update
Browse files- README.md +2 -1
- app.py +55 -20
- examples/Red_Winged_Blackbird_0012_6015.jpg +0 -0
- examples/Red_Winged_Blackbird_0025_5342.jpg +0 -0
- examples/Yellow_Headed_Blackbird_0020_8549.jpg +0 -0
- examples/Yellow_Headed_Blackbird_0026_8545.jpg +0 -0
- sample1.jpeg β examples/sample1.jpeg +0 -0
- sample2.jpeg β examples/sample2.jpeg +0 -0
- requirements.txt +8 -7
README.md
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sdk: gradio
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app_file: app.py
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pinned: false
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---
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# Convolutional Hough Matching Networks
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A demo for Convolutional Hough Matching Networks. [[Paper](https://arxiv.org/abs/2109.05221)] [[Official Github Repo](https://github.com/juhongm999/chm.git)]
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colorFrom: red
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sdk: gradio
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sdk_version: 3.0.5
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app_file: app.py
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pinned: false
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---
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# Convolutional Hough Matching Networks
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A demo for Convolutional Hough Matching Networks. [[Paper](https://arxiv.org/abs/2109.05221)] [[Official Github Repo](https://github.com/juhongm999/chm.git)]
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app.py
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from model.base.geometry import Geometry
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# Downloading the Model
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gdown.download(id="1zsJRlAsoOn5F0GTCprSFYwDDfV85xDy6", output="pas_psi.pt", quiet=False)
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# Model Initialization
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args = dict(
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#
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description = "Performs keypoint transform from a 7x7 gird on the source image to the target image. Use the sliders to adjust the grid."
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article = "<p style='text-align: center'><a href='https://github.com/juhongm999/chm' target='_blank'>Original Github Repo</a></p>"
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iface = gr.Interface(
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fn=generate_correspondences,
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inputs=[
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gr.
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gr.
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gr.
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gr.
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gr.
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gr.
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],
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outputs=[gr.outputs.Plot(type="matplotlib")],
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enable_queue=True,
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title=title,
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description=description,
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article=article,
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examples=[["sample1.jpeg", "sample2.jpeg", 15, 215, 15, 215]],
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)
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from model.base.geometry import Geometry
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# Downloading the Model
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# gdown.download(id="1zsJRlAsoOn5F0GTCprSFYwDDfV85xDy6", output="pas_psi.pt", quiet=False)
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md5 = "6b7b4d7bad7f89600fac340d6aa7708b"
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gdown.cached_download(
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url="1zsJRlAsoOn5F0GTCprSFYwDDfV85xDy6", path="pas_psi.pt", quiet=False, md5=md5
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)
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# Model Initialization
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args = dict(
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)
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# Gradio App
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main = gr.Interface(
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fn=generate_correspondences,
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inputs=[
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gr.Image(shape=(240, 240), type="pil"),
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gr.Image(shape=(240, 240), type="pil"),
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gr.Slider(minimum=1, maximum=240, step=1, default=15, label="Min X"),
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gr.Slider(minimum=1, maximum=240, step=1, default=215, label="Max X"),
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gr.Slider(minimum=1, maximum=240, step=1, default=15, label="Min Y"),
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gr.Slider(minimum=1, maximum=240, step=1, default=215, label="Max Y"),
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],
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allow_flagging="never",
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outputs="plot",
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examples=[
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["./examples/sample1.jpeg", "./examples/sample2.jpeg", 17, 223, 17, 223],
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[
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"./examples/Red_Winged_Blackbird_0012_6015.jpg",
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"./examples/Red_Winged_Blackbird_0025_5342.jpg",
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],
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[
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"./examples/Yellow_Headed_Blackbird_0026_8545.jpg",
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"./examples/Yellow_Headed_Blackbird_0020_8549.jpg",
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],
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)
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blocks = gr.Blocks()
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with blocks:
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gr.Markdown(
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"""
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# Correspondence Matching with Convolutional Hough Matching Networks
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Performs keypoint transform from a 7x7 gird on the source image to the target image. Use the sliders to adjust the grid.
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[Original Paper](https://arxiv.org/abs/2103.16831) - [Github Page](https://github.com/juhongm999/chm)
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"""
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)
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gr.TabbedInterface([main], ["Main"])
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blocks.launch(
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debug=True,
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enable_queue=False,
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)
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examples/Red_Winged_Blackbird_0012_6015.jpg
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examples/Red_Winged_Blackbird_0025_5342.jpg
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examples/Yellow_Headed_Blackbird_0020_8549.jpg
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examples/Yellow_Headed_Blackbird_0026_8545.jpg
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sample1.jpeg β examples/sample1.jpeg
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sample2.jpeg β examples/sample2.jpeg
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requirements.txt
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gradio==
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matplotlib==3.4.3
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numpy==1.21.2
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pandas==1.3.4
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Pillow==8.4.0
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requests==2.26.0
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scipy==1.7.1
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tensorboardX==2.
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gdown
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gradio==3.0.5
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pandas==1.3.4
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requests==2.26.0
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scipy==1.7.1
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tensorboardX==2.5
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matplotlib==3.5.2
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numpy==1.21.5
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Pillow==9.0.1
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scikit-image==0.19.2
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torch==1.11.0
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torchvision==0.12.0
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gdown
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