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"""This module provides the :class:`ImageData` and :class:`ImageRgba` items
of the :class:`Plot`.
"""
__authors__ = ["T. Vincent"]
__license__ = "MIT"
__date__ = "08/12/2020"
from collections import abc
import logging
import numpy
from ....utils.proxy import docstring
from ....utils.deprecation import deprecated_warning
from .core import (
DataItem,
LabelsMixIn,
DraggableMixIn,
ColormapMixIn,
AlphaMixIn,
ItemChangedType,
)
from silx._utils import NP_OPTIONAL_COPY
_logger = logging.getLogger(__name__)
def _convertImageToRgba32(image, copy=True):
"""Convert an RGB or RGBA image to RGBA32.
It converts from floats in [0, 1], bool, integer and uint in [0, 255]
If the input image is already an RGBA32 image,
the returned image shares the same data.
:param image: Image to convert to
:type image: numpy.ndarray with 3 dimensions: height, width, color channels
:param bool copy: True (Default) to get a copy, False, avoid copy if possible
:return: The image converted to RGBA32 with dimension: (height, width, 4)
:rtype: numpy.ndarray of uint8
"""
assert image.ndim == 3
assert image.shape[-1] in (3, 4)
# Convert type to uint8
if image.dtype.name != "uint8":
if image.dtype.kind == "f": # Float in [0, 1]
image = (numpy.clip(image, 0.0, 1.0) * 255).astype(numpy.uint8)
elif image.dtype.kind == "b": # boolean
image = image.astype(numpy.uint8) * 255
elif image.dtype.kind in ("i", "u"): # int, uint
image = numpy.clip(image, 0, 255).astype(numpy.uint8)
else:
raise ValueError("Unsupported image dtype: %s", image.dtype.name)
copy = False # A copy as already been done, avoid next one
# Convert RGB to RGBA
if image.shape[-1] == 3:
new_image = numpy.empty((image.shape[0], image.shape[1], 4), dtype=numpy.uint8)
new_image[:, :, :3] = image
new_image[:, :, 3] = 255
return new_image # This is a copy anyway
else:
return numpy.array(image, copy=copy or NP_OPTIONAL_COPY)
class ImageBase(DataItem, LabelsMixIn, DraggableMixIn, AlphaMixIn):
"""Description of an image
:param numpy.ndarray data: Initial image data
"""
def __init__(self, data=None, mask=None):
DataItem.__init__(self)
LabelsMixIn.__init__(self)
DraggableMixIn.__init__(self)
AlphaMixIn.__init__(self)
if data is None:
data = numpy.zeros((0, 0, 4), dtype=numpy.uint8)
self._data = data
self._mask = mask
self.__valueDataCache = None # Store default data
self._origin = (0.0, 0.0)
self._scale = (1.0, 1.0)
def __getitem__(self, item):
"""Compatibility with PyMca and silx <= 0.4.0"""
deprecated_warning(
"Attributes",
"__getitem__",
since_version="2.0.0",
replacement="Use ImageBase methods",
)
if isinstance(item, slice):
return [self[index] for index in range(*item.indices(5))]
elif item == 0:
return self.getData(copy=False)
elif item == 1:
return self.getName()
elif item == 2:
info = self.getInfo(copy=False)
return {} if info is None else info
elif item == 3:
return None
elif item == 4:
params = {
"info": self.getInfo(),
"origin": self.getOrigin(),
"scale": self.getScale(),
"z": self.getZValue(),
"selectable": self.isSelectable(),
"draggable": self.isDraggable(),
"colormap": None,
"xlabel": self.getXLabel(),
"ylabel": self.getYLabel(),
}
return params
else:
raise IndexError("Index out of range: %s" % str(item))
def _isPlotLinear(self, plot):
"""Return True if plot only uses linear scale for both of x and y
axes."""
linear = plot.getXAxis().LINEAR
if plot.getXAxis().getScale() != linear:
return False
if plot.getYAxis().getScale() != linear:
return False
return True
def _getBounds(self):
if self.getData(copy=False).size == 0: # Empty data
return None
height, width = self.getData(copy=False).shape[:2]
origin = self.getOrigin()
scale = self.getScale()
# Taking care of scale might be < 0
xmin, xmax = origin[0], origin[0] + width * scale[0]
if xmin > xmax:
xmin, xmax = xmax, xmin
# Taking care of scale might be < 0
ymin, ymax = origin[1], origin[1] + height * scale[1]
if ymin > ymax:
ymin, ymax = ymax, ymin
plot = self.getPlot()
if plot is not None and not self._isPlotLinear(plot):
return None
else:
return xmin, xmax, ymin, ymax
@docstring(DraggableMixIn)
def drag(self, from_, to):
origin = self.getOrigin()
self.setOrigin((origin[0] + to[0] - from_[0], origin[1] + to[1] - from_[1]))
def getData(self, copy=True):
"""Returns the image data
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:rtype: numpy.ndarray
"""
return numpy.array(self._data, copy=copy or NP_OPTIONAL_COPY)
def setData(self, data):
"""Set the image data
:param numpy.ndarray data:
"""
previousShape = self._data.shape
self._data = data
self._valueDataChanged()
self._boundsChanged()
self._updated(ItemChangedType.DATA)
if (
self.getMaskData(copy=False) is not None
and previousShape != self._data.shape
):
