Spaces:

Maikou
/

Michelangelo

Runtime error

File size: 14,008 Bytes

# -*- coding: utf-8 -*-
import os
import time
from collections import OrderedDict
from PIL import Image
import torch
import trimesh
from typing import Optional, List
from einops import repeat, rearrange
import numpy as np
from michelangelo.models.tsal.tsal_base import Latent2MeshOutput
from michelangelo.utils.misc import get_config_from_file, instantiate_from_config
from michelangelo.utils.visualizers.pythreejs_viewer import PyThreeJSViewer
from michelangelo.utils.visualizers import html_util

import gradio as gr

from huggingface_hub import snapshot_download

gradio_cached_dir = "./gradio_cached_dir"
os.makedirs(gradio_cached_dir, exist_ok=True)

save_mesh = False

state = ""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

box_v = 1.1
viewer = PyThreeJSViewer(settings={}, render_mode="WEBSITE")

image_model_config_dict = OrderedDict({
    "ASLDM-256-obj": {
        # "config": "./configs/image_cond_diffuser_asl/image-ASLDM-256.yaml",
        # "ckpt_path": "./checkpoints/image_cond_diffuser_asl/image-ASLDM-256.ckpt",
        "config": "/home/user/app/configs/image_cond_diffuser_asl/image-ASLDM-256.yaml",
        "ckpt_path": "checkpoints/image_cond_diffuser_asl/image-ASLDM-256.ckpt",
    },
})

text_model_config_dict = OrderedDict({
    "ASLDM-256": {
        # "config": "./configs/text_cond_diffuser_asl/text-ASLDM-256.yaml",
        # "ckpt_path": "./checkpoints/text_cond_diffuser_asl/text-ASLDM-256.ckpt",
        "config": "./configs/text_cond_diffuser_asl/text-ASLDM-256.yaml",
        "ckpt_path": "checkpoints/text_cond_diffuser_asl/text-ASLDM-256.ckpt",
    },
})

model_path = snapshot_download(repo_id="Maikou/Michelangelo")

class InferenceModel(object):
    model = None
    name = ""


text2mesh_model = InferenceModel()
image2mesh_model = InferenceModel()


def set_state(s):
    global state
    state = s
    print(s)


def output_to_html_frame(mesh_outputs: List[Latent2MeshOutput], bbox_size: float,
                         image: Optional[np.ndarray] = None,
                         html_frame: bool = False):
    global viewer

    for i in range(len(mesh_outputs)):
        mesh = mesh_outputs[i]
        if mesh is None:
            continue

        mesh_v = mesh.mesh_v.copy()
        mesh_v[:, 0] += i * np.max(bbox_size)
        mesh_v[:, 2] += np.max(bbox_size)
        viewer.add_mesh(mesh_v, mesh.mesh_f)

    mesh_tag = viewer.to_html(html_frame=False)

    if image is not None:
        image_tag = html_util.to_image_embed_tag(image)
        frame = f"""
        <table border = "1">
            <tr>
                <td>{image_tag}</td>
                <td>{mesh_tag}</td>
            </tr>
        </table>
        """
    else:
        frame = mesh_tag

    if html_frame:
        frame = html_util.to_html_frame(frame)

    viewer.reset()

    return frame


def load_model(model_name: str, model_config_dict: dict, inference_model: InferenceModel):
    global device

    if inference_model.name == model_name:
        model = inference_model.model
    else:
        assert model_name in model_config_dict

        if inference_model.model is not None:
            del inference_model.model

        config_ckpt_path = model_config_dict[model_name]

        model_config = get_config_from_file(config_ckpt_path["config"])
        if hasattr(model_config, "model"):
            model_config = model_config.model

        ckpt_path = os.path.join(model_path, config_ckpt_path["ckpt_path"])

        model = instantiate_from_config(model_config, ckpt_path=ckpt_path)
        model = model.to(device)
        model = model.eval()

        inference_model.model = model
        inference_model.name = model_name

    return model


def prepare_img(image: np.ndarray):
    image_pt = torch.tensor(image).float()
    image_pt = image_pt / 255 * 2 - 1
    image_pt = rearrange(image_pt, "h w c -> c h w")

    return image_pt

def prepare_model_viewer(fp):
    content = f"""
      <head>
        <script 
          type="module" src="https://ajax.googleapis.com/ajax/libs/model-viewer/3.1.1/model-viewer.min.js">
        </script>
      </head>
      <body>
        <model-viewer 
          style="height: 150px; width: 150px;"
          rotation-per-second="10deg"
          id="t1"
          src="file/gradio_cached_dir/{fp}" 
          environment-image="neutral" 
          camera-target="0m 0m 0m"
          orientation="0deg 90deg 170deg"
          shadow-intensity="1"
          ar:true 
          auto-rotate 
          camera-controls>
        </model-viewer>
      </body>
    """
    return content

def prepare_html_frame(content):
    frame = f"""
    <html>
      <body>
        {content}
      </body>
    </html>
    """
    return frame

def prepare_html_body(content):
    frame = f"""
      <body>
        {content}
      </body>
    """
    return frame

def post_process_mesh_outputs(mesh_outputs):
    # html_frame = output_to_html_frame(mesh_outputs, 2 * box_v, image=None, html_frame=True)
    html_content = output_to_html_frame(mesh_outputs, 2 * box_v, image=None, html_frame=False)
    html_frame = prepare_html_frame(html_content)

    # filename = f"{time.time()}.html"
    filename = f"text-256-{time.time()}.html"
    html_filepath = os.path.join(gradio_cached_dir, filename)
    with open(html_filepath, "w") as writer:
        writer.write(html_frame)

    '''
    Bug: The iframe tag does not work in Gradio.
         The chrome returns "No resource with given URL found" 
    Solutions:
         https://github.com/gradio-app/gradio/issues/884
         Due to the security bitches, the server can only find files parallel to the gradio_app.py.
         The path has format "file/TARGET_FILE_PATH"
    '''

    iframe_tag = f'<iframe src="file/gradio_cached_dir/{filename}" width="600%" height="400" frameborder="0"></iframe>'

    filelist = []
    filenames = []
    for i, mesh in enumerate(mesh_outputs):
        mesh.mesh_f = mesh.mesh_f[:, ::-1]
        mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f)

        name = str(i) + "_out_mesh.obj"
        filepath = gradio_cached_dir + "/" + name
        mesh_output.export(filepath, include_normals=True)
        filelist.append(filepath)
        filenames.append(name)

    filelist.append(html_filepath)
    return iframe_tag, filelist

def image2mesh(image: np.ndarray,
               model_name: str = "subsp+pk_asl_perceiver=01_01_udt=03",
               num_samples: int = 4,
               guidance_scale: int = 7.5,
               octree_depth: int = 7):
    global device, gradio_cached_dir, image_model_config_dict, box_v

    # load model
    model = load_model(model_name, image_model_config_dict, image2mesh_model)

    # prepare image inputs
    image_pt = prepare_img(image)
    image_pt = repeat(image_pt, "c h w -> b c h w", b=num_samples)

    sample_inputs = {
        "image": image_pt
    }
    mesh_outputs = model.sample(
        sample_inputs,
        sample_times=1,
        guidance_scale=guidance_scale,
        return_intermediates=False,
        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
        octree_depth=octree_depth,
    )[0]

    iframe_tag, filelist = post_process_mesh_outputs(mesh_outputs)

    return iframe_tag, gr.update(value=filelist, visible=True)


def text2mesh(text: str,
              model_name: str = "subsp+pk_asl_perceiver=01_01_udt=03",
              num_samples: int = 4,
              guidance_scale: int = 7.5,
              octree_depth: int = 7):
    global device, gradio_cached_dir, text_model_config_dict, text2mesh_model, box_v

    # load model
    model = load_model(model_name, text_model_config_dict, text2mesh_model)

    # prepare text inputs
    sample_inputs = {
        "text": [text] * num_samples
    }
    mesh_outputs = model.sample(
        sample_inputs,
        sample_times=1,
        guidance_scale=guidance_scale,
        return_intermediates=False,
        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
        octree_depth=octree_depth,
    )[0]

    iframe_tag, filelist = post_process_mesh_outputs(mesh_outputs)

    return iframe_tag, gr.update(value=filelist, visible=True)

example_dir = './gradio_cached_dir/example/img_example'

first_page_items = [
    'alita.jpg',
    'burger.jpg'
    'loopy.jpg'
    'building.jpg',
    'mario.jpg',
    'car.jpg',
    'airplane.jpg',
    'bag.jpg',
    'bench.jpg',
    'ship.jpg'
]
raw_example_items = [
    # (os.path.join(example_dir, x), x)
    os.path.join(example_dir, x)
    for x in os.listdir(example_dir)
    if x.endswith(('.jpg', '.png'))
]
example_items = [x for x in raw_example_items if os.path.basename(x) in first_page_items] + [x for x in raw_example_items if os.path.basename(x) not in first_page_items]

example_text = [
    ["A 3D model of a car; Audi A6."],
    ["A 3D model of police car; Highway Patrol Charger"]
    ],

def set_cache(data: gr.SelectData):
    img_name = os.path.basename(example_items[data.index])
    return os.path.join(example_dir, img_name), os.path.join(img_name)

def disable_cache():
    return ""

