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image.py
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image.py
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"""Image class.
"""
from __future__ import annotations
import types
import warnings
from typing import TYPE_CHECKING, List, Sequence, Tuple, Union
import numpy as np
from scipy import ndimage as ndi
from napari.layers._data_protocols import LayerDataProtocol
from napari.layers._multiscale_data import MultiScaleData
from napari.layers.base import Layer, no_op
from napari.layers.image._image_constants import (
ImageRendering,
Interpolation,
Mode,
VolumeDepiction,
)
from napari.layers.image._image_mouse_bindings import (
move_plane_along_normal as plane_drag_callback,
)
from napari.layers.image._image_mouse_bindings import (
set_plane_position as plane_double_click_callback,
)
from napari.layers.image._image_slice import ImageSlice
from napari.layers.image._image_slice_data import ImageSliceData
from napari.layers.image._image_utils import guess_multiscale, guess_rgb
from napari.layers.image._slice import _ImageSliceRequest, _ImageSliceResponse
from napari.layers.intensity_mixin import IntensityVisualizationMixin
from napari.layers.utils._slice_input import _SliceInput
from napari.layers.utils.layer_utils import calc_data_range
from napari.layers.utils.plane import SlicingPlane
from napari.utils import config
from napari.utils._dtype import get_dtype_limits, normalize_dtype
from napari.utils.colormaps import AVAILABLE_COLORMAPS
from napari.utils.events import Event
from napari.utils.events.event import WarningEmitter
from napari.utils.events.event_utils import connect_no_arg
from napari.utils.migrations import rename_argument
from napari.utils.misc import reorder_after_dim_reduction
from napari.utils.naming import magic_name
from napari.utils.translations import trans
if TYPE_CHECKING:
from napari.components import Dims
from napari.components.experimental.chunk import ChunkRequest
# It is important to contain at least one abstractmethod to properly exclude this class
# in creating NAMES set inside of napari.layers.__init__
# Mixin must come before Layer
class _ImageBase(IntensityVisualizationMixin, Layer):
"""Image layer.
Parameters
----------
data : array or list of array
Image data. Can be N >= 2 dimensional. If the last dimension has length
3 or 4 can be interpreted as RGB or RGBA if rgb is `True`. If a
list and arrays are decreasing in shape then the data is treated as
a multiscale image. Please note multiscale rendering is only
supported in 2D. In 3D, only the lowest resolution scale is
displayed.
rgb : bool
Whether the image is rgb RGB or RGBA. If not specified by user and
the last dimension of the data has length 3 or 4 it will be set as
`True`. If `False` the image is interpreted as a luminance image.
colormap : str, napari.utils.Colormap, tuple, dict
Colormap to use for luminance images. If a string must be the name
of a supported colormap from vispy or matplotlib. If a tuple the
first value must be a string to assign as a name to a colormap and
the second item must be a Colormap. If a dict the key must be a
string to assign as a name to a colormap and the value must be a
Colormap.
contrast_limits : list (2,)
Color limits to be used for determining the colormap bounds for
luminance images. If not passed is calculated as the min and max of
the image.
gamma : float
Gamma correction for determining colormap linearity. Defaults to 1.
interpolation : str
Interpolation mode used by vispy. Must be one of our supported
modes.
rendering : str
Rendering mode used by vispy. Must be one of our supported
modes.
depiction : str
3D Depiction mode. Must be one of {'volume', 'plane'}.
The default value is 'volume'.
iso_threshold : float
Threshold for isosurface.
attenuation : float
Attenuation rate for attenuated maximum intensity projection.
name : str
Name of the layer.
metadata : dict
Layer metadata.
scale : tuple of float
Scale factors for the layer.
translate : tuple of float
Translation values for the layer.
rotate : float, 3-tuple of float, or n-D array.
If a float convert into a 2D rotation matrix using that value as an
angle. If 3-tuple convert into a 3D rotation matrix, using a yaw,
pitch, roll convention. Otherwise assume an nD rotation. Angles are
assumed to be in degrees. They can be converted from radians with
np.degrees if needed.
shear : 1-D array or n-D array
Either a vector of upper triangular values, or an nD shear matrix with
ones along the main diagonal.
affine : n-D array or napari.utils.transforms.Affine
(N+1, N+1) affine transformation matrix in homogeneous coordinates.
