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https://github.com/jveitchmichaelis/deeplabel
A cross-platform desktop image annotation tool for machine learning
https://github.com/jveitchmichaelis/deeplabel
annotate-images annotation annotation-tool annotation-tool-offline bounding-boxes deep-learning image-annotation image-labeling-tool kitti labeling object-detection object-detection-label
Last synced: about 1 month ago
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A cross-platform desktop image annotation tool for machine learning
- Host: GitHub
- URL: https://github.com/jveitchmichaelis/deeplabel
- Owner: jveitchmichaelis
- Created: 2017-10-04T16:28:44.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-04-07T11:38:51.000Z (over 2 years ago)
- Last Synced: 2024-08-02T17:31:13.612Z (4 months ago)
- Topics: annotate-images, annotation, annotation-tool, annotation-tool-offline, bounding-boxes, deep-learning, image-annotation, image-labeling-tool, kitti, labeling, object-detection, object-detection-label
- Language: C++
- Homepage:
- Size: 92.1 MB
- Stars: 205
- Watchers: 7
- Forks: 39
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-data-annotation - deeplabel - - image (Image / video / Open source)
- awesome-dataset-tools - DeepLabel - A cross-platform image annotation tool for machine learning (Labeling Tools / Images)
README
# DeepLabel
[](https://github.com/jveitchmichaelis/deeplabel/actions/workflows/build_osx.yml) [](https://github.com/jveitchmichaelis/deeplabel/actions/workflows/build_ubuntu.yml)[](https://github.com/jveitchmichaelis/deeplabel/actions/workflows/build_windows.yml)
DeepLabel is a cross-platform tool for annotating images with labelled bounding boxes. A typical use-case for the program is labelling ground truth data for object-detection machine learning applications. DeepLabel runs as a standalone app and compiles on Windows, Linux and Mac.
DeepLabel is written in C++ and is resource-efficient, typically using less than 100MB RAM to run. There is a GUI or you can run on the command line for batch/automated processing.
Deeplabel also supports running inference using state-of-the-art object detection models like Faster-RCNN and YOLOv4. With support out-of-the-box for CUDA, you can quickly label an entire dataset using an existing model.
**If you use DeepLabel for research or commercial purposes, please cite here!** [](https://zenodo.org/badge/latestdoi/105791274)
Download the [latest release](https://github.com/jveitchmichaelis/deeplabel/releases/latest)! If you are an OS X user, check the `Actions` tab to download an automated and self-contained DMG build. The Github CI runs on every push and attempts to build DeepLabel for Windows, Mac and Linux. You can check the action workflows for hints on how to compile everything if you're having trouble.
**Note: Deeplabel for Windows now packages CUDA and CUDNN for inference. This results an enormous distributable size as the cudnn inference libraries are 600MB+ alone. The CUDA runtime adds another 300MB. I'm looking into uploading simultaneous non-CUDA releases to save space. The alternative is to ask you to install CUDA and CUDNN yourself, but you'd need the correct version which is a pain.**
Ready made binaries for Windows and OS X are on the release page. It is recommended that you build for Linux yourself, but it's not difficult.
## Workflow
DeepLabel was built with convenience in mind. Image locations, classes and labels are stored in a local sqlite database (called a _project_, in the application). When a label is added or removed, this is immediately reflected in the database.
A typical workflow for DeepLabel is:
1. Create a new project database
2. Add images, or import an existing project in a variety of common ML formats
3. Load in a class list, or manually add classes
4. Label/inspect the images
5. Export data in the desired format
## Command Line Interface
Deeplabel now has a convenient command line interface to facilitate import and export of data:
```
(base) PS C:\Users\Josh> deeplabel.exe -h
Usage: deeplabel.exe [options] mode
Options:
-?, -h, --help Displays help on
commandline options.
--help-all Displays help including Qt
specific options.
-v, --version Displays version
information.
