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@@ -33,3 +33,86 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_final/confusion_matrix.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_final/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_final/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_final/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_final/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_00/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_00/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_00/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_00/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_00/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_00/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_01/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_01/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_01/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_01/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_01/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_01/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_02/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_02/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_02/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_02/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_02/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_02/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_03/BoxF1_curve.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_03/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_03/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_03/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_03/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_03/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_03/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_04/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_04/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_04/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_04/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_04/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_04/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_05/BoxF1_curve.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_05/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_05/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_05/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_05/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_05/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_05/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_06/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_06/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_06/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_06/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_06/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_06/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_07/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_07/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_07/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_07/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_07/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_07/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/BoxF1_curve.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/BoxR_curve.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_08/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_09/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_09/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_09/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_09/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_09/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_kfold/fold_09/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/finetune_v3/results.png filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/finetune_v3/train_batch0.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/finetune_v3/train_batch1.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/finetune_v3/train_batch2.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/finetune_v3/val_batch0_labels.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/finetune_v3/val_batch0_pred.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/16.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/23.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/26.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/3.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/37.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/42.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/55.jpg filter=lfs diff=lfs merge=lfs -text
+train/runs/obb/jenga_v3/pred_val/64.jpg filter=lfs diff=lfs merge=lfs -text

train/labeling.py

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+#!/usr/bin/env python3
+import argparse
+import os
+import math
+import cv2
+
+WIN = "corner_picker_png"
+
+def order_points_clockwise(pts):
+    """
+    pts: list[(x,y)] length=4
+    반환: 중심 기준 각도 정렬(시계방향) + 시작점을 좌상단(최소 x+y)로 회전
+    """
+    cx = sum(p[0] for p in pts) / 4.0
