app.py

import gradio as gr
import spaces
import os
import uuid
import subprocess
import torch 

print("check torch and cuda version, they must be 2.4.0 + 12.1:")
print(torch.__version__)
print(torch.version.cuda)

# download model
print("Downloading model weights")
os.system('wget -q https://huggingface.co/ThunderVVV/HaWoR/resolve/main/external/metric_depth_vit_large_800k.pth -P ./thirdparty/Metric3D/weights/')
os.system('wget -q https://huggingface.co/ThunderVVV/HaWoR/resolve/main/external/droid.pth -P ./weights/external/')
os.system('wget -q https://huggingface.co/ThunderVVV/HaWoR/resolve/main/external/detector.pt -P ./weights/external/')
os.system('wget -q https://huggingface.co/ThunderVVV/HaWoR/resolve/main/hawor/checkpoints/hawor.ckpt -P ./weights/hawor/checkpoints/')
os.system('wget -q https://huggingface.co/ThunderVVV/HaWoR/resolve/main/hawor/checkpoints/infiller.pt -P ./weights/hawor/checkpoints/')
os.system('wget -q https://huggingface.co/ThunderVVV/HaWoR/resolve/main/hawor/model_config.yaml -P ./weights/hawor/')


def install_cuda_toolkit():
    CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda_12.1.0_530.30.02_linux.run"
    CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
    subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
    subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
    subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])

    os.environ["CUDA_HOME"] = "/usr/local/cuda"
    os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
    os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
        os.environ["CUDA_HOME"],
        "" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"],
    )
    os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
    

print("Compling other packages")
install_cuda_toolkit()
os.system('pip install ./thirdparty/DROID-SLAM')
os.system('pip install ./thirdparty/DROID-SLAM/thirdparty/lietorch')
os.environ["FORCE_CUDA"] = "1"
os.system('pip install git+https://github.com/facebookresearch/pytorch3d.git@stable')

import numpy as np
import joblib
import cv2
import imageio
from easydict import EasyDict
from scripts.scripts_test_video.detect_track_video import detect_track_video
from scripts.scripts_test_video.hawor_video import hawor_motion_estimation, hawor_infiller
from scripts.scripts_test_video.hawor_slam import hawor_slam
from hawor.utils.process import get_mano_faces, run_mano, run_mano_left
from lib.eval_utils.custom_utils import load_slam_cam
from lib.vis.run_vis2 import lookat_matrix, run_vis2_on_video, run_vis2_on_video_cam
from lib.vis.renderer_world import Renderer

# @spaces.GPU(duration=200)
def render_reconstruction(input_video, img_focal): 
    args = EasyDict()
    args.video_path = input_video
    args.input_type = 'file'
    args.checkpoint = './weights/hawor/checkpoints/hawor.ckpt'
    args.infiller_weight = './weights/hawor/checkpoints/infiller.pt'
    args.vis_mode = 'world'
    args.img_focal = img_focal
    
    start_idx, end_idx, seq_folder, imgfiles = detect_track_video(args)

    if os.path.exists(f'{seq_folder}/tracks_{start_idx}_{end_idx}/frame_chunks_all.npy'):
        print("skip hawor motion estimation")
        frame_chunks_all = joblib.load(f'{seq_folder}/tracks_{start_idx}_{end_idx}/frame_chunks_all.npy')
        img_focal = args.img_focal
    else:
        frame_chunks_all, img_focal = hawor_motion_estimation(args, start_idx, end_idx, seq_folder)

    slam_path = os.path.join(seq_folder, f"SLAM/hawor_slam_w_scale_{start_idx}_{end_idx}.npz")
    if not os.path.exists(slam_path):
        hawor_slam(args, start_idx, end_idx)
    R_w2c_sla_all, t_w2c_sla_all, R_c2w_sla_all, t_c2w_sla_all = load_slam_cam(slam_path)

    out_path = infiller_and_vis(args, start_idx, end_idx, frame_chunks_all, R_w2c_sla_all, t_w2c_sla_all, R_c2w_sla_all, t_c2w_sla_all, seq_folder, imgfiles)
    return out_path