The first (N, N) entries correspond to a linear transform and
the final column is a length N translation vector and a 1 or a napari
`Affine` transform object. Applied as an extra transform on top of the
provided scale, rotate, and shear values.
opacity : float
Opacity of the layer visual, between 0.0 and 1.0.
blending : str
One of a list of preset blending modes that determines how RGB and
alpha values of the layer visual get mixed. Allowed values are
{'opaque', 'translucent', and 'additive'}.
visible : bool
Whether the layer visual is currently being displayed.
multiscale : bool
Whether the data is a multiscale image or not. Multiscale data is
represented by a list of array like image data. If not specified by
the user and if the data is a list of arrays that decrease in shape
then it will be taken to be multiscale. The first image in the list
should be the largest. Please note multiscale rendering is only
supported in 2D. In 3D, only the lowest resolution scale is
displayed.
cache : bool
Whether slices of out-of-core datasets should be cached upon retrieval.
Currently, this only applies to dask arrays.
plane : dict or SlicingPlane
Properties defining plane rendering in 3D. Properties are defined in
data coordinates. Valid dictionary keys are
{'position', 'normal', 'thickness', and 'enabled'}.
experimental_clipping_planes : list of dicts, list of ClippingPlane, or ClippingPlaneList
Each dict defines a clipping plane in 3D in data coordinates.
Valid dictionary keys are {'position', 'normal', and 'enabled'}.
Values on the negative side of the normal are discarded if the plane is enabled.
Attributes
----------
data : array or list of array
Image data. Can be N dimensional. If the last dimension has length
3 or 4 can be interpreted as RGB or RGBA if rgb is `True`. If a list
and arrays are decreasing in shape then the data is treated as a
multiscale image. Please note multiscale rendering is only
supported in 2D. In 3D, only the lowest resolution scale is
displayed.
metadata : dict
Image metadata.
rgb : bool
Whether the image is rgb RGB or RGBA if rgb. If not
specified by user and the last dimension of the data has length 3 or 4
it will be set as `True`. If `False` the image is interpreted as a
luminance image.
multiscale : bool
Whether the data is a multiscale image or not. Multiscale data is
represented by a list of array like image data. The first image in the
list should be the largest. Please note multiscale rendering is only
supported in 2D. In 3D, only the lowest resolution scale is
displayed.
mode : str
Interactive mode. The normal, default mode is PAN_ZOOM, which
allows for normal interactivity with the canvas.
In TRANSFORM mode the image can be transformed interactively.
colormap : 2-tuple of str, napari.utils.Colormap
The first is the name of the current colormap, and the second value is
the colormap. Colormaps are used for luminance images, if the image is
rgb the colormap is ignored.
colormaps : tuple of str
Names of the available colormaps.
contrast_limits : list (2,) of float
Color limits to be used for determining the colormap bounds for
luminance images. If the image is rgb the contrast_limits is ignored.
contrast_limits_range : list (2,) of float
Range for the color limits for luminance images. If the image is
rgb the contrast_limits_range is ignored.
gamma : float
Gamma correction for determining colormap linearity.
interpolation : str
Interpolation mode used by vispy. Must be one of our supported
modes.
rendering : str
Rendering mode used by vispy. Must be one of our supported
modes.
depiction : str
3D Depiction mode used by vispy. Must be one of our supported modes.
iso_threshold : float
Threshold for isosurface.
attenuation : float
Attenuation rate for attenuated maximum intensity projection.
plane : SlicingPlane or dict
Properties defining plane rendering in 3D. Valid dictionary keys are
{'position', 'normal', 'thickness'}.
experimental_clipping_planes : ClippingPlaneList
Clipping planes defined in data coordinates, used to clip the volume.
Notes
-----
_data_view : array (N, M), (N, M, 3), or (N, M, 4)
Image data for the currently viewed slice. Must be 2D image data, but
can be multidimensional for RGB or RGBA images if multidimensional is
`True`.
_colorbar : array
Colorbar for current colormap.
