不用opencv，实现clahe算法

如果你不想使用OpenCV库，你可以手动实现CLAHE算法。以下是一个基于NumPy库的简单实现示例：

import numpy as np

def calculate_histogram(image):
    histogram = np.zeros(256, dtype=np.uint32)
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            pixel_value = image[i, j]
            histogram[pixel_value] += 1
    return histogram

def apply_clahe(image, clip_limit=2.0, grid_size=(8, 8)):
    # 计算图像的直方图
    histogram = calculate_histogram(image)

    # 计算累积直方图
    cdf = np.cumsum(histogram)

    # 计算CLAHE增强后的图像
    enhanced_image = np.zeros_like(image)
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            pixel_value = image[i, j]

            # 计算分块的起始和结束坐标
            block_start_i = (i // grid_size[0]) * grid_size[0]
            block_end_i = block_start_i + grid_size[0]
            block_start_j = (j // grid_size[1]) * grid_size[1]
            block_end_j = block_start_j + grid_size[1]

            # 计算分块内的直方图
            block_histogram = histogram[pixel_value]
            block_histogram += cdf[pixel_value] - cdf[block_start_i:block_end_i, block_start_j:block_end_j].mean()

            # 限制对比度增强
            block_histogram = np.clip(block_histogram, 0, clip_limit)

            # 将像素值映射到增强后的直方图范围
            block_histogram = (block_histogram / clip_limit) * 255

            # 更新增强后的像素值
            enhanced_image[i, j] = block_histogram[pixel_value]

    return enhanced_image

在上述代码中，`calculate_histogram`函数计算输入图像的直方图。`apply_clahe`函数接收输入图像、对比度限制（clip_limit）和分块大小（grid_size）作为参数，并应用CLAHE增强算法。

请注意，这只是一个简单的CLAHE实现示例，可能没有考虑到所有细节和优化。CLAHE算法涉及到图像分块、直方图计算、限制对比度增强等步骤，因此实现完整的CLAHE算法可能需要更复杂的代码。