This repository contains the files for running the Patchify GUI.

Overview

Repository Name >> Train-Test-Validation-Dataset-Generation

App Name >> Patchify

Description >> This app is designed for crop images and creating small patches of a large image e.g. Satellite/Aerial Images, which will then be used for training and testing Deep Learning models specifically semantic segmentation models.

Functionalities: Patchify is capable of:

  • Crop the large image into small patches based on the user-defined patch window-size and patch stride/step independently in two x and y directions.
  • Augmenting the cropped dataset to expand the size of the training dataset and make the model to improve the model performance with better generalizing for unseen samples.
  • Dividing the created dataset into different Train, Test, and Validation dataset with user defined percentages.

A picture of Patchify App is shown below:

Parameters:

  • Input Image: is the input large image need to be cropped into small patches. It can be whether raster or its label image. (The produced results will in the same format as the input image)

  • Export Folder: is the directory for saving the generated cropped patches.

  • Window Size: is the size of the cropping window which is equal to the size of the generated small patches. (X is the patch/cropped images' length in X direction and Y is their length in Y direction.)

  • Stride: is the step size of the moving window for generating the patches. It can move in different step sizes in X and Y directions.

  • Output name: is the constant part of the generated patches' name.

  • Training Percentage: is the percentage of Total generated patches goes into Training Dataset.

  • Testing Percentage: is the percentage of Total generated patches goes into Testing Dataset.

  • Validation Percentage: is the percentage of Total generated patches goes into Validation Dataset.

  • Original Image: is the original version of the cropped patch at the location of moving/sliding window.

  • Rotate 90 Degrees: is the version of original image rotated 90 degrees clockwise.

  • Rotate 180 Degrees: is the version of original image rotated 180 degrees clockwise.

  • Rotate 270 Degrees: is the version of original image rotated 270 degrees clockwise.

  • Flip Vertically: is the version of original image flipped vertically.

  • Flip Horizontally: is the version of original image flipped horizontally.

  • Flip Verticall and Horizontally: is the version of original image flipped both vertically and horizontally .

  • Start Patching: starts the patching operations based on the selected parameters.

  • Cancel: is the button for stopping the patching operations and/or closing the Patchify App.

  • Augmentation section has two buttoms. All button selects all the augmentation methods. In case a different format should be checked manually, the Custom Selection can be selected.

Important Notes:

  • if none of the Train, Testing, Validation percentages is filled, Then the Results will only produce Total cropped patches and the dataset spliting section won't run.
  • Make sure you have selected an image, the destination folder for storing and the generated patch name before pressing "Start Patchify" button.

Implementation:

patchify.py is the only file you need to run. But before make sure you have installed all the required python libraries including opencv, PyQt5. Be sure to use the latest version of pip along with python 3.7

Owner
Salar Ghaffarian
Remote Sensing and GIScientist - MSc in Geomatics Engineering - I am specialist in using Deep learning, Computer vision, and machine learning methods.
Salar Ghaffarian
a project for 3D multi-object tracking

a project for 3D multi-object tracking

155 Jan 04, 2023
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

OpenPCDet OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. It is also the official code release o

OpenMMLab 3.2k Dec 31, 2022
The official implementation of the research paper "DAG Amendment for Inverse Control of Parametric Shapes"

DAG Amendment for Inverse Control of Parametric Shapes This repository is the official Blender implementation of the paper "DAG Amendment for Inverse

Elie Michel 157 Dec 26, 2022
Detectron2 is FAIR's next-generation platform for object detection and segmentation.

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up r

Facebook Research 23.3k Jan 08, 2023
A universal memory dumper using Frida

Fridump Fridump (v0.1) is an open source memory dumping tool, primarily aimed to penetration testers and developers. Fridump is using the Frida framew

551 Jan 07, 2023
This script scrapes and stores the availability of timeslots for Car Driving Test at all RTA Serivce NSW centres in the state.

This script scrapes and stores the availability of timeslots for Car Driving Test at all RTA Serivce NSW centres in the state. Dependencies Account wi

Balamurugan Soundararaj 21 Dec 14, 2022
Collection of generative models in Tensorflow

tensorflow-generative-model-collections Tensorflow implementation of various GANs and VAEs. Related Repositories Pytorch version Pytorch version of th

3.8k Dec 30, 2022
Naszilla is a Python library for neural architecture search (NAS)

A repository to compare many popular NAS algorithms seamlessly across three popular benchmarks (NASBench 101, 201, and 301). You can implement your ow

270 Jan 03, 2023
Codes for our paper The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders published to EMNLP 2021.

The Stem Cell Hypothesis Codes for our paper The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders published to EMNLP

Emory NLP 5 Jul 08, 2022
Cooperative Driving Dataset: a dataset for multi-agent driving scenarios

Cooperative Driving Dataset (CODD) The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple

Eduardo Henrique Arnold 124 Dec 28, 2022
Video Background Music Generation with Controllable Music Transformer (ACM MM 2021 Oral)

CMT Code for paper Video Background Music Generation with Controllable Music Transformer (ACM MM 2021 Best Paper Award) [Paper] [Site] Directory Struc

Zhaokai Wang 198 Dec 27, 2022
Unit-Convertor - Unit Convertor Built With Python

Python Unit Converter This project can convert Weigth,length and ... units for y

Mahdis Esmaeelian 1 May 31, 2022
Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

Gty 44 Dec 17, 2022
QI-Q RoboMaster2022 CV Algorithm

QI-Q RoboMaster2022 CV Algorithm

2 Jan 10, 2022
Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels.

The Face Synthetics dataset Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels. It was introduced in ou

Microsoft 608 Jan 02, 2023
Inference pipeline for our participation in the FeTA challenge 2021.

feta-inference Inference pipeline for our participation in the FeTA challenge 2021. Team name: TRABIT Installation Download the two folders in https:/

Lucas Fidon 2 Apr 13, 2022
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

Tensorpack is a neural network training interface based on TensorFlow. Features: It's Yet Another TF high-level API, with speed, and flexibility built

Tensorpack 6.2k Jan 09, 2023
Python parser for DTED data.

DTED Parser This is a package written in pure python (with help from numpy) to parse and investigate Digital Terrain Elevation Data (DTED) files. This

Ben Bonenfant 12 Dec 18, 2022
PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"

SLAPS-GNN This repo contains the implementation of the model proposed in SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

60 Dec 22, 2022
Deploy pytorch classification model using Flask and Streamlit

Deploy pytorch classification model using Flask and Streamlit

Ben Seo 1 Nov 17, 2021