app.py

import os
os.environ['DISPLAY'] = ':0.0'
os.environ['PYOPENGL_PLATFORM'] = 'egl'
os.system('pip install /home/user/app/pyrender')
os.system('pip install eventlet')
os.system('pip install triangle==20220202')

import gradio as gr
import random
import torch
import time
import cv2
import numpy as np
import OpenGL.GL as gl
import imageio
import pytorch_lightning as pl
import moviepy.editor as mp
from pathlib import Path
from mGPT.data.build_data import build_data
from mGPT.models.build_model import build_model
from mGPT.config import parse_args
from scipy.spatial.transform import Rotation as RRR
import mGPT.render.matplot.plot_3d_global as plot_3d
from mGPT.render.pyrender.hybrik_loc2rot import HybrIKJointsToRotmat
from mGPT.render.pyrender.smpl_render import SMPLRender
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa
from huggingface_hub import snapshot_download
import eventlet

# Load model
cfg = parse_args(phase="webui")  # parse config file
cfg.FOLDER = 'cache'
output_dir = Path(cfg.FOLDER)
output_dir.mkdir(parents=True, exist_ok=True)
pl.seed_everything(cfg.SEED_VALUE)
if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")

model_path = snapshot_download(repo_id="bill-jiang/MotionGPT-base")

datamodule = build_data(cfg, phase="test")
model = build_model(cfg, datamodule)
state_dict = torch.load(f'{model_path}/motiongpt_s3_h3d.tar',
                        map_location="cpu")["state_dict"]
model.load_state_dict(state_dict)
model.to(device)

audio_processor = WhisperProcessor.from_pretrained(cfg.model.whisper_path)
audio_model = WhisperForConditionalGeneration.from_pretrained(
    cfg.model.whisper_path).to(device)
forced_decoder_ids_zh = audio_processor.get_decoder_prompt_ids(
    language="zh", task="translate")
forced_decoder_ids_en = audio_processor.get_decoder_prompt_ids(
    language="en", task="translate")

# HTML Style
Video_Components = """
<div class="side-video" style="position: relative;">
    <video width="340" autoplay loop>
        <source src="file/{video_path}" type="video/mp4">
    </video>
    <a class="videodl-button" href="file/{video_path}" download="{video_fname}" title="Download Video">
        <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#000000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-video"><path d="m22 8-6 4 6 4V8Z"/><rect width="14" height="12" x="2" y="6" rx="2" ry="2"/></svg>
    </a>
    <a class="npydl-button" href="file/{motion_path}" download="{motion_fname}" title="Download Motion">
        <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#000000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-box"><path d="M14.5 22H18a2 2 0 0 0 2-2V7.5L14.5 2H6a2 2 0 0 0-2 2v4"/><polyline points="14 2 14 8 20 8"/><path d="M2.97 13.12c-.6.36-.97 1.02-.97 1.74v3.28c0 .72.37 1.38.97 1.74l3 1.83c.63.39 1.43.39 2.06 0l3-1.83c.6-.36.97-1.02.97-1.74v-3.28c0-.72-.37-1.38-.97-1.74l-3-1.83a1.97 1.97 0 0 0-2.06 0l-3 1.83Z"/><path d="m7 17-4.74-2.85"/><path d="m7 17 4.74-2.85"/><path d="M7 17v5"/></svg>
    </a>
</div>
"""

Video_Components_example = """
<div class="side-video" style="position: relative;">
    <video width="340" autoplay loop controls>
        <source src="file/{video_path}" type="video/mp4">
    </video>
    <a class="npydl-button" href="file/{video_path}" download="{video_fname}" title="Download Video">
        <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-video"><path d="m22 8-6 4 6 4V8Z"/><rect width="14" height="12" x="2" y="6" rx="2" ry="2"/></svg>
    </a>
</div>
"""