+    cy = sum(p[1] for p in pts) / 4.0
+
+    # 각도 기준 정렬 (atan2: -pi~pi). OpenCV 좌표계(y down)에서도 일관되게만 유지하면 OK.
+    pts_ang = sorted(pts, key=lambda p: math.atan2(p[1] - cy, p[0] - cx))
+
+    # 시작점을 "좌상단"에 가깝게(최소 x+y)로 맞춰서 회전
+    start_idx = min(range(4), key=lambda i: pts_ang[i][0] + pts_ang[i][1])
+    pts_ord = pts_ang[start_idx:] + pts_ang[:start_idx]
+
+    # 시계/반시계가 뒤집힐 수 있어, 면적 부호로 한 번 더 고정(원하면)
+    # 여기서는 일관성만 확보하면 되므로 그대로 둠.
+    return pts_ord
+
+def to_yolo_obb_line(class_id, pts, w, h):
+    """
+    YOLOv8 OBB 라벨 포맷:
+    <class_id> x1 y1 x2 y2 x3 y3 x4 y4  (모두 0~1 정규화)
+    """
+    vals = []
+    for (x, y) in pts:
+        xn = x / float(w)
+        yn = y / float(h)
+        # 혹시 모를 경계값 클램프
+        xn = 0.0 if xn < 0.0 else 1.0 if xn > 1.0 else xn
+        yn = 0.0 if yn < 0.0 else 1.0 if yn > 1.0 else yn
+        vals.extend([xn, yn])
+
+    return f"{int(class_id)} " + " ".join(f"{v:.6f}" for v in vals)
+
+def ensure_dir(p):
+    d = os.path.dirname(p)
+    if d:
+        os.makedirs(d, exist_ok=True)
+
+def default_label_path(image_path, labels_root):
+    """
+    예: images/train/1.png -> labels_root/train/1.txt (가능하면)
+    아니면 그냥 labels_root/1.txt
+    """
+    base = os.path.splitext(os.path.basename(image_path))[0] + ".txt"
+    norm = image_path.replace("\\", "/")
+    parts = norm.split("/")
+    if "images" in parts:
+        idx = parts.index("images")
+        if idx + 1 < len(parts):
+            split = parts[idx + 1]  # train/val/test
+            return os.path.join(labels_root, split, base)
+    return os.path.join(labels_root, base)
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("image_path", help="input PNG/JPG path")
+    ap.add_argument("--labels-root", default="labels", help="labels root dir (default: labels/)")
+    ap.add_argument("--class-id", type=int, default=0, help="class id (default: 0)")
+    ap.add_argument("--append", action="store_true", help="append line to label file (multiple objects)")
+    ap.add_argument("--no-reorder", action="store_true", help="do not reorder points, use click order")
+    args = ap.parse_args()
+
+    img = cv2.imread(args.image_path, cv2.IMREAD_COLOR)
+    if img is None:
+        raise FileNotFoundError(f"Failed to read: {args.image_path}")
+
+    h, w = img.shape[:2]
+    pts = []
+    cur = None
+
+    out_path = default_label_path(args.image_path, args.labels_root)
+
+    def save_yolo_label():
+        nonlocal pts
+        pts_use = pts[:]
+        if not args.no_reorder:
+            pts_use = order_points_clockwise(pts_use)
+
+        line = to_yolo_obb_line(args.class_id, pts_use, w, h)
+
+        ensure_dir(out_path)
+        mode = "a" if args.append else "w"
+        with open(out_path, mode) as f:
+            f.write(line + "\n")
+
+        print("\n--- YOLO OBB label saved ---")
+        print(f"image: {args.image_path}")
+        print(f"label: {os.path.abspath(out_path)}")
+        print(f"line : {line}")
+        print("format: <class_id> x1 y1 x2 y2 x3 y3 x4 y4 (normalized 0~1)")
+        print("---------------------------\n")
+
+    def on_mouse(event, x, y, flags, param):
+        nonlocal cur, pts
+        cur = (x, y)
+        if event == cv2.EVENT_LBUTTONDOWN:
+            pts.append((x, y))
+            print(f"[{len(pts)}] uv=({x},{y})")
+            if len(pts) == 4:
+                save_yolo_label()
+                # 다음 객체도 찍을 수 있게 유지하려면 pts.clear()를 하지 말고,
+                # 한 장에 1개만 라벨이면 clear해서 다시 찍게.
+                pts.clear()
+
+    cv2.namedWindow(WIN, cv2.WINDOW_NORMAL)
+    cv2.setMouseCallback(WIN, on_mouse)
+
+    print("Controls: left-click add(4 points -> save) | u undo | r reset | q quit")
+    print(f"Image size: {w}x{h}")
+    print(f"Output label: {os.path.abspath(out_path)}")
+    print(f"class_id={args.class_id}, append={args.append}, reorder={not args.no_reorder}")
+
+    while True:
+        vis = img.copy()
+
+        # draw clicked points
+        for i, (u, v) in enumerate(pts):
+            cv2.circle(vis, (u, v), 6, (255, 0, 255), -1)
+            cv2.putText(
+                vis, f"P{i+1}({u},{v})", (u + 8, v + 18),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2
+            )
+
+        # draw cursor
+        if cur is not None:
+            u, v = cur
+            cv2.circle(vis, (u, v), 4, (0, 255, 255), -1)
+            cv2.putText(
+                vis, f"uv=({u},{v})", (u + 10, v - 10),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2
+            )
+
+        # 안내 텍스트
+        cv2.putText(
+            vis, f"click 4 corners -> save ({os.path.basename(out_path)})",
+            (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2
+        )
+
+        cv2.imshow(WIN, vis)
+        key = cv2.waitKey(20) & 0xFF
+
+        if key == ord('q') or key == 27:
+            break
+        elif key == ord('r'):
+            pts.clear()
+            print("Reset points")
+        elif key == ord('u'):
+            if pts:
+                removed = pts.pop()
+                print(f"Undo: removed {removed}")
+
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    main()

