效果

 

 

注意：Dlib检测人脸在Release版耗时与CPU有关,本人I7 10代约100ms左右。建议人脸检测可以考虑使用Yolov5进行，之后将检测到的人脸输入给Dlib做特征或其他。

 

代码

#include <iostream>

#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <dlib/dnn.h>
#include <dlib/gui_widgets.h>
#include <dlib/clustering.h>
#include <dlib/string.h>
#include <dlib/image_io.h>
#include <dlib/image_transforms.h>
#include <dlib/opencv.h>

#include "opencv2/opencv.hpp"

void line_one_face_detections(cv::Mat &img, std::vector<dlib::full_object_detection> fs)
{
	int i, j;
	for (j = 0; j < fs.size(); j++)
	{

		cv::Point p1, p2;
		for (i = 0; i < 67; i++)
		{
			//if (i != 48 && i != 64 && i != 38 && i != 43 && i != 29) { continue; }

			// 下巴到脸颊 0 ~ 16
			//左边眉毛 17 ~ 21
			//右边眉毛 21 ~ 26
			//鼻梁     27 ~ 30
			//鼻孔        31 ~ 35
			//左眼        36 ~ 41
			//右眼        42 ~ 47
			//嘴唇外圈  48 ~ 59
			//嘴唇内圈  59 ~ 67
			switch (i)
			{
			case 16:
			case 21:
			case 26:
			case 30:
			case 35:
			case 41:
			case 47:
			case 59:
				i++;
				break;
			default:
				break;
			}

			p1.x = fs[j].part(i).x();
			p1.y = fs[j].part(i).y();
			p2.x = fs[j].part(i + 1).x();
			p2.y = fs[j].part(i + 1).y();
			cv::line(img, p1, p2, cv::Scalar(0, 0, 255), 2, 4, 0);
			cv::circle(img, p1, 1, cv::Scalar(0, 0, 255), 3, 4, 0);

// 			char str[25] = { 0 };
// 			itoa(i, str, 10);
// 			cv::putText(img, str, cv::Point(p1.x, p1.y), 1, 1, cv::Scalar(0, 0, 255), 1, 4, 0);
		}
	}
}

int main()
{
	clock_t  start_t, end_t;

	//cv::VideoCapture vc(0);
	cv::VideoCapture vc("./test.mp4");
	if (vc.isOpened())
	{
		// 加载dlib的人脸检测器
		dlib::frontal_face_detector detector = dlib::get_frontal_face_detector();

		// 加载人脸形状探测器
		dlib::shape_predictor sp;
		dlib::deserialize("./Face/shape_predictor_68_face_landmarks.dat") >> sp;

		// 循环操作
		cv::Mat SrcMat, Mat;
		while (1)
		{
			// 读取一帧图像
			vc >> SrcMat;
			if (SrcMat.empty()) { break; }

			// 每5帧做一次,因为dlib人脸检测耗时约100ms（i7-10750H的CPU下测试）
			static unsigned short rFrameRate = 0;
			if (++rFrameRate <= 6) { continue; }rFrameRate = 0;

			// 提取灰度图
			cv::cvtColor(SrcMat, Mat, cv::COLOR_BGR2GRAY);

			// Mat转化为dlib的matrix
			dlib::array2d<dlib::bgr_pixel> dimg;
			dlib::assign_image(dimg, dlib::cv_image<uchar>(Mat));

			// 获取一系列人脸所在区域
			start_t = (double)clock();
			std::vector<dlib::rectangle> dets = detector(dimg);
			end_t = (double)clock();
			int64_t curTime = 1000 * (end_t - start_t) / (double)CLOCKS_PER_SEC;
			std::cout << "total ms:" << curTime;
			std::cout << "\tNumber of faces detected: " << dets.size() << std::endl;
			if (dets.size() > 0)
			{
				//获取人脸特征点分布
				std::vector<dlib::full_object_detection> shapes;
				for (int i = 0; i < dets.size(); i++)
				{
					dlib::full_object_detection shape = sp(dimg, dets[i]); //获取指定一个区域的人脸形状
					shapes.push_back(shape);
				}

				//指出每个检测到的人脸的位置
				for (int i = 0; i < dets.size(); i++)
				{
					//画出人脸所在区域
					cv::Rect r;
					r.x = dets[i].left();
					r.y = dets[i].top();
					r.width = dets[i].width();
					r.height = dets[i].height();
					cv::rectangle(SrcMat, r, cv::Scalar(0,255, 0, 0), 3, 1, 0);
				}

				// 特征绘制
				line_one_face_detections(SrcMat, shapes);
			}

			// 刷新图片
			cv::resize(SrcMat, SrcMat, cv::Size(480, 320));
			cv::imshow("Mat", SrcMat);
			cv::waitKey(1);
		}
		vc.release();
	}

	system("pause");
	return 0;
}

 

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笔者 - jxd