python图像增强算法
时间: 2023-10-24 10:26:47 浏览: 53
在Python中,有多种图像增强算法可以使用。其中一种常见的算法是图像反转。该算法通过求取每个像素的暗通道,然后根据透射率对每个通道进行去雾操作,从而增强图像的效果。以下是一个示例代码:
```python
import cv2
import argparse
import numpy as np
def hazeRemoval(img, w=0.7, t0=0.1):
# 求每个像素的暗通道
darkChannel = img.min(axis=2)
# 取暗通道的最大值最为全球大气光
A = darkChannel.max()
darkChannel = darkChannel.astype(np.double)
# 利用公式求得透射率
t = 1 - w * (darkChannel / A)
# 设定透射率的最小值
t[t < t0 = t0
J = img
# 对每个通道分别进行去雾
J[:, :, 0 = (img[:, :, 0 - (1 - t) * A) / t
J[:, :, 1 = (img[:, :, 1 - (1 - t) * A) / t
J[:, :, 2 = (img[:, :, 2 - (1 - t) * A) / t
return J
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--image', required=True)
args = vars(ap.parse_args())
hazeImage = cv2.imread(args["image"])
result = hazeRemoval(hazeImage.copy())
cv2.imshow("HazeRemoval", np.hstack([hazeImage, result]))
cv2.waitKey(0)
if __name__ == '__main__':
main()
```
另外一个常见的图像增强算法是通过对图像的像素进行阈值处理来增强图像的对比度。以下是一个示例代码:
```python
import cv2
import numpy as np
img = cv2.imread("test3.jpg", 0)
cv2.imshow("img", img)
h, w = img.shape
for i in range(h):
for j in range(w):
if 100 < img[i][j < 180:
img[i][j = 150
else:
img[i][j = 25
cv2.imshow("img_test", img)
cv2.waitKey()
```
这段代码将读取一张灰度图像,并通过设置阈值来对像素进行增强,从而改变图像的对比度。你可以根据具体需求调整阈值和像素值来实现不同的图像增强效果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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