For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

Related tags

Deep LearningImgAlign
Overview

ImgAlign

For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

Usage

Make sure OpenCV is installed, 'pip install opencv-python' (OpenCV not yet working on python 3.10).

For now, the options are: mode (0 or 1), HR file name, LR file name, and scale (integer) in that other: ImgAlign.py mode HR LR scale

Example:

ImgAlign.py 0 HR.png LR.png 2

This is still very much a work in progress. I have fairly limited coding knowledge, but am always trying to pick up new things.

I'd like to add batch functionality so that it will automatically process each picture with matching names in HR and LR directories. I also need to make the argument input nicer.

This cannot handle rotations at the moment, but I am going to try to add that feature soon.

ImgAlign can scale height and width independently, but being more similar tends to give better results. For instance, DVD images are stored at 720x480 resolution, but are almost always displayed at 720x540 or 640x480 (Also known as anamorphic, where SARโ‰ PAR). To match that with a 1920x1080 image (SAR=PAR), you'd get better results prescaling the the LR image (or HR image) to the intended 720x540 or 640x480 (1920x1280, 1620x1080, 1440x960, etc. for HR) than leaving it at 720x480, although either way works.

Mode 0 is true to the LR file, meaning it maintains the resolution, aspect ratio, and orientation of the LR image, cropping where needed. The HR image is cropped, scaled, and translated accordingly.

Mode 1 is true to the HR image, maintaining its resolution, orientaion, and aspect ratio. The LR image is cropped, scaled, translated to match. I have not added a boundary check for this mode yet, so the HR image should be fully contained within the LR image, or else black bars will likely be added. I also haven't yet added a check to make sure the HR resolution is evenly divisible by scale, so be sure it is before using This mode only outputs a new LR image because, as stated, the HR should be contained in the other image, so no cropping is needed.

Starting Point/Credit

I used lines of code from this site to get started with basic alignment: https://learnopencv.com/feature-based-image-alignment-using-opencv-c-python/

You might also like...
Script that receives an Image (original) and a set of images to be used as
Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"

picinpics Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)
Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling This repo contains the official implementation for the paper On Path Int

Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution [arXiv 2021].

Implementation of the ๐Ÿ˜‡ Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones
Implementation of the ๐Ÿ˜‡ 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

Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

YOLOv4-large This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork. YOLOv4-CSP YOLOv4-tiny YOLOv4-

[SIGGRAPH'22] StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
[SIGGRAPH'22] StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

[Project] [PDF] This repository contains code for our SIGGRAPH'22 paper "StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets" by Axel Sauer, Katja

FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizat

Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

Releases(Official_Release)
  • Official_Release(Dec 25, 2021)

    Now supports full homography mapping (warping), use option -f or --full to enable. Better alignment algorithm implemented for more accurate matching. 4x scale now much more reliable. Batch processing now does not halt when a match isn't found. Generates a log file for failed matches.

    Source code(tar.gz)
    Source code(zip)
    ImgAlign.exe(52.11 MB)
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021 [WIP] The code for CVPR 2021 paper 'Disentangled Cycle Consistency for H

ChongjianGE 94 Dec 11, 2022
Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks

pix2vox [Demonstration video] Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks. Generated samples Single-category generation M

Takumi Moriya 232 Nov 14, 2022
PSPNet in Chainer

PSPNet This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Training Requirement Python 3.4.4+ Chainer 3.0.0b1+

Shunta Saito 76 Dec 12, 2022
Internship Assessment Task for BaggageAI.

BaggageAI Internship Task Problem Statement: You are given two sets of images:- background and threat objects. Background images are the background x-

Arya Shah 10 Nov 14, 2022
A tf.keras implementation of Facebook AI's MadGrad optimization algorithm

MADGRAD Optimization Algorithm For Tensorflow This package implements the MadGrad Algorithm proposed in Adaptivity without Compromise: A Momentumized,

20 Aug 18, 2022
Adaout is a practical and flexible regularization method with high generalization and interpretability

Adaout Adaout is a practical and flexible regularization method with high generalization and interpretability. Requirements python 3.6 (Anaconda versi

lambett 1 Feb 09, 2022
This repository contains several jupyter notebooks to help users learn to use neon, our deep learning framework

neon_course This repository contains several jupyter notebooks to help users learn to use neon, our deep learning framework. For more information, see

