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samples/convolution/src/com/amd/aparapi/sample/convolution/Convolve.java

+/*
+Copyright (c) 2010-2011, Advanced Micro Devices, Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
+following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this list of conditions and the following
+disclaimer. 
+
+Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
+disclaimer in the documentation and/or other materials provided with the distribution. 
+
+Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products
+derived from this software without specific prior written permission. 
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
+INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 
+WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+If you use the software (in whole or in part), you shall adhere to all applicable U.S., European, and other export
+laws, including but not limited to the U.S. Export Administration Regulations ("EAR"), (15 C.F.R. Sections 730 through
+774), and E.U. Council Regulation (EC) No 1334/2000 of 22 June 2000.  Further, pursuant to Section 740.6 of the EAR,
+you hereby certify that, except pursuant to a license granted by the United States Department of Commerce Bureau of 
+Industry and Security or as otherwise permitted pursuant to a License Exception under the U.S. Export Administration 
+Regulations ("EAR"), you will not (1) export, re-export or release to a national of a country in Country Groups D:1,
+E:1 or E:2 any restricted technology, software, or source code you receive hereunder, or (2) export to Country Groups
+D:1, E:1 or E:2 the direct product of such technology or software, if such foreign produced direct product is subject
+to national security controls as identified on the Commerce Control List (currently found in Supplement 1 to Part 774
+of EAR).  For the most current Country Group listings, or for additional information about the EAR or your obligations
+under those regulations, please refer to the U.S. Bureau of Industry and Security's website at http://www.bis.doc.gov/. 
+
+*/
+
+package com.amd.aparapi.sample.convolution;
+
+import java.awt.Dimension;
+import java.awt.Graphics;
+import java.awt.image.BufferedImage;
+import java.awt.image.ConvolveOp;
+import java.awt.image.DataBufferInt;
+import java.io.File;
+import java.io.IOException;
+
+import javax.imageio.ImageIO;
+import javax.swing.JComponent;
+import javax.swing.JFrame;
+import javax.swing.WindowConstants;
+
+import com.amd.aparapi.Kernel;
+import com.amd.aparapi.Range;
+
+/**
+ * An example Aparapi application which demonstrates image manipulation via convolution filter
+ * 
+ * Converted to use int buffer and some performance tweaks by Gary Frost
+ * http://processing.org/learning/pixels/
+ * 
+ * @author Gary Frost
+ */
+public class Convolve{
+   // http://docs.gimp.org/en/plug-in-convmatrix.html
+
+   final static class ConvolutionFilter{
+      private float[] weights;
+
+      private int offset;
+
+      ConvolutionFilter(float _nw, float _n, float ne, float _w, float _o, float _e, float _sw, float _s, float _se, int _offset) {
+         weights = new float[] {
+               _nw,
+               _w,
+               ne,
+               _w,
+               _o,
+               _e,
+               _sw,
+               _s,
+               _se
+         };
+         offset = _offset;
+      }
+
+   }
+
+   public static class ConvolutionKernel extends Kernel{
+
+      private final float[] filter = new float[9];
+
+      private final int[] inputData;
+
+      private final int[] outputData;
+
+      private final int width;
+
+      private final int height;
+
+      private int offset;
+
+      public ConvolutionKernel(int _width, int _height, BufferedImage _inputImage, BufferedImage _outputImage) {
+         inputData = ((DataBufferInt) _inputImage.getRaster().getDataBuffer()).getData();
+         outputData = ((DataBufferInt) _outputImage.getRaster().getDataBuffer()).getData();
+         width = _width;
+         height = _height;
+
+         // setExplicit(true); // This gives us a performance boost
