Module « scipy.signal »

Fonction convolve2d - module scipy.signal

Signature de la fonction convolve2d

def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0) 

Description

convolve2d.__doc__

    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 : 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 convolution
           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
    -------
    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
    >>> ascent = misc.ascent()
    >>> 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(ascent, scharr, boundary='symm', mode='same')

    >>> import matplotlib.pyplot as plt
    >>> fig, (ax_orig, ax_mag, ax_ang) = plt.subplots(3, 1, figsize=(6, 15))
    >>> ax_orig.imshow(ascent, 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()