delta.pipeline.ROI

class delta.pipeline.ROI(img_stack, fluo_stack, roi_nb, first_frame, box, config)

ROI processor object.

__init__(img_stack, fluo_stack, roi_nb, first_frame, box, config)

Initialize ROI.

Parameters

img_stackList[Image]

Image stack.

fluo_stackList[List[Image]]

Fluo stack. Shape: (frames, fluo_channels, Y, X).

roi_nbint

ROI index.

first_frameint

Index of the first frame (in general 0 or 1).

boxCroppingBox

CroppingBox for ROI.

configConfig

DeLTA configuration.

Returns

None.

Methods

__init__(img_stack, fluo_stack, roi_nb, ...)	Initialize ROI.
chunks_predict(inputs, model[, batch_size])	Run keras model, but by splitting the input data into "chunks".
compare(other[, level])	Compare this ROI with another and print the differences.
from_xarray(dataset)	Create a ROI from an xarray.Dataset.
get_fluo(frame)	Return the ROI fluo images at a given frame.
get_img(frame)	Return the ROI image at a given frame.
get_labels(frame)	Return the ROI labels at a given frame.
get_seg(frame)	Return the ROI segmentation mask at a given frame.
get_segmentation_inputs(frame)	Compile segmentation inputs for ROI.
get_tracking_inputs(frame)	Compile tracking inputs for ROI from seg_stack.
load_netcdf(filename[, group])	Load a ROI from a netCDF file.
process_segmentation_outputs(logits, frame)	Process outputs after they have been segmented.
process_tracking_outputs(logits, frame, boxes)	Process output from tracking U-Net.
segment(frames[, model])	Segment img_stack and store the results in seg_stack.
to_netcdf(filename, **kwargs)	Save the ROI as a netCDF file.
to_xarray()	Convert the ROI into a xarray.Dataset.
track(frames[, model, progress_bar])	Track cells in the ROI.

Attributes

roi_nb	The ROI index number
box	ROI crop box
first_frame	Index of the first frame
img_stack	Input images stack
seg_stack	Segmentation images stack
lineage	Lineage object for ROI
label_stack	Labelled images stack
config	Configuration parameters object
scaling	Resizing ratios along Y and X