图像处理经典算法及OpenCV程序

基于opencv的use摄像头视频采集程序 ........................................................................... 1 基于opencv的两个摄像头数据采集.................................................................................. 3 能激发你用代码做视频的冲动程序 ................................................................................... 6 图像反转（就是把黑的变白，白的变黑）........................................................................11 图像格式的转换 ............................................................................................................. 12 从摄像头或者AVI文件中得到视频流，对视频流进行边缘检测....................................... 13 采用Canny算子进行边缘检测........................................................................................ 15 角点检测........................................................................................................................ 18 图像的旋转加缩放（效果很拽，用地球做就像谷歌地球似的） ....................................... 21 Log-Polar极坐标变换 ..................................................................................................... 22 对图像进行形态学操作（图像的开闭，腐蚀和膨胀运算） .............................................. 24 用不同的核进行图像的二维滤波..................................................................................... 27 图像域的填充................................................................................................................. 30 寻找轮廓实现视频流的运动目标检测（超推荐一下） ..................................................... 35 采用金字塔方法进行图像分割 ........................................................................................ 40 图像的亮度变换 ............................................................................................................. 43 单通道图像的直方图 ...................................................................................................... 46 计算和显示彩色图像的二维色调-饱和度图像 .................................................................. 48 图像的直方图均匀化 ...................................................................................................... 50 用Hongh变换检测线段 .................................................................................................. 52 利用Hough变换检测圆（是圆不是椭圆） ...................................................................... 57 距离变换........................................................................................................................ 59 椭圆曲线拟合................................................................................................................. 64 由点集序列或数组创建凸外形 ........................................................................................ 68 Delaunay三角形和Voronoi划分的迭代式构造 ................................................................ 71 利用背景建模检测运动物体（推荐）.............................................................................. 78 运动模板检测（摄像头） ............................................................................................... 81 显示如何利用Camshift算法进行彩色目标的跟踪............................................................ 86

基于opencv的use摄像头视频采集程序

准备工作：你得把opencv库装到电脑上，并把各种头文件，源文件，lib库都连到vc上，然后设置一下系统环境变量，这里这方面就不说了，好像我前面的文章有说过，不懂也可百度一下。

建立一个基于WIN32控制台的工程CameraUSB,在新建一个c++元文件,写代码: #include \#include \#include \#include \

void callback(IplImage* image); int main() {

int ncams=cvcamGetCamerasCount( );//返回可以访问的摄像头数目 HWND MyWin;

// 设置系统属性

cvcamSetProperty(0, CVCAM_PROP_ENABLE, CVCAMTRUE); //选择第一个摄像头 //camera

cvcamSetProperty(0, CVCAM_PROP_RENDER, CVCAMTRUE); //We'll render stream

// 在本例中

// 假设创建一个窗口，并且窗口的ID是在变量 MyWin 中定义 // MyWin 是窗口 HWND 的类型

MyWin=(HWND)cvGetWindowHandle(\

cvcamSetProperty(0,CVCAM_PROP_WINDOW,&MyWin); // Selects a window for

//video rendering //回调函数将处理每一帧

cvcamSetProperty(0,CVCAM_PROP_CALLBACK,callback); cvcamInit( ); cvcamStart( );

// 现在程序开始工作 cvWaitKey(0); cvcamStop( ); cvcamExit( ); return 0; }

// 在图像中画兰色水平线

void callback(IplImage* image) {

IplImage* image1 = image; int i,j;

assert (image);

for(i=0; iheight; i+=10) {

for(j=(image1->widthStep)*i; j<(image1->widthStep)*(i+1); j+=image1->nChannels) {

image1->imageData[j] = (char)255; image1->imageData[j+1] = 0;

image1->imageData[j+2] = 0; } } }

嘿嘿,就这么简单就完事了。 不懂可留言问

基于opencv的两个摄像头数据采集

实现功能：同时采集两路USB摄像头数据，并显示，具有图片保存功能（点击左键保存图片，并暂停视频；右键继续视频）。步骤就不说了，很简单，直接放代码了：

#include #include

#include #include \#include

void StereoCallback(IplImage *frame1,IplImage *frame2);

void onMouse1(int Event,int x,int y,int flags,void *param); void onMouse2(int Event,int x,int y,int flags,void *param); IplImage *image1,*image2;

char *strleft[4]={\char

*strright[4]={\void main() {

HWND CaptureWindow1=0; //不赋值也行 HWND CaptureWindow2=0;

//int ncams=cvcamGetCamerasCount(); //获取摄像头的个数，在这里可有可无 //用对话框的形式来选取摄像头 int *CameraNumber;

int nSelected = cvcamSelectCamera(&CameraNumber); /* //灰色图像

image1=cvCreateImage(cvSize(320,240),IPL_DEPTH_8U,1);

image2=cvCreateImage(cvSize(320,240),IPL_DEPTH_8U,1); */

//彩色图像

image1=cvCreateImage(cvSize(320,240),IPL_DEPTH_8U,3); image2=cvCreateImage(cvSize(320,240),IPL_DEPTH_8U,3);

//初始化两个摄像头

cvNamedWindow(\

CaptureWindow1=(HWND)cvGetWindowHandle(\ cvcamSetProperty(CameraNumber[0], CVCAM_PROP_ENABLE, CVCAMTRUE);

cvcamSetProperty(CameraNumber[0], CVCAM_PROP_RENDER, CVCAMTRUE);

cvcamSetProperty(CameraNumber[0], CVCAM_PROP_WINDOW, &CaptureWindow1);

// cvSetMouseCallback(\

cvNamedWindow(\

CaptureWindow2=(HWND)cvGetWindowHandle(\ cvcamSetProperty(CameraNumber[1], CVCAM_PROP_ENABLE, CVCAMTRUE);

cvcamSetProperty(CameraNumber[1], CVCAM_PROP_RENDER, CVCAMTRUE);

cvcamSetProperty(CameraNumber[1], CVCAM_PROP_WINDOW, &CaptureWindow2);

// cvSetMouseCallback(\

//让两个摄像头同步

cvcamSetProperty(CameraNumber[0],CVCAM_STEREO_CALLBACK,(void*)&StereoCallback);

//启动程序

cvcamInit(); cvcamStart();

cvSetMouseCallback(\cvSetMouseCallback(\ cvWaitKey(0);

cvcamStop(); free(CameraNumber); cvcamExit();

cvDestroyWindow(\ cvDestroyWindow(\}

void StereoCallback(IplImage* frame1,IplImage *frame2) {

/* //把图像转换成灰度图并保存到image中 cvCvtColor(frame1,image1,CV_RGB2GRAY); cvCvtColor(frame2,image2,CV_RGB2GRAY); */