"""
_colormaps = AVAILABLE_COLORMAPS
@rename_argument("interpolation", "interpolation2d", "0.6.0")
def __init__(
self,
data,
*,
rgb=None,
colormap='gray',
contrast_limits=None,
gamma=1,
interpolation2d='nearest',
interpolation3d='linear',
rendering='mip',
iso_threshold=None,
attenuation=0.05,
name=None,
metadata=None,
scale=None,
translate=None,
rotate=None,
shear=None,
affine=None,
opacity=1,
blending='translucent',
visible=True,
multiscale=None,
cache=True,
depiction='volume',
plane=None,
experimental_clipping_planes=None,
):
if name is None and data is not None:
name = magic_name(data)
if isinstance(data, types.GeneratorType):
data = list(data)
if getattr(data, 'ndim', 2) < 2:
raise ValueError(
trans._('Image data must have at least 2 dimensions.')
)
# Determine if data is a multiscale
self._data_raw = data
if multiscale is None:
multiscale, data = guess_multiscale(data)
elif multiscale and not isinstance(data, MultiScaleData):
data = MultiScaleData(data)
# Determine if rgb
rgb_guess = guess_rgb(data.shape)
if rgb and not rgb_guess:
raise ValueError(
trans._(
"'rgb' was set to True but data does not have suitable dimensions."
)
)
elif rgb is None:
rgb = rgb_guess
# Determine dimensionality of the data
ndim = len(data.shape)
if rgb:
ndim -= 1
super().__init__(
data,
ndim,
name=name,
metadata=metadata,
scale=scale,
translate=translate,
rotate=rotate,
shear=shear,
affine=affine,
opacity=opacity,
blending=blending,
visible=visible,
multiscale=multiscale,
cache=cache,
experimental_clipping_planes=experimental_clipping_planes,
)
self.events.add(
mode=Event,
interpolation=WarningEmitter(
trans._(
"'layer.events.interpolation' is deprecated please use `interpolation2d` and `interpolation3d`",
deferred=True,
),
type='select',
),
interpolation2d=Event,
interpolation3d=Event,
rendering=Event,
plane=Event,
depiction=Event,
iso_threshold=Event,
attenuation=Event,
)
self._array_like = True
# Set data
self.rgb = rgb
self._data = data
if self.multiscale:
self._data_level = len(self.data) - 1
# Determine which level of the multiscale to use for the thumbnail.
# Pick the smallest level with at least one axis >= 64. This is
# done to prevent the thumbnail from being from one of the very
# low resolution layers and therefore being very blurred.
big_enough_levels = [
np.any(np.greater_equal(p.shape, 64)) for p in data
]
if np.any(big_enough_levels):
self._thumbnail_level = np.where(big_enough_levels)[0][-1]
else:
self._thumbnail_level = 0
else:
self._data_level = 0
self._thumbnail_level = 0
displayed_axes = self._slice_input.displayed
self.corner_pixels[1][displayed_axes] = self.level_shapes[
self._data_level
][displayed_axes]
self._new_empty_slice()
# Set contrast limits, colormaps and plane parameters
self._gamma = gamma
self._attenuation = attenuation
self._plane = SlicingPlane(thickness=1, enabled=False, draggable=True)
self._mode = Mode.PAN_ZOOM
# Whether to calculate clims on the next set_view_slice
self._should_calc_clims = False
if contrast_limits is None:
if not isinstance(data, np.ndarray):
dtype = normalize_dtype(getattr(data, 'dtype', None))
if np.issubdtype(dtype, np.integer):
self.contrast_limits_range = get_dtype_limits(dtype)
else:
self.contrast_limits_range = (0, 1)
self._should_calc_clims = dtype != np.uint8
else:
self.contrast_limits_range = self._calc_data_range()
else:
self.contrast_limits_range = contrast_limits
self._contrast_limits = tuple(self.contrast_limits_range)
if iso_threshold is None:
cmin, cmax = self.contrast_limits_range
self._iso_threshold = cmin + (cmax - cmin) / 2
else:
self._iso_threshold = iso_threshold
# using self.colormap = colormap uses the setter in *derived* classes,
# where the intention here is to use the base setter, so we use the
# _set_colormap method. This is important for Labels layers, because
# we don't want to use get_color before set_view_slice has been
# triggered (self.refresh(), below).
self._set_colormap(colormap)
self.contrast_limits = self._contrast_limits
self._interpolation2d = Interpolation.NEAREST
self._interpolation3d = Interpolation.NEAREST
self.interpolation2d = interpolation2d
self.interpolation3d = interpolation3d
self.rendering = rendering
self.depiction = depiction
if plane is not None:
self.plane = plane
connect_no_arg(self.plane.events, self.events, 'plane')
# Trigger generation of view slice and thumbnail
self.refresh()
def _new_empty_slice(self):
"""Initialize the current slice to an empty image."""
