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feat: add box matching operations (#130)
* feat: add matching functions Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * feat: add ground truth class (#129) * feat: add ground truth class Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * feat: add support of setting transforms Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> --------- Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * feat: add matching functions Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * fix: resolve matching algorithm error Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * refactor: rename method from smaller_is_better to is_smaller_score_better Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * feat: add score calculation for plane distance Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * docs: update API reference Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * chore: remove duplicated Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * chore: remove unused matching/context.py Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> * test: add unit test for matching params Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp> --------- Signed-off-by: ktro2828 <kotaro.uetake@tier4.jp>
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‎docs/apis/evaluation.md‎

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# `evaluation`
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## `matching`
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<!-- prettier-ignore-start -->
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::: t4_devkit.evaluation.matching.parameter
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::: t4_devkit.evaluation.matching.context
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::: t4_devkit.evaluation.matching.scorer
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::: t4_devkit.evaluation.matching.policy
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::: t4_devkit.evaluation.matching.algorithm
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<!-- prettier-ignore-end -->
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## `result`
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<!-- prettier-ignore-start -->
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::: t4_devkit.evaluation.result.box
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::: t4_devkit.evaluation.result.status
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<!-- prettier-ignore-end -->

‎mkdocs.yaml‎

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- Serialize Schema: apis/schema/serialize.md
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- t4_devkit.dataclass: apis/dataclass.md
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- t4_devkit.filtering: apis/filtering.md
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- t4_devkit.evaluation: apis/evaluation.md
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- t4_devkit.viewer: apis/viewer.md
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- t4_devkit.typing: apis/typing.md
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- t4_devkit.common: apis/common.md

‎t4_devkit/dataclass/box.py‎

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@@ -231,6 +231,33 @@ def corners(self, box_scale: float = 1.0) -> NDArrayF64:
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# Rotate and translate
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return np.dot(self.rotation.rotation_matrix, corners).T + self.position
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def diff_yaw(self, other: Box3D) -> float:
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"""Return the yaw difference between the two boxes.
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Args:
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other (Box3D): Another box.
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Raises:
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ValueError: Both boxes must have the same `frame_id`.
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Returns:
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Yaw difference in the range of [-pi, pi].
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"""
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if self.frame_id != other.frame_id:
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raise ValueError(f"Invalid frame comparison: {self.frame_id=} and {other.frame_id=}")
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yaw1, *_ = self.rotation.yaw_pitch_roll
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yaw2, *_ = other.rotation.yaw_pitch_roll
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def _clip(diff: float) -> float:
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if diff < -np.pi:
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diff += 2 * np.pi
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elif diff > np.pi:
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diff -= 2 * np.pi
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return diff
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return _clip(yaw2 - yaw1)
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@define(eq=False)
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class Box2D(BaseBox):

‎t4_devkit/evaluation/__init__.py‎

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from .dataset import * # noqa
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from .matching import * # noqa
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from .result import * # noqa
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from .matching import * # noqa
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from .result import * # noqa

‎t4_devkit/evaluation/matching/__init__.py‎

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from .algorithm import * # noqa
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from .parameter import * # noqa
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from .policy import * # noqa
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from .scorer import * # noqa

