Welcome to the AliGater Classifier. This is a tool for quality control (QC) of images produced by AliGater.
You can download AGClassifier by cloning this repository. We recommend using a virtual environment to install the dependencies. The following commands will create a conda virtual environment with the required dependencies and activate it:
conda create --file agclassifier.yaml -y
conda activate agclassifierAGClassifier can be started by running the AGClassifier.py script:
python AGClassifier.pyThe GUI will then open and ask the user to provide path to the folder containing the images to be classified. The program will create an 'output' folder in the same directory as the images. This is where the output .yaml file will be created. Finally, AGClassifier will also attempt to automatically find the .pickle file that contains the layout particular to the gate. If a single .pickle file exists in the parent directory of the images folder, that file will be used automatically. If no file is found or there are several, the user will be asked to select a file.
Once the paths are defined, the fun begins! The main window will open next. This is where the images are displayed. The user can then use the buttons to either approve the image or select the necessary corrections.
Select the big START button to commence the QC process.
Good luck!
The corrections selected by the user will all be stored in a single corrections.yaml file. This file will be saved
in the folder specified by the user at startup.