scipy.signal.correlate2d

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

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, in2 : array_like

Two-dimensional input arrays to be convolved.

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.

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.

Examples

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

>>> from scipy import signal
>>> from scipy import misc
>>> face = misc.face(gray=True) - misc.face(gray=True).mean()
>>> template = np.copy(face[300:365, 670:750])  # right eye
>>> template -= template.mean()
>>> face = face + np.random.randn(*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(1, 3)
>>> 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()

(Source code)