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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 functools import partial
from tsr.system import TSR
from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
import argparse
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",
)
# adjust the chunk size to balance between speed and memory usage
model.renderer.set_chunk_size(8192)
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)
rv = []
for format in formats:
mesh_path = tempfile.NamedTemporaryFile(suffix=f".{format}", delete=False)
mesh.export(mesh_path.name)
rv.append(mesh_path.name)
return rv
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(title="TripoSR") as demo:
gr.Markdown(
"""
图像生成3d模型
"""
)
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,
maximum=1024,
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: The model shown here is flipped. Download to get correct results.")
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=[
"examples/hamburger.png",
"examples/poly_fox.png",
"examples/robot.png",
"examples/teapot.png",
"examples/tiger_girl.png",
"examples/horse.png",
"examples/flamingo.png",
"examples/unicorn.png",
"examples/chair.png",
"examples/iso_house.png",
"examples/marble.png",
"examples/police_woman.png",
"examples/captured.jpeg",
],
inputs=[input_image],
outputs=[processed_image, output_model_obj, output_model_glb],
cache_examples=False,
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() |