opencv——访问图像元素(imagedata widthstep)

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怎么访问图像元素 (坐标起点相对于图像原点 image origin 从 0 开始，或者是左上角 (img->origin=IPL_ORIGIN_TL) 或者是左下角 (img->origin=IPL_ORIGIN_BL)

假设有 8-bit 1－通道的图像 I (IplImage* img): I(x,y) ~ ((uchar*)(img->imageData + img->widthStep*y))[x]

假设有 8-bit 3-通道的图像 I (IplImage* img): I(x,y)blue ~ ((uchar*)(img->imageData + img->widthStep*y))[x*3] I(x,y)green ~ ((uchar*)(img->imageData + img->widthStep*y))[x*3+1] I(x,y)red ~ ((uchar*)(img->imageData + img->widthStep*y))[x*3+2]

如果增加点 (100,100) 的亮度 30 ，那么可以: CvPoint pt = {100,100}; ((uchar*)(img->imageData + img->widthStep*pt.y))[pt.x*3] += 30; ((uchar*)(img->imageData + img->widthStep*pt.y))[pt.x*3+1] += 30; ((uchar*)(img->imageData + img->widthStep*pt.y))[pt.x*3+2] += 30;

或者更有效的 CvPoint pt = {100,100}; uchar* temp_ptr = &((uchar*)(img->imageData + img->widthStep*pt.y))[x*3]; temp_ptr[0] += 30; temp_ptr[1] += 30; temp_ptr[2] += 30;

假设有 32-bit 浮点数, 1-通道 图像 I (IplImage* img): I(x,y) ~ ((float*)(img->imageData + img->widthStep*y))[x]

现在，通用方法：假设有 N-通道，类型为 T 的图像： I(x,y)c ~ ((T*)(img->imageData + img->widthStep*y))[x*N + c] 或者你可使用宏 CV_IMAGE_ELEM( image_header, elemtype, y, x_Nc ) I(x,y)c ~ CV_IMAGE_ELEM( img, T, y, x*N + c )

也有针对各种图像(包括 4-通道)和矩阵的函数(cvGet2D, cvSet2D), 但是它们都很慢. ——————————————————————————–

如何访问矩阵元素? 方法是类似的 (都是针对 0 起点的列和行)

设有 32-bit 浮点数的实数矩阵 M (CvMat* mat): M(i,j) ~ ((float*)(mat->data.ptr + mat->step*i))[j]

设有 64-bit 浮点数的复数矩阵 M (CvMat* mat): Re M(i,j) ~ ((double*)(mat->data.ptr + mat->step*i))[j*2] Im M(i,j) ~ ((double*)(mat->data.ptr + mat->step*i))[j*2+1]

设有单通道矩阵，有宏 CV_MAT_ELEM( matrix, elemtype, row, col ), 例如对 32-bit 浮点数的实数矩阵 M(i,j) ~ CV_MAT_ELEM( mat, float, i, j ), 假如初始化 3×3 单位阵： CV_MAT_ELEM( mat, float, 0, 0 ) = 1.f; CV_MAT_ELEM( mat, float, 0, 1 ) = 0.f; CV_MAT_ELEM( mat, float, 0, 2 ) = 0.f; CV_MAT_ELEM( mat, float, 1, 0 ) = 0.f; CV_MAT_ELEM( mat, float, 1, 1 ) = 1.f; CV_MAT_ELEM( mat, float, 1, 2 ) = 0.f; CV_MAT_ELEM( mat, float, 2, 0 ) = 0.f; CV_MAT_ELEM( mat, float, 2, 1 ) = 0.f; CV_MAT_ELEM( mat, float, 2, 2 ) = 1.f;

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如何在 OpenCV 中处理我自己的数据 设你有 300×200 32-bit 浮点数 image/array, 也就是对一个有 60000 个元素的数组.

int cols = 300, rows = 200; float* myarr = new float[rows*cols];

// step 1) initializing CvMat header CvMat mat = cvMat( rows, cols, CV_32FC1, // 32-bit floating-point, single channel type myarr // user data pointer (no data is copied) ); // step 2) using cv functions, e.g. calculating l2 (Frobenius) norm double norm = cvNorm( &mat, 0, CV_L2 );

… delete myarr;

其它情况在参考手册中有描述.见 cvCreateMatHeader, cvInitMatHeader, cvCreateImageHeader, cvSetData etc. ——————————————————————————–

如何加载和显示图像

#include “cv.h” #include “highgui.h”

int main( int argc, char** argv ) { IplImage* img; if( argc == 2 && (img = cvLoadImage( argv[1], 1)) != 0 ) { cvNamedWindow( “Image view”, 1 ); cvShowImage( “Image view”, img ); cvWaitKey(0); // very important, contains event processing loop inside cvDestroyWindow( “Image view” ); cvReleaseImage( &img ); return 0; } return -1; }

void cvLaplace (IplImage* src, IplImage* dst, int apertureSize=3); void cvSobel (IplImage* src, IplImage* dst, int dx, int dy, int apertureSize=3); void cvCanny (IplImage* img, IplImage* edges, double lowThresh, double highThresh, int apertureSize=3);

void cvPreCornerDetect (IplImage* img, IplImage* corners, Int apertureSize); void cvCornerEigenValsAndVecs (IplImage* img, IplImage* eigenvv, int blockSize, int apertureSize=3); void cvCornerMinEigenVal (IplImage* img, IplImage* eigenvv, int blockSize, int apertureSize=3); void cvGoodFeaturesToTrack (IplImage* image, IplImage* eigImage, IplImage* tempImage, CvPoint2D32f* corners, int* cornerCount, double qualityLevel,double minDistance); //对已经粗检测出的角点进行亚像素精准定位 void cvFindCornerSubPix (IplImage* img, CvPoint2D32f* corners, int count,CvSize win, CvSize zeroZone, CvTermCriteria criteria);

//金字塔分解与重构 void cvPyrDown (IplImage* src, IplImage* dst, IplFilter filter=IPL_GAUSSIAN_5x5); void cvPyrUp (IplImage* src, IplImage* dst, IplFilter filter=IPL_GAUSSIAN_5x5);

void cvThreshold (IplImage* src, IplImage* dst, float thresh, float maxvalue,CvThreshType type);

void cvProject3D ( CvPoint3D32f* points3D, int count, CvPoint2D32f* points2D,int xindx, int yindx);

void cvFindFundamentalMatrix (int* points1, int* points2, int numpoints, int method, CvMatrix3* matrix);

//很好用的平滑函数 void cvSmooth( const CvArr* src, CvArr* dst,int smoothtype=CV_GAUSSIAN,int param1=3, int param2=0, double param3=0 ); CV_BLUR_NO_SCALE CV_BLUR CV_GAUSSIAN CV_MEDIAN CV_BILATERAL

其他辅助函数： void cvPutText( CvArr* img, const char* text, CvPoint org, const CvFont* font, CvScalar color );

cvCvtColor(image, gray, CV_BGR2GRAY);//彩色图像灰度化

cvCvtPlaneToPix( planes[0], planes[1], planes[2], 0, currentimage); cvSplit(colorimage,plane[0],plane[1],plane[2],0);

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