tests/test_encoder/test_geometric.py

"""Tests for encoder/geometric.py -- spatial-code schema construction and derivation."""

import json
import re

import numpy as np

import geometric


def test_dump_spatial_code(tmp_path):
    path = tmp_path / "scene.json"
    geometric.dump_spatial_code({"objects": {}, "appearance order": []}, path)
    assert path.exists()
    assert '"appearance order"' in path.read_text()


def test_raw_bundle_dispatches_to_explicit_derivation(monkeypatch):
    expected = (
        {
            "spatial code schema": geometric.EXPLICIT_SPATIAL_CODE_SCHEMA,
            "objects": {},
            "room": {"floor area": "0.0 square meters"},
            "closest classes distance meters from": {},
            "appearance order": [],
        },
        {},
        {},
        1,
        np.array([0, 1, 0], dtype=np.float32),
        0.0,
    )
    seen = {}

    def fake(scene):
        seen["scene"] = scene
        return expected

    monkeypatch.setattr(geometric, "build_explicit_spatial_code", fake)
    raw = {
        "depth": np.ones((1, 2, 2), np.float32),
        "intr": np.eye(3, dtype=np.float32)[None],
        "c2w": np.eye(4, dtype=np.float32)[None],
        "conf": None,
        "ftimes": np.array([0.0], np.float32),
        "per": {"chair": {}},
    }
    scene = {"raw_inputs": raw}
    assert geometric.build_spatial_code(scene) is expected
    assert seen["scene"] is scene


def test_explicit_is_a_derivation_of_compact(monkeypatch):
    """build_explicit_spatial_code() must always build compact FIRST and derive from it --
    not measure geometry independently."""
    compact_expected = (
        {
            "spatial code schema": geometric.COMPACT_SPATIAL_CODE_SCHEMA,
            "objects": {},
            "room": {},
        },
        {},
        {},
        1,
        np.array([0, 1, 0], dtype=np.float32),
        None,
    )
    seen = {}

    def fake_compact(scene):
        seen["scene"] = scene
        return compact_expected

    monkeypatch.setattr(geometric, "build_compact_spatial_code", fake_compact)
    scene = {"raw_inputs": None}
    code, *_ = geometric.build_explicit_spatial_code(scene)
    assert seen["scene"] is scene
    assert code["objects"] == {}


def test_exact_math_is_integrated_into_geometric_module():
    assert callable(geometric.build_explicit_spatial_code)
    assert callable(geometric.dump_spatial_code)
    assert not hasattr(geometric, "_reference")


def test_position_reader_accepts_current_and_legacy_formatting():
    assert geometric.pos3(
        {
            "position": {
                "x coordinate": "1.25 meters",
                "y coordinate": "-2.0 meters",
                "height above floor": "0.5 meters",
            }
        }
    ) == [1.25, -2.0, 0.5]
    assert geometric.pos3(
        {
            "position": {
                "floor_x_meters": 1.25,
                "floor_y_meters": -2.0,
                "height_above_floor_meters": 0.5,
            }
        }
    ) == [1.25, -2.0, 0.5]


def test_floor_level_v1_v2_math_is_shared(monkeypatch):
    points = np.array([[0, 0, z] for z in [0, 0, 0, 1, 10]], np.float32)
    gravity = np.array([0, 0, 1], np.float32)
    monkeypatch.delenv("VSI_CODE_V2", raising=False)
    v1 = geometric._floor_level(points, gravity)
    monkeypatch.setenv("VSI_CODE_V2", "1")
    v2 = geometric._floor_level(points, gravity)
    assert 0 <= v1 < 0.2
    assert v2 == 0.0


METERS = re.compile(r"^-?\d+(?:\.\d+)? meters$")
SQUARE_METERS = re.compile(r"^\d+(?:\.\d+)? square meters$")


def _schema_instance(x, y, z, size, first_time=0.0):
    pts = np.array(
        [
            [x - size / 2, y, z],
            [x + size / 2, y, z],
            [x, y - size / 2, z],
            [x, y + size / 2, z],
        ],
        dtype=np.float32,
    )
    return {
        "pts": pts,
        "best_pts": pts,
        "n": len(pts),
        "nframes": 1,
        "first_time": first_time,
        "frames": {0},
    }


def _schema_scene():
    chair = _schema_instance(0.0, 0.0, 0.5, 0.8, first_time=0.0)
    table = _schema_instance(1.0, 0.0, 0.7, 1.2, first_time=1.0)
    floor = np.array(
        [[x, y, 0.0] for x in np.linspace(-1, 2, 5) for y in np.linspace(-1, 1, 5)],
        dtype=np.float32,
    )
    return {
        "instances": {"chair": [chair], "table": [table]},
        "stats": {"chair": {"peak": 1}, "table": {"peak": 3}},
        "scene_pts": np.concatenate([chair["pts"], table["pts"], floor], axis=0),
        "cameras": None,
    }


