face landmark detection

face_landmark_detection.py

# python3.12 needed for mediapipe!
# pip install mediapipe opencv-python numpy

import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import cv2
import numpy as np

def draw_landmarks_on_image(rgb_image, detection_result):
    face_landmarks_list = detection_result.face_landmarks
    annotated_image = np.copy(rgb_image)

    # loop through the detected faces to visualize.
    for idx in range(len(face_landmarks_list)):
        face_landmarks = face_landmarks_list[idx]

        # draw the face landmarks.
        face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
        face_landmarks_proto.landmark.extend([
            landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in face_landmarks
        ])

        solutions.drawing_utils.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks_proto,
            connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp.solutions.drawing_styles
            .get_default_face_mesh_tesselation_style())
        solutions.drawing_utils.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks_proto,
            connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp.solutions.drawing_styles
            .get_default_face_mesh_contours_style())
        solutions.drawing_utils.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks_proto,
            connections=mp.solutions.face_mesh.FACEMESH_IRISES,
              landmark_drawing_spec=None,
              connection_drawing_spec=mp.solutions.drawing_styles
              .get_default_face_mesh_iris_connections_style())

    return annotated_image

if __name__ == '__main__':
    base_options = python.BaseOptions(model_asset_path='face_landmarker_v2_with_blendshapes.task')
    options = vision.FaceLandmarkerOptions(base_options=base_options,
                                           output_face_blendshapes=True,
                                           output_facial_transformation_matrixes=True,
                                           num_faces=1)
    detector = vision.FaceLandmarker.create_from_options(options)

    cap = cv2.VideoCapture(0)

    if not cap.isOpened():
        print('error: could not open camera!')
        exit()

    while True:
        ret, frame = cap.read()
        if not ret:
            print('failed to capture image!')
            break

        frame = cv2.flip(frame, 1)
        frame = cv2.resize(frame, (450, 280))

        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame_rgb)

        detection_result = detector.detect(image)

        black_background = np.zeros_like(frame)
        annotated_image = draw_landmarks_on_image(black_background, detection_result)

        cv2.imshow('face', cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))

        if cv2.waitKey(1) & 0xFF == ord('q'): break

    cap.release()
    cv2.destroyAllWindows()