# Data shape changed, so mask shape changes.
# Send event, mask is lazily updated in getMaskData
self._updated(ItemChangedType.MASK)
def getMaskData(self, copy=True):
"""Returns the mask data
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:rtype: Union[None,numpy.ndarray]
"""
if self._mask is None:
return None
# Update mask if it does not match data shape
shape = self.getData(copy=False).shape[:2]
if self._mask.shape != shape:
# Clip/extend mask to match data
newMask = numpy.zeros(shape, dtype=self._mask.dtype)
newMask[: self._mask.shape[0], : self._mask.shape[1]] = self._mask[
: shape[0], : shape[1]
]
self._mask = newMask
return numpy.array(self._mask, copy=copy or NP_OPTIONAL_COPY)
def setMaskData(self, mask, copy=True):
"""Set the image data
:param numpy.ndarray data:
:param bool copy: True (Default) to make a copy,
False to use as is (do not modify!)
"""
if mask is not None:
mask = numpy.array(mask, copy=copy or NP_OPTIONAL_COPY)
shape = self.getData(copy=False).shape[:2]
if mask.shape != shape:
_logger.warning(
"Inconsistent shape between mask and data %s, %s", mask.shape, shape
)
# Clip/extent is done lazily in getMaskData
elif self._mask is None:
return # No update
self._mask = mask
self._valueDataChanged()
self._updated(ItemChangedType.MASK)
def _valueDataChanged(self):
"""Clear cache of default data array"""
self.__valueDataCache = None
def _getValueData(self, copy=True):
"""Return data used by :meth:`getValueData`
:param bool copy:
:rtype: numpy.ndarray
"""
return self.getData(copy=copy)
def getValueData(self, copy=True):
"""Return data (converted to int or float) with mask applied.
Masked values are set to Not-A-Number.
It returns a 2D array of values (int or float).
:param bool copy:
:rtype: numpy.ndarray
"""
if self.__valueDataCache is None:
data = self._getValueData(copy=False)
mask = self.getMaskData(copy=False)
if mask is not None:
if numpy.issubdtype(data.dtype, numpy.floating):
dtype = data.dtype
else:
dtype = numpy.float64
data = numpy.array(data, dtype=dtype, copy=True)
data[mask != 0] = numpy.nan
self.__valueDataCache = data
return numpy.array(self.__valueDataCache, copy=copy or NP_OPTIONAL_COPY)
def getRgbaImageData(self, copy=True):
"""Get the displayed RGB(A) image
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:returns: numpy.ndarray of uint8 of shape (height, width, 4)
"""
raise NotImplementedError("This MUST be implemented in sub-class")
def getOrigin(self):
"""Returns the offset from origin at which to display the image.
:rtype: 2-tuple of float
"""
return self._origin
def setOrigin(self, origin):
"""Set the offset from origin at which to display the image.
:param origin: (ox, oy) Offset from origin
:type origin: float or 2-tuple of float
"""
if isinstance(origin, abc.Sequence):
origin = float(origin[0]), float(origin[1])
else: # single value origin
origin = float(origin), float(origin)
if origin != self._origin:
self._origin = origin
self._boundsChanged()
self._updated(ItemChangedType.POSITION)
def getScale(self):
"""Returns the scale of the image in data coordinates.
