obs推流算法 摄像头显示

obs+腾讯会议推流
腾讯视频会议打开后 通过mediapipe 加obs推流
总结
开始腾讯会议总是推出，我找不到原因
后来发现原来是fps太少了
因为单纯obs软件的可以，但是通过pycharm的代码 到腾讯会议
总是提示插件错误
我思考了 突然发现fps=60
下述代码注意一个问题
需要改源码才可以实现覆盖脸的操作
下面源码还不够

import cv2
import mediapipe as mp
import pyvirtualcam


mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils

# For static images:
IMAGE_FILES = []
with mp_face_detection.FaceDetection(
    model_selection=1, min_detection_confidence=0.5) as face_detection:
  for idx, file in enumerate(IMAGE_FILES):
    image = cv2.imread(file)
    # Convert the BGR image to RGB and process it with MediaPipe Face Detection.
    results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    # Draw face detections of each face.
    if not results.detections:
      continue
    annotated_image = image.copy()
    for detection in results.detections:
      print('Nose tip:')
      print(mp_face_detection.get_key_point(
          detection, mp_face_detection.FaceKeyPoint.NOSE_TIP))
      mp_drawing.draw_detection(annotated_image, detection)
    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)

# For webcam input:
cap = cv2.VideoCapture(0)
with pyvirtualcam.Camera(width=640, height=480, fmt=pyvirtualcam.PixelFormat.BGR, fps=120) as cam:

  with mp_face_detection.FaceDetection(
      model_selection=0, min_detection_confidence=0.5) as face_detection:
    while cap.isOpened():
      success, image = cap.read()
      if not success:
        print("Ignoring empty camera frame.")
        # If loading a video, use 'break' instead of 'continue'.
        continue

      # To improve performanceprint(, optionally mark the image as not writeable to
      # pass by reference.
      image.flags.writeable = False
      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
      results = face_detection.process(image)

      # h,w = image.shape()

      # Draw the face detection annotations on the image.
      image.flags.writeable = True
      image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
      if results.detections:
        for detection in results.detections:


          print(detection)
          mp_drawing.draw_detection(image, detection)

          cam.send(image)
          cam.sleep_until_next_frame()
          # mp_drawing.draw_detection()
      # Flip the image horizontally for a selfie-view display.
      cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
      if cv2.waitKey(5) & 0xFF == 27:
        break
cap.release()