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# scipy.signal.convolve2d

`scipy.signal.``convolve2d`(in1, in2, mode='full', boundary='fill', fillvalue=0)

  Convolve two 2-dimensional arrays.

  Convolve 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 convolution 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:

  out : ndarray

  > A 2-dimensional array containing a subset of the discrete linear convolution of in1 with in2.

  Examples

  Compute the gradient of an image by 2D convolution with a complex Scharr operator. (Horizontal operator is real, vertical is imaginary.) Use symmetric boundary condition to avoid creating edges at the image boundaries.

      >>> from scipy import signal
      >>> from scipy import misc
      >>> face = misc.face(gray=True)
      >>> scharr = np.array([[ -3-3j, 0-10j,  +3 -3j],
      ...                    [-10+0j, 0+ 0j, +10 +0j],
      ...                    [ -3+3j, 0+10j,  +3 +3j]]) # Gx + j*Gy
      >>> grad = signal.convolve2d(face, scharr, boundary='symm', mode='same')

      >>> import matplotlib.pyplot as plt
      >>> fig, (ax_orig, ax_mag, ax_ang) = plt.subplots(1, 3)
      >>> ax_orig.imshow(face, cmap='gray')
      >>> ax_orig.set_title('Original')
      >>> ax_orig.set_axis_off()
      >>> ax_mag.imshow(np.absolute(grad), cmap='gray')
      >>> ax_mag.set_title('Gradient magnitude')
      >>> ax_mag.set_axis_off()
      >>> ax_ang.imshow(np.angle(grad), cmap='hsv') # hsv is cyclic, like angles
      >>> ax_ang.set_title('Gradient orientation')
      >>> ax_ang.set_axis_off()
      >>> fig.show()

  (Source code)