Library generating 4x/8x super resoltion using deep convolutional neural networks.
Project description
Super Resolution
Image Super-Resolution using Deep Convolutional Neural Networks.
Installing
Install and update using pip:
pip3 install super-resolution
Or
git checkout https://github.com/fengwang/super_resolution.git
cd super_resolution
python3 -m pip install -e .
Usage
Command line:
super_resolution INPUT_IMAGE_PATH OUTPUT_IMAGE_PATH_4X
Using Python API:
# uncomment the follow three lines if you have a Nvidia GPU but you do not want to enable it.
#import os
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"]=''
from super_resolution import cartoon_upsampling_4x
large_image = cartoon_upsampling_4x( './a_tiny_input_image.png', './a_4x_larger_output_image.png' )
from super_resolution import cartoon_upsampling_8x
large_image = cartoon_upsampling_8x( './a_tiny_input_image.png', './a_8x_larger_output_image.png' )
Details
- The super resolution model is inherited from
Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network, Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681-4690.
- The training images are downloaded from Konachan (NSFW).
License
- BSD
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
super_resolution-0.2.1.tar.gz
(28.3 MB
view hashes)
Built Distribution
Close
Hashes for super_resolution-0.2.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 178a03742ae234d861ea3bd5f306f49a3c8db0501ffc676badb9646a20464285 |
|
MD5 | 73246a7d171e5b48abb19a6f4ce427e4 |
|
BLAKE2b-256 | feb38e00bbd32d15ca0e76923113bef4b660356342da808c6cb32479d8c7e97d |