app.py

import sys
import os
import OpenGL.GL as gl
os.environ["PYOPENGL_PLATFORM"] = "egl"
os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"
os.system('pip install /home/user/app/pyrender')

sys.argv = ['VQ-Trans/GPT_eval_multi.py']
os.chdir('VQ-Trans')

sys.path.append('/home/user/app/VQ-Trans')
sys.path.append('/home/user/app/pyrender')

import options.option_transformer as option_trans
from huggingface_hub import snapshot_download
model_path = snapshot_download(repo_id="vumichien/T2M-GPT")

args = option_trans.get_args_parser()

args.dataname = 't2m'
args.resume_pth = f'{model_path}/VQVAE/net_last.pth'
args.resume_trans = f'{model_path}/VQTransformer_corruption05/net_best_fid.pth'
args.down_t = 2
args.depth = 3
args.block_size = 51

import clip
import torch
import numpy as np
import models.vqvae as vqvae
import models.t2m_trans as trans
from utils.motion_process import recover_from_ric
import visualization.plot_3d_global as plot_3d
from models.rotation2xyz import Rotation2xyz
import numpy as np
from trimesh import Trimesh
import gc

import torch
from visualize.simplify_loc2rot import joints2smpl
import pyrender
# import matplotlib.pyplot as plt

import io
import imageio
from shapely import geometry
import trimesh
from pyrender.constants import RenderFlags
import math
# import ffmpeg
# from PIL import Image
import hashlib
import gradio as gr
import moviepy.editor as mp

## load clip model and datasets
is_cuda = torch.cuda.is_available()
device = torch.device("cuda" if is_cuda else "cpu")
print(device)
clip_model, clip_preprocess = clip.load("ViT-B/32", device=device, jit=False, download_root='./')  # Must set jit=False for training

if is_cuda:
    clip.model.convert_weights(clip_model)
    
clip_model.eval()
for p in clip_model.parameters():
    p.requires_grad = False

net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers
                       args.nb_code,
                       args.code_dim,
                       args.output_emb_width,
                       args.down_t,
                       args.stride_t,
                       args.width,
                       args.depth,
                       args.dilation_growth_rate)


trans_encoder = trans.Text2Motion_Transformer(num_vq=args.nb_code, 
                                embed_dim=1024, 
                                clip_dim=args.clip_dim, 
                                block_size=args.block_size, 
                                num_layers=9, 
                                n_head=16, 
                                drop_out_rate=args.drop_out_rate, 
                                fc_rate=args.ff_rate)


print('loading checkpoint from {}'.format(args.resume_pth))
ckpt = torch.load(args.resume_pth, map_location='cpu')
net.load_state_dict(ckpt['net'], strict=True)
net.eval()
    
print('loading transformer checkpoint from {}'.format(args.resume_trans))
ckpt = torch.load(args.resume_trans, map_location='cpu')
trans_encoder.load_state_dict(ckpt['trans'], strict=True)
trans_encoder.eval()

mean = torch.from_numpy(np.load(f'{model_path}/meta/mean.npy'))
std = torch.from_numpy(np.load(f'{model_path}/meta/std.npy'))

if is_cuda:
    net.cuda()
    trans_encoder.cuda()
    mean = mean.cuda()
    std = std.cuda()

def render(motions, device_id=0, name='test_vis'):
    frames, njoints, nfeats = motions.shape
    MINS = motions.min(axis=0).min(axis=0)
    MAXS = motions.max(axis=0).max(axis=0)

    height_offset = MINS[1]
    motions[:, :, 1] -= height_offset
    trajec = motions[:, 0, [0, 2]]
    is_cuda = torch.cuda.is_available()
    # device = torch.device("cuda" if is_cuda else "cpu")
    j2s = joints2smpl(num_frames=frames, device_id=0, cuda=is_cuda)
    rot2xyz = Rotation2xyz(device=device)
    faces = rot2xyz.smpl_model.faces

    if not os.path.exists(f'output/{name}_pred.pt'): 
        print(f'Running SMPLify, it may take a few minutes.')
        motion_tensor, opt_dict = j2s.joint2smpl(motions)  # [nframes, njoints, 3]

