opencv特征提取代码总结

特征提取代码总结 来自 /source/3208155#acomment 特征提取代码总结 颜色提取 颜色直方图提取： Code: #include #include #include using namespace std; int main( int argc, char* argv ) IplImage * src= cvLoadImage(“E:Downloadtest1.jpg“,1); IplImage* hsv = cvCreateImage( cvGetSize(src), 8, 3 ); IplImage* h_plane = cvCreateImage( cvGetSize(src), 8, 1 ); IplImage* s_plane = cvCreateImage( cvGetSize(src), 8, 1 ); IplImage* v_plane = cvCreateImage( cvGetSize(src), 8, 1 ); IplImage* planes = h_plane, s_plane ; /* H 分量划分为 16个等级，S 分量划分为 8个等级*/ int h_bins = 16, s_bins = 8; int hist_size = h_bins, s_bins; /* H 分量的变化范围*/ float h_ranges = 0, 180 ; /* S 分量的变化范围*/ float s_ranges = 0, 255 ; float* ranges = h_ranges, s_ranges ; /* 输入图像转换到 HSV颜色空间*/ cvCvtColor( src, hsv, CV_BGR2HSV ); cvCvtPixToPlane( hsv, h_plane, s_plane, v_plane, 0 ); /* 创建直方图，二维, 每个维度上均分*/ CvHistogram * hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges, 1 ); /* 根据 H,S两个平面数据统计直方图*/ cvCalcHist( planes, hist, 0, 0 ); /* 获取直方图统计的最大值，用于动态显示直方图*/ float max_value; cvGetMinMaxHistValue( hist, 0, /* 设置直方图显示图像*/ int height = 240; int width = (h_bins*s_bins*6); IplImage* hist_img = cvCreateImage( cvSize(width,height), 8, 3 ); cvZero( hist_img ); /* 用来进行 HSV到 RGB颜色转换的临时单位图像*/ IplImage * hsv_color = cvCreateImage(cvSize(1,1),8,3); IplImage * rgb_color = cvCreateImage(cvSize(1,1),8,3); int bin_w = width / (h_bins * s_bins); for(int h = 0; h #include “cv.h“ #include “highgui.h“ #define MAX_CORNERS 100 int main(void) int cornersCount=MAX_CORNERS;/得到的角点数目 CvPoint2D32f cornersMAX_CORNERS;/输出角点集合 IplImage *srcImage = 0,*grayImage = 0,*corners1 = 0,*corners2 = 0; int i; CvScalar color = CV_RGB(255,0,0); cvNamedWindow(“image“,1); /Load the image to be processed srcImage = cvLoadImage(“E:Download1.jpg“,1); grayImage = cvCreateImage(cvGetSize(srcImage),IPL_DEPTH_8U,1); /copy the source image to copy image after converting the format /复制并转为灰度图像 cvCvtColor(srcImage,grayImage,CV_BGR2GRAY); /create empty images os same size as the copied images /两幅临时位浮点图像，cvGoodFeaturesToTrack 会用到 corners1 = cvCreateImage(cvGetSize(srcImage),IPL_DEPTH_32F,1); corners2 = cvCreateImage(cvGetSize(srcImage),IPL_DEPTH_32F,1); cvGoodFeaturesToTrack(grayImage,corners1,corners2,corners, printf(“num corners found: %dn“,cornersCount); /开始画出每个点 if (cornersCount0) for (i=0;i #include #include int main(int argc, char* argv) IplImage* src = cvLoadImage( “E:Download2.jpg“ , 0 ); IplImage* dst; IplImage* color_dst; CvMemStorage* storage = cvCreateMemStorage(0); CvSeq* lines = 0; int i; if( !src ) return -1; dst = cvCreateImage( cvGetSize(src), 8, 1 ); color_dst = cvCreateImage( cvGetSize(src), 8, 3 ); cvCanny( src, dst, 50, 200, 3 ); cvCvtColor( dst, color_dst, CV_GRAY2BGR ); #if 0 lines = cvHoughLines2( dst, storage, CV_HOUGH_STANDARD, 1, CV_PI/180, 100, 0, 0 ); for( i = 0; i total,100); i+ ) float* line = (float*)cvGetSeqElem(lines,i); float rho = line0; float theta = line1; CvPoint pt1, pt2; double a = cos(theta), b = sin(theta); double x0 = a*rho, y0 = b*rho; pt1.x = cvRound(x0 + 1000*(-b); pt1.y = cvRound(y0 + 1000*(a); pt2.x = cvRound(x0 - 1000*(-b); pt2.y = cvRound(y0 - 1000*(a); cvLine( color_dst, pt1, pt2, CV_RGB(255,0,0), 3, CV_AA, 0 ); #else lines = cvHoughLines2( dst, storage, CV_HOUGH_PROBABILISTIC, 1, CV_PI/180, 50, 50, 10 ); for( i = 0; i total; i+ ) CvPoint* line = (CvPoint*)cvGetSeqElem(lines,i); cvLine( color_dst, line0, line1, CV_RGB(255,0,0), 3, CV_AA, 0 ); #endif cvNamedWindow( “Source“, 1 ); cvShowImage( “Source“, src ); cvNamedWindow( “Hough“, 1 ); cvShowImage( “Hough“, color_dst ); cvWaitKey(0); return 0; 运行效果截图： Hough 圆提取： Code： #include #include #include #include using namespace std; int main(int argc, char* argv) IplImage* img; img=cvLoadImage(“E:Download3.jpg“, 1); IplImage* gray = cvCreateImage( cvGetSize(img), 8, 1 ); CvMemStorage* storage = cvCreateMemStorage(0); cvCvtColor( img, gray, CV_BGR2GRAY ); cvSmooth( gray, gray, CV_GAUSSIAN, 5, 15 ); / smooth it, otherwise a lot of false circles may be detected CvSeq* circles = cvHoughCircles( gray, storage, CV_HOUGH_GRADIENT, 2, gray- height/4, 200, 100 ); int i; for( i = 0; i total; i+ ) float* p = (float*)cvGetSeqElem( circles, i ); cvCircle( img, cvPoint(cvRound(p0),cvRound(p1), 3, CV_RGB(0,255,0), - 1, 8, 0 ); cvCircle( img, cvPoint(cvRound(p0),cvRound(p1), cvRound(p2), CV_RGB(255,0,0), 3, 8, 0 ); couttotal #include #include int thresh = 50; IplImage* img = 0; IplImage* img0 = 0; CvMemStorage* storage = 0; CvPoint pt4;const char* wndname = “Square Detection Demo“; double angle( CvPoint* pt1, CvPoint* pt2, CvPoint* pt0 ) double dx1 = pt1-x - pt0-x; double dy1 = pt1-y - pt0-y; double dx2 = pt2-x - pt0-x; double dy2 = pt2-y - pt0-y; return (dx1*dx2 + dy1*dy2)/sqrt(dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10); CvSeq* findSquares4( IplImage* img, CvMemStorage* storage ) CvSeq* contours; int i, c, l, N = 11; CvSize sz = cvSize( img-width IplImage* timg = cvCloneImage( img ); IplImage* gray = cvCreateImage( sz, 8, 1 ); IplImage* pyr = cvCreateImage( cvSize(sz.width/2, sz.height/2), 8, 3 ); IplImage* tgray; CvSeq* result; double s, t; CvSeq* squares = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvPoint), storage ); cvSetImageROI( timg, cvRect( 0, 0, sz.width, sz.height ); / down-scale and upscale the image to filter out the noise cvPyrDown( timg, pyr, 7 ); cvPyrUp( pyr, timg, 7 ); tgray = cvCreateImage( sz, 8, 1 ); / find squares in every color plane of the image for( c = 0; c total = 4 for( i = 0; i = 2 ) t = fabs(angle( (CvPoint*)cvGetSeqElem( result, i ),(CvPoint*)cvGetSeqElem( result, i- 2 ),(CvPoint*)cvGetSeqElem( result, i-1 ); s = s t ? s : t; if( s h_next; cvReleaseImage( cvReleaseImage( cvReleaseImage( cvReleaseImage( return squares; / the function draws all the squares in the image void drawSquares( IplImage* img, CvSeq* squares ) CvSeqReader reader; IplImage* cpy = cvCloneImage( img ); int i; cvStartReadSeq( squares, for( i = 0; i total; i += 4 ) CvPoint* rect = pt; int count = 4; memcpy( pt, reader.ptr, squares-elem_size ); CV_NEXT_SEQ_ELEM( squares-elem_size, reader ); memcpy( pt + 1, reader.ptr, squares-elem_size ); CV_NEXT_SEQ_ELEM( squares-elem_size, reader ); memcpy( pt + 2, reader.ptr, squares-elem_size ); CV_NEXT_SEQ_ELEM( squares-elem_size, reader ); memcpy( pt + 3, reader.ptr, squares-elem_size ); CV_NEXT_SEQ_ELEM( squares-elem_size, reader ); cvPolyLine( cpy, cvShowImage( wndname, cpy ); cvReleaseImage( void on_trackbar( int a ) if( img ) drawSquares( img, findSquares4( img, storage ) ); char* names = “1.jpg“, 0 ; int main(int argc, char* argv) int i, c; storage = cvCreateMemStorage(0); for( i = 0; namesi != 0; i+ ) img0 = cvLoadImage( namesi, 1 ); if( !img0 ) printf(“Couldnt load %sn“, namesi ); continue; img = cvCloneImage( img0 ); cvNamedWindow( wndname, 1 ); cvCreateTrackbar( “canny thresh“, wndname, on_trackbar(0); c = cvWaitKey(0); cvReleaseImage( cvReleaseImage( cvClearMemStorage( storage ); if( c = 27 ) break; cvDestroyWindow( wndname ); return 0; 运行效果截图： 边缘直方图提取： Code： #include “cv.h“ #include “highgui.h“ #include #include #define PI 3.14 int main() IplImage *src = 0; / source imagre IplImage *histimg = 0; / histogram image CvHistogram *hist = 0; / define multi_demention histogram IplImage* canny; CvMat* canny_m; IplImage* dx; / the sobel x difference IplImage* dy; / the sobel y difference CvMat* gradient; / value of gradient CvMat* gradient_dir; / direction of gradient CvMat* dx_m; / format transform to matrix CvMat* dy_m; CvMat* mask; CvSize size; IplImage* gradient_im; int i,j; float theta; int hdims = 8; / 划分 HIST的个数，越高越精确 float hranges_arr = -PI/2,PI/2; / 直方图的上界和下界 float* hranges = hranges_arr; float max_val; / int bin_w; src=cvLoadImage(“E:Downloadtest.jpg“, 0); / force to gray image if(src=0) return -1; cvNamedWindow( “Histogram“, 0 ); /cvNamedWindow( “src“, 0); size=cvGetSize(src); canny=cvCreateImage(cvGetSize(src),8,1);/边缘图像 dx=cvCreateImage(cvGetSize(src),32,1);/x方向上的差分/此处的数据类型为 U 不怕溢出吗？ dy=cvCreateImage(cvGetSize(src),32,1); gradient_im=cvCreateImage(cvGetSize(src),32,1);/梯度图像 canny_m=cvCreateMat(size.height,size.width,CV_32FC1);/边缘矩阵 dx_m=cvCreateMat(size.height,size.width,CV_32FC1); dy_m=cvCreateMat(size.height,size.width,CV_32FC1); gradient=cvCreateMat(size.height,size.width,CV_32FC1);/梯度矩阵 gradient_dir=cvCreateMat(size.height,size.width,CV_32FC1);/梯度方向矩阵 mask=cvCreateMat(size.height,size.width,CV_32FC1);/掩码 cvCanny(src,canny,60,180,3);/边缘检测 cvConvert(canny,canny_m);/把图像转换为矩阵 cvSobel(src,dx,1,0,3);/ 一阶 X方向的图像差分:dx cvSobel(src,dy,0,1,3);/ 一阶 Y方向的图像差分:dy cvConvert(dx,dx_m); cvConvert(dy,dy_m); cvAdd(dx_m,dy_m,gradient); / value of gradient/梯度不是等于根号下 x的导数的平方加上 y 导数的平方吗？ cvDiv(dx_m,dy_m,gradient_dir); / direction for(i=0;ibins, hist-bins, max_val ? 255. / max_val : 0., 0 ); / 缩放 bin 到区间0,255 ，比例系数 cvZero( histimg ); bin_w = histimg-width /16; / hdims: 条的个数，则 bin_w 为条的宽度 / 画直方图 for( i = 0; i bins,i)*histimg-height/255 ); / 返回单通道数组的指定元素， 返回直方图第 i条的大小，val 为 histimg中的 i条的高度 CvScalar color = CV_RGB(255,255,0); /(hsv2rgb(i*180.f/hdims);/直方图颜色 cvRectangle( histimg, cvPoint(100+i*bin_w,histimg- height),cvPoint(100+(i+1)*bin_w,(int)(histimg-height - val), color, 1, 8, 0 ); / 画直方图画矩形，左下角，右上角坐标 cvShowImage( “src“, src); cvShowImage( “Histogram“, histimg ); cvWaitKey(0); cvDestroyWindow(“src“); cvDestroyWindow(“Histogram“); cvReleaseImage( cvReleaseImage( cvReleaseHist ( return 0; 运行效果截图： 视频流中边缘检测： Code： #include “highgui.h“ #include “cv.h“ #include “stdio.h“ #include int main(int argc,char * argv) IplImage * laplace = 0; IplImage * colorlaplace = 0; IplImage * planes3 = 0,0,0; CvCapture *capture = 0; /从摄像头读取 /*if(argc = 1 |( argc=2 */ /从文件中读取 /* else if(argc = 2)*/ capture = cvCaptureFromAVI(“1.avi“); if(!capture) fprintf(stderr,“Could not initialize capturing.n“); return -1; cvNamedWindow(“Laplacian“,1); cvNamedWindow(“video“,1); /循环捕捉，直到用户按键跳出循环体 for(;) IplImage * frame =0; /抓起一祯 frame = cvQueryFrame(capture); if(!frame) break; if(!laplace) /创建图像 for(int i=0;iwidth,frame-height),IPL_DEPTH_8U,1); laplace = cvCreateImage(cvSize(frame-width,frame-height),IPL_DEPTH_16S,1); colorlaplace=cvCreateImage(cvSize(frame-width,frame-height),IPL_DEPTH_8U,3); cvCvtPixToPlane(frame,planes0,planes1,planes2,0); for(int i=0;iorigin = frame-origin; /高斯滤波，平滑图像 / cvSmooth(colorlaplace, colorlaplace, CV_GAUSSIAN, 1, 0, 0); /形态学滤波,闭运算 cvDilate(colorlaplace, colorlaplace, 0, 1);/膨胀 cvErode(colorlaplace, colorlaplace, 0, 1);/腐蚀 cvShowImage(“video“, frame); cvShowImage(“Laplacian“,colorlaplace); if(cvWaitKey(10)0) break; cvReleaseCapture( cvDestroyWindow(“Laplacian“); cvDestroyWindow(“video“); return 0; 运行效果截图：