如何对浮点数numpy数组进行高斯滤波(模糊)

本文介绍了如何对浮点数numpy数组进行高斯滤波(模糊)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

我有一个float64类型的numpy数组a.如何使用高斯滤波器对这些数据进行模糊处理?

I have got a numpy array a of type float64. How can I blur this data with a Gauss filter?

我尝试过

from PIL import Image, ImageFilter image = Image.fromarray(a) filtered = image.filter(ImageFilter.GaussianBlur(radius=7))

，但这会产生ValueError: 'image has wrong mode'. (它的模式为F.)

, but this yields ValueError: 'image has wrong mode'. (It has mode F.)

我可以通过将a与某个常数相乘，然后四舍五入为整数来创建合适模式的图像.那应该可以，但是我想有一个更直接的方法.

I could create an image of suitable mode by multiplying a with some constant, then rounding to integer. That should work, but I would like to have a more direct way.

(我使用的是Pillow 2.7.0.)

(I am using Pillow 2.7.0.)

推荐答案

如果有二维numpy数组a，则可以直接在其上使用高斯滤波器，而无需先使用Pillow将其转换为图像. scipy具有功能 gaussian_filter 一样.

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_filter that does the same.

from scipy.ndimage.filters import gaussian_filter blurred = gaussian_filter(a, sigma=7)