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name Punditsdkoslkdosdkoskdo

crop center portion of a numpy image

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?

Something along these lines -

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 -

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]])
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A more general solution based on @Divakar 's answer:

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

>>> 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:

>>> 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.

Have a nice day.

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      • 1
    • Hi, I have 3D image with the shape of (WxHxD) (281, 389, 104), but once I am trying to run cropND, I receive an error: Traceback (most recent call last): File "", line 1, in cropND(img,(256,256)) File "", line 2, in cropND start = tuple(map(lambda a, da: a//2-da//2, img.shape, bounding)) File "", line 2, in start = tuple(map(lambda a, da: a//2-da//2, img.shape, bounding)) TypeError: unsupported operand type(s) for //: 'NoneType' and 'int'

Thanks, Divakar.

Your answer got me going the right direction. I came up with this using negative slice offsets to count 'from the end':

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
  • 1
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A simple modification from @Divakar 's answer that preserves the image channel:

    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]
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