Source code for rastervision.core.rv_pipeline.object_detection_config

from typing import Optional

from rastervision.pipeline.config import Field, register_config, validator
from rastervision.core.rv_pipeline import (
    ChipOptions, RVPipelineConfig, PredictOptions, WindowSamplingConfig)
from 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()