Examples of Mat

• org.opencv.core.Mat
  code:

  public:

  //... a lot of methods......

  / *! includes several bit-fields:

  - the magic signature

  - continuity flag

  - depth

  - number of channels

  • /

  int flags;

  //! the array dimensionality, >= 2

  int dims;

  //! the number of rows and columns or (-1, -1) when the array has more than 2 dimensions

  int rows, cols;

  //! pointer to the data

  uchar* data;

  //! pointer to the reference counter;

  // when array points to user-allocated data, the pointer is NULL

  int* refcount;

  // other members...

  };

  The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms may be better stored in a SparseMat). The data layout of the array

  M is defined by the array M.step[], so that the address of element (i_0,...,i_(M.dims-1)), where 0 <= i_k<M.size[k], is computed as:

  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)

  In case of a 2-dimensional array, the above formula is reduced to:

  addr(M_(i,j)) = M.data + M.step[0]*i + M.step[1]*j

  Note that M.step[i] >= M.step[i+1] (in fact, M.step[i] >= M.step[i+1]*M.size[i+1]). This means that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize().

  So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV 1.x. It is also compatible with the majority of dense array types from the standard toolkits and SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this compatibility, it is possible to make a Mat header for user-allocated data and process it in-place using OpenCV functions.

  There are many different ways to create a Mat object. The most popular options are listed below:

  • Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) constructor. A new array of the specified size and type is allocated. type has the same meaning as in the cvCreateMat method.

  For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 means a 2-channel (complex) floating-point array, and so on.

  // C++ code:

  // 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));

  As noted in the introduction to this chapter, create() allocates only a new array when the shape or type of the current array are different from the specified ones.

  • Create a multi-dimensional array:

  // C++ code:

  // create a 100x100x100 8-bit array

  int sz[] = {100, 100, 100};

  Mat bigCube(3, sz, CV_8U, Scalar.all(0));

  It passes the number of dimensions =1 to the Mat constructor but the created array will be 2-dimensional with the number of columns set to 1. So, Mat.dims is always >= 2 (can also be 0 when the array is empty).

  • Use a copy constructor or assignment operator where there can be an array or expression on the right side (see below). As noted in the introduction, the array assignment is an O(1) operation because it only copies the header and increases the reference counter. The Mat.clone() method can be used to get a full (deep) copy of the array when you need it.
  • Construct a header for a part of another array. It can be a single row, single column, several rows, several columns, rectangular region in the array (called a *minor* in algebra) or a diagonal. Such operations are also O(1) because the new header references the same data. You can actually modify a part of the array using this feature, for example:

  // C++ code:

  // add the 5-th row, multiplied by 3 to the 3rd row

  M.row(3) = M.row(3) + M.row(5)*3;

  // now copy the 7-th column to the 1-st column

  // M.col(1) = M.col(7); // this will not work

  Mat M1 = M.col(1);

  M.col(7).copyTo(M1);

  // create a new 320x240 image

  Mat img(Size(320,240),CV_8UC3);

  // select a ROI

  Mat roi(img, Rect(10,10,100,100));

  // fill the ROI with (0,255,0) (which is green in RGB space);

  // the original 320x240 image will be modified

  roi = Scalar(0,255,0);

  Due to the additional datastart and dataend members, it is possible to compute a relative sub-array position in the main *container* array using locateROI():

  // C++ code:

  Mat A = Mat.eye(10, 10, CV_32S);

  // extracts A columns, 1 (inclusive) to 3 (exclusive).

  Mat B = A(Range.all(), Range(1, 3));

  // extracts B rows, 5 (inclusive) to 9 (exclusive).

  // that is, C ~ A(Range(5, 9), Range(1, 3))

  Mat C = B(Range(5, 9), Range.all());

  Size size; Point ofs;

  C.locateROI(size, ofs);

  // size will be (width=10,height=10) and the ofs will be (x=1, y=5)

  As in case of whole matrices, if you need a deep copy, use the clone() method of the extracted sub-matrices.

