update

prepare_dataset.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+"""
+Připraví dataset pro trénink na serveru.
+Vždy zahrne: data/lanov_custom/
+Podmíněně:   data/hk_custom/       (pokud existuje)
+             data/yolo_visdrone/   (pokud existuje, jen třídy 0–3)
+
+Výstup: dataset_lanov.zip
+  lanov/train/images+labels, val/images+labels, dataset.yaml
+  best.pt
+"""
+import random, shutil, zipfile
+from pathlib import Path
+
+VAL_RATIO    = 0.2
+BG_PER_SPLIT = 50
+SEED         = 42
+CLASS_NAMES  = ["car", "van", "truck", "bus"]
+OUT          = Path("dataset_lanov")
+ZIP_OUT      = Path("dataset_lanov.zip")
+
+random.seed(SEED)
+
+# ---------------------------------------------------------------------------
+# Zdroje dat  (img_dir, lbl_dir, prefix pro unikátní názvy souborů)
+# ---------------------------------------------------------------------------
+SOURCES = [
+    ("data/lanov_custom/train/images", "data/lanov_custom/train/labels", "lanov"),
+]
+
+# Podmíněné zdroje — stejný YOLO formát, stejné třídy
+for name, path in [
+    ("hk",       "data/hk_custom/train"),
+    ("visdrone", "data/yolo_visdrone/train"),
+]:
+    img_dir = Path(path) / "images"
+    lbl_dir = Path(path) / "labels"
+    if img_dir.exists() and lbl_dir.exists():
+        n = sum(1 for l in lbl_dir.glob("*.txt") if l.stat().st_size > 0)
+        print(f"+ {name}: {n} anotovaných dlaždic")
+        SOURCES.append((str(img_dir), str(lbl_dir), name))
+    else:
+        print(f"- {name}: nenalezeno, přeskočeno")
+
+# ---------------------------------------------------------------------------
+# Načtení všech labelů ze všech zdrojů
+# ---------------------------------------------------------------------------
+all_annotated = []   # (lbl_path, img_path, prefix)
+all_background = []
+
+for img_dir, lbl_dir, prefix in SOURCES:
+    img_dir = Path(img_dir)
+    lbl_dir = Path(lbl_dir)
+    for lbl in sorted(lbl_dir.glob("*.txt")):
+        img = img_dir / (lbl.stem + ".jpg")
+        if not img.exists():
+            img = img_dir / (lbl.stem + ".png")
+        entry = (lbl, img if img.exists() else None, prefix)
+        if lbl.stat().st_size > 0:
+            all_annotated.append(entry)
+        else:
+            all_background.append(entry)
+
+random.shuffle(all_annotated)
+random.shuffle(all_background)
+
+n_val = max(1, int(len(all_annotated) * VAL_RATIO))
+val_ann   = all_annotated[:n_val]
+train_ann = all_annotated[n_val:]
+train_bg  = all_background[:BG_PER_SPLIT]
+val_bg    = all_background[BG_PER_SPLIT : BG_PER_SPLIT * 2]
+
+print(f"\nAnotované: {len(all_annotated)}  (train {len(train_ann)}, val {len(val_ann)})")
+print(f"Background: {len(all_background)} (train {len(train_bg)}, val {len(val_bg)})")
+
+# ---------------------------------------------------------------------------
+# Kopírování do OUT/
+# ---------------------------------------------------------------------------
+if OUT.exists():
+    shutil.rmtree(OUT)
+
+for split_name, split in [("train", train_ann + train_bg), ("val", val_ann + val_bg)]:
+    img_out = OUT / split_name / "images"
+    lbl_out = OUT / split_name / "labels"
+    img_out.mkdir(parents=True, exist_ok=True)
+    lbl_out.mkdir(parents=True, exist_ok=True)
+    for lbl_path, img_path, prefix in split:
+        stem = f"{prefix}_{lbl_path.stem}"
+        shutil.copy2(lbl_path, lbl_out / f"{stem}.txt")
+        if img_path:
+            shutil.copy2(img_path, img_out / f"{stem}{img_path.suffix}")
+
+# ---------------------------------------------------------------------------
+# dataset.yaml
+# ---------------------------------------------------------------------------
+(OUT / "dataset.yaml").write_text(
+    f"path: .\ntrain: train/images\nval:   val/images\nnc: {len(CLASS_NAMES)}\nnames: {CLASS_NAMES}\n"
+)
+
+# ---------------------------------------------------------------------------
+# ZIP
+# ---------------------------------------------------------------------------
+if ZIP_OUT.exists():
+    ZIP_OUT.unlink()
+
+weights = Path("best.pt")
+with zipfile.ZipFile(ZIP_OUT, "w", zipfile.ZIP_DEFLATED) as zf:
+    for f in sorted(OUT.rglob("*")):
+        if f.is_file():
+            zf.write(f, "lanov/" + str(f.relative_to(OUT)))
+    if weights.exists():
+        zf.write(weights, "best.pt")
+        print("Přidáno: best.pt")
+
+size_mb = ZIP_OUT.stat().st_size / 1e6
+print(f"\nHotovo: {ZIP_OUT}  ({size_mb:.1f} MB)")
+print(f"\nNa serveru spusť:")
+print(f"  unzip dataset_lanov.zip")
+print(f"  pip install ultralytics")
+print(f"  yolo train model=best.pt data=lanov/dataset.yaml epochs=50 imgsz=256 batch=32")