Image to Amogus Converter

Converts given image (.png, .jpg, etc) to an amogus gif!

Usage

Place image in the /target/ folder (or anywhere realistically, assuming you can get its path). Next, type the following into the console:

$ python amogus.py path [-size]

Where path is a required string relating to the path of the target image and -size is an optional parameter relating to the size of the outputted .gif (see -h for details).

$ python amogus.py -h
usage: image_to_amogus [-h] [-size SIZE] path

positional arguments:
  path        Path to target image, where the /target/ folder is the current
              directory.

optional arguments:
  -h, --help  show this help message and exit
  -size SIZE  Integer representing the size length of square images, and the
              length of the shortest side in rectangular images (16 by
              default).

Example

Here's an example of the program run on the Girl with a Pearl Earring.

Examples:

$ python amogus.py example.jpg
example_amogus.gif saved in ../output/ folder. So sussy! ඞ

$ python amogus.py example.jpg -size=8
example_amogus.gif saved in ../output/ folder. So sussy! ඞ

$ python amogus.py example.jpg -size=32
example_amogus.gif saved in ../output/ folder. So sussy! ඞ

$ python amogus.py example.jpg -size=64
example_amogus.gif saved in ../output/ folder. So sussy! ඞ

Converts given image (png, jpg, etc) to amogus gif.

Related tags

Overview

Image to Amogus Converter

Usage

Example

Owner

Hank Magan

A real-time speech emotion recognition application using Scikit-learn and gradio

ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)

A template repository for submitting a job to the Slurm Cluster installed at the DISI - University of Bologna

Repository for code and dataset for our EMNLP 2021 paper - “So You Think You’re Funny?”: Rating the Humour Quotient in Standup Comedy.

Neurolab is a simple and powerful Neural Network Library for Python

A basic reminder tool written in Python.

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

Collection of machine learning related notebooks to share.

Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach

CVPR2020 Counterfactual Samples Synthesizing for Robust VQA

Code for Active Learning at The ImageNet Scale.

PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

An AutoML Library made with Optuna and PyTorch Lightning

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

This is an unofficial PyTorch implementation of Meta Pseudo Labels

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)