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import os
import tempfile
import time
from functools import lru_cache
from typing import Any
import gradio as gr
import numpy as np
import rembg
import torch
from gradio_litmodel3d import LitModel3D
import spaces
from PIL import Image
import sf3d.utils as sf3d_utils
from sf3d.system import SF3D
rembg_session = rembg.new_session()
COND_WIDTH = 512
COND_HEIGHT = 512
COND_DISTANCE = 1.6
COND_FOVY_DEG = 40
BACKGROUND_COLOR = [0.5, 0.5, 0.5]
# Cached. Doesn't change
c2w_cond = sf3d_utils.default_cond_c2w(COND_DISTANCE)
intrinsic, intrinsic_normed_cond = sf3d_utils.create_intrinsic_from_fov_deg(
COND_FOVY_DEG, COND_HEIGHT, COND_WIDTH
)
model = SF3D.from_pretrained(
"stabilityai/stable-fast-3d",
config_name="config.yaml",
weight_name="model.safetensors",
)
model.eval().cuda()
example_files = [
os.path.join("demo_files/examples", f) for f in os.listdir("demo_files/examples")
]
@spaces.GPU
def run_model(input_image):
start = time.time()
with torch.no_grad():
with torch.autocast(device_type="cuda", dtype=torch.float16):
model_batch = create_batch(input_image)
model_batch = {k: v.cuda() for k, v in model_batch.items()}
trimesh_mesh, _glob_dict = model.generate_mesh(model_batch, 1024)
trimesh_mesh = trimesh_mesh[0]
# Create new tmp file
tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".glb")
trimesh_mesh.export(tmp_file.name, file_type="glb")
print("Generation took:", time.time() - start, "s")
return tmp_file.name
def create_batch(input_image: Image) -> dict[str, Any]:
img_cond = (
torch.from_numpy(
np.asarray(input_image.resize((COND_WIDTH, COND_HEIGHT))).astype(np.float32)
/ 255.0
)
.float()
.clip(0, 1)
)
mask_cond = img_cond[:, :, -1:]
rgb_cond = torch.lerp(
torch.tensor(BACKGROUND_COLOR)[None, None, :], img_cond[:, :, :3], mask_cond
)
batch_elem = {
"rgb_cond": rgb_cond,
"mask_cond": mask_cond,
"c2w_cond": c2w_cond.unsqueeze(0),
"intrinsic_cond": intrinsic.unsqueeze(0),
"intrinsic_normed_cond": intrinsic_normed_cond.unsqueeze(0),
}
# Add batch dim
batched = {k: v.unsqueeze(0) for k, v in batch_elem.items()}
return batched
@lru_cache
def checkerboard(squares: int, size: int, min_value: float = 0.5):
base = np.zeros((squares, squares)) + min_value
base[1::2, ::2] = 1
base[::2, 1::2] = 1
repeat_mult = size // squares
return (
base.repeat(repeat_mult, axis=0)
.repeat(repeat_mult, axis=1)[:, :, None]
.repeat(3, axis=-1)
)
def remove_background(input_image: Image) -> Image:
return rembg.remove(input_image, session=rembg_session)
def resize_foreground(
image: Image,
ratio: float,
) -> Image:
image = np.array(image)
assert image.shape[-1] == 4
alpha = np.where(image[..., 3] > 0)
y1, y2, x1, x2 = (
alpha[0].min(),
alpha[0].max(),
alpha[1].min(),
alpha[1].max(),
)
# crop the foreground
fg = image[y1:y2, x1:x2]
# pad to square
size = max(fg.shape[0], fg.shape[1])
ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
new_image = np.pad(
fg,
((ph0, ph1), (pw0, pw1), (0, 0)),
mode="constant",
constant_values=((0, 0), (0, 0), (0, 0)),
)
# compute padding according to the ratio
new_size = int(new_image.shape[0] / ratio)
# pad to size, double side
ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
ph1, pw1 = new_size - size - ph0, new_size - size - pw0
new_image = np.pad(
new_image,
((ph0, ph1), (pw0, pw1), (0, 0)),
mode="constant",
constant_values=((0, 0), (0, 0), (0, 0)),
)
new_image = Image.fromarray(new_image, mode="RGBA").resize(
(COND_WIDTH, COND_HEIGHT)
)
return new_image
def square_crop(input_image: Image) -> Image:
# Perform a center square crop
min_size = min(input_image.size)
left = (input_image.size[0] - min_size) // 2
top = (input_image.size[1] - min_size) // 2
right = (input_image.size[0] + min_size) // 2
bottom = (input_image.size[1] + min_size) // 2
return input_image.crop((left, top, right, bottom)).resize(
(COND_WIDTH, COND_HEIGHT)
)
def show_mask_img(input_image: Image) -> Image:
img_numpy = np.array(input_image)
alpha = img_numpy[:, :, 3] / 255.0
chkb = checkerboard(32, 512) * 255
new_img = img_numpy[..., :3] * alpha[:, :, None] + chkb * (1 - alpha[:, :, None])
