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app.py
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# Copyright (C) 2024-present Yuanjing Shengsheng (Beijing) Technology Co., Ltd. All rights reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details].
import os
import math
import tempfile
import cv2
import numpy as np
import torch
import imageio
from rembg import remove
from PIL import Image
from torchvision.transforms import v2
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from typing import List, Optional
from torchvision.transforms import ToTensor
from einops import rearrange, repeat
from src.geolrm_wrapper import GeoLRM
from src.utils.camera_util import (
FOV_to_intrinsics,
get_sv3d_input_cameras,
get_circular_camera_poses,
)
from src.utils.infer_util import save_video
from sgm.util import instantiate_from_config
def get_render_cameras(batch_size=1, M=120, radius=1.5, elevation=20.0):
"""
Get the rendering camera parameters.
"""
c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
Ks = FOV_to_intrinsics(39.6).unsqueeze(0).repeat(M, 1, 1).float()
c2ws = c2ws[None].repeat(batch_size, 1, 1, 1)
Ks = Ks[None].repeat(batch_size, 1, 1, 1)
return c2ws, Ks
config_path = 'configs/geolrm.yaml'
config = OmegaConf.load('configs/geolrm.yaml')
config_name = os.path.basename(config_path).replace('.yaml', '')
model_config = config.model_config
infer_config = config.infer_config
device = torch.device('cuda')
# load reconstruction model
print('Loading reconstruction model ...')
model = GeoLRM(**model_config['params'])
model = model.to(device)
model = model.eval()
# make output directories
output_path = 'tmp_gradio'
image_path = os.path.join(output_path, config_name, 'images')
# mesh_path = os.path.join(output_path, config_name, 'meshes')
gauss_path = os.path.join(output_path, config_name, 'gaussians')
video_path = os.path.join(output_path, config_name, 'videos')
sv3d_path = os.path.join(output_path, config_name, 'sv3d')
os.makedirs(image_path, exist_ok=True)
# os.makedirs(mesh_path, exist_ok=True)
os.makedirs(gauss_path, exist_ok=True)
os.makedirs(video_path, exist_ok=True)
os.makedirs(sv3d_path, exist_ok=True)
# SV3D model
sv3d_config = OmegaConf.load('configs/sv3d_p.yaml')
sv3d_config.model.params.conditioner_config.params.emb_models[0]\
.params.open_clip_embedding_config.params.init_device = 'cuda'
sv3d_config.model.params.sampler_config.params.verbose = False
sv3d_config.model.params.sampler_config.params.num_steps = 50
sv3d_config.model.params.sampler_config.params.guider_config.params.num_frames = 21
sv3d_model = instantiate_from_config(sv3d_config.model).to(device).eval()
# Preprocess
def check_input_image(input_image):
if input_image is None:
raise gr.Error("No image uploaded!")
def preprocess(image, do_remove_background):
if do_remove_background:
image.thumbnail([768, 768], Image.Resampling.LANCZOS)
image = remove(image.convert("RGBA"), alpha_matting=True)
# resize object in frame
image_arr = np.array(image)
in_w, in_h = image_arr.shape[:2]
ret, mask = cv2.threshold(
np.array(image.split()[-1]), 0, 255, cv2.THRESH_BINARY
)
x, y, w, h = cv2.boundingRect(mask)
max_size = max(w, h)
side_len = in_w
padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
center = side_len // 2
padded_image[
center - h // 2 : center - h // 2 + h,
center - w // 2 : center - w // 2 + w,
] = image_arr[y : y + h, x : x + w]
# resize frame to 576x576
rgba = Image.fromarray(padded_image).resize((576, 576), Image.LANCZOS)
# white bg
rgba_arr = np.array(rgba) / 255.0
rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
input_image = Image.fromarray((rgb * 255).astype(np.uint8))
return input_image
def sample_videos(
processed_image, sample_seed,
num_frames: Optional[int] = 21,
version: str = "sv3d_p",
fps_id: int = 6,
motion_bucket_id: int = 127,
cond_aug: float = 1e-5,
decoding_t: int = 14, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
device: str = "cuda",
output_folder: Optional[str] = sv3d_path,
elevations_deg: Optional[float | List[float]] = 10.0, # For SV3D
azimuths_deg: Optional[List[float]] = None, # For SV3D
):
seed_everything(sample_seed)
if isinstance(elevations_deg, float) or isinstance(elevations_deg, int):
elevations_deg = [elevations_deg] * num_frames
assert (
len(elevations_deg) == num_frames
), f"Please provide 1 value, or a list of {num_frames} values for elevations_deg! Given {len(elevations_deg)}"
polars_rad = [np.deg2rad(90 - e) for e in elevations_deg]
if azimuths_deg is None:
azimuths_deg = np.linspace(0, 360, num_frames + 1)[1:] % 360
assert (
len(azimuths_deg) == num_frames
), f"Please provide a list of {num_frames} values for azimuths_deg! Given {len(azimuths_deg)}"
azimuths_rad = [np.deg2rad((a - azimuths_deg[-1]) % 360) for a in azimuths_deg]
azimuths_rad[:-1].sort()
model = sv3d_model
image = ToTensor()(processed_image)
image = image * 2.0 - 1.0
image = image.unsqueeze(0).to(device)
H, W = image.shape[2:]
print(f"Image shape: {image.shape}")
assert image.shape[1] == 3
F = 8
C = 4
shape = (num_frames, C, H // F, W // F)
if (H, W) != (576, 1024) and "sv3d" not in version:
print(
"WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
)
if (H, W) != (576, 576) and "sv3d" in version:
print(
"WARNING: The conditioning frame you provided is not 576x576. This leads to suboptimal performance as model was only trained on 576x576."
