Library generating 4x 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
# 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
cartoon_upsampling_4x( './a_tiny_image.png', './a_4x_larger_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-0.1.2.tar.gz
(9.0 MB
view hashes)
Built Distribution
Close
Hashes for super_resolution-0.1.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 50ba89cd6312af3dfa290b083ddb99c8e2ce635d65cdd668ac7741aa6c56b1fb |
|
MD5 | b036269145f47eed75e2e0e01df2f687 |
|
BLAKE2b-256 | 3389f78002d97d2d9540151dd46ccdc8831472ce0a60a89add6875b8961226f1 |