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Merge pull request #9049 from Cartucho:improve_mask_tutorial_codes
This commit is contained in:
Alexander Alekhin
commit
89172c08a2
@ -12,7 +12,7 @@ static void help(char* progName)
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<< "This program shows how to filter images with mask: the write it yourself and the"
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<< "filter2d way. " << endl
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<< "Usage:" << endl
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<< progName << " [image_name -- default ../data/lena.jpg] [G -- grayscale] " << endl << endl;
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<< progName << " [image_path -- default ../data/lena.jpg] [G -- grayscale] " << endl << endl;
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}
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@ -45,7 +45,7 @@ int main( int argc, char* argv[])
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Sharpen( src, dst0 );
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t = ((double)getTickCount() - t)/getTickFrequency();
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cout << "Hand written function times passed in seconds: " << t << endl;
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cout << "Hand written function time passed in seconds: " << t << endl;
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imshow( "Output", dst0 );
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waitKey();
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@ -62,7 +62,7 @@ int main( int argc, char* argv[])
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filter2D( src, dst1, src.depth(), kernel );
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//![filter2D]
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t = ((double)getTickCount() - t)/getTickFrequency();
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cout << "Built-in filter2D time passed in seconds: " << t << endl;
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cout << "Built-in filter2D time passed in seconds: " << t << endl;
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imshow( "Output", dst1 );
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@ -1,102 +1,109 @@
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import org.opencv.core.*;
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import org.opencv.core.Core;
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import org.opencv.core.CvType;
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import org.opencv.core.Mat;
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import org.opencv.core.Scalar;
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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.*;
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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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public void run(String[] args) {
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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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String filename = "../data/lena.jpg";
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Image img = toBufferedImage( I );
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displayImage("Input Image" , img, 0, 200 );
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int img_codec = Imgcodecs.IMREAD_COLOR;
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if (args.length != 0) {
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filename = args[0];
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if (args.length >= 2 && args[1].equals("G"))
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img_codec = Imgcodecs.IMREAD_GRAYSCALE;
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}
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Mat src = Imgcodecs.imread(filename, img_codec);
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if (src.empty()) {
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System.out.println("Can't open image [" + filename + "]");
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System.out.println("Program Arguments: [image_path -- default ../data/lena.jpg] [G -- grayscale]");
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System.exit(-1);
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}
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Image img = toBufferedImage(src);
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displayImage("Input", 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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Mat dst0 = sharpen(src, 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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t = ((double) System.currentTimeMillis() - t) / 1000;
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System.out.println("Hand written function time 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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Image img2 = toBufferedImage(dst0);
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displayImage("Output", 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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//![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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kern.put(row, col, 0, -1, 0, -1, 5, -1, 0, -1, 0);
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//![kern]
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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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Mat dst1 = new Mat();
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//![filter2D]
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Imgproc.filter2D(src, dst1, src.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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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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Image img3 = toBufferedImage(dst1);
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displayImage("Output", 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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public static double saturate(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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public Mat sharpen(Mat myImage, Mat Result) {
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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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//! [8_bit]
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//! [create_channels]
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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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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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//! [basic_method_loop]
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for (int j = 1; j < myImage.rows() - 1; ++j) {
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for (int i = 1; i < myImage.cols() - 1; ++i) {
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double sum[] = new double[nChannels];
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for(int k = 0; k < nChannels; ++k) {
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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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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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sum[k] = saturate(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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//! [basic_method_loop]
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//! [borders]
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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.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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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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@ -104,23 +111,22 @@ class MatMaskOperationsRun {
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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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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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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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public void displayImage(String title, Image img, int x, int y) {
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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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@ -131,9 +137,9 @@ class MatMaskOperationsRun {
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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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new MatMaskOperationsRun().run(args); // run code
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}
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}
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@ -1,57 +1,100 @@
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import sys
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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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## [basic_method]
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def is_grayscale(my_image):
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return len(my_image.shape) < 3
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def saturated(sum_value):
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if sum_value > 255:
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sum_value = 255
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if sum_value < 0:
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sum_value = 0
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return sum_value
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def sharpen(my_image):
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if is_grayscale(my_image):
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height, width = my_image.shape
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else:
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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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for j in range(1, height - 1):
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for i in range(1, width - 1):
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if is_grayscale(my_image):
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sum_value = 5 * my_image[j, i] - my_image[j + 1, i] - my_image[j - 1, i] \
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- my_image[j, i + 1] - my_image[j, i - 1]
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result[j, i] = saturated(sum_value)
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else:
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for k in range(0, n_channels):
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sum_value = 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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result[j, i, k] = saturated(sum_value)
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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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def main(argv):
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filename = "../data/lena.jpg"
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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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img_codec = cv2.IMREAD_COLOR
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if argv:
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filename = sys.argv[1]
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if len(argv) >= 2 and sys.argv[2] == "G":
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img_codec = cv2.IMREAD_GRAYSCALE
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cv2.imshow('Output Image', J)
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src = cv2.imread(filename, img_codec)
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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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if src is None:
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print "Can't open image [" + filename + "]"
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print "Usage:\nmat_mask_operations.py [image_path -- default ../data/lena.jpg] [G -- grayscale]"
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return -1
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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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cv2.namedWindow("Input", cv2.WINDOW_AUTOSIZE)
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cv2.namedWindow("Output", cv2.WINDOW_AUTOSIZE)
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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("Input", src)
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t = round(time.time())
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cv2.imshow('filter2D Output Image', K)
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dst0 = sharpen(src)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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t = (time.time() - t) / 1000
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print "Hand written function time passed in seconds: %s" % t
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cv2.imshow("Output", dst0)
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cv2.waitKey()
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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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dst1 = cv2.filter2D(src, -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("Output", dst1)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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return 0
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if __name__ == "__main__":
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main(sys.argv[1:])
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