import cv2
import pyaudio
import wave
import threading
import numpy as np
import time
from queue import Queue
import webrtcvad
import os
import threading
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from funasr import AutoModel
import pygame
import edge_tts
import asyncio
from time import sleep

# 参数设置
AUDIO_RATE = 16000        # 音频采样率
AUDIO_CHANNELS = 1        # 单声道
CHUNK = 1024              # 音频块大小
VAD_MODE = 3              # VAD 模式 (0-3, 数字越大越敏感)
OUTPUT_DIR = "./output"   # 输出目录
NO_SPEECH_THRESHOLD = 1   # 无效语音阈值，单位：秒

# 确保输出目录存在
os.makedirs(OUTPUT_DIR, exist_ok=True)

# 队列用于音频和视频同步缓存
audio_queue = Queue()
video_queue = Queue()

# 全局变量
last_active_time = time.time()
recording_active = True
segments_to_save = []
saved_intervals = []
last_vad_end_time = 0  # 上次保存的 VAD 有效段结束时间

# 初始化 WebRTC VAD
vad = webrtcvad.Vad()
vad.set_mode(VAD_MODE)

# 音频录制线程
def audio_recorder():
    global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time
    
    p = pyaudio.PyAudio()
    stream = p.open(format=pyaudio.paInt16,
                    channels=AUDIO_CHANNELS,
                    rate=AUDIO_RATE,
                    input=True,
                    frames_per_buffer=CHUNK)
    
    audio_buffer = []
    print("音频录制已开始")
    
    while recording_active:
        data = stream.read(CHUNK)
        audio_buffer.append(data)
        
        # 每 0.5 秒检测一次 VAD
        if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5:
            # 拼接音频数据并检测 VAD
            raw_audio = b''.join(audio_buffer)
            vad_result = check_vad_activity(raw_audio)
            
            if vad_result:
                print("检测到语音活动")
                last_active_time = time.time()
                segments_to_save.append((raw_audio, time.time()))
            else:
                print("静音中...")
            
            audio_buffer = []  # 清空缓冲区
        
        # 检查无效语音时间
        if time.time() - last_active_time > NO_SPEECH_THRESHOLD:
            # 检查是否需要保存
            if segments_to_save and segments_to_save[-1][1] > last_vad_end_time:
                save_audio_video()
                last_active_time = time.time()
            else:
                pass
                # print("无新增语音段，跳过保存")
    
    stream.stop_stream()
    stream.close()
    p.terminate()

# 视频录制线程
def video_recorder():
    global video_queue, recording_active
    
    cap = cv2.VideoCapture(0)  # 使用默认摄像头
    print("视频录制已开始")
    
    while recording_active:
        ret, frame = cap.read()
        if ret:
            video_queue.put((frame, time.time()))
            
            # 实时显示摄像头画面
            cv2.imshow("Real Camera", frame)
            if cv2.waitKey(1) & 0xFF == ord('q'):  # 按 Q 键退出
                break
        else:
            print("无法获取摄像头画面")
    
    cap.release()
    cv2.destroyAllWindows()

# 检测 VAD 活动
def check_vad_activity(audio_data):
    # 将音频数据分块检测
    num, rate = 0, 0.4
    step = int(AUDIO_RATE * 0.02)  # 20ms 块大小
    flag_rate = round(rate * len(audio_data) // step)

    for i in range(0, len(audio_data), step):
        chunk = audio_data[i:i + step]
        if len(chunk) == step:
            if vad.is_speech(chunk, sample_rate=AUDIO_RATE):
                num += 1

    if num > flag_rate:
        return True
    return False

# 保存音频和视频
def save_audio_video():
    global segments_to_save, video_queue, last_vad_end_time, saved_intervals
    
    if not segments_to_save:
        return
    
    # 获取有效段的时间范围
    start_time = segments_to_save[0][1]
    end_time = segments_to_save[-1][1]
    
    # 检查是否与之前的片段重叠
    if saved_intervals and saved_intervals[-1][1] >= start_time:
        print("当前片段与之前片段重叠，跳过保存")
        segments_to_save.clear()
        return
    
