Awesome
CDR Converter
We provide the python script to convert the dataset proposed in CDR: A Categorized and Diverse Real-World Reflection Removal Dataset based on different options.
Setup
The script requires Python 3.5+ and cv2. Please download our data (~7.45GB) from this link. We have 1063 triplets (M, R, T) in total.
Usage
python convert.py --datapath <DATAPATH> --csvpath <CSVPATH> --output <OUTPUTDIR> --[options]
You can choose to generate one (or a subset) of our dataset by setting the following self-explanatory arguments:
--train
--val
--test
--type
--reflection
--ghost
--motion
For example, if you want to down only train set,
python convert.py --datapath <DATAPATH> --csvpath <CSVPATH> --output <OUTPUTDIR> --train
Note that these arguments can be combined to generate a set satisfying all options,
python convert.py --datapath <DATAPATH> --csvpath <CSVPATH> --output <OUTPUTDIR> --test --type SRST --reflection medium
will generate testset with SRST type AND medium reflection.
Considering some methods may require input image in size of a multiple 32, we also provide an argument --crop32
, which will generate images in size of its nearest 32's multiples.
Some crops in our dataset may have large ratio of longer side / shorter side, you can remove those crops by --remove_extreme
Our dataset is in high resolution, so we also support downsampling option by specifying --downsample_scale
argument followed by an integer.
Output
You must specify the output folder with --output
argument.
Folders structure
You should expect the original data structure looks like
data/
└── isprgb_crop
└── with_gt
├── C1
├── C10
├── C11
├── C2
├── C3
├── C4
├── C5
├── C6
├── C7
├── C8
├── C9
├── H1
├── N1
├── N2
├── N3
├── N4
├── N5
├── N6
└── N7
- isprgb_crop: cropped data inside valid regions
Each leaf directory will contain .png
files accordingly. Also, there is a four unique digit number (e.g. 5532, 5531) for each M and R image, while the corresponding T image is named as "M_R" (e.g. 5532_5531).
For normal benchmarking (as written in our script), only isprgb_crop/ folder will be used, so we only make this folder public for the first release.
<!-- However, you are also welcome to play with the original data. But please ensure that only valid region bounded by mask are valid for _T = M-R_. -->Citation
If you find this dataset or code useful, please kindly reference: