Source Code Cross Referenced for MlibConvolveNxNOpImage.java in  » 6.0-JDK-Modules » Java-Advanced-Imaging » com » sun » media » jai » mlib » Java Source Code / Java DocumentationJava Source Code and Java Documentation

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Java Source Code / Java Documentation » 6.0 JDK Modules » Java Advanced Imaging » com.sun.media.jai.mlib 
Source Cross Referenced  Class Diagram Java Document (Java Doc) 


001:        /*
002:         * $RCSfile: MlibConvolveNxNOpImage.java,v $
003:         *
004:         * Copyright (c) 2005 Sun Microsystems, Inc. All rights reserved.
005:         *
006:         * Use is subject to license terms.
007:         *
008:         * $Revision: 1.4 $
009:         * $Date: 2005/08/16 00:17:28 $
010:         * $State: Exp $
011:         */
012:        package com.sun.media.jai.mlib;
013:
014:        import java.awt.Rectangle;
015:        import java.awt.image.DataBuffer;
016:        import java.awt.image.SampleModel;
017:        import java.awt.image.Raster;
018:        import java.awt.image.RenderedImage;
019:        import java.awt.image.WritableRaster;
020:        import java.awt.image.renderable.ParameterBlock;
021:        import java.awt.image.renderable.RenderedImageFactory;
022:        import javax.media.jai.AreaOpImage;
023:        import javax.media.jai.BorderExtender;
024:        import javax.media.jai.ImageLayout;
025:        import javax.media.jai.KernelJAI;
026:        import javax.media.jai.OpImage;
027:        import java.util.Map;
028:        import com.sun.medialib.mlib.*;
029:
030:        // import com.sun.media.jai.test.OpImageTester;
031:
032:        /**
033:         * An OpImage class to perform convolution on a source image.
034:         *
035:         * <p> This class implements a convolution operation. Convolution is a
036:         * spatial operation that computes each output sample by multiplying
037:         * elements of a kernel with the samples surrounding a particular
038:         * source sample.
039:         *
040:         * <p> For each destination sample, the kernel is rotated 180 degrees
041:         * and its "key element" is placed over the source pixel corresponding
042:         * with the destination pixel.  The kernel elements are multiplied
043:         * with the source pixels under them, and the resulting products are
044:         * summed together to produce the destination sample value.
045:         * 
046:         * <p> Example code for the convolution operation on a single sample
047:         * dst[x][y] is as follows, assuming the kernel is of size M rows x N
048:         * columns and has already been rotated through 180 degrees.  The
049:         * kernel's key element is located at position (xKey, yKey):
050:         *
051:         * <pre>
052:         * dst[x][y] = 0;
053:         * for (int i = -xKey; i < M - xKey; i++) {
054:         *     for (int j = -yKey; j < N - yKey; j++) {
055:         *         dst[x][y] += src[x + i][y + j] * kernel[xKey + i][yKey + j];
056:         *     }
057:         * }
058:         * </pre>
059:         *
060:         * <p> Convolution, or any neighborhood operation, leaves a band of
061:         * pixels around the edges undefined, e.g., for a 3x3 kernel, only
062:         * four kernel elements and four source pixels contribute to the
063:         * destination pixel located at (0,0).  Such pixels are not includined
064:         * in the destination image, unless a non-null BorderExtender is provided.
065:         *
066:         * <p> The Kernel cannot be bigger in any dimension than the image data.
067:         *
068:         * @see KernelJAI
069:         */
070:        final class MlibConvolveNxNOpImage extends AreaOpImage {
071:
072:            /**
073:             * The kernel with which to do the convolve operation.
074:             */
075:            protected KernelJAI kernel;
076:
077:            /** Kernel variables. */
078:            private int kw, kh;
079:            float kData[];
080:            double doublekData[];
081:            int intkData[];
082:            int shift = -1;
083:
084:            /**
085:             * Creates a MlibConvolveNxNOpImage given the image source and
086:             * pre-rotated convolution kernel.  The image dimensions are
087:             * derived from the source image.  The tile grid layout,
088:             * SampleModel, and ColorModel may optionally be specified by an
089:             * ImageLayout object.
090:             *
091:             * @param source a RenderedImage.
092:             * @param extender a BorderExtender, or null.
093:
094:             *        or null.  If null, a default cache will be used.
095:             * @param layout an ImageLayout optionally containing the tile grid layout,
096:             *        SampleModel, and ColorModel, or null.
097:             * @param kernel the pre-rotated convolution KernelJAI.
098:             */
099:            public MlibConvolveNxNOpImage(RenderedImage source,
100:                    BorderExtender extender, Map config, ImageLayout layout,
101:                    KernelJAI kernel) {
102:                super (source, layout, config, true, extender, kernel
103:                        .getLeftPadding(), kernel.getRightPadding(), kernel
104:                        .getTopPadding(), kernel.getBottomPadding());
105:
106:                this .kernel = kernel;
107:                kw = kernel.getWidth();
108:                kh = kernel.getHeight();
109:
110:                // kx and ky are centered in AreaOpImage, not here.
111:
112:                kData = kernel.getKernelData();
113:
114:                int count = kw * kh;
115:
116:                // A little inefficient but figuring out what datatype
117:                // mediaLibAccessor will want is tricky.