)
if motion_bucket_id > 255:
print(
"WARNING: High motion bucket! This may lead to suboptimal performance."
)
if fps_id < 5:
print("WARNING: Small fps value! This may lead to suboptimal performance.")
if fps_id > 30:
print("WARNING: Large fps value! This may lead to suboptimal performance.")
value_dict = {}
value_dict["cond_frames_without_noise"] = image
value_dict["motion_bucket_id"] = motion_bucket_id
value_dict["fps_id"] = fps_id
value_dict["cond_aug"] = cond_aug
value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
if "sv3d_p" in version:
value_dict["polars_rad"] = polars_rad
value_dict["azimuths_rad"] = azimuths_rad
input_img_name = tempfile.mktemp()
with torch.no_grad():
with torch.autocast(device):
batch, batch_uc = get_batch(
get_unique_embedder_keys_from_conditioner(model.conditioner),
value_dict,
[1, num_frames],
T=num_frames,
device=device,
)
c, uc = model.conditioner.get_unconditional_conditioning(
batch,
batch_uc=batch_uc,
force_uc_zero_embeddings=[
"cond_frames",
"cond_frames_without_noise",
],
)
for k in ["crossattn", "concat"]:
uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
randn = torch.randn(shape, device=device)
additional_model_inputs = {}
additional_model_inputs["image_only_indicator"] = torch.zeros(
2, num_frames
).to(device)
additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
def denoiser(input, sigma, c):
return model.denoiser(
model.model, input, sigma, c, **additional_model_inputs
)
samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
model.en_and_decode_n_samples_a_time = decoding_t
samples_x = model.decode_first_stage(samples_z)
if "sv3d" in version:
samples_x[-1:] = value_dict["cond_frames_without_noise"]
samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
os.makedirs(output_folder, exist_ok=True)
save_path = os.path.join(output_folder, f"{input_img_name}")
imageio.imwrite(
save_path + ".png", processed_image
)
vid = (
(rearrange(samples, "t c h w -> t h w c") * 255)
.cpu()
.numpy()
.astype(np.uint8)
)
vid_path = save_path + ".mp4"
imageio.mimwrite(vid_path, vid)
return vid_path
def get_unique_embedder_keys_from_conditioner(conditioner):
return list(set([x.input_key for x in conditioner.embedders]))
def get_batch(keys, value_dict, N, T, device):
batch = {}
batch_uc = {}
for key in keys:
if key == "fps_id":
batch[key] = (
torch.tensor([value_dict["fps_id"]])
.to(device)
.repeat(int(math.prod(N)))
)
elif key == "motion_bucket_id":
batch[key] = (
torch.tensor([value_dict["motion_bucket_id"]])
.to(device)
.repeat(int(math.prod(N)))
)
elif key == "cond_aug":
batch[key] = repeat(
torch.tensor([value_dict["cond_aug"]]).to(device),
"1 -> b",
b=math.prod(N),
)
elif key == "cond_frames" or key == "cond_frames_without_noise":
batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=N[0])
elif key == "polars_rad" or key == "azimuths_rad":
batch[key] = torch.tensor(value_dict[key]).to(device).repeat(N[0])
else:
batch[key] = value_dict[key]
if T is not None:
batch["num_video_frames"] = T
for key in batch.keys():
if key not in batch_uc and isinstance(batch[key], torch.Tensor):
batch_uc[key] = torch.clone(batch[key])
return batch, batch_uc
def video_to_tensor(video_path):
cap = cv2.VideoCapture(video_path)
frames = []
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
cap.release()
video_np = np.array(frames)
video_tensor = torch.from_numpy(video_np).permute(0, 3, 1, 2).float()
return video_tensor
def make3d(sv3d_video, num_gaussians):
model.serializer.max_num_points = num_gaussians
name = os.path.basename(sv3d_video).split('.')[0]
images = video_to_tensor(sv3d_video).unsqueeze(0).to(device) / 255.0
images = v2.functional.resize(images, 560, interpolation=3, antialias=True).clamp(0, 1)
