# Quickstart¶

In this page we explain how to start tracking a video with idtracker.ai v3. For more information about the different functionalities of this new version visit the pages Graphical user interface (GUI), Tracking from the terminal and Advanced parameters.

The full process of tracking this example video takes around 5-7 minutes in one of our computers (Requirements). The time varies with the amount of interaction with the GUI that the user needs to explore the video and set the parameters.

## Step 0. Install idtracker.ai¶

If you haven’t installed the software yet, go to the Installation and requirements page where you will find the installation instructions.

## Step 1. Test the installation¶

Open a terminal (Anaconda Prompt in Windows) and activate the Conda environment where you installed idtracker.ai.

conda activate name_of_the_environment


If you don’t remember the name of the environment, you can type:

conda info --envs


to list all the environment in your computer.

Run the command:

idtrackerai_test


This command will download a small example video and will execute idtracker.ai with default parameters.

By default this command will download an example video in a subfolder called /data in the same folder where idtracker.ai is installed. If you want t download the video and save the results of the tracking in a different folder you can run the command:

idtrackerai_test -o absolute/path/to/the/save_folder


where you should substitute the absolute/path/to/the/save_folder with the path of the folder where you want the data to be downloaded and saved.

In an installation with GPU support the test took around 6 minutes in our computers with a download speed of 45.7MB/s. In an installation without GPU support the test took 37 minutes in our computers with a download speed of 28.6MB/s. At the end of the test, the terminal should have an output similar to this one:

If you installed idtracker.ai without GPU support and you don’t want to wait so long, you can run the following command:

idtrackerai_test -o absolute/path/to/the/save_folder --no_identities


This will run the test but it will skip the part of the algorithm that uses the GPU to train the convolutional neural networks to identify the animals.

In our computers this test took 2 minutes. The terminal at the end of the process should look like this one:

If this is the first time that you are using this system, we recommend to start with the video example of 8 adult zebrafish (Danio rerio). If you have already run the test giving a specific folder to save the results, you already have the example video to start. Otherwise, you can download it from this link.

At the end of this page you will find the link to a video of 100 juvenile zebrafish. We recommend to start with the video of 8 fish as it is faster to track and it is good to get use to the system.

## Step 3. Copy the video to an adequate location¶

Copy the video to a folder where you want the output files to be placed. Depending on the length of the video, the number of animals, and the number of pixels per animal, idtracker.ai will generate different amounts of data, so there must be free space on the disk to allocate the output files. We recommend using solid state disks (SSD) as the saving and loading of the multiple objects that idtracker.ai generates will be faster.

## Step 4. Launch the idtrackerai GUI and open a video¶

The next steps will assume that you installed idtrackerai with GUI support. Check the Installation and requirements page for the installation instructions.

To launch the GUI open a terminal, activate the Conda environment where you installed idtracker.ai and run the command

idtrackerai


After opening the idtracker.ai user interface, click the button Open and browse to the folder where you saved the example video and double click to open it.

Press the “PLAY” button to visualize the video and see how the preprocessing parameters affect the different frames in the video. Use the “PAUSE” button to pause the video. Scroll up/down on top of the preview window to zoom out/in in the frame. Press any number from 1-9 to fast forward the video. Scroll up/down on top of the box indicating the frame number to increase/drecrease the frame number. You can explore any frame by typing the number inside of the box. Drag the gray square in the track bar to move to different frames in the video.

## Step 5. Set the preprocessing parameters¶

NOTE: The default values of the parameters that appear in the window ensure a good tracking performance for this video. Modifying them might imply a decrease on the tracking performance

It is very important for this tracking system to know the number of animals to be tracked. Make sure that the value in the box Number of animals is equal to the number of animals that appear in the video (8 in this case). For a good performance of the algorithm, there must be multiple parts in the video where the number of blobs detected (marked in red in the preview window) is equal to the Number of animals indicated in this text box.

You can get more information about the number of blobs detected by checking the option Segmented blobs info. Toggling this box will show a graph like this one:

If only see a white windows, move to a different frame for the graph to update.

The title of the graph indicates the the number of blobs detected, together with the area of the smallest blob. In the graph, each bar indicates the area in pixels of all the detected blobs. The horizontal gray line indicates the minimum area.

