FaceTrack.rar_Facetrack_face track_人脸跟踪

//#include "stdafx.h" #define _AFXDLL //spc #include "afx.h" #include "cv.h" #include "highgui.h" #include <stdio.h> #include <ctype.h> #include<iostream> using namespace std; static CvHaarClassifierCascade* faceCascade = 0; const char* cascade_name = "haarcascade_frontalface_alt2.xml"; IplImage *image = 0, *hsv = 0, *hue = 0, *mask[10] = { 0 }, *backproject[10] = { 0 }, *histimg[10] = { 0 };//backproject为反向投影图像 CvHistogram *hist[10] = { 0 };//存储颜色直方图 int nFaces; double area0[100] = { 0 }, area1[100] = { 0 };//某一跟踪窗口的面积 int backproject_mode = 0; int select_object = 0; int track_object = 0; CvPoint origin; CvRect selection[10]; CvRect track_window[10] = { 0 }; CvBox2D track_box; CvConnectedComp track_comp; int hdims = 24; float hranges_arr[] = { 0, 180 }; float* hranges = hranges_arr; int vmin = 90, vmax = 256, smin = 90; CvScalar hsv2rgb(float hue); void detectFaceInImage(IplImage *inputImg, CvHaarClassifierCascade* cascade); int main() { CvCapture* capture = 0; //capture = cvCaptureFromCAM(0); capture = cvCaptureFromFile("1.mp4"); //spc //capture = cvCreateCameraCapture(0); //spc if (!capture) { fprintf(stderr, "Could not initialize capturing...\n"); return -1; } cvNamedWindow("CamShiftDemo", 1); cvCreateTrackbar("Vmin", "CamShiftDemo", &vmin, 256, 0);//Vmin被创建的trackbar名字,CamShiftDemo为工具条属于的窗口，vmin这个变量指定创建时的滑块位置 cvCreateTrackbar("Vmax", "CamShiftDemo", &vmax, 256, 0); cvCreateTrackbar("Smin", "CamShiftDemo", &smin, 256, 0); IplImage* frame = 0; frame = cvQueryFrame(capture); while (1) { int c; frame = cvQueryFrame(capture);//获取摄像头的序列，将图像序列存在frame中 if (!frame) break; if (!image)//image是进行处理的图像，开始的时候image = 0；若image =0,则要分配初始空间 { image = cvCreateImage(cvGetSize(frame), 8, 3); image->origin = frame->origin; hsv = cvCreateImage(cvGetSize(frame), 8, 3); hue = cvCreateImage(cvGetSize(frame), 8, 1); } cvCopy(frame, image, 0); cvCvtColor(image, hsv, CV_RGB2HSV); faceCascade = (CvHaarClassifierCascade*)cvLoad(cascade_name, 0, 0, 0); if (!faceCascade) { fprintf(stderr, "ERROR: Could not load classifier cascade\n"); return -1; } if (!track_object) { detectFaceInImage(image, faceCascade); }//检测人脸区域，将检测到的一系列人脸区域存储到selection[]中,得到初始的搜索窗口 else//track_object = 1或者是-1，进行人脸跟踪 { int _vmin = vmin, _vmax = vmax; cvSplit(hsv, hue, 0, 0, 0);//提取人脸图像的Hue分量 if (track_object<0)//track_object < 0 ,表示刚刚检测到人脸区域，然后进行人脸区域直方图计算 { for (int n = 0; n < nFaces; n++) { mask[n] = cvCreateImage(cvGetSize(frame), 8, 1);//得到全黑的mask cvZero(mask[n]); backproject[n] = cvCreateImage(cvGetSize(frame), 8, 1); hist[n] = cvCreateHist(1, &hdims, CV_HIST_ARRAY, &hranges, 1); cvSetImageROI(mask[n], selection[n]);//此时是检测到的人脸区域用于得到掩膜 cvAddS(mask[n], cvScalar(255), mask[n]);//得到mask掩膜，用于求人脸部分的hue分量的直方图 float max_val = 0.f; cvSetImageROI(hue, selection[n]); cvDilate(hue, hue, NULL, 2); cvErode(hue, hue, NULL, 2); cvSmooth(hue, hue, CV_MEDIAN, 3, 0, 0, 0); cvCalcHist(&hue, hist[n], 0, mask[n]);//建立人脸区域Hue分量的颜色直方图 cvResetImageROI(hue);//释放ROI区域 cvResetImageROI(mask[n]); track_window[n] = selection[n];//此时的跟踪窗口是上步中人脸检测得到的区域。 