How to use DLStream
Just run
cd DeepLabStream
python app.py
You will see the main control panel of a GUI app.
To start working with DeepLabStream, press the Start Stream button. It will activate the camera manager and show you the current view from the connected cameras.
After that you can Start Analysis to start pose estimation and receive a pose estimations for each frame, or, additionally, you can Start Recording to record a
video of the current feed (visible in the stream window). You will see your current video timestamp (counted in frames) and FPS after you pressed the Start Analysis button.
As you can see, we track three points that represent three body parts of the mouse - nose, neck and tail root. Every single frame where the animal was tracked is saved to the dataframe, which would create a .csv file after the analysis is finished.
After you finish with tracking and/or recording the video, you can stop either function by specifically pressing on corresponding "stop" button
(so, Stop Analysis or Stop Recording) or you can stop the app and refresh all the timing at once, by pressing Stop Streaming button.
DeepLabStream was build specifically for closed-loop experiments, so with a properly implemented experiment protocol, running experiments on this system is as easy as
pressing the Start Experiment button. Depending on your protocol and experimental goals, experiments could run and finish without any further engagement from the user.
In the provided ExampleExperiment two regions of interest (ROIs) are created inside an arena. The experiment is designed to count the number of times the mouse enters a ROI and trigger a corresponding visual stimulus on a screen.
The high contrast stimuli (image files) are located within the experiments/src folder and specified within the experiments.py ExampleExperiments Class.
As a visual representation of this event, the border of the ROI will turn green.
All experimental output will be stored to a .csv file for easy postprocessing. Check out Working with DLStream output for further details.
Look at the Introduction to experiments to get an idea how to design your own experiment in DeepLabStream or learn how to adapt one of the already published experiments at Adapting an existing experiment.