@spaces.GPU(duration=80)
def infiller_and_vis(args, start_idx, end_idx, frame_chunks_all, R_w2c_sla_all, t_w2c_sla_all, R_c2w_sla_all, t_c2w_sla_all, seq_folder, imgfiles):
    pred_trans, pred_rot, pred_hand_pose, pred_betas, pred_valid = hawor_infiller(args, start_idx, end_idx, frame_chunks_all)

    # vis sequence for this video
    hand2idx = {
        "right": 1,
        "left": 0
    }
    vis_start = 0
    vis_end = pred_trans.shape[1] - 1
            
    # get faces
    faces = get_mano_faces()
    faces_new = np.array([[92, 38, 234],
            [234, 38, 239],
            [38, 122, 239],
            [239, 122, 279],
            [122, 118, 279],
            [279, 118, 215],
            [118, 117, 215],
            [215, 117, 214],
            [117, 119, 214],
            [214, 119, 121],
            [119, 120, 121],
            [121, 120, 78],
            [120, 108, 78],
            [78, 108, 79]])
    faces_right = np.concatenate([faces, faces_new], axis=0)

    # get right hand vertices
    hand = 'right'
    hand_idx = hand2idx[hand]
    pred_glob_r = run_mano(pred_trans[hand_idx:hand_idx+1, vis_start:vis_end], pred_rot[hand_idx:hand_idx+1, vis_start:vis_end], pred_hand_pose[hand_idx:hand_idx+1, vis_start:vis_end], betas=pred_betas[hand_idx:hand_idx+1, vis_start:vis_end])
    right_verts = pred_glob_r['vertices'][0]
    right_dict = {
            'vertices': right_verts.unsqueeze(0),
            'faces': faces_right,
        }

    # get left hand vertices
    faces_left = faces_right[:,[0,2,1]]
    hand = 'left'
    hand_idx = hand2idx[hand]
    pred_glob_l = run_mano_left(pred_trans[hand_idx:hand_idx+1, vis_start:vis_end], pred_rot[hand_idx:hand_idx+1, vis_start:vis_end], pred_hand_pose[hand_idx:hand_idx+1, vis_start:vis_end], betas=pred_betas[hand_idx:hand_idx+1, vis_start:vis_end])
    left_verts = pred_glob_l['vertices'][0]
    left_dict = {
            'vertices': left_verts.unsqueeze(0),
            'faces': faces_left,
        }

    R_x = torch.tensor([[1,  0,  0],
                        [0, -1,  0],
                        [0,  0, -1]]).float()
    R_c2w_sla_all = torch.einsum('ij,njk->nik', R_x, R_c2w_sla_all)
    t_c2w_sla_all = torch.einsum('ij,nj->ni', R_x, t_c2w_sla_all)
    R_w2c_sla_all = R_c2w_sla_all.transpose(-1, -2)
    t_w2c_sla_all = -torch.einsum("bij,bj->bi", R_w2c_sla_all, t_c2w_sla_all)
    left_dict['vertices'] = torch.einsum('ij,btnj->btni', R_x, left_dict['vertices'].cpu())
    right_dict['vertices'] = torch.einsum('ij,btnj->btni', R_x, right_dict['vertices'].cpu())
    
    # simple visualization
    bin_size = 128
    max_faces_per_bin = 20000
    img = cv2.imread(imgfiles[0])
    renderer = Renderer(img.shape[1], img.shape[0], 1800, 'cuda', 
                    bin_size=bin_size, max_faces_per_bin=max_faces_per_bin)

    output_pth = os.path.join(seq_folder, f"vis_{vis_start}_{vis_end}")
    if not os.path.exists(output_pth):
        os.makedirs(output_pth)
    image_names = imgfiles[vis_start:vis_end]
    print(f"vis {vis_start} to {vis_end}")
    # vis_video_path = run_vis2_on_video(left_dict, right_dict, output_pth, img_focal, image_names, R_c2w=R_c2w_sla_all[vis_start:vis_end], t_c2w=t_c2w_sla_all[vis_start:vis_end], interactive=False)
    faces_left = torch.from_numpy(faces_left).cuda()
    faces_right = torch.from_numpy(faces_right).cuda()
    faces_all = torch.stack((faces_left, faces_right))