wrapper = _weakref_hide(self)
self._slice = ImageSlice(
self._get_empty_image(), wrapper._raw_to_displayed, self.rgb
)
self._empty = True
def _get_empty_image(self):
"""Get empty image to use as the default before data is loaded."""
if self.rgb:
return np.zeros((1,) * self._slice_input.ndisplay + (3,))
else:
return np.zeros((1,) * self._slice_input.ndisplay)
def _get_order(self) -> Tuple[int]:
"""Return the ordered displayed dimensions, but reduced to fit in the slice space."""
order = reorder_after_dim_reduction(self._slice_input.displayed)
if self.rgb:
# if rgb need to keep the final axis fixed during the
# transpose. The index of the final axis depends on how many
# axes are displayed.
return order + (max(order) + 1,)
else:
return order
@property
def _data_view(self):
"""Viewable image for the current slice. (compatibility)"""
return self._slice.image.view
def _calc_data_range(self, mode='data'):
"""
Calculate the range of the data values in the currently viewed slice
or full data array
"""
if mode == 'data':
input_data = self.data[-1] if self.multiscale else self.data
elif mode == 'slice':
data = self._slice.image.view # ugh
input_data = data[-1] if self.multiscale else data
else:
raise ValueError(
trans._(
"mode must be either 'data' or 'slice', got {mode!r}",
deferred=True,
mode=mode,
)
)
return calc_data_range(input_data, rgb=self.rgb)
@property
def dtype(self):
return self._data.dtype
@property
def data_raw(self):
"""Data, exactly as provided by the user."""
return self._data_raw
@property
def data(self) -> LayerDataProtocol:
"""Data, possibly in multiscale wrapper. Obeys LayerDataProtocol."""
return self._data
@data.setter
def data(
self, data: Union[LayerDataProtocol, Sequence[LayerDataProtocol]]
):
self._data_raw = data
# note, we don't support changing multiscale in an Image instance
self._data = MultiScaleData(data) if self.multiscale else data # type: ignore
self._update_dims()
self.events.data(value=self.data)
if self._keep_auto_contrast:
self.reset_contrast_limits()
self._set_editable()
def _get_ndim(self):
"""Determine number of dimensions of the layer."""
return len(self.level_shapes[0])
@property
def _extent_data(self) -> np.ndarray:
"""Extent of layer in data coordinates.
Returns
-------
extent_data : array, shape (2, D)
"""
shape = self.level_shapes[0]
return np.vstack([np.zeros(len(shape)), shape])
@property
def data_level(self):
"""int: Current level of multiscale, or 0 if image."""
return self._data_level
@data_level.setter
def data_level(self, level):
if self._data_level == level:
return
self._data_level = level
self.refresh()
@property
def level_shapes(self) -> np.ndarray:
"""array: Shapes of each level of the multiscale or just of image."""
shapes = self.data.shapes if self.multiscale else [self.data.shape]
if self.rgb:
shapes = [s[:-1] for s in shapes]
return np.array(shapes)
@property
def downsample_factors(self) -> np.ndarray:
"""list: Downsample factors for each level of the multiscale."""
return np.divide(self.level_shapes[0], self.level_shapes)
@property
def iso_threshold(self):
"""float: threshold for isosurface."""
return self._iso_threshold
@iso_threshold.setter
def iso_threshold(self, value):
self._iso_threshold = value
self._update_thumbnail()
self.events.iso_threshold()
@property
def attenuation(self):
"""float: attenuation rate for attenuated_mip rendering."""
return self._attenuation
@attenuation.setter
def attenuation(self, value):
self._attenuation = value
self._update_thumbnail()
self.events.attenuation()
@property
def interpolation(self):
"""Return current interpolation mode.
