一.Mat对象常用方法
void copyTo(Mat mat) //复制
void convertTo(Mat dst, int type) //用来切换数据类型
Mat clone() //完全复制
int channels()
int depth()
bool empty();
uchar* ptr(i=0)
部分复制：一般情况下只会复制Mat对象的头和指针部分，不会复制数据部分
Mat A= imread(imgFilePath);
Mat B(A) // 只复制
完全复制：如果想把Mat对象的头部和数据部分一起复制，可以通过如下两个API实现
Mat F = A.clone();
Mat G; A.copyTo(G);
A.copyTo(B, mask)，表示得到一个附加掩膜mask的矩阵B
mask参数的格式，A.copyto(B, mask)，mask作为一个掩模板，如果在某个像素点(i, j)其值为1（只看第一通道，所以mask单通道即可），则把A.at(i, j)处的值直接赋给B.at(i, j)，如果其值为0则B.at(i, j)处保留其原始像素值。这样能够使得图标边缘的颜色和原图保持一致。
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二.imwrite函数的用法
bool imwrite(const string& filename, InputArray img, const vector<int>& params=vector<int>() )
imwrite函数作用是把程序中的Mat类型的矩阵保存为图像到指定位置
（1）参数filename为所需保存图像的文件目录和文件名。这里的文件名需要带有图像格式后缀的，目前OpenCV该函数只支持JPEG,PNG,PPM,PGM,PBM，TIFF等。并不是所有Mat类型都支持。
（2）参数img为图像数据来源，其类型为Mat。注意也不是所有格式的Mat型数据都能被使用保存为图片，目前OpenCV主要只支持单通道和3通道的图像，并且此时要求其深度为8bit和16bit无符号(即CV_16U)。所以其他一些数据类型是不支持的，比如说float型等。如果Mat类型数据的深度和通道数不满足上面的要求，则需要使用convertTo()函数和cvtColor()函数来进行转换。
convertTo()函数负责转换数据类型不同的Mat，即可以将类似float型的Mat转换到imwrite()函数能够接受的类型。而cvtColor()函数是负责转换不同通道的Mat，因为该函数的第4个参数就可以设置目的Mat数据的通道数（只是我们一般没有用到它，一般情况下这个函数是用来进行色彩空间转换的）。
（3）参数params是用来设置对应图片格式的参数的，因为一般情况下这些图片格式都是经过了压缩的，这里就是设置这些压缩参数来控制图片的质量。该参数是一个vector<int>类型，里面分别存入paramId_1, paramValue_1, paramId_2, paramValue_2, ... 也就是说存入一对属性值。如果不设置该参数的话，则程序会自动根据所保存的图像格式采用一个默认的参数。
params是个vector<int>类型。这个参数有三种，如以下格式：
JPEG，参数为CV_IMWRITE_JPEG_QUALITY，它的值是从0到100，值越小压缩的越多，默认值是95.
