Python 如何对浮点 numpy 数组进行高斯滤波（模糊）
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How to gauss-filter (blur) a floating point numpy array
提问by Robert Pollak
I have got a numpy array aof type float64. How can I blur this data with a Gauss filter?
我有一个 numpya类型的数组float64。如何使用高斯滤波器模糊这些数据？
I have tried
我试过了
from PIL import Image, ImageFilter
image = Image.fromarray(a)
filtered = image.filter(ImageFilter.GaussianBlur(radius=7))
, but this yields ValueError: 'image has wrong mode'. (It has mode F.)
，但这会产生ValueError: 'image has wrong mode'。（它有模式F。）
I could create an image of suitable mode by multiplying awith some constant, then rounding to integer. That should work, but I would like to have a more direct way.
我可以通过乘以a某个常数，然后舍入为整数来创建合适模式的图像。这应该有效，但我想有一个更直接的方法。
(I am using Pillow 2.7.0.)
（我使用枕头 2.7.0。）
采纳答案by Carsten
If you have a two-dimensional numpy array a, you can use a Gaussian filter on it directly without using Pillow to convert it to an image first. scipy has a function gaussian_filterthat does the same.
如果你有一个二维的 numpy array a，你可以直接在它上面使用高斯滤波器，而不用先用 Pillow 把它转换成图像。scipy 具有gaussian_filter相同的功能。
from scipy.ndimage.filters import gaussian_filter
blurred = gaussian_filter(a, sigma=7)
回答by Filipe Alves
Here is my approach using only numpy. It is prepared with a simple 3x3 kernel, minor changes could make it work with custom sized kernels.
这是我仅使用 numpy 的方法。它是用一个简单的 3x3 内核准备的，稍作改动就可以使其与自定义大小的内核一起使用。
def blur(a):
kernel = np.array([[1.0,2.0,1.0], [2.0,4.0,2.0], [1.0,2.0,1.0]])
kernel = kernel / np.sum(kernel)
arraylist = []
for y in range(3):
temparray = np.copy(a)
temparray = np.roll(temparray, y - 1, axis=0)
for x in range(3):
temparray_X = np.copy(temparray)
temparray_X = np.roll(temparray_X, x - 1, axis=1)*kernel[y,x]
arraylist.append(temparray_X)
arraylist = np.array(arraylist)
arraylist_sum = np.sum(arraylist, axis=0)
return arraylist_sum
