opencv中常见的与图像操作有关的数据容器有Mat&＃xff0c;cvMat和IplImage&＃xff0c;这三种类型都可以代表和显示图像&＃xff0c;但是&＃xff0c;Mat类型侧重于计算&＃xff0c;数学性较高&＃xff0c;openCV对Mat类型的计算也进行了优化。而CvMat和IplImage类型更侧重于“图像”&＃xff0c;opencv对其中的图像操作&＃xff08;缩放、单通道提取、图像阈值操作等&＃xff09;进行了优化。在opencv2.0之前&＃xff0c;opencv是完全用C实现的&＃xff0c;但是&＃xff0c;IplImage类型与CvMat类型的关系类似于面向对象中的继承关系。实际上&＃xff0c;CvMat之上还有一个更抽象的基类----CvArr&＃xff0c;这在源代码中会常见。
1. IplImage
opencv中的图像信息头&＃xff0c;该结构体定义&＃xff1a;
typedef struct _IplImage { int nSize; int ID; int nChannels; int alphaChannel; int depth; char colorModel[4]; char channelSeq[4]; int dataOrder; int origin; int align; int width; int height; struct _IplROI *roi; struct _IplImage *maskROI; void *imageId; struct _IplTileInfo *tileInfo; int imageSize; char *imageData; int widthStep; int BorderMode[4]; int BorderConst[4]; char *imageDataOrigin; } IplImage;
dataOrder中的两个取值&＃xff1a;交叉存取颜色通道是颜色数据排列将会是BGRBGR...的交错排列。分开的颜色通道是有几个颜色通道就分几个颜色平面存储。roi是IplROI结构体&＃xff0c;该结构体包含了xOffset,yOffset,height,width,coi成员变量&＃xff0c;其中xOffset,yOffset是x,y坐标&＃xff0c;coi代表channelof interest(感兴趣的通道)&＃xff0c;非0的时候才有效。访问图像中的数据元素&＃xff0c;分间接存储和直接存储&＃xff0c;当图像元素为浮点型时&＃xff0c;(uchar*) 改为 (float *)&＃xff1a;
IplImage* img&＃61;cvLoadImage("lena.jpg", 1);CvScalar s; s&＃61;cvGet2D(img,i,j); cvSet2D(img,i,j,s); IplImage* img; //malloc memory by cvLoadImage or cvCreateImage
for(int row &＃61; 0; row < img->height; row&＃43;&＃43;){for (int col &＃61; 0; col width; col&＃43;&＃43;){b &＃61; CV_IMAGE_ELEM(img, UCHAR, row, col * img->nChannels &＃43; 0); g &＃61; CV_IMAGE_ELEM(img, UCHAR, row, col * img->nChannels &＃43; 1); r &＃61; CV_IMAGE_ELEM(img, UCHAR, row, col * img->nChannels &＃43; 2);}}IplImage* img; //malloc memory by cvLoadImage or cvCreateImage
uchar b, g, r; // 3 channels
for(int row &＃61; 0; row < img->height; row&＃43;&＃43;){for (int col &＃61; 0; col width; col&＃43;&＃43;){b &＃61; ((uchar *)(img->imageData &＃43; row * img->widthStep))[col * img->nChannels &＃43; 0]; g &＃61; ((uchar *)(img->imageData &＃43; row * img->widthStep))[col * img->nChannels &＃43; 1]; r &＃61; ((uchar *)(img->imageData &＃43; row * img->widthStep))[col * img->nChannels &＃43; 2];}}
初始化使用IplImage *&＃xff0c;是一个指向结构体IplImage的指针&＃xff1a;
IplImage * cvLoadImage(const char * filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); //load images from specified image
IplImage * cvCreateImage(CvSize size, int depth, int channels); //allocate memory