Text_Components = """
<h3 class="side-content" >{msg}</h3>
"""


def motion_token_to_string(motion_token, lengths, codebook_size=512):
    motion_string = []
    for i in range(motion_token.shape[0]):
        motion_i = motion_token[i].cpu(
        ) if motion_token.device.type == 'cuda' else motion_token[i]
        motion_list = motion_i.tolist()[:lengths[i]]
        motion_string.append(
            (f'<motion_id_{codebook_size}>' +
             ''.join([f'<motion_id_{int(i)}>' for i in motion_list]) +
             f'<motion_id_{codebook_size + 1}>'))
    return motion_string


def render_motion(data, feats, method='fast'):
    fname = time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime(
        time.time())) + str(np.random.randint(10000, 99999))
    video_fname = fname + '.mp4'
    feats_fname = fname + '.npy'
    output_npy_path = os.path.join(output_dir, feats_fname)
    output_mp4_path = os.path.join(output_dir, video_fname)
    np.save(output_npy_path, feats)

    if method == 'slow':
        if len(data.shape) == 4:
            data = data[0]
        data = data - data[0, 0]
        pose_generator = HybrIKJointsToRotmat()
        pose = pose_generator(data)
        pose = np.concatenate([
            pose,
            np.stack([np.stack([np.eye(3)] * pose.shape[0], 0)] * 2, 1)
        ], 1)
        shape = [768, 768]
        render = SMPLRender(cfg.RENDER.SMPL_MODEL_PATH)

        r = RRR.from_rotvec(np.array([np.pi, 0.0, 0.0]))
        pose[:, 0] = np.matmul(r.as_matrix().reshape(1, 3, 3), pose[:, 0])
        vid = []
        aroot = data[:, 0]
        aroot[:, 1:] = -aroot[:, 1:]
        params = dict(pred_shape=np.zeros([1, 10]),
                      pred_root=aroot,
                      pred_pose=pose)
        render.init_renderer([shape[0], shape[1], 3], params)
        for i in range(data.shape[0]):
            renderImg = render.render(i)
            vid.append(renderImg)

        out = np.stack(vid, axis=0)
        output_gif_path = output_mp4_path[:-4] + '.gif'
        imageio.mimwrite(output_gif_path, out, duration=50)
        out_video = mp.VideoFileClip(output_gif_path)
        out_video.write_videofile(output_mp4_path)
        del out, render

    elif method == 'fast':
        output_gif_path = output_mp4_path[:-4] + '.gif'
        if len(data.shape) == 3:
            data = data[None]
        if isinstance(data, torch.Tensor):
            data = data.cpu().numpy()
        pose_vis = plot_3d.draw_to_batch(data, [''], [output_gif_path])
        out_video = mp.VideoFileClip(output_gif_path)
        out_video.write_videofile(output_mp4_path)
        del pose_vis

    return output_mp4_path, video_fname, output_npy_path, feats_fname


def load_motion(motion_uploaded, method):
    file = motion_uploaded['file']

    feats = torch.tensor(np.load(file), device=model.device)
    if len(feats.shape) == 2:
        feats = feats[None]
    # feats = model.datamodule.normalize(feats)

    # Motion tokens
    motion_lengths = feats.shape[0]
    motion_token, _ = model.vae.encode(feats)

    motion_token_string = model.lm.motion_token_to_string(
        motion_token, [motion_token.shape[1]])[0]
    motion_token_length = motion_token.shape[1]

    # Motion rendered
    joints = model.datamodule.feats2joints(feats.cpu()).cpu().numpy()
    output_mp4_path, video_fname, output_npy_path, joints_fname = render_motion(
        joints,
        feats.to('cpu').numpy(), method)

    motion_uploaded.update({
        "feats": feats,
        "joints": joints,
        "motion_video": output_mp4_path,
        "motion_video_fname": video_fname,
        "motion_joints": output_npy_path,
        "motion_joints_fname": joints_fname,
        "motion_lengths": motion_lengths,
        "motion_token": motion_token,
        "motion_token_string": motion_token_string,
        "motion_token_length": motion_token_length,
    })

