opencv学习

编译

gcc Test.c -o Test `pkg-config --cflags --libs opencv`

判断是否正确读入的方法：

if( argc != 2 || !(src=imread(argv[1], 1)).data )
　　return -1;

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if( src.empty() )

　　{ return -1; }

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颜色通道转换：

cvtColor(src, gray, COLOR_BGR2GRAY);

cvtColor(src, hsv, COLOR_BGR2HSV);

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Mat属性：

mat.channels();//通道个数

mat.depth();//每个像素用多少位表示

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Mat的构造函数和工厂函数：

Mat M(2,2, CV_8UC3, Scalar(0,0,255));

M.create(4,4, CV_8UC(2));//You cannot initialize the matrix values with this construction. It will only reallocate its matrix data memory if the new size will not fit into the old one.

Mat E = Mat::eye(4,4, CV_64F);

Mat O = Mat::ones(2,2, CV_32F);

Mat Z = Mat::zeros(3,3, CV_8UC1);

Mat C = (Mat_<double>(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0); //For small matrices you may use comma separated initializers

randu(R, Scalar::all(0), Scalar::all(255)); //need to give the lower and upper value for the random values

vector<float> v; v.push_back( (float)CV_PI);  v.push_back(2);  v.push_back(3.01f); cout << "Vector of floats via Mat = " << Mat(v) << endl << endl; //[3.1415;2;3.01]

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Region of Interest：

Mat D(A, Rect(10, 10, 100, 100));

Mat E = A(Range::all(), Range(1,3));

Mat RowClone = C.row(1).clone();//Create a new header for an existing Mat object

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specify the data type to use for storing the elements and the number of channels per matrix point.

CV_[The number of bits per item][Signed or Unsigned][Type Prefix]C[The channel number]

If you need more, you can create the type with the upper macro:

int sz[3] = {2,2,2};

Mat L(3,sz, CV_8UC(1), Scalar::all(0)); //create a matrix with more than two dimensions

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输出格式化 Output formatting

cout << "R (default) = " << endl << R << endl << endl;
cout << "R (python) = " << endl << format(R,"python") << endl << endl;
cout << "R (csv) = " << endl << format(R,"csv" ) << endl << endl;
cout << "R (numpy) = " << endl << format(R,"numpy" ) << endl << endl;
cout << "R (c) = " << endl << format(R,"C" ) << endl << endl;

//参考http://docs.opencv.org/2.4/doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.html#output-formatting

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遍历Mat中的像素有三种方法，简单记录，具体查看http://docs.opencv.org/2.4.13/doc/tutorials/core/how_to_scan_images/how_to_scan_images.html

//第一种方法
for( i = 0; i < nRows; ++i)
{
　　p = I.ptr<uchar>(i);
　　for ( j = 0; j < nCols; ++j)
　　{
　　　　p[j] = table[p[j]]; //其中p[j]就是像素值
　　}
}

//第二种方法
switch(channels)
{
case 1:
    {
        MatIterator_<uchar> it, end;
        for( it = I.begin<uchar>(), end = I.end<uchar>(); it != end; ++it)
            *it = table[*it];
        break;
    }
case 3:
    {
        MatIterator_<Vec3b> it, end;
        for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it)
        {
            (*it)[0] = table[(*it)[0]];
            (*it)[1] = table[(*it)[1]];
            (*it)[2] = table[(*it)[2]];
        }
    }
}

//第三种方法
switch(channels)
{
case 1:
    {
        for( int i = 0; i < I.rows; ++i)
            for( int j = 0; j < I.cols; ++j )
                I.at<uchar>(i,j) = table[I.at<uchar>(i,j)];
        break;
    }
case 3:
    {
     Mat_<Vec3b> _I = I;

     for( int i = 0; i < I.rows; ++i)
        for( int j = 0; j < I.cols; ++j )
           {
               _I(i,j)[0] = table[_I(i,j)[0]];
               _I(i,j)[1] = table[_I(i,j)[1]];
               _I(i,j)[2] = table[_I(i,j)[2]];
        }
     I = _I;
     break;
    }
}

//最后一种方法使用LUT函数
Mat lookUpTable(1, 256, CV_8U);
uchar* p = lookUpTable.data;
for( int i = 0; i < 256; ++i)
    p[i] = table[i];
LUT(I, lookUpTable, J);

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Mat数据类型和像素值的放缩：

Mat a; a.convertTo(dst, data_type, alpha, beta); 

　　

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Mat基本运算

矩阵相减，subtract(src2, src1, dst, noArray(), DataType<sift_wt>::type);  //其中一个是Mask

通道分割，vector<Mat> bgr_planes; split(src, bgr_planes);

矩阵转置，transpose(src, dst);

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数学函数

cv::hal::fastAtan2(Y, X, Ori, len, true); //计算梯度方向
cv::hal::magnitude32f(X, Y, Mag, len); //计算幅值
cv::hal::exp32f(W, W, len); //计算权重