如何在进行目标检测任务的时候播放.wav文件？

请给出代码，如果目标检测使用的是aidemo的模式，MediaManager的init被封装在了PipeLine中，如果需要同时播放音频和运行人脸检测，需要重新调整代码结构，在MediaManager.init之前配置好视频和音频的配置，然后再执行MediaManager.init。

========================24/11/11===============================
这里给出示例的代码：

from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
from media.pyaudio import *                     # 音频模块
from media.media import *                       # 软件抽象模块，主要封装媒体数据链路以及媒体缓冲区
import media.wave as wave                       # wav音频处理模块
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import time
import utime
import image
import random
import gc
import sys
import aicube
import _thread

audio_run=True

# 自定义目标检测任务类
class DetectionApp(AIBase):
    def __init__(self,kmodel_path,labels,model_input_size=[640,640],anchors=[10.13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326],model_type="AnchorBaseDet",confidence_threshold=0.5,nms_threshold=0.25,nms_option=False,strides=[8,16,32],rgb888p_size=[1280,720],display_size=[1920,1080],debug_mode=0):
        super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
        # kmodel路径
        self.kmodel_path=kmodel_path
        # 类别标签
        self.labels=labels
        # 模型输入分辨率
        self.model_input_size=model_input_size
        # 检测任务的锚框
        self.anchors=anchors
        # 模型类型，支持"AnchorBaseDet","AnchorFreeDet","GFLDet"三种模型
        self.model_type=model_type
        # 检测框类别置信度阈值
        self.confidence_threshold=confidence_threshold
        # 检测框NMS筛选阈值
        self.nms_threshold=nms_threshold
        # NMS选项，如果为True做类间NMS,如果为False做类内NMS
        self.nms_option=nms_option
        # 输出特征图的降采样倍数
        self.strides=strides
        # sensor给到AI的图像分辨率，宽16字节对齐
        self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
        # 视频输出VO分辨率，宽16字节对齐
        self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
        # 调试模式
        self.debug_mode=debug_mode
        # 检测框预置颜色值
        self.color_four=[(255, 220, 20, 60), (255, 119, 11, 32), (255, 0, 0, 142), (255, 0, 0, 230),
                         (255, 106, 0, 228), (255, 0, 60, 100), (255, 0, 80, 100), (255, 0, 0, 70),
                         (255, 0, 0, 192), (255, 250, 170, 30), (255, 100, 170, 30), (255, 220, 220, 0),
                         (255, 175, 116, 175), (255, 250, 0, 30), (255, 165, 42, 42), (255, 255, 77, 255),
                         (255, 0, 226, 252), (255, 182, 182, 255), (255, 0, 82, 0), (255, 120, 166, 157)]
        # Ai2d实例，用于实现模型预处理
        self.ai2d=Ai2d(debug_mode)
        # 设置Ai2d的输入输出格式和类型
        self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)

    # 配置预处理操作，这里使用了pad和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
    def config_preprocess(self,input_image_size=None):
        with ScopedTiming("set preprocess config",self.debug_mode > 0):
            # 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，您可以通过设置input_image_size自行修改输入尺寸
            ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
            # 计算padding参数
            top,bottom,left,right=self.get_padding_param()
            # 配置padding预处理
            self.ai2d.pad([0,0,0,0,top,bottom,left,right], 0, [114,114,114])
            # 配置resize预处理
            self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
            # build预处理过程，参数为输入tensor的shape和输出tensor的shape
            self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])

    # 自定义当前任务的后处理,这里调用了aicube模块的后处理接口
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            # AnchorBaseDet模型的后处理
            if self.model_type == "AnchorBaseDet":
                det_boxes = aicube.anchorbasedet_post_process( results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(labels), self.confidence_threshold, self.nms_threshold, self.anchors, self.nms_option)
            # GFLDet模型的后处理
            elif self.model_type == "GFLDet":
                det_boxes = aicube.gfldet_post_process( results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(labels), self.confidence_threshold, self.nms_threshold, self.nms_option)
            # AnchorFreeDet模型的后处理
            elif self.model_type=="AnchorFreeDet":
                det_boxes = aicube.anchorfreedet_post_process( results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(labels), self.confidence_threshold, self.nms_threshold, self.nms_option)
            else:
                det_boxes=None
            return det_boxes

    # 将结果绘制到屏幕上
    def draw_result(self,pl,det_boxes):
        with ScopedTiming("draw osd",self.debug_mode > 0):
            if det_boxes:
                pl.osd_img.clear()
                for det_boxe in det_boxes:
                    # 获取每一个检测框的坐标，并将其从原图分辨率坐标转换到屏幕分辨率坐标，将框和类别信息绘制在屏幕上
                    x1, y1, x2, y2 = det_boxe[2],det_boxe[3],det_boxe[4],det_boxe[5]
                    sx=int(x1 * self.display_size[0] // self.rgb888p_size[0])
                    sy=int(y1 * self.display_size[1] // self.rgb888p_size[1])
                    w = int(float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0])
                    h = int(float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1])
                    pl.osd_img.draw_rectangle(sx , sy , w , h , color=self.get_color(det_boxe[0]))
                    label = self.labels[det_boxe[0]]
                    score = str(round(det_boxe[1],2))
                    pl.osd_img.draw_string_advanced(sx, sy-50,32, label + " " + score , color=self.get_color(det_boxe[0]))
            else:
                pl.osd_img.clear()
                pl.osd_img.draw_rectangle(0, 0, 128, 128, color=(0,0,0,0))