There are four main parameters that affect the number of blobs detected in a given frame. The Intensity thresholds (minimum and maximum) and the Area thresholds (minimum and maximum). Connected pixels which intensity values are inside of the range defined by the intensity thresholds will be detected as a blob if the number of pixels that define the blob (area of the blob) is inside of the range defined by the area thresholds.

To modify the different thresholds, you can type the new value inside of the text box, scroll up/down with the cursor placed on top of the box, or drag the extremes of the blue bars.

Check the Graphical user interface (GUI) section to get more information about the Subtract background box and the Resolution reduction parameter.

Sometimes you might want to discard the beginning or the end of a video. You can do this by setting the starting and ending frames of the Tracking interval.

Check the Graphical user interface (GUI) section to get more information about the Multiple box that will allow you to set multiple tracking intervals.

## Step 6. Set a region of interest¶

In the example video, the animals can be easily separated from the background using only the Intensity thresholds and the Area thresholds. However, it can happen that there are other detected blobs in the frame that do not correspond to any animal (e.g. reflections, parts of the experimental rig,…). If these objects appear consistently in a part of the frame where the animals do not appear, you can mask the objects by setting one or multiple regions of interest (ROI).

Toggle the box Apply ROI. Three buttons and a white box will appear below.

Click on the Rectangle button. Then, in the preview window, click on one of the corners of the rectangle that you want to draw and drag to the position of the opposite corner. This should draw a green rectangle.

Only the pixels inside of the ROI will be considered when applying the Intensity thresholds and the Area thresholds. To delete the ROI, click on the list of points created in the white box. They will highlight in blue. Then click the minus sign (-) button on the top right of the box to delete it. If you do not want to apply any ROI, uncheck the Apply ROI box.

Check the Graphical user interface (GUI) section to get more information about how to draw Polygons and Ellipses.

NOTE: To track the example video with good performance results you don’t need to set any ROI

## Step 7. Set the session name and start tracking the video¶

Before pressing the Track video button, add the name of the tracking session in the top right Session text box. The results of the tracking will be saved in a folder with the name “Session_sessionname” where “sessionname” will be the text that appear in the Session text box.

This new version allow the user to save the preprocessing parameters as they appear in the main windows. This can be done with the Save parameters button. Saving the preprocessing parameters is useful to track the video later from the command line. Check the tracking_from_terminal.rst section to get more information about how to save the parameters and track multiple videos sequentially.

For now, click the Track video button to start tracking the video. The system will compute the different steps necessary to track the video and the Progress bar will advance accordingly. Note that no feedback is given to the user in the form of windows or graphs. You can check the progress of the tracking in the terminal.

In Linux you use the commands

top


or

htop


to monitor the CPU and memory usage. And the command

watch -n -1 nvidia-smi


to monitor the GPU usage.

In Windows you can check Windows System Resource Manager.

At the end of the tracking, a window will pop up showing that the tracking has finished and the estimated accuracy. Also, the terminal will show a message indicating the estimated accuracy and the value of the DATA_POLICY advanced parameter (see Advanced parameters).

Check the Graphical user interface (GUI) section to get more information about the effects of toggling the box Track without identities.

## Step 8. Validate the trajectories¶

Once the tracking has finished, the button Validate trajectories will activate. This button will open a new window that will show the results of the tracking for every frame of the video. You will be able to correct the identities of the animals that were misidentified and to change the position of the centroids of individual and crossing animals.

Check the instructions of the validation GUI in ./validation_GUI.rst page.

## Step 9. Output files¶

The data generated during the tracking process and the trajectories files are stored in the session folder. If the name of the session was “quickstart” the name of the folder will be “Session_quickstart”. Depending on the value of the DATA_POLICY advanced parameter (see Advanced parameters), the content of the session folder will vary. In this case, the content of the folder should be similar to this one.

The trajectories are stored in the subfolders “trajectories” and “trajectories_wo_gaps”. The “trajectories.npy” file contains the trajectories with gaps (NaN) when the animals were touching or crossing. The “trajectories_wo_gaps.npy” file contains the trajectories with the gaps interpolated. There might still be some gaps where the interpolation was not consistent.

## Try the 100 zebrafish sample video¶

You can download the video from this link. Note that the size of this video is 22.4GB, so it should take around 30 minutes to download it at an average rate of 12Mb/s.

Due to the higher frame size of this video (3500x3584) you might notice a decrease of speed when adjusting the preprocessing parameters.

Tracking time and preprocessing parameters…