code: import os
  import cv2
  import numpy as np
  import requests
  import pyfakewebcam
  import traceback
  import time
  
  def get_mask(frame, bodypix_url=os.environ.get("BODYPIX_URL","http://bodypix:9000")):
      _, data = cv2.imencode(".jpg", frame)
      r = requests.post(
          url=bodypix_url,
          data=data.tobytes(),
          headers={'Content-Type': 'application/octet-stream'})
      mask = np.frombuffer(r.content, dtype=np.uint8)
      mask = mask.reshape((frame.shape[0], frame.shape[1]))
      return mask
  
  def post_process_mask(mask):
      mask = cv2.dilate(mask, np.ones((10,10), np.uint8) , iterations=1)
      mask = cv2.blur(mask.astype(float), (10,10))
      return mask
  
  def shift_image(img, dx, dy):
      img = np.roll(img, dy, axis=0)
      img = np.roll(img, dx, axis=1)
      if dy>0:
          img[:dy, :] = 0
      elif dy<0:
          img[dy:, :] = 0
      if dx>0:
          img[:, :dx] = 0
      elif dx<0:
          img[:, dx:] = 0
      return img
  
  def hologram_effect(img):
      # add a blue tint
      holo = cv2.applyColorMap(img, cv2.COLORMAP_WINTER)
      # add a halftone effect
      bandLength, bandGap = 2, 3
      for y in range(holo.shape[0]):
          if y % (bandLength+bandGap) < bandLength:
              holo[y,:,:] = holo[y,:,:] * np.random.uniform(0.1, 0.3)
      # add some ghosting
      holo_blur = cv2.addWeighted(holo, 0.2, shift_image(holo.copy(), 5, 5), 0.8, 0)
      holo_blur = cv2.addWeighted(holo_blur, 0.4, shift_image(holo.copy(), -5, -5), 0.6, 0)
      # combine with the original color, oversaturated
      out = cv2.addWeighted(img, 0.5, holo_blur, 0.6, 0)
      return out
  
  def get_frame(cap, background_scaled, speed=True, effect=None):
      _, frame = cap.read()
      # fetch the mask with retries (the app needs to warmup and we're lazy)
      # e v e n t u a l l y c o n s i s t e n t
      mask = None
      while mask is None:
          try:
  
              if speed:
                  shrinked_frame = cv2.resize(frame, (width//2, height//2)) 
                  shrinked_mask = get_mask(shrinked_frame)
                  mask = cv2.resize(shrinked_mask, (width, height))
              else:
                  mask = get_mask(frame)
  
          except requests.RequestException:
              print("mask request failed, retrying")
              traceback.print_exc()
              time.sleep(5)
      
      # post-process mask and frame
      mask = post_process_mask(mask)
      if effect is not None:        
          effect_fun = globals()[effect + '_effect']
          frame = effect_fun(frame)
  
      # composite the foreground and background
      inv_mask = 1-mask
      for c in range(frame.shape[2]):       
         frame[:,:,c] = frame[:,:,c]*mask + background_scaled[:,:,c]*inv_mask
  
  
      return frame
  
  
  if __name__ == '__main__':
  
      actual_device = os.environ.get('ACTUAL_CAMERA','/dev/video0')
      fake_device = os.environ.get('FAKE_CAMERA','/dev/video20')
      width = int(os.environ.get('CAMERA_WIDTH',640))
      height = int(os.environ.get('CAMERA_HEIGHT',360))
      cam_fps = int(os.environ.get('CAMERA_FPS',24))
      is_background_video = os.environ.get('IS_VID_BACKGROUND', 'false') == 'true'
      background_file_path = os.environ.get('BACKGROUND_FILE', '/data/background.jpg')
      effect = os.environ.get('EFFECT', None)
  
      # setup access to the *real* webcam
      cap = cv2.VideoCapture(actual_device)
      
      cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
      cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
      cap.set(cv2.CAP_PROP_FPS, cam_fps)
  
      # setup the fake camera
      fake = pyfakewebcam.FakeWebcam(fake_device, width, height)
  
      # load the virtual background
      _background_scaled = {}
      if is_background_video:
          def get_background_scaled(width, height):
              if int(time.time()) % 10 == 0 or len(_background_scaled) == 0:
                  updated_background_file_size = os.stat(background_file_path).st_size
                  if updated_background_file_size != _background_scaled.get('size', None):
                      if 'cap' in _background_scaled:
                          _background_scaled['cap'].release()
                      _background_scaled['cap'] = cv2.VideoCapture(background_file_path)
                      _background_scaled['size'] = updated_background_file_size
              background_cap = _background_scaled['cap']
              success, frame = background_cap.read()
              if success:
                  return cv2.resize(frame, (width, height))
              background_cap.set(cv2.CAP_PROP_POS_FRAMES, 1)
              return get_background_scaled(width, height)
      else:
          def get_background_scaled(width, height):
              if int(time.time()) % 10 == 0 or len(_background_scaled) == 0:
                  updated_background_file_size = os.stat(background_file_path).st_size
                  if updated_background_file_size != _background_scaled.get('size', None):
                      background = cv2.imread(background_file_path)
                      _background_scaled['frame'] = cv2.resize(background,(width, height))
                      _background_scaled['size'] = updated_background_file_size
              return _background_scaled['frame']
  
      while True:
          frame = get_frame(cap, get_background_scaled(width, height), effect=effect)
          # fake webcam expects RGB
          frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
          fake.schedule_frame(frame)
  
apis: numpy.random.uniform numpy.ones numpy.roll numpy.frombuffer