Spaces:
Sleeping
Sleeping
File size: 4,278 Bytes
9ba6f9a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | import logging
import os
import tempfile
import time
os.environ["OMP_NUM_THREADS"] = "1"
import gradio as gr
import numpy as np
import rembg
import torch
from PIL import Image
from functools import partial
from tsr.system import TSR
from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
HEADER = """
"""
if torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"
d = os.environ.get("DEVICE", None)
if d != None:
device = d
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):
with torch.no_grad():
scene_codes = model(image, device=device)
mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0]
mesh = to_gradio_3d_orientation(mesh)
mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
mesh_path2 = tempfile.NamedTemporaryFile(suffix=".glb", delete=False)
mesh.export(mesh_path.name)
mesh.export(mesh_path2.name)
torch.cuda.empty_cache()
return mesh_path.name, mesh_path2.name
def run_example(image_pil):
preprocessed = preprocess(image_pil, False, 0.9)
mesh_name, mesh_name2 = generate(preprocessed, 256)
return preprocessed, mesh_name, mesh_name2
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="Mesh Resolution",
minimum=128,
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 = gr.Model3D(
label="Output Model",
interactive=False,
)
with gr.Tab("glb"):
output_model2 = gr.Model3D(
label="Output Model",
interactive=False,
)
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, output_model2],
)
demo.queue(max_size=10)
demo.launch()
|