2.CvMat
首先&＃xff0c;我们需要知道&＃xff0c;第一&＃xff0c;在OpenCV中没有向量(vector)结构。任何时候需要向量&＃xff0c;都只需要一个列矩阵(如果需要一个转置或者共轭向量&＃xff0c;则需要一个行矩阵)。第二&＃xff0c;OpenCV矩阵的概念与我们在线性代数课上学习的概念相比&＃xff0c;更抽象&＃xff0c;尤其是矩阵的元素&＃xff0c;并非只能取简单的数值类型&＃xff0c;可以是多通道的值。CvMat的结构&＃xff1a;
typedef struct CvMat { int type; int step; int* refcount; union {uchar* ptr;short* s;int* i;float* fl;double* db;} data; union {int rows;int height;};union {int cols; int width;};} CvMat;
创建CvMat数据&＃xff1a;
CvMat * cvCreateMat(int rows, int cols, int type); CV_INLine CvMat cvMat((int rows, int cols, int type, void* data CV_DEFAULT); CvMat * cvInitMatHeader(CvMat * mat, int rows, int cols, int type, void * data CV_DEFAULT(NULL), int step CV_DEFAULT(CV_AUTOSTEP));
对矩阵数据进行访问&＃xff1a;
cvmSet( CvMat* mat, int row, int col, double value);cvmGet( const CvMat* mat, int row, int col );CvScalar cvGet2D(const CvArr * arr, int idx0, int idx1); //CvArr只作为函数的形参void cvSet2D(CvArr* arr, int idx0, int idx1, CvScalar value);
CvMat * cvmat &＃61; cvCreateMat(4, 4, CV_32FC1);cvmat->data.fl[row * cvmat->cols &＃43; col] &＃61; (float)3.0;CvMat * cvmat &＃61; cvCreateMat(4, 4, CV_64FC1);cvmat->data.db[row * cvmat->cols &＃43; col] &＃61; 3.0;
CvMat * cvmat &＃61; cvCreateMat(4, 4, CV_64FC1);CV_MAT_ELEM(*cvmat, double, row, col) &＃61; 3.0;
if (CV_MAT_DEPTH(cvmat->type) &＃61;&＃61; CV_32F)CV_MAT_ELEM_CN(*cvmat, float, row, col * CV_MAT_CN(cvmat->type) &＃43; ch) &＃61; (float)3.0; // ch为通道值
if (CV_MAT_DEPTH(cvmat->type) &＃61;&＃61; CV_64F)CV_MAT_ELEM_CN(*cvmat, double, row, col * CV_MAT_CN(cvmat->type) &＃43; ch) &＃61; 3.0; // ch为通道值
for (int row &＃61; 0; row rows; row&＃43;&＃43;){ p &＃61; cvmat ->data.fl &＃43; row * (cvmat->step / 4);for (int col &＃61; 0; col cols; col&＃43;&＃43;) { *p &＃61; (float) row &＃43; col; *(p&＃43;1) &＃61; (float)row &＃43; col &＃43; 1; *(p&＃43;2) &＃61; (float)row &＃43; col &＃43; 2; p &＃43;&＃61; 3; }}CvMat * vector &＃61; cvCreateMat(1,3, CV_32SC2);CV_MAT_ELEM(*vector, CvPoint, 0, 0) &＃61; cvPoint(100,100);CvMat * vector &＃61; cvCreateMat(1,3, CV_64FC4);CV_MAT_ELEM(*vector, CvScalar, 0, 0) &＃61; CvScalar(0, 0, 0, 0);
复制矩阵操作&＃xff1a;
CvMat* M1 &＃61; cvCreateMat(4,4,CV_32FC1);CvMat* M2;M2&＃61;cvCloneMat(M1);
3.Mat
Mat是opencv2.0推出的处理图像的新的数据结构&＃xff0c;现在越来越有趋势取代之前的cvMat和lplImage&＃xff0c;相比之下Mat最大的好处就是能够更加方便的进行内存管理&＃xff0c;不再需要程序员手动管理内存的释放。opencv2.3中提到Mat是一个多维的密集数据数组&＃xff0c;可以用来处理向量和矩阵、图像、直方图等等常见的多维数据。