//拷贝图像到全局变量image中 该函数这样用存在问题 // cvCopy(frame1,image1); // cvCopy(frame2,image2); image1=cvCloneImage(frame1); image2=cvCloneImage(frame2); //对截取的图像翻转

cvFlip(image1,image1,0); cvFlip(image2,image2,0);

}

void onMouse1(int Event,int x,int y,int flags,void *param) {

static int num=0;

if(Event==CV_EVENT_LBUTTONDOWN) {

if(num==4)num=0;//只是固定定义了保存4张图片，为了不让程序非法而设置的复原

cvcamPause(); //图像保存

cvSaveImage(strleft[num],image1); // cvSaveImage(strright[num],image2); // cvSaveImage(\ // cvSaveImage(\ }

if(Event==CV_EVENT_RBUTTONDOWN) {

cvcamResume(); num++; } }

void onMouse2(int Event,int x,int y,int flags,void *param) {

static int num=0;

if(Event==CV_EVENT_LBUTTONDOWN) {

if(num==4)num=0;//只是固定定义了保存4张图片，为了不让程序非法而设置的复原

cvcamPause(); //图像保存

// cvSaveImage(strleft[num],image1); cvSaveImage(strright[num],image2); // cvSaveImage(\ // cvSaveImage(\ }

if(Event==CV_EVENT_RBUTTONDOWN) {

cvcamResume(); num++; } }

能激发你用代码做视频的冲动程序

这个程序是基于opencv的，连接库就不说了，直接建立一个基于win32的控制台程序，写代码就OK了。

/* 程序名：drawing..c

功能：展示OpenCV的图像绘制功能 */

#include \

#include \#include #include #define NUMBER 100 #define DELAY 5

char wndname[] = \

CvScalar random_color(CvRNG* rng) //函数 cvRNG 初始化随机数生成器并返回其状态，RNG 随机数生成器 {

int icolor = cvRandInt(rng); //函数 cvRandInt 返回均匀分布的随机 32-bit 无符号整型值并更新 RNG 状态

return CV_RGB(icolor&255, (icolor>>8)&255, (icolor>>16)&255); //

创建 一个色彩值 }

int main( int argc, char** argv ) {

int line_type = CV_AA; // change it to 8 to see non-antialiased graphics int i;

CvPoint pt1,pt2; //基于二维整形坐标轴的点 double angle;

CvSize sz; //矩形框大小，以像素为精度 CvPoint ptt[6]; CvPoint* pt[2]; int arr[2]; CvFont font; CvRNG rng;

int width = 1000, height = 700;

int width3 = width*3, height3 = height*3; CvSize text_size; int ymin = 0;

// Load the source image

IplImage* image = cvCreateImage( cvSize(width,height), 8, 3 ); IplImage* image2;

// Create a window

cvNamedWindow(wndname, 1 );

cvZero( image ); //#define cvZero cvSetZero void cvSetZero( CvArr* arr ); arr 要被清空数组

cvShowImage(wndname,image); rng = cvRNG((unsigned)-1); pt[0] = &(ptt[0]); pt[1] = &(ptt[3]); arr[0] = 3; arr[1] = 3;

for (i = 0; i< NUMBER; i++) {

pt1.x=cvRandInt(&rng) % width3 - width; pt1.y=cvRandInt(&rng) % height3 - height; pt2.x=cvRandInt(&rng) % width3 - width; pt2.y=cvRandInt(&rng) % height3 - height;

cvLine( image, pt1, pt2, random_color(&rng), cvRandInt(&rng), line_type, 0 );//绘制连接两个点的线段 cvShowImage(wndname,image); cvWaitKey(DELAY); }

for (i = 0; i< NUMBER; i++) {

pt1.x=cvRandInt(&rng) % width3 - width; pt1.y=cvRandInt(&rng) % height3 - height; pt2.x=cvRandInt(&rng) % width3 - width; pt2.y=cvRandInt(&rng) % height3 - height;

cvRectangle( image,pt1, pt2, random_color(&rng),

cvRandInt(&rng)-1, line_type, 0 );//绘制简单、指定粗细或者带填充的 矩形

cvShowImage(wndname,image); cvWaitKey(DELAY); }

for (i = 0; i< NUMBER; i++) {

pt1.x=cvRandInt(&rng) % width3 - width; pt1.y=cvRandInt(&rng) % height3 - height; sz.width =cvRandInt(&rng) 0; sz.height=cvRandInt(&rng) 0;

angle = (cvRandInt(&rng)00)*0.180;

cvEllipse( image, pt1, sz, angle, angle - 100, angle + 200, random_color(&rng), cvRandInt(&rng)-1, line_type, 0 );//函数cvEllipse用来绘制或者填充一个简单的椭圆弧或椭圆扇形 cvShowImage(wndname,image); cvWaitKey(DELAY); }

for (i = 0; i< NUMBER; i++) {

pt[0][0].x=cvRandInt(&rng) % width3 - width; pt[0][0].y=cvRandInt(&rng) % height3 - height; pt[0][1].x=cvRandInt(&rng) % width3 - width; pt[0][1].y=cvRandInt(&rng) % height3 - height; pt[0][2].x=cvRandInt(&rng) % width3 - width; pt[0][2].y=cvRandInt(&rng) % height3 - height; pt[1][0].x=cvRandInt(&rng) % width3 - width; pt[1][0].y=cvRandInt(&rng) % height3 - height;

pt[1][1].x=cvRandInt(&rng) % width3 - width; pt[1][1].y=cvRandInt(&rng) % height3 - height; pt[1][2].x=cvRandInt(&rng) % width3 - width; pt[1][2].y=cvRandInt(&rng) % height3 - height;

cvPolyLine( image, pt, arr, 2, 1, random_color(&rng),

cvRandInt(&rng), line_type, 0 );//函数cvPolyLine 绘制一个简单的或多样的多角曲线

cvShowImage(wndname,image); cvWaitKey(DELAY); }

for (i = 0; i< NUMBER; i++) {

pt[0][0].x=cvRandInt(&rng) % width3 - width; pt[0][0].y=cvRandInt(&rng) % height3 - height; pt[0][1].x=cvRandInt(&rng) % width3 - width; pt[0][1].y=cvRandInt(&rng) % height3 - height; pt[0][2].x=cvRandInt(&rng) % width3 - width; pt[0][2].y=cvRandInt(&rng) % height3 - height; pt[1][0].x=cvRandInt(&rng) % width3 - width; pt[1][0].y=cvRandInt(&rng) % height3 - height; pt[1][1].x=cvRandInt(&rng) % width3 - width; pt[1][1].y=cvRandInt(&rng) % height3 - height; pt[1][2].x=cvRandInt(&rng) % width3 - width; pt[1][2].y=cvRandInt(&rng) % height3 - height;

cvFillPoly( image, pt, arr, 2, random_color(&rng), line_type, 0 );//函数cvFillPoly用于一个单独被多变形轮廓所限定的区域内进行填充 cvShowImage(wndname,image); cvWaitKey(DELAY); }

for (i = 0; i< NUMBER; i++) {

pt1.x=cvRandInt(&rng) % width3 - width; pt1.y=cvRandInt(&rng) % height3 - height;

cvCircle( image, pt1, cvRandInt(&rng)00, random_color(&rng), cvRandInt(&rng)-1, line_type, 0 );//函数cvCircle绘制或填充一个给定圆心和半径的圆 cvShowImage(wndname,image); cvWaitKey(DELAY); }

for (i = 1; i< NUMBER; i++) {

pt1.x=cvRandInt(&rng) % width3 - width; pt1.y=cvRandInt(&rng) % height3 - height;

cvInitFont( &font, cvRandInt(&rng) % 8, (cvRandInt(&rng)0)*0.05+0.1, (cvRandInt(&rng)0)*0.05+0.1,

