mirror of https://github.com/opencv/opencv.git
GSoC 2016 - Adding ALIASES for tutorial (#7041)
* GSoC 2016 - Adding toggle files to be used by tutorials. Add a toggle option for tutorials. * adds a button on the HTML tutorial pages to switch between blocks * the default option is for languages: one can write a block for C++ and another one for Python without re-writing the tutorial Add aliases to the doxyfile. * adding alises to make a link to previous and next tutorial. * adding alias to specify the toggle options in the tutorials index. * adding alias to add a youtube video directly from link. Add a sample tutorial (mat_mask_opertaions) using the developed aliases: * youtube alias * previous and next tutorial alias * buttons * languages info for tutorial table of content * code referances with snippets (and associated sample code files) * Removing the automatic ordering. Adding specific toggles for cpp, java and python. Move all the code to the footer / header and Doxyfile. Updating documentation.pull/7190/merge
Cartucho
8 years ago
committed by
Maksim Shabunin
parent
36b5abf6b7
commit
fcddfa4f86
13 changed files with 567 additions and 92 deletions
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#include <iostream> |
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/**
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* @function main |
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* @brief Main function |
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*/ |
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int main( void ) |
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{ |
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//! [hello_world]
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std::cout << "Hello World!"; |
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//! [hello_world]
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return 0; |
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} |
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import org.opencv.core.*; |
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import org.opencv.imgcodecs.Imgcodecs; |
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import org.opencv.imgproc.Imgproc; |
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import javax.swing.*; |
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import java.awt.Image; |
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import java.awt.image.BufferedImage; |
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import java.awt.image.DataBufferByte; |
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class MatMaskOperationsRun { |
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public void run() { |
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//! [laod_image]
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Mat I = Imgcodecs.imread("../data/lena.jpg"); |
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if(I.empty()) |
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System.out.println("Error opening image"); |
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//! [laod_image]
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Image img = toBufferedImage( I ); |
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displayImage("Input Image" , img, 0, 200 ); |
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double t = System.currentTimeMillis(); |
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Mat J = sharpen(I, new Mat()); |
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t = ((double)System.currentTimeMillis() - t)/1000; |
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System.out.println("Hand written function times passed in seconds: " + t); |
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Image img2 = toBufferedImage( J ); |
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displayImage("Output Image" , img2, 400, 400 ); |
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Mat K = new Mat(); |
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//![kern]
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Mat kern = new Mat( 3, 3, CvType.CV_8S ); |
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int row = 0, col = 0; |
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kern.put(row ,col, 0, -1, 0, -1, 5, -1, 0, -1, 0 ); |
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//![kern]
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System.out.println("kern = \n" + kern.dump()); |
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t = System.currentTimeMillis(); |
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//![filter2D]
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Imgproc.filter2D(I, K, I.depth(), kern ); |
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//![filter2D]
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t = ((double)System.currentTimeMillis() - t)/1000; |
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System.out.println("Built-in filter2D time passed in seconds: " + t); |
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Image img3 = toBufferedImage( J ); |
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displayImage("filter2D Output Image" , img3, 800, 400 ); |
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} |
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//! [basic_method]
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public static double saturateCastUchar(double x) { |
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return x > 255.0 ? 255.0 : (x < 0.0 ? 0.0 : x); |
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} |
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public Mat sharpen(Mat myImage, Mat Result) |
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{ |
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//! [8_bit]
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myImage.convertTo(myImage, CvType.CV_8U); |
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//! [8_bit]
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//! [create_channels]
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int nChannels = myImage.channels(); |
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Result.create(myImage.size(),myImage.type()); |
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//! [create_channels]
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//! [basic_method_loop]
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for(int j = 1 ; j < myImage.rows()-1; ++j) |
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{ |
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for(int i = 1 ; i < myImage.cols()-1; ++i) |
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{ |
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double sum[] = new double[nChannels]; |
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for(int k = 0; k < nChannels; ++k) { |
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double top = -myImage.get(j - 1, i)[k]; |
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double bottom = -myImage.get(j + 1, i)[k]; |
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double center = (5 * myImage.get(j, i)[k]); |
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double left = -myImage.get(j , i - 1)[k]; |
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double right = -myImage.get(j , i + 1)[k]; |
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sum[k] = saturateCastUchar(top + bottom + center + left + right); |
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} |
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Result.put(j, i, sum); |
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} |
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} |
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//! [basic_method_loop]
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//! [borders]