+         //  put(inputData); // Because we are using explicit buffer management we must put the imageData array
+      }
+
+      @Override public void run() {
+
+         int x = getGlobalId(0);
+         int y = getGlobalId(1);
+         int lx = getLocalId(0);
+         int ly = getLocalId(1);
+         int w = getGlobalSize(0);
+         int h = getGlobalSize(1);
+         // System.out.println(x+","+y+" "+lx+","+ly+" "+w+","+h);
+         if (x > 1 && x < (w - 1) && y > 1 && y < (h - 1)) {
+
+            int result = 0;
+            // We handle each color separately using rgbshift as an 8 bit mask for red, green, blue
+            for (int rgbShift = 0; rgbShift < 24; rgbShift += 8) { // 0,8,16
+               int channelAccum = 0;
+               float accum = 0;
+
+               for (int count = 0; count < 9; count++) {
+                  int dx = (count % 3) - 1; // 0,1,2 -> -1,0,1
+                  int dy = (count / 3) - 1; // 0,1,2 -> -1,0,1
+
+                  int rgb = (inputData[((y + dy) * w) + (x + dx)]);
+                  int channelValue = ((rgb >> rgbShift) & 0xff);
+                  accum += filter[count];
+                  channelAccum += channelValue * filter[count++];
+
+               }
+               channelAccum /= accum;
+               channelAccum += offset;
+               channelAccum = max(0, min(channelAccum, 0xff));
+               result |= (channelAccum << rgbShift);
+            }
+            outputData[y * w + x] = result;
+         }
+      }
+
+      public void convolve(ConvolutionFilter _filter) {
+         System.arraycopy(_filter.weights, 0, filter, 0, _filter.weights.length);
+         offset = _filter.offset;
+         put(filter);
+         execute(Range.create2D(width, height, 8, 8));
+         get(outputData);
+      }
+   }
+
+   public static final int PAD = 1024;
+
+   public static int padValue(int value) {
+      return (PAD - (value % PAD));
+   }
+
+   public static int padTo(int value) {
+      return (value + padValue(value));
+   }
+
+   public static void main(String[] _args) throws IOException, InterruptedException {
+
+      JFrame frame = new JFrame("Convolution");
+
+      BufferedImage testCard = ImageIO.read(new File(_args[0]));
+
+      java.awt.image.Kernel conv = new java.awt.image.Kernel(3, 3, new float[] {
+            Float.parseFloat(_args[1]),
+            Float.parseFloat(_args[2]),
+            Float.parseFloat(_args[3]),
+            Float.parseFloat(_args[4]),
+            Float.parseFloat(_args[5]),
+            Float.parseFloat(_args[6]),
+            Float.parseFloat(_args[7]),
+            Float.parseFloat(_args[8]),
+            Float.parseFloat(_args[9])
+      });
+      final ConvolveOp convOp = new ConvolveOp(conv, ConvolveOp.EDGE_NO_OP, null);
+
+      int imageHeight = testCard.getHeight();
+
+      int imageWidth = testCard.getWidth();
+
+      final int width = padTo(imageWidth);// now multiple of 64
+
+      final int height = padTo(imageHeight); // now multiple of 64
+
+      final BufferedImage inputImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
+
+      inputImage.getGraphics().drawImage(testCard, padValue(imageWidth) / 2, padValue(imageHeight) / 2, null);
+
+      final BufferedImage outputImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
+      
+      convOp.filter(inputImage, outputImage);
+
+     // outputImage.getGraphics().drawImage(testCard, padValue(imageWidth) / 2, padValue(imageHeight) / 2, null);
+      final ConvolutionKernel lifeKernel = new ConvolutionKernel(width, height, inputImage, outputImage);
+
+      // Create a component for viewing the offsecreen image
+      @SuppressWarnings("serial") JComponent viewer = new JComponent(){
+         @Override public void paintComponent(Graphics g) {
+
+            g.drawImage(outputImage, 0, 0, width, height, 0, 0, width, height, this);
+         }
+      };
+
+      viewer.setPreferredSize(new Dimension(width, height));
+      frame.getContentPane().add(viewer);
+
+      // Swing housekeeping
+      frame.pack();
+      frame.setVisible(true);
+      frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
+
+      viewer.repaint();
+
+   }
+}