Module « scipy.signal »

Fonction correlate2d - module scipy.signal

Signature de la fonction correlate2d

def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0) 

Description

correlate2d.__doc__

    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()