`dacapo.blockwise.empanada_function`

Module Contents

`segment_function`(input_array, block, **parameters)
`stack_inference`(engine, volume, axis_name)
`orthoplane_inference`(engine, volume)
`empanada_segmenter`(image[, model_config, use_gpu, ...])
`stack_postprocessing`(trackers, model_config[, ...])	Relabels and filters each class defined in trackers. Yields a numpy
`tracker_consensus`(trackers, model_config[, ...])	Calculate the orthoplane consensus from trackers. Yields a numpy

dacapo.blockwise.empanada_function.segment_function(input_array, block, **parameters)

dacapo.blockwise.empanada_function.stack_inference(engine, volume, axis_name)

dacapo.blockwise.empanada_function.orthoplane_inference(engine, volume)

dacapo.blockwise.empanada_function.empanada_segmenter(image, model_config='MitoNet_v1', use_gpu=True, use_quantized=False, multigpu=False, downsampling=1, confidence_thr=0.5, center_confidence_thr=0.1, min_distance_object_centers=21, fine_boundaries=True, semantic_only=False, median_slices=11, min_size=10000, min_extent=50, maximum_objects_per_class=1000000, inference_plane='xy', orthoplane=True, return_panoptic=False, pixel_vote_thr=1, allow_one_view=False)

dacapo.blockwise.empanada_function.stack_postprocessing(trackers, model_config, label_divisor=1000, min_size=200, min_extent=4, dtype=np.uint32): Relabels and filters each class defined in trackers. Yields a numpy or zarr volume along with the name of the class that is segmented.

dacapo.blockwise.empanada_function.tracker_consensus(trackers, model_config, pixel_vote_thr=2, cluster_iou_thr=0.75, allow_one_view=False, min_size=200, min_extent=4, dtype=np.uint32): Calculate the orthoplane consensus from trackers. Yields a numpy or zarr volume along with the name of the class that is segmented.