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Runtime error
Runtime error
update demo.py
Browse files
demo.py
CHANGED
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@@ -98,256 +98,247 @@ example_videos = gr.components.Dataset(
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model = Model(device=args.device)
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with gr.Blocks(theme=args.theme, css="style.css") as demo:
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gr.Markdown(DESCRIPTION)
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with gr.Box():
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gr.Markdown(
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"""## Step 1(Select Style)
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- Select **Style Type**.
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- Type with `-d` means it supports style degree adjustment.
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- Type without `-d` usually has better toonification quality.
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"""
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)
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with gr.Row():
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with gr.Column():
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gr.Markdown("""Select Style Type""")
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with gr.Row():
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style_type = gr.Radio(
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label="Style Type",
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choices=[
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"cartoon1",
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"cartoon1-d",
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"cartoon2-d",
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"cartoon3-d",
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"cartoon4",
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"cartoon4-d",
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"cartoon5-d",
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"comic1-d",
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"comic2-d",
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"arcane1",
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"arcane1-d",
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"arcane2",
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"arcane2-d",
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"caricature1",
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"caricature2",
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"pixar",
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"pixar-d",
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"illustration1-d",
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"illustration2-d",
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"illustration3-d",
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"illustration4-d",
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"illustration5-d",
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],
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)
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exstyle = gr.Variable()
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with gr.Row():
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loadmodel_button = gr.Button("Load Model")
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with gr.Row():
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load_info = gr.Textbox(
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label="Process Information",
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interactive=False,
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value="No model loaded.",
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)
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with gr.Column():
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gr.Markdown(
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"""Reference Styles
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"""
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)
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"""## Step 2 (Preprocess Input Image / Video)
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- Drop an image/video containing a near-frontal face to the **Input Image**/**Input Video**.
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- Hit the **Rescale Image**/**Rescale First Frame** button.
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- Rescale the input to make it best fit the model.
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- The final image result will be based on this **Rescaled Face**. Use padding parameters to adjust the background space.
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- **<font color=red>Solution to [Error: no face detected!]</font>**: VToonify uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. You can try several times or use other images until a face is detected, then switch back to the original image.
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- For video input, further hit the **Rescale Video** button.
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- The final video result will be based on this **Rescaled Video**. To avoid overload, video is cut to at most **100/300** frames for CPU/GPU, respectively.
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"""
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)
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with gr.Row():
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with gr.Box():
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with gr.Column():
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gr.Markdown(
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"""Choose the padding parameters.
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"""
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)
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with gr.Row():
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top = gr.Slider(128, 256, value=200, step=8, label="top")
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with gr.Row():
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bottom = gr.Slider(
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128, 256, value=200, step=8, label="bottom"
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)
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with gr.Row():
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left = gr.Slider(128, 256, value=200, step=8, label="left")
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with gr.Row():
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right = gr.Slider(
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128, 256, value=200, step=8, label="right"
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)
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with gr.Box():
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with gr.Column():
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gr.Markdown("""Input""")
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with gr.Row():
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input_image = gr.Image(label="Input Image", type="filepath")
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with gr.Row():
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preprocess_image_button = gr.Button("Rescale Image")
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with gr.Row():
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input_video = gr.Video(
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label="Input Video",
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mirror_webcam=False,
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type="filepath",
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)
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with gr.Row():
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preprocess_video0_button = gr.Button("Rescale First Frame")
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preprocess_video1_button = gr.Button("Rescale Video")
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with gr.Box():
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with gr.Column():
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gr.Markdown("""View""")
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with gr.Row():
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input_info = gr.Textbox(
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label="Process Information",
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interactive=False,
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value="n.a.",
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)
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with gr.Row():
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aligned_face = gr.Image(
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label="Rescaled Face", type="numpy", interactive=False
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)
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instyle = gr.Variable()
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with gr.Row():
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aligned_video = gr.Video(
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label="Rescaled Video", type="mp4", interactive=False
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)
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with gr.Row():
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with gr.Column():
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paths = [
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"./vtoonify/data/pexels-andrea-piacquadio-733872.jpg",
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"./vtoonify/data/i5R8hbZFDdc.jpg",
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"./vtoonify/data/yRpe13BHdKw.jpg",
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"./vtoonify/data/ILip77SbmOE.jpg",
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"./vtoonify/data/077436.jpg",
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"./vtoonify/data/081680.jpg",
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]
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example_images = gr.Dataset(
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components=[input_image],
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samples=[[path] for path in paths],
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label="Image Examples",
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)
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with gr.Column():
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# example_videos = gr.Dataset(components=[input_video], samples=[['./vtoonify/data/529.mp4']], type='values')
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# to render video example on mouse hover/click
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example_videos.render()
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with gr.
