# 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 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.

  Examples

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

      >>> from scipy import signal
      >>> from scipy import misc
      >>> rng = np.random.default_rng()
      >>> 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 + 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()

scipy.signal.convolve2d scipy.signal.sepfir2d