scripts/make_deblur_visual.py

"""
Qualitative comparison: blurry input vs deblurred outputs.
No GT reference. Just visual quality comparison.

Columns: Blur input | EVSSM | VD-Diff | TURTLE (if available)
"""
import numpy as np
from pathlib import Path
from PIL import Image, ImageDraw

BASE = Path(__file__).parent.parent

METHODS = [
    ("blur",   "Blur Input",  BASE / "data/scannet_blur_proto/vddiff/test/blur"),
    ("evssm",  "EVSSM",       BASE / "data/evssm_deblurred"),
    ("vdiff",  "VD-Diff",     BASE / "data/vdiff_deblurred"),
    ("turtle", "TURTLE",      BASE / "data/turtle_deblurred"),
]

def label_bar(text, w, h=36, bg=(30,30,30), fg=(255,255,255)):
    img = Image.new("RGB", (w, h), bg)
    draw = ImageDraw.Draw(img)
    bbox = draw.textbbox((0,0), text)
    tw, th = bbox[2]-bbox[0], bbox[3]-bbox[1]
    draw.text(((w-tw)//2, (h-th)//2), text, fill=fg)
    return np.array(img)

def load(path, scale=None):
    img = Image.open(path).convert("RGB")
    if scale and scale < 1.0:
        w, h = img.size
        img = img.resize((int(w*scale), int(h*scale)), Image.LANCZOS)
    return np.array(img)

def make_row(scene, frame_idx, scale=0.45, crop=None):
    fname = f"{frame_idx:06d}.png"
    cols = []
    for key, label, root in METHODS:
        path = root / scene / fname
        if not path.exists():
            continue
        img = load(path, scale=scale if crop is None else None)
        if crop:
            y1,x1,y2,x2 = crop
            img = np.array(Image.open(path).convert("RGB"))[y1:y2, x1:x2]
        bar = label_bar(label, img.shape[1])
        cols.append(np.vstack([bar, img]))
    if not cols:
        return None
    max_h = max(c.shape[0] for c in cols)
    return np.hstack([np.vstack([c, np.zeros((max_h-c.shape[0], c.shape[1], 3), np.uint8)]) for c in cols])

def save_grid(rows, title, out_path):
    title_bar = label_bar(title, rows[0].shape[1], h=30, bg=(60,30,30))
    grid = np.vstack([title_bar] + rows)
    Image.fromarray(grid).save(out_path, quality=93)
    print(f"  → {out_path}")

out_dir = BASE / "outputs/eval/qualitative_ngt"
out_dir.mkdir(parents=True, exist_ok=True)

scenes = ["scene0000_00", "scene0000_01", "scene0000_02"]
frame_idxs = [0, 15, 30, 45]

crops = {
    "full":   None,
    "center": (280, 380, 620, 900),   # y1,x1,y2,x2 — around centre
    "top":    (0,   300, 280, 900),   # top region
}

for scene in scenes:
    print(f"\n[{scene}]")
    for crop_name, crop in crops.items():
        rows = [r for fi in frame_idxs if (r := make_row(scene, fi, scale=0.42, crop=crop)) is not None]
        if rows:
            save_grid(rows, f"{scene}  [{crop_name}]",
                      out_dir / f"{scene}_{crop_name}.jpg")