    return motion_uploaded


def add_text(history, text, motion_uploaded, data_stored, method):
    data_stored = data_stored + [{'user_input': text}]

    text = f"""<h3>{text}</h3>"""
    history = history + [(text, None)]
    if 'file' in motion_uploaded.keys():
        motion_uploaded = load_motion(motion_uploaded, method)
        output_mp4_path = motion_uploaded['motion_video']
        video_fname = motion_uploaded['motion_video_fname']
        output_npy_path = motion_uploaded['motion_joints']
        joints_fname = motion_uploaded['motion_joints_fname']
        history = history + [(Video_Components.format(
            video_path=output_mp4_path,
            video_fname=video_fname,
            motion_path=output_npy_path,
            motion_fname=joints_fname), None)]

    return history, gr.update(value="",
                              interactive=False), motion_uploaded, data_stored


def add_audio(history, audio_path, data_stored, language='en'):
    audio, sampling_rate = librosa.load(audio_path, sr=16000)
    input_features = audio_processor(
        audio, sampling_rate, return_tensors="pt"
    ).input_features  # whisper training sampling rate, do not modify
    input_features = torch.Tensor(input_features).to(device)

    if language == 'English':
        forced_decoder_ids = forced_decoder_ids_en
    else:
        forced_decoder_ids = forced_decoder_ids_zh
    predicted_ids = audio_model.generate(input_features,
                                         forced_decoder_ids=forced_decoder_ids)
    text_input = audio_processor.batch_decode(predicted_ids,
                                              skip_special_tokens=True)
    text_input = str(text_input).strip('[]"')
    data_stored = data_stored + [{'user_input': text_input}]
    gr.update(value=data_stored, interactive=False)
    history = history + [(text_input, None)]

    return history, data_stored


def add_file(history, file, txt, motion_uploaded):
    motion_uploaded['file'] = file.name
    txt = txt.replace(" <Motion_Placeholder>", "") + " <Motion_Placeholder>"
    return history, gr.update(value=txt, interactive=True), motion_uploaded


def bot(history, motion_uploaded, data_stored, method):

    motion_length, motion_token_string = motion_uploaded[
        "motion_lengths"], motion_uploaded["motion_token_string"]

    input = data_stored[-1]['user_input']
    prompt = model.lm.placeholder_fulfill(input, motion_length,
                                          motion_token_string, "")
    data_stored[-1]['model_input'] = prompt
    batch = {
        "length": [motion_length],
        "text": [prompt],
    }

    outputs = model(batch, task="t2m")
    out_feats = outputs["feats"][0]
    out_lengths = outputs["length"][0]
    out_joints = outputs["joints"][:out_lengths].detach().cpu().numpy()
    out_texts = outputs["texts"][0]
    output_mp4_path, video_fname, output_npy_path, joints_fname = render_motion(
        out_joints,
        out_feats.to('cpu').numpy(), method)

    motion_uploaded = {
        "feats": None,
        "joints": None,
        "motion_video": None,
        "motion_lengths": 0,
        "motion_token": None,
        "motion_token_string": '',
        "motion_token_length": 0,
    }

    data_stored[-1]['model_output'] = {
        "feats": out_feats,
        "joints": out_joints,
        "length": out_lengths,
        "texts": out_texts,
        "motion_video": output_mp4_path,
        "motion_video_fname": video_fname,
        "motion_joints": output_npy_path,
        "motion_joints_fname": joints_fname,
    }

    if '<Motion_Placeholder>' == out_texts:
        response = [
            Video_Components.format(video_path=output_mp4_path,
                                    video_fname=video_fname,
                                    motion_path=output_npy_path,
                                    motion_fname=joints_fname)
        ]
    elif '<Motion_Placeholder>' in out_texts:
        response = [
            Text_Components.format(
                msg=out_texts.split("<Motion_Placeholder>")[0]),
            Video_Components.format(video_path=output_mp4_path,
                                    video_fname=video_fname,
                                    motion_path=output_npy_path,
                                    motion_fname=joints_fname),
            Text_Components.format(
                msg=out_texts.split("<Motion_Placeholder>")[1]),
        ]
    else:
        response = f"""<h3>{out_texts}</h3>"""

