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OPENCV_function1.md

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输入

载入图片

Mat srcImage = imread("1.jpg");

载入视频

VideoCapture capture("1.avi"); while(1){ Mat frame; capture >> frame; imshow("读取视频", frame); waitKey(30); }

摄像头读入视频

VideoCapture capture(0); while(1){ Mat frame; capture >> frame; imshow("读取视频", frame); waitKey(30); }

XML和YAML文件的读取

//初始化 FileStorage fs2("test.yaml", FileStorage::READ);
// 第一种方法，对FileNode操作 int frameCount = (int)fs2["frameCount"];

// 第二种方法，使用FileNode运算符> > std::string date; fs2["calibrationDate"] >> date;
Mat cameraMatrix2, distCoeffs2;
fs2["cameraMatrix"] >> cameraMatrix2;
fs2["distCoeffs"] >> distCoeffs2;

输入

输出图片

imshow

imshow("原始图", srcImage); waitKey(0);

XML和YAML文件的写入

//初始化 FileStorage fs("test.yaml", FileStorage::WRITE); Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1);
Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0);
fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs;

添加滑动条

createTrackbar()

//-----------------------------------【全局变量声明部分】--------------------------------------
//		描述：全局变量声明
//-----------------------------------------------------------------------------------------------
const int g_nMaxAlphaValue = 100;//Alpha值的最大值
int g_nAlphaValueSlider;//滑动条对应的变量
double g_dAlphaValue;
double g_dBetaValue;
//声明存储图像的变量
Mat g_srcImage1;
Mat g_srcImage2;
Mat g_dstImage;
//-----------------------------------【on_Trackbar( )函数】--------------------------------
//		描述：响应滑动条的回调函数
//------------------------------------------------------------------------------------------
void on_Trackbar( int, void* )
{
	//求出当前alpha值相对于最大值的比例
	g_dAlphaValue = (double) g_nAlphaValueSlider/g_nMaxAlphaValue ;
	//则beta值为1减去alpha值
	g_dBetaValue = ( 1.0 - g_dAlphaValue );
	//根据alpha和beta值进行线性混合
	addWeighted( g_srcImage1, g_dAlphaValue, g_srcImage2, g_dBetaValue, 0.0, g_dstImage);
	//显示效果图
	imshow( WINDOW_NAME, g_dstImage );
}
int main( int argc, char** argv)
{
  //加载图像 (两图像的尺寸需相同)
	g_srcImage1 = imread("1.jpg");
  g_srcImage2 = imread("2.jpg");
  //设置滑动条初值为70
	g_nAlphaValueSlider = 70;
	//创建窗体
  namedWindow(WINDOW_NAME, 1);
  //在创建的窗体中创建一个滑动条控件
	char TrackbarName[50];
	sprintf( TrackbarName, "透明值 %d", g_nMaxAlphaValue );
	createTrackbar( TrackbarName, WINDOW_NAME, &g_nAlphaValueSlider, g_nMaxAlphaValue, on_Trackbar );
	//结果在回调函数中显示
	on_Trackbar( g_nAlphaValueSlider, 0 );
	//按任意键退出
	waitKey(0);
return 0;
}

鼠标操作

void on_MouseHandle(int event, int x, int y, int flags, vold* param); void DrawRectangle(cv::Mat& img, cv::Rect box); main() {... setMouseCallback(WINDOW_NAME,on_MouseHandle,(void*)&srcImage); ... } void on_MouseHandle((int event, int x, int y, int flags, void* param) { Mat& image = * (cv::Mat*) param; switch(event){ case EVENT_MOUSEMOVE: case EVENT_LBUTTONDOWN: case EVENT_LBUTTONUP: } }

imwrite

** 阿尔法通道(Alpha Channel)是指一张图片的透明和半透明度 **

// createAlpahMat():创建带alpha通道的Mat
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.cols - i))/((float)mat.rows) * UCHAR_MAX);
    rgba[3] = saturate_cast<uchar>( 0.5 * (rgba[1] + rgba[2]));
  }
  }
}
int main()
{
  Mat mat(480, 640, CV_8UC4);
  createAlphaMat(mat);
  vector<int>compression_params;
  compression_params.push_back(IMWRITE_PNG_COMPRESSION);
  compression_params.push_back(9);
  try{
      imwrite("透明Alpha值图", mat, compression_params);
      imshow("", mat);
      waitKey(0）；
      }
}

