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74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
"""template.py - Template matching
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"""
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import numpy as np
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import cv
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import _template
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#XXX add to opencv backend once backend system in place
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def match_template_cv(image, template, out=None, method="norm-coeff"):
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"""Finds a template in an image using normalized correlation.
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Parameters
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----------
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image : array_like, dtype=float
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Image to process.
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template : array_like, dtype=float
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Template to locate.
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out: array_like, dtype=float, optional
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Optional destination.
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Returns
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-------
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output : ndarray, dtype=float
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Correlation results between 0.0 and 1.0, maximum indicating the most probable match.
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"""
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if out == None:
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out = np.empty((image.shape[0] - template.shape[0] + 1,image.shape[1] - template.shape[1] + 1), dtype=image.dtype)
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if method == "norm-corr":
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cv.MatchTemplate(image, template, out, cv.CV_TM_CCORR_NORMED)
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elif method == "norm-corr":
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cv.MatchTemplate(image, template, out, cv.CV_TM_CCOEFF_NORMED)
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else:
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raise ValueError("Unknown template method: %s" % method)
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return out
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def match_template(image, template, method="norm-coeff"):
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"""Finds a template in an image using normalized correlation.
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Parameters
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----------
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image : array_like, dtype=float
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Image to process.
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template : array_like, dtype=float
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Template to locate.
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method: str (default 'norm-coeff')
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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) = Sigma(x',y')[T(x', y').I(x + x', y + y')] / N
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N = sqrt(Sigma(x',y')[T(x', y')**2].Sigma(x',y')[I(x + x', y + y')**2])
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'norm-corr':
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R(x,y) = Sigma(x',y)[T'(x', y').I'(x + x', y + y')] / N
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N = sqrt(Sigma(x',y)[T'(x', y')**2].Sigma(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).Sigma(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).Sigma(x'',y'')[I(x + x'', y + y'')]
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Returns
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-------
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output : ndarray, dtype=float
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Correlation results between 0.0 and 1.0, maximum indicating the most
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probable match.
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"""
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if method == "norm-corr":
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method_num = 0
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elif method == "norm-coeff":
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method_num = 1
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else:
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raise ValueError("Unknown template method: %s" % method)
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return _template.match_template(image, template, method_num)
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