chenjiashuo/T2exture_datasets

T2exture Dataset

Prepared synthetic and real thermal sequences for the two-stage T2exture method.

Download

python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='chenjiashuo/T2exture_datasets', repo_type='dataset', local_dir='datasets')"

Layout

datasets/
  train.txt
  valid.txt
  test.txt
  dataset_manifest.json
  sim/<scene>/
    texture/001.npy       # prepared target residual X
    passive/001.npy       # passive/source-off state S^off
    source_on/001.npy     # optional raw S^on for exact anchor construction
  flow/s10/<scene>/001_002_011.npz
  source_off/amt-s/<scene>/001.npy
  source_off/amt-l/<scene>/001.npy
  source_off/amt-g/<scene>/001.npy
  real/<sequence>/...

Synthetic frames are single-channel NumPy arrays. Real benchmark frames are grayscale PNG images. The prepared synthetic files store X directly in texture/; source_on/ is optional and is copied when present by the dataset preparation script.

Protocol

The default protocol uses an active-frame stride of 10, a centered passive context of 5, and flow/s10:

C_t = [S^off_{t-2}, S^off_{t-1}, S^off_t, S^off_{t+1}, S^off_{t+2}]

Formal T2exture-S/L/G runs use the matching Stage 1 cache:

T2exture-S -> source_off/amt-s
T2exture-L -> source_off/amt-l
T2exture-G -> source_off/amt-g

These caches contain estimated S_hat^off frames at active keyframes. Stage 2 uses them both to correct active texture anchors and to fill the passive context at active keyframes.

Local Check

After downloading this dataset as datasets/, run from the code repository root:

python -B scripts/preflight.py --data-root datasets --config train.yaml --require-source-off

The check verifies split files, frame shape, flow files, and all three Stage 1 cache folders.

Excluded Files

Training logs, evaluation outputs, generated visual sheets, previews, and local caches are not dataset artifacts and should stay outside this repository.