:rtype: 2-tuple of float
"""
return self._scale
def setScale(self, scale):
"""Set the scale of the image
:param scale: (sx, sy) Scale of the image
:type scale: float or 2-tuple of float
"""
if isinstance(scale, abc.Sequence):
scale = float(scale[0]), float(scale[1])
else: # single value scale
scale = float(scale), float(scale)
if scale != self._scale:
self._scale = scale
self._boundsChanged()
self._updated(ItemChangedType.SCALE)
class ImageDataBase(ImageBase, ColormapMixIn):
"""Base class for colormapped 2D data image"""
def __init__(self):
ImageBase.__init__(self, numpy.zeros((0, 0), dtype=numpy.float32))
ColormapMixIn.__init__(self)
def _getColormapForRendering(self):
colormap = self.getColormap()
if colormap.isAutoscale():
# NOTE: Make sure getColormapRange comes from the original object
vrange = colormap.getColormapRange(self)
# Avoid backend to compute autoscale: use item cache
colormap = colormap.copy()
colormap.setVRange(*vrange)
return colormap
def getRgbaImageData(self, copy=True):
"""Get the displayed RGB(A) image
:returns: Array of uint8 of shape (height, width, 4)
:rtype: numpy.ndarray
"""
return self.getColormap().applyToData(self)
def setData(self, data, copy=True):
"""Set the image data
:param numpy.ndarray data: Data array with 2 dimensions (h, w)
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
"""
data = numpy.array(data, copy=copy or NP_OPTIONAL_COPY)
assert data.ndim == 2
if data.dtype.kind == "b":
_logger.warning("Converting boolean image to int8 to plot it.")
data = numpy.asarray(data, dtype=numpy.int8)
elif numpy.iscomplexobj(data):
_logger.warning("Converting complex image to absolute value to plot it.")
data = numpy.absolute(data)
super().setData(data)
def _updated(self, event=None, checkVisibility=True):
# Synchronizes colormapped data if changed
if event in (ItemChangedType.DATA, ItemChangedType.MASK):
self._setColormappedData(self.getValueData(copy=False), copy=False)
super()._updated(event=event, checkVisibility=checkVisibility)
[docs]
class ImageData(ImageDataBase):
"""Description of a data image with a colormap"""
def __init__(self):
ImageDataBase.__init__(self)
self._alternativeImage = None
self.__alpha = None
def _addBackendRenderer(self, backend):
"""Update backend renderer"""
plot = self.getPlot()
assert plot is not None
if not self._isPlotLinear(plot):
# Do not render with non linear scales
return None
if (
self.getAlternativeImageData(copy=False) is not None
or self.getAlphaData(copy=False) is not None
):
dataToUse = self.getRgbaImageData(copy=False)
else:
dataToUse = self.getData(copy=False)
if dataToUse.size == 0:
return None # No data to display
return backend.addImage(
dataToUse,
origin=self.getOrigin(),
scale=self.getScale(),
colormap=self._getColormapForRendering(),
alpha=self.getAlpha(),
)
def __getitem__(self, item):
"""Compatibility with PyMca and silx <= 0.4.0"""
deprecated_warning(
"Attributes",
"__getitem__",
since_version="2.0.0",
replacement="Use ImageData methods",
)
if item == 3:
return self.getAlternativeImageData(copy=False)
params = ImageBase.__getitem__(self, item)
if item == 4:
params["colormap"] = self.getColormap()
return params
[docs]
def getRgbaImageData(self, copy=True):
"""Get the displayed RGB(A) image
:returns: Array of uint8 of shape (height, width, 4)
:rtype: numpy.ndarray
"""
alternative = self.getAlternativeImageData(copy=False)
if alternative is not None:
return _convertImageToRgba32(alternative, copy=copy)
else:
image = super().getRgbaImageData(copy=copy)
alphaImage = self.getAlphaData(copy=False)
if alphaImage is not None:
# Apply transparency
image[:, :, 3] = image[:, :, 3] * alphaImage
return image
[docs]
def getAlternativeImageData(self, copy=True):
"""Get the optional RGBA image that is displayed instead of the data
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:rtype: Union[None,numpy.ndarray]
"""
if self._alternativeImage is None:
return None
else:
return numpy.array(self._alternativeImage, copy=copy or NP_OPTIONAL_COPY)
def getAlphaData(self, copy=True):
"""Get the optional transparency image applied on the data
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:rtype: Union[None,numpy.ndarray]
"""
if self.__alpha is None:
return None
else:
return numpy.array(self.__alpha, copy=copy or NP_OPTIONAL_COPY)
def setData(self, data, alternative=None, alpha=None, copy=True):
"""Set the image data and optionally an alternative RGB(A) representation
:param numpy.ndarray data: Data array with 2 dimensions (h, w)
:param alternative: RGB(A) image to display instead of data,
shape: (h, w, 3 or 4)
:type alternative: Union[None,numpy.ndarray]
:param alpha: An array of transparency value in [0, 1] to use for