(cvRandInt(&rng)%5)*0.1, cvRound(cvRandInt(&rng)),

line_type );//字体结构初始化。函数 cvRound, cvFloor, cvCeil 用一种舍入方法将输入浮点数转换成整数。 cvRound 返回和参数最接近的整数值

cvPutText( image, \Petroleum University!\pt1, &font, random_color(&rng));//在图像中加入文本 cvShowImage(wndname,image); cvWaitKey(DELAY); }

cvInitFont( &font, CV_FONT_HERSHEY_COMPLEX, 3, 3, 0.0, 5, line_type );

cvGetTextSize( \设置字符串文本的宽度和高度

pt1.x = (width - text_size.width)/2; pt1.y = (height + text_size.height)/2; image2 = cvCloneImage(image);

for( i = 0; i < 255; i++ ) {

cvSubS( image2, cvScalarAll(i), image, 0 );//函数 cvSubS 从原数组的每个元素中减去一个数量

cvPutText( image, \CV_RGB(255,i,i));

cvShowImage(wndname,image); cvWaitKey(DELAY); }

// Wait for a key stroke; the same function arranges events processing cvWaitKey(0);

cvReleaseImage(&image); cvReleaseImage(&image2); cvDestroyWindow(wndname);

return 0; }

效果图：太帅了

图像反转（就是把黑的变白，白的变黑）

黑的变白了，白的变黑了 源码：

#include #include #include

#include

int main(int argc,char* argv[]) {

IplImage* img=0;

int height,width,step,channels; UCHAR* data; int i,j,k; if(argc<2) {

printf(\ exit(0); }

img=cvLoadImage(argv[1]); if(!img) {

printf(\ exit(0); }

height=img->height; width=img->width; step=img->widthStep;

channels=img->nChannels;

data=(UCHAR*)img->imageData;

printf(\channels\\n\

cvNamedWindow(\cvMoveWindow(\

for(i=0;i

for(k=0;k

data[i*step+j*channels+k]=255-data[i*step+j*channels+k];

cvShowImage(\cvWaitKey(0);

cvReleaseImage(&img); return 0; }

图像格式的转换

首先要准备一张图片，和几个txt文档，把txt文档的扩展名改成一个你要把图片转换成的格式

我用的原始图片是jpg的，txt改成bmp的

使用时，运行-cmd-cd 转到你的目录- Convert.exe 1.jpg 2.bmp 运行就能把图像1.jpg转换成2.bmp了 源码如下：

/* 程序名：convert.c 功能：图像格式的转换 */

#include

#include #include

int main( int argc, char** argv ) {

IplImage* src;

// -1: the loaded image will be loaded as is (with number of channels depends on the file). if(argc != 3) {

printf(\JPG,BMP,TIF,PNG,PPM\\n\

printf(\ return 0; }

if( ( strstr(argv[1],\&& strstr(argv[1],\

&& strstr(argv[1],\&& strstr(argv[1],\&& strstr(argv[1],\

|| ( strstr(argv[2],\&& strstr(argv[2],\&& strstr(argv[2],\&& strstr(argv[2],\

&& strstr(argv[2],\的用法是不是在a数组内查看是否有b数组。。。没有则输出NULL {

printf(\CONV only support JPG,BMP,TIF,PPM,TGA and PPM\\n\ } else {

if( (src=cvLoadImage(argv[1], -1))!= 0 ) { cvSaveImage( argv[2], src); cvReleaseImage(&src);

printf(\ } else {

printf(\ } }

return 0; }

发现了个小问题：

原来的jpg图像只有102KB转换成bmp后变成549KB ，在运行程序把这个bmp转成jpg又只有81KB。这真是汗死我了

从摄像头或者AVI文件中得到视频流，对视频流进行边缘检测

/*

程序名称：laplace.c

功能：从摄像头或者AVI文件中得到视频流，对视频流进行边缘检测，并输出结果。 */

#include \

#include \#include #include

int main( int argc, char** argv ) {

IplImage* laplace = 0;

IplImage* colorlaplace = 0;

IplImage* planes[3] = { 0, 0, 0 }; // 多个图像面 CvCapture* capture = 0;

// 下面的语句说明在命令行执行程序时，如果指定AVI文件，那么处理从 // AVI文件读取的视频流，如果不指定输入变量，那么处理从摄像头获取 // 的视频流 if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 ); else if( argc == 2 )

capture = cvCaptureFromAVI( argv[1] );

if( !capture ) {

fprintf(stderr,\ return -1; }

cvNamedWindow( \// 循环捕捉，直到用户按键跳出循环体 for(;;) {

IplImage* frame = 0; int i;

frame = cvQueryFrame( capture ); if( !frame ) break;

if( !laplace ) {

for( i = 0; i < 3; i++ ) planes[i] =

cvCreateImage( cvSize(frame->width,frame->height), 8, 1 ); laplace = cvCreateImage( cvSize(frame->width,frame->height), IPL_DEPTH_16S, 1 );

colorlaplace =

cvCreateImage( cvSize(frame->width,frame->height), 8, 3 ); }

cvCvtPixToPlane( frame, planes[0], planes[1], planes[2], 0 ); for( i = 0; i < 3; i++ )

{

cvLaplace( planes[i], laplace, 3 ); // 3: aperture_size cvConvertScaleAbs( laplace, planes[i], 1, 0 ); // planes[] = ABS(laplace) }

cvCvtPlaneToPix( planes[0], planes[1], planes[2], 0, colorlaplace );

colorlaplace->origin = frame->origin; cvShowImage(\ if( cvWaitKey(10) >= 0 ) break; }

cvReleaseCapture( &capture ); cvDestroyWindow(\ return 0; }

采用Canny算子进行边缘检测

#include \

#include \

char wndname[] = \

char tbarname[] = \int edge_thresh = 1;

