Please find the pre-course setup instructions here.
- Overview
- Data Handlers in T4Trian
- Setup
- Interface
- Running T4Train
- Visualizations
- Data Sources / Devices
- Machine Learning
- Contribute
- Troubleshooting
T4Train is an open-source,real-time, cross-platform GUI that collects, visualizes, and classifies real-time data streams for interactive applications.
- Easy-to-use: ML-driven interactive applications prototyped and deployed rapidly
- Cross-platform: Mac, Linux, and Windows are supported
- Real-time visualizations: Raw signals and featurized data plotted in real time
- Open source: Framework fully extendable and customizable
We note that this is research-grade code, and with that comes research-grade bugs.
We currently support 7 data handlers: built-in laptop camera, Teensy, Arduino, built-in laptop microphone, microphone that saves audio stream in .wav format, iOS mobile app with UDP connection (gyroscope and accelerometer), and iOS mobile app with BLE (Bluetooth) connection (gyroscope and accelerometer).
Mac, Linux, and Windows are all supported.
| Teensy | Microphone | Camera | iOS Mobile |
|---|---|---|---|
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 |
 |
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 |
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| Windows | macOS (Intel) | Linux | Raspberry Pi | |
|---|---|---|---|---|
| iOS | ✅ | ✅ | ✅ | ✅ |
| Teensy | ✅ | ✅ | ✅ | ✅ |
| Microphone | ✅ | ✅ | ✅ | ✅ |
| Camera | ✅ | ✅ | ✅ | ✅ |
T4Train requires Python 3 and several dependencies listed in requirements.txt.
Follow setup-README.md for setup instructions
for your computer.
After you've installed your dependencies, go back to the Running T4Train on how to get T4Train running.
ui.py uses Python's PyQt5 library to create the interface. The following is a screenshot of the interface with a configuration featuring the labels "wave," "shake," and "strike," 3 accelerometer input channels, and FFT data featurization:
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Top left corner box: a list of training labels and the number of data frames collected for each label. The label highlighted in red indicates the "current" label, for which recorded data frames are annotated. Users can arbitrarily switch between labels using the keyboard's arrow keys to collect new frames using the spacebar or delete collected frames using backspace as described in a later section. We make these design choices to improve system status visibility and offer better user control in collecting data per label.
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Top right box: a step progress bar, which visualizes a training pipeline's necessary steps. On the far left, the user sees the optional step of loading pre-recorded data from a file. To its right is a completion node for each of the training labels, suggesting that one can only proceed to the training step once each label has at least one frame of annotated data. Once the user completes a step, the corresponding node is highlighted in red to indicate its completion status. Following the data collection steps is a completion node suggesting to hit "T" to train, and node to hit "S" to save the resulting ML model and training data. The step progress bar serves as a guide for beginners and can help users diagnose issues with T4Train (e.g., the model cannot train unless the label nodes are filled). The progress bar can be hidden via the application menu if the user no longer needs it.
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Below the label selector: real-time plots of each channel's raw signal data, provided by the data handler child process. Since users are given the option to featurize data, the UI provides an additional set of horizontally ordered plots for each channel's featurized signals. Users can toggle between different application menu features to find a featurization method that suits their use case. Since features, rather than raw signals, are what the machine learning trains on, the user must be able to see the relationship between signals and features and know precisely what the machine learning is receiving as input. By viewing the features in realtime, a user can toggle through the feature options to select an optimal schema. For example, if a user notices frequency changes in the raw signal, they may prefer extracting FFT or derivative features from the data. For the camera data handler, the default behavior displays the camera frames, the generated keypoints, and hand skeleton are presented below the plots.
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Footer: a message board that relays system status updates to the user, such as whether data is being collected or the current prediction of a generated ML model. It can be easily extended to express other system status messages. The lower right corner is an FPS counter that relays the data stream's speed, useful for debugging custom hardware or sensors. At the very top of the interface is the context menu, which includes all the commands found via keyboard shortcuts, featurization and ML settings, and options to modify the UI, such as increase or decrease font size.
