OpenCV的Mat类型以及基本函数使用

OpenCV的Mat类型以及基本函数使用

Mat和IplImage的区别

Mat和IplImage的主要区别

在OpenCV中IplImage是表示一个图像的结构体,也是从OpenCV1.0到目前最为重要的一个结构;在之前的图像表示用IplImage,而且之前的OpenCV是用C语言编写的,提供的接口也是C语言接口。

Mat是后来OpenCV封装的一个C++类,用来表示一个图像,和IplImage表示基本一致,但是Mat还添加了一些图像函数。

IplImage

IplImage数据结构的定义在opencv\build\include\opencv2\core\types_c.h文件中。

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typedef struct _IplImage
{
int nSize; /* sizeof(IplImage) */
int ID; /* version (=0)*/
int nChannels; /* Most of OpenCV functions support 1,2,3 or 4 channels */
int alphaChannel; /* Ignored by OpenCV */
int depth; /* Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S,
IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */
char colorModel[4]; /* Ignored by OpenCV */
char channelSeq[4]; /* ditto */
int dataOrder; /* 0 - interleaved color channels, 1 - separate color channels.
cvCreateImage can only create interleaved images */
int origin; /* 0 - top-left origin,
1 - bottom-left origin (Windows bitmaps style). */
int align; /* Alignment of image rows (4 or 8).
OpenCV ignores it and uses widthStep instead. */
int width; /* Image width in pixels. */
int height; /* Image height in pixels. */
struct _IplROI *roi; /* Image ROI. If NULL, the whole image is selected. */
struct _IplImage *maskROI; /* Must be NULL. */
void *imageId; /* " " */
struct _IplTileInfo *tileInfo; /* " " */
int imageSize; /* Image data size in bytes
(==image->height*image->widthStep
in case of interleaved data)*/
char *imageData; /* Pointer to aligned image data. */
int widthStep; /* Size of aligned image row in bytes. */
int BorderMode[4]; /* Ignored by OpenCV. */
int BorderConst[4]; /* Ditto. */
char *imageDataOrigin; /* Pointer to very origin of image data
(not necessarily aligned) -
needed for correct deallocation */
}
IplImage;

可见,IplImage是一个表示图像的结构体:C语言操作OpenCV的数据结构。地位等同于Mat,可以说是历史版本了。

Mat

Mat这个数据结构定义在opencv\build\include\opencv2\core\core.hpp这个文件。

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class CV_EXPORTS Mat
{
public:
//! default constructor
Mat();
//! constructs 2D matrix of the specified size and type
// (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
Mat(int rows, int cols, int type);
Mat(Size size, int type);
//! constucts 2D matrix and fills it with the specified value _s.
Mat(int rows, int cols, int type, const Scalar& s);
Mat(Size size, int type, const Scalar& s);

//! constructs n-dimensional matrix
Mat(int ndims, const int* sizes, int type);
Mat(int ndims, const int* sizes, int type, const Scalar& s);

//! copy constructor
Mat(const Mat& m);
//! constructor for matrix headers pointing to user-allocated data
Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP);
Mat(Size size, int type, void* data, size_t step=AUTO_STEP);
Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0);

//! creates a matrix header for a part of the bigger matrix
Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all());
Mat(const Mat& m, const Rect& roi);
Mat(const Mat& m, const Range* ranges);
//! converts old-style CvMat to the new matrix; the data is not copied by default
Mat(const CvMat* m, bool copyData=false);
//! converts old-style CvMatND to the new matrix; the data is not copied by default
Mat(const CvMatND* m, bool copyData=false);
//! converts old-style IplImage to the new matrix; the data is not copied by default
Mat(const IplImage* img, bool copyData=false);
//! builds matrix from std::vector with or without copying the data

......

protected:
void initEmpty();
};

Mat是OpenCV最基本的数据结构,Mat即矩阵(Matrix)的缩写我们在读取图片的时候就是将图片定义为Mat类型,其重载的构造函数一大堆。

其中有一个构造函数可以很方便的直接将IplImage转化为Mat

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Mat(const IplImage* img, bool copyData=false);

基本函数使用

imread

功能:从一个文件中载入图片

定义:

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Mat imread( const string& filename, int flags=1 );

■第一个参数,const string&类型的filename,这是我们需要载入的图片路径名。

在Windows操作系统下,OpenCV的imread函数支持常用的图片类型,比如bmp,jpg,jpeg,png等等。

■第二个参数,int类型的flags,为载入标识,它指定一个加载图像的颜色类型。可以看到它自带缺省值1.所以有时候这个参数在调用时我们可以忽略。如果在调用时忽略这个参数,就表示载入三通道的彩色图像。具体原因看下面的解释。

flags是int型的变量,我们可以按如下方式取值:

