| """여러 주행 타일 (nusim.recon.tile) — 자르기·색 맞춤·카메라 번호, CPU.""" |
|
|
| from dataclasses import dataclass, field |
|
|
| import numpy as np |
| import pytest |
|
|
| from nusim.recon.tile import crop_scene, fit_color |
|
|
|
|
| @dataclass |
| class _F: |
| timestamp: int |
| ego_translation: list |
|
|
|
|
| @dataclass |
| class _S: |
| frames: dict |
| lidar: list = field(default_factory=list) |
|
|
|
|
| def _scene(xs): |
| fr = [_F(i * 100_000, [x, 0.0, 0.0]) for i, x in enumerate(xs)] |
| return _S(frames={"CAM_FRONT": fr}, lidar=list(fr)) |
|
|
|
|
| def test_crop_scene_keeps_time_span_inside_tile(): |
| s = _scene([-100, -50, -30, 0, 30, 50, 100]) |
| c = crop_scene(s, np.array([0.0, 0.0]), 40) |
| xs = [f.ego_translation[0] for f in c.frames["CAM_FRONT"]] |
| assert xs == [-30, 0, 30] and len(c.lidar) == 3 |
|
|
|
|
| def test_crop_scene_none_when_outside(): |
| assert crop_scene(_scene([100, 200]), np.array([0.0, 0.0]), 40) is None |
|
|
|
|
| def test_fit_color_recovers_affine_and_ignores_masked(): |
| rng = np.random.default_rng(0) |
| pred = rng.random((20, 30, 3)).astype(np.float32) * 0.5 + 0.2 |
| A = np.array([[0.9, 0.05, 0], [0, 1.1, 0], [0.02, 0, 0.8]], np.float32) |
| gt = np.clip(pred @ A.T + 0.03, 0, 1) |
| keep = np.ones((20, 30), bool) |
| keep[:5] = False |
| gt_bad = gt.copy() |
| gt_bad[:5] = 1.0 |
| out = fit_color(pred, gt_bad, keep) |
| assert np.allclose(out[keep], gt[keep], atol=1e-4) |
|
|
|
|
| def test_trainer_names_tile_cameras(): |
| torch = pytest.importorskip("torch") |
| from nusim.recon.train import CameraAdjust |
|
|
| adj = CameraAdjust(12) |
| with torch.no_grad(): |
| adj.delta[7, 3] = 0.1 |
| js = adj.to_json() |
| assert "CAM_FRONT" in js and "CAM_FRONT#1" in js and len(js) == 12 |
| assert js["CAM_FRONT#1"]["translation_m"] == pytest.approx(0.1) |
|
|
