mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-06 03:25:21 +08:00
Ankit Agrawal
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
# TODO : Doc, Tests, PEP8 check
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import numpy as np
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def downsample(image, factors, how = 'sum'):
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is0 = image.shape[0]
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is1 = image.shape[1]
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f0 = factors[0]
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f1 = factors[1]
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if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
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return "Use resample for non-integer downsampling"
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cropped = image[: is0 - (is0 % f0), : is1 - (is1 % f1)]
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out = np.zeros((cropped.shape[0] / f0, cropped.shape[1] / f1))
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if how == 'sum':
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for i in range(cropped.shape[0]):
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for j in range(cropped.shape[1]):
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out[int(i / f0)][int(j / f1)] += cropped[i][j]
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return out
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if how == 'mean':
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for i in range(cropped.shape[0]):
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for j in range(cropped.shape[1]):
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out[int(i / f0)][int(j / f1)] += cropped[i][j] / float(f0 * f1)
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return out
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def upsample(image, factors, how = 'divide'):
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is0 = image.shape[0]
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is1 = image.shape[1]
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f0 = factors[0]
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f1 = factors[1]
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if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
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return "Use resample for non-integer upsampling"
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out = np.zeros((f0 * image.shape[0], f1 * image.shape[1]))
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if how == 'divide':
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for i in range(out.shape[0]):
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for j in range(out.shape[1]):
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out[i][j] = (image[i / f0][j / f1]) / float(f0 * f1)
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return out
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if how == 'uniform':
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for i in range(out.shape[0]):
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for j in range(out.shape[1]):
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out[i][j] = (image[i / f0][j / f1])
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return out
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