IplImage *image = 0, *cedge = 0, *gray = 0, *edge = 0; // 定义跟踪条的 callback 函数 void on_trackbar(int h) {

cvSmooth( gray, edge, CV_BLUR, 3, 3, 0 ); cvNot( gray, edge );

// 对灰度图像进行边缘检测

cvCanny(gray, edge, (float)edge_thresh, (float)edge_thresh*3, 3); cvZero( cedge ); // copy edge points

cvCopy( image, cedge, edge ); // 显示图像

cvShowImage(wndname, cedge); }

int main( int argc, char** argv ) {

char* filename = argc == 2 ? argv[1] : (char*)\

if( (image = cvLoadImage( filename, 1)) == 0 ) return -1;

// Create the output image

cedge = cvCreateImage(cvSize(image->width,image->height), IPL_DEPTH_8U, 3);

// 将彩色图像转换为灰度图像

gray = cvCreateImage(cvSize(image->width,image->height), IPL_DEPTH_8U, 1);

edge = cvCreateImage(cvSize(image->width,image->height), IPL_DEPTH_8U, 1);

cvCvtColor(image, gray, CV_BGR2GRAY); // Create a window

cvNamedWindow(wndname, 1);

// create a toolbar

cvCreateTrackbar(tbarname, wndname, &edge_thresh, 100, on_trackbar);

// Show the image on_trackbar(1);

// Wait for a key stroke; the same function arranges events processing cvWaitKey(0);

cvReleaseImage(&image); cvReleaseImage(&gray); cvReleaseImage(&edge); cvDestroyWindow(wndname); return 0; }

/*******代码中的函数说明 1、cvSmooth，其函数声明为：

cvSmooth( const void* srcarr, void* dstarr, int smoothtype,int param1, int param2, double param3 )

cvSmooth函数的作用是对图象做各种方法的图象平滑。其中，srcarr为输入图象；dstarr为输出图象；

param1为平滑操作的第一个参数；param2为平滑操作的第二个参数（如果param2值为0，则表示它被设为param1）； param3是对应高斯参数的标准差。

参数smoothtype是图象平滑的方法选择，主要的平滑方法有以下五种： CV_BLUR_NO_SCALE：简单不带尺度变换的模糊，即对每个象素在 param1×param2领域求和。

CV_BLUR：对每个象素在param1×param2邻域求和并做尺度变换 1/（param1?param2）。

CV_GAUSSIAN：对图像进行核大小为param1×param2的高斯卷积。

CV_MEDIAN：对图像进行核大小为param1×param1 的中值滤波（邻域必须是方的）。

CV_BILATERAL：双向滤波，应用双向 3x3 滤波，彩色设置为param1，空间设置为param2。

2、void cvNot(const CvArr* src,CvArr* dst);

函数cvNot()会将src中的每一个元素的每一位取反，然后把结果赋给dst。 因此，一个值为0x00的8位图像将被映射到0xff，而值为0x83的图像将被映射到0x7c。

3、void cvCanny( const CvArr* image, CvArr* edges, double threshold1,double threshold2, int aperture_size=3 ); 采用 Canny 算法做边缘检测 image 输入图像 edges

输出的边缘图像 threshold1 第一个阈值 threshold2 第二个阈值 aperture_size

Sobel 算子内核大小

4、void cvCopy( const CvArr* src, CvArr* dst, const CvArr* mask=NULL ); 在使用这个函数之前，你必须用cvCreateImage（）一类的函数先开一段内存，然后传递给dst。

cvCopy会把src中的数据复制到dst的内存中。

5、cvCreateTrackbar

创建trackbar并将它添加到指定的窗口。

int cvCreateTrackbar( const char* trackbar_name, const char* window_name, int* value, int count, CvTrackbarCallback on_change ); trackbar_name

被创建的trackbar名字。 window_name

窗口名字，这个窗口将为被创建trackbar的父对象。 value

整数指针，它的值将反映滑块的位置。这个变量指定创建时的滑块位置。 count

滑块位置的最大值。最小值一直是0。 on_change

每次滑块位置被改变的时候，被调用函数的指针。这个函数应该被声明为void Foo(int);

如果没有回调函数，这个值可以设为NULL。

函数cvCreateTrackbar用指定的名字和范围来创建trackbar（滑块或者范围控制），指定与trackbar位置同步的变量，

并且指定当trackbar位置被改变的时候调用的回调函数。被创建的trackbar显示在指定窗口的顶端。 */

角点检测

原始图：

处理后图：

源代码：

#include #include \

#include \#define max_corners 100

int main( int argc, char** argv ) {

int cornerCount=max_corners;

CvPoint2D32f corners[max_corners];

IplImage *srcImage = 0, *grayImage = 0, *corners1 = 0, *corners2 = 0;

int i;

CvScalar color = CV_RGB(255,0,0);

char* filename = argc == 2 ? argv[1] : (char*)\注意相对路径

cvNamedWindow( \1 ); // create HighGUI window with name \

//Load the image to be processed

srcImage = cvLoadImage(filename, 1);

grayImage = cvCreateImage(cvGetSize(srcImage), IPL_DEPTH_8U, 1);

//copy the source image to copy image after converting the format

cvCvtColor(srcImage, grayImage, CV_BGR2GRAY);

//create empty images of same size as the copied images

corners1= cvCreateImage(cvGetSize(srcImage), IPL_DEPTH_32F, 1); corners2= cvCreateImage(cvGetSize(srcImage),IPL_DEPTH_32F, 1);

cvGoodFeaturesToTrack (grayImage, corners1, corners2, corners, &cornerCount, 0.05, 5, 0,

3, // block size

0, // not use harris 0.4 );

printf(\

// draw circles at each corner location in the gray image and //print out a list the corners if(cornerCount>0) {

for (i=0; i

cvCircle(srcImage, cvPoint((int)(corners[i].x), (int)(corners[i].y)), 6,

color, 2, CV_AA, 0); } }

cvShowImage( \

cvReleaseImage(&srcImage); cvReleaseImage(&grayImage); cvReleaseImage(&corners1); cvReleaseImage(&corners2);

cvWaitKey(0); // wait for key. The function has return 0; }

友情链接一下，这是别人写的：

http://hi.http://www.wodefanwen.com//xiaoduo170/blog/item/2816460175c8330779ec2c64.html

图像的旋转加缩放（效果很拽，用地球做就像谷歌地球似的）

#include \

#include \#include \

int main( int argc, char** argv ) {

IplImage* src;

/* the first command line parameter must be image file name */ if( argc==2 && (src = cvLoadImage(argv[1], -1))!=0) {

IplImage* dst = cvCloneImage( src ); int delta = 1; int angle = 0;

int opt = 1; // 1： 旋转加缩放 // 0: 仅仅旋转 double factor;

cvNamedWindow( \ cvShowImage( \

for(;;) {

float m[6];

