从视频中提取图片，对图片做人脸检测并截取人脸区域

Posted 2020-10-02 amazingym

环境配置：VS2013+opencv2.4.10+libface.lib

参考博客：http://blog.csdn.net/augusdi/article/details/11042329

                 http://www.1024do.com/?p=1296

首先给出视频处理的函数video_process.hpp

#include <stdio.h>
#include <opencv2/opencv.hpp>
#include "facedetect-dll.h"
#include<opencv2/highgui/highgui.hpp>  

#pragma comment(lib,"libfacedetect.lib")
//#pragma comment(lib,"libfacedetect-x64.lib")
using namespace cv;

#define DETECT_BUFFER_SIZE 0x20000 //facedetect
#define UNKNOWN_FLOW_THRESH 1e9  //facedetect

#define NUM_FRAME 100  //Video_to_imag中控制截取帧数

//函数声明
void Video_to_image(char* filename, char* Savepath);
/*
函数功能：读取视频的每一帧，并将其按帧数命名保存
例如：Video_to_image("F:\\tp\\1.mp4", "F:\\image");
*/
void video_to_image(char* Filename, char* Savepath);
/*
函数功能：截取视频前三帧图片并将其保存，帧数间隔默认5
可用count_tmp和jiangge控制读取帧数和帧数间隔
用法示例：video_to_image(videopath, "F:\\截图\\1_")
保存文件名为\\后字符和输入序号的拼接
*/
int image_cut(char* Filename, char* Savepath);
/*
函数功能：对图片进行人脸检测吧，并截取保存人脸及周边区域
其中用到了libfacedetect.lib
用法示例：image_cut("F:\\截图\\1_1.jpg","F:\\截图\\CUT1_1.jpg" );
*/

 

给出视频处理的函数video_process.cpp ,对应上面三个函数

#include<video_process.h>

void Video_to_image(char* filename, char* Savepath)
{
    printf("------------- video to image ... ----------------\n");

    CvCapture* capture = cvCaptureFromAVI(filename);//初始化一个视频文件捕捉器 
    cvQueryFrame(capture);//获取视频信息  
    int frameH = (int)cvGetCaptureProperty(capture, CV_CAP_PROP_FRAME_HEIGHT);
    int frameW = (int)cvGetCaptureProperty(capture, CV_CAP_PROP_FRAME_WIDTH);
    int fps = (int)cvGetCaptureProperty(capture, CV_CAP_PROP_FPS);
    int numFrames = (int)cvGetCaptureProperty(capture, CV_CAP_PROP_FRAME_COUNT);
    printf("video height : %dntvideo width : %dntfps : %dntframe numbers : %dn", frameH, frameW, fps, numFrames);//打印视频信息
    //定义和初始化变量  
    int i = 0;
    IplImage* img = 0;
    char image_name[18];

    cvNamedWindow("mainWin", CV_WINDOW_AUTOSIZE);

    //读取和显示  
    while (1)
    {
        img = cvQueryFrame(capture); //获取一帧图片  
        cvShowImage("mainWin", img); //将其显示  
        char key = cvWaitKey(20);
        sprintf(image_name, "%s%d%s", Savepath, ++i, ".jpg");//保存的图片名  
        cvSaveImage(image_name, img);
        //cvSaveImage( image_name, img);   //保存一帧图片 
        if (i == 0)
        {
            sprintf(image_name, "%s//%d%s", Savepath, i, ".jpg");
            cvSaveImage(image_name, img);   //保存一帧图片 
        }
        if (i == numFrames) break;
        //if (i == NUM_FRAME) break;
        i++;
    }
    cvReleaseCapture(&capture);
    cvDestroyWindow("mainWin");
    cvWaitKey();

}

void video_to_image(char* Filename, char* Savepath)
{
    printf("------------- video to image ... ----------------n");
    //初始化一个视频文件捕捉器  
    CvCapture *capture = NULL;
    IplImage *frame = NULL;
    char *AviFileName = Filename;// "F:\\tp\\1.mp4";//视频的目录
    char *AviSavePath = Savepath;//"F:\\截图\\";//图片保存的位置
    const int jiange = 5;//间隔5帧保存一次图片
    capture = cvCaptureFromAVI(AviFileName);
    cvNamedWindow("AVI player", 1);
    int count_tmp = 0;//计数总帧数
    int i = 1;
    char tmpfile[100] = { ‘\0‘ };
    while (count_tmp<15)  //每段视频保留3帧
    {
        if (cvGrabFrame(capture))
        {
            if (count_tmp % jiange == 0)
            {
                frame = cvRetrieveFrame(capture);
                cvShowImage("AVI player", frame);//显示当前帧
                sprintf(tmpfile, "%s%d.jpg", AviSavePath, i);//使用帧号作为图片名
                cvSaveImage(tmpfile, frame);
                i++;
            }
            if (cvWaitKey(10) >= 0) //延时
                break;
            ++count_tmp;
        }
        else
        {
            break;
        }
    }
    cvReleaseCapture(&capture);
    cvDestroyWindow("AVI player");
    std::cout << "总帧数" << count_tmp << std::endl;
    cvWaitKey();
    return;
}

int image_cut(char* Filename, char* Savepath)
{
    Mat image = imread(Filename);
    if (image.empty())
    {
        fprintf(stderr, "Can not load the image file %s.\n");
        return -1;
    }
    Mat gray;
    cvtColor(image, gray, CV_BGR2GRAY);
    int * pResults = NULL;
    //pBuffer is used in the detection functions.
    //If you call functions in multiple threads, please create one buffer for each thread!
    unsigned char * pBuffer = (unsigned char *)malloc(DETECT_BUFFER_SIZE);
    if (!pBuffer)
    {
        fprintf(stderr, "Can not alloc buffer.\n");
        return -1;
    }

    int doLandmark = 1;
    ///////////////////////////////////////////
    // reinforced multiview face detection / 68 landmark detection
    // it can detect side view faces, better but slower than facedetect_multiview().
    //////////////////////////////////////////
    //!!! The input image must be a gray one (single-channel)
    //!!! DO NOT RELEASE pResults !!!
    pResults = facedetect_multiview_reinforce(pBuffer, (unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, (int)gray.step,
        1.2f, 3, 48, 0, doLandmark);

    printf("%d faces detected.\n", (pResults ? *pResults : 0));
    int j = 0;
    Mat result_multiview_reinforce = image.clone();
    Mat image_cut = image.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);//(x,y)为检测到人脸左上角像素位置，w为宽，h为高
        rectangle(result_multiview_reinforce, Rect(x, y, w, h), Scalar(0, 255, 0), 0.5);
        int y1 = y - 100;
        int x1 = x - 90;
        int x2 = x + 285;
        int y2 = y + 345;
        if (y1 < 0) //超出边界判断
            y1 = 0;
        if (x1 < 0)
            x1 = 0;
        if (y2 >479)
            y2 = 479;
        if (x2>679)
            x1 = 679;
        image_cut = image_cut(Range(y1, y2), Range(x1, x2));
        //imshow("image_cut", image_cut);
        imwrite(Savepath, image_cut);

    }

    //release the buffer
    free(pBuffer);
    return 0;
}