LabelImg is a graphical image annotation tool.

Overview

LabelImgPlus

LabelImg is a graphical image annotation tool. This project is not updated with new functions now. More functions are supported with LabelImgTool Project(https://github.com/lzx1413/LabelImgTool)

It is written in Python and uses Qt for its graphical interface.

The annotation file will be saved as an XML file. The annotation format is PASCAL VOC format, and the format is the same as ImageNet

task mode change

DET mode

SEG mode

CLS mode

Brush SEG mode(in development: brush branch)

Release software for windows

baiduyun

googledriver

Build source and use it

  • Ubuntu

sudo apt-get install pyqt4-dev-tools

  • Mac install pyqt5

sudo apt-get install python-opencv

pip install lxml

pip install qdarkstyle

./labelImg.py

  • Windows

$ python labelImg.py

Default file framework

|---Images

​ |---images_1

​ |---images_2

|---Annotation

​ |---images_1

​ |---images_2

the file containing annotations will be created by default.

Usage

After cloning the code, you should run $ make all to generate the resource file.

You can then start annotating by running $ ./labelImg.py. For usage instructions you can see Here

At the moment annotations are saved as an XML file. The format is PASCAL VOC format, and the format is the same as ImageNet

You can also see ImageNet Utils to download image, create a label text for machine learning, etc

Label and parsing

support rectangle label and parsing labels

Create pre-defined classes

You can edit the data/predefined_classes.txt to load pre-defined classes

You also can create labels with two levels in data/predefined_sub_classes.txt

And the labels will be ranked by the frequency you use it.

General steps from scratch

  • Build and launch: $ make all; python labelImg.py

  • Click 'Change default saved annotation folder' in Menu/File

  • Click 'Open Dir'

  • Click 'Create RectBox'

The annotation will be saved to the folder you specifiy

Hotkeys

  • Ctrl + r : Change the defult target dir which saving annotation files

  • Ctrl + n : Create a bounding box

  • Ctrl + s : Save

  • Right : Next image

  • Left : Previous image

Online image data mode

the server have to make the images in a folder that clint can get from http/https with get function

  • settings

open File -->RemoteDBSettings(ctrl+m) like that

the remote image list is a file contenting the name of the images (a line is a image) .

the image will be cached in a folder created in the software file named database/pics/XXXX and this will take a lot of memory if there are a lot of images,and this will be modified in the future.

open File -->ChangedDefaultSavedAnnotationDir(ctrl+r) to set the folder to save the results

  1. if your settings are right,you will find the Get Images button becomes enabled and click it ,then you can annotate the images as before

Change list

2019-07-03 support pyqt5 and python 3

17-08-14 add class label function

Todo list

  • add more functions while adding parsing labels
  • refine the setting functions

How to contribute

Send a pull request

License

License

Owner
lzx1413
learning
lzx1413
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Sta

Charles R. Qi 4k Dec 30, 2022
A package, and script, to perform imaging transcriptomics on a neuroimaging scan.

Imaging Transcriptomics Imaging transcriptomics is a methodology that allows to identify patterns of correlation between gene expression and some prop

Alessio Giacomel 10 Dec 27, 2022
CondenseNet V2: Sparse Feature Reactivation for Deep Networks

CondenseNetV2 This repository is the official Pytorch implementation for "CondenseNet V2: Sparse Feature Reactivation for Deep Networks" paper by Le Y

Haojun Jiang 74 Dec 12, 2022
Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Matthias Wright 169 Dec 26, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 27, 2022
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

PGDF This repo is the official implementation of our paper "Sample Prior Guided Robust Model Learning to Suppress Noisy Labels ". Citation If you use

CVSM Group - email: <a href=[email protected]"> 22 Dec 23, 2022
[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

BNN - BN = ? Training Binary Neural Networks without Batch Normalization Codes for this paper BNN - BN = ? Training Binary Neural Networks without Bat

VITA 40 Dec 30, 2022
"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri

"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri Bu Github Reposundaki tüm projeler; kaleme almış olduğum "Projelerle Yapay Zekâ ve Bi

Ümit Aksoylu 4 Aug 03, 2022
Parameterising Simulated Annealing for the Travelling Salesman Problem

Parameterising Simulated Annealing for the Travelling Salesman Problem

Gary Sun 55 Jun 15, 2022
LBK 20 Dec 02, 2022
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 21.1k Dec 29, 2022
A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

A Higher Performance Pytorch Implementation of DeepLab V3 Plus Introduction This repo is an (re-)implementation of Encoder-Decoder with Atrous Separab

linhua 326 Nov 22, 2022
Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)

Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation (ICCV 2021) [中文|EN] 概述 本工作主要探索一种高效的多传感器(激光雷达和摄像头)融合点云语义分割方法。现有的多传感器融合方法主要将点云投影

ICE 126 Dec 30, 2022
Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Demonstration of OpenVINO techniques - Model-division and a simplest-way to support custom layers Description: Model Optimizer in Intel(r) OpenVINO(tm

Yasunori Shimura 12 Nov 09, 2022
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection Introduction This repository includes codes and models of "Effect of De

Amir Abbasi 5 Sep 05, 2022
A Streamlit demo demonstrating the Deep Dream technique. Adapted from the TensorFlow Deep Dream tutorial.

Streamlit Demo: Deep Dream A Streamlit demo demonstrating the Deep Dream technique. Adapted from the TensorFlow Deep Dream tutorial How to run this de

Streamlit 11 Dec 12, 2022
University of Rochester 2021 Summer REU focusing on music sentiment transfer using CycleGAN

Music-Sentiment-Transfer University of Rochester 2021 Summer REU focusing on music sentiment transfer using CycleGAN Poster: Music Sentiment Transfer

Miles Sigel 2 Jan 24, 2022
Decision Transformer: A brand new Offline RL Pattern

DecisionTransformer_StepbyStep Intro Decision Transformer: A brand new Offline RL Pattern. 这是关于NeurIPS 2021 热门论文Decision Transformer的复现。 👍 原文地址: Deci

Irving 14 Nov 22, 2022
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [2021]

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations This repo contains the Pytorch implementation of our paper: Revisit

Wouter Van Gansbeke 80 Nov 20, 2022
This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

SeerNet This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is

3 May 01, 2022