def test_spatial_code_matches_reference_schema():
    code, *_ = geometric.build_spatial_code(_schema_scene())
    assert list(code) == [
        "spatial code schema",
        "objects",
        "room",
        "closest classes distance meters from",
        "appearance order",
    ]
    assert code["spatial code schema"] == geometric.EXPLICIT_SPATIAL_CODE_SCHEMA
    assert code["appearance order"] == ["chair", "table"]
    assert SQUARE_METERS.match(code["room"]["floor area"])
    for class_data in code["objects"].values():
        assert set(class_data) == {"count", "instances"}
        assert class_data["count"] == len(class_data["instances"])
        for instance in class_data["instances"]:
            assert set(instance) == {"position", "longest dimension"}
            assert set(instance["position"]) == {
                "x coordinate",
                "y coordinate",
                "height above floor",
            }
            assert all(METERS.match(value) for value in instance["position"].values())
            assert METERS.match(instance["longest dimension"])
    assert code["objects"]["table"]["count"] == 1
    chair_to_table = code["closest classes distance meters from"]["chair"]["table"]
    assert set(chair_to_table) == {"distance", "closeness rank"}
    assert METERS.match(chair_to_table["distance"])
    assert chair_to_table["closeness rank"] == 1


def test_dumped_json_preserves_schema(tmp_path):
    code, *_ = geometric.build_spatial_code(_schema_scene())
    path = tmp_path / "scene.json"
    geometric.dump_spatial_code(code, path)
    assert json.loads(path.read_text()) == code


def test_compact_spatial_code_exposes_only_reusable_primitives():
    code, *_ = geometric.build_spatial_code(_schema_scene(), "compact")
    assert list(code) == ["spatial code schema", "objects", "room"]
    assert set(code["objects"]) == {"chair", "table"}
    assert len(code["objects"]["chair"]) == 1
    instance = code["objects"]["chair"][0]
    assert set(instance) == {"3D oriented bounding box", "first visible time"}
    box = instance["3D oriented bounding box"]
    assert set(box) == {
        "3D oriented bounding box center coordinates",
        "3D oriented bounding box dimensions",
        "3D oriented bounding box orientation unit vectors",
    }
    assert len(box["3D oriented bounding box center coordinates"]) == 3
    assert len(box["3D oriented bounding box dimensions"]) == 3
    orientation = np.asarray(
        box["3D oriented bounding box orientation unit vectors"], dtype=np.float64
    )
    np.testing.assert_allclose(orientation @ orientation.T, np.eye(3), atol=0.02)
    assert instance["first visible time"] == 0.0
    polygons = code["room"]["floor boundary polygons"]
    assert len(polygons) == 1
    assert len(polygons[0]["outer boundary coordinates"]) >= 3
    for hole in polygons[0]["interior hole boundary coordinates"]:
        assert len(hole) >= 3
        assert all(len(coordinate) == 2 for coordinate in hole)
    assert "closest classes distance meters from" not in code
    assert "appearance order" not in code


def test_explicit_spatial_code_remains_the_default():
    default, *_ = geometric.build_spatial_code(_schema_scene())
    code, *_ = geometric.build_spatial_code(_schema_scene(), "explicit")
    assert default == code


def test_compact_spatial_code_merges_revisit_instances_and_keeps_earliest_time():
    scene = _schema_scene()
    revisit = dict(scene["instances"]["chair"][0])
    revisit.update({"frames": {1}, "first_time": -1.0})
    scene["instances"]["chair"].append(revisit)
    scene["stats"]["chair"] = {"raw": 2, "merged": 2, "peak": 1}

    code, instances, *_ = geometric.build_spatial_code(scene, "compact")

    assert len(instances["chair"]) == 1
    assert len(code["objects"]["chair"]) == 1
    assert code["objects"]["chair"][0]["first visible time"] == -1.0


def test_compact_oriented_box_uses_accumulated_instance_points():
    xs = np.linspace(-2.0, 2.0, 80)
    points = np.stack([xs, np.zeros_like(xs), np.full_like(xs, 0.5)], axis=1)
    instance = {
        "pts": points.astype(np.float32),
        "best_pts": points[38:42].astype(np.float32),
        "conf": None,
    }

    box = geometric._compact_oriented_box(
        instance,
        np.array([1.0, 0.0, 0.0]),
        np.array([0.0, 1.0, 0.0]),
        np.array([0.0, 0.0, 1.0]),
        0.0,
    )

