不要用cv2.createCLAHE的条件下, 用Numpy实现的CLAHE函数,输入是彩图

好的，我可以给你提供一个基于Numpy实现的CLAHE函数，可以处理彩色图像。

import numpy as np

def clahe(img, clip_limit=2.0, grid_size=(8,8), channel='rgb'):
    """
    Numpy implementation of Contrast Limited Adaptive Histogram Equalization (CLAHE) for RGB images.
    
    :param img: input RGB image
    :param clip_limit: threshold for contrast limiting
    :param grid_size: size of the grid for histogram equalization
    :param channel: color channel to apply CLAHE on. 'r', 'g', 'b', or 'rgb'
    :return: CLAHE-enhanced RGB image
    """
    if channel == 'r':
        img = img[:,:,0]
    elif channel == 'g':
        img = img[:,:,1]
    elif channel == 'b':
        img = img[:,:,2]
    
    h, w = img.shape[:2]
    h_grid, w_grid = grid_size
    
    clahe_img = np.zeros_like(img)
    for i in range(h_grid):
        for j in range(w_grid):
            
            # compute histogram of the current grid
            i_min, i_max = int(i*h/h_grid), int((i+1)*h/h_grid)
            j_min, j_max = int(j*w/w_grid), int((j+1)*w/w_grid)
            hist, _ = np.histogram(img[i_min:i_max,j_min:j_max], bins=256, range=(0,256))
            
            # limit histogram
            cdf = np.cumsum(hist)
            cdf = (cdf / cdf[-1]).astype(np.float32)
            cdf = np.maximum(cdf - clip_limit/(h_grid*w_grid), 0)
            cdf = cdf / (1 - clip_limit/(h_grid*w_grid))
            
            # apply histogram equalization
            grid_img = img[i_min:i_max,j_min:j_max]
            grid_img = np.interp(grid_img, np.arange(256), cdf*255).astype(np.uint8)
            
            clahe_img[i_min:i_max,j_min:j_max] = grid_img
    
    if channel == 'rgb':
        clahe_img = np.stack([clahe_img]*3, axis=-1)
    elif channel == 'r':
        clahe_img = np.stack([clahe_img]*3, axis=-1)
        clahe_img[:,:,1:] = img[:,:,1:]
    elif channel == 'g':
        clahe_img = np.stack([img[:,:,0], clahe_img, img[:,:,2]], axis=-1)
    elif channel == 'b':
        clahe_img = np.stack([img[:,:,:2], clahe_img], axis=-1)
    
    return clahe_img

这个函数的输入是一个RGB图像，输出是CLAHE增强后的RGB图像。你可以自己指定一些参数，例如`clip_limit`是限制对比度的阈值，`grid_size`是直方图均衡化的网格大小，`channel`是你想要对哪个颜色通道进行CLAHE增强。如果你想对所有颜色通道进行CLAHE增强，可以将`channel`设置为'rgb'。