Python feature.register_translation方法代码示例

# 需要导入模块: from skimage import feature [as 别名]
# 或者: from skimage.feature import register_translation [as 别名]
def _conventional_xc(exp_disc, sim_disc, upsample_factor):
    """Takes two images of disc and finds the shift between them using
    conventional (phase) cross correlation.

    Parameters
    ----------
    exp_disc : np.array()
        A numpy array of the "experimental" disc
    sim_disc : np.array()
        A numpy array of the disc used as a template
    upsample_factor: int (must be even)
        Factor to upsample by, reciprocal of the subpixel resolution
        (eg 10 ==> 1/10th of a pixel)

    Returns
    -------
    shifts
        Pixel shifts required to register the two images

    """

    shifts, error, _ = register_translation(exp_disc, sim_disc, upsample_factor)
    shifts = np.flip(shifts)  # to comply with hyperspy conventions - see issue#490
    return shifts 

# 需要导入模块: from skimage import feature [as 别名]
# 或者: from skimage.feature import register_translation [as 别名]
def align_images(data):

    numslices=len(data)
    imageshifts = np.zeros((numslices,2))

    # calculate image shifts
    for idx in range(numslices):
        if idx == 0:
            pass
        else:
            image = np.mean(data[idx-1]['data'],0)
            offset_image = np.mean(data[idx]['data'],0)

            ## shifts in pixel precision for speed
            shift, error, diffphase = register_translation(image, offset_image)
            imageshifts[idx,:] = imageshifts[idx-1,:] + shift

    # apply image shifts
    for idx in range(numslices):
        non = lambda s: s if s<0 else None
        mom = lambda s: max(0,s)
        padded = np.zeros_like(data[idx]['data'])
        oy, ox = imageshifts[idx,:]
        padded[:,mom(oy):non(oy), mom(ox):non(ox)] = data[idx]['data'][:,mom(-oy):non(-oy), mom(-ox):non(-ox)]
        data[idx]['data']=padded.copy()
        #tform=SimilarityTransform(translation = imageshifts[idx,:])
        #for idx2 in range(data[idx]['data'].shape[0]):
        #    tformed = warp(data[idx]['data'][idx2,:,:], inverse_map = tform)
        #    data[idx]['data'][idx2,:,:]= tformed

    return data 

# 需要导入模块: from skimage import feature [as 别名]
# 或者: from skimage.feature import register_translation [as 别名]
def itrack_peak(images, row_slice=None, col_slice=None, precision=1 / 10):
    """
    Generator function that tracks a diffraction peak in a stream of images.
    
    Parameters
    ----------
    images : iterable of array-like
        Iterable of diffraction images. This function also supports generators.
    row_slice : slice or None, optional
        Slice object for which image rows to use. If None (default), all rows are used. 
    col_slice : slice or None, optional
        Slice object for which image columns to use. If None (default), all columns are used.
    precision : float, optional
        Precision of the tracking in pixels. A precision of 1/10 (default) means that
        the tracking will be precise up to 1/10 of a pixel.
    
    Yields
    ------
    shift : `~numpy.ndarray`, shape (2,)
        [row, col] shifts of the peak with respect to the position of this peak
        in the first image.
    """
    if row_slice is None:
        row_slice = np.s_[:]

    if col_slice is None:
        col_slice = np.s_[:]

    first = next(images)

    # The shift between the first image and itself needs not
    # be computed!
    yield np.array((0.0, 0.0))

    ref = np.array(first[row_slice, col_slice], copy=True)
    sub = np.empty_like(ref)

    for image in images:
        sub[:] = image[row_slice, col_slice]
        shift, *_ = register_translation(ref, sub, upsample_factor=int(1 / precision))
        yield np.asarray(shift) 

