Python 裁剪 numpy 图像的中心部分
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crop center portion of a numpy image
提问by Gert Gottschalk
Let's say I have a numpy image of some width x and height y. I have to crop the center portion of the image to width cropx and height cropy. Let's assume that cropx and cropy are positive non zero integers and less than the respective image size. What's the best way to apply the slicing for the output image?
假设我有一个宽度为 x 和高度为 y 的 numpy 图像。我必须将图像的中心部分裁剪为宽度cropx 和高度cropy。让我们假设cropx 和cropy 是非零正整数并且小于各自的图像大小。对输出图像应用切片的最佳方法是什么？
回答by Divakar
Something along these lines -
沿着这些路线的东西 -
def crop_center(img,cropx,cropy):
y,x = img.shape
startx = x//2-(cropx//2)
starty = y//2-(cropy//2)
return img[starty:starty+cropy,startx:startx+cropx]
Sample run -
样品运行 -
In [45]: img
Out[45]:
array([[88, 93, 42, 25, 36, 14, 59, 46, 77, 13, 52, 58],
[43, 47, 40, 48, 23, 74, 12, 33, 58, 93, 87, 87],
[54, 75, 79, 21, 15, 44, 51, 68, 28, 94, 78, 48],
[57, 46, 14, 98, 43, 76, 86, 56, 86, 88, 96, 49],
[52, 83, 13, 18, 40, 33, 11, 87, 38, 74, 23, 88],
[81, 28, 86, 89, 16, 28, 66, 67, 80, 23, 95, 98],
[46, 30, 18, 31, 73, 15, 90, 77, 71, 57, 61, 78],
[33, 58, 20, 11, 80, 25, 96, 80, 27, 40, 66, 92],
[13, 59, 77, 53, 91, 16, 47, 79, 33, 78, 25, 66],
[22, 80, 40, 24, 17, 85, 20, 70, 81, 68, 50, 80]])
In [46]: crop_center(img,4,6)
Out[46]:
array([[15, 44, 51, 68],
[43, 76, 86, 56],
[40, 33, 11, 87],
[16, 28, 66, 67],
[73, 15, 90, 77],
[80, 25, 96, 80]])
回答by Losses Don
A more general solution based on @Divakar 's answer:
基于@Divakar 的回答的更通用的解决方案：
def cropND(img, bounding):
start = tuple(map(lambda a, da: a//2-da//2, img.shape, bounding))
end = tuple(map(operator.add, start, bounding))
slices = tuple(map(slice, start, end))
return img[slices]
and if we have an array a
如果我们有一个数组 a
>>> a = np.arange(100).reshape((10,10))
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
[60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
[70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
[80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])
We can clip it with cropND(a, (5,5)), you will get:
我们可以用 剪辑它cropND(a, (5,5))，你会得到：
>>> cropND(a, (5,5))
array([[33, 34, 35, 36, 37],
[43, 44, 45, 46, 47],
[53, 54, 55, 56, 57],
[63, 64, 65, 66, 67],
[73, 74, 75, 76, 77]])
It not only works with 2D image but also 3D image.
它不仅适用于 2D 图像，还适用于 3D 图像。
Have a nice day.
祝你今天过得愉快。
回答by kdebugging
A simple modification from @Divakar 's answer that preserves the image channel:
@Divakar 的答案的一个简单修改，保留了图像通道：
def crop_center(self, img, cropx, cropy):
_, y, x = img.shape
startx = x // 2 - (cropx // 2)
starty = y // 2 - (cropy // 2)
return img[:, starty:starty + cropy, startx:startx + cropx]
回答by Gert Gottschalk
Thanks, Divakar.
谢谢，迪瓦卡尔。
Your answer got me going the right direction. I came up with this using negative slice offsets to count 'from the end':
你的回答让我朝着正确的方向前进。我想出了这个使用负切片偏移来计算“从最后”：
def cropimread(crop, xcrop, ycrop, fn):
"Function to crop center of an image file"
img_pre= msc.imread(fn)
if crop:
ysize, xsize, chan = img_pre.shape
xoff = (xsize - xcrop) // 2
yoff = (ysize - ycrop) // 2
img= img_pre[yoff:-yoff,xoff:-xoff]
else:
img= img_pre
return img
