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LabelImg is a graphical image annotation tool and label object bounding boxes in images
Project description
LabelImg
LabelImg is a graphical image annotation tool.
It is written in Python and uses Qt for its graphical interface.
Annotations are saved as XML files in PASCAL VOC format, the format usedby ImageNet. Besides, it also supports YOLO format
Installation
Build from source
Linux/Ubuntu/Mac requires at least Python2.6 and has been tested with PyQt4.8. However, Python3 or above and PyQt5 are strongly recommended.
Ubuntu Linux
Python 2 + Qt4
Python 3 + Qt5 (Recommended)
macOS
Python 2 + Qt4
Used auto dealers murfreesboro tn. Python 3 + Qt5 (Recommended)
Python 3 Virtualenv (Recommended)
Virtualenv can avoid a lot of the QT / Python version issues
Note: The Last command gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider using the script: build-tools/build-for-macos.sh
Windows
Install Python,PyQt5and install lxml.
Open cmd and go to the labelImg directory
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Windows + Anaconda
Download and install Anaconda (Python 3+)
Open the Anaconda Prompt and go to the labelImg directory
Use Docker
You can pull the image which has all of the installed and required dependencies. Watch a demo video
Usage
Steps (PascalVOC)
- Build and launch using the instructions above.
- Click ‘Change default saved annotation folder’ in Menu/File
- Click ‘Open Dir’
- Click ‘Create RectBox’
- Click and release left mouse to select a region to annotate the rectbox
- You can use right mouse to drag the rect box to copy or move it
The annotation will be saved to the folder you specify.
You can refer to the below hotkeys to speed up your workflow.
Steps (YOLO)
- In data/predefined_classes.txt define the list of classes that will be used for your training.
- Build and launch using the instructions above.
- Right below “Save” button in the toolbar, click “PascalVOC” button to switch to YOLO format.
- You may use Open/OpenDIR to process single or multiple images. When finished with a single image, click save.
A txt file of YOLO format will be saved in the same folder as your image with same name. A file named “classes.txt” is saved to that folder too. “classes.txt” defines the list of class names that your YOLO label refers to.
Note:
- Your label list shall not change in the middle of processing a list of images. When you save an image, classes.txt will also get updated, while previous annotations will not be updated.
- You shouldn’t use “default class” function when saving to YOLO format, it will not be referred.
- When saving as YOLO format, “difficult” flag is discarded.
Create pre-defined classes
You can edit thedata/predefined_classes.txtto load pre-defined classes
Python 3.6 Mac
Hotkeys
| Ctrl + u | Load all of the images from a directory |
| Ctrl + r | Change the default annotation target dir |
| Ctrl + s | Save |
| Ctrl + d | Copy the current label and rect box |
| Space | Flag the current image as verified |
| w | Create a rect box |
| d | Next image |
| a | Previous image |
| del | Delete the selected rect box |
| Ctrl++ | Zoom in |
| Ctrl– | Zoom out |
| ↑→↓← | Keyboard arrows to move selected rect box |
Verify Image:
When pressing space, the user can flag the image as verified, a green background will appear.This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them.
Difficult:
The difficult field is set to 1 indicates that the object has been annotated as “difficult”, for example, an object which is clearly visible but difficult to recognize without substantial use of context.According to your deep neural network implementation, you can include or exclude difficult objects during training.
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License
Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg
Related
- ImageNet Utils todownload image, create a label text for machine learning, etc
History
1.8.1 (2018-12-02)
- Fix issues
- Support zh-Tw strings
1.8.0 (2018-10-21)
- Support drawing sqaure rect
- Add item single click slot
- Fix issues
1.6.1 (2018-04-17)
- Fix issue
1.6.0 (2018-01-29)
- Add more pre-defined labels
- Show cursor pose in status bar
- Fix minor issues
1.5.2 (2017-10-24)
- Assig different colors to different lablels
1.5.0 (2017-9-14)
- Fix the issues
- Add feature: Draw a box easier
1.4.0 (2017-07-07)
- Add feature: auto saving
- Add feature: single class mode
- Fix the issues
1.3.3 (2017-05-31)
- Fix issues
1.3.1 (2017-05-11)
- Fix issues
1.3.0 (2017-04-22)
- Fix issues
- Add difficult tag
- Create new files for pypi
1.2.2 (2017-01-09)
- Fix issues
Release historyRelease notifications | RSS feed
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| Filename, size | File type | Python version | Upload date | Hashes |
|---|---|---|---|---|
| Filename, size labelImg-1.8.3-py2.py3-none-any.whl (248.5 kB) | File type Wheel | Python version py2.py3 | Upload date | Hashes |
| Filename, size labelImg-1.8.3.tar.gz (235.8 kB) | File type Source | Python version None | Upload date | Hashes |
Hashes for labelImg-1.8.3-py2.py3-none-any.whl
Mac Upgrade Python 3
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| SHA256 | 5f0cc08f886b32666a5b09b2aaa3f19381b158f40d286322a0914c2ba60a32e8 |
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Hashes for labelImg-1.8.3.tar.gz
| Algorithm | Hash digest |
|---|---|
| SHA256 | 0ee9b5d2a3689f23963d1765404e082ce446c018f6d3f5eb364fb7633c6280fa |
| MD5 | 55a6040ed592b2598563328ef1a40949 |
| BLAKE2-256 | 2c5a16dbad7cf79c4fc24065ba27821ed87c93960f5c2dbba29c08f6ae65e054 |