        vertices = rot2xyz(torch.tensor(motion_tensor).clone(), mask=None,
                                        pose_rep='rot6d', translation=True, glob=True,
                                        jointstype='vertices',
                                        vertstrans=True)
        vertices = vertices.detach().cpu()
        torch.save(vertices, f'output/{name}_pred.pt')
    else:
        vertices = torch.load(f'output/{name}_pred.pt')
    frames = vertices.shape[3] # shape: 1, nb_frames, 3, nb_joints
    print(vertices.shape)
    MINS = torch.min(torch.min(vertices[0], axis=0)[0], axis=1)[0]
    MAXS = torch.max(torch.max(vertices[0], axis=0)[0], axis=1)[0]

    out_list = []
    
    minx = MINS[0] - 0.5
    maxx = MAXS[0] + 0.5
    minz = MINS[2] - 0.5 
    maxz = MAXS[2] + 0.5
    polygon = geometry.Polygon([[minx, minz], [minx, maxz], [maxx, maxz], [maxx, minz]])
    polygon_mesh = trimesh.creation.extrude_polygon(polygon, 1e-5)

    vid = []
    for i in range(frames):
        if i % 10 == 0:
            print(i)

        mesh = Trimesh(vertices=vertices[0, :, :, i].squeeze().tolist(), faces=faces)

        base_color = (0.11, 0.53, 0.8, 0.5)
        ## OPAQUE rendering without alpha
        ## BLEND rendering consider alpha 
        material = pyrender.MetallicRoughnessMaterial(
            metallicFactor=0.7,
            alphaMode='OPAQUE',
            baseColorFactor=base_color
        )


        mesh = pyrender.Mesh.from_trimesh(mesh, material=material)

        polygon_mesh.visual.face_colors = [0, 0, 0, 0.21]
        polygon_render = pyrender.Mesh.from_trimesh(polygon_mesh, smooth=False)

        bg_color = [1, 1, 1, 0.8]
        scene = pyrender.Scene(bg_color=bg_color, ambient_light=(0.4, 0.4, 0.4))
        
        sx, sy, tx, ty = [0.75, 0.75, 0, 0.10]

        camera = pyrender.PerspectiveCamera(yfov=(np.pi / 3.0))

        light = pyrender.DirectionalLight(color=[1,1,1], intensity=300)

        scene.add(mesh)

        c = np.pi / 2

        scene.add(polygon_render, pose=np.array([[ 1, 0, 0, 0],

        [ 0, np.cos(c), -np.sin(c), MINS[1].cpu().numpy()],

        [ 0, np.sin(c), np.cos(c), 0],

        [ 0, 0, 0, 1]]))

        light_pose = np.eye(4)
        light_pose[:3, 3] = [0, -1, 1]
        scene.add(light, pose=light_pose.copy())

        light_pose[:3, 3] = [0, 1, 1]
        scene.add(light, pose=light_pose.copy())

        light_pose[:3, 3] = [1, 1, 2]
        scene.add(light, pose=light_pose.copy())


        c = -np.pi / 6

        scene.add(camera, pose=[[ 1, 0, 0, (minx+maxx).cpu().numpy()/2],

                                [ 0, np.cos(c), -np.sin(c), 1.5],

                                [ 0, np.sin(c), np.cos(c), max(4, minz.cpu().numpy()+(1.5-MINS[1].cpu().numpy())*2, (maxx-minx).cpu().numpy())],

                                [ 0, 0, 0, 1]
                                ])
        
        # render scene
        r = pyrender.OffscreenRenderer(960, 960)

        color, _ = r.render(scene, flags=RenderFlags.RGBA)
        # Image.fromarray(color).save(outdir+'/'+name+'_'+str(i)+'.png')

        vid.append(color)

        r.delete()

    out = np.stack(vid, axis=0)
    imageio.mimwrite(f'output/results.gif', out, duration=50)
    out_video = mp.VideoFileClip(f'output/results.gif')
    out_video.write_videofile("output/results.mp4")
    del out, vertices
    return f'output/results.mp4'