  • flags >0返回一个3通道的彩色图像。
  • flags =0返回灰度图像。
  • flags <0返回包含Alpha通道的加载的图像。

需要注意的点:输出的图像默认情况下是不载入Alpha通道进来的。如果我们需要载入Alpha通道的话呢,这里就需要取负值。

所以默认值flags=1表示载入三通道的彩色图像。

imshow

功能:显示一个图像

定义:

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void imshow(const string& winname, InputArray mat);

■ 第一个参数,const string&类型的winname,填需要显示的窗口标识名称。

■ 第二个参数,InputArray 类型的mat,填需要显示的图像。

InputArray 类型是什么类型?

通过转到定义,我们可以在opencv\build\include\opencv2\highgui\highgui.hpp文件中找到imshow的原型:

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CV_EXPORTS_W void imshow(const string& winname, InputArray mat);

进一步对InputArray转到定义,在opencv\build\include\opencv2\core\core.hpp文件中查到一个typedef声明:

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typedef const _InputArray& InputArray;

这其实一个类型声明引用,就是说_InputArrayInputArray是一个意思,然后再次对_InputArray进行转到定义,终于,在opencv\build\include\opencv2\core\core.hpp文件中发现了InputArray的真身:

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class CV_EXPORTS _InputArray
{
public:
enum {
KIND_SHIFT = 16,
FIXED_TYPE = 0x8000 << KIND_SHIFT,
FIXED_SIZE = 0x4000 << KIND_SHIFT,
KIND_MASK = ~(FIXED_TYPE|FIXED_SIZE) - (1 << KIND_SHIFT) + 1,

NONE = 0 << KIND_SHIFT,
MAT = 1 << KIND_SHIFT,
MATX = 2 << KIND_SHIFT,
STD_VECTOR = 3 << KIND_SHIFT,
STD_VECTOR_VECTOR = 4 << KIND_SHIFT,
STD_VECTOR_MAT = 5 << KIND_SHIFT,
EXPR = 6 << KIND_SHIFT,
OPENGL_BUFFER = 7 << KIND_SHIFT,
OPENGL_TEXTURE = 8 << KIND_SHIFT,
GPU_MAT = 9 << KIND_SHIFT,
OCL_MAT =10 << KIND_SHIFT
};
_InputArray();

_InputArray(const Mat& m);
_InputArray(const MatExpr& expr);
template<typename _Tp> _InputArray(const _Tp* vec, int n);
template<typename _Tp> _InputArray(const vector<_Tp>& vec);
template<typename _Tp> _InputArray(const vector<vector<_Tp> >& vec);
_InputArray(const vector<Mat>& vec);
template<typename _Tp> _InputArray(const vector<Mat_<_Tp> >& vec);
template<typename _Tp> _InputArray(const Mat_<_Tp>& m);
template<typename _Tp, int m, int n> _InputArray(const Matx<_Tp, m, n>& matx);
_InputArray(const Scalar& s);
_InputArray(const double& val);
// < Deprecated
_InputArray(const GlBuffer& buf);
_InputArray(const GlTexture& tex);
// >
_InputArray(const gpu::GpuMat& d_mat);
_InputArray(const ogl::Buffer& buf);
_InputArray(const ogl::Texture2D& tex);

virtual Mat getMat(int i=-1) const;
virtual void getMatVector(vector<Mat>& mv) const;
// < Deprecated
virtual GlBuffer getGlBuffer() const;
virtual GlTexture getGlTexture() const;
// >
virtual gpu::GpuMat getGpuMat() const;
/*virtual*/ ogl::Buffer getOGlBuffer() const;
/*virtual*/ ogl::Texture2D getOGlTexture2D() const;

virtual int kind() const;
virtual Size size(int i=-1) const;
virtual size_t total(int i=-1) const;
virtual int type(int i=-1) const;
virtual int depth(int i=-1) const;
virtual int channels(int i=-1) const;
virtual bool empty() const;

#ifdef OPENCV_CAN_BREAK_BINARY_COMPATIBILITY
virtual ~_InputArray();
#endif

int flags;
void* obj;
Size sz;
};

