DeepD3 inference
gui
- class deepd3.inference.gui.DoubleSlider(decimals=2, *args, **kargs)
- setMaximum(self, a0: int)
- setMinimum(self, a0: int)
- setSingleStep(self, a0: int)
- setValue(self, a0: int)
- singleStep(self) int
- value(self) int
- class deepd3.inference.gui.ImageView(*args, **kwargs)
- mouseDoubleClickEvent(self, a0: QMouseEvent)
- mousePressEvent(self, a0: QMouseEvent)
- class deepd3.inference.gui.Interface(fn, pred_fn, rois_fn, logs_fn, dimensions={'xy': 0.094, 'z': 0.5})
- _changeOverlay(z)
Hook for z-slider
- Parameters
z (int) – current z index
- changeOverlay(z, preview=False)
When the z-slider is changed, update the overlay image (i.e. the prediction)
- Parameters
z (int) – current z-location in stack
- getSelection()
Sets the current row selection in table and updates ROIs, because the selected ROI has a different color.
- keyPressEvent(self, a0: QKeyEvent)
- populateTable()
Populates ROI table
- roiSelection(xy)
Highlight selected ROI due to left click
- Parameters
xy (QPoint) – Clicking location
- saveSettingsROI3D(settings)
Save test settings to global settings
- Parameters
settings (dict) – 3D ROI settings
- testROIbuilding(xy)
Test ROI building using dedicated interface. Interface is opened at particular stack location where user double-clicked
- Parameters
xy (QPoint) – XY Location of pointer during click
- updateProgress(pval, pmax)
updates progress bar
- Parameters
pval (int) – current value
pmax (int) – target value
- class deepd3.inference.gui.Main
- cleaning()
Clean the prediction using user-specified settings
- exportImageJ()
Export ROIs to ImageJ
- exportPredictions()
Export neural network prediction as TIFF stacks
- exportRoiCentroids()
Export ROI centroids as CSV file
- exportRoiMap()
Export ROI map as TIFF stack
- exportToFolderStructure()
Export ROIs as folder structure
- importAnnotations()
Import annotations to visualize those on the central widget
- open()
Open a z-stack for inference.
If a prediction and/or ROIs already exist, do load these as well.
- previewCleaning(settings)
Preview cleaning settings to specify the settings
- Parameters
settings (dict) – cleaning settings
- roi2d()
Create ROIs from segmentation
- roi3d()
Create ROIs from segmentation in 3D
- save()
Save segmentation predictions and ROIs
- segment()
Segment stack using user-defined settings
- setDimensions()
Set dimensions for z-stack to ensure proper functionality (e.g. distance measures)
- setShowLabels()
Toggles the visualization of labels on central widget
- setShowMaxProjection()
Shows maximum projection of stack and prediction in central widget
- setShowROIs()
Toggle ROIs on central widget
- setShowSegmentation()
Toggle the segmentation visualization on central widget
- zprojection()
Show a maximum and summed intensity z-projection for the full stack in separate windows
- class deepd3.inference.gui.QHLine
- class deepd3.inference.gui.Segment(model_fn=None)
- close(self) bool
- findModel()
Find TensorFlow/Keras model on file system