Numpy调整大小/重新缩放图像 [英] Numpy Resize/Rescale Image

问题描述

我想拍摄图像并改变图像的比例，而它是一个numpy数组。

I would like to take an image and change the scale of the image, while it is a numpy array.

例如我有一个古柯的图像可乐瓶：
bottle-1

For example I have this image of a coca-cola bottle: bottle-1

哪个转换为numpy数组（528,203,3），我想调整大小来说出第二张图片的大小：
bottle-2

Which translates to a numpy array of shape (528, 203, 3) and I want to resize that to say the size of this second image: bottle-2

其形状为（140,54,3）。

如何更改大小？在保持原始图像的同时将图像转换为某种形状？其他答案建议剥离所有其他或第三行，但我想要做的是基本上缩小图像如何通过图像编辑器，但在python代码。是否有任何库可以在numpy / SciPy中执行此操作？

How do I change the size of the image to a certain shape while still maintaining the original image? Other answers suggest stripping every other or third row out, but what I want to do is basically shrink the image how you would via an image editor but in python code. Are there any libraries to do this in numpy/SciPy?

推荐答案

是的，您可以安装 opencv （这是一个用于图像处理和计算机视觉的库），并使用 cv2.resize 功能。例如使用：

Yeah, you can install opencv (this is a library used for image processing, and computer vision), and use the cv2.resize function. And for instance use:

import cv2
import numpy as np

img = cv2.imread('your_image.jpg')
res = cv2.resize(img, dsize=(54, 140), interpolation=cv2.INTER_CUBIC)

这里 img 因此是包含原始图像的numpy数组，而 res 是一个包含已调整大小的图像的numpy数组。一个重要的方面是插值参数：有几种方法可以调整图像大小。特别是在缩小图像时，原始图像的大小不是调整大小后图像大小的倍数。可能的插值模式是：

Here img is thus a numpy array containing the original image, whereas res is a numpy array containing the resized image. An important aspect is the interpolation parameter: there are several ways how to resize an image. Especially since you scale down the image, and the size of the original image is not a multiple of the size of the resized image. Possible interpolation schemas are:

• INTER_NEAREST - a最近邻插值


• INTER_LINEAR - 双线性插值（默认使用）


• INTER_AREA - 使用像素区域关系重新采样。它可能是图像抽取的首选方法，因为它提供了莫尔条纹的
  结果。但是当图像被缩放时，它类似于
  INTER_NEAREST 方法。


• INTER_CUBIC - 4x4像素邻域的双三次插值


• INTER_LANCZOS4 - 8x8像素邻域的Lanczos插值

• INTER_NEAREST - a nearest-neighbor interpolation
• INTER_LINEAR - a bilinear interpolation (used by default)
• INTER_AREA - resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
• INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood
• INTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhood

与大多数选项一样，对于每个调整大小架构，没有最佳选项，有些情况下，一种策略可以优先于另一种策略。

Like with most options, there is no "best" option in the sense that for every resize schema, there are scenarios where one strategy can be preferred over another.

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