This script runs neural style transfer against the provided content image.

Overview

Neural Style Transfer

Content Style Output

Description:

This script runs neural style transfer against the provided content image. The content image must be present in the src/data/content directory. All style images, that are in the directory src/data/style will be applied against it. The outputs will be saved in generated/ directory as .png files. By default, the input images (both style and content images, as they must be of the same dimensionality) will be transformed to the 512x512 size for the algorithm, and output image will be of size 512x512 as well. To change that, please use additional script arguments.

The neural networks architecture is based on the documentation provided by PyTorch, which itself uses Neural-Style algorithm developed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.

Usage examples:

./main.py -i 
   
   
    
    
./main.py --image 
    
    
     
     
python3 main.py -i 
     
     
      
      
python3 main.py --image 
      
      
       
       

./main.py -i 
       
       
         -he 1024 -wi 1024 ./main.py --image 
        
          -height 1024 -width 1024 
         
       
      
      
     
     
    
    
   
   

Script arguments:

Required arguments:
  -i IMAGE, --image IMAGE
                        Content image to be taken as input. The file must be present in src/data/content.

Optional arguments:
  -h, --help            Show help message and exit.
  -he HEIGHT, --height HEIGHT
                        Sets the height of the input and output images. The default value is 512.
  -wi WIDTH, --width WIDTH
                        Sets the width of the input and output images. The default value is 512.

Setup

Python 3.9 must be installed on the operating system. To install the needed python dependencies run:

pip3 install -r requirements.txt

To run the script as ./main.py -i or to run the linter script, one might need to give executable permissions:

chmod -x main.py
chmod -x ./scripts/lint.sh

To run the black formatter and pylint linter, please run:

./scripts/lint.sh

The script ./scripts/lint_check.sh will be run as GitHub action, and will fail if any possible changes are detected.

Owner
Martynas Subonis
Full-Stack Software Engineer
Martynas Subonis
Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

HaloNet - Pytorch Implementation of the Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones. This re

Phil Wang 189 Nov 22, 2022
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t

Algo Phantoms 81 Nov 26, 2022
Code for the paper: Sketch Your Own GAN

Sketch Your Own GAN Project | Paper | Youtube Our method takes in one or a few hand-drawn sketches and customizes an off-the-shelf GAN to match the in

677 Dec 28, 2022
Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Exploring Cross-Image Pixel Contrast for Semantic Segmentation Exploring Cross-Image Pixel Contrast for Semantic Segmentation, Wenguan Wang, Tianfei Z

Tianfei Zhou 510 Jan 02, 2023
K-FACE Analysis Project on Pytorch

Installation Setup with Conda # create a new environment conda create --name insightKface python=3.7 # or over conda activate insightKface #install t

Jung Jun Uk 7 Nov 10, 2022
Non-Vacuous Generalisation Bounds for Shallow Neural Networks

This package requires jax, tensorflow, and numpy. Either tensorflow or scikit-learn can be used for loading data. To run in a nix-shell with required

Felix Biggs 0 Feb 04, 2022
AVD Quickstart Containerlab

AVD Quickstart Containerlab WARNING This repository is still under construction. It's fully functional, but has number of limitations. For example: RE

Carl Buchmann 3 Apr 10, 2022
Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

Explainable_FIQA_WITH_AMVA Note This is the official repository of the paper: Explainability of the Implications of Supervised and Unsupervised Face I

3 May 08, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
Auxiliary Raw Net (ARawNet) is a ASVSpoof detection model taking both raw waveform and handcrafted features as inputs, to balance the trade-off between performance and model complexity.

Overview This repository is an implementation of the Auxiliary Raw Net (ARawNet), which is ASVSpoof detection system taking both raw waveform and hand

6 Jul 08, 2022
Clinica is a software platform for clinical research studies involving patients with neurological and psychiatric diseases and the acquisition of multimodal data

Clinica Software platform for clinical neuroimaging studies Homepage | Documentation | Paper | Forum | See also: AD-ML, AD-DL ClinicaDL About The Proj

ARAMIS Lab 165 Dec 29, 2022
The official PyTorch implementation for the paper "sMGC: A Complex-Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs".

Magnetic Graph Convolutional Networks About The official PyTorch implementation for the paper sMGC: A Complex-Valued Graph Convolutional Network via M

3 Feb 25, 2022
A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning.

Open3DSOT A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning. The official code release of BAT an

Kangel Zenn 172 Dec 23, 2022
AAAI 2022: Stationary diffusion state neural estimation

Stationary Diffusion State Neural Estimation Although many graph-based clustering methods attempt to model the stationary diffusion state in their obj

绽琨 33 Nov 24, 2022
TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"

Simulated+Unsupervised (S+U) Learning in TensorFlow TensorFlow implementation of Learning from Simulated and Unsupervised Images through Adversarial T

Taehoon Kim 569 Dec 29, 2022
TART - A PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions

TART This project is a PyTorch implementation for Transition Matrix Representati

Lee Sael 2 Jan 19, 2022
an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 985 Jan 08, 2023
Code for binary and multiclass model change active learning, with spectral truncation implementation.

Model Change Active Learning Paper (To Appear) Python code for doing active learning in graph-based semi-supervised learning (GBSSL) paradigm. Impleme

Kevin Miller 1 Jul 24, 2022
Use .csv files to record, play and evaluate motion capture data.

Purpose These scripts allow you to record mocap data to, and play from .csv files. This approach facilitates parsing of body movement data in statisti

21 Dec 12, 2022
State-Relabeling Adversarial Active Learning

State-Relabeling Adversarial Active Learning Code for SRAAL [2020 CVPR Oral] Requirements torch = 1.6.0 numpy = 1.19.1 tqdm = 4.31.1 AL Results The

10 Jul 14, 2022