Opencv第三章
2. 下面这个练习是帮助掌握矩阵类型。创造一个三通道二维矩阵,字节类型,大小为100×100,并设置所有数值为0。
a. 在矩阵中使用void cvCircle(CvArr* img, CvPoint center, intradius, CvScalar color, int thickness=1, int line_type=8, int shift=0)画一个圆。
b. 使用第2章所学的方法来显示这幅图像。
#include <cv.h> #include <highgui.h> int main() { CvMat *mat = cvCreateMat(100, 100, CV_32FC3); cvZero(mat); CvSize rect = cvSize(800, 800); IplImage* image = cvCreateImage(rect, IPL_DEPTH_8U, 3); CvPoint center = cvPoint(400, 400); int radius = 400; CvScalar color = cvScalar(100, 100, 100); cvCircle(image,center, radius, color, 1, 8, 0); cvNamedWindow("main", 1); //cvNamedWindow("main1", 1); cvShowImage("mian", image); //cvShowImage("main1", mat); cvWaitKey(0); cvReleaseImage(&image); cvReleaseMat(&mat); return 0; }
3. 创建一个拥有三个通道的二维字节类型矩阵,大小为100x100,并将所有值赋为0。通过函数cvPtr2D 将指针指向中间的通道(“绿色”)。以(20,5)与(40,20)为顶点间画一个绿色的长方形。
#include <cv.h> #include <highgui.h> int main() { CvSize size = cvSize(100, 100); IplImage *image = cvCreateImage(size, IPL_DEPTH_8U, 3); cvZero(image); int left = 20; int right = 40; int top = 5; int bottom = 20; for (; top < bottom; top++) { for (; left < right; left++) { *(cvPtr2D(image, top, left)+1) = 255; //cvPtr2D 参数分别表示在当前的图中,坐标为(top,left)的位置 + 1 为绿色 ;+2 表示红色 ;+0 表示蓝色; } left = 20; } cvNamedWindow("main", 1); cvShowImage("main", image); cvWaitKey(); cvReleaseImage(&image); cvDestroyWindow("main"); return 0; }
4.创建一个大小为100x100的三通道RGB图像。将它的元素全部置0.使用指针算法以(20,5 )与(40,20)为顶点绘制一个绿色平面。
#include <cv.h> #include <highgui.h> int main() { CvSize size = cvSize(100, 100); IplImage *img = cvCreateImage(size, IPL_DEPTH_8U, 3); cvZero(img); int left = 20; int right = 40; int top = 5; int bottom = 20; for (int i = 0; i < img->height;i++) { uchar *ptr = (uchar*)(img->imageData + i*img->widthStep); //imageData 图像数据的指针 //widthStep 校准后的行字节数 for (int j = 0; j < img->width; j++) { ptr[j * 3] = 255; ptr[j * 3 + 1] = 255; ptr[j * 3 + 2] = 255; } } for (; top < bottom; top++) { uchar *ptr = (uchar*)(img->imageData + top * img->widthStep); for (; left < right; left++) { ptr[left * 3] = 0; ptr[left * 3 + 1] = 255; ptr[left * 3 + 2] = 0; } left = 20; } cvNamedWindow("main", 1); cvShowImage("main", img); cvWaitKey(); cvReleaseImage(&img); cvDestroyWindow("main"); return 0; }
4.
创建一个210*210的单通道图像并将其归0.在图像中使用ROI和cvSet()建立一个增长如金字塔状的数组。也就是,外部边界为0,下一个内部边界为20,再下一个内部边界为20,
再下一个内部边界为40,以此类推,直到最后内部值为200,所有的边界应该为10像素的宽度。最后显示这个图形。
#include <cv.h> #include <highgui.h> int main() { CvSize size = cvSize(210, 210); IplImage *img = cvCreateImage(size, IPL_DEPTH_8U, 1); cvZero(img); for (int i = 0; i <= 20; i++) { cvSetImageROI(img, cvRect(100-i*5, 0+i*10, 10+i*10, 10)); cvAddS(img, cvScalar(255), img); } /* cvSetImageROI(img, cvRect(100, 0, 10, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(95, 10, 20, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(90, 20, 30, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(85, 30, 40, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(80, 40, 50, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(75, 50, 60, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(70, 60, 70, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(65, 70, 80, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(60, 80, 90, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(55, 90, 100, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(50, 100, 110, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(45, 110, 120, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(40, 120, 130, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(35, 130, 140, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(30, 140, 150, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(25, 150, 160, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(20, 160, 170, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(15, 170, 180, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(10, 180, 190, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(5, 190, 200, 10)); cvAddS(img, cvScalar(255), img); cvSetImageROI(img, cvRect(0, 200, 210, 10)); cvAddS(img, cvScalar(255), img); */ cvResetImageROI(img); cvNamedWindow("mian", 1); cvShowImage("main", img); cvWaitKey(); cvReleaseImage(&img); cvDestroyWindow("main"); return 0; }