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gr.
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with gr.
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gr.
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""
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with gr.Column():
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gr.Markdown(
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"""
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with gr.Column():
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with gr.Row():
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with gr.Row():
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with gr.Column():
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with gr.Row():
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label="
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with gr.Row():
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args = parse_args()
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args.device = "cuda" if torch.cuda.is_available() else "cpu"
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print("*** Now using %s." % (args.device))
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model = Model(device=args.device)
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with gr.Blocks(theme=args.theme, css="style.css") as demo:
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gr.Markdown(DESCRIPTION)
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with gr.Box():
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gr.Markdown(
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"""## Step 1(Select Style)
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- Select **Style Type**.
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| 113 |
+
- Type with `-d` means it supports style degree adjustment.
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| 114 |
+
- Type without `-d` usually has better toonification quality.
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| 115 |
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"""
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)
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with gr.Row():
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with gr.Column():
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gr.Markdown("""Select Style Type""")
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with gr.Row():
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style_type = gr.Radio(
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label="Style Type",
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choices=[
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"cartoon1",
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"cartoon1-d",
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"cartoon2-d",
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"cartoon3-d",
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"cartoon4",
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"cartoon4-d",
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"cartoon5-d",
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"comic1-d",
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"comic2-d",
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"arcane1",
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"arcane1-d",
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"arcane2",
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"arcane2-d",
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"caricature1",
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"caricature2",
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"pixar",
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"pixar-d",
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"illustration1-d",
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"illustration2-d",
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"illustration3-d",
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"illustration4-d",
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| 146 |
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"illustration5-d",
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],
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)
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exstyle = gr.Variable()
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with gr.Row():
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loadmodel_button = gr.Button("Load Model")
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| 152 |
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with gr.Row():
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load_info = gr.Textbox(
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label="Process Information",
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| 155 |
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interactive=False,
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value="No model loaded.",
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)
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with gr.Column():
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| 159 |
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gr.Markdown(
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"""Reference Styles
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| 161 |
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"""
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| 162 |
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)
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| 163 |
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| 164 |
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with gr.Box():
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| 165 |
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gr.Markdown(
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| 166 |
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"""## Step 2 (Preprocess Input Image / Video)
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| 167 |
+
- Drop an image/video containing a near-frontal face to the **Input Image**/**Input Video**.
|
| 168 |
+
- Hit the **Rescale Image**/**Rescale First Frame** button.
|
| 169 |
+
- Rescale the input to make it best fit the model.
|
| 170 |
+
- The final image result will be based on this **Rescaled Face**. Use padding parameters to adjust the background space.
|
| 171 |
+
- **<font color=red>Solution to [Error: no face detected!]</font>**: VToonify uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. You can try several times or use other images until a face is detected, then switch back to the original image.
|
| 172 |
+
- For video input, further hit the **Rescale Video** button.
|
| 173 |
+
- The final video result will be based on this **Rescaled Video**. To avoid overload, video is cut to at most **100/300** frames for CPU/GPU, respectively.
|
| 174 |
+
|
| 175 |
+
"""
|
| 176 |
+
)
|
| 177 |
+
with gr.Row():
|
| 178 |
+
with gr.Box():
|
| 179 |
with gr.Column():
|
| 180 |
gr.Markdown(
|
| 181 |
+
"""Choose the padding parameters.
