@

1.图片读取

CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR );
enum ImreadModes {

       IMREAD_UNCHANGED            = -1, 
//!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). Ignore EXIF orientation.

       IMREAD_GRAYSCALE            = 0,  
//!< If set, always convert image to the single channel grayscale image (codec internal conversion).

       IMREAD_COLOR                = 1,  
//!< If set, always convert image to the 3 channel BGR color image.

       IMREAD_ANYDEPTH             = 2,  
//!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit.

       IMREAD_ANYCOLOR             = 4,  
//!< If set, the image is read in any possible color format.

       IMREAD_LOAD_GDAL            = 8,  
//!< If set, use the gdal driver for loading the image.

       IMREAD_REDUCED_GRAYSCALE_2  = 16, 
//!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2.

       IMREAD_REDUCED_COLOR_2      = 17, 
//!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2.

       IMREAD_REDUCED_GRAYSCALE_4  = 32, 
//!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4.

       IMREAD_REDUCED_COLOR_4      = 33, 
//!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4.

       IMREAD_REDUCED_GRAYSCALE_8  = 64, 
//!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8.

       IMREAD_REDUCED_COLOR_8      = 65, 
//!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8.

       IMREAD_IGNORE_ORIENTATION   = 128 
//!< If set, do not rotate the image according to EXIF's orientation flag.
     };

2.创建窗口

CV_EXPORTS_W void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE);

• 参数如下

3.图片显示

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

• 参数如下

• 若窗体未创建，会自动进行创建

CV_EXPORTS_W int waitKey(int delay = 0);

控制图片的展示时间，如设置delay=0，则表示一直展示，按SPACE停止展示

如设置delay不为0，则表示停留delay毫秒

4.图片保存

CV_EXPORTS_W bool imwrite( const String& filename, InputArray img,
              const std::vector<int>& params = std::vector<int>());

• 参数如下

5.视频输入输出

CV_WRAP explicit VideoCapture::VideoCapture(const String& filename, int apiPreference = CAP_ANY);
  
CV_WRAP explicit VideoCapture::VideoCapture(const String& filename, int apiPreference, const std::vector<int>& params);

CV_WRAP explicit VideoCapture::VideoCapture(int index, int apiPreference = CAP_ANY);

CV_WRAP explicit VideoCapture::VideoCapture(int index, int apiPreference, const std::vector<int>& params);

CV_WRAP VideoWriter::VideoWriter(const String& filename, int fourcc, double fps,Size frameSize, bool isColor = true);

CV_WRAP VideoWriter::VideoWriter(const String& filename, int fourcc, double fps, const Size& frameSize,const std::vector<int>& params);

CV_WRAP VideoWriter::VideoWriter(const String& filename, int apiPreference, int fourcc, double fps,const Size& frameSize, const std::vector<int>& params);

//fps:帧率
//frameSize：输出视频中每一帧的尺寸

5.1 filename

• 影片档案名称（例如video.avi）
• 图片序列（例如img_%02d.jpg，将读取像这样的样本img_00.jpg, img_01.jpg, img_02.jpg, …）
• 视频流的网址（例如protocol://host:port/script_name?script_params|auth）。请注意，每个视频流或IP摄像机源均具有其自己的URL方案。请参考源流的文档以了解正确的URL。

5.2 index

• 要打开的视频捕获设备的ID。要使用默认后端打开默认摄像头，只需传递0。
• 当apiPreference为CAP_ANY时，使用camera_id + domain_offset（CAP_ *）向后兼容有效。

5.3 fourcc

• 用于编码视频文件的编码器，通过VideoWriter::fourcc函数获得

CV_WRAP static int fourcc(char c1, char c2, char c3, char c4);

• 参数如下

5.4 apiPreference（not important）

首选使用的Capture API后端。如果有多个可用的读取器实现，则可以用于实施特定的读取器实现。

设置读取的摄像头编号，默认CAP_ANY=0,自动检测摄像头。多个摄像头时，使用索引0,1,2，…进行编号调用摄像头。 apiPreference = -1时单独出现窗口，选取相应编号摄像头。

5.5 演示

VideoCapture video("demo.mp4");
Mat fps;
video.read(fps);
VideoWriter video_out("demo_out.avi",VideoWriter::fourcc('P','I','M','1'),30,fps.size());
    while (1){
        Mat fps;
        video>>fps;
        //video.read(fps);
        fps>>video_out;
        //video_out.write(fps);
        imshow("video",fps);
        waitKey(10);//控制帧率
    }

6.通道分离与合并

6.1 分离

API（一）

CV_EXPORTS void split(const Mat& src, Mat* mvbegin);

• 参数如下

API（二）

CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv);

• 参数如下

6.2 合并

API（一）

CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst);