train/make_yolo_split.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import argparse
+import os
+import random
+import shutil
+from pathlib import Path
+
+IMG_EXTS = [".png", ".jpg", ".jpeg", ".bmp", ".webp"]
+
+def is_image(p: Path) -> bool:
+    return p.suffix.lower() in IMG_EXTS
+
+def safe_mkdir(p: Path):
+    p.mkdir(parents=True, exist_ok=True)
+
+def copy_or_link(src: Path, dst: Path, mode: str):
+    safe_mkdir(dst.parent)
+    if dst.exists():
+        dst.unlink()
+    if mode == "copy":
+        shutil.copy2(src, dst)
+    elif mode == "symlink":
+        os.symlink(src, dst)
+    elif mode == "hardlink":
+        os.link(src, dst)
+    else:
+        raise ValueError(f"unknown mode: {mode}")
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--src", default="/home/jeong/data_v3", help="source images dir (contains 1.png~69.png)")
+    ap.add_argument("--labels", default="/home/jeong/data_v3/labels", help="source labels dir (contains 1.txt~69.txt)")
+    ap.add_argument("--out", default="/home/jeong/jetank_ws/data_jenga_v3_yolo", help="output dataset root")
+    ap.add_argument("--val_n", type=int, default=8, help="number of validation images (recommended 6~9 for 69 imgs)")
+    ap.add_argument("--seed", type=int, default=42, help="random seed for split")
+    ap.add_argument("--mode", choices=["copy", "symlink", "hardlink"], default="copy",
+                    help="how to place files into output (copy safest; symlink fastest)")
+    ap.add_argument("--class_names", default="jenga",
+                    help="comma-separated class names for data.yaml (e.g., 'jenga' or 'jenga,jenga_stack')")
+    args = ap.parse_args()
+
+    src_dir = Path(args.src)
+    lab_dir = Path(args.labels)
+    out_dir = Path(args.out)
+
+    if not src_dir.exists():
+        raise FileNotFoundError(f"src not found: {src_dir}")
+    if not lab_dir.exists():
+        raise FileNotFoundError(f"labels not found: {lab_dir}")
+
+    # collect images
+    images = sorted([p for p in src_dir.iterdir() if p.is_file() and is_image(p)])
+    if not images:
+        raise RuntimeError(f"no images found in: {src_dir}")
+
+    # match label by stem
+    pairs = []
+    missing_labels = []
+    for img in images:
+        lbl = lab_dir / f"{img.stem}.txt"
+        if not lbl.exists():
+            missing_labels.append(img.name)
+            continue
+        pairs.append((img, lbl))
+
+    if missing_labels:
+        print("[WARN] missing label files for images:")
+        for n in missing_labels:
+            print("  -", n)
+        print("[WARN] those will be skipped.")
+
+    if len(pairs) < 2:
+        raise RuntimeError("not enough (image,label) pairs to split.")
+
+    # split
+    random.seed(args.seed)
+    random.shuffle(pairs)
+
+    val_n = args.val_n
+    val_n = max(1, min(val_n, len(pairs) - 1))  # ensure at least 1 train
+    val_pairs = pairs[:val_n]
+    train_pairs = pairs[val_n:]
+
+    # output dirs
+    img_train = out_dir / "images" / "train"
+    img_val   = out_dir / "images" / "val"
+    lab_train = out_dir / "labels" / "train"
+    lab_val   = out_dir / "labels" / "val"
+    for d in [img_train, img_val, lab_train, lab_val]:
+        safe_mkdir(d)
+
+    # copy/link
+    for img, lbl in train_pairs:
+        copy_or_link(img, img_train / img.name, args.mode)
+        copy_or_link(lbl, lab_train / lbl.name, args.mode)
+
+    for img, lbl in val_pairs:
+        copy_or_link(img, img_val / img.name, args.mode)
+        copy_or_link(lbl, lab_val / lbl.name, args.mode)
+
+    # write data.yaml (Ultralytics format)
+    class_names = [c.strip() for c in args.class_names.split(",") if c.strip()]
+    if not class_names:
+        class_names = ["jenga"]
+
+    data_yaml = out_dir / "data.yaml"
+    yaml_txt = (
+        f"path: {out_dir}\n"
+        f"train: images/train\n"
+        f"val: images/val\n"
+        f"names:\n"
+        + "".join([f"  {i}: {name}\n" for i, name in enumerate(class_names)])
+    )
+    data_yaml.write_text(yaml_txt, encoding="utf-8")
+
+    print("✅ Done.")
+    print(f"- total pairs: {len(pairs)}")
+    print(f"- train: {len(train_pairs)}")
+    print(f"- val: {len(val_pairs)}")
+    print(f"- out: {out_dir}")
+    print(f"- data.yaml: {data_yaml}")
+
+if __name__ == "__main__":
+    main()

train/runs/obb/jenga_final/args.yaml

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+task: obb
+mode: train
+model: /home/jeong/jetank_ws/src/common/yolov8_obb_pkg/yolov8_obb_pkg/lib/best.pt
+data: /home/jeong/jetank_ws/data_final/data.yaml
+epochs: 300
+time: null
+patience: 80
+batch: 8
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: '0'
+workers: 8
+project: runs/obb
+name: jenga_final
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: true
+close_mosaic: 0
+resume: false
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