Generator

A Python library for generating thumbnails following the FreeDesktop specification

Vignette Vignette is a Python library to create and manage thumbnails following the FreeDesktop standard. Thumbnails are stored in a shared directory so other apps following the standard can reuse them without having to generate their own thumbnails. Vignette can typically be used in file managers, image browsers, etc. Thumbnails are not limited to image files on disk but can be generated for other file types, for example videos or documents but also for any URL, for example a web […]

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Parser

LIN Description File parser written in Python

This tool is able parse LIN Description Files, retrieve signal names and frames from them, as well as encoding messages using frame definitions and decoding them. Disclaimer The tool has been written according the LIN standards 1.3, 2.0, 2.1 and 2.2A, but due to errors in the documentation there’s no guarantee that the library will be able to parse your LDF. In such cases if possible first verify the LDF with a commercial tool such as Vector LDF Explorer or […]

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Transformer

FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting

By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, Jifeng Dai, Hongsheng Li. This repo is the official Pytorch implementation of FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting. Introduction Usage Prerequisites Install git clone https://github.com/ruiliu-ai/FuseFormer.git cd FuseFormer pip install -r requirements.txt Training Dataset preparation Download datasets (YouTube-VOS and DAVIS) into the data folder. mkdir data Training script python train.py -c configs/youtube-vos.json Test Download pre-trained model into checkpoints folder. mkdir checkpoints Test script […]

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3D

Makes a 3D representation of a rubiks cube and solves it step by step

Python solver for a rubik’s cube This program makes a 3D representation of a rubiks cube and solves it step by step. Usage To use this program you need to execute the following commands For 3D visualizations: python visualizer.py For statistics: python stats.py Requirements To use this program you need to install python 3.8.10 or later (although it will probably work on python 3.7) You will also need a recent version of numpy and vpython 7 or later, those can […]

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Neural Network

CMT: Convolutional Neural Networks Meet Vision Transformers

[arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no reference source code so this is a non-official version. 2. Enveriments python 3.7+ pytorch 1.7.1 pillow apex opencv-python You can see this repo to find how to install the apex 3. DataSet Trainig /data/home/imagenet/train/xxx.jpeg, 0 /data/home/imagenet/train/xxx.jpeg, 1 … /data/home/imagenet/train/xxx.jpeg, 999 Testing /data/home/imagenet/test/xxx.jpeg, 0 /data/home/imagenet/test/xxx.jpeg, 1 … /data/home/imagenet/test/xxx.jpeg, 999 4. Training & Inference

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Neural Network

One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing

Unofficial pytorch implementation of paper “One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing” Driving | FOMM | Ours: Free-View: Train: python run.py –config config/vox-256.yaml –device_ids 0,1,2,3,4,5,6,7 Demo: python demo.py –config config/vox-256.yaml –checkpoint path/to/checkpoint –source_image path/to/source –driving_video path/to/driving –relative –adapt_scale –find_best_frame free-view (e.g. yaw=20, pitch=roll=0): python demo.py –config config/vox-256.yaml –checkpoint    

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Transformer

nnFormer: Interleaved Transformer for Volumetric Segmentation

Code for paper “nnFormer: Interleaved Transformer for Volumetric Segmentation “. Please read our preprint at the following link: paper_address. Parts of codes are borrowed from nn-UNet. Installation 1、System requirements This software was originally designed and run on a system running Ubuntu 18.01, with Python 3.6, PyTorch 1.8.1, and CUDA 10.1. For a full list of software packages and version numbers, see the Conda environment file environment.yml. This software leverages graphical processing units (GPUs) to accelerate neural network training and evaluation; […]

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Camera

CTRL-C: Camera calibration TRansformer with Line-Classification

This repository contains the official code and pretrained models for CTRL-C (Camera calibration TRansformer with Line-Classification). Jinwoo Lee, Hyunsung Go, Hyunjoon Lee, Sunghyun Cho, Minhyuk Sung and Junho Kim. ICCV 2021. Single image camera calibration is the task of estimating the camera parameters from a single input image, such as the vanishing points, focal length, and horizon line. In this work, we propose Camera calibration TRansformer with Line-Classification (CTRL-C), an end-to-end neural network-based approach to single image camera calibration, which […]

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Images

The Intrinsic Dimension of Images and Its Impact on Learning

Estimating the instrinsic dimensionality of image datasets Code for: The Intrinsic Dimensionaity of Images and Its Impact On Learning – Phillip Pope and Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein (ICLR 2021, spotlight) Environment This code was developed in the following environment conda create dimensions python=3.6 jupyter matplotlib scikit-learn pytorch==1.5.0 torchvision cudatoolkit=10.2 -c pytorch To generate new data of controlled dimensionality with GANs, you must install: pip install pytorch-pretrained-biggan To use the shortest-path method (Granata and Carnevale 2016) you […]

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Pytest

A Django plugin for pytest

Welcome to pytest-django! pytest-django allows you to test your Django project/applications with the pytest testing tool. Install pytest-django pip install pytest-django Why would I use this instead of Django’s manage.py test command? Running your test suite with pytest-django allows you to tap into the features that are already present in pytest. Here are some advantages: Manage test dependencies with pytest fixtures. Less boilerplate tests: no need to import unittest, create a subclass with methods. Write tests as regular functions. Database […]

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