import cv2
import numpy as np
import os
from ultralytics import YOLO

# ---------------- 配置 ----------------
MODEL_PATH = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/61seg/exp3/weights/best.pt"
IMAGE_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/6111c/1"
OUT_DIR = "./labels"          # 只保存 labels
IMG_SIZE = 640
CONF_THRES = 0.25

# 多边形简化比例（点数控制核心参数）
EPSILON_RATIO = 0.01

IMG_EXTS = (".jpg", ".jpeg", ".png", ".bmp")
# -------------------------------------


def simplify_polygon(poly, epsilon_ratio):
    """
    使用 approxPolyDP 简化多边形
    """
    poly = poly.astype(np.int32)
    perimeter = cv2.arcLength(poly, True)
    epsilon = epsilon_ratio * perimeter
    approx = cv2.approxPolyDP(poly, epsilon, True)
    return approx.reshape(-1, 2)


def extract_simplified_masks(result):
    """
    提取并简化 YOLO mask
    返回：
      [(cls_id, poly), ...]
    """
    simplified = []

    if result.masks is None:
        return simplified

    boxes = result.boxes

    for i, poly in enumerate(result.masks.xy):
        cls_id = int(boxes.cls[i])
        conf = float(boxes.conf[i])

        if conf < CONF_THRES:
            continue

        poly = simplify_polygon(poly, EPSILON_RATIO)

        if len(poly) < 3:
            continue

        simplified.append((cls_id, poly))

    return simplified


def save_yolo_seg_labels(masks, img_shape, save_path):
    """
    保存 YOLO segmentation 标签
    """
    h, w = img_shape[:2]
    lines = []

    for cls_id, poly in masks:
        poly_norm = []
        for x, y in poly:
            poly_norm.append(f"{x / w:.6f}")
            poly_norm.append(f"{y / h:.6f}")

        lines.append(str(cls_id) + " " + " ".join(poly_norm))

    # ⚠️ 没有目标也生成空 txt（YOLO 训练需要）
    with open(save_path, "w") as f:
        if lines:
            f.write("\n".join(lines))


def run_folder_inference():
    out_lbl_dir = os.path.join(OUT_DIR, "labels")
    os.makedirs(out_lbl_dir, exist_ok=True)

    # 模型只加载一次
    model = YOLO(MODEL_PATH)

    img_files = sorted([
        f for f in os.listdir(IMAGE_DIR)
        if f.lower().endswith(IMG_EXTS)
    ])

    print(f"📂 共检测 {len(img_files)} 张图片")

    for idx, img_name in enumerate(img_files, 1):
        img_path = os.path.join(IMAGE_DIR, img_name)
        img = cv2.imread(img_path)

        if img is None:
            print(f"⚠️ 跳过无法读取: {img_name}")
            continue

        results = model(
            img,
            imgsz=IMG_SIZE,
            conf=CONF_THRES,
            verbose=False
        )
        result = results[0]

        masks = extract_simplified_masks(result)

        base_name = os.path.splitext(img_name)[0]
        label_path = os.path.join(out_lbl_dir, base_name + ".txt")

        save_yolo_seg_labels(masks, img.shape, label_path)

        print(f"[{idx}/{len(img_files)}] ✅ {img_name}")

    print("🎉 标签生成完成（仅保存 YOLO Seg 标签）")


if __name__ == "__main__":
    run_folder_inference()