Superpixel Feature Extractor
Dependencies
- PyTorch
- Scikit Learn Image
- OpenAI CLIP
- Salesforce LAVIS
Parameters
| Name | Description |
|---|---|
--image_dir | The directory containnig image inputs |
--save_dir | The directory to save the npz files to |
--num_superpixels | The number of superpixels to generate per image |
--model_id | Which model to use? [BLIP / CLIP / ResNet] |
--whole_img | (Flag) Generate a single feature for the whole image |
--is_masked | (Flag) Black out pixels in the superpixel bounding box that aren’t in the original superpixel |
--patches | (Flag) Generate patch features instead of superpixel features |
Warning: The --patches flag will generate $16 \times 16$ patches for an image that is resized to $224 \times 224$, yielding $14 \times 14 = 196$ patches
Example
python3 main.py --image_dir "/homes/hps01/superpixel-features/test_images" \
--save_dir "/homes/hps01/superpixel-features/test_output/" \
--is_masked \
--model_id "BLIP" \
--num_superpixels 25 \