Spaces:
Running
Running
File size: 3,846 Bytes
7e0376e 5a483b6 7e0376e 5a483b6 7e0376e db20d3e 7e0376e 23e4ec1 7e0376e 23e4ec1 7e0376e 23e4ec1 7e0376e 23e4ec1 7e0376e 23e4ec1 7e0376e 78efa10 7e0376e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
import logging
import os
import tempfile
import time
import gradio as gr
import numpy as np
import rembg
import torch
from PIL import Image
from tsr.system import TSR
from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
HF_TOKEN = os.getenv("HF_TOKEN")
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",
token=HF_TOKEN
)
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):
scene_codes = model(image, device=device)
mesh = model.extract_mesh(scene_codes)[0]
mesh.vertices = to_gradio_3d_orientation(mesh.vertices)
mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
mesh.export(mesh_path.name)
return mesh_path.name
with gr.Blocks() as demo:
gr.Markdown(
"""
## TripoSR Demo
[TripoSR](https://github.com/VAST-AI-Research/TripoSR) is a state-of-the-art open-source model for **fast** feedforward 3D reconstruction from a single image, collaboratively developed by [Tripo AI](https://www.tripo3d.ai/) and [Stability AI](https://stability.ai/).
"""
)
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,
)
with gr.Row():
submit = gr.Button("Generate", elem_id="generate", variant="primary")
with gr.Column():
with gr.Tab("Model"):
output_model = gr.Model3D(
label="Output Model",
interactive=False,
)
gr.Markdown(
"""
Note: The model shown here will be flipped due to some visualization issues. Please download to get the correct result.
"""
)
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],
outputs=[output_model],
)
demo.queue(max_size=10)
demo.launch()
|