可以看到,_InputArray类的里面首先定义了一个枚举,然后定了各个构造函数和虚函数。很多时候,遇到函数原型中的InputArray类型,我们把它简单地当做Mat类型就行了。

imshow 函数用于在指定的窗口中显示图像。如果窗口是用CV_WINDOW_AUTOSIZE(默认值)标志创建的,那么显示图像原始大小。否则,将图像进行缩放以适合窗口。而imshow 函数缩放图像,取决于图像的深度:

  • 如果载入的图像是8位无符号类型(8-bit unsigned),就显示图像本来的样子。
  • 如果图像是16位无符号类型(16-bit unsigned)或32位整型(32-bit integer),便用像素值除以256。也就是说,值的范围是[0,255 x 256]映射到[0,255]。
  • 如果图像是32位浮点型(32-bit floating-point),像素值便要乘以255。也就是说,该值的范围是[0,1]映射到[0,255]。

imwrite

功能:输出图像到文件

定义:

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bool imwrite( const string& filename, InputArray img,
const vector<int>& params=vector<int>());

■ 第一个参数,const string&类型的filename,填需要写入的文件名就行了,带上后缀,比如,“123.jpg”这样。

■ 第二个参数,InputArray类型的img,一般填一个Mat类型的图像数据就行了。

■ 第三个参数,const vector<int>&类型的params,表示为特定格式保存的参数编码,它有默认值vector<int>(),所以一般情况下不需要填写。

cvtcolor

功能:将一个图像的颜色空间转换到另一种(Converts an image from one color space to another.)

参考:cvtcolor

定义:

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void cvtColor( InputArray src, OutputArray dst, int code, int dstCn=0 );

■ 第一个参数,InputArray类型的src ,– Source image

■ 第二个参数,OutputArray类型的dst,Destination image of the same size and depth as src

■ 第三个参数,int类型的code,颜色空间变换代码Color space conversion code。

具体的变换代码参见:opencv\build\include\opencv2\imgproc\types_c.h文件中的第87行,枚举类型。

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/* Constants for color conversion */
enum
{
CV_BGR2BGRA =0,
CV_RGB2RGBA =CV_BGR2BGRA,

CV_BGRA2BGR =1,
CV_RGBA2RGB =CV_BGRA2BGR,

CV_BGR2RGBA =2,
CV_RGB2BGRA =CV_BGR2RGBA,

CV_RGBA2BGR =3,
CV_BGRA2RGB =CV_RGBA2BGR,

CV_BGR2RGB =4,
CV_RGB2BGR =CV_BGR2RGB,

CV_BGRA2RGBA =5,
CV_RGBA2BGRA =CV_BGRA2RGBA,

CV_BGR2GRAY =6,
CV_RGB2GRAY =7,
CV_GRAY2BGR =8,
CV_GRAY2RGB =CV_GRAY2BGR,
CV_GRAY2BGRA =9,
CV_GRAY2RGBA =CV_GRAY2BGRA,
CV_BGRA2GRAY =10,
CV_RGBA2GRAY =11,

CV_BGR2BGR565 =12,
CV_RGB2BGR565 =13,
CV_BGR5652BGR =14,
CV_BGR5652RGB =15,
CV_BGRA2BGR565 =16,
CV_RGBA2BGR565 =17,
CV_BGR5652BGRA =18,
CV_BGR5652RGBA =19,

CV_GRAY2BGR565 =20,
CV_BGR5652GRAY =21,

CV_BGR2BGR555 =22,
CV_RGB2BGR555 =23,
CV_BGR5552BGR =24,
CV_BGR5552RGB =25,
CV_BGRA2BGR555 =26,
CV_RGBA2BGR555 =27,
CV_BGR5552BGRA =28,
CV_BGR5552RGBA =29,

CV_GRAY2BGR555 =30,
CV_BGR5552GRAY =31,

CV_BGR2XYZ =32,
CV_RGB2XYZ =33,
CV_XYZ2BGR =34,
CV_XYZ2RGB =35,

CV_BGR2YCrCb =36,
CV_RGB2YCrCb =37,
CV_YCrCb2BGR =38,
CV_YCrCb2RGB =39,

CV_BGR2HSV =40,
CV_RGB2HSV =41,

CV_BGR2Lab =44,
CV_RGB2Lab =45,

CV_BayerBG2BGR =46,
CV_BayerGB2BGR =47,
CV_BayerRG2BGR =48,
CV_BayerGR2BGR =49,

CV_BayerBG2RGB =CV_BayerRG2BGR,
CV_BayerGB2RGB =CV_BayerGR2BGR,
CV_BayerRG2RGB =CV_BayerBG2BGR,
CV_BayerGR2RGB =CV_BayerGB2BGR,

