feat(auto-annotation): integrate YOLO auto-labeling and enhance data management (#223)

* feat(auto-annotation): initial setup

* chore: remove package-lock.json

* chore: 清理本地测试脚本与 Maven 设置

* chore: change package-lock.json

runtime/ops/annotation/image_semantic_segmentation/process.py

@@ -0,0 +1,166 @@
+import os
+import json
+from pathlib import Path
+from ultralytics import YOLO
+import cv2
+import numpy as np
+
+
+def get_color_by_class_id(class_id: int):
+    """根据 class_id 生成稳定颜色（BGR）"""
+    np.random.seed(class_id)
+    color = np.random.randint(0, 255, size=3).tolist()
+    return tuple(color)
+
+
+def mask_to_polygons(mask: np.ndarray):
+    """将二值 mask 转换为 COCO 风格多边形列表"""
+    contours, _ = cv2.findContours(
+        mask,
+        cv2.RETR_EXTERNAL,
+        cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    polygons = []
+    for contour in contours:
+        if contour.shape[0] < 3:
+            continue
+        polygon = contour.flatten().tolist()
+        polygons.append(polygon)
+
+    return polygons
+
+
+IMAGE_DIR = "C:/Users/meta/Desktop/Datamate/yolo/Photos"
+OUT_IMG_DIR = "outputs_seg/images"
+OUT_JSON_DIR = "outputs_seg/annotations"
+
+MODEL_MAP = {
+    "n": "yolov8n-seg.pt",
+    "s": "yolov8s-seg.pt",
+    "m": "yolov8m-seg.pt",
+    "l": "yolov8l-seg.pt",
+    "x": "yolov8x-seg.pt",
+}
+MODEL_KEY = "x"
+MODEL_PATH = MODEL_MAP[MODEL_KEY]
+
+CONF_THRES = 0.7
+DRAW_BBOX = True
+
+COCO_CLASS_MAP = {
+    0: "person", 1: "bicycle", 2: "car", 3: "motorcycle", 4: "airplane",
+    5: "bus", 6: "train", 7: "truck", 8: "boat", 9: "traffic light",
+    10: "fire hydrant", 11: "stop sign", 12: "parking meter", 13: "bench",
+    14: "bird", 15: "cat", 16: "dog", 17: "horse", 18: "sheep", 19: "cow",
+    20: "elephant", 21: "bear", 22: "zebra", 23: "giraffe", 24: "backpack",
+    25: "umbrella", 26: "handbag", 27: "tie", 28: "suitcase", 29: "frisbee",
+    30: "skis", 31: "snowboard", 32: "sports ball", 33: "kite",
+    34: "baseball bat", 35: "baseball glove", 36: "skateboard",
+    37: "surfboard", 38: "tennis racket", 39: "bottle",
+    40: "wine glass", 41: "cup", 42: "fork", 43: "knife", 44: "spoon",
+    45: "bowl", 46: "banana", 47: "apple", 48: "sandwich", 49: "orange",
+    50: "broccoli", 51: "carrot", 52: "hot dog", 53: "pizza",
+    54: "donut", 55: "cake", 56: "chair", 57: "couch",
+    58: "potted plant", 59: "bed", 60: "dining table", 61: "toilet",
+    62: "tv", 63: "laptop", 64: "mouse", 65: "remote",
+    66: "keyboard", 67: "cell phone", 68: "microwave", 69: "oven",
+    70: "toaster", 71: "sink", 72: "refrigerator", 73: "book",
+    74: "clock", 75: "vase", 76: "scissors", 77: "teddy bear",
+    78: "hair drier", 79: "toothbrush"
+}
+
+TARGET_CLASS_IDS = [0, 2, 5]
+
+os.makedirs(OUT_IMG_DIR, exist_ok=True)
+os.makedirs(OUT_JSON_DIR, exist_ok=True)
+
+if TARGET_CLASS_IDS is not None:
+    for cid in TARGET_CLASS_IDS:
+        if cid not in COCO_CLASS_MAP:
+            raise ValueError(f"Invalid class id: {cid}")
+
+model = YOLO(MODEL_PATH)
+
+image_paths = list(Path(IMAGE_DIR).glob("*.*"))
+
+for img_path in image_paths:
+    img = cv2.imread(str(img_path))
+    if img is None:
+        print(f"[WARN] Failed to read {img_path}")
+        continue
+
+    results = model(img, conf=CONF_THRES)
+    r = results[0]
+
+    h, w = img.shape[:2]
+    annotations = {
+        "image": img_path.name,
+        "width": w,
+        "height": h,
+        "model_key": MODEL_KEY,
+        "conf_threshold": CONF_THRES,
+        "supported_classes": COCO_CLASS_MAP,
+        "selected_class_ids": TARGET_CLASS_IDS,
+        "instances": []
+    }
+
+    if r.boxes is not None and r.masks is not None:
+        for i, box in enumerate(r.boxes):
+            cls_id = int(box.cls[0])
+            if TARGET_CLASS_IDS is not None and cls_id not in TARGET_CLASS_IDS:
+                continue
+
+            conf = float(box.conf[0])
+            x1, y1, x2, y2 = map(float, box.xyxy[0])
+            label = COCO_CLASS_MAP[cls_id]
+
+            mask = r.masks.data[i].cpu().numpy()
+            mask = (mask > 0.5).astype(np.uint8)
+            mask = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST)
+
+            color = get_color_by_class_id(cls_id)
+            img[mask == 1] = (
+                img[mask == 1] * 0.5 + np.array(color) * 0.5
+            ).astype(np.uint8)
+
+            if True:
+                cv2.rectangle(
+                    img,
+                    (int(x1), int(y1)),
+                    (int(x2), int(y2)),
+                    color,
+                    2
+                )
+
+                cv2.putText(
+                    img,
+                    f"{label} {conf:.2f}",
+                    (int(x1), max(int(y1) - 5, 10)),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.5,
+                    color,
+                    1
+                )
+
+            polygons = mask_to_polygons(mask)
+
+            annotations["instances"].append({
+                "label": label,
+                "class_id": cls_id,
+                "confidence": round(conf, 4),
+                "bbox_xyxy": [x1, y1, x2, y2],
+                "segmentation": polygons
+            })
+
+    out_img_path = os.path.join(OUT_IMG_DIR, img_path.name)
+    out_json_path = os.path.join(OUT_JSON_DIR, img_path.stem + ".json")
+
+    cv2.imwrite(out_img_path, img)
+
+    with open(out_json_path, "w", encoding="utf-8") as f:
+        json.dump(annotations, f, indent=2, ensure_ascii=False)
+
+    print(f"[OK] {img_path.name}")
+
+print("Segmentation batch finished.")