如何仅使用numpy和PIL检测图像翻译 [英] how to detect image translation with only numpy and PIL

问题描述

给出两个图像，我需要检测两个图像之间是否存在平移偏移.我只能使用numpy和PIL.

Given two images, I need to detect if there is a translation offset between the two. I am only able to use numpy and PIL.

这篇文章显示了如何使用PIL应用(x，y)翻译，但是还没有找到类似的方法来检测翻译.

This post shows how to apply an (x, y) translation with PIL, but haven't found something similar for how to detect the translation.

根据我的阅读，互相关似乎是解决方案的一部分，并且有

From what I've read, cross-correlation seems to be part of the solution, and there is the numpy.correlate function. However, I don't know how to use the output of this function to detect horizontal and vertical translation coordinates.

第一张图片

第二张图片

推荐答案

由于(几乎)二维数组，因此需要scipy.signal.correlate2d()函数.

Since these are (almost) 2D arrays, you want the scipy.signal.correlate2d() function.

首先，读取图像并将其转换为数组:

First, read your images and cast as arrays:

import numpy as np
from PIL import Image
import requests
import io

image1 = "https://i.stack.imgur.com/lf2lc.png"
image2 = "https://i.stack.imgur.com/MMSdM.png"

img1 = np.asarray(Image.open(io.BytesIO(requests.get(image1).content)))
img2 = np.asarray(Image.open(io.BytesIO(requests.get(image2).content)))

# img2 is greyscale; make it 2D by taking mean of channel values.
img2 = np.mean(img2, axis=-1)

现在我们有了两个图像，我们可以在

Now we have the two images, we can adapt the example in the scipy.signal.correlate2d() documentation:

from scipy import signal

corr = signal.correlate2d(img1, img2, mode='same')

如果出于某种原因要避免使用scipy，则应该等效:

If you want to avoid using scipy for some reason, then this should be equivalent:

pad = np.max(img1.shape) // 2
fft1 = np.fft.fft2(np.pad(img1, pad))
fft2 = np.fft.fft2(np.pad(img2, pad))
prod = fft1 * fft2.conj()
result_full = np.fft.fftshift(np.fft.ifft2(prod))
corr = result_full.real[1+pad:-pad+1, 1+pad:-pad+1]

现在我们可以计算最大相关性的位置:

Now we can compute the position of the maximum correlation:

y, x = np.unravel_index(np.argmax(corr), corr.shape)

现在我们可以可视化结果，再次修改文档示例:

Now we can visualize the result, again adapting the documentation example:

import matplotlib.pyplot as plt

x2, y2 = np.array(img2.shape) // 2

fig, (ax_img1, ax_img2, ax_corr) = plt.subplots(1, 3, figsize=(15, 5))
im = ax_img1.imshow(img1, cmap='gray')
ax_img1.set_title('img1')
ax_img2.imshow(img2, cmap='gray')
ax_img2.set_title('img2')
im = ax_corr.imshow(corr, cmap='viridis')
ax_corr.set_title('Cross-correlation')
ax_img1.plot(x, y, 'ro')
ax_img2.plot(x2, y2, 'go')
ax_corr.plot(x, y, 'ro')
fig.show()

绿点是img2的中心.红点是放置绿点时给出最大相关性的位置.

The green point is the centre of img2. The red point is the position at which placing the green point gives the maximum correlation.

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