newdectect.py

import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_face_mesh = mp.solutions.face_mesh

# For static images:
face_mesh = mp_face_mesh.FaceMesh(
    static_image_mode=True,
    max_num_faces=1,
    min_detection_confidence=0.5)
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
for idx, file in enumerate(file_list):
  image = cv2.imread(file)
  # Convert the BGR image to RGB before processing.
  results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

  # Print and draw face mesh landmarks on the image.
  if not results.multi_face_landmarks:
    continue
  annotated_image = image.copy()
  for face_landmarks in results.multi_face_landmarks:
    print('face_landmarks:', face_landmarks)
    mp_drawing.draw_landmarks(
        image=annotated_image,
        landmark_list=face_landmarks,
        connections=mp_face_mesh.FACE_CONNECTIONS,
        landmark_drawing_spec=drawing_spec,
        connection_drawing_spec=drawing_spec)
  cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
face_mesh.close()

# For webcam input:
face_mesh = mp_face_mesh.FaceMesh(
    min_detection_confidence=0.5, min_tracking_confidence=0.5)
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
cap = cv2.VideoCapture(0)
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

  # Flip the image horizontally for a later selfie-view display, and convert
  # the BGR image to RGB.
  image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
  # To improve performance, optionally mark the image as not writeable to
  # pass by reference.
  image.flags.writeable = False
  results = face_mesh.process(image)

  # Draw the face mesh annotations on the image.
  image.flags.writeable = True
  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
  if results.multi_face_landmarks:
    for face_landmarks in results.multi_face_landmarks:
      mp_drawing.draw_landmarks(
          image=image,
          landmark_list=face_landmarks,
          connections=mp_face_mesh.FACE_CONNECTIONS,
          landmark_drawing_spec=drawing_spec,
          connection_drawing_spec=drawing_spec)
  cv2.imshow('MediaPipe FaceMesh', image)
  if cv2.waitKey(5) & 0xFF == 27:
    break
face_mesh.close()
cap.release()