crosstyan
487dd4e237
- Added property-based tests for camera affinity calculations using Hypothesis, enhancing test coverage and reliability. - Updated `.gitignore` to include `.hypothesis` directory. - Added `hypothesis` and `pytest` as dependencies in `pyproject.toml` and `uv.lock` for improved testing capabilities. - Refactored imports in `playground.py` to include `Sequence` and `Mapping` from `beartype.typing`. - Introduced a new test file `tests/test_affinity.py` for structured testing of affinity calculations.
120 lines
3.5 KiB
Python
120 lines
3.5 KiB
Python
from datetime import datetime, timedelta
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import jax.numpy as jnp
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import numpy as np
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import pytest
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from hypothesis import given, settings, HealthCheck
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from hypothesis import strategies as st
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from app.camera import Camera, CameraParams
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from playground import (
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Detection,
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Tracking,
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calculate_affinity_matrix,
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calculate_camera_affinity_matrix,
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)
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# ----------------------------------------------------------------------------
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# Helper functions to generate synthetic cameras / trackings / detections
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# ----------------------------------------------------------------------------
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def _make_dummy_camera(cam_id: str, rng: np.random.Generator) -> Camera:
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K = jnp.eye(3)
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Rt = jnp.eye(4)
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dist = jnp.zeros(5)
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image_size = jnp.array([1000, 1000])
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params = CameraParams(K=K, Rt=Rt, dist_coeffs=dist, image_size=image_size)
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return Camera(id=cam_id, params=params)
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def _random_keypoints_3d(rng: np.random.Generator, J: int):
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return jnp.asarray(rng.uniform(-1.0, 1.0, size=(J, 3)).astype(np.float32))
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def _random_keypoints_2d(rng: np.random.Generator, J: int):
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return jnp.asarray(rng.uniform(0.0, 1000.0, size=(J, 2)).astype(np.float32))
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def _make_trackings(rng: np.random.Generator, camera: Camera, T: int, J: int):
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now = datetime.now()
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trackings = []
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for i in range(T):
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kps3d = _random_keypoints_3d(rng, J)
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trk = Tracking(
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id=i + 1,
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keypoints=kps3d,
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last_active_timestamp=now
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- timedelta(milliseconds=int(rng.integers(20, 50))),
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)
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trackings.append(trk)
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return trackings
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def _make_detections(rng: np.random.Generator, camera: Camera, D: int, J: int):
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now = datetime.now()
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detections = []
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for _ in range(D):
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kps2d = _random_keypoints_2d(rng, J)
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det = Detection(
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keypoints=kps2d,
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confidences=jnp.ones(J, dtype=jnp.float32),
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camera=camera,
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timestamp=now,
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)
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detections.append(det)
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return detections
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# ----------------------------------------------------------------------------
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# Property-based test: per-camera vs naive slice should match
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# ----------------------------------------------------------------------------
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@settings(max_examples=3, deadline=None, suppress_health_check=[HealthCheck.too_slow])
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@given(
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T=st.integers(min_value=1, max_value=4),
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D=st.integers(min_value=1, max_value=4),
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J=st.integers(min_value=5, max_value=15),
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seed=st.integers(min_value=0, max_value=10000),
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)
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def test_per_camera_matches_naive(T, D, J, seed):
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rng = np.random.default_rng(seed)
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cam = _make_dummy_camera("C0", rng)
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trackings = _make_trackings(rng, cam, T, J)
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detections = _make_detections(rng, cam, D, J)
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# Compute per-camera affinity (fast)
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A_fast = calculate_camera_affinity_matrix(
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trackings,
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detections,
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w_2d=1.0,
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alpha_2d=1.0,
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w_3d=1.0,
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alpha_3d=1.0,
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lambda_a=0.1,
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)
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# Compute naive multi-camera affinity and slice out this camera
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from collections import OrderedDict
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det_dict = OrderedDict({"C0": detections})
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A_naive, _ = calculate_affinity_matrix(
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trackings,
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det_dict,
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w_2d=1.0,
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alpha_2d=1.0,
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w_3d=1.0,
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alpha_3d=1.0,
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lambda_a=0.1,
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)
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# They should be close
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np.testing.assert_allclose(A_fast, np.asarray(A_naive), rtol=1e-5, atol=1e-5)
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if __name__ == "__main__" and pytest is not None:
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pytest.main([__file__])
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