try.py

import cv2
import mtcnn
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils


# For webcam input:
cap = cv2.VideoCapture(0)
cap.set(3,1000) # set Width
cap.set(4,1000) # set Height
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 performance, 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)
    # print(results.detections[0]["RELATIVE_BOUNDING_BOX"])

    # 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["xmin"])
            # break
            mp_drawing.draw_detection(image, detection)
            # cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),3)
        # break
    # 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()