# 需要导入模块: from skimage import feature [as 别名]
# 或者: from skimage.feature import register_translation [as 别名]
def _shift_dft(array_rec, array, frnum, upsample_factor, interpolation, imlib):
    """
    function used in recenter_dft_unsampling
    """
    shift_yx, _, _ = register_translation(array_rec[0], array[frnum],
                                          upsample_factor=upsample_factor)
    y_i, x_i = shift_yx
    array_rec_i = frame_shift(array[frnum], shift_y=y_i, shift_x=x_i,
                              imlib=imlib, interpolation=interpolation)
    return y_i, x_i, array_rec_i 

# 需要导入模块: from skimage import feature [as 别名]
# 或者: from skimage.feature import register_translation [as 别名]
def find_beam_offset_cross_correlation(z, radius_start, radius_finish):
    """Find the offset of the direct beam from the image center by a cross-correlation algorithm.
    The shift is calculated relative to an circle perimeter. The circle can be
    refined across a range of radii during the centring procedure to improve
    performance in regions where the direct beam size changes,
    e.g. during sample thickness variation.

    Parameters
    ----------
    radius_start : int
        The lower bound for the radius of the central disc to be used in the
        alignment.
    radius_finish : int
        The upper bounds for the radius of the central disc to be used in the
        alignment.

    Returns
    -------
    shift: np.array
        np.array containing offset (from center) of the direct beam positon.
    """
    radiusList = np.arange(radius_start, radius_finish)
    errRecord = np.zeros_like(radiusList, dtype="single")
    origin = np.array(
        [[round(np.size(z, axis=-2) / 2), round(np.size(z, axis=-1) / 2)]]
    )

    for ind in np.arange(0, np.size(radiusList)):
        radius = radiusList[ind]
        ref = reference_circle(origin, np.size(z, axis=-2), np.size(z, axis=-1), radius)
        h0 = np.hanning(np.size(ref, 0))
        h1 = np.hanning(np.size(ref, 1))
        hann2d = np.sqrt(np.outer(h0, h1))
        ref = hann2d * ref
        im = hann2d * z
        shift, error, diffphase = register_translation(ref, im, 10)
        errRecord[ind] = error
        index_min = np.argmin(errRecord)

    ref = reference_circle(
        origin, np.size(z, axis=-2), np.size(z, axis=-1), radiusList[index_min]
    )
    h0 = np.hanning(np.size(ref, 0))
    h1 = np.hanning(np.size(ref, 1))
    hann2d = np.sqrt(np.outer(h0, h1))
    ref = hann2d * ref
    im = hann2d * z
    shift, error, diffphase = register_translation(ref, im, 100)

    return shift - 0.5 

# 需要导入模块: from skimage import feature [as 别名]
# 或者: from skimage.feature import register_translation [as 别名]
def run(self, stack: ImageStack, verbose: bool=False, *args) -> TransformsList:
        """
        Iterate over the given axes of an ImageStack and learn the translation transform
        based off the reference_stack passed into :py:class:`Translation`'s constructor.
        Only supports 2d data.

        Parameters
        ----------
        stack : ImageStack
            Stack to calculate the transforms on.
        verbose : bool
            if True, report on transformation progress (default = False)

        Returns
        -------
        List[Tuple[Mapping[Axes, int], SimilarityTransform]] :
            A list of tuples containing axes of the Imagestack and associated
            transform to apply.
        """

        transforms = TransformsList()
        reference_image = np.squeeze(self.reference_stack.xarray)
        for a in stack.axis_labels(self.axes):
            target_image = np.squeeze(stack.sel({self.axes: a}).xarray)
            if len(target_image.shape) != 2:
                raise ValueError(
                    f"Only axes: {self.axes.value} can have a length > 1, "
                    f"please use the MaxProj filter."
                )

            shift, error, phasediff = register_translation(src_image=target_image,
                                                           target_image=reference_image,
                                                           upsample_factor=self.upsampling)
            if verbose:
                print(f"For {self.axes}: {a}, Shift: {shift}, Error: {error}")
            selectors = {self.axes: a}
            # reverse shift because SimilarityTransform stores in y,x format
            shift = shift[::-1]
            transforms.append(selectors,
                              TransformType.SIMILARITY,
                              SimilarityTransform(translation=shift))

        return transforms