-f, --format <[kitti, darknet, gcp, voc, coco, mot export format
, birdsai, tfrecord]>
-o, --output output folder
-i, --input label database
-s, --split validation split
percentage
--no-subfolders export directly to
specified folder
--prefix filename prefix
-n, --names names file
--bucket GCP bucket
--local use local paths for GCP
export
--export-map export label.pbtxt file
--shuffle shuffle images when
splitting
--append-labels append to label files
--export-unlabelled export images without
labels
--images import image path/folder
--annotations import annotation
path/folder
--import-unlabelled import images without
labels
--overwrite overwrite existing
databases
--records mask for TF Records (*
wildcard)
Arguments:
mode [export, import]
```
For example, if you want to export a dataset to TFRecord:
```
deeplabel.exe export -i labels.lbdlb -f TFRecord -n project.names -o ./output/
```
## Data import
Currently you can import data in the following formats:
* Darknet (provide image list and names)
* COCO (provide an annotation .json file and image folder)
* MOT
* TFRecord (parsing works, but full import is not possible yet)
* Pascal VOC
## Data export
Currently you can export in:
* KITTI (e.g. for Nvidia DIGITS)
* Darknet for YOLO
* Pascal VOC
* COCO (experimental)
* Google Cloud Platform (e.g. for AutoML)
* TFRecord (for the Tensorflow Object Detection library)
* Note this uses protobuf directly and there is _no_ dependency on Tensorflow. I believe this is one of the few implementations of TFRecord writing in c++.
* Video (experimental, command line only)
Deeplabel treats your data as "golden" and does not make any attempt to modify it directly. This is a safe approach to avoid accidental corruption of a dataset that you spent months collating. As such, when you export labels, a copy of your data will be created with associated label files. For example, KITTI requires frames to be numerically labelled. In the future, augmentation may also be added, which is another reason to **not** modify your existing images.
When exporting to darknet, you should specify an existing "names" file so that your output labels have consistent class IDs. Similarly, when you export to Pascal VOC, you have the option of exporting a label map file which maps class IDs to labels. This file is quite easy to generate yourself (and you may already have it). The format is:
```
{
item {
name: some_class
id: 1
displayname: some_class
}
```
DeepLabel can automatically split your data into train and validation sets, you can choose what fraction to use (you can set 0% or 100% if you just want a test or validation set).
Since the labelling metadata is in the sqlite database, it should be fairly easy to write a Python script (or whatever) to convert the output to your preferred system. Many frameworks will accept Pascal VOC formatted data, so that's a good start.
## Model inference (automatic tagging)
DeepLabel now supports automatic image tagging from a pre-trained model. A good approach for rapidly tagging thousands of images is to train a "rough" model on a small subset of your data, use that model to tag more data, correct the labels as required, and repeat. Over time, the model will get better and better and you will need to make fewer corrections.
### Supported models
You can load in a Darknet-type model, for example Yolov3, Yolov3-spp and Yolov3-Tiny or a Tensorflow model.
Most of the standard Tensorflow models are supported e.g. Faster-RCNN, Mobilenet-SSD. Currently the assumption is that the model ends with a `DetectionOutput` layer. This is an Nx7 sized tensor where `N` is the number of output labels, after non-maximum suppression has been applied (typically 100 for stock models). The columns are `batch id, class id, confidence, box coords`.
You need to provide:
* The model configuration file (`.cfg, .pbtxt`)
* The model weights (`.weights, .pb`)
* A names file (`.names`)
The extension doesn't really matter, but these are the standard ones.
**Note: ensure that your names file corresponds to the expect label IDs that your model outputs. For example, Darknet uses an 80-class file, Tensorflow models often use a 91-class file, MobileNet-SSD outputs labels starting from 1, Faster-RCNN outputs labels starting from 0, etc.**
You can then run inference on a single image (the magic wand icon), or an entire project (`Detection->Run on Project`). On a modern CPU, Yolov3 takes around 0.5 seconds to tag a full HD image. DeepLabel will use OpenCV's OpenCL backend by default (you can select CPU if necessary), which should provide some acceleration on GPUs.
