Tutorial Mask Operations on Matrices

doc/tutorials/core/mat-mask-operations/mat_mask_operations.markdown

@@ -28,24 +28,39 @@ the zero-zero index) on the pixel you want to calculate and sum up the pixel val
 the overlapped matrix values. It's the same thing, however in case of large matrices the latter
 notation is a lot easier to look over.
 
+Code
+----
+
 @add_toggle_cpp
-Now let us see how we can make this happen by using the basic pixel access method or by using the
-@ref cv::filter2D function.
+You can download this source code from [here
+](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp) or look in the
+OpenCV source code libraries sample directory at
+`samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp`.
+@include samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp
 @end_toggle
 
 @add_toggle_java
-Now let us see how we can make this happen by using the basic pixel access method or by using the
-**Imgproc.filter2D()** function.
+You can download this source code from [here
+](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java) or look in the
+OpenCV source code libraries sample directory at
+`samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java`.
+@include samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java
 @end_toggle
 
 @add_toggle_python
-Now let us see how we can make this happen by using the basic pixel access method or by using the
-**cv2.filter2D()** function.
+You can download this source code from [here
+](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py) or look in the
+OpenCV source code libraries sample directory at
+`samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py`.
+@include samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py
 @end_toggle
 
 The Basic Method
 ----------------
 
+Now let us see how we can make this happen by using the basic pixel access method or by using the
+**filter2D()** function.
+
 Here's a function that will do this:
 @add_toggle_cpp
 @snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp basic_method
@@ -132,37 +147,38 @@ The filter2D function
 Applying such filters are so common in image processing that in OpenCV there exist a function that
 will take care of applying the mask (also called a kernel in some places). For this you first need
 to define an object that holds the mask:
+
 @add_toggle_cpp
 @snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp kern
-
-Then call the @ref cv::filter2D function specifying the input, the output image and the kernel to
-use:
-@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp filter2D
-
-The function even has a fifth optional argument to specify the center of the kernel, a sixth
-for adding an optional value to the filtered pixels before storing them in K and a seventh one
-for determining what to do in the regions where the operation is undefined (borders).
 @end_toggle
 
 @add_toggle_java
 @snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java kern
-
-Then call the **Imgproc.filter2D()** function specifying the input, the output image and the kernel to
-use:
-@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java filter2D
-The function even has a fifth optional argument to specify the center of the kernel, a sixth
-for adding an optional value to the filtered pixels before storing them in K and a seventh one
-for determining what to do in the regions where the operation is undefined (borders).
 @end_toggle
 
 @add_toggle_python
 @snippet samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py kern
+@end_toggle
 
-Then call the **cv2.filter2D()** function specifying the input, the output image and the kernell to
+Then call the **filter2D()** function specifying the input, the output image and the kernel to
 use:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp filter2D
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java filter2D
+@end_toggle
+
+@add_toggle_python
 @snippet samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py filter2D
 @end_toggle
 
+The function even has a fifth optional argument to specify the center of the kernel, a sixth
+for adding an optional value to the filtered pixels before storing them in K and a seventh one
+for determining what to do in the regions where the operation is undefined (borders).
+
 This function is shorter, less verbose and, because there are some optimizations, it is usually faster
 than the *hand-coded method*. For example in my test while the second one took only 13
 milliseconds the first took around 31 milliseconds. Quite some difference.
@@ -172,22 +188,7 @@ For example:
 ![](images/resultMatMaskFilter2D.png)
 
 @add_toggle_cpp
-You can download this source code from [here
-](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp) or look in the
-OpenCV source code libraries sample directory at
-`samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp`.
-
 Check out an instance of running the program on our [YouTube
 channel](http://www.youtube.com/watch?v=7PF1tAU9se4) .
 @youtube{7PF1tAU9se4}
 @end_toggle
-
-@add_toggle_java
-You can look in the OpenCV source code libraries sample directory at
-`samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java`.
-@end_toggle
-
-@add_toggle_python
-You can look in the OpenCV source code libraries sample directory at
-`samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py`.
-@end_toggle

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)