input_c2ws, input_Ks = get_sv3d_input_cameras(batch_size=1, radius=1.5, return_org=True)
input_c2ws, input_Ks = input_c2ws.to(device)[None], input_Ks.to(device)[None]
step = 1
indices = torch.arange(0, 21, step).long().to(device)
images = images[:, indices].contiguous().clone()
input_Ks = input_Ks[:, indices].contiguous().clone()
input_c2ws = input_c2ws[:, indices].contiguous().clone()
print(f"Images shape: {images.shape}")
with torch.no_grad():
# get latents
xyzs, _ = model.serializer(images, input_c2ws, input_Ks)
latents = model.lrm_generator.forward_latents(xyzs, images, input_Ks, input_c2ws)
# get gaussians
gaussians = model.lrm_generator.renderer.get_gaussians(xyzs, latents)
ply_path = os.path.join(gauss_path, f'{name}.ply')
model.lrm_generator.renderer.save_ply(gaussians, ply_path)
# get video
video_path_idx = os.path.join(video_path, f'{name}.mp4')
render_size = infer_config.render_resolution
render_c2ws, render_Ks = get_render_cameras(
batch_size=1,
M=120,
radius=1.5,
elevation=20.0
)
render_c2ws, render_Ks = render_c2ws.to(device), render_Ks.to(device)
out = model.lrm_generator.renderer.render(
gaussians,
render_c2ws,
render_Ks,
render_size=render_size
)
frames = out["img"][0]
save_video(
frames,
video_path_idx,
fps=30,
)
print(f"Video saved to {video_path_idx}")
return video_path_idx, ply_path
import gradio as gr
_HEADER_ = '''
<h2><b>Official 🤗 Gradio Demo</b></h2><h2><a href='https://github.com/alibaba-yuanjing-aigclab/GeoLRM' target='_blank'><b>GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation</b></a></h2>
<h3>
Tips for better results:
- Use high-resolution images for better results.
- Orthographic front-facing images lead to good reconstructions.
- Avoid white objects and overexposed images.
</h3>
'''
_LINKS_ = '''
<h3>Code is available at <a href='https://github.com/alibaba-yuanjing-aigclab/GeoLRM' target='_blank'>GitHub</a></h3>
<h3>Paper is available at <a href='https://arxiv.org/abs/TODO' target='_blank'>arXiv</a></h3>
'''
# TODO
_CITE_ = r"""
```bibtex
```
"""
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",
width=256,
height=256,
type="pil",
elem_id="content_image",
)
processed_image = gr.Image(
label="Processed Image",
image_mode="RGB",
width=256,
height=256,
type="pil",
interactive=False
)
with gr.Row():
with gr.Group():
do_remove_background = gr.Checkbox(
label="Remove Background", value=False
)
sample_seed = gr.Number(value=42, label="Seed Value", precision=0)
num_gaussians = gr.Slider(
label="Number of Tokens (32 3DGS per token)",
minimum=4096,
maximum=16384,
value=8192,
step=4096,
)
with gr.Row():
submit = gr.Button("Generate", elem_id="generate", variant="primary")
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],
label="Examples",
examples_per_page=20
)
with gr.Column():
with gr.Row():
with gr.Column():
with gr.Tab(label="GeoLRM result"):
output_video = gr.Video(
label="Rendered 3D model", format="mp4",
width=379,
autoplay=True,
interactive=False
)
with gr.Tab(label="SV3D result"):
sv3d_video = gr.Video(
label="SV3D video", format="mp4",
width=379,
autoplay=True,
interactive=False
)
with gr.Column():
gaussians = gr.File(
label="3D Gaussians (PLY Format)",
type="file",
width=379,
download=True,
elem_id="gaussians",
interactive=False
)
with gr.Row():
gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
gr.Markdown(_LINKS_)
gr.Markdown(_CITE_)
submit.click(fn=check_input_image, inputs=[input_image]).success(
fn=preprocess,
inputs=[input_image, do_remove_background],
outputs=[processed_image],
).success(
fn=sample_videos,
inputs=[processed_image, sample_seed],
outputs=[sv3d_video],
).success(
fn=make3d,
inputs=[sv3d_video, num_gaussians],
outputs=[output_video, gaussians]
)
demo.queue(max_size=10)
demo.launch(server_name="0.0.0.0", server_port=42339)