6.为一个图像创建多个图像头。读取一个大小至少为100x100的图像。另外创建两个图像头并设置他们的origion,depth,nChannels和widthStep属性同之前读取的图像一致,
在新的图像头中,设置宽度为20,高度为30。最后将imageData指针分别指向像素(5,10)和(50,60)像素位置,传递这两个新的图像头给cvNot。最后显示最初读取的图像,在那个大的图像中有应该有两个矩阵,矩阵内的值是原始值得求反值。
#include <cv.h> #include <highgui.h> int main() { IplImage *img = cvLoadImage("001.jpg",1); IplImage *src1 = cvCreateImageHeader(cvSize(20,30), img->depth, img->nChannels); IplImage *src2 = cvCreateImageHeader(cvSize(20,30), img->depth, img->nChannels); src1->origin = img->origin; src2->origin = img->origin; src1->widthStep = img->widthStep; src2->widthStep = img->widthStep; uchar* tmp1; uchar* tmp2; src1->imageData = img->imageData + 10 * img->widthStep + 5 * img->nChannels; src2->imageData = img->imageData + 60 * img->widthStep + 50 * img->nChannels; //cvAddS(src1, cvScalar(0), src1); //cvAddS(src2, cvScalar(0), src2); cvNot(src1, src1); cvNot(src2, src2); cvReleaseImageHeader(&src1); cvReleaseImageHeader(&src2); //cvNot(img, img); cvNamedWindow("main", 1); cvShowImage("main", img); cvWaitKey(); cvReleaseImage(&img); cvDestroyWindow("main"); return 0; }
使用cvCmp()创建一个掩码。加载一个真实的图像。使用cvSplit()将图像分割成红、绿、蓝三个通道图像。
a.找到并显示绿图
b.克隆这个绿图(分别命名为clone1和clone2)
c.求出这个绿色平面的最大值和最小值
d.将clone1的所有元素赋值为thresh=(unsigned char)((最大值-最小值)/2.0)
e.将clone1所有元素赋值为0,调用函数cvCmp(green_image,clone1,clone2,CV_CMP_GE)
f.最后,使用cvSubs(green_image,thresh/2,green_image,clone2)函数并显示结果
#include <cv.h> #include <highgui.h> int main() { IplImage *img = cvLoadImage("3.jpg", 1); cvNamedWindow("img", 1); cvShowImage("img", img); IplImage *test,*img1,*G_img,*R_img,*B_img; test = cvCreateImage(cvSize(img->width, img->height), img->depth, 1); img1 = cvCreateImage(cvSize(img->width, img->height), img->depth, 1); G_img = cvCreateImage(cvSize(img->width, img->height), img->depth, 3); R_img = cvCreateImage(cvSize(img->width, img->height), img->depth, 3); B_img = cvCreateImage(cvSize(img->width, img->height), img->depth, 3); cvSplit(img, test, 0, 0, 0); CvScalar scalar; for (int i = 0; i < img->nChannels; i++) { scalar.val[0] = 0x1; } for (int i = 0; i < img->height; i++) { for (int j = 0; j < img->width; j++) { cvSet2D(img1, i, j, scalar); } } cvMerge(img1, test, img1, 0, G_img); cvMerge(img1, img1, test, 0, R_img); cvMerge(test, img1, img1, 0, B_img); cvNamedWindow("GREEN", 1); cvShowImage("GREEN", G_img); //显示绿图 cvNamedWindow("RED", 1); cvShowImage("RED", R_img); cvNamedWindow("BLUE", 1); cvShowImage("BLUE", B_img); IplImage *clone1, *clone2; clone1 = cvCloneImage(test); clone2 = cvCloneImage(test); double mmax, mmin; cvMinMaxLoc(test, &mmin, &mmax);//找出最大值和最小值 printf("mmin = %d,mmax = %d\n", mmin, mmax); double thresh = (mmax - mmin) / 2; cvSet(clone1, cvScalar(thresh));//将clone1 的所有元素赋值thresh cvZero(clone2); //将clone1 的所有元素赋值0 cvCmp(test, clone1, clone2, CV_CMP_GE); cvNamedWindow("clone2", 1); cvShowImage("clone2", clone2); cvSubS(test, cvScalar(thresh / 2), test, clone2); cvNamedWindow("test", 1); cvShowImage("test", test); cvWaitKey(); cvReleaseImage(&img); cvReleaseImage(&test); cvReleaseImage(&img1); cvReleaseImage(&G_img); cvReleaseImage(&clone1); cvReleaseImage(&clone2); cvDestroyAllWindows(); return 0; }