gestop

Built on top of mediapipe, this project aims to be a tool to interact with a computer through hand gestures. Out of the box, using this tool, it is possible to:

Use your hand to act as a replacement for the mouse.
Perform hand gestures to control system parameters like screen brightness, volume etc.

In addition, it is possible to extend and customize the functionality of the application in numerous ways:

Remap existing hand gestures to different functions in order to better suit your needs.
Create custom functionality through the use of either python functions or shell scripts.
Collect data and create your own custom gestures to use with existing gestures.

Usage

In addition to the Python libraries in requirements.txt, OpenCV and xdotool are also required by Gestop.

Server

To start the Gestop server, do the following:

Clone this repo and install all its dependencies.
Execute the get_models.sh script to download the models and place the folder in the gestop/ directory.
Start the server with the following command:

python gestop/gesture_receiver.py

Note: Python dependencies can be installed simply by creating a virtual environment and running pip install -r requirements.txt

Client

The client, or the keypoint generator, can be setup either through MediaPipe's C++ API, or through its Python API. The Python API is simpler to setup and is recommended.

MediaPipe Python API

Install the libraries required by hand_tracking.py i.e. opencv and mediapipe. Then, run the code with:

python gestop/keypoint_gen/hand_tracking.py

MediaPipe C++ API

Download mediapipe and set it up. MediaPipe >=0.8.0 is NOT supported and should no be used. Make sure the provided hand tracking example is working to verify if all dependencies are installed.
Clone this repo in the top level directory of mediapipe. Install all of Gestop's dependencies.
Run the instructions below to build and then execute the code.

Note: Run build instructions in the mediapipe/ directory, not inside this directory.

GPU (Linux only)

bazel build -c opt --verbose_failures --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 gestop:hand_tracking_gpu

GLOG_logtostderr=1 bazel-bin/gestop/hand_tracking_gpu --calculator_graph_config_file=gestop/keypoint_gen/hand_tracking_desktop_live.pbtxt

CPU

bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 gestop:hand_tracking_cpu

GLOG_logtostderr=1 bazel-bin/gestop/hand_tracking_cpu --calculator_graph_config_file=gestop/keypoint_gen/hand_tracking_desktop_live.pbtxt

Overview

The hand keypoints are detected using google's MediaPipe. These keypoints are then fed into gesture_receiver.py . The tool recognizes two kinds of gestures:

Static Gestures : Gestures whose meaning can be inferred from a single image itself.
Dynamic Gestures : Gestures which can only be understood through a sequence of images i.e. a video.

Static gestures, by default, are mapped to all functionality relevant to the mouse, such as left mouse click, scroll etc. Combined with mouse tracking, this allows one to replace the mouse entirely. The mouse is tracked simply by moving the hand, where the tip of the index finger reflects the position of the cursor. The gestures related to the mouse actions are detailed below. To train the neural network to recognize static gestures, a dataset was created manually for the available gestures.

For more complicated gestures involving the movement of the hand, dynamic gestures can be used. By default, it consists of various other actions to interface with the system, such as modifying screen brightness, switching workspaces, taking screenshots etc. The data for these dynamic gestures comes from SHREC2017 dataset. Dynamic gestures are detected by holding down the Ctrl key, which freezes the cursor, performing the gesture, and then releasing the key.

The project consists of a few distinct pieces which are:

MediaPipe - Accessed through either the Python API or the C++ API, MediaPipe tracks the hand, generates the keypoints and transmits them.
Gesture Receiver - See gesture_receiver.py, responsible for handling the stream and utilizing the following modules.
Mouse Tracker - See mouse_tracker.py, responsible for moving the cursor using the position of the index finger.
Gesture Recognizer - See gesture_recognizer.py, takes in the keypoints from the mediapipe executable, and converts them into a high level description of the state of the hand, i.e. a gesture name.
Gesture Executor - See gesture_executor.py, uses the gesture name from the previous module, and executes an action.

Notes

For best performance, perform dynamic gestures with right hand only, as all data from SHREC is right hand only.
For dynamic gestures to work properly, you may need to change the keycodes being used in gesture_executor.py. Use the given find_keycode.py script to find the keycodes of the keys used to change screen brightness and volumee. Finally, system shortcuts may need to be remapped so that the shortcuts work even with the Ctrl key held down. For example, in addition to the usual default behaviour of <Prnt_Screen> taking a screenshot, you may need to add <Ctrl+Prnt_Screen> as a shortcut as well.

Customizing Gestop

Available Gestures

Repo Overview

models -> Stores the trained model(s) which can be called by other files for inference
proto -> Holds the definitions of the protobufs used in the project for data transfer
BUILD -> Various build instructions for Bazel
static_data_collection.py -> Script to create a custom static gesture dataset.
dynamic_data_collection.py -> Script to create a custom dynamic gesture dataset.
data/static_gestures_mapping.json -> Stores the encoding of the static gestures as integers
data/dynamic_gestures_mapping.json -> Stores the encoding of the dynamic gestures as integers
data/static_gestures_data.csv -> Dataset created with data_collection.py
data/action_config.json -> Configuration of what gesture maps to what action.
hand_tracking_desktop_live.pbtxt -> Definition of the mediapipe calculators being used. Check out mediaipe for more details.
hand_tracking_landmarks.cc -> Source code for the mediapipe executable. GPU version is Linux only.
model.py -> Definition of the models used.
static_train_model.py -> Trains the "GestureNet" model for static gestures and saves to disk
dynamic_train_model.py -> Trains the "ShrecNet" model for dynamic gestures and saves to disk
find_keycode.py -> A sample program from pynput used to find the keycode of the key that was pressed. Useful in case the brightness and audio keys vary.
gesture_receiver.py -> Handles the stream of data coming from the mediapipe executable by passing it to the various other modules.
mouse_tracker.py -> Functions which implement mouse tracking.
gesture_recognizer.py -> Functions which use the trained neural networks to recognize gestures from keypoints.
gesture_executor.py -> Functions which implement the end action with an input gesture. E.g. Left Click, Reduce Screen Brightness
config.py -> Stores the configuration and state of the application in dataclasses for easy access.
user_config.py -> Stores the definition of all the actions that will be executed when a particular gesture is detected.

Useful Information

Joints of the hand

HandCommander

Video recorded with VokoScreenNG

ekshaks/gestop