ZQCNN亦是一款开源人脸检测、特征点定位的优秀代码库，其基于MTCNN算法构建。

cpu 25ms，侧脸，低头都能检测到，精度不是很高，人脸框不是特别稳。

以下内容主要来自：

https://github.com/zuoqing1988/ZQCNN-MTCNN-vs-libfacedetection

ZQCNN-MTCNN-vs-libfacedetection

依赖库:

windows：mkl, opencv3.4.2

arm-linux: openblas, opencv3.4.2

libfacedetection:下载时间2019-03-25 09:00

该库无任何依赖。点赞～

mtcnn测试代码：

MKL 拷贝dll即可运行。

200多ms，人脸框不稳定，关键点也不是特别准。

#include "ZQ_CNN_Net.h"
#include "ZQ_CNN_MTCNN_old.h"
#include "ZQ_CNN_MTCNN.h"
#include <vector>
#include <iostream>
#include "opencv2/opencv.hpp"
#include "ZQ_CNN_CompileConfig.h"
#include <opencv2\imgproc\types_c.h>
#if ZQ_CNN_USE_BLAS_GEMM
#if __ARM_NEON
#include <openblas/cblas.h>
#else
#include <openblas/cblas.h>
#pragma comment(lib,"libopenblas.lib")
#endif
#elif ZQ_CNN_USE_MKL_GEMM
#include "mkl/mkl.h"
#pragma comment(lib,"mklml.lib")
#else
#pragma comment(lib,"ZQ_GEMM.lib")
#endif
using namespace ZQ;
using namespace std;
using namespace cv;static void Draw(cv::Mat &image, const std::vector<ZQ_CNN_BBox>& thirdBbox)
{std::vector<ZQ_CNN_BBox>::const_iterator it = thirdBbox.begin();for (; it != thirdBbox.end(); it++){if ((*it).exist){if (it->score > 0.7){cv::rectangle(image, cv::Point((*it).col1, (*it).row1), cv::Point((*it).col2, (*it).row2), cv::Scalar(0, 0, 255), 2, 8, 0);}else{cv::rectangle(image, cv::Point((*it).col1, (*it).row1), cv::Point((*it).col2, (*it).row2), cv::Scalar(0, 255, 0), 2, 8, 0);}for (int num = 0; num < 5; num++)circle(image, cv::Point(*(it->ppoint + num) + 0.5f, *(it->ppoint + num + 5) + 0.5f), 1, cv::Scalar(0, 255, 255), -1);}else{printf("not exist!\n");}}
}static void Draw(cv::Mat &image, const std::vector<ZQ_CNN_BBox106>& thirdBbox)
{std::vector<ZQ_CNN_BBox106>::const_iterator it = thirdBbox.begin();for (; it != thirdBbox.end(); it++){if ((*it).exist){if (it->score > 0.7){cv::rectangle(image, cv::Point((*it).col1, (*it).row1), cv::Point((*it).col2, (*it).row2), cv::Scalar(0, 0, 255), 2, 8, 0);}else{cv::rectangle(image, cv::Point((*it).col1, (*it).row1), cv::Point((*it).col2, (*it).row2), cv::Scalar(0, 255, 0), 2, 8, 0);}for (int num = 0; num < 106; num++)circle(image, cv::Point(*(it->ppoint + num * 2) + 0.5f, *(it->ppoint + num * 2 + 1) + 0.5f), 1, cv::Scalar(0, 255, 255), -1);}else{printf("not exist!\n");}}
}int main()
{int num_threads = 1;
#if ZQ_CNN_USE_BLAS_GEMMprintf("set openblas thread_num = %d\n",num_threads);openblas_set_num_threads(num_threads);
#elif ZQ_CNN_USE_MKL_GEMMmkl_set_num_threads(num_threads);
#endifVideoCapture cap = VideoCapture(0);if (!cap.isOpened())//如果视频不能正常打开则返回return 1;Mat image0;while (1){cap >> image0;//等价于cap.read(frame);if (image0.empty())//如果某帧为空则退出循环break;//cv::resize(image0, image0, cv::Size(), 2, 2);if (image0.channels() == 1)cv::cvtColor(image0, image0, CV_GRAY2BGR);//cv::convertScaleAbs(image0, image0, 2.0);/* TIPS: when finding tiny faces for very big image, gaussian blur is very useful for Pnet*/bool run_blur = true;int kernel_size = 3, sigma = 2;if (image0.cols * image0.rows >= 2500 * 1600){run_blur = false;kernel_size = 5;sigma = 3;}else if (image0.cols * image0.rows >= 1920 * 1080){run_blur = false;kernel_size = 3;sigma = 2;}else{run_blur = false;}if (run_blur){cv::Mat blur_image0;int nBlurIters = 1000;double t00 = omp_get_wtime();for (int i = 0; i < nBlurIters; i++)cv::GaussianBlur(image0, blur_image0, cv::Size(kernel_size, kernel_size), sigma, sigma);double t01 = omp_get_wtime();printf("[%d] blur cost %.3f secs, 1 blur costs %.3f ms\n", nBlurIters, t01 - t00, 1000 * (t01 - t00) / nBlurIters);cv::GaussianBlur(image0, image0, cv::Size(kernel_size, kernel_size), sigma, sigma);}std::vector<ZQ_CNN_BBox> thirdBbox;std::vector<ZQ_CNN_BBox106> thirdBbox106;ZQ_CNN_MTCNN mtcnn;std::string result_name;mtcnn.TurnOnShowDebugInfo();//mtcnn.SetLimit(300, 50, 20);const int use_pnet20 = true;bool landmark106 = true;int thread_num = 0;bool special_handle_very_big_face = false;result_name = "resultdet.jpg";if (use_pnet20){if (landmark106){