def predict(clip_text, method='fast'):
    gc.collect()
    print('prompt text instruction: {}'.format(clip_text))
    if torch.cuda.is_available():
        text = clip.tokenize([clip_text], truncate=True).cuda()
    else:
        text = clip.tokenize([clip_text], truncate=True)
    feat_clip_text = clip_model.encode_text(text).float()
    index_motion = trans_encoder.sample(feat_clip_text[0:1], False)
    pred_pose = net.forward_decoder(index_motion)
    pred_xyz = recover_from_ric((pred_pose*std+mean).float(), 22)
    output_name = hashlib.md5(clip_text.encode()).hexdigest()
    if method == 'fast':
        xyz = pred_xyz.reshape(1, -1, 22, 3)
        pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(), title_batch=None, outname=[f'output/results.gif'])
        out_video = mp.VideoFileClip("output/results.gif")
        out_video.write_videofile("output/results.mp4")
        return f'output/results.mp4'
    elif method == 'slow':
        output_path = render(pred_xyz.detach().cpu().numpy().squeeze(axis=0), device_id=0, name=output_name)
        return output_path

        
# ---- Gradio Layout -----
video_out = gr.Video(label="Motion", mirror_webcam=False, interactive=False) 
demo = gr.Blocks()
demo.encrypt = False

with demo:
    gr.Markdown('''
            <div>
            <h1 style='text-align: center'>Generating Human Motion from Textual Descriptions (T2M-GPT)</h1>
            This space uses <a href='https://mael-zys.github.io/T2M-GPT/' target='_blank'><b>T2M-GPT models</b></a> based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions🤗
            </div>
        ''')
    with gr.Row():
        with gr.Column():
            gr.Markdown('''
            <figure>
              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow1.gif" alt="Demo Slow", width="425", height=480/>
              <figcaption> a man starts off in an up right position with botg arms extended out by his sides, he then brings his arms down to his body and claps his hands together. after this he wals down amd the the left where he proceeds to sit on a seat
              </figcaption>
            </figure>
            ''')
        with gr.Column():
            gr.Markdown('''
            <figure>
              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow2.gif" alt="Demo Slow 2", width="425", height=480/>
              <figcaption> a person puts their hands together, leans forwards slightly then swings the arms from right to left
              </figcaption>
            </figure>
            ''')
        with gr.Column():
            gr.Markdown('''
            <figure>
              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow3.gif" alt="Demo Slow 3", width="425", height=480/>
              <figcaption> a man is practicing the waltz with a partner
              </figcaption>
            </figure>
            ''')
    with gr.Row():
        with gr.Column():
            gr.Markdown('''
            ### Generate human motion by **T2M-GPT**
            ##### Step 1. Give prompt text describing human motion
            ##### Step 2. Choice method to render output (Fast: Sketch skeleton; Slow: SMPL mesh, only work with GPU and running time around 2 mins)
            ##### Step 3. Generate output and enjoy
            ''')
    with gr.Column():
        with gr.Row():
            text_prompt = gr.Textbox(label="Text prompt", lines=1, interactive=True)
            method = gr.Dropdown(["slow", "fast"], label="Method", value="slow")
        with gr.Row():
            generate_btn = gr.Button("Generate")
            generate_btn.click(predict, [text_prompt, method], [video_out], api_name="generate")
        with gr.Row():  
            video_out.render()
    with gr.Row(): 
        gr.Markdown('''
            ### You can test by following examples:
            ''')
    examples = gr.Examples(
            examples=[
                ["a person jogs in place, slowly at first, then increases speed. they then back up and squat down.", "slow"],
                ["a man steps forward and does a handstand", "slow"],
                ["a man rises from the ground, walks in a circle and sits back down on the ground", "slow"],
                ["a man starts off in an up right position with botg arms extended out by his sides, he then brings his arms down to his body and claps his hands together. after this he wals down amd the the left where he proceeds to sit on a seat", "slow"],
                ["a person puts their hands together, leans forwards slightly then swings the arms from right to left","slow"],
                ["a man is practicing the waltz with a partner","slow"],
            ],
            label="Examples", 
            inputs=[text_prompt, method],
            outputs=[video_out],
            fn=predict,
            cache_examples=True,
        )

        
demo.launch(debug=True)