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https://github.com/bcmi/Foreground-Object-Search-Dataset-FOSD
[ICCV 2023] The datasets and code used in our paper "Foreground Object Search by Distilling Composite Image Feature", ICCV2023.
https://github.com/bcmi/Foreground-Object-Search-Dataset-FOSD
foreground-object-search image-composition
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[ICCV 2023] The datasets and code used in our paper "Foreground Object Search by Distilling Composite Image Feature", ICCV2023.
- Host: GitHub
- URL: https://github.com/bcmi/Foreground-Object-Search-Dataset-FOSD
- Owner: bcmi
- Created: 2023-07-18T04:54:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-17T09:29:39.000Z (8 months ago)
- Last Synced: 2024-04-21T20:14:32.135Z (7 months ago)
- Topics: foreground-object-search, image-composition
- Language: Python
- Homepage:
- Size: 45.9 KB
- Stars: 17
- Watchers: 6
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Foreground Object Search Dataset FOSD
This is the official repository for the following paper:
> **Foreground Object Search by Distilling Composite Image Feature** [[arXiv]](https://arxiv.org/pdf/2308.04990.pdf)
>
> Bo Zhang, Jiacheng Sui, Li Niu
> Accepted by **ICCV 2023**.
**Our model has been integrated into our image composition toolbox libcom https://github.com/bcmi/libcom. Welcome to visit and try ＼(^▽^)／**
## Requirements
- See requirements.txt for other dependencies.
## Data Preparing
- Download Open-Images-v6 trainset from [Open Images V6 - Download](https://storage.googleapis.com/openimages/web/download_v6.html) and unzip them. We recommend that you use FiftyOne to download the Open-Images-v6 dataset. After the dataset is downloaded, the data structure of Open-Images-v6 dataset should be as follows.
```
Open-Images-v6
├── metadata
├── train
│ ├── data
│ │ ├── xxx.jpg
│ │ ├── xxx.jpg
│ │ ...
│ │
│ └── labels
│ └── masks
│ │ ├── 0
│ │ ├── xxx.png
│ │ ├── xxx.png
│ │ ...
│ │ ├── 1
│ │ ...
│ │
│ ├── segmentations.csv
│ ...
```
- Download S-FOSD annotations, R-FOSD annotations and background images of R-FOSD from [Baidu disk](https://pan.baidu.com/s/1LF_4LbwxbxSBy-zqBkgzDw) (code: 3wvf) and save them to the appropriate location under the `data` directory according to the data structure below.
- Generate backgrounds and foregrounds.
```
python prepare_data/fetch_data.py --open_images_dir
```
The data structure is like this:
```
data
├── metadata
│ ├── classes.csv
│ └── category_embeddings.pkl
├── test
│ ├── bg_set1
│ │ ├── xxx.jpg
│ │ ├── xxx.jpg
│ │ ...
│ │
│ ├── bg_set2
│ │ ├── xxx.jpg
│ │ ├── xxx.jpg
│ │ ...
│ │
│ ├── fg
│ │ ├── xxx.jpg
│ │ ├── xxx.jpg
│ │ ...
│ └── labels
│ └── masks
│ │ ├── 0
│ │ ├── xxx.png
│ │ ├── xxx.png
│ │ ...
│ │ ├── 1
│ │ ...
│ │
│ ├── test_set1.json
│ ├── test_set2.json
│ └── segmentations.csv
│
└── train
├── bg
│ ├── xxx.jpg
│ ├── xxx.jpg
│ ...
│
├── fg
│ ├── xxx.jpg
│ ├── xxx.jpg
│ ...
│
└── labels
└── masks
│ ├── 0
│ ├── xxx.png
│ ├── xxx.png
│ ...
│ ├── 1
│ ...
│
├── train_sfosd.json
├── train_rfosd.json
├── category.json
├── number_per_category.csv
└── segmentations.csv
```
## Pretrained Model
We provide the checkpoint ([Baidu disk](https://pan.baidu.com/s/1_Dh2w08AAqdsw8Cb3l4nfQ) code: 7793) for the evaluation on S-FOSD dataset and checkpoint ([Baidu disk](https://pan.baidu.com/s/17jq1FWKSsEngp7scB4357Q) code: 6kme) for testing on R-FOSD dataset. By default, we assume that the pretrained model is downloaded and saved to the directory `checkpoints`.
## Testing
### Evaluation on S-FOSD Dataset
```
python evaluate/evaluate.py --testOnSet1
```
### Evaluation on R-FOSD Dataset
```
python evaluate/evaluate.py --testOnSet2
```
The evaluation results will be stored to the directory `eval_results`.
If you want to save top 20 results on R-FOSD, add `--saveTop20 ` parameter. The top 20 results on R-FOSD will be stored to the directory `top20` by default.
If you want to save the model's prediction scores on R-FOSD, add `--saveScores` parameter. The model scores on R-FOSD will be stored to the directory `model_scores` by default.
## Training
Please download the pretrained teacher models from [Baidu disk](https://pan.baidu.com/s/1D_zT326PLXZ-C0j5mcCY6A) (code: 40a5) and save the model to directory `checkpoints/teacher`.
To train a new sfosd model, you can simply run:
```
.train/train_sfosd.sh
```
Similarly, train a new rfosd model by:
```
.train/train_rfosd.sh
```
## FOS Score
Our model can be used to evaluate the compatibility between foreground and background in terms of geometry and semantics.
To launch the demo, you can run:
```
python demo/demo_ui.py
```
Here are three steps you can take to get a compatibility score for the foreground and the background.
1) Upload a background image in the left box of the first row
2) Click the left-top point and the right-bottom point of the bounding box in the right box of the first row
3) Upload a foreground image in the left box of the second row, then click 'run' button.
## Other Resources
+ [Awesome-Foreground-Object-Search](https://github.com/bcmi/Awesome-Foreground-Object-Search)
+ [Awesome-Image-Composition](https://github.com/bcmi/Awesome-Image-Composition)
## License
Both background and foreground images of S-FOSD belong to Open-Images. The background images of R-FOSD are collected from Internet and are licensed under a Creative Commons Attribution 4.0 License.