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For example, to rotate an image by 15 degrees, use this matrix:



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The first thing we do is set the number of bins and the minimum and maximum of the histograms. Then we construct the histogram with those parameters. After that, we create a parameter block for the operation. This is very similar to what I did with the FilterParameter class. Next because Java Advanced Images uses PlanarImages instead of BufferedImages, we have to create an output image to perform the operation. Once the operation completes, we simply extract the histogram property from the output image and get the mean values for the red, green, and blue of the image before writing the output to System.out. See Example 6-19.





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In highly distributed applications, it is advantageous to pass parameters by value instead of by reference. Passing parameters by reference can cause increased overhead, because when the server object must work with parameter information, it needs to make calls across processing boundaries and the network. Passing values by reference is also less secure when maintaining data integrity. The client is opening a channel for the data to be manipulated without the client s knowledge or control. On the other hand, passing values by reference may be the better choice when the client and server are in the same process space (they occupy the same cubicle, so to speak) and have a clearly established trust relationship. In this situation, allowing direct access to the memory storage location and passing the parameters by reference may offer a performance advantage over passing the parameters by value. The other situation where passing parameters by reference may be advantageous is if the object is a complex data type, such as another object. In this case, the overhead of copying the data structure and passing it across process and network boundaries outweighs the overhead of making repeated calls across the network.

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This gives you this command line: convert -affine 0.9659,0.2588,-0.2588,0.9659,0,0 -transform input.jpg output.jpg This produces the rotated image shown in Figure 7-95.

Example 6-19. getHistogram(); public int[] getHistogram(BufferedImage bufImg) { // Set up the parameters for the Histogram object. int[] bins = { 256, 256, 256 }; // The number of bins. double[] low = { 0.0D, 0.0D, 0.0D }; // The low value. double[] high = { 256.0D, 256.0D, 256.0D }; // The high value. // Construct the Histogram object. Histogram hist = new Histogram(bins, low, high); // Create the parameter block. ParameterBlock pb = new ParameterBlock(); pb.addSource(bufImg); // Specify the source image pb.add(null); // No ROI pb.add(1); // Sampling pb.add(1); // periods // Perform the histogram operation. PlanarImage output = (PlanarImage) JAI.create("histogram", pb, null); // Retrieve the histogram data. hist = (Histogram) output.getProperty("histogram"); // Print 3-band histogram. int[] mean = new int[] {(int)hist.getMean()[0], (int)hist.getMean()[1],(int)hist.getMean()[2]}; System.out.println("histogram2=" + mean[0] + "," + mean[1] + "," + mean[2]); return mean; } Getting the mean values helps us compute the desired color of an image. From Example 6-17 with colorFilter(), knowing that you first want the red, and second the green, and third the blue can help you optimize the filter to give you an exact color. What I ve found through experimentation is that it s not so much the colors that make a difference, but their relationship to each other that s important. So instead, I ll create a class called a ColorGram that represents not only the colors, but also the ratio of those colors to one another.

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