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| import logging | |
| import os | |
| import shlex | |
| import subprocess | |
| import tempfile | |
| import time | |
| import gradio as gr | |
| import numpy as np | |
| import rembg | |
| import spaces | |
| import torch | |
| from PIL import Image | |
| from functools import partial | |
| subprocess.run(shlex.split('pip install wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl')) | |
| from tsr.system import TSR | |
| from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation | |
| HEADER = """ | |
| # TripoSR Demo | |
| <table bgcolor="#1E2432" cellspacing="0" cellpadding="0" width="450"> | |
| <tr style="height:50px;"> | |
| <td style="text-align: center;"> | |
| <a href="https://stability.ai"> | |
| <img src="https://images.squarespace-cdn.com/content/v1/6213c340453c3f502425776e/6c9c4c25-5410-4547-bc26-dc621cdacb25/Stability+AI+logo.png" width="200" height="40" /> | |
| </a> | |
| </td> | |
| <td style="text-align: center;"> | |
| <a href="https://www.tripo3d.ai"> | |
| <img src="https://www.tripo3d.ai/logo.png" width="170" height="40" /> | |
| </a> | |
| </td> | |
| </tr> | |
| </table> | |
| <table bgcolor="#1E2432" cellspacing="0" cellpadding="0" width="450"> | |
| <tr style="height:30px;"> | |
| <td style="text-align: center;"> | |
| <a href="https://huggingface.co/stabilityai/TripoSR"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model_Card-Huggingface-orange" height="20"></a> | |
| </td> | |
| <td style="text-align: center;"> | |
| <a href="https://github.com/VAST-AI-Research/TripoSR"><img src="https://postimage.me/images/2024/03/04/GitHub_Logo_White.png" width="100" height="20"></a> | |
| </td> | |
| <td style="text-align: center; color: white;"> | |
| <a href="https://arxiv.org/abs/2403.02151"><img src="https://img.shields.io/badge/arXiv-2403.02151-b31b1b.svg" height="20"></a> | |
| </td> | |
| </tr> | |
| </table> | |
| **TripoSR** is a state-of-the-art open-source model for **fast** feedforward 3D reconstruction from a single image, developed in collaboration between [Tripo AI](https://www.tripo3d.ai/) and [Stability AI](https://stability.ai/). | |
| **Tips:** | |
| 1. If you find the result is unsatisfied, please try to change the foreground ratio. It might improve the results. | |
| 2. It's better to disable "Remove Background" for the provided examples since they have been already preprocessed. | |
| 3. Otherwise, please disable "Remove Background" option only if your input image is RGBA with transparent background, image contents are centered and occupy more than 70% of image width or height. | |
| """ | |
| if torch.cuda.is_available(): | |
| device = "cuda:0" | |
| else: | |
| device = "cpu" | |
| model = TSR.from_pretrained( | |
| "stabilityai/TripoSR", | |
| config_name="config.yaml", | |
| weight_name="model.ckpt", | |
| ) | |
| model.renderer.set_chunk_size(131072) | |
| model.to(device) | |
| rembg_session = rembg.new_session() | |
| def check_input_image(input_image): | |
| if input_image is None: | |
| raise gr.Error("No image uploaded!") | |
| def preprocess(input_image, do_remove_background, foreground_ratio): | |
| def fill_background(image): | |
| image = np.array(image).astype(np.float32) / 255.0 | |
| image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5 | |
| image = Image.fromarray((image * 255.0).astype(np.uint8)) | |
| return image | |
| if do_remove_background: | |
| image = input_image.convert("RGB") | |
| image = remove_background(image, rembg_session) | |
| image = resize_foreground(image, foreground_ratio) | |
| image = fill_background(image) | |
| else: | |
| image = input_image | |
| if image.mode == "RGBA": | |
| image = fill_background(image) | |
| return image | |
| def generate(image, mc_resolution, formats=["obj", "glb"]): | |
| scene_codes = model(image, device=device) | |
| mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0] | |
| mesh = to_gradio_3d_orientation(mesh) | |
| mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f".glb", delete=False) | |
| mesh.export(mesh_path_glb.name) | |
| mesh_path_obj = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False) | |
| mesh.apply_scale([-1, 1, 1]) # Otherwise the visualized .obj will be flipped | |
| mesh.export(mesh_path_obj.name) | |
| return mesh_path_obj.name, mesh_path_glb.name | |
| def run_example(image_pil): | |
| preprocessed = preprocess(image_pil, False, 0.9) | |
| mesh_name_obj, mesh_name_glb = generate(preprocessed, 256, ["obj", "glb"]) | |
| return preprocessed, mesh_name_obj, mesh_name_glb | |
| with gr.Blocks() as demo: | |
| gr.Markdown(HEADER) | |
| with gr.Row(variant="panel"): | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_image = gr.Image( | |
| label="Input Image", | |
| image_mode="RGBA", | |
| sources="upload", | |
| type="pil", | |
| elem_id="content_image", | |
| ) | |
| processed_image = gr.Image(label="Processed Image", interactive=False) | |
| with gr.Row(): | |
| with gr.Group(): | |
| do_remove_background = gr.Checkbox( | |
| label="Remove Background", value=True | |
| ) | |
| foreground_ratio = gr.Slider( | |
| label="Foreground Ratio", | |
| minimum=0.5, | |
| maximum=1.0, | |
| value=0.85, | |
| step=0.05, | |
| ) | |
| mc_resolution = gr.Slider( | |
| label="Marching Cubes Resolution", | |
| minimum=32, | |
| maximum=320, | |
| value=256, | |
| step=32 | |
| ) | |
| with gr.Row(): | |
| submit = gr.Button("Generate", elem_id="generate", variant="primary") | |
| with gr.Column(): | |
| with gr.Tab("OBJ"): | |
| output_model_obj = gr.Model3D( | |
| label="Output Model (OBJ Format)", | |
| interactive=False, | |
| ) | |
| gr.Markdown("Note: Downloaded object will be flipped in case of .obj export. Export .glb instead or manually flip it before usage.") | |
| with gr.Tab("GLB"): | |
| output_model_glb = gr.Model3D( | |
| label="Output Model (GLB Format)", | |
| interactive=False, | |
| ) | |
| gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.") | |
| with gr.Row(variant="panel"): | |
| gr.Examples( | |
| examples=[ | |
| os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples")) | |
| ], | |
| inputs=[input_image], | |
| outputs=[processed_image, output_model_obj, output_model_glb], | |
| cache_examples=True, | |
| fn=partial(run_example), | |
| label="Examples", | |
| examples_per_page=20 | |
| ) | |
| submit.click(fn=check_input_image, inputs=[input_image]).success( | |
| fn=preprocess, | |
| inputs=[input_image, do_remove_background, foreground_ratio], | |
| outputs=[processed_image], | |
| ).success( | |
| fn=generate, | |
| inputs=[processed_image, mc_resolution], | |
| outputs=[output_model_obj, output_model_glb], | |
| ) | |
| demo.queue(max_size=10) | |
| demo.launch() | |