#if defined(_WIN32)if (!mtcnn.Init("model/det1-dw20-fast.zqparams", "model/det1-dw20-fast.nchwbin","model/det2-dw24-fast.zqparams", "model/det2-dw24-fast.nchwbin",//"model/det2.zqparams", "model/det2_bgr.nchwbin","model/det3-dw48-fast.zqparams", "model/det3-dw48-fast.nchwbin", thread_num, true,"model/det5-dw64-v3s.zqparams", "model/det5-dw64-v3s.nchwbin"//"model/det3.zqparams", "model/det3_bgr.nchwbin"
#elseif (!mtcnn.Init("../../model/det1-dw20-fast.zqparams", "../../model/det1-dw20-fast.nchwbin","../../model/det2-dw24-fast.zqparams", "../../model/det2-dw24-fast.nchwbin",//"../../model/det2.zqparams", "../../model/det2_bgr.nchwbin","../../model/det3-dw48-fast.zqparams", "../../model/det3-dw48-fast.nchwbin",thread_num, true,"../../model/det5-dw64-v3s.zqparams", "../../model/det5-dw64-v3s.nchwbin"//"../../model/det3.zqparams", "../../model/det3_bgr.nchwbin"
#endif)){cout << "failed to init!\n";return EXIT_FAILURE;}}else{
#if defined(_WIN32)if (!mtcnn.Init("model/det1-dw20-fast.zqparams", "model/det1-dw20-fast.nchwbin","model/det2-dw24-fast.zqparams", "model/det2-dw24-fast.nchwbin",//"model\\det2.zqparams", "model\\det2_bgr.nchwbin","model/det3-dw48-fast.zqparams", "model/det3-dw48-fast.nchwbin",//"model/det3.zqparams", "model/det3_bgr.nchwbin",thread_num, false,"model/det4-dw48-v2n.zqparams", "model/det4-dw48-v2n.nchwbin"//"model/det3.zqparams", "model/det3_bgr.nchwbin"
#elseif (!mtcnn.Init("../../model/det1-dw20-fast.zqparams", "../../model/det1-dw20-fast.nchwbin","../../model/det2-dw24-fast.zqparams", "../../model/det2-dw24-fast.nchwbin",//"model/det2.zqparams", "model/det2_bgr.nchwbin","../../model/det3-dw48-fast.zqparams", "../../model/det3-dw48-fast.nchwbin", thread_num, false,"model/det4-dw48-v2s.zqparams", "model/det4-dw48-v2s.nchwbin"//"../../model/det3.zqparams", "../../model/det3_bgr.nchwbin"
#endif)){cout << "failed to init!\n";return EXIT_FAILURE;}}mtcnn.SetPara(image0.cols, image0.rows, 80, 0.5, 0.6, 0.8, 0.4, 0.5, 0.5, 0.709, 3, 20, 4, special_handle_very_big_face);}else{
#if defined(_WIN32)if (!mtcnn.Init("model/det1.zqparams", "model/det1_bgr.nchwbin","model/det2.zqparams", "model/det2_bgr.nchwbin","model/det3.zqparams", "model/det3_bgr.nchwbin", thread_num))
#elseif (!mtcnn.Init("../../model/det1.zqparams", "../../model/det1_bgr.nchwbin","../../model/det2.zqparams", "../../model/det2_bgr.nchwbin","../../model/det3.zqparams", "../../model/det3_bgr.nchwbin", thread_num))
#endif{cout << "failed to init!\n";return EXIT_FAILURE;}mtcnn.SetPara(image0.cols, image0.rows, 20, 0.6, 0.7, 0.7, 0.4, 0.5, 0.5, 0.709, 4, 12, 2, special_handle_very_big_face);}mtcnn.TurnOffShowDebugInfo();//mtcnn.TurnOnShowDebugInfo();int iters = 100;double t1 = omp_get_wtime();for (int i = 0; i < iters; i++){if (i == iters / 2)mtcnn.TurnOnShowDebugInfo();elsemtcnn.TurnOffShowDebugInfo();if (landmark106 && use_pnet20){if (!mtcnn.Find106(image0.data, image0.cols, image0.rows, image0.step[0], thirdBbox106)){cout << "failed to find face!\n";//return EXIT_FAILURE;continue;}}else{if (!mtcnn.Find(image0.data, image0.cols, image0.rows, image0.step[0], thirdBbox)){cout << "failed to find face!\n";//return EXIT_FAILURE;continue;}}}double t2 = omp_get_wtime();printf("total %.3f s / %d = %.3f ms\n", t2 - t1, iters, 1000 * (t2 - t1) / iters);namedWindow("result");if (landmark106 && use_pnet20)Draw(image0, thirdBbox106);elseDraw(image0, thirdBbox);imwrite(result_name, image0);imshow("result", image0);waitKey(1);}return EXIT_SUCCESS;
}