    history[-1][1] = ""
    for character in response:
        history[-1][1] += character
        time.sleep(0.02)
        yield history, motion_uploaded, data_stored


def bot_example(history, responses):
    history = history + responses
    return history


with open("assets/css/custom.css", "r", encoding="utf-8") as f:
    customCSS = f.read()

with gr.Blocks(css=customCSS) as demo:

    # Examples
    chat_instruct = gr.State([
        (None,
         "👋 Hi, I'm MotionGPT! I can generate realistic human motion from text, or generate text from motion."
         ),
        (None,
         "💡 You can chat with me in pure text like generating human motion following your descriptions."
         ),
        (None,
         "💡 After generation, you can click the button in the top right of generation human motion result to download the human motion video or feature stored in .npy format."
         ),
        (None,
         "💡 With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it!"
         ),
        (None,
         "💡 Of courser, you can also purely chat with me and let me give you human motion in text, here are some examples!"
         ),
        (None,
         "💡 We provide two motion visulization methods. The default fast method is skeleton line ploting which is like the examples below:"
         ),
        (None,
         Video_Components_example.format(
             video_path="assets/videos/example0_fast.mp4",
             video_fname="example0_fast.mp4")),
        (None,
         "💡 And the slow method is SMPL model rendering which is more realistic but slower."
         ),
        (None,
         Video_Components_example.format(
             video_path="assets/videos/example0.mp4",
             video_fname="example0.mp4")),
        (None,
         "💡 If you want to get the video in our paper and website like below, you can refer to the scirpt in our [github repo](https://github.com/OpenMotionLab/MotionGPT#-visualization)."
         ),
        (None,
         Video_Components_example.format(
             video_path="assets/videos/example0_blender.mp4",
             video_fname="example0_blender.mp4")),
        (None, "👉 Follow the examples and try yourself!"),
    ])
    chat_instruct_sum = gr.State([(None, '''
         👋 Hi, I'm MotionGPT! I can generate realistic human motion from text, or generate text from motion.
         
         1. You can chat with me in pure text like generating human motion following your descriptions.
         2. After generation, you can click the button in the top right of generation human motion result to download the human motion video or feature stored in .npy format.
         3. With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it!
         4. Of course, you can also purely chat with me and let me give you human motion in text, here are some examples!
         ''')] + chat_instruct.value[-7:])

    t2m_examples = gr.State([
        (None,
         "💡 You can chat with me in pure text, following are some examples of text-to-motion generation!"
         ),
        ("A person is walking forwards, but stumbles and steps back, then carries on forward.",
         Video_Components_example.format(
             video_path="assets/videos/example0.mp4",
             video_fname="example0.mp4")),
        ("Generate a man aggressively kicks an object to the left using his right foot.",
         Video_Components_example.format(
             video_path="assets/videos/example1.mp4",
             video_fname="example1.mp4")),
        ("Generate a person lowers their arms, gets onto all fours, and crawls.",
         Video_Components_example.format(
             video_path="assets/videos/example2.mp4",
             video_fname="example2.mp4")),
        ("Show me the video of a person bends over and picks things up with both hands individually, then walks forward.",
         Video_Components_example.format(
             video_path="assets/videos/example3.mp4",
             video_fname="example3.mp4")),
        ("Imagine a person is practing balancing on one leg.",
         Video_Components_example.format(
             video_path="assets/videos/example5.mp4",
             video_fname="example5.mp4")),
        ("Show me a person walks forward, stops, turns directly to their right, then walks forward again.",
         Video_Components_example.format(
             video_path="assets/videos/example6.mp4",
             video_fname="example6.mp4")),
        ("I saw a person sits on the ledge of something then gets off and walks away.",
         Video_Components_example.format(
             video_path="assets/videos/example7.mp4",
             video_fname="example7.mp4")),
        ("Show me a person is crouched down and walking around sneakily.",
         Video_Components_example.format(
             video_path="assets/videos/example8.mp4",
             video_fname="example8.mp4")),
    ])