|
| 182 |
+
"""
|
| 183 |
)
|
| 184 |
+
with gr.Row():
|
| 185 |
+
top = gr.Slider(128, 256, value=200, step=8, label="top")
|
| 186 |
+
with gr.Row():
|
| 187 |
+
bottom = gr.Slider(128, 256, value=200, step=8, label="bottom")
|
| 188 |
+
with gr.Row():
|
| 189 |
+
left = gr.Slider(128, 256, value=200, step=8, label="left")
|
| 190 |
+
with gr.Row():
|
| 191 |
+
right = gr.Slider(128, 256, value=200, step=8, label="right")
|
| 192 |
+
with gr.Box():
|
| 193 |
with gr.Column():
|
| 194 |
+
gr.Markdown("""Input""")
|
| 195 |
with gr.Row():
|
| 196 |
+
input_image = gr.Image(label="Input Image", type="filepath")
|
| 197 |
+
with gr.Row():
|
| 198 |
+
preprocess_image_button = gr.Button("Rescale Image")
|
| 199 |
+
with gr.Row():
|
| 200 |
+
input_video = gr.Video(
|
| 201 |
+
label="Input Video",
|
| 202 |
+
mirror_webcam=False,
|
| 203 |
+
type="filepath",
|
| 204 |
)
|
| 205 |
with gr.Row():
|
| 206 |
+
preprocess_video0_button = gr.Button("Rescale First Frame")
|
| 207 |
+
preprocess_video1_button = gr.Button("Rescale Video")
|
| 208 |
+
|
| 209 |
+
with gr.Box():
|
| 210 |
with gr.Column():
|
| 211 |
+
gr.Markdown("""View""")
|
| 212 |
with gr.Row():
|
| 213 |
+
input_info = gr.Textbox(
|
| 214 |
+
label="Process Information",
|
| 215 |
+
interactive=False,
|
| 216 |
+
value="n.a.",
|
| 217 |
)
|
| 218 |
with gr.Row():
|
| 219 |
+
aligned_face = gr.Image(
|
| 220 |
+
label="Rescaled Face", type="numpy", interactive=False
|
| 221 |
+
)
|
| 222 |
+
instyle = gr.Variable()
|
| 223 |
+
with gr.Row():
|
| 224 |
+
aligned_video = gr.Video(
|
| 225 |
+
label="Rescaled Video", type="mp4", interactive=False
|
| 226 |
+
)
|
| 227 |
+
with gr.Row():
|
| 228 |
+
with gr.Column():
|
| 229 |
+
paths = [
|
| 230 |
+
"./vtoonify/data/pexels-andrea-piacquadio-733872.jpg",
|
| 231 |
+
"./vtoonify/data/i5R8hbZFDdc.jpg",
|
| 232 |
+
"./vtoonify/data/yRpe13BHdKw.jpg",
|
| 233 |
+
"./vtoonify/data/ILip77SbmOE.jpg",
|
| 234 |
+
"./vtoonify/data/077436.jpg",
|
| 235 |
+
"./vtoonify/data/081680.jpg",
|
| 236 |
+
]
|
| 237 |
+
example_images = gr.Dataset(
|
| 238 |
+
components=[input_image],
|
| 239 |
+
samples=[[path] for path in paths],
|
| 240 |
+
label="Image Examples",
|
| 241 |
+
)
|
| 242 |
+
with gr.Column():
|
| 243 |
+
# example_videos = gr.Dataset(components=[input_video], samples=[['./vtoonify/data/529.mp4']], type='values')
|
| 244 |
+
# to render video example on mouse hover/click
|
| 245 |
+
example_videos.render()
|
| 246 |
+
|
| 247 |
+
# to load sample video into input_video upon clicking on it
|
| 248 |
+
def load_examples(video):
|
| 249 |
+
# print("****** inside load_example() ******")
|
| 250 |
+
# print("in_video is : ", video[0])
|
| 251 |
+
return video[0]
|
| 252 |
+
|
| 253 |
+
example_videos.click(load_examples, example_videos, input_video)
|
| 254 |
+
|
| 255 |
+
with gr.Box():
|
| 256 |
+
gr.Markdown("""## Step 3 (Generate Style Transferred Image/Video)""")
|
| 257 |
+
with gr.Row():
|
| 258 |
+
with gr.Column():
|
| 259 |
+
gr.Markdown(
|
| 260 |
+
"""
|
| 261 |
+
|
| 262 |
+
- Adjust **Style Degree**.
|
| 263 |
+
- Hit **Toonify!** to toonify one frame. Hit **VToonify!** to toonify full video.
