fixed table and search

public/userarea/photos/matrici/input_images/collage.py

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+from pathlib import Path
+from PIL import Image
+import shutil
+import statistics
+
+# --- Settings ---
+INPUT_DIR = Path("input_images")        # where all images are
+OUTPUT_DIR = Path("input_square")       # where selected square-ish images will be copied
+ASPECT_MAX = 1.20                       # max ratio between longer/shorter side (1.00 = perfect square)
+SIZE_TOLERANCE = 0.35                   # +-35% around median "short side" (set None to disable)
+
+exts = {".png", ".jpg", ".jpeg", ".webp"}
+
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+candidates = []
+short_sides = []
+
+# --- Scan images and compute metrics ---
+files = sorted([p for p in INPUT_DIR.iterdir() if p.is_file() and p.suffix.lower() in exts])
+print(f"Found {len(files)} files in {INPUT_DIR.resolve()}")
+
+for p in files:
+    with Image.open(p) as im:
+        w, h = im.size
+    long_side = max(w, h)
+    short_side = min(w, h)
+    aspect = long_side / short_side
+
+    # Keep only square-ish by aspect ratio
+    if aspect <= ASPECT_MAX:
+        candidates.append((p, w, h, short_side, aspect))
+        short_sides.append(short_side)
+
+print(f"Square-ish (aspect <= {ASPECT_MAX}): {len(candidates)}")
+
+# --- Optional: filter also by similar size (short side around median) ---
+if SIZE_TOLERANCE is not None and len(short_sides) > 0:
+    med = statistics.median(short_sides)
+    lo = med * (1 - SIZE_TOLERANCE)
+    hi = med * (1 + SIZE_TOLERANCE)
+
+    filtered = [c for c in candidates if lo <= c[3] <= hi]
+    print(f"After size filter (median short side={med:.0f}, range {lo:.0f}-{hi:.0f}): {len(filtered)}")
+else:
+    filtered = candidates
+
+# --- Copy selected to output folder ---
+copied = 0
+for (p, w, h, short_side, aspect) in filtered:
+    dst = OUTPUT_DIR / p.name
+    shutil.copy2(p, dst)
+    copied += 1
+
+print(f"Copied {copied} images to {OUTPUT_DIR.resolve()}")