    m2t_examples = gr.State([
        (None,
         "💡 With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it, here are some examples!"
         ),
        ("Please explain the movement shown in <Motion_Placeholder> using natural language.",
         None),
        (Video_Components_example.format(
            video_path="assets/videos/example0.mp4",
            video_fname="example0.mp4"),
         "The person was pushed but didn't fall down"),
        ("What kind of action is being represented in <Motion_Placeholder>? Explain it in text.",
         None),
        (Video_Components_example.format(
            video_path="assets/videos/example4.mp4",
            video_fname="example4.mp4"),
         "The figure has its hands curled at jaw level, steps onto its left foot and raises right leg with bent knee to kick forward and return to starting stance."
         ),
        ("Provide a summary of the motion demonstrated in <Motion_Placeholder> using words.",
         None),
        (Video_Components_example.format(
            video_path="assets/videos/example2.mp4",
            video_fname="example2.mp4"),
         "A person who is standing with his arms up and away from his sides bends over, gets down on his hands and then his knees and crawls forward."
         ),
        ("Generate text for <Motion_Placeholder>:", None),
        (Video_Components_example.format(
            video_path="assets/videos/example5.mp4",
            video_fname="example5.mp4"),
         "The man tries to stand in a yoga tree pose and looses his balance."),
        ("Provide a summary of the motion depicted in <Motion_Placeholder> using language.",
         None),
        (Video_Components_example.format(
            video_path="assets/videos/example6.mp4",
            video_fname="example6.mp4"),
         "Person walks up some steps then leeps to the other side and goes up a few more steps and jumps dow"
         ),
        ("Describe the motion represented by <Motion_Placeholder> in plain English.",
         None),
        (Video_Components_example.format(
            video_path="assets/videos/example7.mp4",
            video_fname="example7.mp4"),
         "Person sits down, then stands up and walks forward. then the turns around 180 degrees and walks the opposite direction"
         ),
        ("Provide a description of the action in <Motion_Placeholder> using words.",
         None),
        (Video_Components_example.format(
            video_path="assets/videos/example8.mp4",
            video_fname="example8.mp4"),
         "This man is bent forward and walks slowly around."),
    ])

    t2t_examples = gr.State([
        (None,
         "💡 Of course, you can also purely chat with me and let me give you human motion in text, here are some examples!"
         ),
        ('Depict a motion as like you have seen it.',
         "A person slowly walked forward in rigth direction while making the circle"
         ),
        ('Random say something about describing a human motion.',
         "A man throws punches using his right hand."),
        ('Describe the motion of someone as you will.',
         "Person is moving left to right in a dancing stance swaying hips, moving feet left to right with arms held out"
         ),
        ('Come up with a human motion caption.',
         "A person is walking in a counter counterclockwise motion."),
        ('Write a sentence about how someone might dance.',
         "A person with his hands down by his sides reaches down for something with his right hand, uses the object to make a stirring motion, then places the item back down."
         ),
        ('Depict a motion as like you have seen it.',
         "A person is walking forward a few feet, then turns around, walks back, and continues walking."
         )
    ])

    Init_chatbot = chat_instruct.value[:
                                       1] + t2m_examples.value[:
                                                               3] + m2t_examples.value[:3] + t2t_examples.value[:2] + chat_instruct.value[
                                                                   -7:]

