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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import sys | |
| import os | |
| import datetime | |
| import gradio as gr | |
| import spaces | |
| def run_on_gpu(input_point_cloud: gr.utils.NamedString, | |
| gen_resolution_global: int, | |
| padding_factor: float, | |
| gen_subsample_manifold_iter: int, | |
| gen_refine_iter: int) -> str: | |
| print('Started inference at {}'.format(datetime.datetime.now())) | |
| print('Inputs:', input_point_cloud, gen_resolution_global, padding_factor, | |
| gen_subsample_manifold_iter, gen_refine_iter) | |
| print('Types:', type(input_point_cloud), type(gen_resolution_global), type(padding_factor), | |
| type(gen_subsample_manifold_iter), type(gen_refine_iter)) | |
| sys.path.append(os.path.abspath('ppsurf')) | |
| import subprocess | |
| import uuid | |
| in_file = '{}'.format(input_point_cloud.name) | |
| rand_hash = uuid.uuid4().hex | |
| out_dir = '/tmp/outputs/{}'.format(rand_hash) | |
| out_file_basename = os.path.basename(in_file) + '.ply' | |
| out_file = os.path.join(out_dir, os.path.basename(in_file), out_file_basename) | |
| os.makedirs(out_dir, exist_ok=True) | |
| model_path = 'models/ppsurf_50nn/version_0/checkpoints/last.ckpt' | |
| args = [ | |
| 'pps.py', 'predict', | |
| '-c', 'ppsurf/configs/poco.yaml', | |
| '-c', 'ppsurf/configs/ppsurf.yaml', | |
| '-c', 'ppsurf/configs/ppsurf_50nn.yaml', | |
| '--ckpt_path', model_path, | |
| '--data.init_args.in_file', in_file, | |
| '--model.init_args.results_dir', out_dir, | |
| '--trainer.logger', 'False', | |
| '--trainer.devices', '1', | |
| '--model.init_args.gen_resolution_global', str(gen_resolution_global), | |
| '--data.init_args.padding_factor', str(padding_factor), | |
| '--model.init_args.gen_subsample_manifold_iter', str(gen_subsample_manifold_iter), | |
| '--model.init_args.gen_refine_iter', str(gen_refine_iter), | |
| ] | |
| sys.argv = args | |
| try: | |
| subprocess.run(['python', 'ppsurf/pps.py'] + args[1:]) # need subprocess to spawn workers | |
| except Exception as e: | |
| gr.Warning("Reconstruction failed:\n{}".format(e)) | |
| print('Finished inference at {}'.format(datetime.datetime.now())) | |
| result_3d_model = out_file | |
| return result_3d_model | |
| def main(): | |
| description_header = '# PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction' | |
| description_col0 = '''## [Github](https://github.com/cg-tuwien/ppsurf) | |
| Supported file formats: | |
| - PLY, STL, OBJ and other mesh files, | |
| - XYZ as whitespace-separated text file, | |
| - NPY and NPZ (key='arr_0'), | |
| - LAS and LAZ (version 1.0-1.4), COPC and CRS. | |
| Best results for 50k-250k points. | |
| ''' | |
| description_col1 = '''## [Project Info](https://www.cg.tuwien.ac.at/research/publications/2024/erler_2024_ppsurf/) | |
| This method is meant for scans of single and few objects. | |
| Quality for scenes and landscapes will be lower. | |
| Inference takes up to 180 seconds. | |
| ''' | |
| # can't render many input types directly in Gradio Model3D | |
| # so we need to convert to supported format | |
| # Gradio can't draw point clouds anyway (2024-03-04), so we skip this for now | |
| # def convert_to_ply(input_point_cloud_upload: gr.utils.NamedString): | |
| # | |
| # # add absolute path to import dirs | |
| # import sys | |
| # import os | |
| # sys.path.append(os.path.abspath('ppsurf')) | |
| # | |
| # # import os | |
| # # os.chdir('ppsurf') | |
| # | |
| # print('Inputs:', input_point_cloud_upload, type(input_point_cloud_upload)) | |
