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Afloat
8f9443f84e
Use a 600 x 800 input to exercise the height as the normalization scale.
The expected horizontal principal point is 379.5 pixels, while main
50e0e3c returns 299.6. All five fixtures fail on main and pass with the
patch using the real importer. Ruff 0.12.2 lint and format checks pass.
The earlier CI run reports the same 19 Pyright diagnostic locations and
messages as PR #3797 at the same base. Both Docker runs stop building
tiny-cuda-nn because pkg_resources is unavailable.
https://github.com/nerfstudio-project/nerfstudio/actions/runs/42401285154
https://github.com/nerfstudio-project/nerfstudio/actions/runs/37900213951
https://github.com/nerfstudio-project/nerfstudio/actions/runs/42401284860
https://github.com/nerfstudio-project/nerfstudio/actions/runs/37900214031
36 lines
1.5 KiB
Python
36 lines
1.5 KiB
Python
"""Regression tests for OpenSfM camera intrinsics in ODM conversions."""
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import json
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from pathlib import Path
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import pytest
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from nerfstudio.process_data.odm_utils import cameras2nerfds
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@pytest.mark.parametrize(
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"camera, expected",
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[
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({"projection_type": "perspective", "focal": 0.5}, {"fl_x": 400, "cx": 399.5, "cy": 299.5}),
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({"projection_type": "brown", "focal": 0.5, "c_x": 0.1, "c_y": -0.05}, {"cx": 479.5, "cy": 259.5}),
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(
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{"projection_type": "brown", "width": 600, "height": 800, "focal": 0.5, "c_x": 0.1, "c_y": -0.05},
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{"fl_x": 400, "fl_y": 400, "cx": 379.5, "cy": 359.5},
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),
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({"projection_type": "fisheye_opencv", "focal": 0.5, "k4": 0.004}, {"k4": 0.004}),
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({"projection_type": "spherical"}, {"fl_x": 400, "fl_y": 600, "cx": 400, "cy": 300}),
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],
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)
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def test_odm_intrinsics(tmp_path, camera, expected):
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cameras_path = tmp_path / "cameras.json"
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cameras_path.write_text(json.dumps({"camera": {"width": 800, "height": 600, **camera}}))
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reconstruction_path = tmp_path / "reconstruction.json"
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shot = {"rotation": [0, 0, 0], "translation": [0, 0, 0]}
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reconstruction_path.write_text(json.dumps([{"shots": {"sample.jpg": shot}}]))
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cameras2nerfds(
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{"sample": Path("sample.jpg")}, cameras_path, tmp_path / "shots.geojson", reconstruction_path, tmp_path
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)
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frame = json.loads((tmp_path / "transforms.json").read_text())["frames"][0]
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for key, value in expected.items():
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assert frame[key] == pytest.approx(value)
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