// Matrix m looks like: //

// [ m0 m1 m2 ] ===> [ A11 A12 b1 ] // [ m3 m4 m5 ] [ A21 A22 b2 ] //

CvMat M = cvMat( 2, 3, CV_32F, m ); int w = src->width; int h = src->height; if(opt) // 旋转加缩放

factor = (cos(angle*CV_PI/180.) + 1.05)*2; else // 仅仅旋转 factor = 1;

m[0] = (float)(factor*cos(-angle*2*CV_PI/180.)); m[1] = (float)(factor*sin(-angle*2*CV_PI/180.)); m[3] = -m[1]; m[4] = m[0];

// 将旋转中心移至图像中间 m[2] = w*0.5f; m[5] = h*0.5f;

// dst(x,y) = A * src(x,y) + b

cvGetQuadrangleSubPix( src, dst, &M);//提取象素四边形，使用子象素精度

cvNamedWindow( \ cvShowImage( \ if( cvWaitKey(5) == 27 ) break;

angle =(int) (angle + delta) % 360; } // for-loop }

return 0; }

Log-Polar极坐标变换

原始图：

效果图：（正变换）

反变换：

正反变换只是函数中一个参数的不同，具体看你所需要的应用。

cvLogPolar函数可以用来模拟人类的中央视觉（foveal vision），并可以用于物体跟踪方面的尺度及旋转不变模板的快速匹配。 源代码：

#include

#include

int main(int argc, char** argv) {

IplImage* src;

if( argc == 2 && (src=cvLoadImage(argv[1],1)) != 0 ) {

IplImage* dst = cvCreateImage( cvSize(256,256), 8, 3 ); IplImage* src2 = cvCreateImage( cvGetSize(src), 8, 3 );

cvLogPolar( src, dst, cvPoint2D32f(src->width/2,src->height/2), 40, CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS ); cvLogPolar( dst, src2,

cvPoint2D32f(src->width/2,src->height/2), 40,

CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS+CV_WARP_INVERSE_MAP ); cvNamedWindow( \ cvShowImage( \

cvNamedWindow( \ cvShowImage( \ cvWaitKey(); }

return 0; }

对图像进行形态学操作（图像的开闭，腐蚀和膨胀运算）

效果图：（什么东东长这么丑啊，汗）

#include

#include #include #include IplImage* src = 0; IplImage* dst = 0;

IplConvKernel* element = 0;

int element_shape = CV_SHAPE_RECT;

//the address of variable which receives trackbar position update int max_iters = 10;

int open_close_pos = 0; int erode_dilate_pos = 0;

// callback function for open/close trackbar void OpenClose(int pos) {

int n = open_close_pos - max_iters; int an = n > 0 ? n : -n;

element = cvCreateStructuringElementEx( an*2+1, an*2+1, an, an, element_shape, 0 ); if( n < 0 ) {

cvErode(src,dst,element,1); cvDilate(dst,dst,element,1); }

else {

cvDilate(src,dst,element,1); cvErode(dst,dst,element,1); }

cvReleaseStructuringElement(&element); cvShowImage(\}

// callback function for erode/dilate trackbar void ErodeDilate(int pos) {

int n = erode_dilate_pos - max_iters; int an = n > 0 ? n : -n;

element = cvCreateStructuringElementEx( an*2+1, an*2+1, an, an, element_shape, 0 ); if( n < 0 ) {

cvErode(src,dst,element,1); } else {

cvDilate(src,dst,element,1); }

cvReleaseStructuringElement(&element); cvShowImage(\}

int main( int argc, char** argv ) {

char* filename = argc == 2 ? argv[1] : (char*)\ if( (src = cvLoadImage(filename,1)) == 0 ) return -1;

printf( \

\

\ \

\ \ dst = cvCloneImage(src);

//create windows for output images cvNamedWindow(\ cvNamedWindow(\

open_close_pos = erode_dilate_pos = max_iters; cvCreateTrackbar(\

\ cvCreateTrackbar(\

\ for(;;) {

int c;

OpenClose(open_close_pos);

ErodeDilate(erode_dilate_pos); c = cvWaitKey(0);

if( (char)c == 27 ) break;

if( (char)c == 'e' )

element_shape = CV_SHAPE_ELLIPSE; else if( (char)c == 'r' )

element_shape = CV_SHAPE_RECT; else if( (char)c == 'c' )

element_shape = CV_SHAPE_CROSS; else if( (char)c == '\\n' )

element_shape = (element_shape + 1) % 3; }

//release images

cvReleaseImage(&src); cvReleaseImage(&dst);

//destroy windows

cvDestroyWindow(\ cvDestroyWindow(\ return 0; }

用不同的核进行图像的二维滤波

函数cvSmooth实现各种方法的图形平滑。

一般来说，图像平滑主要是为了消除噪声。图像的常见噪声主要有加性噪声、乘性噪声和量化噪声等。由于图像的能量主要集在低频部分，而噪声所在频段主要在高频段，因此通常都是采用低通滤波的方法消除噪声。 函数cvFilter2D对图像做卷积运算。

对图像进行线性滤波，支持替换方式操作。当核运算部份超出输入图像时，边界外面的像素值等于离它最近的图像像素值。 效果图：

源代码：

// Filtering for Image with variaty filtering kernel //

// CV_PREWITT_3x3_V A gradient filter (vertical Prewitt operator). // -1 0 1 // -1 0 1 // -1 0 1

// CV_PREWITT_3x3_H A gradient filter (horizontal Prewitt operator). // 1 1 1 // 0 0 0 // -1 -1 -1

// CV_SOBEL_3x3_V A gradient filter (vertical Sobel operator). // -1 0 1 // -2 0 2 // -1 0 1

// CV_SOBEL_3x3_H A gradient filter (horizontal Sobel operator). // 1 2 1 // 0 0 0 // -1 -2 -1

// CV_LAPLACIAN_3x3 A 3x3 Laplacian highpass filter. // -1 -1 -1 // -1 8 -1 // -1 -1 -1

// CV_LAPLACIAN_3x3 A 3x3 Laplacian highpass filter (another kernel) // This kernel is similar with function: cvLaplace with aperture_size=1 // 0 1 0 // 1 -4 1

// 0 1 0 注：直接用cvFilter2D得到的结果与用cvLaplace得到的结果

// 略有不同

// CV_LAPLACIAN_5x5 A 5x5 Laplacian highpass filter. // -1 -3 -4 -3 -1 // -3 0 6 0 -3 // -4 6 20 6 -4 // -3 0 6 0 -3 // -1 -3 -4 -3 -1

// CV_GAUSSIAN_3x3 A 3x3 Gaussian lowpass filter. // This filter uses the kernel A/16,where // 1 2 1 // A = 2 4 2 // 1 2 1

// These filter coefficients correspond to a 2-dimensional Gaussian // distribution with standard deviation 0.85. //

