facelandmark_media.py

# IMPORTING LIBRARIES
import cv2
import mediapipe as mp

# INITIALIZING OBJECTS
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_face_mesh = mp.solutions.face_mesh

drawing_spec = mp_drawing.DrawingSpec(thickness = 1, circle_radius = 1)
cap = cv2.VideoCapture(r'D:\computer_vision\data\171124_C1_HD_002.mp4')

# DETECT THE FACE LANDMARKS
with mp_face_mesh.FaceMesh(min_detection_confidence = 0.5, min_tracking_confidence = 0.5) as face_mesh:
    while True:
        success, image = cap.read()

        # Flip the image horizontally and convert the color space from BGR to RGB
        image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)

        # To improve performance
        image.flags.writeable = False

        # Detect the face landmarks
        results = face_mesh.process(image)

        # To improve performance
        image.flags.writeable = True

        # Convert back to the BGR color space
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

        # Draw the face mesh annotations on the image.
        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.FACEMESH_TESSELATION,
                    landmark_drawing_spec = None,
                    connection_drawing_spec = mp_drawing_styles
                        .get_default_face_mesh_tesselation_style())

        # Display the image
        cv2.imshow('MediaPipe FaceMesh', image)

        # Terminate the process
        if cv2.waitKey(5) & 0xFF == 27:
            break

cap.release()