输出文件

图像处理

腐蚀

Mat element = getStructuringElement(MORPH_RECT, Size(15, 15)); Mat dstImage; erode(srcImage, dstImage, element);

模糊

Mat dstImage; blur( srcImage, dstImage, Size(7, 7));

图像混合

cv::addWeighted(): 图像线性混合 图像自定义区域混合： Mat srcImage4= imread("dota_pa.jpg",1); Mat logoImage= imread("dota_logo.jpg"); Mat imageROI;//【2】定义一个Mat类型并给其设定ROI区域 imageROI = srcImage4(Rect(200,250,logoImage.cols, logoImage.rows)); addWeighted(imageROI, 0.5, logoImage, 0.3, 0., imageROI):

分离颜色通道&多通道图像混合

split(srcImage,channels); imageBlueChannel = channels.at(0); imageGreenChannel= channels.at(1); imageRedChannel= channels.at(2); merge(channels, stcImage); //【5】将三个单通道重新合并成一个三通道

图像对比度、亮度值调整

static void ContrastAndBright(int, void * ){ for( int y = 0; y < g_srcImage.rows; y++ ){ for( int x = 0; x < g_srcImage.cols; x++ ){ for( int c = 0; c < 3; c++ ){ g_dstImage.at(y,x)[c] = saturate_cast( (g_nContrastValue0.01)( g_srcImage.at(y,x)[c] ) + g_nBrightValue ); }}}} main(){ //创建轨迹条 createTrackbar("对比度：", "【效果图窗口】",&g_nContrastValue, 300,ContrastAndBright ); createTrackbar("亮 度：", "【效果图窗口】",&g_nBrightValue, 200,ContrastAndBright ); //调用回调函数 ContrastAndBright(g_nContrastValue,0); ContrastAndBright(g_nBrightValue,0); }

canny边缘检测

Mat dstImage, edge, grayImage; dstImage.create(src.Image.size(), srcImage.type() ); cvtColor( srcImage, grayImage, COLOR_BGR2GRAY); //【2】将原图像转换为灰度图像 blur(grayImage, edge, Size(3, 3) ); //【3】先用使用 3x3内核来降噪 Canny( edge, edge, 3, 9, 3); //【4】运行Canny算子

离散傅里叶变换

//【1】以灰度模式读取原始图像并显示 Mat srcImage = imread("1.jpg", 0); //【2】将输入图像延扩到最佳的尺寸，边界用0补充 int m = getOptimalDFTSize( srcImage.rows ); int n = getOptimalDFTSize( srcImage.cols ); //将添加的像素初始化为0. Mat padded;
copyMakeBorder(srcImage, padded, 0, m - srcImage.rows, 0, n - srcImage.cols, BORDER_CONSTANT, Scalar::all(0)); //【3】为傅立叶变换的结果(实部和虚部)分配存储空间。 //将planes数组组合合并成一个多通道的数组complexI Mat planes[] = {Mat_(padded), Mat::zeros(padded.size(), CV_32F)}; Mat complexI; merge(planes, 2, complexI);

//【4】进行就地离散傅里叶变换 dft(complexI, complexI);

