各种小程序代码
void COpenCV::OnOpencvCvcanny()
{
// TODO: 在此添加命令处理程序代码
IplImage* imgColor = cvLoadImage("C:\\Users\\Administrator\\Desktop\\159.jpg", 0);
IplImage* contoursImage = cvCreateImage(cvSize(imgColor->width, imgColor->height), 8, 1);
IplImage* img = cvCreateImage(cvSize(imgColor->width, imgColor->height), 8, 1);
IplImage* imgmid = cvCloneImage(imgColor);
CvSeq* contours = 0;
//提取图像img的轮廓信息
cvCanny(imgmid, img, 80, 180, 3);
//cvSaveImage("midimg/Canny.jpg", img);
cvNamedWindow("image", 1);
cvShowImage("image", imgColor);
cvNamedWindow("contours", 1);
cvShowImage("contours", img);
cvWaitKey(0);
cvSaveImage("C:\\Users\\Administrator\\Desktop\\cvCanny.jpg", img);
//cvReleaseMemStorage(&storage);
cvReleaseImage(&img);
cvReleaseImage(&contoursImage);
cvReleaseImage(&imgColor);
}
void COpenCV::OnOpencvHist()
{
// TODO: 在此添加命令处理程序代码
IplImage* src=cvLoadImage("D:\\18.jpg", 1);
//Compute the HSV image and decompose it into separate planes.
IplImage* hsv = cvCreateImage(cvGetSize(src), 8, 3);
//rgb色彩空间转换到hsv色彩空间
cvCvtColor(src, hsv, CV_BGR2HSV);
cvNamedWindow("hsv", 1);
cvShowImage("hsv", hsv);
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 };
//分割为单通道图像
cvCvtPixToPlane(hsv, h_plane, s_plane, v_plane, 0);
cvNamedWindow("h_plane", 1);
cvShowImage("h_plane", h_plane);
cvNamedWindow("s_plane", 1);
cvShowImage("s_plane", s_plane);
cvNamedWindow("v_plane", 1);
cvShowImage("v_plane", v_plane);
//Build the histogram and copute its contents.
/** H 分量划分为30个等级，S分量划分为32个等级 */
int h_bins = 30;
int s_bins = 32;
CvHistogram* hist;
{
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 };
/* 创建直方图，二维, 每个维度上均分 */
hist = cvCreateHist(
2, // int dims
hist_size, // int* sizes
CV_HIST_ARRAY,// int type
ranges, // float** ranges
1 //uniform
);
}
// 根据H,S两个平面数据统计直方图
cvCalcHist(planes, hist, 0, 0); //Compute histogram
//归一化处理
cvNormalizeHist(hist, 1.0); //Normalize it
//Creat an image to use to visualize our histogram.
int scale = 10;
IplImage* hist_img = cvCreateImage(cvSize(h_bins*scale, s_bins*scale),8,3);
cvZero(hist_img);
//populate our visualization with litter gray squares.
