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Maikou commited on Jan 22, 2024

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Delete gradio_app.py

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-# -*- 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
-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",
-    },
-})
-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",
-    },
-})
-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
-        model = instantiate_from_config(model_config, ckpt_path=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 a coupe; Audi A6.")
-                gr.Markdown("2. A 3D model of a motorcar; Hummer H2 SUT.")
-                gr.Markdown("3. A 3D model of an airplane; Airbus.")
-                gr.Markdown("4. A 3D model of a fighter aircraft; Attack Fighter.")
-                gr.Markdown("5. A 3D model of a chair; Simple Wooden Chair.")
-                gr.Markdown("6. A 3D model of a laptop computer; Dell Laptop.")
-                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(f'line:404: {cache_dir}')
-        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(server_name="0.0.0.0", server_port=8008, share=False)