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https://github.com/kinhong/OpenLabeler

OpenLabeler is an open source desktop application for annotating objects for AI appplications
https://github.com/kinhong/OpenLabeler

annotations cross-platform deep-learning desktop-application detection docker labeling-tool nueral-networks recognition tensorflow

Last synced: 3 months ago
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OpenLabeler is an open source desktop application for annotating objects for AI appplications

Host: GitHub
URL: https://github.com/kinhong/OpenLabeler
Owner: kinhong
License: apache-2.0
Created: 2019-02-18T19:23:00.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-03-15T17:18:07.000Z (8 months ago)
Last Synced: 2024-07-24T23:02:45.407Z (4 months ago)
Topics: annotations, cross-platform, deep-learning, desktop-application, detection, docker, labeling-tool, nueral-networks, recognition, tensorflow
Language: Java
Homepage:
Size: 17.2 MB
Stars: 109
Watchers: 7
Forks: 11
Open Issues: 5
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

Awesome Lists containing this project

awesome-dataset-tools - OpenLabeler - Open source desktop application for annotating objects (Labeling Tools / Images)

README

        # OpenLabeler

[![GitHub license](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](https://raw.githubusercontent.com/kinhong/openlabeler/master/LICENSE)

[![Downloads](https://img.shields.io/badge/download-all%20releases-brightgreen.svg)](https://github.com/kinhong/openlabeler/releases/)

## Introduction

**OpenLabeler** is an open-source application for annotating objects. It can generate the PASCAL VOC format XML annotation file for artificial intelligence and deep learning training. This application's unique aspect is its ability to use inference (with [TensorFlow](https://www.tensorflow.org)) to improve accuracy and speed up the annotation process.

OpenLabeler is written in [OpenJDK](https://openjdk.java.net)/[OpenJFX](https://openjfx.io) (version 21.x).

![Application](assets/app.png)

A few highlights:

* Fast labeling (no need for Open/Save File actions)

![General Preferences](assets/pref-general.png)

* Multi-level undo/redo

* Annotation "hints" (using TensorFlow inference) 

* Pre-built installation packages for macOS (tested on macOS Sonoma), Linux (tested on Ubuntu 20.04 LTS), and Windows (tested on Windows 10 Pro)

## Inference

OpenLabeler can help improve the speed and accuracy of annotation by offering labeling "hints" from a saved model using TensorFlow (currently, only x86/x86_64 machines are supported).

For example, you have thousands of images to annotate. After labeling the first 300 or so images, you could train a model using these 300 samples, then configure OpenLabeler to use this intermediary model to give you labeling suggestions for the remaining images, thereby speeding up the annotation task.

![Inference Preferences](assets/pref-inference.png)

The **Label Map File** is the label map file in protobuf format (`.pbtxt`).

The **Saved Model Location** is the *folder* where the `.pb` file is located. If it is at `/opt/model/saved_model/saved_model.pb`, then the location should be specified as `/opt/model/saved_model`. Also, the `.pb` file must be named `saved_model.pb`.

OpenLabeler supports graphs with the `image_tensor` and `encoded_image_string_tensor` operations/input types.

The protobuf sources is located in https://github.com/tensorflow/models/tree/master/research/object_detection/protos

## Training Support

*Note: This is currently an experimental feature.*

OpenLabeler can be used to start/stop a training process in TensorFlow running inside a [Docker](https://www.docker.com) container. Containers with [TensorFlow 2](https://www.tensorflow.org/install/docker) and [Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) dependencies have been pre-built for your convenience. To use this feature:

1. [Install Docker](https://docs.docker.com/install) on your host machine

2. Choose a pre-built, `kinhong/openlabeler:tf-2.3.1` or `kinhong/openlabeler:tf-2.3.1-gpu`, [docker image](https://cloud.docker.com/repository/docker/kinhong/openlabeler/tags) from [Docker Hub](https://hub.docker.com/) and pull it to your docker host

3. Download a base model from the [TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md) for transfer learning

4. Configure the Training Preference settings (and add the label map entries)

![Train Preferences](assets/pref-train.png)

5. You can then start, stop, continue, restart training, or export the inference graph

## Shortcut Keys

OpenLabeler supports the following shortcut keys:

| Key Combination  | Action

| ------------- | -------------

| Ctrl (or ⌘) + o | Open media file

| Ctrl (or ⌘) + d | Open media directory

| Ctrl (or ⌘) + s | Save changes

| Ctrl (or ⌘) + x | Cut

| Ctrl (or ⌘) + c | Copy

| Ctrl (or ⌘) + v | Paste

| ⌫ (Backspace or Delete)  | Delete selected box

| Ctrl (or ⌘) + p | Go to previous media file

| Ctrl (or ⌘) + n | Go to next media file

| Ctrl (or ⌘) + g | Go to next unlabeled media file

| Ctrl (or ⌘) + h | Show inference hints

| Ctrl (or ⌘) + Shift + h | Hide inference hits

| Ctrl (or ⌘) + z | Undo

| Ctrl (or ⌘) + Shift + z | Redo

| Ctrl (or ⌘) + ↑→↓← (Arrow Keys) | Move selected bounding box  

| Any character(s) | Match/change label by prefix (on recent labels) of the selected box 

## Installation

If you have previously installed OpenLabeler, uninstall it first.

Download and execute the `.pkg`, `.deb` or `.msi` installation packages for macOS, Linux, and Windows respectively on the [releases](https://github.com/kinhong/OpenLabeler/releases) page.

## Recommended Directory Structure

```

+project

  +images

  +annotations

  +data

    -label_map file

    -train TFRecord file

    -eval TFRecord file

  +models

    +model

      +checkpoint

      +saved_model

      -pipeline config file

      -model.config (created by OpenLabeler)

      +temp (created by OpenLabeler)

        -ckpt-xyz...

        ...

      +fine_tuned_model (created by OpenLabeler)

```

 

## Build

This application can be built using [Apache Maven](https://maven.apache.org) with CLI.

First make sure the environment variable JAVA_HOME has been set accordingly

### macOS

1. Download and install [OpenJDK 21](http://jdk.java.net/21)

2. Download and install [Maven](https://maven.apache.org/install.html)

```

cd 

mvn clean package -Drevision=x.y.z

```

The macOS .pkg installer can be found under the app/target/package directory.

### Linux

```

sudo add-apt-repository ppa:openjdk-r/ppa \

sudo apt update -q \

sudo apt install -y openjdk-21-jdk

sudo apt install maven

sudo apt install binutils

sudo apt install fakeroot

cd 

mvn clean package -Drevision=x.y.z

```

The Linux .deb bundle can be found under the app/target/package directory.

### Windows

1. Download [OpenJDK 21](https://learn.microsoft.com/en-us/java/openjdk/download#openjdk-21) for Windows and unzip to a directory with no spaces (e.g., `C:\java\jdk-21`)

2. Download [Maven](https://maven.apache.org/download.cgi) and unzip to a directory with no spaces (e.g., `C:\java\apache-maven`)

3. Download [Wix Toolset](https://github.com/wixtoolset/wix3) and unzip to a directory with no spaces (e.g., `c:\wix`)

3. Make sure java, mvn and wix executables are in your Windows PATH (e.g., `set PATH=%PATH%;C:\java\jdk-21\bin;C:\java\apache-maven\bin;c:\wix`)

```DOS .bat

cd 

mvn clean package -Drevision=x.y.z

```

The Windows .msi file can be found under the app\target\package directory.

## License

[Apache License 2.0](LICENSE.md)