‎t4_devkit/evaluation/matching/algorithm.py‎

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from __future__ import annotations
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from abc import ABC, abstractmethod
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from copy import deepcopy
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from typing import TYPE_CHECKING, Sequence, TypeVar
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import numpy as np
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from ..result import BoxMatch
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if TYPE_CHECKING:
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from t4_devkit.dataclass import BoxLike
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from t4_devkit.typing import NDArrayF64
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from .policy import MatchingPolicyLike
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from .scorer import MatchingScorerLike
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__all__ = ["GreedyMatcher", "MatchingAlgorithmLike"]
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# ===== Base Class for Matching Algorithm =====
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class MatchingAlgorithmImpl(ABC):
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"""Abstract base class for matching algorithm class."""
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def __init__(
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self,
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scorer: MatchingScorerLike,
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policy: MatchingPolicyLike,
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matchable_threshold: float,
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) -> None:
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super().__init__()
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self._scorer = scorer
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self._policy = policy
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self._matchable_threshold = matchable_threshold
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def __call__(
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self,
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estimations: Sequence[BoxLike],
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ground_truths: Sequence[BoxLike],
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) -> list[BoxMatch]:
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"""Execute matching.
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Args:
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estimations (Sequence[BoxLike]): Sequence of estimations.
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ground_truths (Sequence[BoxLike]): Sequence of ground truths.
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Returns:
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list[BoxMatch]: List of matches.
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"""
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score_table = self._score_table(estimations, ground_truths)
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return self._do_matching(estimations, ground_truths, score_table)
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def _score_table(
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self,
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estimations: Sequence[BoxLike],
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ground_truths: Sequence[BoxLike],
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) -> NDArrayF64:
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"""Create a score table.
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Args:
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estimations (Sequence[BoxLike]): Sequence of estimations.
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ground_truths (Sequence[BoxLike]): Sequence of ground truths.
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Returns:
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NDArrayF64: Score table in the shape of (NumEst, NumGT).
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"""
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num_rows, num_cols = len(estimations), len(ground_truths)
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table: NDArrayF64 = np.full((num_rows, num_cols), fill_value=np.nan)
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for i, box1 in enumerate(estimations):
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for j, box2 in enumerate(ground_truths):
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if box1.frame_id != box2.frame_id:
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continue
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score = self._scorer(box1, box2)
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# check if boxes distance and label is matchable
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if self._scorer.is_better_than(
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score, self._matchable_threshold
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) and self._policy.is_matchable(box1, box2):
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table[i, j] = score
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return table
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def _get_indices(self, score_table: NDArrayF64) -> tuple[int, int]:
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"""Return indices of estimation and ground truth in the score table at the best score.
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Args:
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score_table (NDArrayF64): Score table in the shape of (NumEst, NumGt).
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Returns:
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Estimation index and ground truth index.
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"""
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estimation_idx, ground_truth_idx = (
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np.unravel_index(np.nanargmin(score_table), score_table.shape)
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if self._scorer.is_smaller_score_better()
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else np.unravel_index(np.nanargmax(score_table), score_table.shape)
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)
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return estimation_idx, ground_truth_idx
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@abstractmethod
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def _do_matching(
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self,
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estimations: Sequence[BoxLike],
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ground_truths: Sequence[BoxLike],
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score_table: NDArrayF64,
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) -> list[BoxMatch]:
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pass
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MatchingAlgorithmLike = TypeVar("MatchingAlgorithmLike", bound=MatchingAlgorithmImpl)
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# ===== Specific Matching Algorithms =====
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class GreedyMatcher(MatchingAlgorithmImpl):
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def _do_matching(
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self,
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estimations: Sequence[BoxLike],
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ground_truths: Sequence[BoxLike],
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score_table: NDArrayF64,
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) -> list[BoxMatch]:
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tmp_estimations = list(deepcopy(estimations))
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tmp_ground_truths = list(deepcopy(ground_truths))
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output: list[BoxMatch] = []
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# 1. match the nearest matchable estimations and GTs
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num_estimations, *_ = score_table.shape
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for _ in range(num_estimations):
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if np.isnan(score_table).all():
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break
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estimation_idx, ground_truth_idx = self._get_indices(score_table)
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estimation_picked = tmp_estimations.pop(estimation_idx)
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ground_truth_picked = tmp_ground_truths.pop(ground_truth_idx)
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output.append(BoxMatch(estimation_picked, ground_truth_picked))
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# remove picked estimations and GTs
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score_table = np.delete(score_table, estimation_idx, axis=0)
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score_table = np.delete(score_table, ground_truth_idx, axis=1)
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# 2. assign remaining estimations(=FPs) and GTs(=FNs)
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output += [BoxMatch(estimation=estimation) for estimation in tmp_estimations]
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output += [BoxMatch(ground_truth=ground_truth) for ground_truth in tmp_ground_truths]
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return output

