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