import os
from pathlib import Path
import cv2
import numpy as np
import shutil
from ultralytics import YOLO

# ---------------------------
# 三分类类别定义（必须与模型训练时的顺序一致！）
# ---------------------------
CLASS_NAMES = {
    0: "模具车1",
    1: "模具车2",
    2: "有遮挡"
}

# ---------------------------
# 加载 ROI 列表
# ---------------------------
def load_global_rois(txt_path):
    rois = []
    if not os.path.exists(txt_path):
        print(f"❌ ROI 文件不存在: {txt_path}")
        return rois
    with open(txt_path, 'r') as f:
        for line in f:
            s = line.strip()
            if s:
                try:
                    x, y, w, h = map(int, s.split(','))
                    rois.append((x, y, w, h))
                except Exception as e:
                    print(f"⚠️ 无法解析 ROI 行 '{s}': {e}")
    return rois

# ---------------------------
# 裁剪并 resize ROI
# ---------------------------
def crop_and_resize(img, rois, target_size=640):
    crops = []
    h_img, w_img = img.shape[:2]
    for i, (x, y, w, h) in enumerate(rois):
        if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
            print(f"⚠️ ROI 超出图像边界，跳过: ({x}, {y}, {w}, {h})")
            continue
        roi = img[y:y+h, x:x+w]
        roi_resized = cv2.resize(roi, (target_size, target_size), interpolation=cv2.INTER_AREA)
        crops.append((roi_resized, i))
    return crops

# ---------------------------
# 单张图片推理函数（3分类）
# ---------------------------
def classify_image(image, model):
    results = model(image)
    pred_probs = results[0].probs.data.cpu().numpy().flatten()
    class_id = int(pred_probs.argmax())
    confidence = float(pred_probs[class_id])
    class_name = CLASS_NAMES.get(class_id, f"未知类别({class_id})")
    return class_name, confidence

# ---------------------------
# 批量推理主函数（移动原图）
# ---------------------------
def batch_classify_images(model_path, input_folder, output_root, roi_file, target_size=640):

    # 加载模型
    model = YOLO(model_path)

    # 创建输出目录
    output_root = Path(output_root)
    output_root.mkdir(parents=True, exist_ok=True)

    class_dirs = {}
    for name in CLASS_NAMES.values():
        d = output_root / name
        d.mkdir(exist_ok=True)
        class_dirs[name] = d

    # 加载 ROI
    rois = load_global_rois(roi_file)
    if not rois:
        print("❌ 没有有效 ROI，退出程序")
        return

    # 定义严重性等级（数值越小越“正常”，用于取最严重结果）
    # 根据你的业务调整：例如“有遮挡”最严重
    severity_rank = {
        "模具车1": 0,
        "模具车2": 1,
        "有遮挡": 2
    }

    input_folder = Path(input_folder)
    image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff'}
    processed_count = 0

    for img_path in input_folder.glob("*.*"):
        if img_path.suffix.lower() not in image_extensions:
            continue

        try:
            print(f"\n📄 处理: {img_path.name}")
            img = cv2.imread(str(img_path))
            print(f"图像尺寸: {img.shape[1]} x {img.shape[0]}")
            if img is None:
                print(f"❌ 无法读取图像: {img_path.name}")
                continue

            crops = crop_and_resize(img, rois, target_size)
            if not crops:
                print(f"⚠️ 无有效 ROI 裁剪区域: {img_path.name}")
                continue

            detected_classes = []
            for roi_img, roi_idx in crops:
                final_class, conf = classify_image(roi_img, model)
                detected_classes.append(final_class)
                print(f" 🔍 ROI{roi_idx}: {final_class} (conf={conf:.2f})")

            # 选择最严重的类别（severity_rank 值最大者）
            most_severe_class = max(detected_classes, key=lambda x: severity_rank.get(x, -1))

            # 移动原图（不是裁剪图！）
            dst_path = class_dirs[most_severe_class] / img_path.name
            shutil.move(str(img_path), str(dst_path))
            print(f"📦 已移动 -> [{most_severe_class}] {dst_path}")

            processed_count += 1

        except Exception as e:
            print(f"❌ 处理失败 {img_path.name}: {e}")

    print(f"\n🎉 批量处理完成！共处理 {processed_count} 张图像。")


# ---------------------------
# 主程序入口
# ---------------------------
if __name__ == "__main__":
    model_path = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/cls_resize_muju/exp_cls2/weights/best.pt"
    input_folder = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/61/浇筑满"
    output_root = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/61"
    roi_file = "/home/hx/yolo/muju_cls/roi_coordinates/muju_roi.txt"
    target_size = 640

    batch_classify_images(
        model_path=model_path,
        input_folder=input_folder,
        output_root=output_root,
        roi_file=roi_file,
        target_size=target_size
    )