/** 获取直方图统计的最大值，用于动态显示直方图 */
float max_value = 0;
cvGetMinMaxHistValue(hist, 0, &max_value, 0, 0);
for (int h = 0; h < h_bins; h++)
{
for (int 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("Source", 1);
cvShowImage("Source", src);
cvNamedWindow("H-S Histogram", 1);
cvShowImage("H-S Histogram", hist_img);
cvWaitKey(0);
}
void COpenCV::OnOpencvMatchtemplate()
{
// TODO: 在此添加命令处理程序代码
IplImage *src, *templ, *ftmp[6];
int i;
//Read in the source image to be searched;
if ((src = cvLoadImage("D:\\19.jpg", 1)) == 0)
{
printf("Error on reading src image %s\n", "D:\\19.jpg");
}
//Read in the template to be used for matching:
if ((templ = cvLoadImage("D:\\20.jpg", 1)) == 0)
{
printf("Error on reading template image %s\n", "D:\\20.jpg");
}
//allocate output images:
int iwidth = src->width - templ->width + 1;
int iheight = src->height - templ->height + 1;
for (i = 0; i < 6; i++)
{
ftmp[i] = cvCreateImage(cvSize(iwidth, iheight), 32, 1);
}
//Do the matching of the template witch the image:
for (i = 0; i < 6; i++)
{
double min_val;
double max_val;
CvPoint min_loc;
CvPoint max_loc;
cvMatchTemplate(src, templ, ftmp[i], i);
cvNormalize(ftmp[i], ftmp[i], 1, 0, CV_MINMAX);
cvMinMaxLoc(ftmp[i], &min_val, &max_val, &min_loc, &max_loc, NULL);
cvRectangle(src, cvPoint(min_loc.x, min_loc.y), cvPoint((min_loc.x + templ->width), (min_loc.y + templ->height)), CV_RGB(0, 255, 0), 1);
}
//Displa
cvNamedWindow("Template", 0);
cvShowImage("Template", templ);
cvNamedWindow("image", 0);
cvShowImage("image", src);
cvNamedWindow("SQDIFF", 0);
cvShowImage("SQDIFF", ftmp[0]);
cvNamedWindow("SQDIFF_NORMED", 0);
cvShowImage("SQDIFF_NORMED", ftmp[1]);
cvNamedWindow("CCORR", 0);
cvShowImage("CCORR", ftmp[2]);
cvNamedWindow("CCORR_NORMED", 0);
cvShowImage("CCORR_NORMED", ftmp[3]);
cvNamedWindow("CCOEFF", 0);
cvShowImage("CCOEFF", ftmp[4]);
cvNamedWindow("CCOEFF_NORMED", 0);
cvShowImage("CCOEFF_NORMED", ftmp[5]);
}
void COpenCV::OnOpencvContours2()
{
// TODO: 在此添加命令处理程序代码
cvNamedWindow("box", 0);
IplImage* img_8uc1 = cvLoadImage("C:\\Users\\Administrator\\Desktop\\159.jpg", CV_LOAD_IMAGE_GRAYSCALE);
IplImage* img_edge = cvCreateImage(cvGetSize(img_8uc1), 8, 1);
IplImage* img_8uc3 = cvCreateImage(cvGetSize(img_8uc1), 8, 3);
cvThreshold(img_8uc1, img_edge, 128, 255, CV_THRESH_BINARY);
CvMemStorage* storage = cvCreateMemStorage();
CvSeq* first_contours = NULL;
int NC = cvFindContours(
img_edge,
storage,
&first_contours,
sizeof(CvContour),
CV_RETR_LIST
);
int n = 0;
printf("Total Contours Deleted:%d\n", NC);
for (CvSeq* c = first_contours; c != NULL; c = c->h_next)
{
cvCvtColor(img_8uc1, img_8uc3, CV_GRAY2BGR);
cvDrawContours(
img_8uc3,
c,
cvScalarAll(255),
cvScalarAll(255),
100
);
cvShowImage("box", img_8uc3);
for (int i = 0; i < c->total; ++i)
{
CvPoint* p = CV_GET_SEQ_ELEM(CvPoint, c, i);
}
cvWaitKey(0);
n++;
}
cvCvtColor(img_8uc1, img_8uc3, CV_GRAY2BGR);
cvShowImage("box", img_8uc3);
cvNamedWindow("src", 0);
cvShowImage("src", img_8uc1);
cvNamedWindow("edge", 0);
cvShowImage("edge", img_edge);
cvWaitKey(0);
cvDestroyAllWindows();
cvReleaseImage(&img_8uc1);
cvReleaseImage(&img_8uc3);
cvReleaseImage(&img_edge);
}
void COpenCV::OnOpencvAddgaussiannoise()
{
// TODO: 在此添加命令处理程序代码
Mat image;
image = imread("D:\\11.jpg", 1); // Read the file
if (!image.data) // Check for invalid input
{
cout << "Could not open or find the image" << std::endl;
}
namedWindow("Display window", WINDOW_AUTOSIZE); // Create a window for display.
imshow("Display window", image); // Show our image inside it.
