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README.md
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title: BBBicycles
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emoji: 🐨
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colorFrom: purple
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.5
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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license: mit
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---
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app.py
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import gradio as gr
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from PIL import Image
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elements = {
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"Becane": ("data/becane_1_0_2_014/before/img6", "data/becane_1_0_2_014/after/img7", "models/RoadBecane_f.stl"),
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"BTWIN": ("data/btwin_1_26_3_114/before/img0", "data/btwin_1_26_3_114/after/img13", "models/BTWIN.stl"),
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"Classic Road": ("data/croad_4_26_4_112/before/img3", "data/croad_4_26_4_112/after/img7", "models/ClassicRoad.stl"),
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"Domane": ("data/domane_2_0_39_012/before/img6", "data/domane_2_0_39_012/after/img13", "models/domane.stl"),
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"Enduro": ("data/enduro_2_18_18_105/before/img6", "data/enduro_2_18_18_105/after/img10", "models/enduro.stl"),
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"G1": ("data/g1_2_36_14_010/before/img6", "data/g1_2_36_14_010/after/img8", "models/g1.stl"),
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"GBike": ("data/gbike_3_31_8_018/before/img2", "data/gbike_3_31_8_018/after/img8", "models/gbike.stl"),
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"Holland": (
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"data/holland_4_24_18_00/before/img4", "data/holland_4_24_18_00/after/img7", "models/holland.stl"),
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"Huffy": (
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"data/huffy_6_4_35_012/before/img4", "data/huffy_6_4_35_012/after/img12", "models/huffy.stl"),
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"Kuota": (
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"data/kuota_6_5_30_112/before/img4", "data/kuota_6_5_30_112/after/img12", "models/kuota.stl"),
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"MFactory": (
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"data/mfactory_1_17_8_00/before/img4", "data/mfactory_1_17_8_00/after/img12", "models/mfactory.stl"),
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"Mirage": (
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"data/mirage_6_11_36_117/before/img4", "data/mirage_6_11_36_117/after/img12", "models/mirage.stl"),
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"Old Bike": (
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"data/oldbike_3_30_15_100/before/img4", "data/oldbike_3_30_15_100/after/img12", "models/oldbike.stl"),
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"Freeride": (
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"data/freeride_6_24_0_00/before/img4", "data/freeride_6_24_0_00/after/img12", "models/freeride_1Step_f.stl"),
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"RondoRuut": (
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"data/rondo_2_11_39_106/before/img4", "data/rondo_2_11_39_106/after/img12",
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"models/RondoRuutClean_1Step_f.stl"),
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"MTB Ghost": (
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"data/ghost_1_0_4_116/before/img4", "data/ghost_1_0_4_116/after/img12", "models/MTB_Ghost.stl"),
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"Scalpel": (
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"data/scalpel_6_2_12_101/before/img4", "data/scalpel_6_2_12_101/after/img12", "models/scalpel.stl"),
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"Verdona": (
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"data/verdona_1_14_10_118/before/img4", "data/verdona_1_14_10_118/after/img12", "models/verdona.stl"),
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"Vintage": (
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"data/vintage_6_6_33_011/before/img4", "data/vintage_6_6_33_011/after/img12", "models/Vintage.stl"),
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"WBike": (
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"data/wbike_3_19_23_00/before/img4", "data/wbike_3_19_23_00/after/img12", "models/wbike.stl"),
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}
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def generate_ui(key):
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img_b, img_a, model = elements[key]
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gr.Markdown("### Before damage")
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with gr.Row():
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with gr.Row():
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with gr.Column(scale=1):
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gr.Markdown("#### Render")
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img_bn = gr.Image(f"{img_b}.png")
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with gr.Column(scale=1):
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gr.Markdown("#### Background")
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img_bb = gr.Image(f"{img_b}_background.png")
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with gr.Column(scale=1):
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gr.Markdown("#### Foreground")
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img_bf = gr.Image(f"{img_b}_foreground.png")
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with gr.Column(scale=1):
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gr.Markdown("#### Segmentation")
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img_bs = gr.Image(f"{img_b}_segmentation.png")
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gr.Markdown("### After damage")
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with gr.Row():
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with gr.Column(scale=1):
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gr.Markdown("#### Render")
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img_an = gr.Image(f"{img_a}.png")
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with gr.Column(scale=1):
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gr.Markdown("#### Background")
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img_ab = gr.Image(f"{img_a}_background.png")
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with gr.Column(scale=1):
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gr.Markdown("#### Foreground")
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img_af = gr.Image(f"{img_a}_foreground.png")
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with gr.Column(scale=1):
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gr.Markdown("#### Segmentation")
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img_as = gr.Image(f"{img_a}_segmentation.png")
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model = gr.Model3D(model, label="3D model preview")
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return img_bn, img_bb, img_bf, img_bs, img_an, img_ab, img_af, img_as, model
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def get_values(key):
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img_b, img_a, model = elements[key]
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gr.Markdown("### Before damage")
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img_bn = Image.open(f"{img_b}.png")
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img_bb = Image.open(f"{img_b}_background.png")
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img_bf = Image.open(f"{img_b}_foreground.png")
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img_bs = Image.open(f"{img_b}_segmentation.png")
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img_an = Image.open(f"{img_a}.png")
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img_ab = Image.open(f"{img_a}_background.png")
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img_af = Image.open(f"{img_a}_foreground.png")
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img_as = Image.open(f"{img_a}_segmentation.png")
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return img_bn, img_bb, img_bf, img_bs, img_an, img_ab, img_af, img_as, model
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block = gr.Blocks()
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with block:
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with open("page.md", "r") as f:
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gr.Markdown(f.read())
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first_key = list(elements.keys())[0]
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dropdown = gr.Dropdown(choices=list(elements.keys()), value=first_key, label="Model", interactive=True)
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img_bn, img_bb, img_bf, img_bs, img_an, img_ab, img_af, img_as, model = generate_ui(first_key)
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dropdown.change(get_values, inputs=[dropdown],
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outputs=[img_bn, img_bb, img_bf, img_bs, img_an, img_ab, img_af,
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img_as, model])
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block.queue(concurrency_count=40, max_size=20).launch(max_threads=150)
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page.md
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# Dataset Card for BBBicycles
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## Dataset Summary
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Bent & Broken Bicycles (BBBicycles) dataset is a benchmark set for the novel task of **damaged object re-identification**, which aims to identify the same object in multiple images even in the presence of breaks, deformations, and missing parts.
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## Dataset Structure
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The final dataset contains:
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- Total of 39,200 image
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- 2,800 unique IDs
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- 20 models
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- 140 IDs for each model
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<table border-collapse="collapse">
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<tr>
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<td><b style="font-size:25px">Information for each ID:</b></td>
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<td><b style="font-size:25px">Information for each render:</b></td>
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</tr>
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<tr>
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<td>
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<ul>
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<li>Model</li>
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<li>Type</li>
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<li>Texture type</li>
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<li>Stickers</li>
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</ul>
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</td>
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<td>
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<ul>
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<li>Background</li>
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<li>Viewing Side</li>
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<li>Focal Length</li>
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<li>Presence of dirt</li>
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</ul>
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</td>
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</tr>
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</table>
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