track_object = 1;//计算得到图像的直方图后，是track_objedt=1 ，方便后面进行跟踪，且不在计算初始的搜索窗口的颜色直方图以及掩膜图像 } } else { for (int k = 0; k < nFaces; k++) { cvZero(mask[k]); cvSetImageROI(mask[k], track_window[k]);//此时是用跟踪到的人脸区域得到掩膜图像 cvAddS(mask[k], cvScalar(255), mask[k]); cvResetImageROI(mask[k]);//得到mask掩膜，用于求人脸部分的hue分量的直方图 } } //下面是利用上面得到的直方图，以及掩膜图像得到反向投影图像 for (int m = 0; m < nFaces; m++) { cvCalcBackProject(&hue, backproject[m], hist[m]);//计算人脸部分的hue分量在整幅图像中的反向投影图 cvAnd(backproject[m], mask[m], backproject[m], 0); cvDilate(backproject[m], backproject[m], NULL, 2); cvErode(backproject[m], backproject[m], NULL, 2); } for (int m = 0; m < nFaces; m++) { cvCamShift(backproject[m], track_window[m], cvTermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1), &track_comp, &track_box);//使用camshift进行人脸跟踪 area0[m] = track_window[m].height * track_window[m].width; area1[m] = track_comp.rect.height * track_comp.rect.width; track_window[m] = track_comp.rect;//重新设置搜索窗口 if (image->origin) track_box.angle = -track_box.angle; cvEllipseBox(image, track_box, CV_RGB(255, 0, 0), 3, CV_AA, 0); } for (int l = 0; l < nFaces; l++) { if ((0.4 * area0[l] <= area1[l]) && (area1[l] <= 1.1 * area0[l])) track_object = 1; else track_object = 0; } } cvShowImage("CamShiftDemo", image);//显示的是人脸图像的跟踪情况 c = cvWaitKey(5);//此处过大的话时间就会等很久，在这个小问题上一定要注意，否则的话跟踪效率很低。 if (c == 27)//ESC，退出程序 break; } cvReleaseCapture(&capture); cvDestroyWindow("CamShiftDemo"); return 0; } //下面是颜色空间 CvScalar hsv2rgb(float hue) { int rgb[3], p, sector; static const int sector_data[][3] = { { 0, 2, 1 }, { 1, 2, 0 }, { 1, 0, 2 }, { 2, 0, 1 }, { 2, 1, 0 }, { 0, 1, 2 } }; hue *= 0.033333333333333333333333333333333f; sector = cvFloor(hue); p = cvRound(255 * (hue - sector)); p ^= sector & 1 ? 255 : 0; rgb[sector_data[sector][0]] = 255; rgb[sector_data[sector][1]] = 0; rgb[sector_data[sector][2]] = p; return cvScalar(rgb[2], rgb[1], rgb[0], 0); } void detectFaceInImage(IplImage* inputImg, CvHaarClassifierCascade* cascade) { CvSize minFeatureSize = cvSize(50, 50); int flags = CV_HAAR_DO_CANNY_PRUNING; float search_scale_factor = 1.2f; IplImage *detectImg; IplImage *greyImg = 0; CvMemStorage* storage; CvSize size; int ms; storage = cvCreateMemStorage(0); cvClearMemStorage(storage); // If the image is color, use a greyscale copy of the image. detectImg = (IplImage*)inputImg; if (inputImg->nChannels > 1) { size = cvSize(inputImg->width, inputImg->height); greyImg = cvCreateImage(size, IPL_DEPTH_8U, 1); cvCvtColor(inputImg, greyImg, CV_BGR2GRAY); detectImg = greyImg; // Use the greyscale image. } // Detect all the faces in the greyscale image. double t = (double)cvGetTickCount(); CvSeq* rects = cvHaarDetectObjects(detectImg, cascade, storage, search_scale_factor, 2, flags, minFeatureSize); t = (double)cvGetTickCount() - t; ms = cvRound(t / ((double)cvGetTickFrequency() * 1000.0)); nFaces = rects->total; printf("Face Detection took %d ms and found %d objects\n", ms, nFaces); if (nFaces > 0) { for (int i = 0; i < nFaces; i++) { selection[i] = *(CvRect*)cvGetSeqElem(rects, i); cvRectangle(inputImg, cvPoint(selection[i].x - 3, selection[i].y - 3), cvPoint(selection[i].x + selection[i].width + 2, selection[i].y + selection[i].height + 2), CV_RGB(255, 0, 0), 3); track_object = -1; } } else selection[0] = cvRect(-1, -1, -1, -1); if (greyImg) cvReleaseImage(&greyImg); cvReleaseMemStorage(&storage); }