// Add Gaussian noise here
AddGaussian(image);
namedWindow("Noisy image", WINDOW_AUTOSIZE); // Create a window for display.
imshow("Noisy image", image); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
}
void COpenCV::AddGaussian(Mat& I)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
int channels = I.channels();
int nRows = I.rows;
int nCols = I.cols * channels;
if (I.isContinuous())
{
nCols *= nRows;
nRows = 1;
}
int i, j;
uchar* p;
for (i = 0; i < nRows; ++i){
p = I.ptr<uchar>(i);
for (j = 0; j < nCols; ++j){
double val = p[j] + generateGaussianNoise() * 128;
if (val < 0)
val = 0;
if (val > 255)
val = 255;
p[j] = (uchar)val;
}
}
}
void COpenCV::OnOpencvTestnoise()
{
// TODO: 在此添加命令处理程序代码
}
void COpenCV::OnOpencvCvboxpoints()
{
// TODO: 在此添加命令处理程序代码
//画缺陷的外接矩形
IplImage *src = cvLoadImage("D:\\21.jpg", CV_LOAD_IMAGE_GRAYSCALE);
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contour = 0;
cvNamedWindow("image0", 1);
cvShowImage("image0", src);
int hei = src->height;
int wid = src->width;
uchar *data = (uchar*)src->imageData;
int widstep = src->widthStep;
int channel = src->nChannels;
IplImage *dst = cvCreateImage(cvSize(wid, hei), IPL_DEPTH_8U, 3);
// invert the image
for (int i = 0; i<hei; i++)
{
for (int j = 0; j<wid; j++)
{
if (data[i*widstep + j*channel]>120)
{
data[i*widstep + j*channel] = 0;
}
else
{
data[i*widstep + j*channel] = 255;
}
}
}
cvNamedWindow("image", 0);
cvShowImage("image", src);
printf("图像的高为：%d,宽为：%d\n\n", hei, wid);
cvCvtColor(src, dst, CV_GRAY2BGR);;
cvFindContours(src, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
for (; contour != 0; contour = contour->h_next)
{
CvBox2D rect = cvMinAreaRect2(contour, storage);
CvPoint2D32f rect_pts0[4];
cvBoxPoints(rect, rect_pts0);
int npts = 4, k = 0;
int aaa = 0, bbb = 0;
CvPoint rect_pts[4], *pt = rect_pts;
printf("连通区域最小外接矩形顶点坐标分别为:\n");
for (int i = 0; i<4; i++)
{
rect_pts[i] = cvPointFrom32f(rect_pts0[i]);
printf("%d %d\n", rect_pts[i].x, rect_pts[i].y);
aaa = (int)sqrt((pow((rect_pts[0].x - rect_pts[1].x), 2) + pow((rect_pts[0].y
- rect_pts[1].y), 2)));
bbb = (int)sqrt((pow((rect_pts[0].x - rect_pts[3].x), 2) + pow((rect_pts[0].
