实时视频人脸识别尝试（使用ShiqiYu老师的人脸识别库）

2019-11-11 05:46:18

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使用ShiqiYu老师的 face landmark detection方法进行人脸识别与人脸转动角度的计算，使用的是ShiqiYu老师在2017年1月更新的代码。Github地址：https://github.com/ShiqiYu/libfacedetection其代码较2015年的版本做了一些修改，其中提供了frontal,frontal-surveillance,multiview,multiview_reinforce四种方法。每种方法的耗费时间与检测的人脸转向的范围有所不同，在Github中对其效果差异做了详细说明。使用multiview_reinforce为例进行编写，此次改动在调用函数中做了参数的修改需要注意。各个参数的含义如下：FACEDETECTDLL_API int * facedetect_multiview_reinforce(unsigned char * result_buffer, //buffer memory for storing face detection results, !!its size must be 0xC004 Bytes!!unsigned char * gray_image_data, int width, int height, int step, //input image, it must be gray (single-channel) image!float scale, //scale factor for scan windowsint min_neighbors, //how many neighbors each candidate rectangle should have to retain itint min_object_width, //Minimum possible face size. Faces smaller than that are ignored.int max_object_width = 0, //Maximum possible face size. Faces larger than that are ignored. It is the largest posible when max_object_width=0.int doLandmark = 0); // landmark detection在提供的example例子上进行了修改，使用OpenCV接口读取摄像头的图片，进行实时操作。代码如下：

//2017.2.6    #include <stdio.h>  #include <opencv2/opencv.hpp>  #include "facedetect-dll.h"    #PRagma comment(lib,"libfacedetect.lib")  //#pragma comment(lib,"libfacedetect-x64.lib")    //define the buffer size. Do not change the size!  #define DETECT_BUFFER_SIZE 0x20000  using namespace cv;    void OnMouseAction(int event, int x, int y, int falgs, void *ustc);  bool stime = false;    int main(int argc, char* argv[])  {    	Mat gray;  	Mat frame;  	Mat new_frame;  	int resize_height = 480;  	int resize_width = 640;    	int * pResults = NULL;         unsigned char * pBuffer = (unsigned char *)malloc(DETECT_BUFFER_SIZE);      if(!pBuffer)      {          fprintf(stderr, "Can not alloc buffer./n");          return -1;      }  	VideoCapture capture(0);    	while (stime==false)  	{  		capture >> frame;  		cv::resize(frame, new_frame, cv::Size(resize_width, resize_height), (0.0), (0.0), cv::INTER_LINEAR);  		cvtColor(new_frame, gray, CV_BGR2GRAY);  		int doLandmark = 1;    		pResults = facedetect_multiview_reinforce(pBuffer, (unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, (int)gray.step,  			1.2f, 3, 24, 0, doLandmark);    		//printf("%d faces detected./n", (pResults ? *pResults : 0));  		Mat result_multiview_reinforce = new_frame.clone();;  		//print the detection results  		for (int i = 0; i < (pResults ? *pResults : 0); i++)  		{  			short * p = ((short*)(pResults + 1)) + 142 * i;  			int x = p[0];  			int y = p[1];  			int w = p[2];  			int h = p[3];  			int neighbors = p[4];  			int angle = p[5];    			printf("face_rect=[%d, %d, %d, %d], neighbors=%d, angle=%d/n", x, y, w, h, neighbors, angle);  			rectangle(result_multiview_reinforce, Rect(x, y, w, h), Scalar(0, 255, 0), 2);  			if (doLandmark)  			{  				for (int j = 0; j < 68; j++)  					circle(result_multiview_reinforce, Point((int)p[6 + 2 * j], (int)p[6 + 2 * j + 1]), 1, Scalar(0, 255, 0));  			}  		}  		imshow("Results_multiview_reinforce", result_multiview_reinforce);  		waitKey(30);  		setMouseCallback("Results_multiview_reinforce",OnMouseAction);  	}      //release the buffer      free(pBuffer);    	return 0;  }    void OnMouseAction(int event, int x, int y, int falgs, void *ustc)  {  	if (event==CV_EVENT_LBUTTONDOWN)  	{  		stime = true;  	}   	else  	{  		stime = false;   	}  } 效果图如下：评价一下性能的话，识别效果确实不错。不足点的话一是当人脸有部分遮挡、低头或仰头时无法识别，因此更适合应用于正面下人脸左右摆动识别，如各种身份确认等领域。不是用在安防等领域的检测跟踪的应用背景中。