• 参数如下

API（二）

CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst);

• 参数如下

7.图片色彩模式转换

7.1 API

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

• 参数如下

7.2 转换类型和转换码

• RGB和BGR（opencv默认的彩色图像的颜色空间是BGR）颜色空间的转换

cv::COLOR_BGR2RGB

cv::COLOR_RGB2BGR

cv::COLOR_RGBA2BGRA

cv::COLOR_BGRA2RGBA

• 向RGB和BGR图像中增添alpha通道

cv::COLOR_RGB2RGBA

cv::COLOR_BGR2BGRA

• 从RGB和BGR图像中去除alpha通道

cv::COLOR_RGBA2RGB

cv::COLOR_BGRA2BGR

• 从RBG和BGR颜色空间转换到灰度空间

cv::COLOR_RGB2GRAY

cv::COLOR_BGR2GRAY

cv::COLOR_RGBA2GRAY

cv::COLOR_BGRA2GRAY

• 从灰度空间转换到RGB和BGR颜色空间

cv::COLOR_GRAY2RGB

cv::COLOR_GRAY2BGR

cv::COLOR_GRAY2RGBA

cv::COLOR_GRAY2BGRA

• RGB和BGR颜色空间与BGR565颜色空间之间的转换

cv::COLOR_RGB2BGR565

cv::COLOR_BGR2BGR565

cv::COLOR_BGR5652RGB

cv::COLOR_BGR5652BGR

cv::COLOR_RGBA2BGR565

cv::COLOR_BGRA2BGR565

cv::COLOR_BGR5652RGBA

cv::COLOR_BGR5652BGRA

• 灰度空间与BGR565之间的转换

cv::COLOR_GRAY2BGR555

cv::COLOR_BGR5552GRAY

• RGB和BGR颜色空间与CIE XYZ之间的转换

cv::COLOR_RGB2XYZ

cv::COLOR_BGR2XYZ

cv::COLOR_XYZ2RGB

cv::COLOR_XYZ2BGR

• RGB和BGR颜色空间与uma色度（YCrCb空间）之间的转换

cv::COLOR_RGB2YCrCb

cv::COLOR_BGR2YCrCb

cv::COLOR_YCrCb2RGB

cv::COLOR_YCrCb2BGR

• RGB和BGR颜色空间与HSV颜色空间之间的相互转换

cv::COLOR_RGB2HSV

cv::COLOR_BGR2HSV

cv::COLOR_HSV2RGB

cv::COLOR_HSV2BGR

• RGB和BGR颜色空间与HLS颜色空间之间的相互转换

cv::COLOR_RGB2HLS

cv::COLOR_BGR2HLS

cv::COLOR_HLS2RGB

cv::COLOR_HLS2BGR

• RGB和BGR颜色空间与CIE Lab颜色空间之间的相互转换

cv::COLOR_RGB2Lab

cv::COLOR_BGR2Lab

cv::COLOR_Lab2RGB

cv::COLOR_Lab2BGR

• RGB和BGR颜色空间与CIE Luv颜色空间之间的相互转换

cv::COLOR_RGB2Luv

cv::COLOR_BGR2Luv

cv::COLOR_Luv2RGB

cv::COLOR_Luv2BGR

• Bayer格式（raw data）向RGB或BGR颜色空间的转换

cv::COLOR_BayerBG2RGB

cv::COLOR_BayerGB2RGB

cv::COLOR_BayerRG2RGB

cv::COLOR_BayerGR2RGB

cv::COLOR_BayerBG2BGR

cv::COLOR_BayerGB2BGR

cv::COLOR_BayerRG2BGR

cv::COLOR_BayerGR2BGR

8.改变图片的对比度和亮度

8.1 概述

Mat.ptr(i,j)=Mat.ptr(i,j)*a+b

a：控制对比度增益

b：控制亮度增益

8.2 手动（使用saturate_cast函数确保输出值不溢出范围）

Mat xuenai = imread("xuenai.jpg");
imshow("xuenai", xuenai);
for(int i=0;i<xuenai.rows;i++){
        for(int j=0;j<xuenai.cols;j++){
            for(int k=0;k<xuenai.channels();k++) {
                xuenai.at<Vec3b>(i, j)[k] = saturate_cast<uchar>(xuenai.at<Vec3b>(i, j)[k] *                 1.2 + 30);
            }
        }
    }
imshow("xuenai_convertTo",xuenai);
waitKey();

8.3 API

void Mat::convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const;

• 参数如下

• 不能进行原地运算

8.4 效果

Mat xuenai = imread("xuenai.jpg");
imshow("xuenai", xuenai);
xuenai.convertTo(xuenai,-1,1.2,30);
imshow("xuenai_convertTo",xuenai);
waitKey();