with gr.Blocks() as app:
    gr.Markdown("# Michelangelo")
    gr.Markdown("## [Github](https://github.com/NeuralCarver/Michelangelo) | [Arxiv](https://arxiv.org/abs/2306.17115) | [Project Page](https://neuralcarver.github.io/michelangelo/)")
    gr.Markdown("Michelangelo is a conditional 3D shape generation system that trains based on the shape-image-text aligned latent representation.")
    gr.Markdown("### Hint:")
    gr.Markdown("1. We provide two APIs: Image-conditioned generation and Text-conditioned generation")
    gr.Markdown("2. Note that the Image-conditioned model is trained on multiple 3D datasets like ShapeNet and Objaverse")
    gr.Markdown("3. We provide some examples for you to try. You can also upload images or text as input.")
    gr.Markdown("4. Welcome to share your amazing results with us, and thanks for your interest in our work!")
    
    with gr.Row():
        with gr.Column():
            
            with gr.Tab("Image to 3D"):
                img = gr.Image(label="Image")
                gr.Markdown("For the best results, we suggest that the images uploaded meet the following three criteria: 1. The object is positioned at the center of the image, 2. The image size is square, and 3. The background is relatively clean.")
                btn_generate_img2obj = gr.Button(value="Generate")
                
                with gr.Accordion("Advanced settings", open=False):
                    image_dropdown_models = gr.Dropdown(label="Model", value="ASLDM-256-obj",choices=list(image_model_config_dict.keys()))
                    num_samples = gr.Slider(label="samples", value=4, minimum=1, maximum=8, step=1)
                    guidance_scale = gr.Slider(label="Guidance scale", value=7.5, minimum=3.0, maximum=10.0, step=0.1)
                    octree_depth = gr.Slider(label="Octree Depth (for 3D model)", value=7, minimum=4, maximum=8, step=1)

                
                cache_dir = gr.Textbox(value="", visible=False)
                examples = gr.Gallery(label='Examples', value=example_items, elem_id="gallery", allow_preview=False, columns=[4], object_fit="contain")
            
            with gr.Tab("Text to 3D"):
                prompt = gr.Textbox(label="Prompt", placeholder="A 3D model of motorcar; Porche Cayenne Turbo.")
                gr.Markdown("For the best results, we suggest that the prompt follows 'A 3D model of CATEGORY; DESCRIPTION'. For example, A 3D model of motorcar; Porche Cayenne Turbo.")
                btn_generate_txt2obj = gr.Button(value="Generate")
                
                with gr.Accordion("Advanced settings", open=False):
                    text_dropdown_models = gr.Dropdown(label="Model", value="ASLDM-256",choices=list(text_model_config_dict.keys()))
                    num_samples = gr.Slider(label="samples", value=4, minimum=1, maximum=8, step=1)
                    guidance_scale = gr.Slider(label="Guidance scale", value=7.5, minimum=3.0, maximum=10.0, step=0.1)
                    octree_depth = gr.Slider(label="Octree Depth (for 3D model)", value=7, minimum=4, maximum=8, step=1)

                gr.Markdown("#### Examples:")
                gr.Markdown("1. A 3D model of an airplane; Airbus.")
                gr.Markdown("2. A 3D model of a fighter aircraft; Attack Fighter.")
                gr.Markdown("3. A 3D model of a chair; Simple Wooden Chair.")
                gr.Markdown("4. A 3D model of a laptop computer; Dell Laptop.")
                gr.Markdown("5. A 3D model of a coupe; Audi A6.")
                gr.Markdown("6. A 3D model of a motorcar; Hummer H2 SUT.")
                gr.Markdown("7. A 3D model of a lamp; ceiling light.")
                gr.Markdown("8. A 3D model of a rifle; AK47.")
                gr.Markdown("9. A 3D model of a knife; Sword.")
                gr.Markdown("10. A 3D model of a vase; Plant in pot.")

        with gr.Column():
            model_3d = gr.HTML()
            file_out = gr.File(label="Files", visible=False)

        outputs = [model_3d, file_out]
           
        img.upload(disable_cache, outputs=cache_dir)
        examples.select(set_cache, outputs=[img, cache_dir])
        print(os.path.abspath(os.path.dirname(__file__)), flush=True)
        model_path = snapshot_download(repo_id="Maikou/Michelangelo")
        print(model_path, flush=True)
        print(f'line:404: {cache_dir}', flush=True)
        btn_generate_img2obj.click(image2mesh, inputs=[img, image_dropdown_models, num_samples,
                                                       guidance_scale, octree_depth],
                                   outputs=outputs, api_name="generate_img2obj")

        btn_generate_txt2obj.click(text2mesh, inputs=[prompt, text_dropdown_models, num_samples,
                                                      guidance_scale, octree_depth],
                                   outputs=outputs, api_name="generate_txt2obj")



app.launch()