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Utilities for converting, visualizing, and annotating raw hexstring Jacquard Gesture Data (length = 15)
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Script for training a new gesture using annotated data
- python 3
- numpy
- scipy
- keras
- seaborn
- matplotlib 2.1.1 (version 3 seems to break the visualization, PRs are more than appreciated)!
Script to convert raw hex readings of Jacquard Data (stored in ./data/raw/ as a csv) to numpy compatible csv (outputed into ./data/converted/)- run with python convert_threads.py <name>
Example: python convert_threads.py forcetouch_data.csv
Script to visualize converted CSV Jacquard Data (stored in ./data/converted/ as a csv) as a function of time- run with python visualize_threads.py forcetouch_data.csv
Example: python visualize_threads.py forcetouch_data.csv
Note: Inspiration for visualizing the time series thread data came from the infamous arcade game Dance Dance Revolution, or DDR. Just as timing is indicated by when a DDR step is about to scroll beyond the visible window, the timing of each thread reading is indicated by when it reaches the last visible row before disappearing offscreen.
Script to visualize csv thread readings- run with python annotate_threads.py forcetouch_data.csv
Example: python annotate_threads.py forcetouch_data.csv
Note: This script displays the same visualization as the aforementioned visualize_threads.py does, except with a title indicating whether the script is recording a Positive Annotation (recording 1's indicating that yes, this is the gesture we are trying to detect) or a Negative Annotation (recording 0's indicating that no, this is something we should NOT identify as our new gesture). Clicking anywhere on the plot will toggle between positive and negative annotations, and basically makes this like a game of DDR where you want to time your toggling clicks so that you get positive annotations for the duration of your gesture and then get negative annotations for anything else.
The script will initially prompt you with the following message:
Often times the VERY first row of data is the beginning of the gesture you're trying to recognize. In that case, would you like to start annotating with 1's? (y/n)
This simply refers to how your dataset might start off with a positive or negative instance of the gesture you are annotating for. Typing y will start the annotations with positive labels and n will start the annotations with negative labels.
As a heads up, you need to click the visualization window an extra time in the VERY beginning to make the visualization your active window. Only with an active window will your clicks be registered as toggling between positive and negative annotations.
TLDR:
If your data starts with the gesture you're trying to recognize, select y in order to begin annotating with 1's.
If your data starts with an already implemented gesture, select n in order to begin annotating with 0's.
Script to train a model to recognize gestures based on the annotation(s) stored in ./data/annoations/. While storing checkpoints of the model in ./model/checkpoints, it also converts/exports an iOS compatible coreML model into ./model/coreml
Example 1: python train_gesture.py to use ALL annotated data in ./data/annotated/* to train the model
Example 2: python train_gesture.py test.csv hex_data.csv to use only specified files, in this case ./data/annotated/test.csv and ./data/annotated/hex_data.csv, to train the model