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Upload app.py with huggingface_hub

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  1. app.py +51 -0
app.py ADDED
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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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+
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+
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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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+
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+ mp_face_mesh = mp.solutions.face_mesh
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+
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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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+
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+ # Run MediaPipe Face Mesh.
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+
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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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+
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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_CONTOURS,
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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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+
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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/1907.06724' target='_blank'>Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs</a> | <a href='https://github.com/google/mediapipe' target='_blank'>Github Repo</a></p>"
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+
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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)