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

# ---------------------------
# 类别映射
# ---------------------------
CLASS_NAMES = {
    0: "未堆料",
    1: "小堆料",
    2: "大堆料",
    3: "未浇筑满",
    4: "浇筑满"
}


# ---------------------------
# 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:
            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


# ---------------------------
# class1/class2 加权判断
# ---------------------------
def weighted_small_large(pred_probs, threshold=0.4, w1=0.3, w2=0.7):
    p1 = float(pred_probs[1])
    p2 = float(pred_probs[2])
    total = p1 + p2
    if total > 0:
        score = (w1 * p1 + w2 * p2) / total
    else:
        score = 0.0
    final_class = "大堆料" if score >= threshold else "小堆料"
    return final_class, score, p1, p2


# ---------------------------
# 单张图片推理函数
# ---------------------------
def classify_image_weighted(image, model, threshold=0.4):
    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})")

    # class1/class2 使用加权得分
    if class_id in [1, 2]:
        final_class, score, p1, p2 = weighted_small_large(pred_probs, threshold=threshold)
    else:
        final_class = class_name
        score = confidence
        p1 = float(pred_probs[1])
        p2 = float(pred_probs[2])

    return final_class, score, p1, p2


# ---------------------------
# 实时视频流推理函数
# ---------------------------
def real_time_inference(rtsp_url, model_path, roi_file, target_size=640, threshold=0.4):
    """
    从RTSP流实时推理
    :param rtsp_url: RTSP流URL
    :param model_path: 模型路径
    :param roi_file: ROI文件路径
    :param target_size: 目标尺寸
    :param threshold: 分类阈值
    """
    # 加载模型
    model = YOLO(model_path)

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

    # 打开RTSP流
    cap = cv2.VideoCapture(rtsp_url)

    if not cap.isOpened():
        print(f"❌ 无法打开视频流: {rtsp_url}")
        return

    print(f"✅ 成功连接到视频流: {rtsp_url}")
    print("按 'q' 键退出，按 's' 键保存当前帧")

    frame_count = 0
    while True:
        ret, frame = cap.read()
        if not ret:
            print("❌ 无法读取帧，可能连接已断开")
            break

        frame_count += 1
        print(f"\n处理第 {frame_count} 帧")

        try:
            # 裁剪并调整ROI
            crops = crop_and_resize(frame, rois, target_size)

            for roi_resized, roi_idx in crops:
                final_class, score, p1, p2 = classify_image_weighted(roi_resized, model, threshold=threshold)

                print(f"ROI {roi_idx} -> 类别: {final_class}, 加权分数: {score:.2f}, "
                      f"class1 置信度: {p1:.2f}, class2 置信度: {p2:.2f}")

                # 判断是否溢料
                if "大堆料" in final_class or "浇筑满" in final_class:
                    print(f"🚨 检测到溢料: ROI {roi_idx} - {final_class}")

                # 可视化（可选）
                cv2.imshow(f'ROI {roi_idx}', roi_resized)

            # 显示原始帧
            cv2.imshow('Original Frame', frame)

        except Exception as e:
            print(f"处理帧时出错: {e}")
            continue

        # 键盘控制
        key = cv2.waitKey(1) & 0xFF
        if key == ord('q'):  # 按q退出
            break
        elif key == ord('s'):  # 按s保存当前帧
            cv2.imwrite(f"frame_{frame_count}.jpg", frame)
            print(f"保存帧到 frame_{frame_count}.jpg")

    # 清理资源
    cap.release()
    cv2.destroyAllWindows()
    print("✅ 视频流处理结束")


# ---------------------------
# 主函数 - 实时推理示例
# ---------------------------
# if __name__ == "__main__":
#     # RTSP流URL
#     rtsp_url = "rtsp://admin:XJ123456@192.168.1.51:554/streaming/channels/101"
#
#     # 配置参数
#     model_path = r"models/overflow.pt"
#     roi_file = r"./roi_coordinates/1_rois.txt"
#     target_size = 640
#     threshold = 0.4
#
#     print("开始实时视频流推理...")
#     real_time_inference(rtsp_url, model_path, roi_file, target_size, threshold)