correlate2d — SciPy v1.15.2 Manual (original) (raw)

scipy.signal.

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 correlateand 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()