What’s new in idtracker.ai v4

• Works with Python 3.7.

• Remove Kivy submodules and stop support for old Kivy GUI.

• Neural network training is done with Pytorch 1.10.0.

• Identification images are saved as uint 8.

• Crossing detector images are the same as the identification images. This saves computing time and makes the process of generating the images faster.

• Improve data pipeline for the crossing detector.

• Parallel saving and loading of identification images (only for Linux)

• Simplify code for connecting blobs from frame to frame.

• Remove unnecessary execution of the blobs connection algorithm.

• Background subtraction considers the ROI

• Allows to save trajectories as csv with the advanced parameter CONVERT_TRAJECTORIES_DICT_TO_CSV_AND_JSON (using the local_settings.py file).

• Allows to change the output width (and height) of the individual-centered videos with the advanced parameter INDIVIDUAL_VIDEO_WIDTH_HEIGHT (using the local_settings.py file).

• Horizontal layout for graphical user interface (GUI). This layout can be deactivated using the local_settings.py setting NEW_GUI_LAYOUT=False.

• Width and height of GUI can be changed using the local_settings.py using the GUI_MINIMUM_HEIGHT and GUI_MINIMUM_WIDTH variables.

• Add ground truth button to validation GUI.

• Added “Add setup points” featrue to store landmark points in the video frame that will be stored in the trajectories.npy and trajectories_wo_gaps.npy in the key setup_poitns. Users can use this points to perform behavioural analysis that requires landmarks of the experimental setup.

• Improved code formatting using the black formatter.

• Better factorization of the TrackerApi.

• Some bugs fixed.

• Better documentation of main idtracker.ai objects (video, blob, list_of_blobs, fragment, list_of_fragments, global_fragment and list_of_global_fragments).

• Dropped support for MacOS