mirror of https://github.com/opencv/opencv.git
Extending template_matching tutorial with Java (#8043)
* Extending template_matching tutorial with Java * adding mask to java version of the tutorial * adding the python toggle and code * updating table of content * adding py and java to table of content * adding mask to python * going back to markdown with duplicated text * non duplicated textpull/1539/merge
Cartucho
8 years ago
committed by
Maksim Shabunin
parent
3b669149d2
commit
2055bcc807
6 changed files with 479 additions and 103 deletions
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import org.opencv.core.*; |
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import org.opencv.core.Point; |
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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 javax.swing.event.ChangeEvent; |
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import javax.swing.event.ChangeListener; |
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import java.awt.*; |
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import java.awt.image.BufferedImage; |
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import java.awt.image.DataBufferByte; |
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import java.util.*; |
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class MatchTemplateDemoRun implements ChangeListener{ |
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//! [declare]
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/// Global Variables
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Boolean use_mask = false; |
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Mat img = new Mat(), templ = new Mat(); |
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Mat mask = new Mat(); |
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int match_method; |
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JLabel imgDisplay = new JLabel(), resultDisplay = new JLabel(); |
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//! [declare]
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public void run(String[] args) { |
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if (args.length < 2) |
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{ |
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System.out.println("Not enough parameters"); |
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System.out.println("Program arguments:\n<image_name> <template_name> [<mask_name>]"); |
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System.exit(-1); |
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} |
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//! [load_image]
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/// Load image and template
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img = Imgcodecs.imread( args[0], Imgcodecs.IMREAD_COLOR ); |
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templ = Imgcodecs.imread( args[1], Imgcodecs.IMREAD_COLOR ); |
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//! [load_image]
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if(args.length > 2) { |
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use_mask = true; |
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mask = Imgcodecs.imread( args[2], Imgcodecs.IMREAD_COLOR ); |
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} |
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if(img.empty() || templ.empty() || (use_mask && mask.empty())) |
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{ |
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System.out.println("Can't read one of the images"); |
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System.exit(-1); |
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} |
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matchingMethod(); |
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createJFrame(); |
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} |
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private void matchingMethod() { |
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Mat result = new Mat(); |
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//! [copy_source]
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/// Source image to display
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Mat img_display = new Mat(); |
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img.copyTo( img_display ); |
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//! [copy_source]
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//! [create_result_matrix]
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/// Create the result matrix
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int result_cols = img.cols() - templ.cols() + 1; |
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int result_rows = img.rows() - templ.rows() + 1; |
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result.create( result_rows, result_cols, CvType.CV_32FC1 ); |
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//! [create_result_matrix]
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//! [match_template]
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/// Do the Matching and Normalize
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Boolean method_accepts_mask = (Imgproc.TM_SQDIFF == match_method || |
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match_method == Imgproc.TM_CCORR_NORMED); |
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if (use_mask && method_accepts_mask) |
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{ Imgproc.matchTemplate( img, templ, result, match_method, mask); } |
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else |
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{ Imgproc.matchTemplate( img, templ, result, match_method); } |
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//! [match_template]
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//! [normalize]
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Core.normalize( result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat() ); |
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//! [normalize]
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//! [best_match]
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/// Localizing the best match with minMaxLoc
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double minVal; double maxVal; |
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Point matchLoc; |
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Core.MinMaxLocResult mmr = Core.minMaxLoc( result ); |
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//! [best_match]
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//! [match_loc]
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/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values.