After installing all of the general dependencies and dependencies for your chosen data handler, run the following in T4Train's root directory to start the T4Train program:
$ python ui.py
config.ini is the config file that ui.py parses to determine the different labels, channels, machine learning algorithm, data source, sampling rate, frame length, and number of bins you will be training.
To change the labels, replace the list of labels in the LABELS section of config.ini. For example, if you want your labels to be fist, open, and thumbs up, your LABELS field in your config.ini should look like this:
[GLOBAL]
LABELS=[fist, open, thumbs up]
To change the number of channels of waves you want to plot on the interface, replace the CHANNELS field in your config.ini. For example, if you want to display three channels, your CHANNELS section in your config.ini should look like this:
[GLOBAL]
CHANNELS=3
Microphone currently only supports 2 channels.
To change the machine learning algorithm you want to use during training and predicting, change the index of the algorithm in the config.ini (zero indexed).
[GLOBAL]
CURR_ALGO_INDEX=3
To change the data source file you want to use as a data source,
change the DS_FILE_NUM index of the data handler in the config.ini (zero indexed).
Note that the data source python script names listed after DS_FILENAMES can be
modified to support newly created data sources.
[DS]
; 0, 1, 2, 3, 4, 5, 6, 7
ds_filenames=[ds_camera, ds_teensy, ds_arduino, ds_microphone, ds_microphonewav, ds_mobile_udp, ds_mobile_ble, ds_nano33]
ds_file_num =3
In this case, DS_FILE_NUM sets the data source to ds_microphone
For mobile data sources ONLY, you can change the sampling rate and frame length in config.ini:
[GLOBAL]
FRAME_LENGTH=3000
[DS]
SAMPLE_RATE=50
[ML]
NUM_BINS=750
The sampling rate, frame length, and number of bins are hardcoded into other data source files to optimize the ML and UI, so changing these fields for Microphone or Teensy will not impact either.
To use the interface, there are several different controls:
- Up/Down for Label Selection
- Right Click Label to Edit
- L for Load
- Space for Collect
- Backspace for Delete
- T for Train
- S for Save
- M for Machine Learning
- C for Confusion
- I for Importance
- Featurization
ui_labels.py is a class that manages the labels from config.ini.
To select a different label, the user can use the up/down arrow keys. On down, ui.py will call move_down to highlight the label below the current selected label. Similarly, move_up will be called on up.
def move_down(self):
"""Moves selected label to one below."""
def move_up(self):
"""Moves selected label to one above."""
If you wish to rename, add, or delete labels from the main window, right click on any label. A context menu will appear that lets you delete or edit the name for the currently clicked label. You can also append a new default label from the same context menu.
If you have previously used T4Train and saved your training data files and/or the model file, you can place the files in saved_files/import/ in the project directory. You can then press l, which will copy all the files in saved_files/import/ and paste them in the project directory.
By loading past files, you can continue your session and collect more training data, delete some data, retrain, or save again.
You can also load data with the application menu in the PyQt UI under the "Commands" tab.
Hit spacebar to collect the next number of instances of frames of data (number of instances can be changed in the config.ini file). The data will be saved using numpy's save function to a file in the project directory with the name trainingdata[label].npy, [label] being the current selected label. If the file already exists, the data will be appended to the file.
Each collection will be in a numpy ndarray of the shape (frames, channels, 1502), where frames is the number of frames collected (default is 10) and channels is the number of channels specified in config.ini.
You can also collect frames with the application menu in the PyQt UI under the "Commands" tab.
Hit backspace to delete the most recent number of instances frames of data for the selected label (number of instances can be changed in the config.ini file). The data will be deleted from the file in the project directory with the name trainingdata[label].npy, [label] being the current selected label. If the file does not exist (there are no saved data for the selected label), then nothing will happen.
You can also delete frames with the application menu in the PyQt UI under the "Commands" tab.
Hit t to send a signal to ml.py. ml.py will then train a model on .npy files with the format trainingdata[label] with [label] being the label name.
After training, ml.py will continuously read tmpframe.npy and predict its label. The prediction will be written to prediction.npy.
In the meantime, ui.py will continuously read prediction.npy and display the prediction onto the interface.
