opencv——访问图像元素(imagedata widthstep)

大家好，又见面了，我是你们的朋友全栈君。

http://blog.sina.com.cn/s/blog_759803690101gtpx.html

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.
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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.
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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