    # Variables
    motion_uploaded = gr.State({
        "feats": None,
        "joints": None,
        "motion_video": None,
        "motion_lengths": 0,
        "motion_token": None,
        "motion_token_string": '',
        "motion_token_length": 0,
    })
    data_stored = gr.State([])

    gr.Markdown('''
                # MotionGPT: Human Motion as a Foreign Language
                
                <p align="left">
                <a href="https://github.com/OpenMotionLab/MotionGPT">Github Repo</a> •
                <a href="https://motion-gpt.github.io/">Project Page</a> •
                <a href="https://arxiv.org/abs/2306.14795">Arxiv Paper</a> •
                <a href="https://github.com/OpenMotionLab/MotionGPT#-citation">Citation
                </p>
                ''')

    chatbot = gr.Chatbot(Init_chatbot,
                         elem_id="mGPT",
                         height=600,
                         label="MotionGPT",
                         avatar_images=(None,
                                        ("assets/images/avatar_bot.jpg")),
                         bubble_full_width=False)

    with gr.Row():
        with gr.Column(scale=0.85):
            with gr.Row():
                txt = gr.Textbox(
                    label="Text",
                    show_label=False,
                    elem_id="textbox",
                    placeholder=
                    "Enter text and press ENTER or speak to input. You can also upload motion.",
                    container=False)

            with gr.Row():
                aud = gr.Audio(source="microphone",
                               label="Speak input",
                               type='filepath')
                btn = gr.UploadButton("📁 Upload motion",
                                      elem_id="upload",
                                      file_types=["file"])
                # regen = gr.Button("🔄 Regenerate", elem_id="regen")
                clear = gr.ClearButton([txt, chatbot, aud], value='🗑️ Clear')

            with gr.Row():
                gr.Markdown('''
                ### You can get more examples (pre-generated for faster response) by clicking the buttons below:
                ''')

            with gr.Row():
                instruct_eg = gr.Button("Instructions", elem_id="instruct")
                t2m_eg = gr.Button("Text-to-Motion", elem_id="t2m")
                m2t_eg = gr.Button("Motion-to-Text", elem_id="m2t")
                t2t_eg = gr.Button("Random description", elem_id="t2t")

        with gr.Column(scale=0.15, min_width=150):
            method = gr.Dropdown(["slow", "fast"],
                                 label="Visulization method",
                                 interactive=True,
                                 elem_id="method",
                                 value="slow")

            language = gr.Dropdown(["English", "中文"],
                                   label="Speech language",
                                   interactive=True,
                                   elem_id="language",
                                   value="English")

    txt_msg = txt.submit(
        add_text, [chatbot, txt, motion_uploaded, data_stored, method],
        [chatbot, txt, motion_uploaded, data_stored],
        queue=False).then(bot, [chatbot, motion_uploaded, data_stored, method],
                          [chatbot, motion_uploaded, data_stored])

    txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)

    file_msg = btn.upload(add_file, [chatbot, btn, txt, motion_uploaded],
                          [chatbot, txt, motion_uploaded],
                          queue=False)
    aud_msg = aud.stop_recording(
        add_audio, [chatbot, aud, data_stored, language],
        [chatbot, data_stored],
        queue=False).then(bot, [chatbot, motion_uploaded, data_stored, method],
                          [chatbot, motion_uploaded, data_stored])
    # regen_msg = regen.click(bot,
    #                         [chatbot, motion_uploaded, data_stored, method],
    #                         [chatbot, motion_uploaded, data_stored],
    #                         queue=False)

    instruct_msg = instruct_eg.click(bot_example, [chatbot, chat_instruct_sum],
                                     [chatbot],
                                     queue=False)
    t2m_eg_msg = t2m_eg.click(bot_example, [chatbot, t2m_examples], [chatbot],
                              queue=False)
    m2t_eg_msg = m2t_eg.click(bot_example, [chatbot, m2t_examples], [chatbot],
                              queue=False)
    t2t_eg_msg = t2t_eg.click(bot_example, [chatbot, t2t_examples], [chatbot],
                              queue=False)

    chatbot.change(scroll_to_output=True)

if __name__ == "__main__":
    demo.launch(debug=True)