Pseudocylindrical convolutions for Learned Omnidirectional Image Compression
Requirmed packages:
- pytorch
- cv2 (python-opencv)
- numpy
Install:
- python setup.py install
- cd coder & python setup_linux.py install
Running the codec for 360-degree images:
- Encoding:
- python pseudo_codec.py --enc --img-file image_names.txt --code-file code_names.txt --model-idx 3 --ssim
- python pseudo_codec.py --enc --img-list a.png b.png --code-list code_a code_b --model-idx 3 --ssim
- Decoding:
- python pseudo_codec.py --dec --out-file decoded_image_names.txt --code-file code_names.txt --model-idx 3 --ssim
- python pseudo_codec.py --dec --out-list a_dec.png b_dec.png --code-list code_a code_b --model-idx 3 --ssim
- Testing (Decoding and evaluate the performance):
- python pseudo_codec.py --test --img-file source_image_names.txt --code-file code_names.txt --model-idx 3 --ssim
- python pseudo_codec.py --test --img-list a.png b.png --code-list code_a code_b --model-idx 3 --ssim
- python pseudo_codec.py --test --img-list a.png b.png --code-list code_a code_b --model-idx 3 --ssim