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https://github.com/andraniksargsyan/qartezator

Qartezator is an aerial image-to-map translator
https://github.com/andraniksargsyan/qartezator

aerial-imagery generative-adversarial-network image-to-image-translation

Last synced: about 2 months ago
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Qartezator is an aerial image-to-map translator

Host: GitHub
URL: https://github.com/andraniksargsyan/qartezator
Owner: AndranikSargsyan
License: mit
Created: 2023-05-20T16:32:17.000Z (over 1 year ago)
Default Branch: master
Last Pushed: 2023-05-29T15:25:06.000Z (over 1 year ago)
Last Synced: 2024-05-06T00:26:00.946Z (8 months ago)
Topics: aerial-imagery, generative-adversarial-network, image-to-image-translation
Language: Jupyter Notebook
Homepage:
Size: 3.88 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# 🌍 Qartezator: Yet another aerial image-to-map translator

Qartezator is your translator between aerial images and maps.

![Qartezator teaser](https://github.com/AndranikSargsyan/qartezator/blob/master/assets/teaser.gif)

## Environment setup

Clone the repo: `git clone https://github.com/AndranikSargsyan/qartezator.git`

Set up virtualenv:
```bash
cd qartezator
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```

If you need torch+cuda, you can use the following command
```bash
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
```

## Demo
Download all models from [this link](https://drive.google.com/drive/folders/1dFtVLJXO7fuq9lYFIXMMquFS5cO1t4i4?usp=sharing) and place inside `models/` directory.

### StreamLit

Start StreamLit demo by running:
```bash
streamlit run demo.py
```

### CLI Inference
```bash
python -m qartezator.inference -m PATH-TO-MODEL -i PATH-TO-IMAGE -o OUTPUT-PATH
```

## Training

Download training data from [here](https://drive.google.com/uc?id=1nq4yAQ5HSVOGL5B2juPU_L5WElKKVBPj) and extract into data/
directory.

To start the training run

```bash
python -m qartezator.train --config-path ./qartezator/configs/qartezator-fourier.yaml
```

## Evaluation
Download test images from [here](https://drive.google.com/file/d/1RdrgqyxRY0cdaHzGnjsZYCiDrd7T-34C/view?usp=share_link).

To do the inference on test set, run

```bash
python scripts/predict_many.py --source-dir SOURCE_IMG_DIR --model-path TRACED_MODEL_PATH --output-dir OUTPUT_DIR
```
please see more argument options in the script.

To evaluate **PSNR**, **SSIM** and **L1**, use

```bash
python scripts/cal_psnr_ssim_l1.py --gt-path TARGET_MAPS_DIR --pred-path PREDICTED_MAPS_DIR
```

To calculate **FID** use

```bash
python -m pytorch_fid PREDICTED_MAPS_PATH TARGET_MAPS_PATH
```

## Results

### Qartezator-Fourier

Aerial image
Target map
Predicted map