Example Usage: Commandline: Recaption all images from folder /home/feng/Downloads/images to folder /home/feng/Downloads/recaptioned_images. python3 ./recaption_images.py -i /home/feng/Downloads/images -o /home/feng/Downloads/recaptioned_images Python: from recaption_images import recaption_images recaption_images( ‘/home/feng/Downloads/images’, ‘/home/feng/Downloads/recaptioned_images’ ) # the first argument is for the input image folder, the second argument is for the output image folder The images names in the input folder

Official PyTorch Implementation for IICNet: A Generic Framework for Reversible Image Conversion (ICCV2021). Demo Video | Supplements Introduction Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e.g., short videos) and an embedding image, where the original content can be restored from the embedding when necessary. This work develops Invertible Image Conversion Net (IICNet) as a generic solution to various RIC tasks due to its strong capacity and task-independent design. Unlike previous encoder-decoder based methods, […]

Python implementation of image filters Use Python to adjust brightness and contrast, add blur, and detect edges! Follow along tutorial: https://youtu.be/4ifdUQmZqhM In order to download this code, either click the green button at the top right and download as ZIP, or use git clone https://github.com/kying18/pyphotoshop.git. You will need to pip install -r requirements.txt (or use pip3 if you are getting a module not found error). In the folder, you will find these files: image.py: contains the Image class that will […]

This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al. Requirements Training Copy two files to {torchvision_path}/models cp {__init__.py,evloution.py} {torchvision_path}/models Run python main.py -a evolution {cifar10_data_dir} cf. How to know {torchvision_path}? import torchvision print(torchvision.__file__) Note The numbers of channels are not in the paper and it is set by me similar with vgg. You need to adjust these for better performance. If you want to adsjust    

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 […]

Image2Reverb Image2Reverb: Cross-Modal Reverb Impulse Response SynthesisNikhil Singh, Jeff Mentch, Jerry Ng, Matthew Beveridge, Iddo Drori Code for the ICCV 2021 paper [arXiv]. Image2Reverb is a method for generating audio impulse responses, to simulate the acoustic reverberation of a given environment, from a 2D image of it. Dependencies Model/Data: PyTorch>=1.7.0 PyTorch Lightning torchvision torchaudio librosa PyRoomAcoustics PIL Eval/Preprocessing: PySoundfile SciPy Scikit-Learn python-acoustics google-images-download matplotlib Resources Model Checkpoint Code Acknowlegdements We borrow and adapt code snippets from GANSynth (and this PyTorch […]

MMEditing Documentation actions codecov PyPI LICENSE Average time to resolve an issue Percentage of issues still open MMEditing is an open source image and video editing toolbox based on PyTorch. It is a part of the OpenMMLab project. The master branch works with PyTorch 1.3 to 1.6. Documentation: https://mmediting.readthedocs.io/en/latest/. Major features Modular design We decompose the editing framework into different components and one can easily construct a customized editor framework by combining different modules. Support of multiple tasks in editing […]

This repository contains the official PyTorch implementation for the paper: SofGAN: A Portrait Image Generator with Dynamic Styling. We propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space. Experiments on SofGAN show that our system can generate high quality portrait images with independently controllable geometry and texture attributes. Installation Install environment: git clone https://github.com/apchenstu/sofgan.git –recursive conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.2 -c pytorch pip install tqdm argparse scikit-image […]

Mutual-Channel-Loss Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)DOI Changelog 2020/09/14 update the code: CUB-200-2011_ResNet18.py Training with ResNet18 (TRAINED FROM SCRATCH). 2020/04/19 add the hyper-parameter fine-tune results. 2020/04/18 clean the code for better understanding. Dataset CUB-200-2011 Requirements python 3.6 PyTorch 1.2.0 torchvision Training Download datasets Train: python CUB-200-2011.py, the alpha and beta are the hyper-parameters of the MC-Loss Description : PyTorch CUB-200-2011 Training with VGG16 (TRAINED FROM SCRATCH). Hyper-parameter Loss = […]

MIA is shorthand for “Multiparametric Image Analysis”. It is intended to be a complete image processing environment mainly targeted at the analysis and visualization of large amounts of MRI data. MRI data analysis often requires a complex succession of data processing pipelines applied to a set of data acquired in an MRI exam or over several MRI exams. This analysis may need to be repeated a large number of times in studies involving a large number of acquisition sessions. Such […]