PNG，参数为CV_IMWRITE_PNG_COMPRESSION,它的值是从0到9，值越大表示图片尺寸越小，压缩时间越长。默认值是3。
PPM，PGM或者PBM，参数为CV_IMWRITE_PXM_BINARY，它的值是0或者1。默认值是1。
例子1
#include<opencv2/opencv.hpp>
#include<vector>
using namespace cv;
using namespace std;
int main()
{
Mat imgsrc = imread("lena.jpg",0);//读取一个图像
if(imgsrc.data == NULL)
return -1;
namedWindow("my",WINDOW_AUTOSIZE);
imshow("my",imgsrc);
vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(100);
imwrite("my.jpg",imgsrc,compression_params);
waitKey(0);
destroyWindow("my");
imgsrc.release();
return 0;
}例子2
#include <vector>
#include <stdio.h>
#include<opencv2/opencv.hpp>
using namespace cv;
using namespace std;
void createAlphaMat(Mat &mat)
{
for (int i = 0; i < mat.rows; ++i) {
for (int j = 0; j < mat.cols; ++j) {
Vec4b&rgba = mat.at<Vec4b>(i, j);
rgba[0] = UCHAR_MAX;
rgba[1] = saturate_cast<uchar>((float(mat.cols - j)) / ((float)mat.cols) *UCHAR_MAX);
rgba[2] = saturate_cast<uchar>((float(mat.rows - i)) / ((float)mat.rows) *UCHAR_MAX);
rgba[3] = saturate_cast<uchar>(0.5 * (rgba[1] + rgba[2]));
}
}
}
int main()
{
//创建带alpha通道的Mat
Mat mat(480, 640, CV_8UC4);
createAlphaMat(mat);
vector<int>compression_params;
compression_params.push_back(IMWRITE_PNG_COMPRESSION);
compression_params.push_back(9);
//显示图片
try{
imwrite("透明Alpha值图.png", mat, compression_params);
imshow("生成的png图", mat);
fprintf(stdout, "PNG图片文件的alpha数据保存完毕~\n可以在工程目录下查看由imwrite函数生成的图片\n");
waitKey(0);
}
catch (runtime_error& ex) {
fprintf(stderr, "图像转换成PNG格式发生错误：%s\n", ex.what());
return 1;
}
return 0;
}
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三.convertTo函数的用法
void Mat::convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const；
输入参数：
m 目标矩阵，如果m的大小与原矩阵不一样，或者数据类型与参数不匹配，那么在函数convertTo内部会先给m重新分配空间。
rtype 指定从原矩阵进行转换后的数据类型，即目标矩阵m的数据类型。当然，矩阵m的通道数应该与原矩阵一样的。如果rtype是负数，那么m矩阵的数据类型应该与原矩阵一样。
alpha 缩放因子，默认值是1。即把原矩阵中的每一个元素都乘以alpha。
beta 增量，默认值是0。即把原矩阵中的每一个元素都乘以alpha，再加上beta。
功能：
把一个矩阵从一种数据类型转换到另一种数据类型，同时可以带上缩放因子和增量，公式如下：
m(x,y)=saturate_cast<rType>(alpha*(*this)(x,y)+beta);
由于有数据类型的转换，所以需要用saturate_cast<rType>来处理数据的溢出。
需要注意的是：
convertTo——用来切换数据类型，如float类型变为uchar类型，但是不可用来改变通道数，也就是说不能将灰度图转化为彩色图
cvtColor——用来转换图像的颜色，也就是改变通道数，可以将灰度图像转化为彩色图像
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四.cvtColor函数的用法
void cvtColor(
InputArray src,
OutputArray dst,
int code,
int dstCn = 0
);参数的定义
（1）InputArray类型的points，输入图像。
（2）OutputArray类型的dst，输出图像。
（3）int类型的code，颜色空间转换代码（具体请看“ColorConversionCodes”）。
（4）bool类型的returnPoints，目标图像中的通道数；如果参数为0，则通道数自动从src和code派生。
颜色空间转换代码ColorConversionCodes定义在enum ColorConversionCodes结构体当中，结构体如下：
enum ColorConversionCodes {
COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image
COLOR_RGB2RGBA = COLOR_BGR2BGRA,
COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image
COLOR_RGBA2RGB = COLOR_BGRA2BGR,
COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)
COLOR_RGB2BGRA = COLOR_BGR2RGBA,
COLOR_RGBA2BGR = 3,
COLOR_BGRA2RGB = COLOR_RGBA2BGR,
COLOR_BGR2RGB = 4,
COLOR_RGB2BGR = COLOR_BGR2RGB,
COLOR_BGRA2RGBA = 5,
COLOR_RGBA2BGRA = COLOR_BGRA2RGBA,
COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"
COLOR_RGB2GRAY = 7,
COLOR_GRAY2BGR = 8,
COLOR_GRAY2RGB = COLOR_GRAY2BGR,
COLOR_GRAY2BGRA = 9,
COLOR_GRAY2RGBA = COLOR_GRAY2BGRA,
COLOR_BGRA2GRAY = 10,
