File size: 4,474 Bytes
db613b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | from __future__ import annotations
from pathlib import Path
import numpy as np
import pyarrow.parquet as pq
from kitti_c2l.assets import generate_frame_asset, generate_sequence_assets
from kitti_c2l.kitti_calib import load_rig_chain
from kitti_c2l.schema import ASSETS_COLUMN_NAMES, ASSETS_SCHEMA, frame_id
from kitti_c2l.sequence_mapping import SequenceInfo
from kitti_c2l.writer import write_assets, write_assets_sharded
def _fake_seq_info(frame_count: int = 4) -> SequenceInfo:
return SequenceInfo(sequence="04", date="2011_09_30", drive="0016", start=0, end=frame_count - 1, frame_count=frame_count, split="train")
def test_frame_id_format():
assert frame_id("04", 2) == "04_000002"
def test_generate_frame_asset_matches_raw_files(fake_kitti_root: Path):
seq_info = _fake_seq_info()
rig = load_rig_chain(fake_kitti_root / "2011_09_30", camera_id=2)
row = generate_frame_asset(seq_info, fake_kitti_root, frame_index=1, image_width=rig.image_width, image_height=rig.image_height)
raw_image_path = fake_kitti_root / "2011_09_30" / "2011_09_30_drive_0016_sync" / "image_02" / "data" / "0000000001.png"
raw_lidar_path = fake_kitti_root / "2011_09_30" / "2011_09_30_drive_0016_sync" / "velodyne_points" / "data" / "0000000001.bin"
assert row.image_png == raw_image_path.read_bytes()
assert row.points_bin == raw_lidar_path.read_bytes()
assert row.frame_id == "04_000001"
assert row.raw_frame_index == 1
def test_generate_sequence_assets_count(fake_kitti_root: Path):
seq_info = _fake_seq_info()
rows = list(generate_sequence_assets(seq_info, fake_kitti_root))
assert len(rows) == 4
frame_ids = {row.frame_id for row in rows}
assert len(frame_ids) == 4 # one row per unique frame, no stage multiplication
def test_write_assets_round_trip(fake_kitti_root: Path, tmp_path: Path):
seq_info = _fake_seq_info()
rows = list(generate_sequence_assets(seq_info, fake_kitti_root))
output_path = tmp_path / "frames_train.parquet"
count = write_assets(rows, output_path)
assert count == 4
table = pq.read_table(output_path)
assert table.schema.equals(ASSETS_SCHEMA)
read_rows = table.to_pylist()
assert set(read_rows[0].keys()) == set(ASSETS_COLUMN_NAMES)
for original, read_back in zip(rows, read_rows, strict=True):
assert read_back["frame_id"] == original.frame_id
assert bytes(read_back["image_png"]) == original.image_png
assert bytes(read_back["points_bin"]) == original.points_bin
def test_write_assets_sharded_splits_by_target_size(fake_kitti_root: Path, tmp_path: Path):
seq_info = _fake_seq_info()
rows = list(generate_sequence_assets(seq_info, fake_kitti_root))
row_bytes = len(rows[0].image_png) + len(rows[0].points_bin)
# Force multiple shards: cap each shard at roughly 1 row's worth of bytes.
shard_paths = write_assets_sharded(rows, tmp_path, split="train", target_shard_bytes=row_bytes)
assert len(shard_paths) == 4 # one row per shard
for i, path in enumerate(shard_paths):
assert path.name == f"frames_train-{i:05d}-of-00004.parquet"
assert path.exists()
all_rows = []
for path in shard_paths:
all_rows.extend(pq.read_table(path).to_pylist())
assert len(all_rows) == 4
assert {r["frame_id"] for r in all_rows} == {row.frame_id for row in rows}
def test_write_assets_sharded_single_shard_when_under_target(fake_kitti_root: Path, tmp_path: Path):
seq_info = _fake_seq_info()
rows = list(generate_sequence_assets(seq_info, fake_kitti_root))
shard_paths = write_assets_sharded(rows, tmp_path, split="test", target_shard_bytes=10**9)
assert len(shard_paths) == 1
assert shard_paths[0].name == "frames_test-00000-of-00001.parquet"
def test_write_assets_sharded_empty_input(tmp_path: Path):
shard_paths = write_assets_sharded([], tmp_path, split="train")
assert shard_paths == []
def test_asset_points_bin_decodes_to_same_points_as_raw_file(fake_kitti_root: Path):
seq_info = _fake_seq_info()
rows = list(generate_sequence_assets(seq_info, fake_kitti_root))
row = rows[2]
raw_lidar_path = fake_kitti_root / "2011_09_30" / "2011_09_30_drive_0016_sync" / "velodyne_points" / "data" / "0000000002.bin"
expected = np.fromfile(raw_lidar_path, dtype=np.float32).reshape(-1, 4)
decoded = np.frombuffer(row.points_bin, dtype=np.float32).reshape(-1, 4)
np.testing.assert_array_equal(decoded, expected)
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