top_percent = 0.001;

这是一个用于计算大气光照值的参数，用于确定图像中亮度最高的前0.1%的像素作为大气光照值。具体来说，top_percent的值为0.001表示取亮度最高的前0.1%的像素作为大气光照值。这个值可以根据具体情况进行调整，例如当图像质量较差或者亮度变化较大时，可以适当增大top_percent的值，以提高大气光照值的准确性。

相关问题

基于Python的图像去雾项目代码实现

基于Python的图像去雾项目的代码实现可以分为以下几个步骤：

1. 导入所需的库：

   import cv2
   import numpy as np

2. 加载图像：

   img = cv2.imread('input.jpg')

3. 预处理：

   gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

4. 估计大气光：

   dark_channel = np.min(gray, axis=2)
   top_percent = 0.001
   pixels = dark_channel.size
   num_pixels = int(pixels * top_percent)
   dark_channel_flat = dark_channel.flatten()
   dark_channel_flat_sorted = np.sort(dark_channel_flat)
   atmospheric_light = np.mean(dark_channel_flat_sorted[-num_pixels:])

5. 估计透射率：

   window_size = 15
   omega = 0.95
   mean_filter = cv2.blur(gray, (window_size, window_size))
   transmission = 1 - omega * dark_channel / mean_filter

6. 修复透射率：

   guided_filter = cv2.ximgproc.createGuidedFilter(gray, window_size, eps)
   transmission_filtered = guided_filter.filter(transmission)

7. 恢复原始图像：

   t = np.maximum(transmission_filtered, 0.1)
   restored = np.empty_like(img)
   for i in range(3):
       restored[:, :, i] = (img[:, :, i] - atmospheric_light) / t + atmospheric_light
       restored[:, :, i] = np.clip(restored[:, :, i], 0, 255)

8. 保存结果：

   cv2.imwrite('output.jpg', restored)

以上是一个简单的基于Python的图像去雾项目的代码实现。你可以根据需要进行调整和优化。

opencv实现DeHaze

以下是使用OpenCV实现DeHaze的步骤：

1.读取图像并转换为浮点数格式：

import cv2
import numpy as np

img = cv2.imread('image.jpg')
img = img.astype('float64') / 255.0

2.计算暗通道：

patch_size = 15
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)

3.计算大气光：

top_percent = 0.001
height, width = img.shape[:2]
num_pixels = height * width
num_top_pixels = int(num_pixels * top_percent)
dark_flat = dark_channel.flatten()
indices = dark_flat.argsort()[-num_top_pixels:]
atmosphere = np.zeros(3)
for i in range(num_top_pixels):
    atmosphere += img.item(indices[i] / width, indices[i] % width, :).astype('float64')
atmosphere /= num_top_pixels

4.计算透射率：

omega = 0.95
transmission = 1 - omega * dark_channel / atmosphere.max()

5.恢复原始图像：

tmin = 0.1
transmission = cv2.max(transmission, tmin)
img_recovered = np.zeros_like(img)
for i in range(3):
    img_recovered[:, :, i] = (img[:, :, i] - atmosphere[i]) / transmission + atmosphere[i]
img_recovered = cv2.min(cv2.max(img_recovered, 0), 1)
img_recovered = (img_recovered * 255).astype('uint8')