cv2.createCLAHE

cv2.createCLAHE是OpenCV中的一个函数，用于创建一个局部对比度自适应直方图均衡化（CLAHE）对象。CLAHE是一种将图像分成小块并对每个块进行直方图均衡化的技术，可以在增强图像细节的同时避免过度增强噪声。使用cv2.createCLAHE函数可以设置CLAHE对象的各种参数，例如块大小、直方图bin数等。CLAHE对象可以用于对图像进行直方图均衡化，从而改善图像对比度。下面是一个示例代码：

import cv2

# 读取图像
img = cv2.imread('image.jpg', 0)

# 创建CLAHE对象
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))

# 对图像进行直方图均衡化
img_clahe = clahe.apply(img)

# 显示原图和处理后的图像
cv2.imshow('Original Image', img)
cv2.imshow('CLAHE Image', img_clahe)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个示例中，我们使用cv2.createCLAHE函数创建了一个CLAHE对象，并设置了clipLimit和tileGridSize参数。然后，我们使用CLAHE对象对图像进行直方图均衡化，并显示原始图像和处理后的图像。

相关问题

cv2.createclahe函数中cliplimit参数解释

cv2.createclahe函数中的cliplimit参数是指对比度限制，它控制了CLAHE算法中直方图像的剪裁程度，即在计算直方图生成LUT（look-up table）时，超过cliplimit的像素数会被截断，这有助于避免一些异常像素值的影响，同时也能加快处理速度。

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

CLAHE（Contrast Limited Adaptive Histogram Equalization）是一种非线性映射方式，用于增强图像的对比度。下面是用Numpy实现的CLAHE函数，可以输入彩图。

import numpy as np

def clahe(img, clip_limit=2.0, grid_size=(8, 8)):
    """
    Contrast Limited Adaptive Histogram Equalization (CLAHE)
    :param img: input image, should be a numpy array with shape (H, W, C)
    :param clip_limit: threshold for contrast limiting
    :param grid_size: size of grid for histogram equalization
    :return: CLAHE-enhanced image
    """
    # convert image to LAB color space
    lab_img = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
    clahe_img = np.zeros_like(lab_img)
    # apply CLAHE to each channel of LAB image separately
    for i in range(3):
        clahe_img[..., i] = clahe_channel(lab_img[..., i], clip_limit, grid_size)
    # convert back to RGB color space
    clahe_img = cv2.cvtColor(clahe_img, cv2.COLOR_LAB2RGB)
    return clahe_img

def clahe_channel(channel, clip_limit=2.0, grid_size=(8, 8)):
    """
    Apply CLAHE to a single channel
    :param channel: input channel, should be a numpy array with shape (H, W)
    :param clip_limit: threshold for contrast limiting
    :param grid_size: size of grid for histogram equalization
    :return: CLAHE-enhanced channel
    """
    h, w = channel.shape
    # compute histogram of image
    hist, bins = np.histogram(channel.flatten(), 256, [0, 256])
    # compute cumulative distribution function (CDF) of histogram
    cdf = hist.cumsum()
    cdf = (cdf / cdf[-1]) * 255.0
    # compute equalized image using CDF
    equalized = np.interp(channel.flatten(), bins[:-1], cdf)
    equalized = equalized.reshape((h, w))
    # compute contrast limited equalization
    grid_h, grid_w = grid_size
    gh = int(np.ceil(h / grid_h))
    gw = int(np.ceil(w / grid_w))
    for i in range(grid_h):
        for j in range(grid_w):
            # extract grid from equalized image
            grid = equalized[i*gh:(i+1)*gh, j*gw:(j+1)*gw]
            # compute histogram of grid
            hist, bins = np.histogram(grid.flatten(), 256, [0, 256])
            # compute cumulative distribution function (CDF) of histogram
            cdf = hist.cumsum()
            cdf = (cdf / cdf[-1]) * 255.0
            # clip CDF to contrast limit
            cdf = np.clip(cdf, 0, clip_limit * gw * gh / 256.0)
            # compute equalized grid using clipped CDF
            equalized_grid = np.interp(grid.flatten(), bins[:-1], cdf)
            equalized_grid = equalized_grid.reshape(grid.shape)
            # insert equalized grid into output image
            clahe_img[i*gh:(i+1)*gh, j*gw:(j+1)*gw] = equalized_grid
    return clahe_img

这个函数首先将输入的彩图转换为LAB色彩空间，然后分别对L、A、B三个通道应用CLAHE算法，最后再将LAB图像转换回RGB色彩空间。CLAHE算法的核心是对图像分成若干个网格，然后在每个网格中执行直方图均衡化。为了避免增强噪声，CLAHE算法还引入了一个对比度限制的机制，具体来说就是对每个网格的直方图进行裁剪，使得直方图的范围不超过一个阈值。这里的阈值是通过clip_limit参数控制的。