More specifically, when you hit t, the UI will append all the training data into one file called training_data.npy. In addition to this, the UI will also creates a file called training_labels.npy, which is a list of labels for each collection of training data.
For example, if the labels are [touch, no touch, wiimote] and you collect 10
frames for each label, the training data files will be training_data_touch.npy,
training_data_no_touch.npy, and training_data_wiimote.npy. When you hit
t to train, the UI will create training_data.npy, which will be all the
training data (30 frames). Since there are three groups of 10 frames, the UI
will also create training_labels.npy, which will be the labels in a list
([touch, no touch, wiimote]), in the same order that they are in
training_data.npy.
The ML will then take these two files to train a classification model. After training, the ML will spit the current frame's prediction to prediction.npy, which will be projected onto the UI.
The following three functions are located in utils.py and prep the input files for the ML.
def get_training_data_files_and_labels(labels_raw_text):
"""Gets all training data file names and its sanitized labels."""
def write_training_labels(training_data_files, labels, filename):
"""Write npy file of labels (input for ML)."""
def compile_all_training_data(training_data_files, filename):
"""Compiles all training data files into one file (input for ML)."""
The UI will call these functions in prepare_ml_input_files().
def prepare_ml_input_files(self):
"""Create training_data.npy and training_labels.npy for training."""
You can also train with the application menu in the PyQt UI under the "Commands" tab.
Hit s to copy all trainingdata[label].npy files and send a signal to ml.py, which will save the model (if it exists) as model.npy. These files will be placed in savedfiles/%YYYY%MM*%DD-%HH*%MM/.
You can also save with the application menu in the PyQt UI under the "Commands" tab.
Hit m to toggle between machine learning models to use in ml.py. The footer of the UI will display which to algorithm you have toggled.
You can toggle between to SVM ('svm'), Random Forest ('rf'), Neural Net ('mlp'), or a voting classifier ('voting').
You can also toggle between the algorithms with the application menu in the PyQt UI under the "ML Algorithm" tab, and you can instantly boot-up T4Train with a certain algorithm if you change the current algorithm index in the config.ini file.
*Note: if you toggle to a different model after training, the current model will be erased. Save your model before you toggle if you want to keep your model.*
Hit c to generate a confusion matrix in confusion_matrix.csv. This calls the following function in ml.py:
def confusion_matrix():
"""Generates a confusion matrix from the training data"""
This function will call scikit learn's K-folds cross validation to split the training data into 10 different train/test splits. Then, from each fold, ml.py will generate a model on the training data, classify on the test data, find the accuracy of the classification, and generate a confusion matrix using scikit learn's confusion matrix function.
# of Predicted Result
A B C
# of A ___ ___ ___
Actual B ___ ___ ___
Result C ___ ___ ___
You can aso generate a confusion matrix with the application menu in the PyQt UI under the "Commands" tab.
Hit i to generate feature importances in feature_importances.csv. This calls the following function in ml.py:
def feature_importances():
"""""Generates a confusion matrix from a random forest"""
The function trains a random forest classifier on the training data and returns the feature importances from that model.
You can also generate feature importances with the application menu in the PyQt UI under the "Commands" tab.
The application menu in the PyQt UI has featurization options under the "Featurization" tab. The available featurizations change depending on the data source. Featurization calls the following function in utils.py:
def featurize(input_frame, featurization_type=Featurization.Raw, numbins=60, sample_rate=None):
"""Featurizes data using the enum Featurization class in utils"""
This function bins the data into the number of bins specified in config.ini (for mobile only, fixed for other data sources), then applies the selected featurization method onto the binned data. Ths function featurizes the data on the ML side in ml.py, but also in the UI featurized plots in ui.py.
*IMPORTANT NOTE: DO NOT featurize the data you collect differently from the data the program predicts from. This means that the featurization method should stay the same while collecting data and after hitting t to train.*
For all data sources, the raw signals of each channel are plotted on top in real time, then the featurized plots visualizing the featurized data for each channel are plotted below the raw signals.
For camera, in addition to the raw and featurized plots, the camera's live frames are displayed in the UI, along with keypoints. This video window can be resized by adjusting the edges with your mouse in the UI.
