dacapo.experiments.tasks.post_processors.watershed_post_processor
Classes
A post-processor that applies a watershed transformation to the |
Module Contents
- class dacapo.experiments.tasks.post_processors.watershed_post_processor.WatershedPostProcessor(offsets: List[funlib.geometry.Coordinate])
A post-processor that applies a watershed transformation to the prediction.
- offsets
List of offsets for the watershed transformation.
- enumerate_parameters()
Enumerate all possible parameters of this post-processor.
- set_prediction()
Set the prediction array.
- process()
Process the prediction with the given parameters.
Note
This post-processor uses the watershed_function.py script to apply the watershed transformation. The offsets are used to define the neighborhood for the watershed transformation.
- offsets
- enumerate_parameters()
Enumerate all possible parameters of this post-processor. Should return instances of
PostProcessorParameters.- Returns:
A generator of parameters.
- Return type:
Generator[WatershedPostProcessorParameters]
- Raises:
NotImplementedError – If the method is not implemented.
Examples
>>> for parameters in post_processor.enumerate_parameters(): ... print(parameters)
Note
This method should be implemented by the subclass. It should return a generator of instances of
WatershedPostProcessorParameters.
- set_prediction(prediction_array_identifier)
Set the prediction array identifier.
- Parameters:
prediction_array_identifier – The identifier of the array containing the model’s prediction.
- Raises:
NotImplementedError – If the method is not implemented in the subclass.
Examples
>>> post_processor = MyPostProcessor() >>> post_processor.set_prediction("prediction")
Note
This method must be implemented in the subclass. It should set the prediction_array_identifier attribute.
- process(parameters: dacapo.experiments.tasks.post_processors.watershed_post_processor_parameters.WatershedPostProcessorParameters, output_array_identifier: dacapo.store.array_store.LocalArrayIdentifier, num_workers: int = 16, block_size: funlib.geometry.Coordinate = Coordinate((256, 256, 256)))
Process the prediction with the given parameters.
- Parameters:
parameters (WatershedPostProcessorParameters) – The parameters to use for processing.
output_array_identifier (LocalArrayIdentifier) – The output array identifier.
num_workers (int) – The number of workers to use for processing.
block_size (Coordinate) – The block size to use for processing.
- Returns:
The output array identifier.
- Return type:
- Raises:
NotImplementedError – If the method is not implemented.
Examples
>>> post_processor.process(parameters, output_array_identifier)
Note
This method should be implemented by the subclass. To run the watershed transformation, the method uses the segment_blockwise function from the dacapo.blockwise.scheduler module.