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// For all the other methods, the higher the better
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if( match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED ) |
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{ matchLoc = mmr.minLoc; } |
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else |
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{ matchLoc = mmr.maxLoc; } |
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//! [match_loc]
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//! [imshow]
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/// Show me what you got
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Imgproc.rectangle(img_display, matchLoc, new Point(matchLoc.x + templ.cols(), |
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matchLoc.y + templ.rows()), new Scalar(0, 0, 0), 2, 8, 0); |
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Imgproc.rectangle(result, matchLoc, new Point(matchLoc.x + templ.cols(), |
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matchLoc.y + templ.rows()), new Scalar(0, 0, 0), 2, 8, 0); |
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Image tmpImg = toBufferedImage(img_display); |
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ImageIcon icon = new ImageIcon(tmpImg); |
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imgDisplay.setIcon(icon); |
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result.convertTo(result, CvType.CV_8UC1, 255.0); |
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tmpImg = toBufferedImage(result); |
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icon = new ImageIcon(tmpImg); |
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resultDisplay.setIcon(icon); |
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//! [imshow]
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} |
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public void stateChanged(ChangeEvent e) { |
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JSlider source = (JSlider) e.getSource(); |
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if (!source.getValueIsAdjusting()) { |
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match_method = (int)source.getValue(); |
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matchingMethod(); |
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} |
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} |
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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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private void createJFrame() { |
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String title = "Source image; Control; Result image"; |
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JFrame frame = new JFrame(title); |
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frame.setLayout(new GridLayout(2, 2)); |
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frame.add(imgDisplay); |
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//! [create_trackbar]
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int min = 0, max = 5; |
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JSlider slider = new JSlider(JSlider.VERTICAL, min, max, match_method); |
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//! [create_trackbar]
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slider.setPaintTicks(true); |
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slider.setPaintLabels(true); |
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// Set the spacing for the minor tick mark
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slider.setMinorTickSpacing(1); |
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// Customizing the labels
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Hashtable labelTable = new Hashtable(); |
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labelTable.put( new Integer( 0 ), new JLabel("0 - SQDIFF") ); |
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labelTable.put( new Integer( 1 ), new JLabel("1 - SQDIFF NORMED") ); |
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labelTable.put( new Integer( 2 ), new JLabel("2 - TM CCORR") ); |
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labelTable.put( new Integer( 3 ), new JLabel("3 - TM CCORR NORMED") ); |
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labelTable.put( new Integer( 4 ), new JLabel("4 - TM COEFF") ); |
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labelTable.put( new Integer( 5 ), new JLabel("5 - TM COEFF NORMED : (Method)") ); |
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slider.setLabelTable( labelTable ); |
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slider.addChangeListener(this); |
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frame.add(slider); |
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frame.add(resultDisplay); |
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frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); |
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frame.pack(); |
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frame.setVisible(true); |
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} |
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} |
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public class MatchTemplateDemo |
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{ |
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public static void main(String[] args) { |
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// load the native OpenCV library
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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// run code
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new MatchTemplateDemoRun().run(args); |
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} |
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} |
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@ -0,0 +1,96 @@ |
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import sys |
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import cv2 |
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## [global_variables] |
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use_mask = False |
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img = None |
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templ = None |
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mask = None |
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image_window = "Source Image" |
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result_window = "Result window" |
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match_method = 0 |
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max_Trackbar = 5 |
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## [global_variables] |
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def main(argv): |
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if (len(sys.argv) < 3): |
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print 'Not enough parameters' |
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print 'Usage:\nmatch_template_demo.py <image_name> <template_name> [<mask_name>]' |
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return -1 |
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## [load_image] |
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global img |
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global templ |
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img = cv2.imread(sys.argv[1], cv2.IMREAD_COLOR) |
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templ = cv2.imread(sys.argv[2], cv2.IMREAD_COLOR) |
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if (len(sys.argv) > 3): |
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global use_mask |
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use_mask = True |
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global mask |
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mask = cv2.imread( sys.argv[3], cv2.IMREAD_COLOR ) |
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if ((img is None) or (templ is None) or (use_mask and (mask is None))): |
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print 'Can\'t read one of the images' |
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return -1 |
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## [load_image] |
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## [create_windows] |
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cv2.namedWindow( image_window, cv2.WINDOW_AUTOSIZE ) |
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cv2.namedWindow( result_window, cv2.WINDOW_AUTOSIZE ) |
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## [create_windows] |
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## [create_trackbar] |
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trackbar_label = 'Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED' |
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cv2.createTrackbar( trackbar_label, image_window, match_method, max_Trackbar, MatchingMethod ) |
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## [create_trackbar] |
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MatchingMethod(match_method) |
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## [wait_key] |
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cv2.waitKey(0) |
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return 0 |
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## [wait_key] |
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def MatchingMethod(param): |
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global match_method |
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match_method = param |
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## [copy_source] |
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img_display = img.copy() |
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## [copy_source] |
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## [match_template] |
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method_accepts_mask = (cv2.TM_SQDIFF == match_method or match_method == cv2.TM_CCORR_NORMED) |
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if (use_mask and method_accepts_mask): |
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result = cv2.matchTemplate(img, templ, match_method, None, mask) |
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else: |
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result = cv2.matchTemplate(img, templ, match_method) |
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## [match_template] |
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## [normalize] |
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cv2.normalize( result, result, 0, 1, cv2.NORM_MINMAX, -1 ) |
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## [normalize] |
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## [best_match] |
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minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(result, None) |
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## [best_match] |
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## [match_loc] |
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if (match_method == cv2.TM_SQDIFF or match_method == cv2.TM_SQDIFF_NORMED): |
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matchLoc = minLoc |
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else: |
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matchLoc = maxLoc |
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## [match_loc] |
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## [imshow] |
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cv2.rectangle(img_display, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 ) |
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cv2.rectangle(result, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 ) |
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cv2.imshow(image_window, img_display) |
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cv2.imshow(result_window, result) |
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## [imshow] |
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pass |
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if __name__ == "__main__": |
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main(sys.argv[1:]) |
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