    side_source = torch.tensor([0.463, -0.478, 2.456])
    side_target = torch.tensor([0.026, -0.481, -3.184])
    up = torch.tensor([1.0, 0.0, 0.0])
    view_camera = lookat_matrix(side_source, side_target, up)
    cam_R = view_camera[:3, :3].unsqueeze(0).cuda()
    cam_T = view_camera[:3, 3].unsqueeze(0).cuda()
    vis_video_imgs = []
    out_path = f'{seq_folder}/vis_output_{str(uuid.uuid4())}.mp4' 
    writer = imageio.get_writer(out_path, fps=30, mode='I', 
                        format='FFMPEG', macro_block_size=1)
    renderer.set_ground(100, 0, 0)
    for img_i, _ in enumerate(image_names):

        vertices_left = left_dict['vertices'][:, img_i]
        vertices_right = right_dict['vertices'][:, img_i]

        cameras, lights = renderer.create_camera_from_cv(cam_R, cam_T)
        verts_color = torch.tensor([0.207, 0.596, 0.792, 1.0]).unsqueeze(0).repeat(2, 1)
        vertices_i = torch.stack((vertices_left, vertices_right))
        rend, _ = renderer.render_multiple(vertices_i.cuda(), faces_all.cuda(), verts_color.cuda(), cameras, lights)

        writer.append_data(rend)

    writer.close()
    print("finish")
        
    return out_path     



header = ('''
<div class="embed_hidden" style="text-align: center;">
    <h1> <b>HaWoR</b>: World-Space Hand Motion Reconstruction from Egocentric Videos</h1>
    <h3>
        <a href="" target="_blank" rel="noopener noreferrer">Jinglei Zhang</a><sup>1</sup>,        
        <a href="https://jiankangdeng.github.io/" target="_blank" rel="noopener noreferrer">Jiankang Deng</a><sup>2</sup>,
        <br>
        <a href="https://scholar.google.com/citations?user=syoPhv8AAAAJ&hl=en" target="_blank" rel="noopener noreferrer">Chao Ma</a><sup>1</sup>,
        <a href="https://rolpotamias.github.io" target="_blank" rel="noopener noreferrer">Rolandos Alexandros Potamias</a><sup>2</sup>
    </h3>
    <h3>
        <sup>1</sup>Shanghai Jiao Tong University;
        <sup>2</sup>Imperial College London
    </h3>
</div>
<div style="display:flex; gap: 0.3rem; justify-content: center; align-items: center;" align="center">
<a href=''><img src='https://img.shields.io/badge/Arxiv-arxiv-A42C25?style=flat&logo=arXiv&logoColor=A42C25'></a> 
<a href=''><img src='https://img.shields.io/badge/Paper-PDF-yellow?style=flat&logo=arXiv&logoColor=yellow'></a> 
<a href='https://hawor-project.github.io/'><img src='https://img.shields.io/badge/Project-Page-%23df5b46?style=flat&logo=Google%20chrome&logoColor=%23df5b46'></a> 
<a href='https://github.com/ThunderVVV/HaWoR'><img src='https://img.shields.io/badge/GitHub-Code-black?style=flat&logo=github&logoColor=white'></a> 
<a href='https://huggingface.co/spaces/ThunderVVV/HaWoR'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-green'></a>
''')


with gr.Blocks(title="HaWoR: World-Space Hand Motion Reconstruction from Egocentric Videos", css=".gradio-container") as demo:

    gr.Markdown(header)

    with gr.Row():
        with gr.Column():
            input_video = gr.Video(label="Input video", sources=["upload"])
            img_focal = gr.Number(label="Focal Length", value=600)
            # threshold = gr.Slider(value=0.3, minimum=0.05, maximum=0.95, step=0.05, label='Detection Confidence Threshold')
            #nms = gr.Slider(value=0.5, minimum=0.05, maximum=0.95, step=0.05, label='IoU NMS Threshold')
            submit = gr.Button("Submit", variant="primary")
        
        
        with gr.Column():
            reconstruction = gr.Video(label="Reconstruction",show_download_button=True)
            # hands_detected = gr.Textbox(label="Hands Detected")
    
        submit.click(fn=render_reconstruction, inputs=[input_video, img_focal], outputs=[reconstruction])

    with gr.Row():
        
        example_images = gr.Examples([
            ['./example/video_0.mp4'],
            ['./example/segment_017.mp4'],
            ['./example/segment_018.mp4']
            ], 
            inputs=input_video)
    
demo.launch(debug=True)