118:                intkData = new int[count];
119:                doublekData = new double[count];
120:                for (int i = 0; i < count; i++) {
121:                    doublekData[i] = (double) kData[i];
122:                }
123:            }
124:
125:            private synchronized void setShift(int formatTag) {
126:                if (shift == -1) {
127:                    int mediaLibDataType = MediaLibAccessor
128:                            .getMediaLibDataType(formatTag);
129:                    shift = Image.ConvKernelConvert(intkData, doublekData, kw,
130:                            kh, mediaLibDataType);
131:                }
132:            }
133:
134:            /**
135:             * Performs convolution on a specified rectangle. The sources are
136:             * cobbled.
137:             *
138:             * @param sources an array of source Rasters, guaranteed to provide all
139:             *                necessary source data for computing the output.
140:             * @param dest a WritableRaster tile containing the area to be computed.
141:             * @param destRect the rectangle within dest to be processed.
142:             */
143:            protected void computeRect(Raster[] sources, WritableRaster dest,
144:                    Rectangle destRect) {
145:
146:                Raster source = sources[0];
147:                Rectangle srcRect = mapDestRect(destRect, 0);
148:
149:                int formatTag = MediaLibAccessor.findCompatibleTag(sources,
150:                        dest);
151:
152:                MediaLibAccessor srcAccessor = new MediaLibAccessor(source,
153:                        srcRect, formatTag, true);
154:                MediaLibAccessor dstAccessor = new MediaLibAccessor(dest,
155:                        destRect, formatTag, true);
156:                int numBands = getSampleModel().getNumBands();
157:
158:                mediaLibImage[] srcML = srcAccessor.getMediaLibImages();
159:                mediaLibImage[] dstML = dstAccessor.getMediaLibImages();
160:                for (int i = 0; i < dstML.length; i++) {
161:                    switch (dstAccessor.getDataType()) {
162:                    case DataBuffer.TYPE_BYTE:
163:                    case DataBuffer.TYPE_USHORT:
164:                    case DataBuffer.TYPE_SHORT:
165:                    case DataBuffer.TYPE_INT:
166:                        if (shift == -1) {
167:                            setShift(formatTag);
168:                        }
169:
170:                        if (kw == 2) {
171:                            Image.Conv2x2(dstML[i], srcML[i], intkData, shift,
172:                                    ((1 << numBands) - 1),
173:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
174:                        } else if (kw == 3) {
175:                            Image.Conv3x3(dstML[i], srcML[i], intkData, shift,
176:                                    ((1 << numBands) - 1),
177:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
178:                        } else if (kw == 4) {
179:                            Image.Conv4x4(dstML[i], srcML[i], intkData, shift,
180:                                    ((1 << numBands) - 1),
181:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
182:                        } else if (kw == 5) {
183:                            Image.Conv5x5(dstML[i], srcML[i], intkData, shift,
184:                                    ((1 << numBands) - 1),
185:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
186:                        } else if (kw == 7) {
187:                            Image.Conv7x7(dstML[i], srcML[i], intkData, shift,
188:                                    ((1 << numBands) - 1),
189:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
190:                        }
191:                        break;
192:                    case DataBuffer.TYPE_FLOAT:
193:                    case DataBuffer.TYPE_DOUBLE:
194:                        if (kw == 2) {
195:                            Image.Conv2x2_Fp(dstML[i], srcML[i], doublekData,
196:                                    ((1 << numBands) - 1),
197:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
198:                        } else if (kw == 3) {
199:                            Image.Conv3x3_Fp(dstML[i], srcML[i], doublekData,
200:                                    ((1 << numBands) - 1),
201:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
202:                        } else if (kw == 4) {
203:                            Image.Conv4x4_Fp(dstML[i], srcML[i], doublekData,
204:                                    ((1 << numBands) - 1),
205:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
206:                        } else if (kw == 5) {
207:                            Image.Conv5x5_Fp(dstML[i], srcML[i], doublekData,
208:                                    ((1 << numBands) - 1),
209:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
210:                        } else if (kw == 7) {
211:                            Image.Conv7x7_Fp(dstML[i], srcML[i], doublekData,
212:                                    ((1 << numBands) - 1),
213:                                    Constants.MLIB_EDGE_DST_NO_WRITE);
214:                        }
215:                        break;
216:                    default:
217:                        String className = this .getClass().getName();
218:                        throw new RuntimeException(JaiI18N
219:                                .getString("Generic2"));
220:                    }
221:                }
222:
223:                if (dstAccessor.isDataCopy()) {
224:                    dstAccessor.copyDataToRaster();
225:                }
226:            }
227:
228:            //     public static OpImage createTestImage(OpImageTester oit) {
229:            //         float data[] = {0.05f,0.10f,0.05f,
230:            //                         0.10f,0.40f,0.10f,
231:            //                         0.05f,0.10f,0.05f};
232:            //         KernelJAI kJAI = new KernelJAI(3,3,1,1,data);
233:            //         return new MlibConvolve3x3OpImage(oit.getSource(), null, null,
234:            //                                           new ImageLayout(oit.getSource()),
235:            //                                           kJAI);
236:            //     }
237:
238:            //     public static void main (String args[]) {
239:            //         String classname = "com.sun.media.jai.mlib.MlibConvolve3x3OpImage";
240:            //         OpImageTester.performDiagnostics(classname,args);
241:            //     }
242:        }
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