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Superpixel Features

Generate features for superpixels and patches using pretrained models. Features are saved as .npz files with keys for the features ('feats'), superpixel bounding box ('bbox'), and region adjacency edges ('rag').

In terms of feature space, the code supports ResNet, CLIP, and BLIPv2 features. Currently, only SLIC and Watershed superpixel segmentation algorithms are implemented (via scikit-image) however, patching is also supported (but not with the --rag flag).

To generate a collection of superpixels for the COCO dataset, see runner.sh for an example of how this can be achieved.

For compatiblity with the Karpathy Split of the COCO dataset, merge_and_clean.py is provided. This script will move and rename the superpixel feature files such that they can be used in place of the BUTD files in the original Karpathy Split JSON files.

Dependencies

python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
python3 -m pip install scikit-image
python3 -m pip install salesforce-lavis
python3 -m pip install clip-openai

Parameters

Name Description
--image_dir The directory containnig image inputs
--save_dir The directory to save the npz files to
--feature_extractor Which model to use? [BLIP / CLIP / ResNet]
--num_superpixels The number of superpixels to generate per image (Not compatible with --whole_img)
--algorithm Which superpixel algorithm to use? [SLIC / watershed]
--whole_img (Flag) Generate a single feature for the whole image (Not compatible with --rag)
--patches (Flag) Generate patch features instead of superpixel features (Not compatible with --rag)
--rag (Flag) Generate the Region Adjacency Graph edges between superpixels

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

Examples

Generate Watershed superpixel CLIP features for the Karpathy Test Set

python3 main.py --image_dir "/home/hsenior/coco/img/test2014/" \
    --save_dir "/home/hsenior/coco/superpixel_features/" \
    --model_id "CLIP" \
    --num_superpixels 25 \
    --algorithm "watershed" \
    --rag

Generate whole image ResNet features for the Karpathy Validation set

python3 main.py --image_dir "/home/hsenior/coco/img/val2014/" \
    --save_dir "/home/hsenior/coco/superpixel_features/" \
    --model_id "CLIP" \
    --whole_img

Generate SLIC superpixel features for the Karpathy Train Set (without the RAG edges)

python3 main.py --image_dir "/home/hsenior/coco/img/train2014/" \
    --save_dir "/home/hsenior/coco/superpixel_features/" \
    --model_id "BLIP" \
    --num_superpixels 75 \
    --algorithm "SLIC" \