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- # Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
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-
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- > [Ming Qian](https://qianmingduowan.github.io/), Jincheng Xiong, [Gui-Song Xia](http://www.captain-whu.com/xia_En.html), [Nan Xue](https://xuenan.net)
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- >
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- > IEEE/CVF International Conference on Computer Vision (ICCV), 2023
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- >
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- > [Project](https://sat2density.github.io/) | [Paper](https://arxiv.org/abs/2303.14672) | [Data]() | [Install.md](docs/INSTALL.md)
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-
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- > <p align="center" float="left">
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- > <img src="docs/figures/demo/case1.sat.gif" alt="drawing" width="19%">
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- > <img src="docs/figures/demo-density/case1.gif" alt="drawing" width="38%">
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- > <img src="docs/figures/demo/case1.render.gif" alt="drawing" width="38%">
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- > </p>
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-
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- > <p align="center" float="left">
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- > <img src="docs/figures/demo/case2.sat.gif" alt="drawing" width="19%">
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- > <img src="docs/figures/demo-density/case2.gif" alt="drawing" width="38%">
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- > <img src="docs/figures/demo/case2.render.gif" alt="drawing" width="38%">
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- > </p>
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-
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- > <p align="center" float="left">
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- > <img src="docs/figures/demo/case3.sat.gif" alt="drawing" width="19%">
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- > <img src="docs/figures/demo-density/case3.gif" alt="drawing" width="38%">
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- > <img src="docs/figures/demo/case3.render.gif" alt="drawing" width="38%">
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- > </p>
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-
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- > <p align="center" float="left">
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- > <img src="docs/figures/demo/case4.sat.gif" alt="drawing" width="19%">
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- > <img src="docs/figures/demo-density/case4.gif" alt="drawing" width="38%">
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- > <img src="docs/figures/demo/case4.render.gif" alt="drawing" width="38%">
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- > </p>
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-
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- ## Checkpoints Downloading
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- > Two checkpoints for CVACT and CVUSA can be found from [thisurl](https://github.com/sat2density/checkpoints/releases). You can also run the following command to download them.
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- ```
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- bash scripts/download_weights.sh
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- ```
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-
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- ## QuickStart Demo
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- ### Video Synthesis
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- #### Example Usage
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- ```
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- python test.py --yaml=sat2density_cvact \
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- --test_ckpt_path=2u87bj8w \
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- --task=test_vid \
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- --demo_img=demo_img/case1/satview-input.png \
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- --sty_img=demo_img/case1/groundview.image.png \
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- --save_dir=results/case1
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- ```
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- ####
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-
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- ### Illumination Interpolation
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- <!-- ```
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- bash inference/quick_demo_interpolation.sh
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- ``` -->
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- ```
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- python test.py --task=test_interpolation \
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- --yaml=sat2density_cvact \
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- --test_ckpt_path=2u87bj8w \
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- --sty_img1=demo_img/case9/groundview.image.png \
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- --sty_img2=demo_img/case7/groundview.image.png \
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- --demo_img=demo_img/case3/satview-input.png \
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- --save_dir=results/case2
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- ```
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-
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- ## Train & Inference
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- - *We trained our model using 1 V100 32GB GPU. The training phase will take about 20 hours.*
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- - *For data preparation, please check out [data.md](dataset/INSTALL.md).*
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-
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-
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-
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-
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- ### Inference
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-
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- To test Center Ground-View Synthesis setting
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- If you want save results, please add --task=vis_test
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- ```bash
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- # CVACT
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- python offline_train_test.py --yaml=sat2density_cvact --test_ckpt_path=2u87bj8w
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- # CVUSA
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- python offline_train_test.py --yaml=sat2density_cvusa --test_ckpt_path=2cqv8uh4
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- ```
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-
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- To test inference with different illumination
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- ```bash
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- # CVACT
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- bash inference/single_style_test_cvact.sh
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- # CVUSA
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- bash inference/single_style_test_cvusa.sh
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- ```
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-
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- To test synthesis ground videos
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- ```bash
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- bash inference/synthesis_video.sh
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- ```
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-
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- ## Training
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-
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- ### Training command
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-
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- ```bash
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- # CVACT
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- CUDA_VISIBLE_DEVICES=X python train.py --yaml=sat2density_cvact
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- # CVUSA
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- CUDA_VISIBLE_DEVICES=X python train.py --yaml=sat2density_cvusa
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- ```
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-
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- ## Citation
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- If you use this code for your research, please cite
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-
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- ```
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- @inproceedings{qian2021sat2density,
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- title={Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs},
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- author={Qian, Ming and Xiong, Jincheng and Xia, Gui-Song and Xue, Nan},
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- booktitle={ICCV},
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- year={2023}
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- }
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- ```
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-
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- ## License
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- This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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- For commercial use, please contact [mingqian@whu.edu.cn].
 
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+ ---
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+ title: sat3density
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+ emoji: 🐠
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+ colorFrom: yellow
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+ colorTo: purple
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+ sdk: gradio
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+ sdk_version: 3.36.1
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+ app_file: app.py
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+ pinned: false
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+ license: mit
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+ ---
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference