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import gradio as gr |
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import re |
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from utils import read_pcd, render_point_cloud, render_pcd_file, set_seed |
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from inference.utils import get_legend |
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from inference.inference import segment_obj, get_heatmap |
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from huggingface_hub import login |
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import os |
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os.chdir("Pointcept/libs/pointops") |
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os.system("python setup.py install") |
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os.chdir("../../../") |
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login(token=os.getenv('hfkey')) |
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parts_dict = { |
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"fireplug": "bonnet of a fireplug,side cap of a fireplug,barrel of a fireplug,base of a fireplug", |
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"mickey": "ear,head,arms,hands,body,legs", |
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"motorvehicle": "wheel of a motor vehicle,seat of a motor vehicle,handle of a motor vehicle", |
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"teddy": "head,body,arms,legs", |
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"lamppost": "lighting of a lamppost,pole of a lamppost", |
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"shirt": "sleeve of a shirt,collar of a shirt,body of a shirt", |
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"capybara": "hat worn by a capybara,head,body,feet", |
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"corgi": "head,leg,body,ear", |
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"pushcar": "wheel,body,handle", |
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"plant": "pot,plant", |
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"chair": "back of chair,leg,seat" |
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} |
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source_dict = { |
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"fireplug":"objaverse", |
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"mickey":"objaverse", |
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"motorvehicle":"objaverse", |
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"teddy":"objaverse", |
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"lamppost":"objaverse", |
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"shirt":"objaverse", |
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"capybara": "wild", |
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"corgi": "wild", |
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"pushcar": "wild", |
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"plant": "wild", |
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"chair": "wild" |
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} |
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def predict(pcd_path, inference_mode, part_queries): |
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set_seed() |
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xyz, rgb, normal = read_pcd(pcd_path) |
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if inference_mode == "Segmentation": |
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parts = [part.strip(" ") for part in re.split(r'[,;.|]', part_queries)] |
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if len(parts)< 2: |
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raise gr.Error("For segmentation mode, please provide 2 or more parts", duration=5) |
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seg_rgb = segment_obj(xyz, rgb, normal, parts).cpu().numpy() |
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legend = get_legend(parts) |
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return render_point_cloud(xyz, seg_rgb, legend=legend) |
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elif inference_mode == "Localization": |
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if "," in part_queries or ";" in part_queries or "." in part_queries: |
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raise gr.Error("For localization mode, please provide only one part", duration=5) |
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heatmap_rgb = get_heatmap(xyz, rgb, normal, part_queries).cpu().numpy() |
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return render_point_cloud(xyz, heatmap_rgb) |
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else: |
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return None |
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def on_select(evt: gr.SelectData): |
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obj_name = evt.value['image']['orig_name'][:-4] |
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src = source_dict[obj_name] |
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return [f"examples/{src}/{obj_name}.pcd", parts_dict[obj_name]] |
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with gr.Blocks(theme=gr.themes.Default(text_size="lg", radius_size="none")) as demo: |
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gr.HTML( |
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'''<h1 text-align="center">Find Any Part in 3D</h1> |
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<p style='font-size: 16px;'>This is a demo for Find3D: Find Any Part in 3D! Two modes are supported: <b>segmentation</b> and <b>localization</b>. |
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For <b>segmentation mode</b>, please provide multiple part queries in the "queries" text box, in the format of comma-separated string, such as "part1,part2,part3". |
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After hitting "Run", the model will segment the object into the provided parts. |
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<br> |
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For <b>localization mode<b>, please only provide one query string in the "queries" text box. After hitting "Run", the model will generate a heatmap for the provided query text. |
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Please click on the buttons below "Objaverse" and "In the Wild" for some examples. You can also upload your own .pcd files.</p> |
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<p style='font-size: 16px;'>Hint: |
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When uploading your own point cloud, please first close the existing point cloud by clicking on the "x" button. |
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<br> |
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We show some sample queries for the provided examples. When working with your own point cloud, feel free to rephrase the query (e.g. "part" vs "part of a object") to achieve better performance!</p> |
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''' |
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) |
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with gr.Row(variant="panel"): |
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with gr.Column(scale=4): |
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file_upload = gr.File( |
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label="Upload Point Cloud File", |
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type="filepath", |
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file_types=[".pcd"], |
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value="examples/objaverse/lamppost.pcd" |
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) |
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inference_mode = gr.Radio( |
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choices=["Segmentation", "Localization"], |
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label="Inference Mode", |
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value="Segmentation", |
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) |
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part_queries = gr.Textbox( |
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label="Part Queries", |
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value="lighting of a lamppost,pole of a lamppost", |
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) |
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run_button = gr.Button( |
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value="Run", |
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variant="primary", |
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) |
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with gr.Column(scale=4): |
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input_image = gr.Image(label="Input Image", visible=False, type='pil', image_mode='RGBA', height=290) |
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input_point_cloud = gr.Plot(label="Input Point Cloud") |
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with gr.Column(scale=4): |
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output_point_cloud = gr.Plot(label="Output Result") |
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with gr.Row(variant="panel"): |
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with gr.Column(scale=6): |
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title = gr.HTML('''<h1 text-align="center">Objaverse</h1> |
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<p style='font-size: 16px;'>Online 3D assets from Objaverse!</p> |
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''') |
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gallery_objaverse = gr.Gallery([("examples/objaverse/lamppost.jpg", "lamppost"), |
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("examples/objaverse/fireplug.jpg", "fireplug"), |
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("examples/objaverse/mickey.jpg", "Mickey"), |
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("examples/objaverse/motorvehicle.jpg", "motor vehicle"), |
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("examples/objaverse/teddy.jpg", "teddy bear"), |
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("examples/objaverse/shirt.jpg", "shirt")], |
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columns=3, |
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allow_preview=False) |
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gallery_objaverse.select(fn=on_select, |
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inputs=None, |
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outputs=[file_upload, part_queries]) |
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with gr.Column(scale=6): |
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title = gr.HTML("""<h1 text-align="center">In the Wild</h1> |
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<p style='font-size: 16px;'>Challenging in-the-wild reconstructions from iPhone photos & AI-generated images!</p> |
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""") |
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gallery_wild = gr.Gallery([("examples/wild/capybara.png", "DALLE-capybara"), |
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("examples/wild/corgi.jpg", "DALLE-corgi"), |
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("examples/wild/plant.jpg", "iPhone-plant"), |
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("examples/wild/pushcar.jpg", "iPhone-pushcar"), |
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("examples/wild/chair.jpg", "iPhone-chair")], |
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columns=3, |
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allow_preview=False) |
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gallery_wild.select(fn=on_select, |
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inputs=None, |
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outputs=[file_upload, part_queries]) |
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file_upload.change( |
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fn=render_pcd_file, |
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inputs=[file_upload], |
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outputs=[input_point_cloud], |
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) |
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run_button.click( |
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fn=predict, |
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inputs=[file_upload, inference_mode, part_queries], |
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outputs=[output_point_cloud], |
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) |
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demo.load( |
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fn=render_pcd_file, |
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inputs=[file_upload], |
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outputs=[input_point_cloud]) |
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demo.launch() |
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