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CLCC: Contrastive Learning for Color Constancy (CVPR 2021)

(Poster & video will be released before 5/31!)

Yi-Chen Lo*, Chia-Che Chang*, Hsuan-Chao Chiu, Yu-Hao Huang, Chia-Ping Chen, Yu-Lin Chang, Kevin Jou

MediaTek Inc., Hsinchu, Taiwan

(*) indicates equal contribution.

Paper

TL;DR:

Code

CLCC is a Python 3 & TensorFlow 1.x implementation based on FC4 codebase.

• Dataset preparation: Download dataset from here. Please make sure your dataset folder is structured as <DATA_DIR>/<DATA_NAME>/<FOLD_ID> (e.g., data/gehler/0).
• Training: Modify config.py (i.e., you may want to rename EXP_NAME and specify training data DATA_NAME, TRAIN_FOLDS, TEST_FOLDS) and execute train.py. Checkpoints will be saved under ckpts/EXP_NAME during training.
• Evaluation: Once training is done, you can evaluate checkpoint with eval.py on a specific test fold. We recommend to refer to scripts/eval_squeezenet_clcc_gehler.sh for 3-fold cross-validation.

Dataset

Since the original data preprocessing code and procedure are quite tedious, we preprocess each fold of dataset and stored in .pkl format for each sample. Each sample contains:

• Raw image: Mask color checker; Subtract black level; Convert to uint16 [0, 65535] BGR numpy array with shape (H, W, 3).
• RGB label: L2-normalized numpy vector with shape (3,).
• Color checker: [0, 4095] BGR numpy array with shape (24, 3) for raw-to-raw mapping presented in our paper (see util/raw2raw.py and also section 4.3 in our paper). A few of them are stored in all zeros due to the failure of color checker detection. Note that we convert it into RGB format during preprocessing in dataloader.py, and our raw-to-raw mapping algorithm also manipulates it in RGB format.

Acknowledgments

• FC4: https://github.com/yuanming-hu/fc4.
• Color checker detection: https://github.com/colour-science/colour-checker-detection. To increase detection accuracy, performing homography with color checker coordinates provided by the original dataset can help a lot.