2个进程并行执行两个多线程下载图片的函数，下载数据会少，单独调用下载函数正常

def CaptureThread(num,path):
  try:
    tables_list.clear()
    count = 0
    getTables()
    CaptureImageUrl('capture_url')
    gap = math.ceil(len(captureurl_list) / int(num)) 
    for i in range(int(num)):
      cap_url = captureurl_list[count:count+gap]
      cap_id = captureid_list[count:count+gap]
      cap_deviceid = capturedeviceid_list[count:count+gap]
      cap_table_name = capturetables_list[count:count+gap]
      t = threading.Thread(target=Capture_ImageDownload,args=(path,cap_url,cap_id,cap_deviceid,cap_table_name))
      threads_list.append(t)
      count += gap
    for t in threads_list:
      t.setDaemon(True)
      time.sleep(0.5)
      t.start()
    for t in threads_list:
      t.join()
  except Exception as e:
    print(e)
    input("Press <enter>..........")
```
```
def SceneThread(num,path):
  try:
    tables_list.clear()
    count = 0
    getTables()
    SceneImageUrl('scene_url')
    gap = math.ceil(len(sceneurl_list) / int(num)) 
    for i in range(int(num)):
      sce_url = sceneurl_list[count:count + gap]
      sce_id = sceneid_list[count:count + gap]
      sce_deviceid = scenedeviceid_list[count:count + gap]
      sce_table_name = scenetables_list[count:count + gap]
      t = threading.Thread(target=Scene_ImageDownload,args=(path, sce_url, sce_id, sce_deviceid, sce_table_name))
      threads_list.append(t)
      count += gap
    for t in threads_list:
      t.setDaemon(True)
      time.sleep(0.5)
      t.start()
    for t in threads_list:
      t.join()
  except Exception as e:
    print(e)
    input("Press <enter>..........")
```

通过两个进程分别调用上面两个函数，运行一会的时候就会停了，单独调用上面函数的时候正常
```
  try:
    p = multiprocessing.Process(target=CaptureThread, args=(t_num, CaptureImg_path,))
    p1 = multiprocessing.Process(target=SceneThread,args=(t_num, SceneImg_path,))
    p.daemon=True
    p1.daemon=True
    p.start()
    p1.start()
    p.join()
    p1.join()
  except Exception as e:
    print(e)
    input("Press <enter>..........")
```

可以看看抛出什么异常了

没任何异常

看看内存使用量 是不是资源不够