main.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('./VQ-Trans')
sys.path.append('./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
from datetime import datetime

## 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 ensure_directory(path):
    """Tạo thư mục nếu chưa tồn tại"""
    if not os.path.exists(path):
        os.makedirs(path)
        print(f"Created directory: {path}")

def get_output_path(output_dir, filename, extension):
    """Tạo đường dẫn đầy đủ cho file output"""
    ensure_directory(output_dir)
    if not filename.endswith(extension):
        filename += extension
    return os.path.join(output_dir, filename)

def render(motions, output_dir='output', filename='results', device_id=0):
    """
    Render motion với tùy chọn thư mục và tên file
    
    Args:
        motions: Motion data
        output_dir: Thư mục lưu kết quả (mặc định: 'output')
        filename: Tên file không có extension (mặc định: 'results')
        device_id: GPU device ID
    """
    try:
        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()
        j2s = joints2smpl(num_frames=frames, device_id=0, cuda=is_cuda)
        rot2xyz = Rotation2xyz(device=device)
        faces = rot2xyz.smpl_model.faces

        # Tạo đường dẫn cho file .pt
        pt_path = get_output_path(output_dir, f'{filename}_pred', '.pt')
        
        if not os.path.exists(pt_path): 
            print(f'Running SMPLify, it may take a few minutes.')
            motion_tensor, opt_dict = j2s.joint2smpl(motions)

            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, pt_path)
        else:
            vertices = torch.load(pt_path)
        
        frames = vertices.shape[3]
        print(f"Vertices shape: {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(f"Processing frame {i}/{frames}")

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

                base_color = (0.11, 0.53, 0.8, 0.5)
                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=np.array([
                    [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]
                ]))
                
                r = pyrender.OffscreenRenderer(960, 960)
                color, _ = r.render(scene, flags=RenderFlags.RGBA)
                vid.append(color)
                r.delete()
                
            except Exception as e:
                print(f"Error processing frame {i}: {str(e)}")
                continue

        if not vid:
            raise Exception("No frames were successfully processed")
            
        out = np.stack(vid, axis=0)
        
        # Tạo đường dẫn cho file GIF và MP4
        gif_path = get_output_path(output_dir, filename, '.gif')
        mp4_path = get_output_path(output_dir, filename, '.mp4')
        
        imageio.mimwrite(gif_path, out, duration=50)
        out_video = mp.VideoFileClip(gif_path)
        out_video.write_videofile(mp4_path)
        
        # Set file permissions để đảm bảo file có thể được truy cập
        try:
            os.chmod(gif_path, 0o644)
            os.chmod(mp4_path, 0o644)
        except Exception as e:
            print(f"Warning: Could not set file permissions: {str(e)}")
        
        print(f"Results saved to: {mp4_path}")
        
        del out, vertices
        return mp4_path
        
    except Exception as e:
        print(f"Error in render function: {str(e)}")
        # Fallback to fast rendering if slow rendering fails
        print("Falling back to fast rendering...")
        return None

def predict(clip_text, method='fast', output_dir='output', filename=''):
    """
    Predict motion with custom output settings
    
    Args:
        clip_text: Text prompt
        method: 'fast' or 'slow'
        output_dir: Output directory
        filename: Custom filename (if empty, will use hash or timestamp)
    
    Returns:
        tuple: (video_path, download_path) để hiển thị và download
    """
    gc.collect()
    print('prompt text instruction: {}'.format(clip_text))
    
    # Đảm bảo output_dir là absolute path và trong thư mục hiện tại
    if not os.path.isabs(output_dir):
        output_dir = os.path.abspath(output_dir)
    
    # Tạo tên file nếu không được cung cấp
    if not filename.strip():
        if method == 'fast':
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"motion_{timestamp}"
        else:
            filename = hashlib.md5(clip_text.encode()).hexdigest()
    
    # Xử lý text với CLIP
    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)
    
    if method == 'fast':
        xyz = pred_xyz.reshape(1, -1, 22, 3)
        
        # Tạo đường dẫn cho fast method
        gif_path = get_output_path(output_dir, filename, '.gif')
        mp4_path = get_output_path(output_dir, filename, '.mp4')
        
        pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(), title_batch=None, outname=[gif_path])
        out_video = mp.VideoFileClip(gif_path)
        out_video.write_videofile(mp4_path)
        
        # Set file permissions để đảm bảo file có thể được truy cập
        os.chmod(mp4_path, 0o644)
        
        print(f"Fast render results saved to: {mp4_path}")
        return mp4_path, mp4_path  # Return both for display and download
        
    elif method == 'slow':
        try:
            output_path = render(pred_xyz.detach().cpu().numpy().squeeze(axis=0), 
                               output_dir=output_dir, 
                               filename=filename, 
                               device_id=0)
            
            if output_path is None:
                # Fallback to fast rendering if slow rendering fails
                print("Slow rendering failed, using fast rendering instead...")
                xyz = pred_xyz.reshape(1, -1, 22, 3)
                
                gif_path = get_output_path(output_dir, filename, '.gif')
                mp4_path = get_output_path(output_dir, filename, '.mp4')
                
                pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(), title_batch=None, outname=[gif_path])
                out_video = mp.VideoFileClip(gif_path)
                out_video.write_videofile(mp4_path)
                
                # Set file permissions
                try:
                    os.chmod(mp4_path, 0o644)
                except:
                    pass
                
                return mp4_path, mp4_path
            
            # Set file permissions để đảm bảo file có thể được truy cập
            try:
                os.chmod(output_path, 0o644)
            except Exception as e:
                print(f"Warning: Could not set file permissions: {str(e)}")
            
            return output_path, output_path  # Return both for display and download
            
        except Exception as e:
            print(f"Error in slow rendering: {str(e)}")
            # Fallback to fast rendering
            print("Falling back to fast rendering...")
            xyz = pred_xyz.reshape(1, -1, 22, 3)
            
            gif_path = get_output_path(output_dir, filename, '.gif')
            mp4_path = get_output_path(output_dir, filename, '.mp4')
            
            pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(), title_batch=None, outname=[gif_path])
            out_video = mp.VideoFileClip(gif_path)
            out_video.write_videofile(mp4_path)
            
            # Set file permissions
            try:
                os.chmod(mp4_path, 0o644)
            except:
                pass
            
            return mp4_path, mp4_path

# ---- Gradio Layout -----
video_out = gr.Video(label="Motion", mirror_webcam=False, interactive=False) 
download_file = gr.File(label="Download Video", visible=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. Choose method to render output (Fast: Sketch skeleton; Slow: SMPL mesh)
            ##### Step 3. Specify output directory and filename (optional)
            ##### Step 4. Generate output and enjoy
            ##### Step 5. Download your video using the download button below
            ''')
    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():
            output_dir = gr.Textbox(label="Output Directory", value="output", interactive=True)
            filename = gr.Textbox(label="Filename (without extension)", placeholder="Leave empty for auto-generated name", interactive=True)
        with gr.Row():
            generate_btn = gr.Button("Generate")
        with gr.Row():  
            video_out.render()
        with gr.Row():
            download_file.render()
        
        # Kết nối button với function và output
        generate_btn.click(
            predict, 
            [text_prompt, method, output_dir, filename], 
            [video_out, download_file], 
            api_name="generate"
        )
        
    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", "output", "jogging_motion"],
                ["a man steps forward and does a handstand", "slow", "output", "handstand_motion"],
                ["a man rises from the ground, walks in a circle and sits back down on the ground", "slow", "output", "circle_walk"],
                ["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", "output", "clap_and_sit"],
                ["a person puts their hands together, leans forwards slightly then swings the arms from right to left","slow", "output", "swing_arms"],
                ["a man is practicing the waltz with a partner","slow", "output", "waltz_dance"],
            ],
            label="Examples", 
            inputs=[text_prompt, method, output_dir, filename],
            outputs=[video_out, download_file],
            fn=predict,
            cache_examples=True,
        )

        
demo.launch(debug=True, server_name="0.0.0.0", server_port=8000, inbrowser=True, share=True, 
           allowed_paths=[os.path.abspath("output"), os.path.abspath("./")])