如何应用 ndimage.generic_filter() [英] How to apply ndimage.generic_filter()

问题描述

我正在尝试学习 ndimage，但我不知道如何generic_filter() 函数有效.文档提到用户功能将应用于用户定义的足迹，但不知何故我无法做到.示例如下:

<预><代码>>>>将 numpy 导入为 np>>>从 scipy 导入 ndimage>>>im = np.ones((20, 20)) * np.arange(20)>>>足迹 = np.array([[0,0,1],... [0,0,0]，... [1,0,0]])...>>>定义测试(x):...返回 x * 0.5...>>>res = ndimage.generic_filter(im, test,footprint=footprint)回溯(最近一次调用最后一次):文件<Engine input>"，第 1 行，在 <module> 中文件C:\Python27\lib\site-packages\scipy\ndimage\filters.py"，第 1142 行，在 generic_filter 中cval、起源、extra_arguments、extra_keywords)类型错误:只有长度为 1 的数组可以转换为 Python 标量

我期望传递给 test() 函数的 x 值是每个数组样本的真实足迹相邻元素，因此在此示例中，数组的形状为 (2,)，但出现上述错误.

我做错了什么?
如何告诉通用过滤器在指定的相邻点上应用简单的值计算?

解决方案

传递给 ndimage.generic_filter 的函数必须将数组映射到标量.该数组将是一维的，并包含 im 中已被 footprint 选择"的值.

对于res 中的每个位置，函数返回的值是分配给该位置的值.这就是函数自然需要返回标量的原因.

例如，

def test(x):返回 (x*0.5).sum()

会起作用.

I'm trying to learn ndimage and I can't figure how generic_filter() function works. Documentation mentions that user function is to be applied to user defined footprint, but somehow I can't make it. Here is example:

>>> import numpy as np
>>> from scipy import ndimage
>>> im = np.ones((20, 20)) * np.arange(20)
>>> footprint = np.array([[0,0,1],
...                       [0,0,0],
...                       [1,0,0]])
... 
>>> def test(x):
...     return x * 0.5
... 
>>> res = ndimage.generic_filter(im, test, footprint=footprint)
Traceback (most recent call last):
  File "<Engine input>", line 1, in <module>
  File "C:\Python27\lib\site-packages\scipy\ndimage\filters.py", line 1142, in generic_filter
    cval, origins, extra_arguments, extra_keywords)
TypeError: only length-1 arrays can be converted to Python scalars

I expected that x value passed to test() function, are those True footprint neighboring elements for each array sample, so in this example arrays with shape (2,), but I get above error.

What am I doing wrong?
How can I tell generic filter to apply simple value calculation on specified neighboring points?

解决方案

The function passed to ndimage.generic_filter must map an array to a scalar. The array will be 1-dimensional, and contain the values from im which have been "selected" by the footprint.

For each location in res, the value returned by the function is the value assigned to that location. That's why, naturally, the function needs to return a scalar.

So, for example,

def test(x):
    return (x*0.5).sum()

would work.

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