test/visualize_color_jitter.py

"""Visual sanity check: 10 samples with vs. without color jitter.

Writes `color_jitter_compare.png` next to this script. Layout:
  row 0 — base image (no augmentation)
  row 1 — color-jittered image (training-time augmentation)
  row 2 — |jittered − base| amplified for visibility
"""
from __future__ import annotations

import sys
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import torch

_REPO_ROOT = Path(__file__).resolve().parents[2]
if str(_REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(_REPO_ROOT))

from tactile_vae.dataset import ColorJitterConfig, TactileParquetDataset

N_SAMPLES = 10
SEED = 0
JITTER = ColorJitterConfig(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.05)
OUT_PNG = Path(__file__).with_name("color_jitter_compare.png")


def _to_hwc(t: torch.Tensor) -> np.ndarray:
    return t.detach().cpu().clamp(0, 1).permute(1, 2, 0).numpy()


def main() -> None:
    ds_plain = TactileParquetDataset(image_size=128, color_jitter=None)
    ds_jit = TactileParquetDataset(image_size=128, color_jitter=JITTER)

    # Sample 10 distinct indices across the dataset for variety.
    n = len(ds_plain)
    rng = np.random.default_rng(SEED)
    indices = sorted(rng.choice(n, size=N_SAMPLES, replace=False).tolist())
    print(f"Comparing {N_SAMPLES} samples (indices: {indices[:3]}... of {n:,})")
    print(f"Jitter config: {JITTER}")

    torch.manual_seed(SEED)  # fix jitter RNG so the PNG is reproducible

    fig, axes = plt.subplots(3, N_SAMPLES, figsize=(2 * N_SAMPLES, 6.6))

    for col, idx in enumerate(indices):
        base = ds_plain[idx]                 # (3,128,128) float in [0,1]
        jittered = ds_jit[idx]
        base_hwc = _to_hwc(base)
        jit_hwc = _to_hwc(jittered)

        # Amplify residual so visible per-pixel changes are visible at a glance.
        diff = np.abs(jit_hwc - base_hwc)
        scale = max(diff.max(), 1e-6)
        diff_vis = np.clip(diff / scale, 0, 1)

        axes[0, col].imshow(base_hwc)
        axes[0, col].set_title(f"idx={idx}", fontsize=8)
        axes[1, col].imshow(jit_hwc)
        axes[1, col].set_title(f"Δmean={float(jit_hwc.mean()-base_hwc.mean()):+.3f}",
                               fontsize=8)
        axes[2, col].imshow(diff_vis)
        axes[2, col].set_title(f"|Δ|max={float(diff.max()):.3f}", fontsize=8)
        for r in range(3):
            axes[r, col].set_xticks([])
            axes[r, col].set_yticks([])

    axes[0, 0].set_ylabel("no jitter", fontsize=10)
    axes[1, 0].set_ylabel("with jitter", fontsize=10)
    axes[2, 0].set_ylabel("|Δ| (norm)", fontsize=10)

    fig.suptitle(
        f"Color jitter comparison (b={JITTER.brightness}, c={JITTER.contrast}, "
        f"s={JITTER.saturation}, h={JITTER.hue})",
        fontsize=11,
    )
    fig.tight_layout(rect=(0, 0, 1, 0.96))
    fig.savefig(OUT_PNG, dpi=120)
    print(f"wrote {OUT_PNG} ({OUT_PNG.stat().st_size/1024:.1f} KB)")


if __name__ == "__main__":
    main()