mirror of
https://github.com/azaion/gps-denied-onboard.git
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Yuzviak
dd9835c0cd
- ruff --fix: removed trailing whitespace (W293), sorted imports (I001) - Manual: broke long lines (E501) in eskf, rotation, vo, gpr, metric, pipeline, rotation tests - Removed unused imports (F401) in models.py, schemas/__init__.py - pyproject.toml: line-length 100→120, E501 ignore for abstract interfaces ruff check: 0 errors. pytest: 195 passed / 8 skipped. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
"""Tests for Metric Refinement (F09)."""
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import numpy as np
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import pytest
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from gps_denied.core.metric import MetricRefinement
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from gps_denied.core.models import ModelManager
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from gps_denied.schemas import GPSPoint
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from gps_denied.schemas.metric import AlignmentResult, ChunkAlignmentResult
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from gps_denied.schemas.satellite import TileBounds
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@pytest.fixture
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def metric():
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manager = ModelManager()
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return MetricRefinement(manager)
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@pytest.fixture
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def bounds():
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# Covers precisely 1 degree lat and lon around 49, 32
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return TileBounds(
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nw=GPSPoint(lat=50.0, lon=32.0),
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ne=GPSPoint(lat=50.0, lon=33.0),
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sw=GPSPoint(lat=49.0, lon=32.0),
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se=GPSPoint(lat=49.0, lon=33.0),
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center=GPSPoint(lat=49.5, lon=32.5),
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gsd=1.0 # dummy
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)
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def test_extract_gps_from_alignment(metric, bounds):
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# Homography is identity -> map center to center
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H = np.eye(3, dtype=np.float64)
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# The image is 256x256 in our mock
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# Center pixel is 128, 128
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gps = metric.extract_gps_from_alignment(H, bounds, (128, 128))
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# 128 is middle -> should be EXACTLY at 49.5 lat and 32.5 lon
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assert np.isclose(gps.lat, 49.5)
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assert np.isclose(gps.lon, 32.5)
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def test_align_to_satellite(metric, bounds, monkeypatch):
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def mock_infer(*args, **kwargs):
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H = np.eye(3, dtype=np.float64)
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return {
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"homography": H, "inlier_count": 80,
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"total_correspondences": 100, "confidence": 0.8,
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"reprojection_error": 1.0,
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}
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engine = metric.model_manager.get_inference_engine("LiteSAM")
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monkeypatch.setattr(engine, "infer", mock_infer)
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uav = np.zeros((256, 256, 3))
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sat = np.zeros((256, 256, 3))
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res = metric.align_to_satellite(uav, sat, bounds)
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assert res is not None
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assert isinstance(res, AlignmentResult)
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assert res.matched is True
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assert res.inlier_count == 80
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# SAT-04: confidence = inlier_ratio
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assert np.isclose(res.confidence, 80 / 100)
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# ---------------------------------------------------------------
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# SAT-03: GSD normalization
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# ---------------------------------------------------------------
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def test_normalize_gsd_downsamples(metric):
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"""UAV frame at 0.16 m/px downsampled to satellite 0.6 m/px."""
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uav = np.zeros((480, 640, 3), dtype=np.uint8)
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out = metric.normalize_gsd(uav, uav_gsd_mpp=0.16, sat_gsd_mpp=0.6)
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# Should be roughly 640 * (0.16/0.6) ≈ 170 wide
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assert out.shape[1] < 640
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assert out.shape[0] < 480
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def test_normalize_gsd_no_downscale_needed(metric):
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"""UAV GSD already coarser than satellite → image unchanged."""
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uav = np.zeros((256, 256, 3), dtype=np.uint8)
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out = metric.normalize_gsd(uav, uav_gsd_mpp=0.8, sat_gsd_mpp=0.6)
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assert out.shape == uav.shape
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def test_normalize_gsd_zero_args(metric):
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"""Zero GSD args → image returned unchanged (guard against divide-by-zero)."""
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uav = np.zeros((100, 100, 3), dtype=np.uint8)
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out = metric.normalize_gsd(uav, uav_gsd_mpp=0.0, sat_gsd_mpp=0.6)
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assert out.shape == uav.shape
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# ---------------------------------------------------------------
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# SAT-04: confidence = inlier ratio via align_to_satellite
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# ---------------------------------------------------------------
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def test_align_confidence_is_inlier_ratio(metric, bounds, monkeypatch):
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"""SAT-04: returned confidence must equal inlier_count / total_correspondences."""
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def mock_infer(*args, **kwargs):
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H = np.eye(3, dtype=np.float64)
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return {"homography": H, "inlier_count": 60, "total_correspondences": 150,
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"confidence": 0.4, "reprojection_error": 1.0}
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engine = metric.model_manager.get_inference_engine("LiteSAM")
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monkeypatch.setattr(engine, "infer", mock_infer)
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res = metric.align_to_satellite(np.zeros((256, 256, 3)), np.zeros((256, 256, 3)), bounds)
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if res is not None:
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assert np.isclose(res.confidence, 60 / 150)
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def test_align_chunk_to_satellite(metric, bounds, monkeypatch):
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def mock_infer(*args, **kwargs):
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H = np.eye(3, dtype=np.float64)
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return {"homography": H, "inlier_count": 80, "confidence": 0.8}
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engine = metric.model_manager.get_inference_engine("LiteSAM")
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monkeypatch.setattr(engine, "infer", mock_infer)
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uavs = [np.zeros((256, 256, 3)) for _ in range(5)]
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sat = np.zeros((256, 256, 3))
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res = metric.align_chunk_to_satellite(uavs, sat, bounds)
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assert res is not None
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assert isinstance(res, ChunkAlignmentResult)
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assert res.matched is True
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assert res.chunk_id == "chunk1"
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