38: Writing Shaders with Pixel Bender in Java

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38: Writing Shaders with Pixel Bender
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stage.addEventListener(Event.ENTER_FRAME, go); } protected function onImgLoad(event:Event):void { testPattern.width = noise.width; testPattern.height = noise.height; } protected function onFullScreen(event:MouseEvent):void { if (stage.displayState == StageDisplayState.FULL_SCREEN) { stage.displayState = StageDisplayState.NORMAL; } else { stage.displayState = StageDisplayState.FULL_SCREEN; } } protected function go(event:Event):void { noise.noise(getTimer(), 0, 255, 7, true); for (var i:int = 0; i < pointers.length; i++) { var p:Point = pointers[i]; p.x = (int(p.x) + 2*(1+i)) % smoothRandomNoise.width; rands[i] = smoothRandomNoise.getPixel(p.x, p.y) / 0x00ffffff; } var sd:ShaderData = shader.data; testPattern.alpha = Math.max(0, rands[6] * 3 - 2); sd.noisyHDisplace.value = [Math.pow(rands[2],4)*40]; sd.vRoll.value = [rands[0] * 80 - 20]; sd.channelSplit.value = [rands[3] * 40 - 10]; sd.sinHDisplaceAmplitudes.value = [20.0, 2.0, 1.0]; sd.sinHDisplaceFrequencies.value = [rands[2]*2+0.4, rands[10]*4+4, rands[11]*8+8]; sd.sinHDisplace.value = [Math.pow(rands[2], 8) * 100]; sd.noiseLayer.value = [Math.pow(rands[10], 4) * 0.8]; sd.blackoutThresh.value = [rands[2]*0.3]; holder.filters = [shaderFilter]; } } }
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In the example, you use a strip of Perlin noise to produce cycles of natural-looking randomness. You have a set of points you move across the strip of noise at different speeds, sampling the cyclical noise, and storing the random numbers in the rands vector. By picking an entry from this vector with a higher index, you get faster-cycling noise. By raising these values in the range zero to one to higher powers with Math.pow(), you make the distribution of the noise cluster toward the edges more. This way, you get more extreme effects less often. Anyway, try it. The effect is akin to Figure 38-1.
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Part VIII: Graphics Programming and Animation
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FIGURE 38-1
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A complex effect given by Example 38-2
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This section investigates two advanced applications of Pixel Bender: using kernels to crunch different kinds of data ef ciently, and asynchronously. These goals are far from mutually exclusive. As I ve said before, even if a Pixel Bender kernel runs on the CPU, it may utilize the CPU better than ActionScript 3.0 would. If you design your programs to break out time-critical vector processing routines into Pixel Bender kernels, you can make better use of the CPU and squeeze out more performance, especially on multicore machines. Whether or not a kernel runs faster than equivalent ActionScript, the kernel is able to run asynchronously to ActionScript code. For huge data sets, simply freeing up AVM2 to handle the application s interface and input handling is enough of a bene t. Rather than hang while the data is processed, the kernel can execute in one or more threads, ring an event back to ActionScript when its results are ready. In other words, you may not be able to move processing off the CPU, but you can move it off the main thread. To take advantage of Pixel Bender for generalized data processing, though, your problem should be compatible with Pixel Bender s pipeline. Rather than run your kernel on every pixel of an image, it runs your kernel on every sample of the data. (Actually, the kernel still runs on an image you just craft this image to store your data.) So the problem should be highly parallelizable. The result for any sample of the data can t rely on the result for another sample. The input has to be one or more sets of data, and the output of the kernel must be a set of data. You must be able to represent the input and output data as sequences or arrays of 32-bit oating-point numbers. Also, you can only use the math functions and data types that Pixel Bender provides, so save it for crunching numbers, vectors, and matrices.
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You can use multiple inputs in a kernel, but all inputs must be image types. Recall that image types in Pixel Bender have between one and four channels, which are stored as 32-bit precision oating-point
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