Python实现双直方图均衡算法
时间: 2023-11-05 14:02:30 浏览: 45
双直方图均衡算法(Dual Histogram Equalization)是一种对比度增强算法,可以在图像处理中使用,下面是Python实现双直方图均衡算法的代码:
```python
import numpy as np
import cv2
def dual_histogram_equalization(img):
# 将图像转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 获取灰度图像的直方图
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
# 获取灰度图像的累积分布函数
cdf = hist.cumsum()
# 归一化
cdf_normalized = cdf * hist.max() / cdf.max()
# 获取灰度图像的归一化累积分布函数
norm_cdf = np.zeros_like(cdf)
for i in range(256):
norm_cdf[i] = cdf_normalized[i] / cdf_normalized[-1]
# 获取滑动窗口的大小
window_size = gray.shape[0] // 10
# 获取滑动窗口的步长
stride = gray.shape[0] // 20
# 初始化输出图像
out = np.zeros_like(gray)
# 对每个滑动窗口进行均衡化
for i in range(0, gray.shape[0] - window_size, stride):
window = gray[i:i+window_size]
# 获取当前窗口的直方图
hist = cv2.calcHist([window], [0], None, [256], [0, 256])
# 获取当前窗口的累积分布函数
cdf = hist.cumsum()
# 归一化
cdf_normalized = cdf * hist.max() / cdf.max()
# 获取当前窗口的归一化累积分布函数
norm_cdf = np.zeros_like(cdf)
for j in range(256):
norm_cdf[j] = cdf_normalized[j] / cdf_normalized[-1]
# 对当前窗口进行均衡化
for j in range(window_size):
out[i+j] = 255 * norm_cdf[window[j]]
return out
```
使用以下代码调用该函数:
```python
img = cv2.imread("input.jpg")
out = dual_histogram_equalization(img)
cv2.imshow("Input", img)
cv2.imshow("Output", out)
cv2.waitKey(0)
```
其中,`input.jpg`是输入图像的文件名。