    # 计算padding参数
    def get_padding_param(self):
        ratiow = float(self.model_input_size[0]) / self.rgb888p_size[0];
        ratioh = float(self.model_input_size[1]) / self.rgb888p_size[1];
        ratio = min(ratiow, ratioh)
        new_w = int(ratio * self.rgb888p_size[0])
        new_h = int(ratio * self.rgb888p_size[1])
        dw = float(self.model_input_size[0]- new_w) / 2
        dh = float(self.model_input_size[1] - new_h) / 2
        top = int(round(dh - 0.1))
        bottom = int(round(dh + 0.1))
        left = int(round(dw - 0.1))
        right = int(round(dw - 0.1))
        return top,bottom,left,right

    # 根据当前类别索引获取框的颜色
    def get_color(self, x):
        idx=x%len(self.color_four)
        return self.color_four[idx]
        

def audio_output_thread(output_stream):
    CHUNK = int(0.3 * 16000)    # 每次读取音频数据的帧数，设置为0.3s的帧数16000*0.3=4800
    reply_wav_file = "/sdcard/examples/utils/wozai.wav"         # 音频路径
    while audio_run:
        wf = wave.open(reply_wav_file, "rb")
        wav_data = wf.read_frames(CHUNK)
        while wav_data:
            output_stream.write(wav_data)
            wav_data = wf.read_frames(CHUNK)
        time.sleep(1) # 时间缓冲，用于播放回复声音
        wf.close()
        


if __name__=="__main__":
    
    # 其它参数
    THRESH = 0.5                # 检测阈值
    SAMPLE_RATE = 16000         # 采样率16000Hz,即每秒采样16000次
    CHANNELS = 1                # 通道数 1为单声道，2为立体声
    FORMAT = paInt16            # 音频输入输出格式 paInt16
    CHUNK = int(0.3 * 16000)    # 每次读取音频数据的帧数，设置为0.3s的帧数16000*0.3=4800
    # 初始化音频流
    p = PyAudio()
    p.initialize(CHUNK)
    
   
    # 添加显示模式，支持"hdmi"和"lcd"
    display_mode="hdmi"
    if display_mode=="hdmi":
        display_size=[1920,1080]
    else:
        display_size=[800,480]
    # kmodel路径
    kmodel_path="/sdcard/examples/ai_test_kmodel/insect_det.kmodel"
    # 检测类别标签
    labels=["leconte","boerner","armandi","linnaeus","coleoptera","acuminatus"]
    # 类别置信度阈值
    confidence_threshold=0.5
    # nms阈值
    nms_threshold = 0.5
    # 训练中使用的锚框，在线训练平台和AICube部署包的deploy_config.json文件中包含该字段，只有AnchorBaseDet需要该参数
    anchors=[30,23,21,33,29,43,44,29,41,39,41,68,71,43,59,61,71,72]
    # 初始化PipeLine，只关注传给AI的图像分辨率，显示的分辨率
    pl=PipeLine(rgb888p_size=[1280,720],display_size=display_size,display_mode=display_mode)
    pl.create()
    
    # 用于播放回复音频
    output_stream = p.open(format=FORMAT,channels=CHANNELS,rate=SAMPLE_RATE,output=True,frames_per_buffer=CHUNK)
    _thread.start_new_thread(audio_output_thread,(output_stream,))
    
    # 检测类实例，关注模型输入分辨率，传给AI的图像分辨率，显示的分辨率
    det=DetectionApp(kmodel_path,labels,model_input_size=[640,640],anchors=anchors,rgb888p_size=[1280,720],display_size=display_size,debug_mode=0)
    # 配置预处理过程
    det.config_preprocess()
    try:
        while True:
            os.exitpoint()
            with ScopedTiming("total",1):
                # 获取当前帧
                img=pl.get_frame()
                # 获得检测框
                det_boxes=det.run(img)
                # 绘制检测框和类别信息
                det.draw_result(pl,det_boxes)
                # 显示当前的绘制结果
                pl.show_image()
                gc.collect()
    except BaseException as e:
        sys.print_exception(e)
        audio_run=False
    finally:
        audio_run=False
        time.sleep(1)
        output_stream.stop_stream()
        output_stream.close()
        det.deinit()
        pl.destroy()

你需要在那个多媒体控制器运行之前配置好他的参数，我建议你把初始化图像和屏幕的部分移到两个线程之前来控制时序。