CCCNN

• Name: GunGyeom James Kim
• OS: Windows 11 / Colab(V100 GPU)

Descripton

PyTorch implementation of Color Constancy Convolutional Network in Color Constancy Using CNNs

S. Bianco, C. Cusano and R. Schettini, "Color constancy using CNNs," 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA, 2015, pp. 81-89, doi: 10.1109/CVPRW.2015.7301275.

TODO

• At testing time, generate a single illuminant estimation per image by pooling the predicted patch illuminants
• Optimize RandomPatches

How to

You can just run train and test on Colab using colab.ipynb or on your terminal by following below requiement and commands

Requirements

• matplotlib 3.5.3
• numpy 1.21.5
• opencv-python 4.7.0.72
• pandas 1.3.5
• torch 1.13.1
• torchvision 0.14.1
• tqdm 4.65.0

Train

The SimpleCube++ dataset

python train.py --train-images-dir ./SimpleCube++/train/PNG \
                --train-labels-file .SimpleCube++/train/gt.csv \
                --eval-images-dir ./SimpleCube++/test/PNG \
                --eval-labels-file ./SimpleCube++/test/gt.csv \
                --outputs-dir ./pth \
                --batch-size 32 \
                --num-epochs 10 \
                --lr 1e-3 \
                --num-patches 5

Test

The SimpleCube++ dataset

python src/test.py --weights-file "pth/srcnn_x3.pth" \
               --image-file "data/butterfly.png" \