图像类型int16到uint8的转换

问题描述：

我有一个数据类型为int16的图像. 因此，当我必须将其范围转换为0-255时，我有两种方法可以在Python中实现.

I have an image with data type int16. So when I have to convert its range to 0-255, I got two ways to do that in Python.

1)直接使用numpy.uint8功能

2)在0-255范围内使用OpenCV cv2.normalize函数，然后使用numpy.uint8.

2) Use OpenCV cv2.normalize function with 0-255 range and then use numpy.uint8.

在Matlab中，我们直接使用uint8函数进行转换. 在

In Matlab, we directly get the conversion using uint8 function. In

在第二种情况下，我使用了NORM_MINMAX，强度值的范围也更改为0-4.

Also in second case, I used NORM_MINMAX and the range of intensity values gets changed to 0-4.

进行转换的正确方法是什么?

What is the correct way to do the conversion?

答

所有这些都做不同的事情.

All of these do different things.

np.uint8仅考虑数字的最低字节.就像在做value & 0xff.

np.uint8 considers only the lowest byte of your number. It's like doing value & 0xff.

>>> img = np.array([2000, -150, 11], dtype=np.int16)
>>> np.uint8(img)
array([208, 106,  11], dtype=uint8)

规范类型为cv2.NORM_MINMAX的

cv2.normalize根据规范化功能

cv2.normalize with the cv2.NORM_MINMAX norm type normalises your values according to the normalisation function

img_new = (img - img.min()) * ((max_new - min_new) / (img.max() - img.min())) + min_new

它可以有效地将一个范围更改为另一个范围，并且之间的所有值都将按比例缩放.根据定义，原始的最小/最大值成为目标的最小/最大值.

It effectively changes one range to another and all the values in the between are scaled accordingly. By definition the original min/max values become the targetted min/max values.

>>> cv2.normalize(img, out, 0, 255, cv2.NORM_MINMAX)
array([255,   0,  19], dtype=int16)

Matlab中的

uint8只会使您的值饱和.高于255的所有内容变为255，低于0的所有内容变为0.

uint8 in Matlab simply saturates your values. Everything above 255 becomes 255 and everything below 0 becomes 0.

>> uint8([2000 -150 11])

ans =

  255    0   11

如果您想复制Matlab的功能，则可以

If you want to replicate Matlab's functionality, you can do

>>> img[img > 255] = 255
>>> img[img < 0] = 0

您要使用哪一个取决于您要执行的操作.如果int16覆盖了像素值的范围，并且您想将其缩放为uint8，那么答案就是cv2.normalize.

图像类型int16到uint8的转换

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