# yolo11_main.py
import cv2
import numpy as np
from collections import deque
import os

# 导入模块（不是函数）
from .aligment_inference import yolov11_cls_inference

# 模型路径
CLS_MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "yolov11_cls_640v6.rknn")

class ClassificationStabilizer:
    """分类结果稳定性校验器，处理瞬时噪声帧"""

    def __init__(self, window_size=5, switch_threshold=2):
        self.window_size = window_size  # 滑动窗口大小（缓存最近N帧结果）
        self.switch_threshold = switch_threshold  # 状态切换需要连续N帧一致
        self.result_buffer = deque(maxlen=window_size)  # 缓存最近结果
        self.current_state = "盖板未对齐"  # 初始状态
        self.consecutive_count = 0  # 当前状态连续出现的次数

    def stabilize(self, current_frame_result):
        """
        输入当前帧的分类结果，返回经过稳定性校验的结果
        Args:
            current_frame_result: 当前帧的原始分类结果（str）
        Returns:
            str: 经过校验的稳定结果
        """
        # 1. 将当前帧结果加入滑动窗口
        self.result_buffer.append(current_frame_result)

        # 2. 统计窗口内各结果的出现次数（多数投票基础）
        result_counts = {}
        for res in self.result_buffer:
            result_counts[res] = result_counts.get(res, 0) + 1    # 使用 result_counts 字典记录每个元素出现的总次数。

        # 3. 找到窗口中出现次数最多的结果（候选结果）
        candidate = max(result_counts, key=result_counts.get)

        # 4. 状态切换校验：只有候选结果连续出现N次才允许切换
        if candidate == self.current_state:
            # 与当前状态一致，重置连续计数
            self.consecutive_count = 0
        else:
            # 与当前状态不一致，累计连续次数
            self.consecutive_count += 1
            # 连续达到阈值，才更新状态
            if self.consecutive_count >= self.switch_threshold:
                self.current_state = candidate
                self.consecutive_count = 0

        return self.current_state

# 初始化稳定性校验器（全局唯一实例，确保状态连续）
cls_stabilizer = ClassificationStabilizer(
    window_size=5,  # 缓存最近5帧
    switch_threshold=2  # 连续2帧一致才切换状态
)

# ====================== 分类接口（可选，保持原逻辑） ======================
def run_yolo_classification(rgb_frame):
    """
    YOLO 图像分类接口函数
    Args:
        rgb_frame: numpy array (H, W, 3), RGB 格式
    Returns:
        str: 分类结果（"盖板对齐" / "盖板未对齐" / "异常"）
    """
    if not isinstance(rgb_frame, np.ndarray):
        print(f"[ERROR] 输入类型错误：需为 np.ndarray，当前为 {type(rgb_frame)}")
        return "异常"

    try:
        cover_cls = yolov11_cls_inference(CLS_MODEL_PATH, rgb_frame, target_size=(640, 640))
    except Exception as e:
        print(f"[WARN] 分类推理失败: {e}")
        cover_cls = "异常"
        
    raw_result = "盖板未对齐"  # 默认值
    # 结果映射
    if cover_cls == "cover_ready":
        raw_result = "盖板对齐"
    elif cover_cls == "cover_noready":
        raw_result = "盖板未对齐"
    else:
        raw_result = "异常"
    # 通过稳定性校验器处理，返回最终结果
    stable_result = cls_stabilizer.stabilize(raw_result)
    print("raw_result, stable_result:",raw_result, stable_result)
    return stable_result