Selects a preset interpolation mode in vispy that determines how volume
is displayed. Makes use of the two Texture2D interpolation methods and
the available interpolation methods defined in
vispy/gloo/glsl/misc/spatial_filters.frag
Options include:
'bessel', 'bicubic', 'linear', 'blackman', 'catrom', 'gaussian',
'hamming', 'hanning', 'hermite', 'kaiser', 'lanczos', 'mitchell',
'nearest', 'spline16', 'spline36'
Returns
-------
str
The current interpolation mode
"""
warnings.warn(
trans._(
"Interpolation attribute is deprecated since 0.4.17. Please use interpolation2d or interpolation3d",
),
category=DeprecationWarning,
stacklevel=2,
)
return str(
self._interpolation2d
if self._slice_input.ndisplay == 2
else self._interpolation3d
)
@interpolation.setter
def interpolation(self, interpolation):
"""Set current interpolation mode."""
warnings.warn(
trans._(
"Interpolation setting is deprecated since 0.4.17. Please use interpolation2d or interpolation3d",
),
category=DeprecationWarning,
stacklevel=2,
)
if self._slice_input.ndisplay == 3:
self.interpolation3d = interpolation
else:
if interpolation == 'bilinear':
interpolation = 'linear'
warnings.warn(
trans._(
"'bilinear' is invalid for interpolation2d (introduced in napari 0.4.17). "
"Please use 'linear' instead, and please set directly the 'interpolation2d' attribute'.",
),
category=DeprecationWarning,
stacklevel=2,
)
self.interpolation2d = interpolation
@property
def interpolation2d(self):
return str(self._interpolation2d)
@interpolation2d.setter
def interpolation2d(self, value):
if value == 'bilinear':
raise ValueError(
trans._(
"'bilinear' interpolation is not valid for interpolation2d. Did you mean 'linear' instead ?",
),
)
self._interpolation2d = Interpolation(value)
self.events.interpolation2d(value=self._interpolation2d)
self.events.interpolation(value=self._interpolation2d)
@property
def interpolation3d(self):
return str(self._interpolation3d)
@interpolation3d.setter
def interpolation3d(self, value):
self._interpolation3d = Interpolation(value)
self.events.interpolation3d(value=self._interpolation3d)
self.events.interpolation(value=self._interpolation3d)
@property
def depiction(self):
"""The current 3D depiction mode.
Selects a preset depiction mode in vispy
* volume: images are rendered as 3D volumes.
* plane: images are rendered as 2D planes embedded in 3D.
plane position, normal, and thickness are attributes of
layer.plane which can be modified directly.
"""
return str(self._depiction)
@depiction.setter
def depiction(self, depiction: Union[str, VolumeDepiction]):
"""Set the current 3D depiction mode."""
self._depiction = VolumeDepiction(depiction)
self._update_plane_callbacks()
self.events.depiction()
def _reset_plane_parameters(self):
"""Set plane attributes to something valid."""
self.plane.position = np.array(self.data.shape) / 2
self.plane.normal = (1, 0, 0)
def _update_plane_callbacks(self):
"""Set plane callbacks depending on depiction mode."""
plane_drag_callback_connected = (
plane_drag_callback in self.mouse_drag_callbacks
)
double_click_callback_connected = (
plane_double_click_callback in self.mouse_double_click_callbacks
)
if self.depiction == VolumeDepiction.VOLUME:
if plane_drag_callback_connected:
self.mouse_drag_callbacks.remove(plane_drag_callback)
if double_click_callback_connected:
self.mouse_double_click_callbacks.remove(
plane_double_click_callback
)
elif self.depiction == VolumeDepiction.PLANE:
if not plane_drag_callback_connected:
self.mouse_drag_callbacks.append(plane_drag_callback)
if not double_click_callback_connected:
self.mouse_double_click_callbacks.append(
plane_double_click_callback
)
@property
def plane(self):
return self._plane
@plane.setter
def plane(self, value: Union[dict, SlicingPlane]):
self._plane.update(value)
self.events.plane()
@property
def loaded(self):
"""Has the data for this layer been loaded yet.
With asynchronous loading the layer might exist but its data
for the current slice has not been loaded.
"""
return self._slice.loaded
@property
def mode(self) -> str:
"""str: Interactive mode
Interactive mode. The normal, default mode is PAN_ZOOM, which
allows for normal interactivity with the canvas.
TRANSFORM allows for manipulation of the layer transform.