  • Make a header for user-allocated data. It can be useful to do the following:
  • Process "foreign" data using OpenCV (for example, when you implement a DirectShow* filter or a processing module for gstreamer, and so on). For example:

  // C++ code:

  void process_video_frame(const unsigned char* pixels,

  int width, int height, int step)

  Mat img(height, width, CV_8UC3, pixels, step);

  GaussianBlur(img, img, Size(7,7), 1.5, 1.5);

  • Quickly initialize small matrices and/or get a super-fast element access.

  // C++ code:

  double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};

  Mat M = Mat(3, 3, CV_64F, m).inv();

  Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and IplImage to Mat. For this purpose, there are special constructors taking pointers to CvMat or IplImage and the optional flag indicating whether to copy the data or not.

  Backward conversion from Mat to CvMat or IplImage is provided via cast operators Mat.operator CvMat() const and Mat.operator IplImage(). The operators do NOT copy the data.

  // C++ code:

  IplImage* img = cvLoadImage("greatwave.jpg", 1);

  Mat mtx(img); // convert IplImage* -> Mat

  CvMat oldmat=mtx; // convert Mat -> CvMat

  CV_Assert(oldmat.cols == img->width && oldmat.rows == img->height &&

  oldmat.data.ptr == (uchar*)img->imageData && oldmat.step == img->widthStep);

  • Use MATLAB-style array initializers, zeros(), ones(), eye(), for example:

  // C++ code:

  // create a double-precision identity martix and add it to M.

  M += Mat.eye(M.rows, M.cols, CV_64F);

  • Use a comma-separated initializer:

  // C++ code:

  // create a 3x3 double-precision identity matrix

  Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);

  With this approach, you first call a constructor of the "Mat_" class with the proper parameters, and then you just put << operator followed by comma-separated values that can be constants, variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation errors.

  Once the array is created, it is automatically managed via a reference-counting mechanism. If the array header is built on top of user-allocated data, you should handle the data by yourself. The array data is deallocated when no one points to it. If you want to release the data pointed by a array header before the array destructor is called, use Mat.release().

  The next important thing to learn about the array class is element access. This manual already described how to compute an address of each array element. Normally, you are not required to use the formula directly in the code. If you know the array element type (which can be retrieved using the method Mat.type()), you can access the elementM_(ij) of a 2-dimensional array as:

  // C++ code:

  M.at(i,j) += 1.f;

  assuming that M is a double-precision floating-point array. There are several variants of the method at for a different number of dimensions.

  If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to the row first, and then just use the plain C operator [] :

  // C++ code:

  // compute sum of positive matrix elements

  // (assuming that M isa double-precision matrix)

  double sum=0;

  for(int i = 0; i < M.rows; i++)

  const double* Mi = M.ptr(i);

  for(int j = 0; j < M.cols; j++)

  sum += std.max(Mi[j], 0.);

  Some operations, like the one above, do not actually depend on the array shape. They just process elements of an array one by one (or elements from multiple arrays that have the same coordinates, for example, array addition). Such operations are called *element-wise*. It makes sense to check whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If yes, process them as a long single row:

  // compute the sum of positive matrix elements, optimized variant

  double sum=0;

  int cols = M.cols, rows = M.rows;

  if(M.isContinuous())

  cols *= rows;

  rows = 1;

  for(int i = 0; i < rows; i++)

  const double* Mi = M.ptr(i);

  for(int j = 0; j < cols; j++)

  sum += std.max(Mi[j], 0.);

  In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is smaller, which is especially noticeable in case of small matrices.

  Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows:

  // C++ code:

  // compute sum of positive matrix elements, iterator-based variant

  double sum=0;

  MatConstIterator_ it = M.begin(), it_end = M.end();

  for(; it != it_end; ++it)

  sum += std.max(*it, 0.);

  The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, including std.sort().

  Note:

  • An example demonstrating the serial out capabilities of cv.Mat can be found at opencv_source_code/samples/cpp/cout_mat.cpp

  @see org.opencv.core.Mat

Examples of org.opencv.core.Mat