|
| 264 |
+
- Estimated time on 1600x1440 video of 300 frames: 1 hour (CPU); 2 mins (GPU)
|
| 265 |
+
"""
|
| 266 |
+
)
|
| 267 |
+
style_degree = gr.Slider(
|
| 268 |
+
0, 1, value=0.5, step=0.05, label="Style Degree"
|
| 269 |
+
)
|
| 270 |
+
with gr.Column():
|
| 271 |
+
gr.Markdown(
|
| 272 |
+
"""
|
| 273 |
+
"""
|
| 274 |
+
)
|
| 275 |
+
with gr.Row():
|
| 276 |
+
output_info = gr.Textbox(
|
| 277 |
+
label="Process Information", interactive=False, value="n.a."
|
| 278 |
+
)
|
| 279 |
+
with gr.Row():
|
| 280 |
+
with gr.Column():
|
| 281 |
+
with gr.Row():
|
| 282 |
+
result_face = gr.Image(
|
| 283 |
+
label="Result Image", type="numpy", interactive=False
|
| 284 |
+
)
|
| 285 |
+
with gr.Row():
|
| 286 |
+
toonify_button = gr.Button("Toonify!")
|
| 287 |
+
with gr.Column():
|
| 288 |
+
with gr.Row():
|
| 289 |
+
result_video = gr.Video(
|
| 290 |
+
label="Result Video", type="mp4", interactive=False
|
| 291 |
+
)
|
| 292 |
+
with gr.Row():
|
| 293 |
+
vtoonify_button = gr.Button("VToonify!")
|
| 294 |
+
|
| 295 |
+
gr.Markdown(ARTICLE)
|
| 296 |
+
gr.Markdown(FOOTER)
|
| 297 |
+
|
| 298 |
+
loadmodel_button.click(
|
| 299 |
+
fn=model.load_model, inputs=[style_type], outputs=[exstyle, load_info]
|
| 300 |
+
)
|
| 301 |
+
|
| 302 |
+
style_type.change(fn=update_slider, inputs=style_type, outputs=style_degree)
|
| 303 |
+
|
| 304 |
+
preprocess_image_button.click(
|
| 305 |
+
fn=model.detect_and_align_image,
|
| 306 |
+
inputs=[input_image, top, bottom, left, right],
|
| 307 |
+
outputs=[aligned_face, instyle, input_info],
|
| 308 |
+
)
|
| 309 |
+
preprocess_video0_button.click(
|
| 310 |
+
fn=model.detect_and_align_video,
|
| 311 |
+
inputs=[input_video, top, bottom, left, right],
|
| 312 |
+
outputs=[aligned_face, instyle, input_info],
|
| 313 |
+
)
|
| 314 |
+
preprocess_video1_button.click(
|
| 315 |
+
fn=model.detect_and_align_full_video,
|
| 316 |
+
inputs=[input_video, top, bottom, left, right],
|
| 317 |
+
outputs=[aligned_video, instyle, input_info],
|
| 318 |
+
)
|
| 319 |
+
|
| 320 |
+
toonify_button.click(
|
| 321 |
+
fn=model.image_toonify,
|
| 322 |
+
inputs=[aligned_face, instyle, exstyle, style_degree, style_type],
|
| 323 |
+
outputs=[result_face, output_info],
|
| 324 |
+
)
|
| 325 |
+
vtoonify_button.click(
|
| 326 |
+
fn=model.video_tooniy,
|
| 327 |
+
inputs=[aligned_video, instyle, exstyle, style_degree, style_type],
|
| 328 |
+
outputs=[result_video, output_info],
|
| 329 |
+
)
|
| 330 |
+
|
| 331 |
+
example_images.click(
|
| 332 |
+
fn=set_example_image,
|
| 333 |
+
inputs=example_images,
|
| 334 |
+
outputs=example_images.components,
|
| 335 |
+
)
|
| 336 |
+
|
| 337 |
+
# demo.launch(
|
| 338 |
+
# enable_queue=args.enable_queue,
|
| 339 |
+
# server_port=args.port,
|
| 340 |
+
# share=args.share,
|
| 341 |
+
# )
|
| 342 |
+
|
| 343 |
+
demo.queue(concurrency_count=1, max_size=4)
|
| 344 |
+
demo.launch(server_port=8266)
|