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本文旨在介绍直方图均衡化的概念及其在改善图片对比度中的应用。当图像像素值集中在某一范围内时，直方图均衡化会通过拉伸像素分布来增强图像对比度。首先，我们利用Numpy实现直方图均衡化，观察到高灰度值的集中现象，并通过转换函数将集中分布的直方图映射到更宽的范围。然后，探讨了OpenCV中的cv2.equalizeHist()函数，该函数适用于灰度图像，能有效提升对比度。最后，提到了局部自适应直方图均衡化（CLAHE），它将图像划分为小块并分别均衡化，特别适用于像素变化幅度较小的区域。
Goal:
Learning the concept of histogram equailze and how to use it to improve the contrast of pictures
Theory:
If most of the pixel values in an images are concentrated in one pixel range.For example, if a picture is bright as a whole, all pixels should be very high, but the pixel value distribution of a high quality image should be very wide.So we should horizontally stretch the histogram.
In general,this operation will improve the contrast of the image.
First we use Numpy for histogram equlaization, and then learn how to use OpenCV for it.
Numpy
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