(一) WINDOWS下对比

(测试机器为E5-1650V4 3.6GHz)

编译方式：

ZQCNN用.sln打开

libfacedetection用cmake-gui配置，勾选avx2和DEMO，不勾选int8和neon，配置之后用vs打开，注意在facedetection、facedetection_shared、fdt_demo三个项目的项目属性->C++->代码生成里开启AVX2和快速浮点。

(1)对比keliamoniz1.jpg

结果比较：

(2)对比4.jpg

结果比较：

（3)对比其他图片

结果比较：

（二）ARM-LINUX下对比

(测试机器为firefly rk3399)

编译方式：

ZQCNN: 先编译OpenBLAS, OpenCV3.4.2, 然后编译ZQCNN, 使用命令cmake .. -DSIMD_ARCH_TYPE=arm64 -DBLAS_TYPE=openblas_zq_gemm 具体参见ZQCNN项目的README

libfacedetection: 命令cmake .. -DENABLE_NEON=ON -DCMAKE_BUILD_TYPE=RELEASE

(1)对比keliamoniz1.jpg

结果比较：

(2)对比4.jpg

结果比较：

(3)对比其他图片

结果比较：

总结

根据以上比较结果可知，按照上述编译方式，在上述图像集中，ZQCNN-MTCNN在速度和精度上都优于libfacedetection。

不过要声明一点，该比较中使用的图像数量过少，感兴趣的读者可自行下载编译测试。

ZQCNN使用了mkl、openblas，这些基础加速库可能是其速度占优的原因。

libfacedetection追求无依赖库，在实际项目中，应用可以更广泛。

代码数据下载

作者已经把上述代码及测试图像开源了，在OpenCV中文网公众号对话界面回复“facepk”，即可收到下载地址。

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ZQCNN快速人脸检测

(一) WINDOWS下对比

(测试机器为E5-1650V4 3.6GHz)

（二）ARM-LINUX下对比

(测试机器为firefly rk3399)

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