python,设置大气能见度(单位km)在无雾图像中添加雾霾合成有雾图像
时间: 2023-05-27 18:06:59 浏览: 50
要在Python中添加雾霾效果,可以使用以下步骤:
1. 定义一个函数,该函数将接受输入图像,大气能见度和雾霾强度作为参数。
2. 在函数中,首先计算出雾霾影响因素,即由于空气中的颗粒物质而导致的透明度减少。
3. 然后,根据大气能见度和雾霾强度计算出雾的密度。
4. 接下来,使用雾的密度和雾霾影响因素来生成一个雾图像。
5. 最后,将原始图像与雾图像混合,以创建有雾效果。
以下是一个示例代码,其中包含有雾图像的创建和原始图像的混合:
```python
import cv2
import numpy as np
def add_haze(image, visibility, haze_strength):
"""
Adds haze to an image.
Parameters:
image (numpy.ndarray): The input image.
visibility (float): The visibility distance in kilometers.
haze_strength (float): The strength of the haze effect.
Returns:
numpy.ndarray: The output image with haze effect added.
"""
# Convert visibility distance from kilometers to meters.
visibility *= 1000
# Calculate the haze factor.
haze_factor = 1 - (haze_strength * 0.01)
# Calculate the atmospheric light based on the haze factor.
atmospheric_light = np.ones_like(image) * 255 * haze_factor
# Generate a random noise image.
noise_image = np.random.randint(0, 256, size=image.shape).astype(np.uint8)
# Calculate the distance to the farthest point in the image.
max_distance = np.sqrt(image.shape[0]**2 + image.shape[1]**2)
# Calculate the density of the haze.
density = np.exp(-(visibility / max_distance)**2)
# Calculate the haze image.
haze_image = (image * (1 - density) + atmospheric_light * density + noise_image * (1 - haze_factor)).astype(np.uint8)
# Blend the original image and the haze image.
output_image = cv2.addWeighted(image, haze_factor, haze_image, 1 - haze_factor, 0)
return output_image
# Load the input image.
input_image = cv2.imread('input_image.jpg')
# Add haze to the image.
output_image = add_haze(input_image, 5, 50)
# Display the output image.
cv2.imshow('Output Image', output_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
在这个示例中,我们使用了numpy和OpenCV库来生成随机噪声和混合图像。您可以根据需要调整参数来获得所需的雾霾效果。