bilateralFilter GPU C++示例代码
时间: 2024-10-23 16:05:59 浏览: 32
双边滤波是一种图像处理技术,它不仅平滑了图像的噪声,还能保留边缘信息。在GPU上加速这种操作通常涉及到CUDA编程,因为GPU擅长并行计算。以下是一个简单的CUDA C++示例代码片段,用于双边滤波:
```cpp
#include <cuda_runtime.h>
#include <opencv2/core.hpp>
#include <opencv2/gpu/gpu.hpp>
// CUDA函数模板,用于双边滤波
__global__ void bilateralFilterKernel(unsigned char* input, float* output, int width, int height, int diameter, float sigmaColor, float sigmaSpace) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= 0 && x < width && y >= 0 && y < height) {
float sum = 0.0f;
for (int dx = -diameter; dx <= diameter; dx++) {
for (int dy = -diameter; dy <= diameter; dy++) {
int i = x + dx;
int j = y + dy;
// 检查像素是否在图像范围内
if (i >= 0 && i < width && j >= 0 && j < height) {
float weight = exp(-((dx * dx + dy * dy) / (2.0f * sigmaSpace * sigmaSpace)));
sum += weight * input[(j * width + i)];
}
}
}
output[y * width + x] = saturate_cast<unsigned char>(sum / (2.0f * M_PI * sigmaColor * sigmaSpace));
}
}
void bilateralFilter(const cv::Mat& src, cv::Mat& dst, int diameter, float sigmaColor, float sigmaSpace) {
const int blockSize = 8;
const int gridSize = (src.cols + blockSize - 1) / blockSize * (src.rows + blockSize - 1) / blockSize;
// 将输入转换为GPU可用的数据
cv::Mat src_gpu(src), dst_gpu(dst.size(), CV_8UC1);
cudaMemcpy(dst_gpu.data, src.data, src.total() * sizeof(unsigned char), cudaMemcpyHostToDevice);
dim3 threadsPerBlock(blockSize, blockSize);
dim3 numBlocks(gridSize, gridSize);
bilateralFilterKernel<<<numBlocks, threadsPerBlock>>>(dst_gpu.data, dst_gpu.data, src.cols, src.rows, diameter, sigmaColor, sigmaSpace);
// 将结果从GPU复制回CPU
cudaMemcpy(dst.data, dst_gpu.data, dst.total() * sizeof(unsigned char), cudaMemcpyDeviceToHost);
}
```
这个代码首先定义了一个CUDA kernel,然后在主机上创建CUDA设备内存,并将数据复制到那里。接着,在GPU上执行filtering,最后把结果从GPU拷贝回CPU。
注意:在实际应用中,你还需要设置适当的OpenCV和CUDA库路径,并确保正确安装了CUDA工具链。
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