init project
Browse files
app.py
CHANGED
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@@ -447,7 +447,7 @@ def get_3D_model_from_scene(outdir, scene, min_conf_thr=3, as_pointcloud=False,
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# return cog_seg_maps, rev_cog_seg_maps, multi_view_clip_feats
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@spaces.GPU(duration=30)
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def get_reconstructed_scene(outdir, filelist, schedule='linear', niter=300, min_conf_thr=3.0,
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as_pointcloud=True, mask_sky=False, clean_depth=True, transparent_cams=True, cam_size=0.05,
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scenegraph_type='complete', winsize=1, refid=0):
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@@ -537,11 +537,15 @@ def get_reconstructed_scene(outdir, filelist, schedule='linear', niter=300, min_
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outfile = get_3D_model_from_scene(outdir, scene, min_conf_thr, as_pointcloud, mask_sky,
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clean_depth, transparent_cams, cam_size)
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scene.to('cpu')
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torch.cuda.empty_cache()
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return
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# @spaces.GPU(duration=30)
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# def get_3D_object_from_scene(outdir, text, threshold, scene, min_conf_thr, as_pointcloud,
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# mask_sky, clean_depth, transparent_cams, cam_size):
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@@ -569,7 +573,8 @@ recon_fun = functools.partial(get_reconstructed_scene, tmpdirname)
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with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 100%};""", title="PE3R Demo") as demo:
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# scene state is save so that you can change conf_thr, cam_size... without rerunning the inference
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scene = gradio.State(None)
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gradio.HTML('<h2 style="text-align: center;">PE3R Demo</h2>')
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with gradio.Column():
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inputfiles = gradio.File(file_count="multiple")
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@@ -587,7 +592,7 @@ with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 10
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run_btn.click(fn=recon_fun,
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inputs=[inputfiles],
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outputs=[
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# find_btn.click(fn=get_3D_object_from_scene_fun,
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# inputs=[text_input, threshold, scene, min_conf_thr, as_pointcloud, mask_sky,
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# return cog_seg_maps, rev_cog_seg_maps, multi_view_clip_feats
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@spaces.GPU#(duration=30)
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def get_reconstructed_scene(outdir, filelist, schedule='linear', niter=300, min_conf_thr=3.0,
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as_pointcloud=True, mask_sky=False, clean_depth=True, transparent_cams=True, cam_size=0.05,
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scenegraph_type='complete', winsize=1, refid=0):
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outfile = get_3D_model_from_scene(outdir, scene, min_conf_thr, as_pointcloud, mask_sky,
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clean_depth, transparent_cams, cam_size)
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# scene.to('cpu')
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# print(scene)
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# print(scene.imgs)
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# print(scene.cogs) scene,
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torch.cuda.empty_cache()
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return outfile
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# @spaces.GPU #(duration=30)
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# def get_3D_object_from_scene(outdir, text, threshold, scene, min_conf_thr, as_pointcloud,
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# mask_sky, clean_depth, transparent_cams, cam_size):
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with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 100%};""", title="PE3R Demo") as demo:
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# scene state is save so that you can change conf_thr, cam_size... without rerunning the inference
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# scene = gradio.State(None)
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gradio.HTML('<h2 style="text-align: center;">PE3R Demo</h2>')
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with gradio.Column():
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inputfiles = gradio.File(file_count="multiple")
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run_btn.click(fn=recon_fun,
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inputs=[inputfiles],
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outputs=[outmodel]) # , outgallery, scene,
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# find_btn.click(fn=get_3D_object_from_scene_fun,
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# inputs=[text_input, threshold, scene, min_conf_thr, as_pointcloud, mask_sky,
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