""" Demo built with gradio """ import pickle as pkl import sys, os import os.path as osp from typing import Iterable, Optional from functools import partial import trimesh from torch.utils.data import DataLoader import cv2 from accelerate import Accelerator from tqdm import tqdm from glob import glob sys.path.append(os.getcwd()) import hydra import torch import numpy as np import imageio import gradio as gr import plotly.graph_objs as go import training_utils import traceback from configs.structured import ProjectConfig from demo import DemoRunner from dataset.demo_dataset import DemoDataset md_description=""" # HDM Interaction Reconstruction Demo ### Official Implementation of the paper \"Template Free Reconstruction of Human Object Interaction\", CVPR'24. [Project Page](https://virtualhumans.mpi-inf.mpg.de/procigen-hdm/)|[Code](https://github.com/xiexh20/HDM)|[Dataset](https://edmond.mpg.de/dataset.xhtml?persistentId=doi:10.17617/3.2VUEUS )|[Paper](https://virtualhumans.mpi-inf.mpg.de/procigen-hdm/paper-lowreso.pdf) Upload your own human object interaction image and get full 3D reconstruction! ## Citation ``` @inproceedings{xie2023template_free, title = {Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generation}, author = {Xie, Xianghui and Bhatnagar, Bharat Lal and Lenssen, Jan Eric and Pons-Moll, Gerard}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, } ``` """ def plot_points(colors, coords): """ use plotly to visualize 3D point with colors """ trace = go.Scatter3d(x=coords[:, 0], y=coords[:, 1], z=coords[:, 2], mode='markers', marker=dict( size=2, color=colors )) layout = go.Layout( scene=dict( xaxis=dict( title="", showgrid=False, zeroline=False, showline=False, ticks='', showticklabels=False ), yaxis=dict( title="", showgrid=False, zeroline=False, showline=False, ticks='', showticklabels=False ), zaxis=dict( title="", showgrid=False, zeroline=False, showline=False, ticks='', showticklabels=False ), ), margin=dict(l=0, r=0, b=0, t=0), showlegend=False ) fig = go.Figure(data=[trace], layout=layout) return fig def inference(runner: DemoRunner, cfg: ProjectConfig, rgb, mask_hum, mask_obj, std_coverage, input_seed, input_cls): """ given user input, run inference :param runner: :param cfg: :param rgb: (h, w, 3), np array :param mask_hum: (h, w, 3), np array :param mask_obj: (h, w, 3), np array :param std_coverage: float value, used to estimate camera translation :param input_seed: random seed :param input_cls: the object category of the input image :return: path to the 3D reconstruction, and an interactive 3D figure for visualizing the point cloud """ log = "" try: # Set random seed training_utils.set_seed(int(input_seed)) data = DemoDataset([], (cfg.dataset.image_size, cfg.dataset.image_size), std_coverage) batch = data.image2batch(rgb, mask_hum, mask_obj) if input_cls != 'general': log += f"Reloading fine-tuned checkpoint of category {input_cls}\n" runner.reload_checkpoint(input_cls) out_stage1, out_stage2 = runner.forward_batch(batch, cfg) points = out_stage2.points_packed().cpu().numpy() colors = out_stage2.features_packed().cpu().numpy() fig = plot_points(colors, points) # save tmp point cloud outdir = './results' os.makedirs(outdir, exist_ok=True) trimesh.PointCloud(points, colors).export(outdir + f"/pred_std{std_coverage}_seed{input_seed}_stage2_{input_cls}.ply") trimesh.PointCloud(out_stage1.points_packed().cpu().numpy(), out_stage1.features_packed().cpu().numpy()).export( outdir + f"/pred_std{std_coverage}_seed{input_seed}_stage1_{input_cls}.ply") log += 'Successfully reconstructed the image.' outfile = outdir + f"/pred_std{std_coverage}_seed{input_seed}_stage2_{input_cls}.ply" except Exception as e: log = traceback.format_exc() fig, outfile = None, None return fig, outfile, log @hydra.main(config_path='configs', config_name='configs', version_base='1.1') def main(cfg: ProjectConfig): # Setup model runner = DemoRunner(cfg) # runner = None # without model initialization, it shows one line of thumbnail # TODO: add instructions on how to get masks # TODO: add instructions on how to use the demo, input output, example outputs etc. # Setup interface demo = gr.Blocks(title="HDM Interaction Reconstruction Demo") with demo: gr.Markdown(md_description) gr.HTML("""

HDM Demo

""") gr.HTML("""

Instruction: Upload RGB, human, object masks and then click reconstruct.

""") gr.HTML("""

You can use these methods to obtain the masks:

""") # Input data with gr.Row(): input_rgb = gr.Image(label='Input RGB', type='numpy') input_mask_hum = gr.Image(label='Human mask', type='numpy') with gr.Row(): input_mask_obj = gr.Image(label='Object mask', type='numpy') with gr.Column(): # TODO: add hint for this value here input_std = gr.Number(label='Gaussian std coverage', value=3.5) input_seed = gr.Number(label='Random seed', value=42) # TODO: add description outside label input_cls = gr.Dropdown(label='Object category (we have fine tuned the model for specific categories, ' 'reconstructing with these model should lead to better result ' 'for specific categories.) ', choices=['general', 'backpack', 'ball', 'bottle', 'box', 'chair', 'skateboard', 'suitcase', 'table'], value='general') # Output visualization with gr.Row(): pc_plot = gr.Plot(label="Reconstructed point cloud") out_pc_download = gr.File(label="3D reconstruction for download") # this allows downloading with gr.Row(): out_log = gr.TextArea(label='Output log') gr.HTML("""
""") # Control with gr.Row(): button_recon = gr.Button("Start Reconstruction", interactive=True, variant='secondary') button_recon.click(fn=partial(inference, runner, cfg), inputs=[input_rgb, input_mask_hum, input_mask_obj, input_std, input_seed, input_cls], outputs=[pc_plot, out_pc_download, out_log]) gr.HTML("""
""") # Example input example_dir = cfg.run.code_dir_abs+"/examples" rgb, ps, obj = 'k1.color.jpg', 'k1.person_mask.png', 'k1.obj_rend_mask.png' example_images = gr.Examples([ [f"{example_dir}/017450/{rgb}", f"{example_dir}/017450/{ps}", f"{example_dir}/017450/{obj}", 3.0, 42, 'skateboard'], [f"{example_dir}/002446/{rgb}", f"{example_dir}/002446/{ps}", f"{example_dir}/002446/{obj}", 3.0, 42, 'ball'], [f"{example_dir}/053431/{rgb}", f"{example_dir}/053431/{ps}", f"{example_dir}/053431/{obj}", 3.8, 42, 'chair'], [f"{example_dir}/158107/{rgb}", f"{example_dir}/158107/{ps}", f"{example_dir}/158107/{obj}", 3.8, 42, 'chair'], ], inputs=[input_rgb, input_mask_hum, input_mask_obj, input_std, input_seed, input_cls],) # demo.launch(share=True) # Enabling queue for runtime>60s, see: https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062 demo.queue().launch() if __name__ == '__main__': from argparse import ArgumentParser # parser = ArgumentParser() # parser.add_argument('-share', default=False, action='store_true', help='allow a temporal public url') # args = parser.parse_args() main()