import mediapipe as mp import gradio as gr import cv2 import torch # Images torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c789e54661bfb432c5522a36553f.jpeg', 'face1.jpg') torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c86622e8cb58d490e35b01cb9996.jpeg', 'face2.jpg') mp_face_mesh = mp.solutions.face_mesh # Prepare DrawingSpec for drawing the face landmarks later. mp_drawing = mp.solutions.drawing_utils drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1) # Run MediaPipe Face Mesh. def inference(image): with mp_face_mesh.FaceMesh( static_image_mode=True, max_num_faces=2, min_detection_confidence=0.5) as face_mesh: # Convert the BGR image to RGB and process it with MediaPipe Face Mesh. results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) annotated_image = image.copy() for face_landmarks in results.multi_face_landmarks: mp_drawing.draw_landmarks( image=annotated_image, landmark_list=face_landmarks, connections=mp_face_mesh.FACEMESH_CONTOURS, landmark_drawing_spec=drawing_spec, connection_drawing_spec=drawing_spec) return annotated_image title = "Face Mesh" description = "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." article = "

Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs | Github Repo

" gr.Interface( inference, [gr.inputs.Image(label="Input")], gr.outputs.Image(type="pil", label="Output"), title=title, description=description, article=article, examples=[ ["face1.jpg"], ["face2.jpg"] ]).launch(debug=True)