y - rect_pts[3].y), 2)));
if (aaa<bbb)
{
k = aaa;
aaa = bbb;
bbb = k;
}
}
printf("最小外接矩形的长为：%d宽为：%d。\n\n", aaa, bbb);
cvPolyLine(dst, &pt, &npts, 1, 1, CV_RGB(255, 0, 0), 1);
}
cvNamedWindow("image5", 1);
cvShowImage("image5", dst);
cvWaitKey(0);
cvDestroyWindow("image");
cvDestroyWindow("image5");
cvReleaseImage(&src);
}
void COpenCV::OnOpencv2Mythros()
{
//获取单通道的值，并对像素进行处理
// TODO: 在此添加命令处理程序代码
IplImage* imgSource = cvLoadImage("C:\\Users\\Administrator\\Desktop\\no2.jpg",0);
cvNamedWindow("Image", CV_WINDOW_AUTOSIZE);
cvShowImage("Image", imgSource);
if(imgSource != 0)//imgSource为IplImage*
{
for (int j = 0; j < imgSource->width; ++j)
{
for (int i = 0; i < imgSource->height; ++i)
{
//获取(i, j)处的单道图像像素值
double dPixelVal = cvGetReal2D(imgSource, i, j);
if (dPixelVal>51 && dPixelVal <= 102)
{
//设定(i, j)处的单通道图像像素值
cvSetReal2D(imgSource, i, j, 51);
}
}
}
}
cvSaveImage("C:\\Users\\Administrator\\Desktop\\no2.jpg", imgSource);
cvWaitKey(0);
cvDestroyWindow("Image");
cvReleaseImage(&imgSource);
}
void COpenCV::OnOpencv2Cvavg()
{
// TODO: 在此添加命令处理程序代码
IplImage *src1 = cvLoadImage("C:\\Users\\Administrator\\Desktop\\no2.jpg");
CvScalar cs,cs1;
cvAvgSdv(src1,&cs,&cs1);
cout<<"平均值："<<endl; cout<<cs.val[0] << endl;
cout<<cs.val[1] << endl;
cout<<cs.val[2] << endl;
cout<<cs.val[3] << endl;
cout <<endl; cout <<"标准差"<<endl;
cout<<cs1.val[0] << endl;
cout<<cs1.val[1] << endl;
cout<<cs1.val[2] << endl;
cout<<cs1.val[3] << endl;
}
void COpenCV::OnOpencvImagematching()
{
// TODO: 在此添加命令处理程序代码
double dbMax = 0; //最大像素值
double dSigmaST, dSigmaS, dSigmaT; //中间结果
double R; //相似性测量
int i, j, m, n;
int nMaxWidth, nMaxHeight;
CvScalar pixelSrc, pixelTem, pixel0, pixel1;
IplImage* TemplateSrc = cvLoadImage("D:\\20.jpg"); //模板图像
IplImage* src = cvLoadImage("D:\\24.jpg"); //原图
//计算dSigmaT
dSigmaT = 0;
for (n = 0; n < TemplateSrc->height; n++)
{
for (m = 0; m < TemplateSrc->width; m++)
{
pixelTem = cvGet2D(TemplateSrc, n, m);
dSigmaT += (double)pixelTem.val[0] * pixelTem.val[0];
}
}
//找到图像中最大相似性出现的位置
for (j = 0; j < src->height - TemplateSrc->height + 1; j++)
{
for (i = 0; i < src->width - TemplateSrc->width + 1; i++)
{
dSigmaST = 0;
dSigmaS = 0;
for (n = 0; n < TemplateSrc->height; n++)
{
for (m = 0; m < TemplateSrc->width; m++)
{
pixelSrc = cvGet2D(src, j + n, i + m);
pixelTem = cvGet2D(TemplateSrc, n, m);
dSigmaS += (double)pixelSrc.val[0] * pixelSrc.val[0];
dSigmaST += (double)pixelSrc.val[0] * pixelTem.val[0];
}
}
R = dSigmaST / (sqrt(dSigmaS)*sqrt(dSigmaT)); //计算相似性
if (R > dbMax) //与最大相似性比较
{
dbMax = R;
nMaxHeight = j;
nMaxWidth = i;
}
}
}
//对目标像素进行赋值
for (j = nMaxHeight; j <TemplateSrc->height + nMaxHeight; j++)
{
for (i = nMaxWidth; i < TemplateSrc->width + nMaxWidth; i++)
{
pixel0 = cvGet2D(src, j, i);
pixel1.val[0] = pixel0.val[0] / 2;
pixel1.val[1] = pixel0.val[1] / 2;
pixel1.val[2] = pixel0.val[2] / 2;
cvSet2D(src, j, i, pixel1);
}
}
cvNamedWindow("White", CV_WINDOW_AUTOSIZE);
cvShowImage("White", src);
while (1)
{
if (cvWaitKey(100) == 27) break;
}
cvDestroyWindow("White");
cvReleaseImage(&src);
exit(0);
}