wehub-resource-sync
90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
"""
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Helper functions used internally for object detection tasks.
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"""
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from typing import Any, Dict, List, Optional
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import numpy as np
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from cleanlab.internal.numerics import softmax
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def bbox_xyxy_to_xywh(bbox: List[float]) -> Optional[List[float]]:
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"""Converts bounding box coodrinate types from x1y1,x2y2 to x,y,w,h"""
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if len(bbox) == 4:
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x1, y1, x2, y2 = bbox
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w = x2 - x1
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h = y2 - y1
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return [x1, y1, w, h]
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else:
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print("Wrong bbox shape", len(bbox))
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return None
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def softmin1d(scores: np.ndarray, temperature: float = 0.99, axis: int = 0) -> float:
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"""Returns softmin of passed in scores."""
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scores = np.array(scores)
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softmax_scores = softmax(x=-1 * scores, temperature=temperature, axis=axis, shift=True)
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return np.dot(softmax_scores, scores)
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def calculate_bounding_box_areas(rectangle_coordinates):
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"""Calculate areas of bounding boxes represented by numpy array with x1, y1, x2, y2 format"""
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widths = rectangle_coordinates[:, 2] - rectangle_coordinates[:, 0]
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heights = rectangle_coordinates[:, 3] - rectangle_coordinates[:, 1]
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return widths * heights
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def assert_valid_aggregation_weights(aggregation_weights: Dict[str, Any]) -> None:
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"""assert aggregation weights are in the proper format"""
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weights = np.array(list(aggregation_weights.values()))
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if (not np.isclose(np.sum(weights), 1.0)) or (np.min(weights) < 0.0):
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raise ValueError(
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f"""Aggregation weights should be non-negative and must sum to 1.0
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"""
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)
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def assert_valid_inputs(
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labels: List[Dict[str, Any]],
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predictions,
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method: Optional[str] = None,
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threshold: Optional[float] = None,
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):
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"""Asserts proper input format."""
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if len(labels) != len(predictions):
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raise ValueError(
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f"labels and predictions length needs to match. len(labels) == {len(labels)} while len(predictions) == {len(predictions)}."
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)
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# Typecheck labels and predictions
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if not isinstance(labels[0], dict):
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raise ValueError(
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f"Labels has to be a list of dicts. Instead it is list of {type(labels[0])}."
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)
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# check last column of predictions is probabilities ( < 1.)?
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if not isinstance(predictions[0], (list, np.ndarray)):
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raise ValueError(
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f"Prediction has to be a list or np.ndarray. Instead it is type {type(predictions[0])}."
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)
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if not predictions[0][0].shape[1] == 5:
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raise ValueError(
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f"Prediction values have to be of format [x1,y1,x2,y2,pred_prob]. Please refer to the documentation for predicted probabilities under object_detection.rank.get_label_quality_scores for details"
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)
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valid_methods = ["objectlab"]
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if method is not None and method not in valid_methods:
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raise ValueError(
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f"""
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{method} is not a valid object detection scoring method!
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Please choose a valid scoring_method: {valid_methods}
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"""
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)
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if threshold is not None and threshold > 1.0:
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raise ValueError(
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f"""
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Threshold is a cutoff of predicted probabilities and therefore should be <= 1.
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"""
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)
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