Nervana 92 Jan 03, 2023
[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

Junha Lee 10 Dec 02, 2022
Easy and Efficient Object Detector

EOD Easy and Efficient Object Detector EOD (Easy and Efficient Object Detection) is a general object detection model production framework. It aim on p

381 Jan 01, 2023
TensorFlow2 Classification Model Zoo playing with TensorFlow2 on the CIFAR-10 dataset.

Training CIFAR-10 with TensorFlow2(TF2) TensorFlow2 Classification Model Zoo. I'm playing with TensorFlow2 on the CIFAR-10 dataset. Architectures LeNe

Chia-Hung Yuan 16 Sep 27, 2022
An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

์•Œ๊ณ ๋ฆฌ์ฆ˜ ์Šคํ„ฐ๋”” ๐Ÿ”ฅ ๋ถ€์ŠคํŠธ์บ ํ”„ ์›น๋ชจ๋ฐ”์ผ 6๊ธฐ iOS 10์กฐ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์Šคํ„ฐ๋”” ์ž…๋‹ˆ๋‹ค. ๊ฐœ์ธ์ ์ธ ์‚ฌ์ • ๋“ฑ์œผ๋กœ S034, S055๋งŒ ์ฐธ๊ฐ€ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์Šคํ„ฐ๋”” ๋ชฉ์  ์ƒ์ง„: ์ฝ”ํ…Œ ํ•ฉ๊ฒฉ + ๋ถ€์บ ๋๋‚˜๊ณ  ์•„์นจ์— ์ผ์–ด๋‚˜๊ธฐ ์œ„ํ•ด ํ•„์š”ํ•œ ์‚ฌ์ดํด ๊ธฐ์™„: ๊พธ์ค€ํ•˜๊ฒŒ ์ž๋ฆฌ์— ์•‰์•„ ๊ณต๋ถ€ํ•˜๊ธฐ +

2 Jan 11, 2022
Deploy pytorch classification model using Flask and Streamlit

Deploy pytorch classification model using Flask and Streamlit

Ben Seo 1 Nov 17, 2021
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

Self-labelling via simultaneous clustering and representation learning ๐Ÿ†— ๐Ÿ†— ๐ŸŽ‰ NEW models (20th August 2020): Added standard SeLa pretrained torchvis

Yuki M. Asano 469 Jan 02, 2023
Final project code: Implementing MAE with downscaled encoders and datasets, for ESE546 FA21 at University of Pennsylvania

546 Final Project: Masked Autoencoder Haoran Tang, Qirui Wu 1. Training To train the network, please run mae_pretraining.py. Please modify folder path

Haoran Tang 0 Apr 22, 2022
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition

VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition Usage First, install PyTorch 1.7.1+, torchvision 0.8.2

40 Dec 12, 2022
Benchmark for evaluating open-ended generation

OpenMEVA Contributed by Jian Guan, Zhexin Zhang. Thank Jiaxin Wen for DeBugging. OpenMEVA is a benchmark for evaluating open-ended story generation me

25 Nov 15, 2022
PyTorch code for JEREX: Joint Entity-Level Relation Extractor

JEREX: "Joint Entity-Level Relation Extractor" PyTorch code for JEREX: "Joint Entity-Level Relation Extractor". For a description of the model and exp

LAVIS - NLP Working Group 50 Dec 01, 2022
The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

ASMA-GAN Anisotropic Stroke Control for Multiple Artists Style Transfer Proceedings of the 28th ACM International Conference on Multimedia The officia

Six_God 146 Nov 21, 2022
This is an open solution to the Home Credit Default Risk challenge ๐Ÿก

Home Credit Default Risk: Open Solution This is an open solution to the Home Credit Default Risk challenge ๐Ÿก . More competitions ๐ŸŽ‡ Check collection

minerva.ml 427 Dec 27, 2022
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport This GitHub page provides code for reproducing the results i

Andrew Zammit Mangion 1 Nov 08, 2021