return Image.fromarray(new_img.astype(np.uint8), mode="RGB")
def run_button(run_btn, input_image, background_state, foreground_ratio):
if run_btn == "Run":
glb_file: str = run_model(background_state)
return (
gr.update(),
gr.update(),
gr.update(),
gr.update(),
gr.update(value=glb_file, visible=True),
gr.update(visible=True),
)
elif run_btn == "Remove Background":
rem_removed = remove_background(input_image)
sqr_crop = square_crop(rem_removed)
fr_res = resize_foreground(sqr_crop, foreground_ratio)
return (
gr.update(value="Run", visible=True),
sqr_crop,
fr_res,
gr.update(value=show_mask_img(fr_res), visible=True),
gr.update(value=None, visible=False),
gr.update(visible=False),
)
def requires_bg_remove(image, fr):
if image is None:
return (
gr.update(visible=False, value="Run"),
None,
None,
gr.update(value=None, visible=False),
gr.update(visible=False),
gr.update(visible=False),
)
alpha_channel = np.array(image.getchannel("A"))
min_alpha = alpha_channel.min()
if min_alpha == 0:
print("Already has alpha")
sqr_crop = square_crop(image)
fr_res = resize_foreground(sqr_crop, fr)
return (
gr.update(value="Run", visible=True),
sqr_crop,
fr_res,
gr.update(value=show_mask_img(fr_res), visible=True),
gr.update(visible=False),
gr.update(visible=False),
)
return (
gr.update(value="Remove Background", visible=True),
None,
None,
gr.update(value=None, visible=False),
gr.update(visible=False),
gr.update(visible=False),
)
def update_foreground_ratio(img_proc, fr):
foreground_res = resize_foreground(img_proc, fr)
return (
foreground_res,
gr.update(value=show_mask_img(foreground_res)),
)
with gr.Blocks() as demo:
img_proc_state = gr.State()
background_remove_state = gr.State()
gr.Markdown("""
# SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement
**SF3D** is a state-of-the-art method for 3D mesh reconstruction from a single image.
This demo allows you to upload an image and generate a 3D mesh model from it.
**Tips**
1. If the image already has an alpha channel, you can skip the background removal step.
2. You can adjust the foreground ratio to control the size of the foreground object. This can influence the shape
3. You can upload your own HDR environment map to light the 3D model.
""")
with gr.Row(variant="panel"):
with gr.Column():
with gr.Row():
input_img = gr.Image(
type="pil", label="Input Image", sources="upload", image_mode="RGBA"
)
preview_removal = gr.Image(
label="Preview Background Removal",
type="pil",
image_mode="RGB",
interactive=False,
visible=False,
)
foreground_ratio = gr.Slider(
label="Foreground Ratio",
minimum=0.5,
maximum=1.0,
value=0.85,
step=0.05,
)
foreground_ratio.change(
update_foreground_ratio,
inputs=[img_proc_state, foreground_ratio],
outputs=[background_remove_state, preview_removal],
)
run_btn = gr.Button("Run", variant="primary", visible=False)
with gr.Column():
output_3d = LitModel3D(
label="3D Model",
visible=False,
clear_color=[0.0, 0.0, 0.0, 0.0],
tonemapping="aces",
contrast=1.0,
scale=1.0,
)
with gr.Column(visible=False, scale=1.0) as hdr_row:
gr.Markdown("""## HDR Environment Map
Select an HDR environment map to light the 3D model. You can also upload your own HDR environment maps.
""")
with gr.Row():
hdr_illumination_file = gr.File(
label="HDR Env Map", file_types=[".hdr"], file_count="single"
)
example_hdris = [
os.path.join("demo_files/hdri", f)
for f in os.listdir("demo_files/hdri")
]
hdr_illumination_example = gr.Examples(
examples=example_hdris,
inputs=hdr_illumination_file,
)
hdr_illumination_file.change(
lambda x: gr.update(env_map=x.name if x is not None else None),
inputs=hdr_illumination_file,
outputs=[output_3d],
)
examples = gr.Examples(
examples=example_files,
inputs=input_img,
)
input_img.change(
requires_bg_remove,
inputs=[input_img, foreground_ratio],
outputs=[
run_btn,
img_proc_state,
background_remove_state,
preview_removal,
output_3d,
hdr_row,
],
)
run_btn.click(
run_button,
inputs=[
run_btn,
input_img,
background_remove_state,
foreground_ratio,
],
outputs=[
run_btn,
img_proc_state,
background_remove_state,
preview_removal,
output_3d,
hdr_row,
],
)
demo.launch()
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