| # input_shape: str = input_point_cloud_upload.name | |
| # if not input_shape.endswith('.ply'): | |
| # # load file | |
| # from ppsurf.source.occupancy_data_module import OccupancyDataModule | |
| # pts_np = OccupancyDataModule.load_pts(input_shape) | |
| # | |
| # # convert to ply | |
| # import trimesh | |
| # mesh = trimesh.Trimesh(vertices=pts_np[:, :3]) | |
| # input_shape = input_shape + '.ply' | |
| # mesh.export(input_shape) | |
| # | |
| # print('ls:\n', subprocess.run(['ls', os.path.dirname(input_shape)])) | |
| # | |
| # # show in viewer | |
| # print(type(input_tabs)) | |
| # # print(type(input_point_cloud_viewer)) | |
| # # input_tabs.selected = 'pc_viewer' | |
| # # input_point_cloud_viewer.value = input_shape | |
| if (SPACE_ID := os.getenv('SPACE_ID')) is not None: | |
| description_col1 += (f'\n<p>For faster inference without waiting in queue, ' | |
| f'you may duplicate the space and upgrade to GPU in settings. ' | |
| f'<a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true">' | |
| f'<img style="display: inline; margin-top: 0em; margin-bottom: 0em" ' | |
| f'src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>') | |
| with gr.Blocks(css='style.css') as demo: | |
| # descriptions | |
| gr.Markdown(description_header) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(description_col0) | |
| with gr.Column(): | |
| gr.Markdown(description_col1) | |
| # inputs and outputs | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_point_cloud_upload = gr.File(show_label=False, file_count='single') | |
| # with gr.Tabs() as input_tabs: # re-enable when Gradio supports point clouds | |
| # with gr.TabItem(label='Input Point Cloud Upload', id='pc_upload'): | |
| # input_point_cloud_upload.upload( | |
| # fn=convert_to_ply, | |
| # inputs=[ | |
| # input_point_cloud_upload, | |
| # ], | |
| # outputs=[ | |
| # # input_point_cloud_viewer, # not available here | |
| # ]) | |
| # with gr.TabItem(label='Input Point Cloud Viewer', id='pc_viewer'): | |
| # input_point_cloud_viewer = gr.Model3D(show_label=False) | |
| gen_resolution_global = gr.Slider( | |
| label='Grid Resolution (larger for more details)', | |
| minimum=17, maximum=513, value=129, step=2) | |
| padding_factor = gr.Slider( | |
| label='Padding Factor (larger if object is cut off at boundaries)', | |
| minimum=0, maximum=1.0, value=0.05, step=0.05) | |
| gen_subsample_manifold_iter = gr.Slider( | |
| label='Subsample Manifold Iterations (larger for larger point clouds)', | |
| minimum=3, maximum=30, value=10, step=1) | |
| gen_refine_iter = gr.Slider( | |
| label='Edge Refinement Iterations (larger for more details)', | |
| minimum=3, maximum=30, value=10, step=1) | |
| with gr.Column(): | |
| result_3d_model = gr.Model3D(label='Reconstructed 3D model') | |
| # progress_text = gr.Text(label='Progress') | |
| # with gr.Tabs(): | |
| # with gr.TabItem(label='Reconstructed 3D model'): | |
| # result_3d_model = gr.Model3D(show_label=False) | |
| # with gr.TabItem(label='Output mesh file'): | |
| # output_file = gr.File(show_label=False) | |
| with gr.Row(): | |
| run_button = gr.Button('Reconstruct with PPSurf') | |
| run_button.click(fn=run_on_gpu, | |
| inputs=[ | |
| input_point_cloud_upload, | |
| gen_resolution_global, | |
| padding_factor, | |
| gen_subsample_manifold_iter, | |
| gen_refine_iter, | |
| ], | |
| outputs=[ | |
| result_3d_model, | |
| # output_file, | |
| # progress_text, | |
| ]) | |
| demo.queue(max_size=5) | |
| demo.launch(debug=True) | |
| if __name__ == '__main__': | |
| print(os.environ) | |
| main() | |