from typing import Optional
from rastervision.pipeline.config import Field, register_config, validator
from rastervision.core.rv_pipeline import (
ChipOptions, RVPipelineConfig, PredictOptions, WindowSamplingConfig)
from rastervision.core.data.label_store import ObjectDetectionGeoJSONStoreConfig
from rastervision.core.evaluation import ObjectDetectionEvaluatorConfig
[docs]@register_config('object_detection_window_sampling')
class ObjectDetectionWindowSamplingConfig(WindowSamplingConfig):
ioa_thresh: float = Field(
0.8,
description='When a box is partially outside of a training chip, it '
'is not clear if (a clipped version) of the box should be included in '
'the chip. If the IOA (intersection over area) of the box with the '
'chip is greater than ioa_thresh, it is included in the chip. '
'Defaults to 0.8.')
clip: bool = Field(
False,
description='Clip bounding boxes to window limits when retrieving '
'labels for a window.')
neg_ratio: Optional[float] = Field(
None,
description='The ratio of negative chips (those containing no '
'bounding boxes) to positive chips. This can be useful if the '
'statistics of the background is different in positive chips. For '
'example, in car detection, the positive chips will always contain '
'roads, but no examples of rooftops since cars tend to not be near '
'rooftops. Defaults to None.')
neg_ioa_thresh: float = Field(
0.2,
description='A window will be considered negative if its max IoA with '
'any bounding box is less than this threshold. Defaults to 0.2.')
[docs]@register_config('object_detection_chip_options')
class ObjectDetectionChipOptions(ChipOptions):
sampling: ObjectDetectionWindowSamplingConfig = Field(
..., description='Window sampling config.')
[docs]@register_config('object_detection_predict_options')
class ObjectDetectionPredictOptions(PredictOptions):
stride: Optional[int] = Field(
None,
description='Stride of the sliding window for generating chips. '
'Defaults to half of ``chip_sz``.')
merge_thresh: float = Field(
0.5,
description=
('If predicted boxes have an IOA (intersection over area) greater than '
'merge_thresh, then they are merged into a single box during postprocessing. '
'This is needed since the sliding window approach results in some false '
'duplicates.'))
score_thresh: float = Field(
0.5,
description=
('Predicted boxes are only output if their score is above score_thresh.'
))
[docs] @validator('stride', always=True)
def validate_stride(cls, v: Optional[int], values: dict) -> dict:
if v is None:
chip_sz: int = values['chip_sz']
return chip_sz // 2
return v
[docs]@register_config('object_detection')
class ObjectDetectionConfig(RVPipelineConfig):
"""Configure an :class:`.ObjectDetection` pipeline."""
chip_options: Optional[ObjectDetectionChipOptions]
predict_options: Optional[ObjectDetectionPredictOptions]
[docs] def build(self, tmp_dir):
from rastervision.core.rv_pipeline.object_detection import ObjectDetection
return ObjectDetection(self, tmp_dir)
[docs] def get_default_label_store(self, scene):
return ObjectDetectionGeoJSONStoreConfig()
[docs] def get_default_evaluator(self):
return ObjectDetectionEvaluatorConfig()