def __call__(self, data): self.randomize() d = dict(data) if not self._do_transform: return d zoomer = Zoom(self._zoom, self.order, self.mode, self.cval, self.prefilter, self.use_gpu, self.keep_size) for key in self.keys: d[key] = zoomer(d[key]) return d
def __call__(self, data): # match the spatial dim of first item self.randomize() d = dict(data) if not self._do_transform: return d zoomer = Zoom(self._zoom, keep_size=self.keep_size) for idx, key in enumerate(self.keys): d[key] = zoomer(d[key], mode=self.mode[idx], align_corners=self.align_corners[idx]) return d
def __call__(self, data): self.randomize() d = dict(data) if not self._do_transform: return d zoomer = Zoom(self._zoom, align_corners=self.align_corners, keep_size=self.keep_size) for idx, key in enumerate(self.keys): d[key] = zoomer(d[key], interp_order=self.interp_order[idx]) return d
def __init__( self, keys: KeysCollection, zoom: Union[Sequence[float], float], mode: InterpolateModeSequence = InterpolateMode.AREA, align_corners=None, keep_size: bool = True, ): super().__init__(keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.zoomer = Zoom(zoom=zoom, keep_size=keep_size)
def __init__( self, keys: KeysCollection, zoom, interp_order: str = "area", align_corners: Optional[bool] = None, keep_size: bool = True, ): super().__init__(keys) self.zoomer = Zoom(zoom=zoom, align_corners=align_corners, keep_size=keep_size) self.interp_order = ensure_tuple_rep(interp_order, len(self.keys))
def __init__( self, keys: KeysCollection, zoom: Union[Sequence[float], float], mode: InterpolateModeSequence = InterpolateMode.AREA, padding_mode: NumpyPadModeSequence = NumpyPadMode.EDGE, align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None, keep_size: bool = True, ) -> None: super().__init__(keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.zoomer = Zoom(zoom=zoom, keep_size=keep_size)
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: # match the spatial dim of first item self.randomize() d = dict(data) if not self._do_transform: return d zoomer = Zoom(self._zoom, keep_size=self.keep_size) for idx, key in enumerate(self.keys): d[key] = zoomer( d[key], mode=self.mode[idx], padding_mode=self.padding_mode[idx], align_corners=self.align_corners[idx], ) return d
def __call__(self, data): self.randomize() d = dict(data) if not self._do_transform: return d zoomer = Zoom(self._zoom, use_gpu=self.use_gpu, keep_size=self.keep_size) for idx, key in enumerate(self.keys): d[key] = zoomer( d[key], order=self.interp_order[idx], mode=self.mode[idx], cval=self.cval[idx], prefilter=self.prefilter[idx], ) return d
def __init__(self, keys, zoom, order=3, mode="constant", cval=0, prefilter=True, use_gpu=False, keep_size=False): super().__init__(keys) self.zoomer = Zoom(zoom=zoom, use_gpu=use_gpu, keep_size=keep_size) self.order = ensure_tuple_rep(order, len(self.keys)) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.cval = ensure_tuple_rep(cval, len(self.keys)) self.prefilter = ensure_tuple_rep(prefilter, len(self.keys))
def __init__(self, keys, zoom, order=3, mode='constant', cval=0, prefilter=True, use_gpu=False, keep_size=False): super().__init__(keys) self.zoomer = Zoom(zoom=zoom, order=order, mode=mode, cval=cval, prefilter=prefilter, use_gpu=use_gpu, keep_size=keep_size)
def __init__( self, keys, zoom, interp_order=InterpolationCode.SPLINE3, mode="constant", cval=0, prefilter=True, use_gpu=False, keep_size=False, ): super().__init__(keys) self.zoomer = Zoom(zoom=zoom, use_gpu=use_gpu, keep_size=keep_size) self.interp_order = ensure_tuple_rep(interp_order, len(self.keys)) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.cval = ensure_tuple_rep(cval, len(self.keys)) self.prefilter = ensure_tuple_rep(prefilter, len(self.keys))
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: # match the spatial dim of first item self.randomize() d = dict(data) if not self._do_transform: return d img_dims = data[self.keys[0]].ndim if len(self._zoom) == 1: # to keep the spatial shape ratio, use same random zoom factor for all dims self._zoom = ensure_tuple_rep(self._zoom[0], img_dims - 1) elif len(self._zoom) == 2 and img_dims > 3: # if 2 zoom factors provided for 3D data, use the first factor for H and W dims, second factor for D dim self._zoom = ensure_tuple_rep(self._zoom[0], img_dims - 2) + ensure_tuple(self._zoom[-1]) zoomer = Zoom(self._zoom, keep_size=self.keep_size) for idx, key in enumerate(self.keys): d[key] = zoomer( d[key], mode=self.mode[idx], padding_mode=self.padding_mode[idx], align_corners=self.align_corners[idx], ) return d