COLOR_RGBA2GRAY = 11,
COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)
COLOR_RGB2BGR565 = 13,
COLOR_BGR5652BGR = 14,
COLOR_BGR5652RGB = 15,
COLOR_BGRA2BGR565 = 16,
COLOR_RGBA2BGR565 = 17,
COLOR_BGR5652BGRA = 18,
COLOR_BGR5652RGBA = 19,
COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images)
COLOR_BGR5652GRAY = 21,
COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images)
COLOR_RGB2BGR555 = 23,
COLOR_BGR5552BGR = 24,
COLOR_BGR5552RGB = 25,
COLOR_BGRA2BGR555 = 26,
COLOR_RGBA2BGR555 = 27,
COLOR_BGR5552BGRA = 28,
COLOR_BGR5552RGBA = 29,
COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images)
COLOR_BGR5552GRAY = 31,
COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"
COLOR_RGB2XYZ = 33,
COLOR_XYZ2BGR = 34,
COLOR_XYZ2RGB = 35,
COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"
COLOR_RGB2YCrCb = 37,
COLOR_YCrCb2BGR = 38,
COLOR_YCrCb2RGB = 39,
COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
COLOR_RGB2HSV = 41,
COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
COLOR_RGB2Lab = 45,
COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
COLOR_RGB2Luv = 51,
COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
COLOR_RGB2HLS = 53,
COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR
COLOR_HSV2RGB = 55,
COLOR_Lab2BGR = 56,
COLOR_Lab2RGB = 57,
COLOR_Luv2BGR = 58,
COLOR_Luv2RGB = 59,
COLOR_HLS2BGR = 60,
COLOR_HLS2RGB = 61,
COLOR_BGR2HSV_FULL = 66,
COLOR_RGB2HSV_FULL = 67,
COLOR_BGR2HLS_FULL = 68,
COLOR_RGB2HLS_FULL = 69,
COLOR_HSV2BGR_FULL = 70,
COLOR_HSV2RGB_FULL = 71,
COLOR_HLS2BGR_FULL = 72,
COLOR_HLS2RGB_FULL = 73,
COLOR_LBGR2Lab = 74,
COLOR_LRGB2Lab = 75,
COLOR_LBGR2Luv = 76,
COLOR_LRGB2Luv = 77,
COLOR_Lab2LBGR = 78,
COLOR_Lab2LRGB = 79,
COLOR_Luv2LBGR = 80,
COLOR_Luv2LRGB = 81,
COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV
COLOR_RGB2YUV = 83,
COLOR_YUV2BGR = 84,
COLOR_YUV2RGB = 85,
//! YUV 4:2:0 family to RGB
COLOR_YUV2RGB_NV12 = 90,
COLOR_YUV2BGR_NV12 = 91,
COLOR_YUV2RGB_NV21 = 92,
COLOR_YUV2BGR_NV21 = 93,
COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21,
COLOR_YUV2RGBA_NV12 = 94,
COLOR_YUV2BGRA_NV12 = 95,
COLOR_YUV2RGBA_NV21 = 96,
COLOR_YUV2BGRA_NV21 = 97,
COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
COLOR_YUV2RGB_YV12 = 98,
COLOR_YUV2BGR_YV12 = 99,
COLOR_YUV2RGB_IYUV = 100,
COLOR_YUV2BGR_IYUV = 101,
COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV,
COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
COLOR_YUV2RGBA_YV12 = 102,
COLOR_YUV2BGRA_YV12 = 103,
COLOR_YUV2RGBA_IYUV = 104,
COLOR_YUV2BGRA_IYUV = 105,
COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
COLOR_YUV2GRAY_420 = 106,
COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
//! YUV 4:2:2 family to RGB
COLOR_YUV2RGB_UYVY = 107,
COLOR_YUV2BGR_UYVY = 108,
//COLOR_YUV2RGB_VYUY = 109,
//COLOR_YUV2BGR_VYUY = 110,
COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGBA_UYVY = 111,
COLOR_YUV2BGRA_UYVY = 112,
//COLOR_YUV2RGBA_VYUY = 113,
//COLOR_YUV2BGRA_VYUY = 114,
COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGB_YUY2 = 115,
COLOR_YUV2BGR_YUY2 = 116,
COLOR_YUV2RGB_YVYU = 117,
COLOR_YUV2BGR_YVYU = 118,
COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGBA_YUY2 = 119,
COLOR_YUV2BGRA_YUY2 = 120,
