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app.py
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def sepia(input_img, intensity):
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import gradio as gr
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from matplotlib import gridspec
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@@ -10,10 +8,10 @@ import tensorflow as tf
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from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
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feature_extractor = SegformerFeatureExtractor.from_pretrained(
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"
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model = TFSegformerForSemanticSegmentation.from_pretrained(
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"
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)
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def ade_palette():
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color_seg[seg.numpy() == label, :] = color
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# Show image + mask
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pred_img = np.array(input_img) *
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pred_img = pred_img.astype(np.uint8)
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fig = draw_plot(pred_img, seg)
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return fig
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inputs=[gr.inputs.Image(shape=(400, 600)), gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5)],
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outputs='plot',
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examples=[["cheonggyecheon_stream_in_seoul_city.jpg", 0.5], ["Incheon_stadium.jpg", 0.7],
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["Incheon_city.jpg", 0.3]],
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title="Semantic Segmentation",
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description="This is a demo of semantic segmentation using Segformer.",
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allow_flagging=False,
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)
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demo.launch()
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import gradio as gr
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from matplotlib import gridspec
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from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
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feature_extractor = SegformerFeatureExtractor.from_pretrained(
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"mattmdjaga/segformer_b2_clothes"
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)
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model = TFSegformerForSemanticSegmentation.from_pretrained(
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"mattmdjaga/segformer_b2_clothes"
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)
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def ade_palette():
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color_seg[seg.numpy() == label, :] = color
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# Show image + mask
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pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
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pred_img = pred_img.astype(np.uint8)
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fig = draw_plot(pred_img, seg)
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return fig
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demo = gr.Interface(fn=sepia,
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inputs=gr.Image(shape=(400, 600)),
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outputs=['plot'],
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examples=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
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allow_flagging='never')
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demo.launch()
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