import asyncio
from datetime import datetime

import aiohttp
import cv2
import numpy as np
from aiortc import RTCPeerConnection, RTCSessionDescription, RTCConfiguration
from ocr.ocr_violation_detector import OCRViolationDetector
from ws.ws import send_message_to_client


async def rtc_frame_receiver(url, frame_queue, stop_event):
    """
    接收RTC帧并往队列放入cv2格式的帧数据
    当队列已满时直接丢弃新帧，不阻塞等待
    当stop_event被设置时停止接收
    """
    pc = RTCPeerConnection(RTCConfiguration(iceServers=[]))

    # 累计帧计数器和丢弃帧计数器
    total_frames = 0
    dropped_frames = 0

    @pc.on("track")
    async def on_track(track):
        nonlocal total_frames, dropped_frames
        if track.kind == "video":
            print("接收到视频轨道、开始接收视频帧")
            while not stop_event.is_set():  # 检查是否需要停止
                # 接收当前帧并累计计数
                frame = await track.recv()
                total_frames += 1

                # 转换为cv2兼容的BGR格式numpy数组
                frame_cv2 = frame.to_ndarray(format='bgr24')

                # 验证是否为cv2兼容格式
                if isinstance(frame_cv2, np.ndarray) and frame_cv2.ndim == 3 and frame_cv2.shape[2] == 3:
                    # 检查队列是否已满
                    if frame_queue.full():
                        # 队列已满，丢弃当前帧
                        dropped_frames += 1
                        print(f"第{total_frames}帧：队列已满，丢弃该帧（累计丢弃: {dropped_frames}）")
                    else:
                        # 队列未满，放入当前帧
                        await frame_queue.put(frame_cv2)
                        print(f"第{total_frames}帧：已放入队列")
                else:
                    print("帧格式转换失败，不是有效的cv2格式")

    # 创建并设置本地offer
    offer = await pc.createOffer()
    print("已创建本地 SDP Offer")
    await pc.setLocalDescription(offer)

    # 发送offer到服务器
    async with aiohttp.ClientSession() as session:
        print("开始向服务器发送 SDP Offer")
        try:
            async with session.post(
                    url,
                    data=offer.sdp.encode(),
                    headers={
                        "Content-Type": "application/sdp",
                        "Content-Length": str(len(offer.sdp))
                    },
                    ssl=False
            ) as response:
                if response.status == 200:
                    print("已接收到服务器的响应、开始处理 SDP Answer")
                    answer_sdp = await response.text()
                    await pc.setRemoteDescription(RTCSessionDescription(sdp=answer_sdp, type='answer'))
                else:
                    print(f"服务器响应错误: {response.status}")
                    stop_event.set()
        except Exception as e:
            print(f"发送SDP Offer失败: {str(e)}")
            stop_event.set()

    try:
        # 保持连接，直到收到停止信号
        while not stop_event.is_set():
            await asyncio.sleep(1)
    except KeyboardInterrupt:
        print("用户中断")
    finally:
        print(f"开始关闭 RTCPeerConnection，共接收{total_frames}帧，丢弃{dropped_frames}帧")
        await pc.close()
        print("已关闭 RTCPeerConnection")


async def frame_consumer(ip, frame_queue, stop_event):
    """
    从队列中阻塞读取cv2帧并处理（队列为空时阻塞等待）
    检测到违规内容后设置stop_event以终止所有任务
    """
    # 创建OCR检测器实例
    ocr_detector = OCRViolationDetector(
        forbidden_words_path=r"D:\Git\bin\video\ocr\forbidden_words.txt",
        ocr_confidence_threshold=0.5, )

    while not stop_event.is_set():  # 检查是否需要停止
        try:
            # 阻塞等待队列中的帧
            current_frame = await frame_queue.get()

            # 进行OCR检测
            has_violation, words, confidences = ocr_detector.detect(current_frame)
            print(f"检测结果: {'有违规内容' if has_violation else '无违规内容'}")
            print(f"检测到的词: {words}")
            print(f"置信度: {confidences}")

            # 输出所有检测到的违禁词
            if has_violation:
                print(f"测试结果：图片中共检测到 {len(words)} 个违禁词：")
                response_data = {
                    "status": "stop",
                    "timestamp": datetime.now().isoformat(),
                    "violations": [{"word": w, "confidence": c} for w, c in zip(words, confidences)]
                }
                await send_message_to_client(ip, response_data)

                for word, conf in zip(words, confidences):
                    print(f"- {word}（置信度：{conf:.4f}）")

                # 检测到违规，设置停止事件
                print("检测到违规内容，准备关闭AI检测")
                stop_event.set()

            # 标记任务完成，允许生产者放入新的帧
            frame_queue.task_done()

        except Exception as e:
            print(f"处理帧时发生错误: {str(e)}")
            frame_queue.task_done()


def process_webrtc_stream(ip, webrtc_url):
    """
    处理WEBRTC流并持续打印OCR检测结果，检测到违规后关闭
    队列大小为1，满时直接丢弃新帧

    Args:
        ip: IP地址
        webrtc_url: WEBRTC服务器地址
    """
    # 创建队列（大小为1）和停止事件
    frame_queue = asyncio.Queue(maxsize=1)  # 只存储一帧
    stop_event = asyncio.Event()  # 用于控制任务停止的事件

    # 定义事件循环中的主任务
    async def main_task():
        # 创建任务
        receiver_task = asyncio.create_task(rtc_frame_receiver(webrtc_url, frame_queue, stop_event))
        consumer_task = asyncio.create_task(frame_consumer(ip, frame_queue, stop_event))

        # 等待任务完成
        await asyncio.gather(receiver_task, consumer_task)

        # 确保队列处理完毕
        await frame_queue.join()

    try:
        # 运行事件循环
        asyncio.run(main_task())
    except KeyboardInterrupt:
        print("用户中断处理流程")
        stop_event.set()
    finally:
        # 确保关闭所有cv2窗口
        cv2.destroyAllWindows()
        print("AI检测已关闭")