labelimg

We have hosted the application labelimg in order to run this application in our online workstations with Wine or directly.

Quick description about 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 used by ImageNet. Besides, it also supports YOLO and CreateML formats. Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended. Virtualenv can avoid a lot of the QT / Python version issues. Build and launch using the instructions. 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 rect box. 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 hotkeys to speed up your workflow.

Features:

• 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 can edit the data/predefined_classes.txt to load pre-defined classes
• When pressing space, the user can flag the image as verified, a green background will appear
• The difficult field is set to 1 indicates that the object has been annotated as "difficult"
• According to your deep neural network implementation, you can include or exclude difficult objects during training

Programming Language: Python.
Categories: