: bbox_8 Dim: 1 :12800

Output: 3 Name: bbox_8 Dim: 2 :4

Output: 4 Name: bbox_16 Dim: 0 :1

Output: 4 Name: bbox_16 Dim: 1 :3200

Output: 4 Name: bbox_16 Dim: 2 :4

Output: 5 Name: bbox_32 Dim: 0 :1

Output: 5 Name: bbox_32 Dim: 1 :800

Output: 5 Name: bbox_32 Dim: 2 :4

Output: 6 Name: kps_8 Dim: 0 :1

Output: 6 Name: kps_8 Dim: 1 :12800

Output: 6 Name: kps_8 Dim: 2 :10

Output: 7 Name: kps_16 Dim: 0 :1

Output: 7 Name: kps_16 Dim: 1 :3200

Output: 7 Name: kps_16 Dim: 2 :10

Output: 8 Name: kps_32 Dim: 0 :1

Output: 8 Name: kps_32 Dim: 1 :800

Output: 8 Name: kps_32 Dim: 2 :10

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

generate_bboxes_kps num: 52

Default Version Done! Detected Face Num: 9

LITEORT_DEBUG LogId: ../hub/onnx/cv/scrfd_2.5g_bnkps_shape640x640.onnx

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

input_node_dims: 1

input_node_dims: 3

input_node_dims: 640

input_node_dims: 640

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

Output: 0 Name: score_8 Dim: 0 :1

Output: 0 Name: score_8 Dim: 1 :12800

Output: 0 Name: score_8 Dim: 2 :1

Output: 1 Name: score_16 Dim: 0 :1

Output: 1 Name: score_16 Dim: 1 :3200

Output: 1 Name: score_16 Dim: 2 :1

Output: 2 Name: score_32 Dim: 0 :1

Output: 2 Name: score_32 Dim: 1 :800

Output: 2 Name: score_32 Dim: 2 :1

Output: 3 Name: bbox_8 Dim: 0 :1

Output: 3 Name: bbox_8 Dim: 1 :12800

Output: 3 Name: bbox_8 Dim: 2 :4

Output: 4 Name: bbox_16 Dim: 0 :1

Output: 4 Name: bbox_16 Dim: 1 :3200

Output: 4 Name: bbox_16 Dim: 2 :4

Output: 5 Name: bbox_32 Dim: 0 :1

Output: 5 Name: bbox_32 Dim: 1 :800

Output: 5 Name: bbox_32 Dim: 2 :4

Output: 6 Name: kps_8 Dim: 0 :1

Output: 6 Name: kps_8 Dim: 1 :12800

Output: 6 Name: kps_8 Dim: 2 :10

Output: 7 Name: kps_16 Dim: 0 :1

Output: 7 Name: kps_16 Dim: 1 :3200

Output: 7 Name: kps_16 Dim: 2 :10

Output: 8 Name: kps_32 Dim: 0 :1

Output: 8 Name: kps_32 Dim: 1 :800

Output: 8 Name: kps_32 Dim: 2 :10

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

generate_bboxes_kps num: 138

ONNXRuntime Version Done! Detected Face Num: 23

LITEMNN_DEBUG LogId: ../hub/mnn/cv/scrfd_2.5g_bnkps_shape640x640.mnn

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

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

Dimension Type: (CAFFE/PyTorch/ONNX)NCHW

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

getSessionOutputAll done!

Output: bbox_16:        **Tensor shape**: 1, 3200, 4, 

Output: bbox_32:        **Tensor shape**: 1, 800, 4, 

Output: bbox_8:         **Tensor shape**: 1, 12800, 4, 

Output: kps_16:         **Tensor shape**: 1, 3200, 10, 

Output: kps_32:         **Tensor shape**: 1, 800, 10, 

Output: kps_8:  **Tensor shape**: 1, 12800, 10, 

Output: score_16:       **Tensor shape**: 1, 3200, 1, 

Output: score_32:       **Tensor shape**: 1, 800, 1, 

Output: score_8:        **Tensor shape**: 1, 12800, 1, 

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

generate_bboxes_kps num: 34

MNN Version Done! Detected Face Num: 5

LITENCNN_DEBUG LogId: ../hub/ncnn/cv/scrfd_2.5g_bnkps_shape640x640.opt.param

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

score_8: c=1,h=12800,w=1

score_16: c=1,h=3200,w=1

score_32: c=1,h=800,w=1

bbox_8: c=1,h=12800,w=4

bbox_16: c=1,h=3200,w=4

bbox_32: c=1,h=800,w=4

kps_8: c=1,h=12800,w=10

kps_16: c=1,h=3200,w=10

kps_32: c=1,h=800,w=10

generate_bboxes_kps num: 16

NCNN Version Done! Detected Face Num: 2

LITETNN_DEBUG LogId: ../hub/tnn/cv/scrfd_2.5g_bnkps_shape640x640.opt.tnnproto

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

input.1: [1 3 640 640 ]

Input Data Format: NCHW

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

bbox_16: [1 3200 4 ]

bbox_32: [1 800 4 ]

bbox_8: [1 12800 4 ]

kps_16: [1 3200 10 ]

kps_32: [1 800 10 ]

kps_8: [1 12800 10 ]

score_16: [1 3200 1 ]

score_32: [1 800 1 ]

score_8: [1 12800 1 ]

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

generate_bboxes_kps num: 49

TNN Version Done! Detected Face Num: 7

Testing Successful !

```  

![](resources/9.jpg)
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