// CV_GAUSSIAN_5x5 A 5x5 Gaussian lowpass filter. // This filter uses the kernel A/571,where // 2 7 12 7 2 // 7 31 52 31 7 // A = 12 52 127 52 12 // 7 31 52 31 7 // 2 7 12 7 2 #include

#include #include

int main( int argc, char** argv ) {

IplImage *src = 0, *dst = 0, *dst2 = 0; /*float k[9] = { 0, 1, 0, 1,-4, 1,

0, 1, 0}; */

float k[9] = { 1.f/16, 2.f/16, 1.f/16,

2.f/16, 4.f/16, 2.f/16,

1.f/16, 2.f/16, 1.f/16}; // 这里高斯核滤波器归一化

CvMat Km;

//cvInitMatHeader( &Km, 3, 3, CV_32FC1, k, CV_AUTOSTEP ); Km = cvMat( 3, 3, CV_32F, k ); // 0: force to gray image

src = cvLoadImage(\ dst = cvCloneImage( src );

cvNamedWindow(\ cvShowImage(\

cvNamedWindow(\

cvFilter2D( src, dst, &Km, cvPoint(-1,-1)); cvShowImage(\ cvWaitKey(0);

cvReleaseImage( &src ); cvReleaseImage( &dst ); return 0; }

图像域的填充

效果图：

源代码：

#include \

#include \#include #include

IplImage* color_img0; IplImage* mask;

IplImage* color_img;

IplImage* gray_img0 = NULL; IplImage* gray_img = NULL; int ffill_case = 1;

int lo_diff = 20, up_diff = 20; int connectivity = 4; int is_color = 1; int is_mask = 0;

int new_mask_val = 255;

void on_mouse( int event, int x, int y, int flags, void* param ) {

if( !color_img ) return;

switch( event ) {

case CV_EVENT_LBUTTONDOWN:

{

CvPoint seed = cvPoint(x,y);

int lo = ffill_case == 0 ? 0 : lo_diff; int up = ffill_case == 0 ? 0 : up_diff;

int flags = connectivity + (new_mask_val << 8) + (ffill_case == 1 ? CV_FLOODFILL_FIXED_RANGE : 0); int b = rand() & 255, g = rand() & 255, r = rand() & 255; CvConnectedComp comp;

if( is_mask )

cvThreshold( mask, mask, 1, 128, CV_THRESH_BINARY );

if( is_color ) {

CvScalar color = CV_RGB( r, g, b ); cvFloodFill( color_img, seed, color, CV_RGB( lo, lo, lo ), CV_RGB( up, up, up ), &comp, flags, is_mask ? mask : NULL );

cvShowImage( \ } else {

CvScalar brightness = cvRealScalar((r*2 + g*7 + b + 5)/10);

cvFloodFill( gray_img, seed, brightness, cvRealScalar(lo),

cvRealScalar(up), &comp, flags, is_mask ? mask : NULL );

cvShowImage( \ }

printf(\ if( is_mask )

cvShowImage( \ }

break; } }

int main( int argc, char** argv ) {

char* filename = argc >= 2 ? argv[1] : (char*)\

if( (color_img0 = cvLoadImage(filename,1)) == 0 ) return 0;

printf( \

\

\ \

\ \

\- use gradient floodfill with fixed(absolute) range\\n\ \range\\n\

\ \

color_img = cvCloneImage( color_img0 );

gray_img0 = cvCreateImage( cvSize(color_img->width, color_img->height), 8, 1 );

cvCvtColor( color_img, gray_img0, CV_BGR2GRAY ); gray_img = cvCloneImage( gray_img0 ); mask = cvCreateImage( cvSize(color_img->width + 2, color_img->height + 2), 8, 1 );

cvNamedWindow( \

cvCreateTrackbar( \ cvCreateTrackbar( \ cvSetMouseCallback( \ for(;;) {

int c;

if( is_color )

cvShowImage( \ else

cvShowImage( \ c = cvWaitKey(0); switch( (char) c ) {

case '\\x1b':

printf(\ goto exit_main; case 'c':

if( is_color ) {

printf(\

cvCvtColor( color_img, gray_img, CV_BGR2GRAY ); is_color = 0; } else {

printf(\

cvCopy( color_img0, color_img, NULL ); cvZero( mask ); is_color = 1; }

break; case 'm':

if( is_mask ) {

cvDestroyWindow( \ is_mask = 0; } else {

cvNamedWindow( \ cvZero( mask );

cvShowImage( \ is_mask = 1; }

break; case 'r':

printf(\ cvCopy( color_img0, color_img, NULL ); cvCopy( gray_img0, gray_img, NULL ); cvZero( mask ); break; case 's':

printf(\ ffill_case = 0; break; case 'f':

printf(\ ffill_case = 1; break; case 'g':

printf(\(floating range) floodfill mode is set\\n\

ffill_case = 2; break; case '4':

printf(\ connectivity = 4; break; case '8':

printf(\ connectivity = 8; break; } } exit_main:

cvDestroyWindow( \ cvReleaseImage( &gray_img ); cvReleaseImage( &gray_img0 ); cvReleaseImage( &color_img ); cvReleaseImage( &color_img0 ); cvReleaseImage( &mask ); return 1; }

寻找轮廓实现视频流的运动目标检测（超推荐一下）

效果视频我上传了，浏览网址（个人感觉很牛，有点像生化危机里的一个场景）：

http://tinypic.com/m/a2epo8/2

如果上面的卡，可以连这个，就是有点发散图形：

http://i41.tinypic.com/54xcsm.jpg

也不说什么了，直接给代码吧（有一句话想说，实际上如果你是拿来做实际项目的，可能并不要学习里面的算法，直接利用里面的模板，也就是外接的调用函数就可以了）：

#include \

#include \#include

#include #include #include #include

// various tracking parameters (in seconds) const double MHI_DURATION = 0.5; const double MAX_TIME_DELTA = 0.5; const double MIN_TIME_DELTA = 0.05; const int N = 3;

//

const int CONTOUR_MAX_AERA = 16; // ring image buffer IplImage **buf = 0; int last = 0;

// temporary images

IplImage *mhi = 0; // MHI: motion history image CvFilter filter = CV_GAUSSIAN_5x5; CvConnectedComp *cur_comp, min_comp; CvConnectedComp comp; CvMemStorage *storage; CvPoint pt[4];

// 参数：

// img – 输入视频帧 // dst – 检测结果

void update_mhi( IplImage* img, IplImage* dst, int diff_threshold ) {

double timestamp = clock()/100.; // get current time in seconds CvSize size = cvSize(img->width,img->height); // get current frame size

int i, j, idx1, idx2; IplImage* silh; uchar val; float temp;

IplImage* pyr = cvCreateImage( cvSize((size.width & -2)/2, (size.height & -2)/2), 8, 1 ); CvMemStorage *stor;