If you're not seeing any detections, you can adjust the confidence threshold (`Detection->Set Threshold`). If your detection boxes look right, but the classe labels are wrong, check your names file is correct.
Download Tensorflow models for testing from OpenCV [here](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API).
### Target
You can run models on CPU or OpenCL. All models will work on CPU. You may see a speed up with OpenCL, but the CPU backend is far more heavily optimised (and typically is better than other framework's CPU performance).
OpenCL support is somewhat experimental and you may get an out-of-memory error (and Deeplabel will unceremoniously crash). Try with CPU if this happens. Typical performance is anywhere from 50 ms for Tiny Yolov3 to a couple of seconds for Faster-RCNN
## 16-bit File Handling
Deeplabel supports 16-bit images and will automatically scale the colours to 8-bit grayscale.
You can optionally apply a colourmap to a single channel image, which may improve contrast.
In the future, support should be added for OpenCV's Tensorflow backend.
## Bounding box tracking and refinement
DeepLabel supports multi-threaded object tracking for quickly labelling video sequences. Simply label the first frame, initialise the tracker and select "propagate on next image". The software will initialise a tracker for each bounding box and attempt to track it in the next frame. If tracking is lost, you can re-label the offending image, restart the tracker and keep going. In this way, you can quickly label thousands of images in minutes.
DeepLabel also supports bounding box refinement using some simple thresholding and contour segmentation techniques. This was designed for labelling thermal infrared images, but it should work on colour images too. It works best if the object has a high contrast. It will also probably fail if you have two overlapping objects that look very similar.
An experimental foreground/background segmenter is being tested, but it seems to be a bit overenthusiastic right now.
## Video support
DeepLabel currently supports videos by a brute force route. You can load in a video which will be automatically split into JPEG frames and then loaded into your label database. Currently the entire video will be loaded, though in the future functionality will likely be similar to VOTT with frame skip options and no need to split the video beforehand. Note that when dealing with videos you will typically need to export them as separate frames anyway.
Any video format that OpenCV (and its backends) can open should work.
Usage
--
Using the software should be fairly straightforward. Once you've created a project database and added images to it, you can get on with the fun part of adding bounding boxes.
First, add the classes that you want.
DeepLabel operates in two modes: draw and select. In **draw** mode, you can click to define the corners of a bounding box rectangle. If you're happy with the box, hit space to confirm. The rectangle will be added to the image with a class label.
If you need to delete a label, switch to **select** mode. Click on a rectangle, it will highlight green, the hit delete or backspace to remove it.
All changes are immediately reflected in the database.
**Navigate** through images using the left/right or a/d keys. You can use ctrl+left/right to quickly advance through your dataset.
You should find a/d to be quite a natural way of navigating without moving your left hand. There is a progress bar to indicate how far through the dataset you've labelled.
Once you're done labelling, open the export menu to copy and rename your images and generate label files. When you export, you need to tell Deeplabel what classes you wish to export (typically via a `.names` file). This means that you can consistently export multiple label files (e.g. one per video sequence) and have the class/class IDs match up.
Building from source
--
It's recommended that you use Qt5, but Qt4 will probably work. You need to have Qt's SQL extensions installed.
**OpenCV Requirements**
This is mostly a pure Qt project, but there are some limitations to what Qt can do with images. In particular, scaling sucks (even with `Qt::SmoothTransform`). Qt's image reader is also not particularly robust, so OpenCV is used there. OpenCV is also used for image augmentation. On OS X or Linux it's expected that you have `pkg-config` installed to handle dependencies.
You need to compile OpenCV with contrib (`-DOPENCV_EXTRA_MODULES_PATH`) for object tracking. You should also compile with (`-DOPENCV_GENERATE_PKGCONFIG`). Only OpenCV 4+ is supported due to API changes. Make sure you checkout the same tagged release for the main repository and the contrib repository.
