生成暗通道先验的python代码

以下是一个生成暗通道先验的Python代码示例：

import numpy as np
import cv2

def get_dark_channel(img, patch_size):
    # 计算暗通道图像
    b, g, r = cv2.split(img)
    min_channel = cv2.min(cv2.min(r, g), b)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (patch_size, patch_size))
    dark_channel = cv2.erode(min_channel, kernel)
    return dark_channel

def get_atmosphere(img, dark_channel, top_percent):
    # 从暗通道图像中获取大气光值
    # 按照暗通道像素值排序，取前top_percent%的像素点
    pixel_num = dark_channel.shape[0] * dark_channel.shape[1]
    flat_dark = dark_channel.reshape(pixel_num)
    flat_img = img.reshape(pixel_num, 3)
    indices = np.argsort(-flat_dark)
    top_num = int(top_percent / 100.0 * pixel_num)
    top_indices = indices[:top_num]
    top_pixels = flat_img[top_indices]
    atmosphere = np.max(top_pixels, axis=0)
    return atmosphere

def get_transmission(img, atmosphere, omega, patch_size):
    # 计算透射率
    b, g, r = cv2.split(img.astype(np.float64))
    transmission = 1.0 - omega * get_dark_channel(img / atmosphere, patch_size)
    transmission = np.clip(transmission, 0, 1)
    return transmission

def dehaze(img, omega=0.95, patch_size=15, top_percent=0.1):
    # 去雾
    dark_channel = get_dark_channel(img, patch_size)
    atmosphere = get_atmosphere(img, dark_channel, top_percent)
    transmission = get_transmission(img, atmosphere, omega, patch_size)
    result = np.zeros(img.shape, img.dtype)
    for i in range(3):
        result[:, :, i] = (img[:, :, i] - atmosphere[i]) / np.maximum(transmission, 0.1) + atmosphere[i]
    return result

# 测试代码
img = cv2.imread('haze_image.jpg')
result = dehaze(img)
cv2.imwrite('dehaze_image.jpg', result)

该代码实现了以下步骤：

1. 通过计算每个像素点的RGB三个通道的最小值，得到暗通道图像。
2. 从暗通道图像中获取大气光值，即暗通道像素值最大的前top_percent%的像素点的RGB三个通道的最大值。
3. 计算透射率，即每个像素点在RGB三个通道的透射率的最小值，通过透射率去除雾霾。
4. 对去除雾霾后的RGB三个通道进行修复，使图像看起来更自然。

这是一种简单有效的暗通道先验去雾方法。