feat: stage8 — Global Place Recognition and Metric Refinement

tests/test_metric.py

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+"""Tests for Metric Refinement (F09)."""
+
+import numpy as np
+import pytest
+
+from gps_denied.core.metric import MetricRefinement
+from gps_denied.core.models import ModelManager
+from gps_denied.schemas.flight import GPSPoint
+from gps_denied.schemas.metric import AlignmentResult, ChunkAlignmentResult
+from gps_denied.schemas.satellite import TileBounds
+
+
+@pytest.fixture
+def metric():
+    manager = ModelManager()
+    return MetricRefinement(manager)
+
+@pytest.fixture
+def bounds():
+    # Covers precisely 1 degree lat and lon around 49, 32
+    return TileBounds(
+        nw=GPSPoint(lat=50.0, lon=32.0),
+        ne=GPSPoint(lat=50.0, lon=33.0),
+        sw=GPSPoint(lat=49.0, lon=32.0),
+        se=GPSPoint(lat=49.0, lon=33.0),
+        center=GPSPoint(lat=49.5, lon=32.5),
+        gsd=1.0 # dummy
+    )
+
+def test_extract_gps_from_alignment(metric, bounds):
+    # Homography is identity -> map center to center
+    H = np.eye(3, dtype=np.float64)
+    # The image is 256x256 in our mock
+    # Center pixel is 128, 128
+    gps = metric.extract_gps_from_alignment(H, bounds, (128, 128))
+    
+    # 128 is middle -> should be EXACTLY at 49.5 lat and 32.5 lon
+    assert np.isclose(gps.lat, 49.5)
+    assert np.isclose(gps.lon, 32.5)
+
+def test_align_to_satellite(metric, bounds, monkeypatch):
+    # Monkeypatch random to ensure matched=True and high inliers
+    def mock_infer(*args, **kwargs):
+        H = np.eye(3, dtype=np.float64)
+        return {"homography": H, "inlier_count": 80, "confidence": 0.8}
+        
+    engine = metric.model_manager.get_inference_engine("LiteSAM")
+    monkeypatch.setattr(engine, "infer", mock_infer)
+    
+    uav = np.zeros((256, 256, 3))
+    sat = np.zeros((256, 256, 3))
+    
+    res = metric.align_to_satellite(uav, sat, bounds)
+    assert res is not None
+    assert isinstance(res, AlignmentResult)
+    assert res.matched is True
+    assert res.inlier_count == 80
+
+def test_align_chunk_to_satellite(metric, bounds, monkeypatch):
+    def mock_infer(*args, **kwargs):
+        H = np.eye(3, dtype=np.float64)
+        return {"homography": H, "inlier_count": 80, "confidence": 0.8}
+    
+    engine = metric.model_manager.get_inference_engine("LiteSAM")
+    monkeypatch.setattr(engine, "infer", mock_infer)
+    
+    uavs = [np.zeros((256, 256, 3)) for _ in range(5)]
+    sat = np.zeros((256, 256, 3))
+    
+    res = metric.align_chunk_to_satellite(uavs, sat, bounds)
+    assert res is not None
+    assert isinstance(res, ChunkAlignmentResult)
+    assert res.matched is True
+    assert res.chunk_id == "chunk1"