akhaliq/FaceMesh

1import mediapipe as mp2import gradio as gr3import cv24import torch5 6 7# Images8torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c789e54661bfb432c5522a36553f.jpeg', 'face1.jpg')9torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c86622e8cb58d490e35b01cb9996.jpeg', 'face2.jpg')10 11mp_face_mesh = mp.solutions.face_mesh12 13# Prepare DrawingSpec for drawing the face landmarks later.14mp_drawing = mp.solutions.drawing_utils 15drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)16 17# Run MediaPipe Face Mesh.18 19def inference(image):20    with mp_face_mesh.FaceMesh(21        static_image_mode=True,22        max_num_faces=2,23        min_detection_confidence=0.5) as face_mesh:24        # Convert the BGR image to RGB and process it with MediaPipe Face Mesh.25        results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))26 27        annotated_image = image.copy()28        for face_landmarks in results.multi_face_landmarks:29            mp_drawing.draw_landmarks(30                image=annotated_image,31                landmark_list=face_landmarks,32                connections=mp_face_mesh.FACEMESH_TESSELATION,33                landmark_drawing_spec=drawing_spec,34                connection_drawing_spec=drawing_spec)35            return annotated_image36 37title = "Face Mesh"38description = "Gradio demo for Face Mesh. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."39article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.06724'>Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs</a> | <a href='https://github.com/google/mediapipe'>Github Repo</a></p>"40 41gr.Interface(42    inference, 43    [gr.inputs.Image(label="Input")], 44    gr.outputs.Image(type="pil", label="Output"),45    title=title,46    description=description,47    article=article, 48    examples=[49              ["face1.jpg"],50              ["face2.jpg"]51    ]).launch()