用opencv 的CUDA实现msr 图像增强

MSR (Multi-Scale Retinex) 图像增强是一种基于多尺度的图像增强算法，可以有效地改善图像的亮度、对比度和细节。而CUDA是一种利用GPU并行计算的技术，能够加速图像处理过程。

下面是使用OpenCV和CUDA实现MSR图像增强的步骤：

1. 导入必要的库

import cv2
import numpy as np
from numba import cuda

2. 定义MSR算法函数

@cuda.jit
def msr_cuda(img, output):
    # 获取线程ID和块ID
    x, y = cuda.grid(2)
    tx, ty = cuda.threadIdx.x, cuda.threadIdx.y
    bx, by = cuda.blockIdx.x, cuda.blockIdx.y
    bw, bh = cuda.blockDim.x, cuda.blockDim.y

    if x < img.shape[0] and y < img.shape[1]:
        # 定义常数和参数
        alpha = 125
        beta = 46
        sigma_list = [15, 80, 250]
        eps = 1e-3

        # 计算每个像素在不同尺度下的权重
        for k in range(len(sigma_list)):
            sigma = sigma_list[k]
            weight = cuda.shared.array((3, 3), dtype=float32)
            for i in range(3):
                for j in range(3):
                    x_, y_ = x + i - 1, y + j - 1
                    if x_ >= 0 and x_ < img.shape[0] and y_ >= 0 and y_ < img.shape[1]:
                        weight[i, j] = 1.0 / (sigma * sigma) * np.exp(-((img[x_, y_] - img[x, y]) / alpha) ** 2 / 2)
                    else:
                        weight[i, j] = 0.0
            weight /= (np.sum(weight) + eps)

            # 计算在当前尺度下的像素值
            value = 0
            for i in range(3):
                for j in range(3):
                    x_, y_ = x + i - 1, y + j - 1
                    if x_ >= 0 and x_ < img.shape[0] and y_ >= 0 and y_ < img.shape[1]:
                        value += weight[i, j] * np.log(img[x_, y_] + eps)
            output[bx * bw + tx, by * bh + ty, k] = beta * np.exp(value)

3. 加载图像并将其转换为CUDA数组

img = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
img_cuda = cuda.to_device(img)

4. 定义Grid和Block大小

threads_per_block = (32, 32)
blocks_per_grid_x = int(np.ceil(img.shape[0] / threads_per_block[0]))
blocks_per_grid_y = int(np.ceil(img.shape[1] / threads_per_block[1]))
blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y)

5. 创建输出数组并调用MSR函数

output = cuda.device_array((img.shape[0], img.shape[1], 3))
msr_cuda[blocks_per_grid, threads_per_block](img_cuda, output)

6. 将CUDA数组转换为OpenCV图像并保存

output_host = output.copy_to_host()
msr_img = np.zeros_like(img)
for k in range(3):
    msr_img[:, :, k] = cv2.normalize(output_host[:, :, k], None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
cv2.imwrite('test_msr.jpg', msr_img)

完成上述步骤后，你就可以使用CUDA加速的MSR算法对图像进行增强了。