Tutorial Mask Operations on Matrices

samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java

@@ -2,14 +2,10 @@ import org.opencv.core.Core;
 import org.opencv.core.CvType;
 import org.opencv.core.Mat;
 import org.opencv.core.Scalar;
+import org.opencv.highgui.HighGui;
 import org.opencv.imgcodecs.Imgcodecs;
 import org.opencv.imgproc.Imgproc;
 
-import javax.swing.*;
-import java.awt.*;
-import java.awt.image.BufferedImage;
-import java.awt.image.DataBufferByte;
-
 class MatMaskOperationsRun {
 
     public void run(String[] args) {
@@ -31,8 +27,10 @@ class MatMaskOperationsRun {
             System.exit(-1);
         }
 
-        Image img = toBufferedImage(src);
-        displayImage("Input", img, 0, 200);
+        HighGui.namedWindow("Input", HighGui.WINDOW_AUTOSIZE);
+        HighGui.namedWindow("Output", HighGui.WINDOW_AUTOSIZE);
+
+        HighGui.imshow( "Input", src );
         double t = System.currentTimeMillis();
 
         Mat dst0 = sharpen(src, new Mat());
@@ -40,8 +38,9 @@ class MatMaskOperationsRun {
         t = ((double) System.currentTimeMillis() - t) / 1000;
         System.out.println("Hand written function time passed in seconds: " + t);
 
-        Image img2 = toBufferedImage(dst0);
-        displayImage("Output", img2, 400, 400);
+        HighGui.imshow( "Output", dst0 );
+        HighGui.moveWindow("Output", 400, 400);
+        HighGui.waitKey();
 
         //![kern]
         Mat kern = new Mat(3, 3, CvType.CV_8S);
@@ -58,8 +57,10 @@ class MatMaskOperationsRun {
         t = ((double) System.currentTimeMillis() - t) / 1000;
         System.out.println("Built-in filter2D time passed in seconds:     " + t);
 
-        Image img3 = toBufferedImage(dst1);
-        displayImage("Output", img3, 800, 400);
+        HighGui.imshow( "Output", dst1 );
+
+        HighGui.waitKey();
+        System.exit(0);
     }
 
     //! [basic_method]
@@ -108,38 +109,12 @@ class MatMaskOperationsRun {
         return Result;
     }
     //! [basic_method]
-
-    public Image toBufferedImage(Mat m) {
-        int type = BufferedImage.TYPE_BYTE_GRAY;
-        if (m.channels() > 1) {
-            type = BufferedImage.TYPE_3BYTE_BGR;
-        }
-        int bufferSize = m.channels() * m.cols() * m.rows();
-        byte[] b = new byte[bufferSize];
-        m.get(0, 0, b); // get all the pixels
-        BufferedImage image = new BufferedImage(m.cols(), m.rows(), type);
-        final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
-        System.arraycopy(b, 0, targetPixels, 0, b.length);
-        return image;
-    }
-
-    public void displayImage(String title, Image img, int x, int y) {
-        ImageIcon icon = new ImageIcon(img);
-        JFrame frame = new JFrame(title);
-        JLabel lbl = new JLabel(icon);
-        frame.add(lbl);
-        frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
-        frame.pack();
-        frame.setLocation(x, y);
-        frame.setVisible(true);
-    }
 }
 
 public class MatMaskOperations {
     public static void main(String[] args) {
         // Load the native library.
         System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
-
-        new MatMaskOperationsRun().run(args); // run code
+        new MatMaskOperationsRun().run(args);
     }
 }

samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py

@@ -1,9 +1,10 @@
+from __future__ import print_function
 import sys
 import time
+
 import numpy as np
 import cv2
 
-
 ## [basic_method]
 def is_grayscale(my_image):
     return len(my_image.shape) < 3
@@ -26,7 +27,6 @@ def sharpen(my_image):
         height, width, n_channels = my_image.shape
 
     result = np.zeros(my_image.shape, my_image.dtype)
-
     ## [basic_method_loop]
     for j in range(1, height - 1):
         for i in range(1, width - 1):
@@ -36,17 +36,16 @@ def sharpen(my_image):
                 result[j, i] = saturated(sum_value)
             else:
                 for k in range(0, n_channels):
-                    sum_value = 5 * my_image[j, i, k] - my_image[j + 1, i, k] - my_image[j - 1, i, k] \
-                                - my_image[j, i + 1, k] - my_image[j, i - 1, k]
+                    sum_value = 5 * my_image[j, i, k] - my_image[j + 1, i, k]  \
+                                - my_image[j - 1, i, k] - my_image[j, i + 1, k]\
+                                - my_image[j, i - 1, k]
                     result[j, i, k] = saturated(sum_value)
     ## [basic_method_loop]
-
     return result
 ## [basic_method]
 
-
 def main(argv):
-    filename = "../data/lena.jpg"
+    filename = "../../../../data/lena.jpg"
 
     img_codec = cv2.IMREAD_COLOR
     if argv:
@@ -57,8 +56,9 @@ def main(argv):
     src = cv2.imread(filename, img_codec)
 
     if src is None:
-        print "Can't open image [" + filename + "]"
-        print "Usage:\nmat_mask_operations.py [image_path -- default ../data/lena.jpg] [G -- grayscale]"
+        print("Can't open image [" + filename + "]")
+        print("Usage:")
+        print("mat_mask_operations.py [image_path -- default ../../../../data/lena.jpg] [G -- grayscale]")
         return -1
 
     cv2.namedWindow("Input", cv2.WINDOW_AUTOSIZE)
@@ -70,7 +70,7 @@ def main(argv):
     dst0 = sharpen(src)
 
     t = (time.time() - t) / 1000
-    print "Hand written function time passed in seconds: %s" % t
+    print("Hand written function time passed in seconds: %s" % t)
 
     cv2.imshow("Output", dst0)
     cv2.waitKey()
@@ -81,13 +81,13 @@ def main(argv):
                        [-1, 5, -1],
                        [0, -1, 0]], np.float32)  # kernel should be floating point type
     ## [kern]
-
     ## [filter2D]
-    dst1 = cv2.filter2D(src, -1, kernel)  # ddepth = -1, means destination image has depth same as input image
+    dst1 = cv2.filter2D(src, -1, kernel)
+    # ddepth = -1, means destination image has depth same as input image
     ## [filter2D]
 
     t = (time.time() - t) / 1000
-    print "Built-in filter2D time passed in seconds:     %s" % t
+    print("Built-in filter2D time passed in seconds:     %s" % t)
 
     cv2.imshow("Output", dst1)