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51 lines
1.6 KiB
Python
51 lines
1.6 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, pad_output=True):
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"""Match a template to an image using normalized correlation.
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The output is an array with values between -1.0 and 1.0, which correspond
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to the probability that the template's *origin* (i.e. its top-left
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corner) is found at that position.
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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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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 -1.0 and 1.0. The `output` is truncated
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(`pad_output = False`) or zero-padded (`pad_output = True`) at the
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bottom and right edges, where the template would otherwise extend
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beyond the image edges.
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"""
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image = _convert(image, np.float32)
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template = _convert(template, np.float32)
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if pad_output:
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pad_size = tuple(np.array(image.shape) + np.array(template.shape) - 1)
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pad_image = np.mean(image) * np.ones(pad_size, dtype=np.float32)
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h, w = image.shape
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i0, j0 = template.shape
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i0 /= 2
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j0 /= 2
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pad_image[i0:i0+h, j0:j0+w] = image
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image = pad_image
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result = _template.match_template(image, template)
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return result
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