To use the iOS T4T app for streaming sensor data, you'll need to install Xcode from the Mac App store, and an iOS device running iOS 12.0 or later. Currently supported sensors include the accelerometer and gyroscope, with hardware-capped sample rates of 100 Hz each.
Follow iOS-README.md for setup instructions.
The teensy data handler receives data via serial from a teensy, reshapes the data by channels, and hands a complete data frame to T4Train. This data handler compared to the Arduino data handler, is optimized to maximize the data throughput for the Teensy 3.6 and Teensy 4.0.
Microphone uses the built-in laptop microphone and python package PyAudio to stream audio data.
It is recommended to collect at at least 3-4 times for each label (spacebar) and to start making sound before and continue a little after you collect for training.
Copy the config_mic.ini setup into config.ini to run T4Train. Both
config_mic.ini and the top of ds_microphone.py detail the config.ini setup
necessary to run T4Train with microphone.
Configuration notes:
- Increasing
INSTANCESincreases data collection time every time you press the spacebar. - Increasing
FRAME_LENGTHincreases FFT resolution but decreases UI plotting speed. - You can find your microphone's sampling rate in your laptop's sound settings
and adjust accordingly in
SAMPLE_RATE. Most laptop microphones are either 44,100Hz or 48,000Hz. Data collection will not be accurate if the rate is wrong. NUM_BINSfor FFT featurization must be between 1 andFRAME_LENGTH / 2(inclusive) and must be between 1 andFRAME_LENGTHfor raw featurization (inclusive).- Increasing
NUM_BINSincreases data resolution but also increases ML fragility. ReduceNUM_BINSto increase ML robustness.
For exterior microphones that stream audio by saving data in .wav files on
your laptop, use the Microphone config.ini setup, but change the folder in
ds_microphonewav.py and change the DS_FILE_NUM to 4.
The camera data handler streams visual data from the webcam, processes the camera frames using OpenCV and MediaPipe (implemented in TensorFlow), and generates hand keypoints to perform hand gesture recognition tasks. Camera frames with keypoints data are also passed to the UI for visualization.
For the camera data handler, only raw data and the featurization of deltas areavailable.
Copy the config_cam.ini setup into config.ini to run T4Train. NUM_BINS is
not used in the camera data and can be ignored.
If the device has a CUDA-enabled GPU, tensorflow-gpu can accelerate the
keypoint detection.
If you have questions about the specifics, email Yasha at yiravan@umich.edu.
Feel free to fork the repo and submit GitHub issues for changes, feature requests, or bug fixes.
- Source Code: github.com/ISC-Lab/T4Train
If you see an error message in terminal about the program not being able to find
ml_pidnum.txt or something like this, kill the program by pressing ctrl+c/cmd+c
in terminal, then keep trying to restart the UI. This will
happen because sometimes the processes do not start fast enough.
For Teensy or Arduino, if you are restarting the UI, sometimes the Teensy falls into an undefined state. Kill the UI (either by closing the UI window or press ctrl+c/cmd+c) and then unplug and replug the Teensy/Arduino.
For Microphone/Microphone WAV, make sure that your microphone is on in your computer's settings.
For Mobile UDP, make sure that the IP address is correct.
Since Windows does not support signals, we use timeloop to run T4Train on Windows. As a result, sometimes T4Train gets stuck after you press t and "Training..." is displayed in the UI footer. Try killing the UI (either by closing the UI window or press ctrl+c/cmd+c) and restarting the UI.
Try changing the featurization method in the UI application menu under the "Featurization" tab. There are certain data sources that work better with certain featurizations, such as Mobile and Microphone with FFT (Fast Fourier Transform).
You can also try trying different algorithms by using our algorithm toggle. Results may vary.
Windows does not support signals and sometimes may not work properly. To get around this, we've created an Ubuntu VM that includes all of the packages needed to run T4Train. To run the VM, you need at least 4GB of RAM and 2 CPU cores.
Please email Yasha at yiravan@umich.edu if you want to go this route.
If you are having issues, please let us know by posting an issue on this repo. Feel free to email Yasha at yiravan@umich.edu or Yang-Hsi at devilsu@umich.edu.