51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
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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# Images
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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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# Prepare DrawingSpec for drawing the face landmarks later.
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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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# Run MediaPipe Face Mesh.
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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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# Convert the BGR image to RGB and process it with MediaPipe 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.FACE_CONNECTIONS,
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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 = "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/2006.10962'>Attention Mesh: High-fidelity Face Mesh Prediction in Real-time</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(debug=True) |