使用PIL和Numpy的Python图像过滤速度太慢 [英] Python image filtering with PIL and Numpy too slow

问题描述

我正在尝试使用PIL和Numpy在Python中实现最简单的高斯滤波器.一切正常，但速度很慢:(无法确定如何加快速度.您能帮忙吗?

I'm trying to implement the simpliest Gauss filter in Python with PIL and Numpy. All works fine but very slowly :( Can't figure out how to speed up. Could you help?

import os, sys  
import Image, ImageEnhance  
import numpy as np  

if (len(sys.argv) > 1):  
    im = Image.open(sys.argv[1])  
    data = np.array(im.resize((200,200)))  
    out_data = np.array(data)  
    chs = len(data[0][0])  
    kernel = np.array([[1.,2,1],  
                       [2,3,2],  
                       [1,2,1]])  
    ctr = 1  
    kernel = kernel/np.sum(kernel)  
    for x in xrange(data.shape[0]):  
        for y in xrange(data.shape[1]):  
            for c in xrange(chs):  
                acc = 0  
                for i in xrange(kernel.shape[0]):  
                    for j in xrange(kernel.shape[1]):  
                        m = x + i - ctr  
                        n = y + j - ctr  
                        if (m >= 0 and n >= 0 and m < data.shape[0] and n < data.shape  [1]):
                            acc += data[m][n][c]*kernel[i][j]  
                out_data[x][y][c] = acc  
    out = Image.new(im.mode, (data.shape[0], data.shape[1]))  
    out = Image.fromarray(out_data)  
    out.show()  
else:  
    print "no file was passed"  

推荐答案

看看高斯模糊滤波器是可分离的，这意味着您可以大大降低算法的复杂性(在研究其他答案(即并行化)的建议之上).

Gaussian blur filters are separable, which means you can reduce the complexity of your algorithm quite a bit (on top of looking into the suggestions from other answers, i.e. parallelization).

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