1 Operations with images {#tutorial_mat_operations} 2 ====================== 3 4 Input/Output 5 ------------ 6 7 ### Images 8 9 Load an image from a file: 10 @code{.cpp} 11 Mat img = imread(filename) 12 @endcode 13 14 If you read a jpg file, a 3 channel image is created by default. If you need a grayscale image, use: 15 16 @code{.cpp} 17 Mat img = imread(filename, 0); 18 @endcode 19 20 @note format of the file is determined by its content (first few bytes) Save an image to a file: 21 22 @code{.cpp} 23 imwrite(filename, img); 24 @endcode 25 26 @note format of the file is determined by its extension. 27 28 @note use imdecode and imencode to read and write image from/to memory rather than a file. 29 30 Basic operations with images 31 ---------------------------- 32 33 ### Accessing pixel intensity values 34 35 In order to get pixel intensity value, you have to know the type of an image and the number of 36 channels. Here is an example for a single channel grey scale image (type 8UC1) and pixel coordinates 37 x and y: 38 @code{.cpp} 39 Scalar intensity = img.at<uchar>(y, x); 40 @endcode 41 intensity.val[0] contains a value from 0 to 255. Note the ordering of x and y. Since in OpenCV 42 images are represented by the same structure as matrices, we use the same convention for both 43 cases - the 0-based row index (or y-coordinate) goes first and the 0-based column index (or 44 x-coordinate) follows it. Alternatively, you can use the following notation: 45 @code{.cpp} 46 Scalar intensity = img.at<uchar>(Point(x, y)); 47 @endcode 48 Now let us consider a 3 channel image with BGR color ordering (the default format returned by 49 imread): 50 @code{.cpp} 51 Vec3b intensity = img.at<Vec3b>(y, x); 52 uchar blue = intensity.val[0]; 53 uchar green = intensity.val[1]; 54 uchar red = intensity.val[2]; 55 @endcode 56 You can use the same method for floating-point images (for example, you can get such an image by 57 running Sobel on a 3 channel image): 58 @code{.cpp} 59 Vec3f intensity = img.at<Vec3f>(y, x); 60 float blue = intensity.val[0]; 61 float green = intensity.val[1]; 62 float red = intensity.val[2]; 63 @endcode 64 The same method can be used to change pixel intensities: 65 @code{.cpp} 66 img.at<uchar>(y, x) = 128; 67 @endcode 68 There are functions in OpenCV, especially from calib3d module, such as projectPoints, that take an 69 array of 2D or 3D points in the form of Mat. Matrix should contain exactly one column, each row 70 corresponds to a point, matrix type should be 32FC2 or 32FC3 correspondingly. Such a matrix can be 71 easily constructed from `std::vector`: 72 @code{.cpp} 73 vector<Point2f> points; 74 //... fill the array 75 Mat pointsMat = Mat(points); 76 @endcode 77 One can access a point in this matrix using the same method Mat::at : 78 @code{.cpp} 79 Point2f point = pointsMat.at<Point2f>(i, 0); 80 @endcode 81 82 ### Memory management and reference counting 83 84 Mat is a structure that keeps matrix/image characteristics (rows and columns number, data type etc) 85 and a pointer to data. So nothing prevents us from having several instances of Mat corresponding to 86 the same data. A Mat keeps a reference count that tells if data has to be deallocated when a 87 particular instance of Mat is destroyed. Here is an example of creating two matrices without copying 88 data: 89 @code{.cpp} 90 std::vector<Point3f> points; 91 // .. fill the array 92 Mat pointsMat = Mat(points).reshape(1); 93 @endcode 94 As a result we get a 32FC1 matrix with 3 columns instead of 32FC3 matrix with 1 column. pointsMat 95 uses data from points and will not deallocate the memory when destroyed. In this particular 96 instance, however, developer has to make sure that lifetime of points is longer than of pointsMat. 97 If we need to copy the data, this is done using, for example, cv::Mat::copyTo or cv::Mat::clone: 98 @code{.cpp} 99 Mat img = imread("image.jpg"); 100 Mat img1 = img.clone(); 101 @endcode 102 To the contrary with C API where an output image had to be created by developer, an empty output Mat 103 can be supplied to each function. Each implementation calls Mat::create for a destination matrix. 104 This method allocates data for a matrix if it is empty. If it is not empty and has the correct size 105 and type, the method does nothing. If, however, size or type are different from input arguments, the 106 data is deallocated (and lost) and a new data is allocated. For example: 107 @code{.cpp} 108 Mat img = imread("image.jpg"); 109 Mat sobelx; 110 Sobel(img, sobelx, CV_32F, 1, 0); 111 @endcode 112 113 ### Primitive operations 114 115 There is a number of convenient operators defined on a matrix. For example, here is how we can make 116 a black image from an existing greyscale image \`img\`: 117 @code{.cpp} 118 img = Scalar(0); 119 @endcode 120 Selecting a region of interest: 121 @code{.cpp} 122 Rect r(10, 10, 100, 100); 123 Mat smallImg = img(r); 124 @endcode 125 A convertion from Mat to C API data structures: 126 @code{.cpp} 127 Mat img = imread("image.jpg"); 128 IplImage img1 = img; 129 CvMat m = img; 130 @endcode 131 132 Note that there is no data copying here. 133 134 Conversion from color to grey scale: 135 @code{.cpp} 136 Mat img = imread("image.jpg"); // loading a 8UC3 image 137 Mat grey; 138 cvtColor(img, grey, COLOR_BGR2GRAY); 139 @endcode 140 Change image type from 8UC1 to 32FC1: 141 @code{.cpp} 142 src.convertTo(dst, CV_32F); 143 @endcode 144 145 ### Visualizing images 146 147 It is very useful to see intermediate results of your algorithm during development process. OpenCV 148 provides a convenient way of visualizing images. A 8U image can be shown using: 149 @code{.cpp} 150 Mat img = imread("image.jpg"); 151 152 namedWindow("image", WINDOW_AUTOSIZE); 153 imshow("image", img); 154 waitKey(); 155 @endcode 156 157 A call to waitKey() starts a message passing cycle that waits for a key stroke in the "image" 158 window. A 32F image needs to be converted to 8U type. For example: 159 @code{.cpp} 160 Mat img = imread("image.jpg"); 161 Mat grey; 162 cvtColor(img, grey, COLOR_BGR2GRAY); 163 164 Mat sobelx; 165 Sobel(grey, sobelx, CV_32F, 1, 0); 166 167 double minVal, maxVal; 168 minMaxLoc(sobelx, &minVal, &maxVal); //find minimum and maximum intensities 169 Mat draw; 170 sobelx.convertTo(draw, CV_8U, 255.0/(maxVal - minVal), -minVal * 255.0/(maxVal - minVal)); 171 172 namedWindow("image", WINDOW_AUTOSIZE); 173 imshow("image", draw); 174 waitKey(); 175 @endcode 176
