如何从 numpy 数组中提取任意一行值? [英] How to extract an arbitrary line of values from a numpy array?

问题描述

我有一个包含一些图像数据的 numpy 数组.我想绘制在图像上绘制的横断面的轮廓".最简单的情况是轮廓平行于图像边缘运行，因此如果图像数组是 imdat，则所选点 (r,c) 处的轮廓是只需 imdat[r](水平)或 imdat[:,c](垂直).

I have a numpy array that contains some image data. I would like to plot the 'profile' of a transect drawn across the image. The simplest case is a profile running parallel to the edge of the image, so if the image array is imdat, then the profile at a selected point (r,c) is simply imdat[r] (horizontal) or imdat[:,c] (vertical).

现在，我想将两个点 (r1,c1) 和 (r2,c2) 作为输入，它们都位于 imdat.我想沿着连接这两个点的线绘制值的轮廓.

Now, I want to take as input two points (r1,c1) and (r2,c2), both lying inside imdat. I would like to plot the profile of the values along the line connecting these two points.

沿着这条线从 numpy 数组中获取值的最佳方法是什么?更一般地说，沿着路径/多边形?

What is the best way to get values from a numpy array, along such a line? More generally, along a path/polygon?

我之前使用过切片和索引，但对于连续切片元素不在同一行或列中的情况，我似乎无法找到一个优雅的解决方案.感谢您的帮助.

I have used slicing and indexing before, but I can't seem to arrive at an elegant solution for such a where consecutive slice elements are not in the same row or column. Thanks for your help.

推荐答案

@Sven 的答案很简单，但对于大型数组来说效率很低.如果您正在处理一个相对较小的数组，您不会注意到其中的区别，如果您想要一个大的(例如 >50 MB)的配置文件，您可能想尝试其他几种方法.不过，您需要使用像素"坐标来处理这些问题，因此会增加一层额外的复杂性.

@Sven's answer is the easy way, but it's rather inefficient for large arrays. If you're dealing with a relatively small array, you won't notice the difference, if you're wanting a profile from a large (e.g. >50 MB) you may want to try a couple of other approaches. You'll need to work in "pixel" coordinates for these, though, so there's an extra layer of complexity.

还有两种更节省内存的方法.1) 使用 scipy.ndimage.map_coordinates 如果您需要双线性或三次插值.2)如果你只是想要最近邻采样，那么就直接使用索引.

There are two more memory-efficient ways. 1) use scipy.ndimage.map_coordinates if you need bilinear or cubic interpolation. 2) if you just want nearest neighbor sampling, then just use indexing directly.

作为第一个例子:

import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt

#-- Generate some data...
x, y = np.mgrid[-5:5:0.1, -5:5:0.1]
z = np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)

#-- Extract the line...
# Make a line with "num" points...
x0, y0 = 5, 4.5 # These are in _pixel_ coordinates!!
x1, y1 = 60, 75
num = 1000
x, y = np.linspace(x0, x1, num), np.linspace(y0, y1, num)

# Extract the values along the line, using cubic interpolation
zi = scipy.ndimage.map_coordinates(z, np.vstack((x,y)))

#-- Plot...
fig, axes = plt.subplots(nrows=2)
axes[0].imshow(z)
axes[0].plot([x0, x1], [y0, y1], 'ro-')
axes[0].axis('image')

axes[1].plot(zi)

plt.show()

使用最近邻插值的等效项如下所示:

The equivalent using nearest-neighbor interpolation would look something like this:

import numpy as np
import matplotlib.pyplot as plt

#-- Generate some data...
x, y = np.mgrid[-5:5:0.1, -5:5:0.1]
z = np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)

#-- Extract the line...
# Make a line with "num" points...
x0, y0 = 5, 4.5 # These are in _pixel_ coordinates!!
x1, y1 = 60, 75
num = 1000
x, y = np.linspace(x0, x1, num), np.linspace(y0, y1, num)

# Extract the values along the line
zi = z[x.astype(np.int), y.astype(np.int)]

#-- Plot...
fig, axes = plt.subplots(nrows=2)
axes[0].imshow(z)
axes[0].plot([x0, x1], [y0, y1], 'ro-')
axes[0].axis('image')

axes[1].plot(zi)

plt.show()

但是，如果您使用最近邻，您可能只需要每个像素的样本，因此您可能会做更多类似的事情，而不是......

However, if you're using nearest-neighbor, you probably would only want samples at each pixel, so you'd probably do something more like this, instead...

import numpy as np
import matplotlib.pyplot as plt

#-- Generate some data...
x, y = np.mgrid[-5:5:0.1, -5:5:0.1]
z = np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)

#-- Extract the line...
# Make a line with "num" points...
x0, y0 = 5, 4.5 # These are in _pixel_ coordinates!!
x1, y1 = 60, 75
length = int(np.hypot(x1-x0, y1-y0))
x, y = np.linspace(x0, x1, length), np.linspace(y0, y1, length)

# Extract the values along the line
zi = z[x.astype(np.int), y.astype(np.int)]

#-- Plot...
fig, axes = plt.subplots(nrows=2)
axes[0].imshow(z)
axes[0].plot([x0, x1], [y0, y1], 'ro-')
axes[0].axis('image')

axes[1].plot(zi)

plt.show()

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