scipy.signal.correlate2d¶
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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: in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as in1. If operating in ‘valid’ mode, either in1 or in2 must be at least as large as the other in every dimension.
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(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()