The Label Tool

The dynsight label_tool is a simple web application that allows users to label images. Picture labelling is a crucial step in many computer vision tasks, such as the creation of initial training dataset to train Convolutional Neural Networks (CNNs) model. The current version of dynsight vision exploits the power of the YOLO models for computer vision tasks. Thus, the label_tool has been specifically designed to work with the YOLO dataset format.

../_images/label_tool.png

How to Use

The label_tool application can be executed in 2 main ways:

  • As a standalone application, run the following command in the environment where dynsight is installed:

$ label_tool

  • From python code:

import dynsight

dynsight.vision.label_tool(port=8888) #port selection is optional

In both cases a localhost server should start and the application should automatically appear in your default web browser.

Tip

In case the application does not open automatically, you can manually open it by copying and pasting the URL provided in the terminal output.

The GUI

The label_tool Graphical User Interface is divided in three main panels:

  • The image panel: where loaded images appear and labels can be drawn.

  • The label menu panel: where labels can be created and edited.

../_images/label_menu.png

  • The commands panel: where all the available commands can be executed.

../_images/label_bar.png

Using the Choose File button, users can select the image(s) they want to label. Once the image is loaded, users can start drawing labels by clicking and dragging on the image panel. The label menu panel allows users to create and edit labels. Finally, the commands panel provides a set of exporting options:

  • Export label: Download a single .txt file in YOLO format containing the labels for the current image.

  • Export dataset: Download a YOLO dataset from the loaded images with the labels and create the initial yaml configuration file to be used in the YOLO training process.

  • Synthesize dataset: Create a synthetic dataset from the drawn labels randomizing the object position in different images (useful when a low number of images is available).