mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-08 16:00:31 +08:00
Tony S Yu
85 lines
2.7 KiB
Python
85 lines
2.7 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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try:
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import cv
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opencv_available = True
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except ImportError:
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opencv_available = False
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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
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probable match.
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"""
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if not opencv_available:
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raise ImportError("Opencv 2.0+ required")
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if out == None:
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out = np.empty((image.shape[0] - template.shape[0] + 1,
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image.shape[1] - template.shape[1] + 1),
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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-coeff":
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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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