fixed yacine code

mediapipe_landmark.py

import cv2
import numpy as np
import mediapipe as mp

# Initialize mediapipe Face Mesh and Hands solutions
mp_face_mesh = mp.solutions.face_mesh
mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles

# Set up the webcam
cap = cv2.VideoCapture(0)

# Initialize Face Mesh and Hands
with mp_face_mesh.FaceMesh(max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh, \
     mp_hands.Hands(max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5) as hands:

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        # Convert the BGR image to RGB for processing
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        # Process the face and hands in the frame
        face_results = face_mesh.process(rgb_frame)
        hand_results = hands.process(rgb_frame)

        # Create a black image to draw the landmarks on
        black_image = np.zeros(frame.shape, dtype=np.uint8)

        # Draw face landmarks if detected
        if face_results.multi_face_landmarks:
            for face_landmarks in face_results.multi_face_landmarks:
                mp_drawing.draw_landmarks(
                    image=black_image,
                    landmark_list=face_landmarks,
                    connections=mp_face_mesh.FACEMESH_TESSELATION,
                    landmark_drawing_spec=None,
                    connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style())
                mp_drawing.draw_landmarks(
                    image=black_image,
                    landmark_list=face_landmarks,
                    connections=mp_face_mesh.FACEMESH_CONTOURS,
                    landmark_drawing_spec=None,
                    connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_contours_style())
                mp_drawing.draw_landmarks(
                    image=black_image,
                    landmark_list=face_landmarks,
                    connections=mp_face_mesh.FACEMESH_IRISES,
                    landmark_drawing_spec=None,
                    connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_iris_connections_style())

        # Draw hand landmarks if detected
        if hand_results.multi_hand_landmarks:
            for hand_landmarks in hand_results.multi_hand_landmarks:
                mp_drawing.draw_landmarks(
                    image=black_image,
                    landmark_list=hand_landmarks,
                    connections=mp_hands.HAND_CONNECTIONS,
                    landmark_drawing_spec=mp_drawing_styles.get_default_hand_landmarks_style(),
                    connection_drawing_spec=mp_drawing_styles.get_default_hand_connections_style())

        # Resize the image to make it bigger
        scale_factor = 1.5  # Adjust this value to change the window size
        width = int(black_image.shape[1] * scale_factor)
        height = int(black_image.shape[0] * scale_factor)
        resized_image = cv2.resize(black_image, (width, height), interpolation=cv2.INTER_LINEAR)

        # Display the annotated image
        cv2.imshow('Face Mesh and Hands', resized_image)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# Release resources
cap.release()
cv2.destroyAllWindows()