OpenCV Tutorial 1: Mat - The basic Image Container

Color Space In OpenCV

There are, however, many other color systems each with their own advantages:

• RGB is the most common as our eyes use something similar, our display systems also compose colors using these.
• The HSV and HLS decompose colors into their hue, saturation and value/luminance components, which is a more natural way for us to describe colors. You might, for example, dismiss the last component, making your algorithm less sensible to the light conditions of the input image.
• YCrCb is used by the popular JPEG image format.
• CIE L*a*b* is a perceptually uniform color space, which comes handy if you need to measure the distance of a given color to another color.

Sample Code

	// create by using the constructor
	Mat M(2,2, CV_8UC3, Scalar(0,0,255));
	cout << "M = " << endl << " " << M << endl << endl;
	// create by using the create function()
	M.create(4,4, CV_8UC(2));
	cout << "M = "<< endl << " " << M << endl << endl;
	// create multidimensional matrices
	int sz[3] = {2,2,2};
	Mat L(3,sz, CV_8UC(1), Scalar::all(0));
	// Cannot print via operator <<
	// Create using MATLAB style eye, ones or zero matrix
	Mat E = Mat::eye(4, 4, CV_64F);
	cout << "E = " << endl << " " << E << endl << endl;
	Mat O = Mat::ones(2, 2, CV_32F);
	cout << "O = " << endl << " " << O << endl << endl;
	Mat Z = Mat::zeros(3,3, CV_8UC1);
	cout << "Z = " << endl << " " << Z << endl << endl;
	// create a 3x3 double-precision identity matrix
	Mat C = (Mat_<double>(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
	cout << "C = " << endl << " " << C << endl << endl;
	Mat RowClone = C.row(1).clone();
	cout << "RowClone = " << endl << " " << RowClone << endl << endl;
	// Fill a matrix with random values
	Mat R = Mat(3, 2, CV_8UC3);
	randu(R, Scalar::all(0), Scalar::all(255));
	// Demonstrate the output formating options
	cout << "R (default) = " << endl << R << endl << endl;
	cout << "R (python) = " << endl << format(R,"python") << endl << endl;
	cout << "R (numpy) = " << endl << format(R,"numpy" ) << endl << endl;
	cout << "R (csv) = " << endl << format(R,"csv" ) << endl << endl;
	cout << "R (c) = " << endl << format(R,"C" ) << endl << endl;
	Point2f P(5, 1);
	cout << "Point (2D) = " << P << endl << endl;
	Point3f P3f(2, 6, 7);
	cout << "Point (3D) = " << P3f << endl << endl;
	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;
	vector<Point2f> vPoints(20);
	for (size_t E = 0; E < vPoints.size(); ++E)
	vPoints[E] = Point2f((float)(E * 5), (float)(E % 7));
	cout << "A vector of 2D Points = " << vPoints << endl << endl;

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