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Module « scipy.signal »

Fonction correlate2d - module scipy.signal

Signature de la fonction correlate2d

def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0) 

Description

help(scipy.signal.correlate2d)

Cross-correlate two 2-dimensional arrays.

Cross correlate `in1` and `in2` with output size determined by `mode`, and
boundary conditions determined by `boundary` and `fillvalue`.

Parameters
----------
in1 : array_like
    First input.
in2 : array_like
    Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
    A string indicating the size of the output:

    ``full``
       The output is the full discrete linear cross-correlation
       of the inputs. (Default)
    ``valid``
       The output consists only of those elements that do not
       rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
       must be at least as large as the other in every dimension.
    ``same``
       The output is the same size as `in1`, centered
       with respect to the 'full' output.
boundary : str {'fill', 'wrap', 'symm'}, optional
    A flag indicating how to handle boundaries:

    ``fill``
       pad input arrays with fillvalue. (default)
    ``wrap``
       circular boundary conditions.
    ``symm``
       symmetrical boundary conditions.

fillvalue : scalar, optional
    Value to fill pad input arrays with. Default is 0.

Returns
-------
correlate2d : ndarray
    A 2-dimensional array containing a subset of the discrete linear
    cross-correlation of `in1` with `in2`.

Notes
-----
When using "same" mode with even-length inputs, the outputs of `correlate`
and `correlate2d` differ: There is a 1-index offset between them.

Examples
--------
Use 2D cross-correlation to find the location of a template in a noisy
image:

>>> import numpy as np
>>> from scipy import signal, datasets, ndimage
>>> rng = np.random.default_rng()
>>> face = datasets.face(gray=True) - datasets.face(gray=True).mean()
>>> face = ndimage.zoom(face[30:500, 400:950], 0.5)  # extract the face
>>> template = np.copy(face[135:165, 140:175])  # right eye
>>> template -= template.mean()
>>> face = face + rng.standard_normal(face.shape) * 50  # add noise
>>> corr = signal.correlate2d(face, template, boundary='symm', mode='same')
>>> y, x = np.unravel_index(np.argmax(corr), corr.shape)  # find the match

>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_template, ax_corr) = plt.subplots(3, 1,
...                                                     figsize=(6, 15))
>>> ax_orig.imshow(face, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_template.imshow(template, cmap='gray')
>>> ax_template.set_title('Template')
>>> ax_template.set_axis_off()
>>> ax_corr.imshow(corr, cmap='gray')
>>> ax_corr.set_title('Cross-correlation')
>>> ax_corr.set_axis_off()
>>> ax_orig.plot(x, y, 'ro')
>>> fig.show()

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