CvSeq *cont, *result, *squares; CvSeqReader reader;

if( !mhi || mhi->width != size.width || mhi->height != size.height ) {

if( buf == 0 ) {

buf = (IplImage**)malloc(N*sizeof(buf[0])); memset( buf, 0, N*sizeof(buf[0])); }

for( i = 0; i < N; i++ ) {

cvReleaseImage( &buf[i] );

buf[i] = cvCreateImage( size, IPL_DEPTH_8U, 1 ); cvZero( buf[i] ); }

cvReleaseImage( &mhi );

mhi = cvCreateImage( size, IPL_DEPTH_32F, 1 ); cvZero( mhi ); // clear MHI at the beginning } // end of if(mhi)

cvCvtColor( img, buf[last], CV_BGR2GRAY ); // convert frame to grayscale

idx1 = last;

idx2 = (last + 1) % N; // index of (last - (N-1))th frame last = idx2;

// 做帧差

silh = buf[idx2];

cvAbsDiff( buf[idx1], buf[idx2], silh ); // get difference between frames

// 对差图像做二值化

cvThreshold( silh, silh, 30, 255, CV_THRESH_BINARY ); // and threshold it

cvUpdateMotionHistory( silh, mhi, timestamp, MHI_DURATION ); // update MHI

cvCvtScale( mhi, dst, 255./MHI_DURATION,

(MHI_DURATION - timestamp)*255./MHI_DURATION ); cvCvtScale( mhi, dst, 255./MHI_DURATION, 0 );

// 中值滤波，消除小的噪声

cvSmooth( dst, dst, CV_MEDIAN, 3, 0, 0, 0 );

// 向下采样，去掉噪声

cvPyrDown( dst, pyr, 7 );

cvDilate( pyr, pyr, 0, 1 ); // 做膨胀操作，消除目标的不连续空洞 cvPyrUp( pyr, dst, 7 ); //

// 下面的程序段用来找到轮廓 //

// Create dynamic structure and sequence. stor = cvCreateMemStorage(0);

cont = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint) , stor);

// 找到所有轮廓

cvFindContours( dst, stor, &cont, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0)); /*

for(;cont;cont = cont->h_next) {

// Number point must be more than or equal to 6 (for cvFitEllipse_32f).

if( cont->total < 6 ) continue;

// Draw current contour.

cvDrawContours(img,cont,CV_RGB(255,0,0),CV_RGB(255,0,0),0,1, 8, cvPoint(0,0));

} // end of for-loop: \*/

// 直接使用CONTOUR中的矩形来画轮廓 for(;cont;cont = cont->h_next) {

CvRect r = ((CvContour*)cont)->rect;

if(r.height * r.width > CONTOUR_MAX_AERA) // 面积小的方形抛弃掉

{

cvRectangle( img, cvPoint(r.x,r.y),

cvPoint(r.x + r.width, r.y + r.height), CV_RGB(255,0,0), 1, CV_AA,0); } }

// free memory

cvReleaseMemStorage(&stor); cvReleaseImage( &pyr ); }

int main(int argc, char** argv) {

IplImage* motion = 0;

CvCapture* capture = 0; //视频获取结构

if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))

//原型：extern int isdigit(char c);

//用法：#include 功能：判断字符c是否为数字 说明：当c为数字0-9时，返回非零值，否则返回零。

capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 ); else if( argc == 2 )

capture = cvCaptureFromAVI( argv[1] ); if( capture ) {

cvNamedWindow( \ for(;;) {

IplImage* image;

if( !cvGrabFrame( capture )) //从摄像头或者视频文件中抓取帧

break; image = cvRetrieveFrame( capture ); //取回由函数cvGrabFrame抓取的图像,返回由函数cvGrabFrame 抓取的图像的指针 if( image ) {

if( !motion ) {

motion =

cvCreateImage( cvSize(image->width,image->height), 8, 1 ); cvZero( motion );

motion->origin = image->origin; ///* 0 - 顶—左结构, 1 - 底—左结构 (Windows bitmaps 风格) */ } }

update_mhi( image, motion, 60 ); cvShowImage( \ if( cvWaitKey(10) >= 0 ) break; }

cvReleaseCapture( &capture ); cvDestroyWindow( \

}

return 0; }

采用金字塔方法进行图像分割

图像分割指的是将数字图像细分为多个图像子区域的过程，在OpenCv中实现了三种跟图像分割相关的算法，它们分别是：分水岭分割算法、金字塔分割算法以及均值漂移分割算法。

分水岭分割算法

分水岭分割算法需要您或者先前算法提供标记，该标记用于指定哪些大致区域是目标，哪些大致区域是背景等等；分水岭分割算法的分割效果严重依赖于提供的标记。OpenCv中的函数cvWatershed实现了该算法

金字塔分割算法

金字塔分割算法由cvPrySegmentation所实现，该函数的使用很简单；需要注意的是图像的尺寸以及金字塔的层数，图像的宽度和高度必须能被2整除，能够被2整除的次数决定了金字塔的最大层数

均值漂移分割算法

均值漂移分割算法由cvPryMeanShiftFiltering所实现，均值漂移分割的金字塔层数只能介于[1,7]之间

友情链接一下，个人感觉比较好的这方面博客：

http://www.cnblogs.com/xrwang/archive/2010/02/28/ImageSegmentation.html 效果图：

#include \

#include \#include

IplImage* image[2] = { 0, 0 }, *image0 = 0, *image1 = 0; CvSize size;

int w0, h0,i;

int threshold1, threshold2; int l,level = 4;

int sthreshold1, sthreshold2; int l_comp;

int block_size = 1000; float parameter; double threshold;

double rezult, min_rezult;

CvFilter filter = CV_GAUSSIAN_5x5; CvConnectedComp *cur_comp, min_comp; CvSeq *comp;

CvMemStorage *storage;

CvPoint pt1, pt2;

void ON_SEGMENT(int a) {

cvPyrSegmentation(image0, image1, storage, &comp, level, threshold1+1, threshold2+1); /*l_comp = comp->total;

i = 0;

min_comp.value = cvScalarAll(0); while(i

cur_comp = (CvConnectedComp*)cvGetSeqElem ( comp, i ); if(fabs(255- min_comp.value.val[0])> fabs(255- cur_comp->value.val[0]) && fabs(min_comp.value.val[1])> fabs(cur_comp->value.val[1]) && fabs(min_comp.value.val[2])> fabs(cur_comp->value.val[2]) ) min_comp = *cur_comp; i++; }*/

cvShowImage(\}

int main( int argc, char** argv ) {

char* filename = argc == 2 ? argv[1] : (char*)\ if( (image[0] = cvLoadImage( filename, 1)) == 0 ) return -1;

cvNamedWindow(\

cvShowImage(\ cvNamedWindow(\

storage = cvCreateMemStorage ( block_size ); image[0]->width &= -(1<height &= -(1<

threshold1 =255; threshold2 =30; ON_SEGMENT(1);

sthreshold1 = cvCreateTrackbar(\&threshold1, 255, ON_SEGMENT);

sthreshold2 = cvCreateTrackbar(\&threshold2, 255, ON_SEGMENT);

cvShowImage(\ cvWaitKey(0);

cvDestroyWindow(\ cvDestroyWindow(\ cvReleaseMemStorage(&storage ); cvReleaseImage(&image[0]); cvReleaseImage(&image0); cvReleaseImage(&image1); return 0; }

