使用NumPy对灰度图像进行直方图均衡 [英] Histogram equalization of grayscale images with NumPy
本文介绍了使用NumPy对灰度图像进行直方图均衡的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
如何对存储在NumPy数组中的多个灰度图像进行直方图均衡?
How to do histogram equalization for multiple grayscaled images stored in a NumPy array easily?
我有这种4D格式的96x96像素NumPy数据:
I have the 96x96 pixel NumPy data in this 4D format:
(1800, 1, 96,96)
推荐答案
穆斯的评论指向此博客很好地完成了这项工作。
Moose's comment which points to this blog entry does the job quite nicely.
为了完整性,我在这里使用更好的变量名称和一个4D中的1000个96x96图像的循环执行给出了一个例子数组中的问题。它很快(在我的电脑上1-2秒)并且只需要NumPy。
For completeness I give an axample here using nicer variable names and a looped execution on 1000 96x96 images which are in a 4D array as in the question. It is fast (1-2 seconds on my computer) and only needs NumPy.
import numpy as np
def image_histogram_equalization(image, number_bins=256):
# from http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html
# get image histogram
image_histogram, bins = np.histogram(image.flatten(), number_bins, normed=True)
cdf = image_histogram.cumsum() # cumulative distribution function
cdf = 255 * cdf / cdf[-1] # normalize
# use linear interpolation of cdf to find new pixel values
image_equalized = np.interp(image.flatten(), bins[:-1], cdf)
return image_equalized.reshape(image.shape), cdf
if __name__ == '__main__':
# generate some test data with shape 1000, 1, 96, 96
data = np.random.rand(1000, 1, 96, 96)
# loop over them
data_equalized = np.zeros(data.shape)
for i in range(data.shape[0]):
image = data[i, 0, :, :]
data_equalized[i, 0, :, :] = image_histogram_equalization(image)[0]
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