示例#1
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 def test_interp_bilinear(self):
     x = np.mgrid[:10, :10].sum(axis=0)
     out = ext.interp_bilinear(x, [[1, 2], [1.5, 1.5]],
                                  [[2, 1], [1.5, 1.5]])
     assert_equal(out, [[3, 3], [3, 3]])
示例#2
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def logpolar(image, angles=None, Rs=None, mode='M', cval=0, output=None,
             _coords_r=None, _coords_c=None, extra_info=False):
    """Perform the log polar transform on an image.

    Input:
    ------
    image : MxNxC array
        An MxN image with C colour bands.
    Rs : int
        Number of samples in the radial direction.
    angles : 1D array of floats
        Angles at which to evaluate. Defaults to 0..2*Pi in 4*Rs steps
        ([1] below suggests 8*Rs, but that causes too much oversampling).
    mode : string
        How values outside borders are handled. 'C' for constant, 'M'
        for mirror and 'W' for wrap.
    cval : int or float
        Used in conjunction with mode 'C', the value outside the border.
    extra_info : bool
        Whether to return the angles and log base in addition
        to the transform.  False by default.

    Returns
    -------
    lpt : ndarray of uint8
        Log polar transform of the input image.
    angles : ndarray of float
        Angles used.  Only returned if `extra_info` is set
        to True.
    log_base : int
        Log base used.  Only returned if `extra_info` is set
        to True.

    Optimisation parameters:
    ------------------------
    _coords_r, _coords_c : 2D array
        Pre-calculated coords, as given by _lpcoords.

    References
    ----------
    .. [1] Matungka, Zheng and Ewing, "Image Registration Using Adaptive
           Polar Transform". IEEE Transactions on Image Processing, Vol. 18,
           No. 10, October 2009.

    """
    if image.ndim < 2 or image.ndim > 3:
        raise ValueError("Input image must be 2 or 3 dimensional.")

    image = np.atleast_3d(image)

    if Rs is None:
        Rs = max(image.shape[:2])

    if _coords_r is None or _coords_c is None:
        _coords_r, _coords_c, angles, log_base = \
                   _lpcoords(image.shape, Rs, angles)

    bands = image.shape[2]
    if output is None:
        output = np.empty(_coords_r.shape + (bands,),dtype=np.uint8)
    else:
        output = np.atleast_3d(np.ascontiguousarray(output))
    for band in range(bands):
        output[...,band] = interp_bilinear(image[...,band],
                                           _coords_r,_coords_c,mode=mode,
                                           cval=cval,output=output[...,band])

    output = output.squeeze()

    if extra_info:
        return output, angles, log_base
    else:
        return output
示例#3
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 def test_interp_bilinear(self):
     x = np.mgrid[:10, :10].sum(axis=0)
     out = ext.interp_bilinear(x, [[1, 2], [1.5, 1.5]],
                               [[2, 1], [1.5, 1.5]])
     assert_equal(out, [[3, 3], [3, 3]])