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import os
from os.path import basename, splitext, join
import tempfile
import gradio as gr
import numpy as np
from PIL import Image
import torch
import cv2
from torchvision.transforms.functional import to_tensor, to_pil_image
from torch import Tensor
from genstereo import GenStereo, AdaptiveFusionLayer
import ssl
from huggingface_hub import hf_hub_download
import spaces
from extern.DAM2.depth_anything_v2.dpt import DepthAnythingV2
ssl._create_default_https_context = ssl._create_unverified_context
IMAGE_SIZE = 512
DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
CHECKPOINT_NAME = 'genstereo'
def download_models():
models = [
{
'repo': 'stabilityai/sd-vae-ft-mse',
'sub': None,
'dst': 'checkpoints/sd-vae-ft-mse',
'files': ['config.json', 'diffusion_pytorch_model.safetensors'],
'token': None
},
{
'repo': 'lambdalabs/sd-image-variations-diffusers',
'sub': 'image_encoder',
'dst': 'checkpoints',
'files': ['config.json', 'pytorch_model.bin'],
'token': None
},
{
'repo': 'FQiao/GenStereo',
'sub': None,
'dst': 'checkpoints/genstereo',
'files': ['config.json', 'denoising_unet.pth', 'fusion_layer.pth', 'pose_guider.pth', 'reference_unet.pth'],
'token': None
},
{
'repo': 'depth-anything/Depth-Anything-V2-Large',
'sub': None,
'dst': 'checkpoints',
'files': [f'depth_anything_v2_vitl.pth'],
'token': None
}
]
for model in models:
for file in model['files']:
hf_hub_download(
repo_id=model['repo'],
subfolder=model['sub'],
filename=file,
local_dir=model['dst'],
token=model['token']
)
# Setup.
download_models()
# DepthAnythingV2
def get_dam2_model():
model_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
}
encoder = 'vitl'
encoder_size_map = {'vits': 'Small', 'vitb': 'Base', 'vitl': 'Large'}
if encoder not in encoder_size_map:
raise ValueError(f"Unsupported encoder: {encoder}. Supported: {list(encoder_size_map.keys())}")
dam2 = DepthAnythingV2(**model_configs[encoder])
dam2_checkpoint = f'checkpoints/depth_anything_v2_{encoder}.pth'
dam2.load_state_dict(torch.load(dam2_checkpoint, map_location='cpu'))
dam2 = dam2.to(DEVICE).eval()
return dam2
# GenStereo
def get_genstereo_model():
genwarp_cfg = dict(
pretrained_model_path='checkpoints',
checkpoint_name=CHECKPOINT_NAME,
half_precision_weights=True
)
genstereo = GenStereo(cfg=genwarp_cfg, device=DEVICE)
return genstereo
# Adaptive Fusion
def get_fusion_model():
fusion_model = AdaptiveFusionLayer()
fusion_checkpoint = join('checkpoints', CHECKPOINT_NAME, 'fusion_layer.pth')
fusion_model.load_state_dict(torch.load(fusion_checkpoint, map_location='cpu'))
fusion_model = fusion_model.to(DEVICE).eval()
return fusion_model
# Crop the image to the shorter side.
def crop(img: Image) -> Image:
W, H = img.size
if W < H:
left, right = 0, W
top, bottom = np.ceil((H - W) / 2.), np.floor((H - W) / 2.) + W
else:
left, right = np.ceil((W - H) / 2.), np.floor((W - H) / 2.) + H
top, bottom = 0, H
return img.crop((left, top, right, bottom))
# Gradio app
with tempfile.TemporaryDirectory() as tmpdir:
with gr.Blocks(
title='StereoGen Demo',
css='img {display: inline;}'
) as demo:
# Internal states.
src_image = gr.State()
src_depth = gr.State()
proj_mtx = gr.State()
src_view_mtx = gr.State()
# Blocks.
gr.Markdown(
"""
# StereoGen: Towards Open-World Generation of Stereo Images and Unsupervised Matching
[](https://qjizhi.github.io/genstereo) |
[](https://huggingface.co/spaces/FQiao/GenStereo) |
[](https://github.com/Qjizhi/GenStereo) |
[](https://huggingface.co/FQiao/GenStereo/tree/main) |
[](https://arxiv.org/abs/2405.17251)
## Introduction
This is an official demo for the paper "[Towards Open-World Generation of Stereo Images and Unsupervised Matching](https://qjizhi.github.io/genstereo)". Given an arbitrary reference image, GenStereo can generate the corresponding right-view image.
## How to Use
1. Upload a reference image to "Left Image"
*(You can also select an image from "Examples")*
2. Adjust the Scale Factor (controls depth intensity)
3. Click "Generate a right image" and view results
"""
)
file = gr.File(label='Left', file_types=['image'])
examples = gr.Examples(
examples=['./assets/COCO_val2017_000000070229.jpg',
'./assets/COCO_val2017_000000092839.jpg',
'./assets/KITTI2015_000003_10.png',
'./assets/KITTI2015_000147_10.png'],
inputs=file
)
with gr.Row():
image_widget = gr.Image(
label='Depth', type='filepath',
interactive=False
)
depth_widget = gr.Image(label='Estimated Depth', type='pil')
# Add scale factor slider
scale_slider = gr.Slider(
label='Scale Factor',
minimum=1.0,
maximum=30.0,
value=15.0,
step=0.1,
)
button = gr.Button('Generate a right image', size='lg', variant='primary')
with gr.Row():
warped_widget = gr.Image(
label='Warped Image', type='pil', interactive=False
)
gen_widget = gr.Image(
label='Generated Right', type='pil', interactive=False
)
def normalize_disp(disp):
return (disp - disp.min()) / (disp.max() - disp.min())
# Callbacks
@spaces.GPU()
def cb_mde(image_file: str):
if not image_file:
# Return None if no image is provided (e.g., when file is cleared).
return None, None, None, None
image = crop(Image.open(image_file).convert('RGB')) # Load image using PIL
image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
image_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
dam2 = get_dam2_model()
depth_dam2 = dam2.infer_image(image_bgr)
depth = torch.tensor(depth_dam2).unsqueeze(0).unsqueeze(0).float()
depth_image = cv2.applyColorMap((normalize_disp(depth_dam2) * 255).astype(np.uint8), cv2.COLORMAP_JET)
return image, depth_image, image, depth
@spaces.GPU()
def cb_generate(image, depth: Tensor, scale_factor):
norm_disp = normalize_disp(depth.cuda())
disp = norm_disp * scale_factor / 100 * IMAGE_SIZE
genstereo = get_genstereo_model()
fusion_model = get_fusion_model()
renders = genstereo(
src_image=image,
src_disparity=disp,
ratio=None,
)
warped = (renders['warped'] + 1) / 2
synthesized = renders['synthesized']
mask = renders['mask']
fusion_image = fusion_model(synthesized.float(), warped.float(), mask.float())
warped_pil = to_pil_image(warped[0])
fusion_pil = to_pil_image(fusion_image[0])
return warped_pil, fusion_pil
# Events
file.change(
fn=cb_mde,
inputs=file,
outputs=[image_widget, depth_widget, src_image, src_depth]
)
button.click(
fn=cb_generate,
inputs=[src_image, src_depth, scale_slider],
outputs=[warped_widget, gen_widget]
)
if __name__ == '__main__':
demo.launch() |