opencv 基本数据结构

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;

 Point_  二维点坐标(x,y)

typedef Point_<int> Point2i;
typedef Point2i Point;
typedef Point_<float> Point2f;
typedef Point_<double> Point2d;

Point3_ 3维点坐标(x,y,z)

typedef Point3_<int> Point3i;
typedef Point3_<float> Point3f;
typedef Point3_<double> Point3d;

Size_  尺寸(width, height)

typedef Size_<int> Size2i;
typedef Size2i Size;
typedef Size_<float> Size2f;

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)

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);

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

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;

Scalar_  四维向量

template<typename_Tp> class Scalar_: public Vec<_Tp, 4> { ... };
typedef Scalar_<double> Scalar;

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 
　　}
}

 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

 

 

InputArray
OutputArray

//Do not explicitly create InputArray, OutputArray instances
void 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));
　　}
}

SparseMat 稀疏矩阵

Algorithm  实现一个算法的框架