CV_BGR2Luv =50,
CV_RGB2Luv =51,
CV_BGR2HLS =52,
CV_RGB2HLS =53,

CV_HSV2BGR =54,
CV_HSV2RGB =55,

CV_Lab2BGR =56,
CV_Lab2RGB =57,
CV_Luv2BGR =58,
CV_Luv2RGB =59,
CV_HLS2BGR =60,
CV_HLS2RGB =61,

CV_BayerBG2BGR_VNG =62,
CV_BayerGB2BGR_VNG =63,
CV_BayerRG2BGR_VNG =64,
CV_BayerGR2BGR_VNG =65,

CV_BayerBG2RGB_VNG =CV_BayerRG2BGR_VNG,
CV_BayerGB2RGB_VNG =CV_BayerGR2BGR_VNG,
CV_BayerRG2RGB_VNG =CV_BayerBG2BGR_VNG,
CV_BayerGR2RGB_VNG =CV_BayerGB2BGR_VNG,

CV_BGR2HSV_FULL = 66,
CV_RGB2HSV_FULL = 67,
CV_BGR2HLS_FULL = 68,
CV_RGB2HLS_FULL = 69,

CV_HSV2BGR_FULL = 70,
CV_HSV2RGB_FULL = 71,
CV_HLS2BGR_FULL = 72,
CV_HLS2RGB_FULL = 73,

CV_LBGR2Lab = 74,
CV_LRGB2Lab = 75,
CV_LBGR2Luv = 76,
CV_LRGB2Luv = 77,

CV_Lab2LBGR = 78,
CV_Lab2LRGB = 79,
CV_Luv2LBGR = 80,
CV_Luv2LRGB = 81,

CV_BGR2YUV = 82,
CV_RGB2YUV = 83,
CV_YUV2BGR = 84,
CV_YUV2RGB = 85,

CV_BayerBG2GRAY = 86,
CV_BayerGB2GRAY = 87,
CV_BayerRG2GRAY = 88,
CV_BayerGR2GRAY = 89,

//YUV 4:2:0 formats family
CV_YUV2RGB_NV12 = 90,
CV_YUV2BGR_NV12 = 91,
CV_YUV2RGB_NV21 = 92,
CV_YUV2BGR_NV21 = 93,
CV_YUV420sp2RGB = CV_YUV2RGB_NV21,
CV_YUV420sp2BGR = CV_YUV2BGR_NV21,

CV_YUV2RGBA_NV12 = 94,
CV_YUV2BGRA_NV12 = 95,
CV_YUV2RGBA_NV21 = 96,
CV_YUV2BGRA_NV21 = 97,
CV_YUV420sp2RGBA = CV_YUV2RGBA_NV21,
CV_YUV420sp2BGRA = CV_YUV2BGRA_NV21,

CV_YUV2RGB_YV12 = 98,
CV_YUV2BGR_YV12 = 99,
CV_YUV2RGB_IYUV = 100,
CV_YUV2BGR_IYUV = 101,
CV_YUV2RGB_I420 = CV_YUV2RGB_IYUV,
CV_YUV2BGR_I420 = CV_YUV2BGR_IYUV,
CV_YUV420p2RGB = CV_YUV2RGB_YV12,
CV_YUV420p2BGR = CV_YUV2BGR_YV12,

CV_YUV2RGBA_YV12 = 102,
CV_YUV2BGRA_YV12 = 103,
CV_YUV2RGBA_IYUV = 104,
CV_YUV2BGRA_IYUV = 105,
CV_YUV2RGBA_I420 = CV_YUV2RGBA_IYUV,
CV_YUV2BGRA_I420 = CV_YUV2BGRA_IYUV,
CV_YUV420p2RGBA = CV_YUV2RGBA_YV12,
CV_YUV420p2BGRA = CV_YUV2BGRA_YV12,

CV_YUV2GRAY_420 = 106,
CV_YUV2GRAY_NV21 = CV_YUV2GRAY_420,
CV_YUV2GRAY_NV12 = CV_YUV2GRAY_420,
CV_YUV2GRAY_YV12 = CV_YUV2GRAY_420,
CV_YUV2GRAY_IYUV = CV_YUV2GRAY_420,
CV_YUV2GRAY_I420 = CV_YUV2GRAY_420,
CV_YUV420sp2GRAY = CV_YUV2GRAY_420,
CV_YUV420p2GRAY = CV_YUV2GRAY_420,

