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# 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)