我正在用Java计算图像的肤色。
它在CPU代码中工作正常，但是当我将其转换为GPU代码时，像素百分比不正确。
对我来说，令人困惑的部分是将像素数据发送到GPU，并在GPU中获取其r，g，b值。
因此，我按照JCuda Pixel Invert Example示例发送像素数据。区别在于示例以int []数组发送像素数据，而我以byte []数组发送像素数据。
这里是代码。
import static jcuda.driver.JCudaDriver.cuCtxCreate;
import static jcuda.driver.JCudaDriver.cuCtxSynchronize;
import static jcuda.driver.JCudaDriver.cuDeviceGet;
import static jcuda.driver.JCudaDriver.cuInit;
import static jcuda.driver.JCudaDriver.cuLaunchKernel;
import static jcuda.driver.JCudaDriver.cuMemAlloc;
import static jcuda.driver.JCudaDriver.cuMemFree;
import static jcuda.driver.JCudaDriver.cuMemcpyDtoH;
import static jcuda.driver.JCudaDriver.cuMemcpyHtoD;
import java.awt.image.BufferedImage;
import java.awt.image.DataBuffer;
import java.awt.image.DataBufferByte;
import java.awt.image.Raster;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;
import ij.IJ;
import jcuda.Pointer;
import jcuda.Sizeof;
import jcuda.driver.CUcontext;
import jcuda.driver.CUdevice;
import jcuda.driver.CUdeviceptr;
import jcuda.driver.CUfunction;
import jcuda.driver.JCudaDriver;
import jcuda.nvrtc.JNvrtc;
public class SkinTone {
public static void CalculateSKintoneGPU(File file) throws IOException {
BufferedImage bufferedImage = ImageIO.read(file);
if (bufferedImage == null || bufferedImage.getData() == null)
return;
Raster raster = bufferedImage.getData();
DataBuffer dataBuffer = raster.getDataBuffer();
DataBufferByte dataBufferInt = (DataBufferByte)dataBuffer;
byte[] pixels = dataBufferInt.getData();
int totalPixels = raster.getHeight() * raster.getWidth();
CUfunction kernelFunction = initlize();
int output[] = execute(kernelFunction, pixels, raster.getWidth(), raster.getHeight());
// Flushing memory
raster = null;
bufferedImage.flush();
bufferedImage = null;
long skintoneThreshold = Math.round(output[0] / (double) totalPixels * 100.0);
System.err.println("Skintone Using GPU: " + output[0]);
System.err.println("Total Pixel Of GPU: " + totalPixels);
System.err.println("SKinTone Percentage Using GPU: " + skintoneThreshold + "%");
}
static int[] execute(CUfunction kernelFunction, byte[] pixels, int w, int h) {
// Allocate memory on the device, and copy the host data to the device
int size = w * h * Sizeof.BYTE;
CUdeviceptr pointer = new CUdeviceptr();
cuMemAlloc(pointer, size);
cuMemcpyHtoD(pointer, Pointer.to(pixels), size);
int numElements = 9;
int s = 0;
// Allocate device output memory
CUdeviceptr deviceOutput = new CUdeviceptr();
cuMemAlloc(deviceOutput, numElements * Sizeof.INT);
// Set up the kernel parameters: A pointer to an array
// of pointers which point to the actual values.
Pointer kernelParameters = Pointer.to(Pointer.to(pointer), Pointer.to(new int[] { w }),
Pointer.to(new int[] { h }), Pointer.to(deviceOutput));
// Call the kernel function
int blockSize = 16;
int gridSize = (Math.max(w, h) + blockSize - 1) / blockSize;
cuLaunchKernel(kernelFunction, gridSize, gridSize, 1, // Grid dimension
blockSize, blockSize, 1, // Block dimension
0, null, // Shared memory size and stream
kernelParameters, null // Kernel- and extra parameters
);
cuCtxSynchronize();
// Allocate host output memory and copy the device output
// to the host.
int hostOutput[] = new int[numElements];
cuMemcpyDtoH(Pointer.to(hostOutput), deviceOutput, numElements * Sizeof.INT);
// Clean up.
cuMemFree(deviceOutput);
cuMemFree(pointer);
return hostOutput;
}
public static CUfunction initlize() {
// Enable exceptions and omit all subsequent error checks
JCudaDriver.setExceptionsEnabled(true);
JNvrtc.setExceptionsEnabled(true);
// Initialize the driver and create a context for the first device.
