Python NonUniformScale.apply方法代码示例

# 需要导入模块: from menpo.transform import NonUniformScale [as 别名]
# 或者: from menpo.transform.NonUniformScale import apply [as 别名]
    def rescale(self, scale, interpolator='scipy', round='ceil', **kwargs):
        r"""
        Return a copy of this image, rescaled by a given factor.
        Landmarks are rescaled appropriately.

        Parameters
        ----------
        scale : float or tuple
            The scale factor. If a tuple, the scale to apply to each dimension.
            If a single float, the scale will be applied uniformly across
            each dimension.
        interpolator : 'scipy', optional
            The interpolator that should be used to perform the warp.

            Default: 'scipy'
        round: {'ceil', 'floor', 'round'}
            Rounding function to be applied to floating point shapes.

            Default: 'ceil'
        kwargs : dict
            Passed through to the interpolator. See `menpo.interpolation`
            for details. Note that mode is set to nearest to avoid numerical
            issues, and cannot be changed here by the user.

        Returns
        -------
        rescaled_image : type(self)
            A copy of this image, rescaled.

        Raises
        ------
        ValueError:
            If less scales than dimensions are provided.
            If any scale is less than or equal to 0.
        """
        # Pythonic way of converting to list if we are passed a single float
        try:
            if len(scale) < self.n_dims:
                raise ValueError(
                    'Must provide a scale per dimension.'
                    '{} scales were provided, {} were expected.'.format(
                        len(scale), self.n_dims
                    )
                )
        except TypeError:  # Thrown when len() is called on a float
            scale = [scale] * self.n_dims

        # Make sure we have a numpy array
        scale = np.asarray(scale)
        for s in scale:
            if s <= 0:
                raise ValueError('Scales must be positive floats.')

        transform = NonUniformScale(scale)
        from menpo.image.boolean import BooleanImage
        # use the scale factor to make the template mask bigger
        template_mask = BooleanImage.blank(transform.apply(self.shape),
                                           round=round)
        # due to image indexing, we can't just apply the pseduoinverse
        # transform to achieve the scaling we want though!
        # Consider a 3x rescale on a 2x4 image. Looking at each dimension:
        #    H 2 -> 6 so [0-1] -> [0-5] = 5/1 = 5x
        #    W 4 -> 12 [0-3] -> [0-11] = 11/3 = 3.67x
        # => need to make the correct scale per dimension!
        shape = np.array(self.shape, dtype=np.float)
        # scale factors = max_index_after / current_max_index
        # (note that max_index = length - 1, as 0 based)
        scale_factors = (scale * shape - 1) / (shape - 1)
        inverse_transform = NonUniformScale(scale_factors).pseudoinverse
        # for rescaling we enforce that mode is nearest to avoid num. errors
        if 'mode' in kwargs:
            raise ValueError("Cannot set 'mode' kwarg on rescale - set to "
                             "'nearest' to avoid numerical errors")
        kwargs['mode'] = 'nearest'
        # Note here we pass warp_mask to warp_to. In the case of
        # Images that aren't MaskedImages this kwarg will
        # harmlessly fall through so we are fine.
        return self.warp_to(template_mask, inverse_transform,
                            warp_landmarks=True,
                            interpolator=interpolator, **kwargs)