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DataType : 将C++数据类型转换为对应的opencv数据类型
enum { CV_8U=0, CV_8S=1, CV_16U=2, CV_16S=3, CV_32S=4, CV_32F=5, CV_64F=6 };
// allocates a 30x40 floating-point matrix // CV_32F
Mat A(30, 40, DataType<float>::type);
Mat B = Mat_<std::complex<double> >(3, 3);
// the statement below will print 6, 2 /*, that is depth == CV_64F, channels == 2*/ CV_64FC2
cout << B.depth() << ", " << B.channels() << endl;
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Point_ 二维点坐标(x,y)
typedef Point_<int> Point2i;
typedef Point2i Point;
typedef Point_<float> Point2f;
typedef Point_<double> Point2d;
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Point3_ 3维点坐标(x,y,z)
typedef Point3_<int> Point3i;
typedef Point3_<float> Point3f;
typedef Point3_<double> Point3d;
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Size_ 尺寸(width, height)
typedef Size_<int> Size2i;
typedef Size2i Size;
typedef Size_<float> Size2f;
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Rect_ 矩形区域(x,y,width,height) ，(x,y)左上角坐标, 范围[x, x + width), [y, y + height)
rect = rect ± point //矩形偏移(shifting a rectangle by a certain offset)
rect = rect ± size //改变大小(expanding or shrinking a rectangle by a certain amount)
rect += point, rect -= point, rect += size, rect -= size //(augmenting operations)
rect = rect1 & rect2 //矩形交集(rectangle intersection)
rect = rect1 | rect2 //包含r1r2的最小矩形(minimum area rectangle containing rect2 and rect3 )
rect &= rect1, rect |= rect1 //(and the corresponding augmenting operations)
rect == rect1, rect != rect1 //(rectangle comparison)
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RotatedRect 旋转矩形
RotatedRect::RotatedRect(const Point2f& center, const Size2f& size, float angle)// 中心点（不是左上角坐标），尺寸，旋转角度
RotatedRect rRect = RotatedRect(Point2f(100,100), Size2f(100,50), 30);
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Matx 小矩阵
template<typename_Tp, int m, int n> class Matx {...};
typedef Matx<float, 1, 2> Matx12f;
typedef Matx<double, 1, 2> Matx12d;
...
typedef Matx<float, 1, 6> Matx16f;
typedef Matx<double, 1, 6> Matx16d;
typedef Matx<float, 2, 1> Matx21f;
typedef Matx<double, 2, 1> Matx21d;
...
typedef Matx<float, 6, 1> Matx61f;
typedef Matx<double, 6, 1> Matx61d;
typedef Matx<float, 2, 2> Matx22f;
typedef Matx<double, 2, 2> Matx22d;
...
typedef Matx<float, 6, 6> Matx66f;
typedef Matx<double, 6, 6> Matx66d;
Matx33f m(1, 2, 3,
4, 5, 6,
7, 8, 9);
cout << sum(Mat(m*m.t())) << endl;//Matx转化为Mat
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Vec 短向量，基于Matx
template<typename_Tp, int n> class Vec : public Matx<_Tp, n, 1> {...};
typedef Vec<uchar, 2> Vec2b;
typedef Vec<uchar, 3> Vec3b;
typedef Vec<uchar, 4> Vec4b;
typedef Vec<short, 2> Vec2s;
typedef Vec<short, 3> Vec3s;
typedef Vec<short, 4> Vec4s;
typedef Vec<int, 2> Vec2i;
typedef Vec<int, 3> Vec3i;
typedef Vec<int, 4> Vec4i;
typedef Vec<float, 2> Vec2f;
typedef Vec<float, 3> Vec3f;
typedef Vec<float, 4> Vec4f;
typedef Vec<float, 6> Vec6f;
typedef Vec<double, 2> Vec2d;
typedef Vec<double, 3> Vec3d;
typedef Vec<double, 4> Vec4d;
typedef Vec<double, 6> Vec6d;
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Scalar_ 四维向量
template<typename_Tp> class Scalar_: public Vec<_Tp, 4> { ... };
typedef Scalar_<double> Scalar;
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Range 范围，（start, end）
Mat m(300,300,CV32F);
Mat part = m(Range::all(), Range(20, 200)); // 相当于matlab的m(:, 20 : 199)对于自定义的函数，可以用如下方法来支持Range
void my_function(..., const Range& r, ....)
{
if(r == Range::all()) {
// process all the data, 使用全部数据
}
else {
// process [r.start, r.end)，根据r中定义, 处理数据 start : end - 1
}
}
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Mat 矩阵结构
•M.data 数据区域的指针
•M.dims 矩阵维度
•M.sizes 维度
•M.elemSize() 每个元素占的字节空间大小，与元素类型相关，如CV_8U
•M.step[] 用来计算元素地址， M.step[i] 表示所有比i大的维度所占空间大小
M.step[i] >= M.step[i+1]*M.sizes[i+1]; //这里大于是因为数据空间可能有空白•2-dimensional matrices are stored row-by-row
•3-dimensional matrices are stored plane-by-plane
addr(M(i(0),...,i(M.dims−1))) = M.data + M.step[0] ∗ i(0)+ M.step[1] ∗ i(1)+ ... + M.step[M.dims − 1] ∗ i(M.dims−1)创建数组：
// make a 7x7 complex matrix filled with 1+3j.
Mat M(7,7,CV_32FC2,Scalar(1,3));
// and now turn M to a 100x60 15-channel 8-bit matrix.
// The old content will be deallocated
M.create(100,60,CV_8UC(15));
// create a 100x100x100 8-bit array
int sz[] = {100, 100, 100};
Mat bigCube(3, sz, CV_8U, Scalar::all(0));创建特殊矩阵：
•diag
•ones
•zeros
•eye
属性相关：
•rows
•cols
•begin
•end
•at
•size
•depth
•type
•elemSize
•total
矩阵操作：
•t
•inv
•mul
•cross
•dot
•reshape
•resize
•reserve
•push_back
•pop_back
赋值相关：
•clone
•copyTo
•convertTo
•assignTo
•setTo
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InputArray
OutputArray
//Do not explicitly create InputArray, OutputArray instancesvoid myAffineTransform(InputArray_src, OutputArray_dst, InputArray_m)
{
// get Mat headers for input arrays. This is O(1) operation,
// unless_src and/or_m are matrix expressions.
Mat src =_src.getMat(), m =_m.getMat();
CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) );
// [re]create the output array so that it has the proper size and type.
// In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize.
_dst.create(src.size(), src.type());
Mat dst =_dst.getMat();
for( int i = 0; i < src.rows; i++ )
for( int j = 0; j < src.cols; j++ )
{
Point2f pt = src.at<Point2f>(i, j);
dst.at<Point2f>(i, j) = Point2f(m.at<float>(0, 0)*pt.x +
m.at<float>(0, 1)*pt.y +
m.at<float>(0, 2),
m.at<float>(1, 0)*pt.x +
m.at<float>(1, 1)*pt.y +
m.at<float>(1, 2));
}
}
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原文地址：http://www.cnblogs.com/yuxinJ/p/4499644.html