"""
return str(self._mode)
_drag_modes = {Mode.TRANSFORM: no_op, Mode.PAN_ZOOM: no_op}
_move_modes = {
Mode.TRANSFORM: no_op,
Mode.PAN_ZOOM: no_op,
}
_cursor_modes = {
Mode.TRANSFORM: 'standard',
Mode.PAN_ZOOM: 'standard',
}
@mode.setter
def mode(self, mode):
mode, changed = self._mode_setter_helper(mode, Mode)
if not changed:
return
assert mode is not None, mode
if mode == Mode.PAN_ZOOM:
self.help = ''
else:
self.help = trans._(
'hold <space> to pan/zoom, hold <shift> to preserve aspect ratio and rotate in 45° increments'
)
self.events.mode(mode=mode)
def _raw_to_displayed(self, raw):
"""Determine displayed image from raw image.
For normal image layers, just return the actual image.
Parameters
----------
raw : array
Raw array.
Returns
-------
image : array
Displayed array.
"""
image = raw
return image
def _set_view_slice(self) -> None:
"""Set the slice output based on this layer's current state."""
# Initializes an ImageSlice for the old experimental async code.
self._new_empty_slice()
# Skip if any non-displayed data indices are out of bounds.
# This can happen when slicing layers with different extents.
indices = self._slice_indices
for d in self._slice_input.not_displayed:
if (indices[d] < 0) or (indices[d] >= self._extent_data[1][d]):
return
# For the old experimental async code.
self._empty = False
# The new slicing code makes a request from the existing state and
# executes the request on the calling thread directly.
# For async slicing, the calling thread will not be the main thread.
request = self._make_slice_request_internal(self._slice_input, indices)
response = request()
self._update_slice_response(response, indices)
def _make_slice_request(self, dims: Dims) -> _ImageSliceRequest:
"""Make an image slice request based on the given dims and this image."""
slice_input = self._make_slice_input(
dims.point, dims.ndisplay, dims.order
)
# TODO: for the existing sync slicing, slice_indices is passed through
# to avoid some performance issues related to the evaluation of the
# data-to-world transform and its inverse. Async slicing currently
# absorbs these performance issues here, but we can likely improve
# things either by caching the world-to-data transform on the layer
# or by lazily evaluating it in the slice task itself.
slice_indices = slice_input.data_indices(self._data_to_world.inverse)
return self._make_slice_request_internal(slice_input, slice_indices)
def _make_slice_request_internal(
self,
slice_input: _SliceInput,
slice_indices,
) -> _ImageSliceRequest:
"""Needed to support old-style sync slicing through _slice_dims and
_set_view_slice.
This is temporary scaffolding that should go away once we have completed
the async slicing project: https://github.com/napari/napari/issues/4795
"""
return _ImageSliceRequest(
dims=slice_input,
data=self.data,
slice_indices=slice_indices,
multiscale=self.multiscale,
corner_pixels=self.corner_pixels,
rgb=self.rgb,
data_level=self.data_level,
thumbnail_level=self._thumbnail_level,
level_shapes=self.level_shapes,
downsample_factors=self.downsample_factors,
lazy=True,
)
def _update_slice_response(
self, response: _ImageSliceResponse, indices
) -> None:
"""Update the slice output state currently on the layer."""
# For the old experimental async code.
slice_data = self._SliceDataClass(
layer=self,
indices=indices,
image=response.data,
thumbnail_source=response.thumbnail,
)
self._transforms[0] = response.tile_to_data
# For the old experimental async code, where loading might be sync
# or async.
self._load_slice(slice_data)
# Maybe reset the contrast limits based on the new slice.
if self._should_calc_clims:
self.reset_contrast_limits_range()
self.reset_contrast_limits()
self._should_calc_clims = False
elif self._keep_auto_contrast:
self.reset_contrast_limits()
@property
def _SliceDataClass(self):
# Use special ChunkedSlideData for async.
if config.async_loading:
from napari.layers.image.experimental._chunked_slice_data import (
ChunkedSliceData,
)
return ChunkedSliceData
return ImageSliceData
def _load_slice(self, data: ImageSliceData):
"""Load the image and maybe thumbnail source.
Parameters
----------
data : Slice
"""
if self._slice.load(data):
# The load was synchronous.
self._on_data_loaded(data, sync=True)
else:
# The load will be asynchronous. Signal that our self.loaded
# property is now false, since the load is in progress.
self.events.loaded()
def _on_data_loaded(self, data: ImageSliceData, sync: bool) -> None:
"""The given data a was loaded, use it now.