COLOR_YUV2RGBA_YVYU = 121,
COLOR_YUV2BGRA_YVYU = 122,
COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2GRAY_UYVY = 123,
COLOR_YUV2GRAY_YUY2 = 124,
//CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
//! alpha premultiplication
COLOR_RGBA2mRGBA = 125,
COLOR_mRGBA2RGBA = 126,
//! RGB to YUV 4:2:0 family
COLOR_RGB2YUV_I420 = 127,
COLOR_BGR2YUV_I420 = 128,
COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420,
COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420,
COLOR_RGBA2YUV_I420 = 129,
COLOR_BGRA2YUV_I420 = 130,
COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
COLOR_RGB2YUV_YV12 = 131,
COLOR_BGR2YUV_YV12 = 132,
COLOR_RGBA2YUV_YV12 = 133,
COLOR_BGRA2YUV_YV12 = 134,
//! Demosaicing
COLOR_BayerBG2BGR = 46,
COLOR_BayerGB2BGR = 47,
COLOR_BayerRG2BGR = 48,
COLOR_BayerGR2BGR = 49,
COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
COLOR_BayerBG2GRAY = 86,
COLOR_BayerGB2GRAY = 87,
COLOR_BayerRG2GRAY = 88,
COLOR_BayerGR2GRAY = 89,
//! Demosaicing using Variable Number of Gradients
COLOR_BayerBG2BGR_VNG = 62,
COLOR_BayerGB2BGR_VNG = 63,
COLOR_BayerRG2BGR_VNG = 64,
COLOR_BayerGR2BGR_VNG = 65,
COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
//! Edge-Aware Demosaicing
COLOR_BayerBG2BGR_EA = 135,
COLOR_BayerGB2BGR_EA = 136,
COLOR_BayerRG2BGR_EA = 137,
COLOR_BayerGR2BGR_EA = 138,
COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA,
COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA,
COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA,
COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA,
//! Demosaicing with alpha channel
COLOR_BayerBG2BGRA = 139,
COLOR_BayerGB2BGRA = 140,
COLOR_BayerRG2BGRA = 141,
COLOR_BayerGR2BGRA = 142,
COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,
COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,
COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,
COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,
COLOR_COLORCVT_MAX = 143
};———————————————————————————————————————————
代码示例
#include"stdafx.h"
#include<opencv2\opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;
double alpha = 1;
double beta = 50;
void changeContrastAndBright(const Mat& src1, Mat& dst1) {
for (int i = 0; i < src1.rows; i++)
{
for (int j = 0; j < src1.cols; j++)
{
for (int k = 0; k < 3; k++)
{
dst1.at<Vec3b>(i, j)[k] =
saturate_cast<uchar>(alpha*(src1.at<Vec3b>(i, j)[k]) + beta);
}
}
}
}
int main() {
Mat src1 = imread("F:/photo/c.jpg",-1);
Mat dst1;
Mat dst2 = Mat::zeros(src1.size(), src1.type());
double time0 = static_cast<double>(getTickCount());
changeContrastAndBright(src1, dst2);
time0 = ((double)getTickCount() - time0) / getTickFrequency();
cout << "直接遍历方法运行时间为：" << time0 << "秒" << endl;
time0 = static_cast<double>(getTickCount());
src1.convertTo(dst1, src1.type(), alpha, beta);
time0 = ((double)getTickCount() - time0) / getTickFrequency();
cout << "convertTo方法运行时间为：" << time0 << "秒" << endl;
namedWindow("src1", 0);
resizeWindow("src1", 450, 450);
imshow("src1", src1);
namedWindow("dst1", 0);
resizeWindow("dst1", 450, 450);
imshow("dst1", dst1);
namedWindow("dst2", 0);
resizeWindow("dst2", 450, 450);
imshow("dst2", dst2);
waitKey();
return 0;
}图像效果
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