//【5】将复数转换为幅值，即=> log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2)) split(complexI, planes); // 将多通道数组complexI分离成几个单通道数组，planes[0] = Re(DFT(I), planes[1] = Im(DFT(I)) magnitude(planes[0], planes[1], planes[0]);// planes[0] = magnitude
Mat magnitudeImage = planes[0]; //【6】进行对数尺度(logarithmic scale)缩放 magnitudeImage += Scalar::all(1); log(magnitudeImage, magnitudeImage);//求自然对数 //【7】剪切和重分布幅度图象限 //若有奇数行或奇数列，进行频谱裁剪
magnitudeImage = magnitudeImage(Rect(0, 0, magnitudeImage.cols & -2, magnitudeImage.rows & -2)); //重新排列傅立叶图像中的象限，使得原点位于图像中心
int cx = magnitudeImage.cols/2; int cy = magnitudeImage.rows/2; Mat q0(magnitudeImage, Rect(0, 0, cx, cy)); // ROI区域的左上 Mat q1(magnitudeImage, Rect(cx, 0, cx, cy)); // ROI区域的右上 Mat q2(magnitudeImage, Rect(0, cy, cx, cy)); // ROI区域的左下 Mat q3(magnitudeImage, Rect(cx, cy, cx, cy)); // ROI区域的右下 //交换象限（左上与右下进行交换） Mat tmp;
q0.copyTo(tmp); q3.copyTo(q0); tmp.copyTo(q3); //交换象限（右上与左下进行交换） q1.copyTo(tmp);
q2.copyTo(q1); tmp.copyTo(q2); //【8】归一化，用0到1之间的浮点值将矩阵变换为可视的图像格式 normalize(magnitudeImage, magnitudeImage, 0, 1, NORM_MINMAX); //【9】显示效果图 imshow("频谱幅值", magnitudeImage);

滤波函数

方框滤波

Mat out; boxFilter(image,out,-1,Size(5,5)); imshow(out)

均值滤波blur函数

Mat dstImage; blur( srcImage, dstImage, Size(7, 7)); imshow( "均值滤波【效果图】" ,dstImage );

高斯滤波

GaussianBlur( image, out, Size( 5, 5 ), 0, 0 );

中值滤波medianBlur

medianBlur ( image, out, 7);

双边滤波bilateralFilter

bilateralFilter ( image, out, 25, 25*2, 25/2 );

膨胀、腐蚀

//进行膨胀操作 Mat element = getStructuringElement(MORPH_RECT, Size(15, 15)); Mat out; dilate(image, out, element); //进行腐蚀操作 Mat element = getStructuringElement(MORPH_RECT, Size(15, 15)); Mat dstImage; erode(srcImage, dstImage, element);

形态学

膨胀 //定义核 Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
//进行形态学操作 morphologyEx(image, image, MORPH_DILATE, element); 图像腐蚀 //定义核 Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
//进行形态学操作 morphologyEx(image, image, MORPH_ERODE, element);

开运算： morphologyEx(image, image, MORPH_OPEN, element); 闭运算： morphologyEx(image, image, MORPH_CLOSE, element);

形态学梯度运算： //定义核 Mat element = getStructuringElement(MORPH_RECT, Size(15, 15)); morphologyEx(image, image, MORPH_GRADIENT, element);

顶帽运算： morphologyEx(image, image, MORPH_TOPHAT, element); 黑帽运算： morphologyEx(image, image, MORPH_BLACKHAT, element);

漫水填充floodFill

Rect ccomp; floodFill(src, Point(50,300), Scalar(155, 255,55), &ccomp, Scalar(20, 20, 20),Scalar(20, 20, 20)); imshow("【效果图】",src);

resize函数

//进行尺寸调整操作 resize(tmpImage,dstImage1,Size( tmpImage.cols/2, tmpImage.rows/2 ),(0,0),(0,0),3); resize(tmpImage,dstImage2,Size( tmpImage.cols2, tmpImage.rows2 ),(0,0),(0,0),3);

图像金字塔

pyrUp函数

//进行向上取样操作 pyrUp( tmpImage, dstImage, Size( tmpImage.cols2, tmpImage.rows2 ) );

pyrUp函数

//进行向下取样操作

阈值函数

//调用阈值函数 threshold(g_grayImage,g_dstImage,g_nThresholdValue,255,g_nThresholdType); pyrDown( tmpImage, dstImage, Size( tmpImage.cols2, tmpImage.rows2 ) );