‎t4_devkit/evaluation/matching/parameter.py‎

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from __future__ import annotations
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from enum import Enum
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import numpy as np
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from attrs import define, field
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from .algorithm import GreedyMatcher, MatchingAlgorithmLike
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from .policy import AllowAnyPolicy, AllowUnknownPolicy, MatchingPolicyLike, StrictPolicy
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from .scorer import CenterDistance, Iou2D, Iou3D, MatchingScorerLike, PlaneDistance
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__all__ = [
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"build_matcher",
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"build_matching_scorer",
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"build_matching_policy",
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"MatchingScorer",
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"MatchingPolicy",
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"MatchingAlgorithm",
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"MatchingParams",
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]
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# ===== Builder Functions =====
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def build_matcher(params: MatchingParams) -> MatchingAlgorithmLike:
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"""Build a matching algorithm from the parameter.
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Examples:
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>>> params = MatchingParams(...)
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>>> matcher = build_matcher(params)
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"""
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scorer = build_matching_scorer(params)
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policy = build_matching_policy(params)
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if params.algorithm == MatchingAlgorithm.GREEDY:
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return GreedyMatcher(
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scorer=scorer,
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policy=policy,
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matchable_threshold=params.matchable_distance,
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)
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else:
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raise ValueError(f"Unexpected algorithm name: {params.algorithm}")
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def build_matching_scorer(params: MatchingParams) -> MatchingScorerLike:
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"""Build a matching scorer from the parameter.
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Examples:
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>>> params = MatchingParams(...)
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>>> scorer = build_matching_scorer(params)
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"""
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if params.scorer == MatchingScorer.CENTER_DISTANCE:
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return CenterDistance()
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elif params.scorer == MatchingScorer.PLANE_DISTANCE:
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return PlaneDistance()
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elif params.scorer == MatchingScorer.IOU2D:
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return Iou2D()
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elif params.scorer == MatchingScorer.IOU3D:
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return Iou3D()
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else:
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raise ValueError(f"Unexpected scorer name: {params.scorer}")
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def build_matching_policy(params: MatchingParams) -> MatchingPolicyLike:
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"""Build a matching policy from the parameter.
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Examples:
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>>> params = MatchingParams(...)
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>>> policy = build_matching_policy(params)
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"""
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if params.policy == MatchingPolicy.STRICT:
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return StrictPolicy()
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elif params.policy == MatchingPolicy.ALLOW_UNKNOWN:
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return AllowUnknownPolicy()
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elif params.policy == MatchingPolicy.ALLOW_ANY:
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return AllowAnyPolicy()
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else:
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raise ValueError(f"Unexpected policy name: {params.policy}")
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# ===== Parameters =====
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class MatchingScorer(str, Enum):
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"""An enum to represent matching scorer names."""
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CENTER_DISTANCE = "CENTER_DISTANCE"
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PLANE_DISTANCE = "PLANE_DISTANCE"
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IOU2D = "IOU2D"
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IOU3D = "IOU3D"
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class MatchingPolicy(str, Enum):
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"""An enum to represent matching policy names."""
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STRICT = "STRICT"
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ALLOW_UNKNOWN = "ALLOW_UNKNOWN"
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ALLOW_ANY = "ALLOW_ANY"
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class MatchingAlgorithm(str, Enum):
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"""An enum to represent matching algorithm names."""
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GREEDY = "GREEDY"
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@define
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class MatchingParams:
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"""A dataclass to represent matching parameters.
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Attributes:
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scorer (MatchingScorer): Name of matching scorer.
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policy (MatchingPolicy): Name of matching policy.
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algorithm (MatchingAlgorithm): Name of matching algorithm.
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matchable_distance (float): Max distance from a GT which a estimation can be matched.
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"""
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scorer: MatchingScorer = field(default=MatchingScorer.CENTER_DISTANCE, converter=MatchingScorer)
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policy: MatchingPolicy = field(default=MatchingPolicy.STRICT, converter=MatchingPolicy)
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algorithm: MatchingAlgorithm = field(
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default=MatchingAlgorithm.GREEDY,
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converter=MatchingAlgorithm,
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)
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matchable_distance: float = field(default=np.inf)

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