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import mediapipe as mp |
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import gradio as gr |
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import cv2 |
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import torch |
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torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c789e54661bfb432c5522a36553f.jpeg', 'face1.jpg') |
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torch.hub.download_url_to_file('https://artbreeder.b-cdn.net/imgs/c86622e8cb58d490e35b01cb9996.jpeg', 'face2.jpg') |
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mp_face_mesh = mp.solutions.face_mesh |
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mp_drawing = mp.solutions.drawing_utils |
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1) |
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def inference(image): |
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with mp_face_mesh.FaceMesh( |
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static_image_mode=True, |
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max_num_faces=2, |
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min_detection_confidence=0.5) as face_mesh: |
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results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) |
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annotated_image = image.copy() |
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for face_landmarks in results.multi_face_landmarks: |
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mp_drawing.draw_landmarks( |
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image=annotated_image, |
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landmark_list=face_landmarks, |
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connections=mp_face_mesh.FACEMESH_TESSELATION, |
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landmark_drawing_spec=drawing_spec, |
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connection_drawing_spec=drawing_spec) |
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return annotated_image |
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title = "Face Mesh" |
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description = "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." |
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article = "<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>" |
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gr.Interface( |
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inference, |
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[gr.inputs.Image(label="Input")], |
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gr.outputs.Image(type="pil", label="Output"), |
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title=title, |
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description=description, |
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article=article, |
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examples=[ |
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["face1.jpg"], |
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["face2.jpg"] |
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]).launch() |
