mirror of
https://github.com/azaion/gps-denied-onboard.git
synced 2026-04-22 22:06:37 +00:00
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)."""
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from gps_denied.core.metric import MetricRefinement
|
|
from gps_denied.core.models import ModelManager
|
|
from gps_denied.schemas 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):
|
|
def mock_infer(*args, **kwargs):
|
|
H = np.eye(3, dtype=np.float64)
|
|
return {
|
|
"homography": H, "inlier_count": 80,
|
|
"total_correspondences": 100, "confidence": 0.8,
|
|
"reprojection_error": 1.0,
|
|
}
|
|
|
|
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
|
|
# SAT-04: confidence = inlier_ratio
|
|
assert np.isclose(res.confidence, 80 / 100)
|
|
|
|
|
|
# ---------------------------------------------------------------
|
|
# SAT-03: GSD normalization
|
|
# ---------------------------------------------------------------
|
|
|
|
def test_normalize_gsd_downsamples(metric):
|
|
"""UAV frame at 0.16 m/px downsampled to satellite 0.6 m/px."""
|
|
uav = np.zeros((480, 640, 3), dtype=np.uint8)
|
|
out = metric.normalize_gsd(uav, uav_gsd_mpp=0.16, sat_gsd_mpp=0.6)
|
|
# Should be roughly 640 * (0.16/0.6) ≈ 170 wide
|
|
assert out.shape[1] < 640
|
|
assert out.shape[0] < 480
|
|
|
|
|
|
def test_normalize_gsd_no_downscale_needed(metric):
|
|
"""UAV GSD already coarser than satellite → image unchanged."""
|
|
uav = np.zeros((256, 256, 3), dtype=np.uint8)
|
|
out = metric.normalize_gsd(uav, uav_gsd_mpp=0.8, sat_gsd_mpp=0.6)
|
|
assert out.shape == uav.shape
|
|
|
|
|
|
def test_normalize_gsd_zero_args(metric):
|
|
"""Zero GSD args → image returned unchanged (guard against divide-by-zero)."""
|
|
uav = np.zeros((100, 100, 3), dtype=np.uint8)
|
|
out = metric.normalize_gsd(uav, uav_gsd_mpp=0.0, sat_gsd_mpp=0.6)
|
|
assert out.shape == uav.shape
|
|
|
|
|
|
# ---------------------------------------------------------------
|
|
# SAT-04: confidence = inlier ratio via align_to_satellite
|
|
# ---------------------------------------------------------------
|
|
|
|
def test_align_confidence_is_inlier_ratio(metric, bounds, monkeypatch):
|
|
"""SAT-04: returned confidence must equal inlier_count / total_correspondences."""
|
|
def mock_infer(*args, **kwargs):
|
|
H = np.eye(3, dtype=np.float64)
|
|
return {"homography": H, "inlier_count": 60, "total_correspondences": 150,
|
|
"confidence": 0.4, "reprojection_error": 1.0}
|
|
|
|
engine = metric.model_manager.get_inference_engine("LiteSAM")
|
|
monkeypatch.setattr(engine, "infer", mock_infer)
|
|
|
|
res = metric.align_to_satellite(np.zeros((256, 256, 3)), np.zeros((256, 256, 3)), bounds)
|
|
if res is not None:
|
|
assert np.isclose(res.confidence, 60 / 150)
|
|
|
|
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"
|