display with shape: (h, w)
:type alpha: Union[None,numpy.ndarray]
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
"""
data = numpy.array(data, copy=copy or NP_OPTIONAL_COPY)
assert data.ndim == 2
if alternative is not None:
alternative = numpy.array(alternative, copy=copy or NP_OPTIONAL_COPY)
assert alternative.ndim == 3
assert alternative.shape[2] in (3, 4)
assert alternative.shape[:2] == data.shape[:2]
self._alternativeImage = alternative
if alpha is not None:
alpha = numpy.array(alpha, copy=copy or NP_OPTIONAL_COPY)
assert alpha.shape == data.shape
if alpha.dtype.kind != "f":
alpha = alpha.astype(numpy.float32)
if numpy.any(numpy.logical_or(alpha < 0.0, alpha > 1.0)):
alpha = numpy.clip(alpha, 0.0, 1.0)
self.__alpha = alpha
super().setData(data)
[docs]
class ImageRgba(ImageBase):
"""Description of an RGB(A) image"""
def __init__(self):
ImageBase.__init__(self, numpy.zeros((0, 0, 4), dtype=numpy.uint8))
def _addBackendRenderer(self, backend):
"""Update backend renderer"""
plot = self.getPlot()
assert plot is not None
if not self._isPlotLinear(plot):
# Do not render with non linear scales
return None
data = self.getData(copy=False)
if data.size == 0:
return None # No data to display
return backend.addImage(
data,
origin=self.getOrigin(),
scale=self.getScale(),
colormap=None,
alpha=self.getAlpha(),
)
[docs]
def getRgbaImageData(self, copy=True):
"""Get the displayed RGB(A) image
:returns: numpy.ndarray of uint8 of shape (height, width, 4)
"""
return _convertImageToRgba32(self.getData(copy=False), copy=copy)
def setData(self, data, copy=True):
"""Set the image data
:param data: RGB(A) image data to set
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
"""
data = numpy.array(data, copy=copy or NP_OPTIONAL_COPY)
if data.ndim != 3:
raise ValueError(
f"RGB(A) image is expected to be a 3D dataset. Got {data.ndim} dimensions"
)
if data.shape[-1] not in (3, 4):
raise ValueError(
f"RGB(A) image is expected to have 3 or 4 elements as last dimension. Got {data.shape[-1]}"
)
super().setData(data)
def _getValueData(self, copy=True):
"""Compute the intensity of the RGBA image as default data.
Conversion: https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion
:param bool copy:
"""
rgba = self.getRgbaImageData(copy=False).astype(numpy.float32)
intensity = (
rgba[:, :, 0] * 0.299 + rgba[:, :, 1] * 0.587 + rgba[:, :, 2] * 0.114
)
intensity *= rgba[:, :, 3] / 255.0
return intensity
class MaskImageData(ImageData):
"""Description of an image used as a mask.
This class is used to flag mask items. This information is used to improve
internal silx widgets.
"""
pass
class ImageStack(ImageData):
"""Item to store a stack of images and to show it in the plot as one
of the images of the stack.
The stack is a 3D array ordered this way: `frame id, y, x`.
So the first image of the stack can be reached this way: `stack[0, :, :]`
"""
def __init__(self):
ImageData.__init__(self)
self.__stack = None
"""A 3D numpy array (or a mimic one, see ListOfImages)"""
self.__stackPosition = None
"""Displayed position in the cube"""
def setStackData(self, stack, position=None, copy=True):
"""Set the stack data
:param stack: A 3D numpy array like
:param int position: The position of the displayed image in the stack
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
"""
if self.__stack is stack:
return
if copy:
stack = numpy.array(stack)
assert stack.ndim == 3
self.__stack = stack
if position is not None:
self.__stackPosition = position
if self.__stackPosition is None:
self.__stackPosition = 0
self.__updateDisplayedData()
def getStackData(self, copy=True):
"""Get the stored stack array.
:param bool copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:rtype: A 3D numpy array, or numpy array like
"""
if copy:
return numpy.array(self.__stack)
else:
return self.__stack
def setStackPosition(self, pos):
"""Set the displayed position on the stack.
This function will clamp the stack position according to
the real size of the first axis of the stack.
:param int pos: A position on the first axis of the stack.
"""
if self.__stackPosition == pos:
return
self.__stackPosition = pos
self.__updateDisplayedData()
def getStackPosition(self):
"""Get the displayed position of the stack.
:rtype: int
"""
return self.__stackPosition
def __updateDisplayedData(self):
"""Update the displayed frame whenever the stack or the stack
position are updated."""
if self.__stack is None or self.__stackPosition is None:
empty = numpy.array([]).reshape(0, 0)
self.setData(empty, copy=False)
return
size = len(self.__stack)
self.__stackPosition = numpy.clip(self.__stackPosition, 0, size)
self.setData(self.__stack[self.__stackPosition], copy=False)