//YUV 4:2:2 formats family
CV_YUV2RGB_UYVY = 107,
CV_YUV2BGR_UYVY = 108,
//CV_YUV2RGB_VYUY = 109,
//CV_YUV2BGR_VYUY = 110,
CV_YUV2RGB_Y422 = CV_YUV2RGB_UYVY,
CV_YUV2BGR_Y422 = CV_YUV2BGR_UYVY,
CV_YUV2RGB_UYNV = CV_YUV2RGB_UYVY,
CV_YUV2BGR_UYNV = CV_YUV2BGR_UYVY,

CV_YUV2RGBA_UYVY = 111,
CV_YUV2BGRA_UYVY = 112,
//CV_YUV2RGBA_VYUY = 113,
//CV_YUV2BGRA_VYUY = 114,
CV_YUV2RGBA_Y422 = CV_YUV2RGBA_UYVY,
CV_YUV2BGRA_Y422 = CV_YUV2BGRA_UYVY,
CV_YUV2RGBA_UYNV = CV_YUV2RGBA_UYVY,
CV_YUV2BGRA_UYNV = CV_YUV2BGRA_UYVY,

CV_YUV2RGB_YUY2 = 115,
CV_YUV2BGR_YUY2 = 116,
CV_YUV2RGB_YVYU = 117,
CV_YUV2BGR_YVYU = 118,
CV_YUV2RGB_YUYV = CV_YUV2RGB_YUY2,
CV_YUV2BGR_YUYV = CV_YUV2BGR_YUY2,
CV_YUV2RGB_YUNV = CV_YUV2RGB_YUY2,
CV_YUV2BGR_YUNV = CV_YUV2BGR_YUY2,

CV_YUV2RGBA_YUY2 = 119,
CV_YUV2BGRA_YUY2 = 120,
CV_YUV2RGBA_YVYU = 121,
CV_YUV2BGRA_YVYU = 122,
CV_YUV2RGBA_YUYV = CV_YUV2RGBA_YUY2,
CV_YUV2BGRA_YUYV = CV_YUV2BGRA_YUY2,
CV_YUV2RGBA_YUNV = CV_YUV2RGBA_YUY2,
CV_YUV2BGRA_YUNV = CV_YUV2BGRA_YUY2,

CV_YUV2GRAY_UYVY = 123,
CV_YUV2GRAY_YUY2 = 124,
//CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
CV_YUV2GRAY_Y422 = CV_YUV2GRAY_UYVY,
CV_YUV2GRAY_UYNV = CV_YUV2GRAY_UYVY,
CV_YUV2GRAY_YVYU = CV_YUV2GRAY_YUY2,
CV_YUV2GRAY_YUYV = CV_YUV2GRAY_YUY2,
CV_YUV2GRAY_YUNV = CV_YUV2GRAY_YUY2,

// alpha premultiplication
CV_RGBA2mRGBA = 125,
CV_mRGBA2RGBA = 126,

CV_RGB2YUV_I420 = 127,
CV_BGR2YUV_I420 = 128,
CV_RGB2YUV_IYUV = CV_RGB2YUV_I420,
CV_BGR2YUV_IYUV = CV_BGR2YUV_I420,

CV_RGBA2YUV_I420 = 129,
CV_BGRA2YUV_I420 = 130,
CV_RGBA2YUV_IYUV = CV_RGBA2YUV_I420,
CV_BGRA2YUV_IYUV = CV_BGRA2YUV_I420,
CV_RGB2YUV_YV12 = 131,
CV_BGR2YUV_YV12 = 132,
CV_RGBA2YUV_YV12 = 133,
CV_BGRA2YUV_YV12 = 134,

CV_COLORCVT_MAX = 135
};

■ 第四个参数,int类型的dstCn,dst中的通道数(channel number ),dstCn默认为0,表示 dst中通道数自动从src和code中获取。

示例:

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//将彩色图像image1变换为灰度图像gray_image1
cvtColor(image1,gray_image1,CV_RGB2GRAY);

综合示例

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// VS2010 + OpenCV2.4.9

#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>

using namespace cv;


int main( )
{
Mat girl=imread("girl.jpg"); //载入图像到Mat
namedWindow("girl.jpg");
imshow("girl.jpg",girl);

//载入图片
Mat image= imread("11.jpg",199);

//载入后先显示
namedWindow("11.jpg");
imshow("11.jpg",image);

//输出一张jpg图片到工程目录下
imwrite("10.jpg",image);

waitKey();

return 0;
}
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