    assert max(box["3D oriented bounding box dimensions"]) > 3.5


def test_compact_floor_boundaries_preserve_disconnected_regions():
    first = np.array(
        [[x, y, 0.0] for x in np.linspace(0, 1, 11) for y in np.linspace(0, 1, 11)]
    )
    second = np.array(
        [[x, y, 0.0] for x in np.linspace(5, 6, 11) for y in np.linspace(0, 1, 11)]
    )

    polygons = geometric._compact_floor_boundary_polygons(
        np.concatenate([first, second]),
        np.array([1.0, 0.0, 0.0]),
        np.array([0.0, 1.0, 0.0]),
    )

    assert len(polygons) == 2
    assert all(len(polygon["outer boundary coordinates"]) >= 3 for polygon in polygons)


def test_compact_spatial_code_suppresses_co_visible_duplicate_tracks():
    points = np.array(
        [[x, y, z] for x in (-0.5, 0.5) for y in (-0.5, 0.5) for z in (0.0, 1.0)],
        dtype=np.float32,
    )
    first = {
        "pts": points,
        "best_pts": points,
        "observations": [points],
        "frames": {0},
        "n": len(points),
        "nframes": 1,
        "first_time": 0.0,
    }
    second = dict(first)
    second.update({"pts": points + 0.01, "best_pts": points + 0.01})
    scene = {
        "instances": {"chair": [first, second]},
        "stats": {"chair": {"raw": 2, "merged": 2, "peak": 2}},
        "scene_pts": np.concatenate([points, points + 0.01]),
        "cameras": None,
    }

    code, instances, *_ = geometric.build_spatial_code(scene, "compact")

    assert len(instances["chair"]) == 1
    assert len(code["objects"]["chair"]) == 1


def test_compact_oriented_box_combines_observation_extents_by_consensus():
    narrow_x = np.linspace(-1.0, 1.0, 80)
    wide_x = np.linspace(-2.0, 2.0, 80)
    narrow = np.stack(
        [narrow_x, np.zeros_like(narrow_x), np.full_like(narrow_x, 0.5)], axis=1
    ).astype(np.float32)
    wide = np.stack(
        [wide_x, np.zeros_like(wide_x), np.full_like(wide_x, 0.5)], axis=1
    ).astype(np.float32)
    instance = {
        "pts": np.concatenate([narrow, wide]),
        "best_pts": narrow,
        "observations": [narrow, wide],
        "conf": None,
    }

    box = geometric._compact_oriented_box(
        instance,
        np.array([1.0, 0.0, 0.0]),
        np.array([0.0, 1.0, 0.0]),
        np.array([0.0, 0.0, 1.0]),
        0.0,
    )

    # The LONGEST axis recovers the fullest observed extent (the wide view's full 4.0 span),
    # not the cross-observation consensus -- a partial view underestimates true length, so the
    # object is at least as long as the fullest clean view saw (see _compact_oriented_box's
    # length-axis decoupling). Width/depth stay on the robust consensus.
    assert 3.9 < max(box["3D oriented bounding box dimensions"]) <= 4.0


def test_compact_instances_keep_peak_co_visible_hypotheses_by_evidence():
    scene = _schema_scene()
    weak = _schema_instance(4.0, 0.0, 0.5, 0.8, first_time=-1.0)
    weak.update({"n": 4, "nframes": 1, "frames": {2}})
    strong = scene["instances"]["chair"][0]
    strong.update({"n": 40, "nframes": 3, "frames": {0, 1, 2}})
    scene["instances"]["chair"] = [weak, strong]
    scene["stats"]["chair"] = {"raw": 2, "merged": 2, "peak": 1}

    code, instances, *_ = geometric.build_spatial_code(scene, "compact")

    assert len(instances["chair"]) == 1
    assert instances["chair"][0]["nframes"] == 3
    assert instances["chair"][0]["first_time"] == -1.0
    assert len(code["objects"]["chair"]) == 1


def test_compact_oriented_box_rejects_one_inconsistent_observation():
    ordinary = np.stack(
        [
            np.linspace(-1.0, 1.0, 80),
            np.zeros(80),
            np.full(80, 0.5),
        ],
        axis=1,
    ).astype(np.float32)
    outlier = ordinary.copy()
    outlier[:, 0] *= 20
    instance = {
        "pts": np.concatenate([ordinary] * 4 + [outlier]),
        "best_pts": ordinary,
        "observations": [ordinary] * 4 + [outlier],
        "conf": None,
    }

    box = geometric._compact_oriented_box(
        instance,
        np.array([1.0, 0.0, 0.0]),
        np.array([0.0, 1.0, 0.0]),
        np.array([0.0, 0.0, 1.0]),
        0.0,
    )

    assert max(box["3D oriented bounding box dimensions"]) < 3.0