可以看到效果是一样的

9.图片混合

CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2,
                              double beta, double gamma, OutputArray dst, int dtype = -1);

• 参数如下

10.图片尺寸调整

CV_EXPORTS_W void resize( InputArray src, OutputArray dst,
                          Size dsize, double fx = 0, double fy = 0,
                          int interpolation = INTER_LINEAR );

• 参数如下

10.1 插值算法(not important)

enum InterpolationFlags{
    /** nearest neighbor interpolation */
    INTER_NEAREST        = 0,
    /** bilinear interpolation */
    INTER_LINEAR         = 1,
    /** bicubic interpolation */
    INTER_CUBIC          = 2,
    /** resampling using pixel area relation. It may be a preferred method for image decimation, as
    it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST
    method. */
    INTER_AREA           = 3,
    /** Lanczos interpolation over 8x8 neighborhood */
    INTER_LANCZOS4       = 4,
    /** Bit exact bilinear interpolation */
    INTER_LINEAR_EXACT = 5,
    /** Bit exact nearest neighbor interpolation. This will produce same results as
    the nearest neighbor method in PIL, scikit-image or Matlab. */
    INTER_NEAREST_EXACT  = 6,
    /** mask for interpolation codes */
    INTER_MAX            = 7,
    /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the
    source image, they are set to zero */
    WARP_FILL_OUTLIERS   = 8,
    /** flag, inverse transformation

    For example, #linearPolar or #logPolar transforms:
    - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$
    - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$
    */
    WARP_INVERSE_MAP     = 16
};

10.2 注意事项

使用注意事项：

• dsize和fx/fy不能同时为0

1. 指定dsize的值，让fx和fy空置直接使用默认值。
2. 让dsize为0，指定好fx和fy的值，比如fx=fy=0.5，那么就相当于把原图两个方向缩小一倍。

11.图像金字塔（常用于神经网络的池化层，对图像进行成倍的放大或缩小）

//缩小一倍
CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst,
                           const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );

//放大一倍
CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst,
                         const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );

• 参数如下

12.二值化（对灰度图）

CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
                               double thresh, double maxval, int type );

• 参数如下

12.1 阈值类型

enum ThresholdTypes {
    THRESH_BINARY     = 0, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{maxval}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
    THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f]
    THRESH_TRUNC      = 2, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
    THRESH_TOZERO     = 3, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
    THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
    THRESH_MASK       = 7,
    THRESH_OTSU       = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value
    THRESH_TRIANGLE   = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value
};

阈值二值化（Threshold Binary）

　　首先指定像素的灰度值的阈值，遍历图像中像素值，如果像素的灰度值大于这个阈值，则将这个像素设置为最大像素值(8位灰度值最大为255)；若像素的灰度值小于阈值，则将该像素点像素值赋值为0。公式以及示意图如下：

阈值反二值化（Threshold Binary Inverted）

　　首先也要指定一个阈值，不同的是在对图像进行阈值化操作时与阈值二值化相反，当像素的灰度值超过这个阈值的时候为该像素点赋值为0；当该像素的灰度值低于该阈值时赋值为最大值。公式及示意图如下：

截断（Truncate）

　　给定像素值阈值，在图像中像素的灰度值大于该阈值的像素点被设置为该阈值，而小于该阈值的像素值保持不变。公式以及示意图如下：

阈值取零（Threshold To Zero）

　　与截断阈值化相反，像素点的灰度值如果大于该阈值则像素值不变，如果像素点的灰度值小于该阈值，则该像素值设置为0.公式以及示意图如下：

阈值反取零（Threshold To Zero Inverted）

　　像素值大于阈值的像素赋值为0，而小于该阈值的像素值则保持不变，公式以及示意图如下：

13.图片裁剪

13.1 方式一

inline
Mat Mat::operator()( const Rect& roi ) const
{
    return Mat(*this, roi);
}

以下为实例

Mat xuenai = imread("xuenai.jpg");
resize(xuenai,xuenai,Size(1000,1000));
imshow("xuenai", xuenai);
Mat tuanzi(xuenai,(Rect(0,0,500,1000)));
imshow("tuanzi",tuanzi);
waitKey();

13.2 方式二

Mat::Mat(const Mat& m, const Rect& roi);

以下为实例

Mat xuenai = imread("xuenai.jpg");
resize(xuenai,xuenai,Size(1000,1000));
imshow("xuenai", xuenai);
Mat tuanzi(xuenai(Rect(0,0,500,1000)));
imshow("tuanzi",tuanzi);
waitKey();