    # 保存音频
    audio_frames = [seg[0] for seg in segments_to_save]
    audio_output_path = f"{OUTPUT_DIR}/audio_0.wav"
    wf = wave.open(audio_output_path, 'wb')
    wf.setnchannels(AUDIO_CHANNELS)
    wf.setsampwidth(2)  # 16-bit PCM
    wf.setframerate(AUDIO_RATE)
    wf.writeframes(b''.join(audio_frames))
    wf.close()
    print(f"音频保存至 {audio_output_path}")
    
    # 保存视频
    video_frames = []
    while not video_queue.empty():
        frame, timestamp = video_queue.get()
        if start_time <= timestamp <= end_time:
            video_frames.append(frame)
    
    if video_frames:
        video_output_path = f"{OUTPUT_DIR}/video_0.avi"
        out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480))
        for frame in video_frames:
            out.write(frame)
        out.release()
        print(f"视频保存至 {video_output_path}")
        Inference()
    else:
        pass
        # print("无可保存的视频帧")
    
    # 记录保存的区间
    saved_intervals.append((start_time, end_time))
    
    # 清空缓冲区
    segments_to_save.clear()

# --- 播放音频 -
def play_audio(file_path):
    try:
        pygame.mixer.init()
        pygame.mixer.music.load(file_path)
        pygame.mixer.music.play()
        while pygame.mixer.music.get_busy():
            time.sleep(1)  # 等待音频播放结束
        print("播放完成！")
    except Exception as e:
        print(f"播放失败: {e}")
    finally:
        pygame.mixer.quit()

async def amain(TEXT, VOICE, OUTPUT_FILE) -> None:
    """Main function"""
    communicate = edge_tts.Communicate(TEXT, VOICE)
    await communicate.save(OUTPUT_FILE)

# -------------- Load QWen2-VL Model ------------
# default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"
)
# ------- 设置分辨率，降低现存占用 -------
min_pixels = 256*28*28
max_pixels = 512*28*28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
# --------------------------------------

# -------- SenceVoice 语音识别 --模型加载-----
model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall"
model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, )
folder_path = "./Test_QWen2_VL/"

def Inference(TEMP_VIDEO_FILE=f"{OUTPUT_DIR}/video_0.avi", TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"):
    file_path = os.path.join(folder_path, "captured_image.jpg")  # 设置保存路径
    cap = cv2.VideoCapture(TEMP_VIDEO_FILE)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_index = int(total_frames // 2)
    # 设置视频帧位置
    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_index)
    ret, frame = cap.read()
    if not ret:
        print(f"无法读取帧索引 {frame_index}")
    else:
        # 显示帧
        cv2.imwrite(file_path, frame)
        # cv2.imshow(f"Frame {frame_index}", frame)

    # -------- SenceVoice 推理 ---------
    input_file = (TEMP_AUDIO_FILE)
    res = model_senceVoice.generate(
        input=input_file,
        cache={},
        language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
        use_itn=False,
    )
    prompt = res[0]['text'].split(">")[-1]
    # ---------SenceVoice --end----------

    # -------- QWen2-VL 模型推理 ---------
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": f"{file_path}",
                },
                {"type": "text", "text": f"{prompt}"},
            ],
        }
    ]

    # Preparation for inference
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")

    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=128)
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    print(output_text)

    # 输入文本
    text = output_text[0]
    # asyncio.run(amain(text, "zh-CN-YunxiaNeural", os.path.join(folder_path,"sft_0.mp3")))
    # play_audio(f'{folder_path}/sft_0.mp3')

    asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3")))
    play_audio(f'{folder_path}/sft_0.mp3')

    # asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3")))
    # play_audio(f'{folder_path}/sft_0.mp3')

    # asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3")))
    # play_audio(f'{folder_path}/sft_0.mp3')

# 主函数
if __name__ == "__main__":
    try:
        # 启动音视频录制线程
        audio_thread = threading.Thread(target=audio_recorder)
        video_thread = threading.Thread(target=video_recorder)
        audio_thread.start()
        video_thread.start()
        
        print("按 Ctrl+C 停止录制")
        while True:
            time.sleep(1)
    
    except KeyboardInterrupt:
        print("录制停止中...")
        recording_active = False
        audio_thread.join()
        video_thread.join()
        print("录制已停止")