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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
      >>> lena = misc.lena() - misc.lena().mean()
      >>> template = np.copy(lena[235:295, 310:370]) # right eye
      >>> template -= template.mean()
      >>> lena = lena + np.random.randn(*lena.shape) * 50 # add noise
      >>> corr = signal.correlate2d(lena, 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(lena, 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)