暗通道先验算法python

暗通道先验算法（Dark Channel Prior）是一种用于图像去雾的算法。在Python中，可以使用以下代码实现该算法：

import numpy as np
import cv2

def dark_channel_prior(image, patch_size=15):
    # 计算暗通道图像
    min_channel = np.min(image, axis=2)
    dark_channel = cv2.erode(min_channel, np.ones((patch_size, patch_size), dtype=np.uint8))

    return dark_channel

def get_atmospheric_light(image, dark_channel, top_percent=0.001):
    # 获取全球大气光值
    flat_dark_channel = dark_channel.flatten()
    num_pixels = len(flat_dark_channel)
    top_pixels = int(num_pixels * top_percent)
    indices = np.argpartition(flat_dark_channel, -top_pixels)[-top_pixels:]
    atmospheric_light = np.max(image.reshape(-1, 3)[indices], axis=0)

    return atmospheric_light

def get_transmission(image, atmospheric_light, omega=0.95, patch_size=15):
    # 估计透射率图像
    normalized_image = image.astype(np.float64) / atmospheric_light.astype(np.float64)
    dark_channel = dark_channel_prior(normalized_image, patch_size)
    transmission = 1 - omega * dark_channel

    return transmission

def dehaze(image, transmission, atmospheric_light, t0=0.1):
    # 对图像进行去雾处理
    transmission = np.clip(transmission, t0, 1)
    result = np.empty_like(image)
    for c in range(3):
        result[:, :, c] = (image[:, :, c].astype(np.float64) - atmospheric_light[c]) / transmission + atmospheric_light[c]
    result = np.clip(result, 0, 255).astype(np.uint8)

    return result

def haze_removal(image_path):
    # 图像去雾主函数
    image = cv2.imread(image_path)
    dark_channel = dark_channel_prior(image)
    atmospheric_light = get_atmospheric_light(image, dark_channel)
    transmission = get_transmission(image, atmospheric_light)
    result = dehaze(image, transmission, atmospheric_light)

    return result

# 示例用法
result_image = haze_removal('input_image.jpg')
cv2.imwrite('output_image.jpg', result_image)

这是一个基本的暗通道先验算法的实现，通过调整参数可以对图像进行去雾处理。你可以将输入图像替换为自己的图像文件，并将输出图像保存在指定的文件路径中。希望这能帮到你！