cuInit(0);
CUdevice device = new CUdevice();
cuDeviceGet(device, 0);
CUcontext context = new CUcontext();
cuCtxCreate(context, 0, device);
// Obtain the CUDA source code from the CUDA file
String cuFileName = "Skintone.cu";
String sourceCode = CudaUtils.readResourceAsString(cuFileName);
if (sourceCode == null) {
IJ.showMessage("Error", "Could not read the kernel source code");
}
// Create the kernel function
return CudaUtils.createFunction(sourceCode, "skintone");
}
public static void CalculateSKintoneCPU(File file) throws IOException {
BufferedImage bufferedImage = ImageIO.read(file);
if (bufferedImage == null || bufferedImage.getData() == null)
return;
Raster raster = bufferedImage.getData();
float[] rgb = new float[4];
int totalPixels = raster.getHeight() * raster.getWidth();
int skinTonePixels = 0;
for (int x = 0; x < raster.getWidth(); x++) {
for (int y = 0; y < raster.getHeight(); y++) {
raster.getPixel(x, y, rgb);
if (skintone(rgb)) {
skinTonePixels++;
}
}
}
// Flushing memory
raster = null;
rgb = null;
bufferedImage.flush();
bufferedImage = null;
long skintoneThreshold = Math.round(skinTonePixels / (double) totalPixels * 100.0);
System.err.println("Skintone Using CPU: " + skinTonePixels);
System.err.println("Total Pixel Of CPU: " + totalPixels);
System.err.println("SKinTone Percentage Using CPU: " + skintoneThreshold + "%");
}
private static boolean skintone(float[] rgb) {
float yCbCr[] = (float[]) convertRGBtoYUV(rgb);
if ((yCbCr[1] >= 80 && yCbCr[1] <= 120) && (yCbCr[2] >= 133 && yCbCr[2] <= 173)) {
return true;
}
return false;
}
private static float[] convertRGBtoYUV(float[] rgb) {
final float[] yCbCr = new float[3];
float r = rgb[0];
float g = rgb[1];
float b = rgb[2];
yCbCr[0] = 16 + (0.299f * r) + (0.587f * g) + (0.144f * b);
yCbCr[1] = 128 + (-0.169f * r) - (0.331f * g) + (0.5f * b);
yCbCr[2] = 128 + (0.5f * r) - (0.419f * g) - (0.081f * b);
return yCbCr;
}
public static void main(String[] args) throws IOException {
File file = new File("C:\\Users\\Aqeel\\git\\jcuda-imagej-example\\src\\test\\resources\\lena512color.png");
CalculateSKintoneCPU(file);
CalculateSKintoneGPU(file);
}
}
核心文件
extern "C"
__global__ void skintone(uchar4* data, int w, int h, int* output)
{
int x = threadIdx.x+blockIdx.x*blockDim.x;
int y = threadIdx.y+blockIdx.y*blockDim.y;
if (x < w && y < h)
{
float r, g, b;
float cb, cr;
int index = y*w+x;
uchar4 pixel = data[index];
r = pixel.x;
g = pixel.y;
b = pixel.z;
cb = 128 + (-0.169f * r) - (0.331f * g) + (0.5f * b);
cr = 128 + (0.5f * r) - (0.419f * g) - (0.081f * b);
if((cb >= 80 && cb <= 120) && (cr >= 133 && cr <= 173)) {
atomicAdd(&output[0], 1);
}
}
}
Complete Example src, Machine Need Nvida Card, Cuda Toolkit V9 and Graphics Drivers
答案 0 :(得分：0)
我通过点击和尝试的方法解决了这个问题。在内核中，我用b更改了r的位置，并解决了问题，我也必须用Java在int数组中发送代码，而不是字节。
extern "C"
__global__ void skintone(uchar4* data, int w, int h, int* output)
{
int x = threadIdx.x+blockIdx.x*blockDim.x;
int y = threadIdx.y+blockIdx.y*blockDim.y;
if (x < w && y < h)
{
float b, g, r;
float cb, cr;
int index = y*w+x;
uchar4 pixel = data[index];
b = (float)pixel.x;
g = (float)pixel.y;
r = (float)pixel.z;
cb = 128 + (-0.169f * r) - (0.331f * g) + (0.5f * b);
cr = 128 + (0.5f * r) - (0.419f * g) - (0.081f * b);
if((cb >= 80 && cb <= 120) && (cr >= 133 && cr <= 173)) {
atomicAdd(&output[0], 1);
}
}
}
Java代码更改。
public static void calculateSkintoneGPU() throws IOException {
BufferedImage img = ImageIO.read(SkinTone.class.getClassLoader().getResource("images.jpg"));
if (img == null || img.getData() == null)
return;
int width = img.getWidth(null);
int height = img.getHeight(null);
int[] pixels = new int[width * height];
PixelGrabber pg = new PixelGrabber(img, 0, 0, width, height, pixels, 0, width);
try {
pg.grabPixels();
} catch (InterruptedException e){};
int totalPixels = width * height;
CUfunction kernelFunction = initlize();
int output[] = execute(kernelFunction, pixels, width, height);
// Flushing memory
img.flush();
img = null;
long skintoneThreshold = Math.round(output[0] / (double) totalPixels * 100.0);
System.err.println("Skintone Using GPU: " + output[0]);
System.err.println("Total Pixel Of GPU: " + totalPixels);
System.err.println("SKinTone Percentage Using GPU: " + skintoneThreshold + "%");
}