bundles / scipy latest / scipy / signal / _signaltools / correlate2d

function

`scipy.signal._signaltools:correlate2d`

source: /scipy/signal/_signaltools.py :1829

Signature

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

Summary

Cross-correlate two 2-dimensional arrays.

Extended Summary

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.

Array API Standard Support

correlate2d has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ⛔                   
JAX                   ✅                     ✅                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

The JAX backend only supports boundary="fill" and fillvalue=0.

See dev-arrayapi for more information.

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()

✓

Aliases

scipy.signal.correlate2d