mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-01 20:07:06 +08:00
Tony S Yu
64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
"""template.py - Template matching
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"""
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import numpy as np
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import _template
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from skimage.util.dtype import _convert
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def match_template(image, template, method='norm-coeff', pad_output=True):
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"""Finds a template in an image using normalized correlation.
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TODO: The output is currently smaller than the input image due to
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cropping at the boundaries equal to the template width.
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Parameters
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----------
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image : array_like
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Image to process.
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template : array_like
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Template to locate.
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method : str
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The correlation method used in scanning.
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T represents the template, I the image and R the result.
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The summation is done over X = 0..w-1 and Y = 0..h-1 of the template.
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'norm-coeff':
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R(x, y) = Sum(X,Y)[T(X, Y) * I(x + X, y + Y)] / N
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N = sqrt(Sum(X,Y)[T(X, Y)**2] * Sum(X,Y)[I(x + X, y + Y)**2])
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'norm-corr':
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R(x,y) = Sum(X,y)[T'(X, Y) * I'(x + X, y + Y)] / N
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N = sqrt(Sum(X,y)[T'(X, Y)**2] * Sum(X,Y)[I'(x + X, y + Y)**2])
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where:
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T'(x, y) = T(X, Y) - 1/(w * h) * Sum(X',Y')[T(X', Y')]
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I'(x + X, y + Y) = I(x + X, y + Y)
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- 1/(w * h) * Sum(X',Y')[I(x + X', y + Y')]
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pad_output : bool
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If True, pad output array to be the same size as the input image.
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Otherwise, the output is an array with shape `(M - m + 1, N - n + 1)`
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for an `(M, N)` image and an `(m, n)` template.
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Returns
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-------
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output : ndarray
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Correlation results between 0.0 and 1.0, which correspond to the match
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probability when the template's *origin* (i.e. its top-left corner) is
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placed at that position. The bottom and right edges of `output` are
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truncated (`pad_output = False`) or zero-padded (`pad_output = True`),
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since otherwise the template would extend beyond the image edges.
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"""
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if method not in ('norm-corr', 'norm-coeff'):
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raise ValueError("Unknown template method: %s" % method)
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image = _convert(image, np.float32)
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template = _convert(template, np.float32)
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result = _template.match_template(image, template, method)
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if pad_output:
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h, w = result.shape
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full_result = np.zeros(image.shape, dtype=np.float32)
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full_result[:h, :w] = result
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return full_result
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else:
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return result
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