class CV_EXPORTS Mat{public&＃xff1a;int flags;&＃xff08;Note &＃xff1a;目前还不知道flags做什么用的&＃xff09;int dims; int rows,cols; uchar *data; int * refcount; ...};
从以上结构体可以看出Mat也是一个矩阵头&＃xff0c;默认不分配内存&＃xff0c;只是指向一块内存(注意读写保护)。初始化使用create函数或者Mat构造函数&＃xff0c;以下整理自opencv2.3.1Manual:
Mat(nrows, ncols, type, fillValue]); M.create(nrows, ncols, type);
例子&＃xff1a;Mat M(7,7,CV_32FC2,Scalar(1,3)); M.create(100, 60, CV_8UC(15));
int sz[] &＃61; {100, 100, 100}; Mat bigCube(3, sz, CV_8U, Scalar:all(0));
double m[3][3] &＃61; {{a, b, c}, {d, e, f}, {g, h, i}};Mat M &＃61; Mat(3, 3, CV_64F, m).inv();
Mat img(Size(320,240),CV_8UC3); Mat img(height, width, CV_8UC3, pixels, step);
IplImage* img &＃61; cvLoadImage("greatwave.jpg", 1);Mat mtx(img,0); // convert IplImage* -> Mat;
访问Mat的数据元素&＃xff1a;
Mat M;M.row(3) &＃61; M.row(3) &＃43; M.row(5) * 3; Mat M1 &＃61; M.col(1);M.col(7).copyTo(M1); Mat M;M.at<double>(i,j); M.at(uchar)(i,j); Vec3i bgr1 &＃61; M.at(Vec3b)(i,j) Vec3s bgr2 &＃61; M.at(Vec3s)(i,j) Vec3w bgr3 &＃61; M.at(Vec3w)(i,j) double sum &＃61; 0.0f;for(int row &＃61; 0; row const double * Mi &＃61; M.ptr<double>(row); for (int col &＃61; 0; col 0.);}double sum&＃61;0;MatConstIterator<double> it &＃61; M.begin<double>(), it_end &＃61; M.end<double>();for(; it !&＃61; it_end; &＃43;&＃43;it) sum &＃43;&＃61; std::max(*it, 0.);
Mat可进行Matlab风格的矩阵操作&＃xff0c;如初始化的时候可以用initializers,zeros(), ones(), eye().除以上内容之外&＃xff0c;Mat还有有3个重要的方法&＃xff1a;
Mat mat &＃61; imread(const String* filename); // 读取图像
imshow(const string frameName, InputArray mat); // 显示图像
imwrite (const string& filename, InputArray img); //储存图像
4. CvMat, Mat, IplImage之间的互相转换
IpIImage -> CvMatCvMat matheader;CvMat * mat &＃61; cvGetMat(img, &matheader);CvMat * mat &＃61; cvCreateMat(img->height, img->width, CV_64FC3);cvConvert(img, mat)
IplImage -> MatMat::Mat(const IplImage* img, bool copyData&＃61;false);例子&＃xff1a;IplImage* iplImg &＃61; cvLoadImage("greatwave.jpg", 1);Mat mtx(iplImg);
Mat -> IplImageMat MIplImage iplimage &＃61; M;
CvMat -> MatMat::Mat(const CvMat* m, bool copyData&＃61;false);
Mat -> CvMat例子(假设Mat类型的imgMat图像数据存在)&＃xff1a;CvMat cvMat &＃61; imgMat;/*Mat -> CvMat, 类似转换到IplImage&＃xff0c;不复制数据只创建矩阵头
![[编程题] LeetCode上的Dynamic Programming(动态规划)类型的题目](https://img1.php1.cn/3cd4a/24cea/42f/ab7ad6031d31eee4.jpeg)
京公网安备 11010802041100号