match_template(image, template, pad_input=False, mode='constant', constant_values=0)
The output is an array with values between -1.0 and 1.0. The value at a given position corresponds to the correlation coefficient between the image and the template.
For :None:None:`pad_input=True` matches correspond to the center and otherwise to the top-left corner of the template. To find the best match you must search for peaks in the response (output) image.
Details on the cross-correlation are presented in . This implementation uses FFT convolutions of the image and the template. Reference presents similar derivations but the approximation presented in this reference is not used in our implementation.
2-D or 3-D input image.
Template to locate. It must be :None:None:`(m <= M, n <= N[, d <= D])`.
If True, pad :None:None:`image` so that output is the same size as the image, and output values correspond to the template center. Otherwise, the output is an array with shape :None:None:`(M - m + 1, N - n + 1)` for an :None:None:`(M, N)` image and an :None:None:`(m, n)` template, and matches correspond to origin (top-left corner) of the template.
Padding mode.
Constant values used in conjunction with mode='constant'
.
Response image with correlation coefficients.
Match a template to a 2-D or 3-D image using normalized correlation.
>>> template = np.zeros((3, 3))This example is valid syntax, but we were not able to check execution
... template[1, 1] = 1
... template array([[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]])
>>> image = np.zeros((6, 6))This example is valid syntax, but we were not able to check execution
... image[1, 1] = 1
... image[4, 4] = -1
... image array([[ 0., 0., 0., 0., 0., 0.], [ 0., 1., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., -1., 0.], [ 0., 0., 0., 0., 0., 0.]])
>>> result = match_template(image, template)This example is valid syntax, but we were not able to check execution
... np.round(result, 3) array([[ 1. , -0.125, 0. , 0. ], [-0.125, -0.125, 0. , 0. ], [ 0. , 0. , 0.125, 0.125], [ 0. , 0. , 0.125, -1. ]])
>>> result = match_template(image, template, pad_input=True)See :
... np.round(result, 3) array([[-0.125, -0.125, -0.125, 0. , 0. , 0. ], [-0.125, 1. , -0.125, 0. , 0. , 0. ], [-0.125, -0.125, -0.125, 0. , 0. , 0. ], [ 0. , 0. , 0. , 0.125, 0.125, 0.125], [ 0. , 0. , 0. , 0.125, -1. , 0.125], [ 0. , 0. , 0. , 0.125, 0.125, 0.125]])
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.feature.template.match_template
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