mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 04:44:43 +08:00
Andreas Mueller
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""template.py - Template matching
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"""
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import numpy as np
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from . import _template
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def match_template(image, template, pad_input=False):
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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 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_input : bool
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If True, pad `image` with image mean so that output is the same size as
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the image, and output values correspond to the template center.
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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, and matches correspond
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to origin (top-left corner) of the 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. For an `(M, N)` image and an
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`(m, n)` template, the `output` is `(M - m + 1, N - n + 1)` when
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`pad_input = False` and `(M, N)` when `pad_input = True`.
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Examples
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--------
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>>> template = np.zeros((3, 3))
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>>> template[1, 1] = 1
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>>> print template
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[[ 0. 0. 0.]
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[ 0. 1. 0.]
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[ 0. 0. 0.]]
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>>> image = np.zeros((6, 6))
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>>> image[1, 1] = 1
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>>> image[4, 4] = -1
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>>> print image
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[[ 0. 0. 0. 0. 0. 0.]
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[ 0. 1. 0. 0. 0. 0.]
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[ 0. 0. 0. 0. 0. 0.]
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[ 0. 0. 0. 0. 0. 0.]
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[ 0. 0. 0. 0. -1. 0.]
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[ 0. 0. 0. 0. 0. 0.]]
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>>> result = match_template(image, template)
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>>> print np.round(result, 3)
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[[ 1. -0.125 0. 0. ]
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[-0.125 -0.125 0. 0. ]
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[ 0. 0. 0.125 0.125]
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[ 0. 0. 0.125 -1. ]]
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>>> result = match_template(image, template, pad_input=True)
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>>> print np.round(result, 3)
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[[-0.125 -0.125 -0.125 0. 0. 0. ]
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[-0.125 1. -0.125 0. 0. 0. ]
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[-0.125 -0.125 -0.125 0. 0. 0. ]
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[ 0. 0. 0. 0.125 0.125 0.125]
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[ 0. 0. 0. 0.125 -1. 0.125]
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[ 0. 0. 0. 0.125 0.125 0.125]]
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"""
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if np.any(np.less(image.shape, template.shape)):
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raise ValueError("Image must be larger than template.")
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image = np.ascontiguousarray(image, dtype=np.float32)
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template = np.ascontiguousarray(template, dtype=np.float32)
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if pad_input:
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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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