13.3 Rect类构造

template<typename _Tp> inline
Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height)
    : x(_x), y(_y), width(_width), height(_height) {}

template<typename _Tp> inline
Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz)
    : x(org.x), y(org.y), width(sz.width), height(sz.height) {}

template<typename _Tp> inline
Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2)
{
    x = std::min(pt1.x, pt2.x);
    y = std::min(pt1.y, pt2.y);
    width = std::max(pt1.x, pt2.x) - x;
    height = std::max(pt1.y, pt2.y) - y;
}

14.基本变换

14.1 翻转

CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode);

• 参数如下

• flipCode 可选值如下

效果

Mat xuenai = imread("xuenai.jpg");
imshow("xuenai", xuenai);
Mat xuenai_flip(xuenai.size(), xuenai.type());
flip(xuenai, xuenai_flip, 0);
imshow("xuenai_flip", xuenai_flip);
waitKet();

14.2 90°旋转

CV_EXPORTS_W void rotate(InputArray src, OutputArray dst, int rotateCode);
enum RotateFlags {
    ROTATE_90_CLOCKWISE = 0, //!<Rotate 90 degrees clockwise
    ROTATE_180 = 1, //!<Rotate 180 degrees clockwise
    ROTATE_90_COUNTERCLOCKWISE = 2, //!<Rotate 270 degrees clockwise
};

• 参数如下

效果

Mat xuenai = imread("xuenai.jpg");
imshow("xuenai", xuenai);
Mat xuenai_rotate(xuenai.size(), xuenai.type());
rotate(xuenai, xuenai_rotate, ROTATE_180);
imshow("xuenai_rotate", xuenai_rotate);
waitKet();

15.仿射变换

15.1 API

CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
                              InputArray M, Size dsize,
                              int flags = INTER_LINEAR,
                              int borderMode = BORDER_CONSTANT,
                              const Scalar& borderValue = Scalar());

• 参数如下

15.2 平移

• 只需将变换矩阵M设置成如下形式：

float delta_x=200,delta_y=200;
float  M_values[]={1,0,delta_x,
                   0,1,delta_y};
Mat M(Size(3,2),CV_32F,M_values);

delta_x：x方向上的偏移量

delta_y：y方向上的偏移量

M_values：必须是浮点类型的数组对象

M：必须是CV_32F，不能用逗号式分隔创建

效果

Mat xuenai = imread("xuenai.jpg");
imshow("xuenai",xuenai);
double  M_values[]={1,0,200,
                    0,1,200};
Mat M(Size(3,2), CV_64F,M_values);
Mat xuenai_shift(xuenai.size(),xuenai.type());
warpAffine(xuenai,xuenai_shift,M,xuenai.size());
imshow("xuenai_shift",xuenai_shift);
waitKet();

15.3 任意角度旋转

获得变换矩阵M

inline
Mat getRotationMatrix2D(Point2f center, double angle, double scale)
{
    return Mat(getRotationMatrix2D_(center, angle, scale), true);
}

• 参数如下

效果

Mat xuenai = imread("xuenai.jpg");
imshow("xuenai", xuenai);
Mat M= getRotationMatrix2D(Point2f(xuenai.cols/2,xuenai.rows/2),45,1);
Mat xuenai_rotate(xuenai.size(),xuenai.type());
warpAffine(xuenai,xuenai_rotate,M,xuenai.size());
imshow("xuenai_flip",xuenai_rotate);

15.4 仿射（不破坏几何关系）

获得变换矩阵M

CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );

• 参数如下

• 三个点要一一对应

16.透射变换（破坏几何关系）

16.1 API

进行变换

CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst,
                                   InputArray M, Size dsize,
                                   int flags = INTER_LINEAR,
                                   int borderMode = BORDER_CONSTANT,
                                   const Scalar& borderValue = Scalar());

• 参数如下

已知变换后图片，逆推变换矩阵M

CV_EXPORTS_W Mat getPerspectiveTransform(InputArray src, InputArray dst, int solveMethod = DECOMP_LU);

• 参数如下

获得变换矩阵M

CV_EXPORTS Mat getPerspectiveTransform(const Point2f src[], const Point2f dst[], int solveMethod = DECOMP_LU);

• 参数如下

• 四个点要一一对应

16.2 效果

Mat origin = imread("origin.jpg");
Point2f point2F_origin[4]={Point2f (405,105),Point2f(2469,217),Point2f(2573,3489),Point2f(349,3547)};
Point2f point2F_tansform[4]={Point2f (0,0),Point2f(2500,0),Point2f(2500,3500),Point2f(0,3500)};
Mat M=getPerspectiveTransform(point2F_origin,point2F_tansform);
Mat transfrom(origin.size(),origin.type());
warpPerspective(origin,transfrom,M,Size(2500,3500));
resize(origin,origin,Size(500,700));
resize(transfrom,transfrom,Size(500,700));
imshow("origin",origin);
imshow("transform",transfrom);

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