This routine is called synchronously from _load_async() above, or
it is called asynchronously sometime later when the ChunkLoader
finishes loading the data in a worker thread or process.
Parameters
----------
data : ChunkRequest
The request that was satisfied/loaded.
sync : bool
If True the chunk was loaded synchronously.
"""
# Transpose after the load.
data.transpose(self._get_order())
# Pass the loaded data to the slice.
if not self._slice.on_loaded(data):
# Slice rejected it, was it for the wrong indices?
return
# Notify the world.
if self.multiscale:
self.events.scale()
self.events.translate()
# Announcing we are in the loaded state will make our node visible
# if it was invisible during the load.
self.events.loaded()
if not sync:
# TODO_ASYNC: Avoid calling self.refresh(), because it would
# call our _set_view_slice(). Do we need a "refresh without
# set_view_slice()" method that we can call?
self.events.set_data(value=self._slice) # update vispy
self._update_thumbnail()
def _update_thumbnail(self):
"""Update thumbnail with current image data and colormap."""
if not self.loaded:
# ASYNC_TODO: Do not compute the thumbnail until we are loaded.
# Is there a nicer way to prevent this from getting called?
return
image = self._slice.thumbnail.view
if self._slice_input.ndisplay == 3 and self.ndim > 2:
image = np.max(image, axis=0)
# float16 not supported by ndi.zoom
dtype = np.dtype(image.dtype)
if dtype in [np.dtype(np.float16)]:
image = image.astype(np.float32)
raw_zoom_factor = np.divide(
self._thumbnail_shape[:2], image.shape[:2]
).min()
new_shape = np.clip(
raw_zoom_factor * np.array(image.shape[:2]),
1, # smallest side should be 1 pixel wide
self._thumbnail_shape[:2],
)
zoom_factor = tuple(new_shape / image.shape[:2])
if self.rgb:
# warning filter can be removed with scipy 1.4
with warnings.catch_warnings():
warnings.simplefilter("ignore")
downsampled = ndi.zoom(
image, zoom_factor + (1,), prefilter=False, order=0
)
if image.shape[2] == 4: # image is RGBA
colormapped = np.copy(downsampled)
colormapped[..., 3] = downsampled[..., 3] * self.opacity
if downsampled.dtype == np.uint8:
colormapped = colormapped.astype(np.uint8)
else: # image is RGB
if downsampled.dtype == np.uint8:
alpha = np.full(
downsampled.shape[:2] + (1,),
int(255 * self.opacity),
dtype=np.uint8,
)
else:
alpha = np.full(downsampled.shape[:2] + (1,), self.opacity)
colormapped = np.concatenate([downsampled, alpha], axis=2)
else:
# warning filter can be removed with scipy 1.4
with warnings.catch_warnings():
warnings.simplefilter("ignore")
downsampled = ndi.zoom(
image, zoom_factor, prefilter=False, order=0
)
low, high = self.contrast_limits
downsampled = np.clip(downsampled, low, high)
color_range = high - low
if color_range != 0:
downsampled = (downsampled - low) / color_range
downsampled = downsampled**self.gamma
color_array = self.colormap.map(downsampled.ravel())
colormapped = color_array.reshape(downsampled.shape + (4,))
colormapped[..., 3] *= self.opacity
self.thumbnail = colormapped
def _get_value(self, position):
"""Value of the data at a position in data coordinates.
Parameters
----------
position : tuple
Position in data coordinates.
Returns
-------
value : tuple
Value of the data.
"""
if self.multiscale:
# for multiscale data map the coordinate from the data back to
# the tile
coord = self._transforms['tile2data'].inverse(position)
else:
coord = position
coord = np.round(coord).astype(int)
raw = self._slice.image.raw
if self.rgb:
shape = raw.shape[:-1]
else:
shape = raw.shape
if self.ndim < len(coord):
# handle 3D views of 2D data by omitting extra coordinate
offset = len(coord) - len(shape)
coord = coord[[d + offset for d in self._slice_input.displayed]]
else:
coord = coord[self._slice_input.displayed]
if all(0 <= c < s for c, s in zip(coord, shape)):
value = raw[tuple(coord)]
else:
value = None
if self.multiscale:
value = (self.data_level, value)
return value
def _get_offset_data_position(self, position: List[float]) -> List[float]:
"""Adjust position for offset between viewer and data coordinates.