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Result.row(0).setTo(new Scalar(0)); |
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Result.row(Result.rows()-1).setTo(new Scalar(0)); |
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Result.col(0).setTo(new Scalar(0)); |
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Result.col(Result.cols()-1).setTo(new Scalar(0)); |
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//! [borders]
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return Result; |
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} |
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//! [basic_method]
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public Image toBufferedImage(Mat m) { |
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int type = BufferedImage.TYPE_BYTE_GRAY; |
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if ( m.channels() > 1 ) { |
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type = BufferedImage.TYPE_3BYTE_BGR; |
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} |
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int bufferSize = m.channels()*m.cols()*m.rows(); |
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byte [] b = new byte[bufferSize]; |
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m.get(0,0,b); // get all the pixels
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BufferedImage image = new BufferedImage(m.cols(),m.rows(), type); |
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final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData(); |
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System.arraycopy(b, 0, targetPixels, 0, b.length); |
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return image; |
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} |
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public void displayImage(String title, Image img, int x, int y) |
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{ |
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ImageIcon icon=new ImageIcon(img); |
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JFrame frame=new JFrame(title); |
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JLabel lbl=new JLabel(icon); |
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frame.add(lbl); |
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frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); |
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frame.pack(); |
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frame.setLocation(x, y); |
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frame.setVisible(true); |
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} |
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} |
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public class MatMaskOperations { |
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public static void main(String[] args) { |
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// Load the native library.
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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new MatMaskOperationsRun().run(); |
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} |
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} |
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@ -0,0 +1,9 @@ |
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public class Documentation { |
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public static void main (String[] args) { |
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//! [hello_world]
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System.out.println ("Hello World!"); |
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//! [hello_world]
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} |
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} |
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import time |
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import numpy as np |
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import cv2 |
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## [basic_method] |
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def sharpen(my_image): |
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my_image = cv2.cvtColor(my_image, cv2.CV_8U) |
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height, width, n_channels = my_image.shape |
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result = np.zeros(my_image.shape, my_image.dtype) |
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## [basic_method_loop] |
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for j in range (1, height-1): |
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for i in range (1, width-1): |
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for k in range (0, n_channels): |
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sum = 5 * my_image[j, i, k] - my_image[j + 1, i, k] - my_image[j - 1, i, k]\ |
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- my_image[j, i + 1, k] - my_image[j, i - 1, k]; |
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if sum > 255: |
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sum = 255 |
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if sum < 0: |
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sum = 0 |
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result[j, i, k] = sum |
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## [basic_method_loop] |
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return result |
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## [basic_method] |
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I = cv2.imread("../data/lena.jpg") |
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cv2.imshow('Input Image', I) |
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t = round(time.time()) |
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J = sharpen(I) |
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t = (time.time() - t)/1000 |
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print "Hand written function times passed in seconds: %s" % t |
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cv2.imshow('Output Image', J) |
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t = time.time() |
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## [kern] |
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kernel = np.array([ [0,-1,0], |
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[-1,5,-1], |
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[0,-1,0] ],np.float32) # kernel should be floating point type |
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## [kern] |
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## [filter2D] |
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K = cv2.filter2D(I, -1, kernel) # ddepth = -1, means destination image has depth same as input image. |
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## [filter2D] |
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t = (time.time() - t)/1000 |
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print "Built-in filter2D time passed in seconds: %s" % t |
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cv2.imshow('filter2D Output Image', K) |
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cv2.waitKey(0) |
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cv2.destroyAllWindows() |
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@ -0,0 +1,5 @@ |
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print('Not showing this text because it is outside the snippet') |
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## [hello_world] |
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print('Hello world!') |
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## [hello_world] |
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