mirror of
https://github.com/azaion/gps-denied-onboard.git
synced 2026-04-22 22:26:38 +00:00
Yuzviak
75 lines
2.5 KiB
Python
75 lines
2.5 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.flight 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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# Monkeypatch random to ensure matched=True and high inliers
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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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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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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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