图像的亮度变换

郁闷，以前用过MatLab，很长时间没用了，都不知道怎么使了，据说做这个效果很不错。 效果图：

源代码：

#include \

#include \/*

src and dst are grayscale, 8-bit images; Default input value:

[low, high] = [0,1]; X-Direction [bottom, top] = [0,1]; Y-Direction gamma ;

if adjust successfully, return 0, otherwise, return non-zero. */

int ImageAdjust(IplImage* src, IplImage* dst, double low, double high, // X方向：low and high are the intensities of src

double bottom, double top, // Y方向：mapped to bottom and top of dst double gamma ) {

if( low<0 && low>1 && high <0 && high>1&&

bottom<0 && bottom>1 && top<0 && top>1 && low>high) return -1;

double low2 = low*255; double high2 = high*255;

double bottom2 = bottom*255; double top2 = top*255;

double err_in = high2 - low2; double err_out = top2 - bottom2;

int x,y; double val;

// intensity transform

for( y = 0; y < src->height; y++) {

for (x = 0; x < src->width; x++) {

val = ((uchar*)(src->imageData + src->widthStep*y))[x]; val = pow((val - low2)/err_in, gamma) * err_out + bottom2; if(val>255) val=255; if(val<0) val=0; // Make sure src is in the range [low,high]

((uchar*)(dst->imageData + dst->widthStep*y))[x] = (uchar) val;

} }

return 0; }

int main( int argc, char** argv ) {

IplImage *src = 0, *dst = 0;

if( argc != 2 || (src=cvLoadImage(argv[1], 0)) == NULL) // force to gray image

return -1;

cvNamedWindow( \ cvNamedWindow( \

// Image adjust

dst = cvCloneImage(src);

// 输入参数 [0,0.5] 和 [0.5,1], gamma=1

if( ImageAdjust( src, dst, 0, 0.5, 0.5, 1, 1)!=0) return -1;

cvShowImage( \ cvShowImage( \ cvWaitKey(0);

cvDestroyWindow(\ cvDestroyWindow(\ cvReleaseImage( &src ); cvReleaseImage( &dst );

return 0; }

单通道图像的直方图

原始图：

效果图：

源代码：

#include \

#include \#include #include

int main( int argc, char** argv ) {

IplImage *src = 0;

IplImage *histimg = 0; CvHistogram *hist = 0;

int hdims = 50; // 划分HIST的个数，越高越精确 float hranges_arr[] = {0,255}; float* hranges = hranges_arr; int bin_w; float max_val; int i;

if( argc != 2 || (src=cvLoadImage(argv[1], 0)) == NULL) // force to gray image

return -1;

cvNamedWindow( \ cvNamedWindow( \

hist = cvCreateHist( 1, &hdims, CV_HIST_ARRAY, &hranges, 1 ); // 计

算直方图

histimg = cvCreateImage( cvSize(320,200), 8, 3 ); cvZero( histimg );

cvCalcHist( &src, hist, 0, 0 ); // 计算直方图

cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 ); // 只找最大值 cvConvertScale( hist->bins, hist->bins, max_val ? 255. / max_val : 0., 0 ); // 缩放 bin 到区间 [0,255] cvZero( histimg );

bin_w = histimg->width / hdims; // hdims: 条的个数，则 bin_w 为条的宽度

// 画直方图

for( i = 0; i < hdims; i++ ) {

double val = ( cvGetReal1D(hist->bins,i)*histimg->height/255 ); CvScalar color = CV_RGB(255,255,0); //(hsv2rgb(i*180.f/hdims); cvRectangle( histimg, cvPoint(i*bin_w,histimg->height), cvPoint((i+1)*bin_w,(int)(histimg->height - val)), color, 1, 8, 0 ); }

cvShowImage( \

cvShowImage( \ cvWaitKey(0);

cvDestroyWindow(\

cvDestroyWindow(\ cvReleaseImage( &src );

cvReleaseImage( &histimg ); cvReleaseHist ( &hist );

return 0; }

计算和显示彩色图像的二维色调-饱和度图像

对这篇内容很郁闷，不知道以后用来干什么，声明一下，哥不是搞图像处理的。（业余爱好）

效果图：（好好的一张图，给处理成人不像人，鬼不像鬼）

#include

#include

int main( int argc, char** argv ) {

IplImage* src;

if( argc == 2 && (src=cvLoadImage(argv[1], 1))!= 0) {

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 };

IplImage* hsv = cvCreateImage( cvGetSize(src), 8, 3 ); int h_bins = 30, s_bins = 32;

int hist_size[] = {h_bins, s_bins};

float h_ranges[] = { 0, 180 }; /* hue varies from 0 (~0°red) to

180 (~360°red again) */

float s_ranges[] = { 0, 255 }; /* saturation varies from 0 (black-gray-white) to 255 (pure spectrum color) */ float* ranges[] = { h_ranges, s_ranges }; int scale = 10;

IplImage* hist_img =

cvCreateImage( cvSize(h_bins*scale,s_bins*scale), 8, 3 ); CvHistogram* hist; float max_value = 0; int h, s;

cvCvtColor( src, hsv, CV_BGR2HSV );

cvCvtPixToPlane( hsv, h_plane, s_plane, v_plane, 0 );

hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges, 1 ); cvCalcHist( planes, hist, 0, 0 );

cvGetMinMaxHistValue( hist, 0, &max_value, 0, 0 ); cvZero( hist_img );

for( h = 0; h < h_bins; h++ ) {

for( s = 0; s < s_bins; s++ ) {

float bin_val = cvQueryHistValue_2D( hist, h, s ); int intensity = cvRound(bin_val*255/max_value);

cvRectangle( hist_img, cvPoint( h*scale, s*scale ), cvPoint( (h+1)*scale - 1, (s+1)*scale - 1), CV_RGB(intensity,intensity,intensity), CV_FILLED ); } }

cvNamedWindow( \ cvShowImage( \

cvNamedWindow( \

cvShowImage( \ cvWaitKey(0); } }

图像的直方图均匀化

直方图均衡化算法可以归一化图像的亮度，并增强图像的对比度 效果图：

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