``` bash
git clone https://github.com/opencv/opencv
git clone https://github.com/opencv/opencv_contrib
cd opencv && git checkout 4.5.3 && cd ../
cd opencv_contrib && git checkout 4.5.3 && cd ../
cd opencv
mkdir build && cd build
cmake .. -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=OFF -DOPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules
make -j8
sudo make install
```
On Mac, Homebrew automatically include pkg-config support and the contrib packages.
**Linux**
[See Github Action for representative build instructions.](https://github.com/jveitchmichaelis/deeplabel/blob/master/.github/workflows/build_ubuntu.yml)
Build opencv using your preferred method (e.g. above). You need Qt5 installed - not just Qt Creator.
```
sudo apt install git build-essential qt5-default libprotobuf-dev protobuf-compiler
```
Proto files are already compiled within the `src/proto` folder, but if you need to, you can compile with:
```
protoc --proto_path ./src/proto --cpp_out ./src/proto feature.proto example.proto
```
Clone the repository, then:
```bash
git submodule update --init --recursive
qmake -makefile -o Makefile DeepLabel.pro
make -j4
```
**Mac**
Install dependencies using Homebrew:
``` bash
brew install qt opencv protobuf
```
Note that qt is not linked by default, so either force link it (`brew link -f qt`) or follow the post-install instructions to see where qmake is installed.
Clone the repo, open the pro file in Qt Creator and build. Deployment is automatic on Windows and OS X. Alternatively:
```bash
git submodule update --init --recursive
qmake -makefile -o Makefile DeepLabel.pro
make -j4
```
`madeployqt` is automatically run after compilation, and on OS X will build a `.dmg` file. This does have the irritating side effect of linking and copying every `dylib` OpenCV has to offer so feel free to dig into the package and delete some of the dylibs that you don't need. This is a tradeoff between output file size and convenience.
An extra script is provided to fix paths to certain libraries on OS X.
**Windows**
Unfortunately you need to install OpenCV from source, because the official binary releases don't include the contrib modules (which include tracking algorithms and DNN support). Or just download a DeepLabel release from [here](https://github.com/jveitchmichaelis/deeplabel/releases).
You will also need protobuf installed - make sure you compile with DLLs enabled, not static libraries.
Once you've installed OpenCV...(!)
Clone the repo, update the submodules to fetch QtAwesome, open the pro file in Qt Creator and modify the paths to your opencv install. Build as normal. Make sure you copy all the OpenCV DLLs after install.
Notes
--
#### Overlapping bounding boxes
DeepLabel doesn't care if your bounding boxes overlap, but selecting overlapping bounding boxes is a tricky problem from a UI point of view. Currently the solution is simple: all rectangles containing the cursor will be highlighted and if you hit delete, the most recent one will be deleted.
#### Image paths
Image paths in the database are stored relative to the database location. This means you can easily copy over files to another system, provided you keep the relative structure of the files.
#### Support for other descriptors (e.g. occluded, truncated)
In the future I'd like to add the ability to mark labels as occluded or truncated. I haven't decided on the best way to implement this yet - most detector networks don't use this information, but it's useful for stats.
#### Database schema
DeepLabel uses a simple relational database with the following schema:
CREATE TABLE classes (class_id INTEGER PRIMARY KEY ASC, name varchar(32))
CREATE TABLE images (image_id INTEGER PRIMARY KEY ASC, path varchar(256))
CREATE TABLE labels (label_id INTEGER PRIMARY KEY ASC, image_id int, class_id int, x int, y int, width int, height int)
These fields are the bare minimum and more may be added later (see note on descriptors above). It may also be useful to include metadata about images for statistics purposes.
#### License
This code is MIT licensed - feel free to fork and use without restriction, commercially or privately, but please do cite. Copyright Josh Veitch-Michaelis 2017 - present.