def onShow(self, checked: bool):
"""Respond to the 'show' button. Set the current image as
input data to the controller. This causes the activations
(and the classification result) to be displayed in the
'Activations' panel.
"""
if not self._toolbox:
return
#image = self._maximization.get_snapshot()
image = self._imageView.getImage()
if image is None:
return
#a,b = self._maximization.get_min(), self._maximization.get_max()
#image2 = (image-a)*255/(b-a)
description = ("Artificial input generated to maximize "
f"activation of unit {self._config.UNIT_INDEX} "
f"in layer {self._config.LAYER_KEY} "
f"of network {self._config.NETWORK_KEY}")
# FIXME[check]: could we use self._maximization.description here?
# FIXME[hack]: label is only self._config.UNIT_INDEX, if the
# network is a classifier an we have selected the last
# (i.e. output) layer.
data = Data()
data.array = image
data.image = True
data.description = description
data.label = self._config.UNIT_INDEX
self._toolbox.set_input(data)
def _read_frame_at(self, data: Data, time: float):
"""Fetch a video frame at a given timepoint from this
:py:class:`Video`.
Arguments
---------
time: float
The temporal position (point in time) of the frame
to fetch in seconds (fractions may be given).
"""
data.array = self._backend.read_frame(time=time)
data.index = self._backend.frame
def _get_index(self, data: Data, index: int, **kwargs) -> None:
"""Implementation of the :py:class:`Indexed` datasource interface.
The index will be interpreted as frame number.
Arguments
---------
index: int
The frame number of the frame to fetch from the video.
If no frame is given, the next frame of the video will
be fetched (advancing the frame number by 1).
"""
data.array = self._backend[index]
data.frame = self._backend.frame
data.index = index or data.frame
data.time = self._backend.time
def _get_random(self,
data: Data,
shape: Tuple[int, ...] = None,
distribution: str = None,
**kwargs) -> None:
# pylint: disable=arguments-differ
"""Generate a random datapoint. Parameters can be given as arguments
or will be taken from object attributes.
Arguments
---------
shape: tuple
The shape of the data to be generated.
distribution: str
The distribution (either `uniform` or `normal`).
"""
shape = shape or self.shape
distribution = distribution or self.distribution
data.array = (np.random.rand(
*shape) if distribution == 'uniform' else np.random.randn(*shape))