scipy.signal.

correlate2d#

scipy.signal.correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0)[source] #

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:

in1array_like

First input.

in2array_like

Second input. Should have the same number of dimensions as in1.

modestr {‘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.

boundarystr {‘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.

fillvaluescalar, optional

Value to fill pad input arrays with. Default is 0.

Returns:

correlate2dndarray: 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:

>>> importnumpyasnp
>>> fromscipyimport 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

>>> importmatplotlib.pyplotasplt
>>> 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()

../../_images/scipy-signal-correlate2d-1.png