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There are lots of repositories that offer gesture recognition via deep learning. But hardly any that provides an end-to-end solution for gesture recognition and control of an application. So here, we offer a gesture recognition and control system, that you can easily customise for your own applications.
Video available on Youtube at https://youtu.be/28S2qK9o4ME
In order to train the gesture recognition system, we will use TwentyBN's Jester Dataset. This dataset consists of 148,092 labeled videos, depicting 25 different classes of human hand gestures. This dataset is made available under the Creative Commons Attribution 4.0 International license CC BY-NC-ND 4.0. It can be used for academic research free of charge. In order to get access to the dataset you will need to register.
The Jester dataset is provided as one large TGZ archive and has a total download size of 22.8 GB, split into 23 parts of about 1 GB each. After downloading all the parts, you can extract the videos using:
cat 20bn-jester-v1-?? | tar zx
More information, including alternative ways to download the dataset, is available in the Jester Dataset website.
- Clone this repo:
git clone https://github.com/eleow/Gesture-Recognition-and-Control.git- Install prerequisites in requirements.txt
In the root folder you will find a couple of config files. Any of these files can be used for training of the network. These files need to be modified to indicate the location of both the CSV files and the videos from the Jester dataset. The default location is ./annotations/ for the CSV files and ../20bn-jester-v1/ for the videos.
These config files also contain the parameters to be used during training and quick testing, such as the number of epochs, batch size, learning rate, etc... Feel free to modify these parameters as you see fit.
Please note that the default number of epochs used for training is set to -1 in the config.json file, which corresponds to 999999 epochs.
The model.py module already has a simple 3D CNN model that you can use to train your gesture recognition system. You are encouraged to modify model.py to create your own 3D CNN architecture
It is recommended that you quickly test your models before you train them on the full Jester dataset, as training takes a LOOOONNNNGG time. When quickly testing models we suggest you use the config_quick_testing.json file and the CPU. To do this, use the following command:
python train.py --config ./config_quick_testing.json --use_gpu=False
It is likely that for your particular usecase, you might not want to have all possible classes. Due to computational limitations and/or time constraints, you might also not want to train on all 148,092 samples.
Use createDataCSV.py to create a subset based on num_samples and selected_classes.
After configuring your config.json, and setting up your annotation files, train your model using train.py.
You can choose whether you want to train the network using only a CPU or a GPU. Due to the very large size of the Jester dataset it is strongly recommended that you only perform the training using a GPU. This is controlled by the use_gpu flag. Also specify the GPU IDs to use by the --gpus flag.
python train.py --config ./config.json --use_gpu=True --gpus 0
A sample configuration file mapping.ini is provided for mapping gestures to VLC shortcuts. The general syntax to specify a mapping in the INI file is <gesture> = <type>,<key1>,<key2>,..., where gesture can be one of the classes specified in Jester Dataset, and type can be either press, hotkey or typewrite.
[MAPPING]
Stop Sign = press,space
Swiping Up = hotkey,ctrl,up
Swiping Down = hotkey,ctrl,down
Turning Hand Clockwise = press,p
Turning Hand Counterclockwise = press,n
Swiping Left = hotkey,alt,left
Swiping Right = hotkey,alt,rightUse webcam.py to start your webcam, load your trained model, perform inference, and affect your desired application by sending keystrokes based on the recognised gestures.
You can load a video file to do the same, by using the --video flag
usage: webcam.py [-h] [-e EXECUTE] [-d DEBUG] [-u USE_GPU] [-g GPUS]
[-v VIDEO] [-vb VERBOSE] [-cp CHECKPOINT] [-m MAPPING]
optional arguments:
-h, --help show this help message and exit
-e EXECUTE, --execute EXECUTE
Bool indicating whether to map output to
keyboard/mouse commands
-d DEBUG, --debug DEBUG
In debug mode, show webcam input
-u USE_GPU, --use_gpu USE_GPU
Bool indicating whether to use GPU. False - CPU, True
- GPU
-g GPUS, --gpus GPUS GPU ids to use
-v VIDEO, --video VIDEO
Path to video file if using an offline file
-vb VERBOSE, --verbose VERBOSE
Verbosity mode. 0- Silent. 1- Print info messages. 2-
Print info and debug messages
-cp CHECKPOINT, --checkpoint CHECKPOINT
Location of model checkpoint file
-m MAPPING, --mapping MAPPING
Location of mapping file for gestures to commands
- If you get an error similar to
data_loader.py", line 73, in get_frame_names frame_names += [frame_names[-1]] * (num_frames_necessary - num_frames) IndexError: list index out of range, ensure that the entire Jester dataset has been properly extracted and that all folders are present.
Distributed under the MIT License
Repository is modified based on code by Udacity, which in turn, is based on TwentyBN's GulpIO-benchmarks repository, written by Raghav Goyal and the TwentyBN team.
