Hand gesture recognition with Mediapipe

MediaPipe is a framework for building multimodal (eg. video, audio, any time series data), cross platform (i.e Android, iOS, web, edge devices) applied ML pipelines. With MediaPipe, a perception pipeline can be built as a graph of modular components, including, for instance, inference models (e.g., TensorFlow, TFLite) and media processing functions.

Installation

1. To install mediapipe follow these instructions for linux environments(That's what I use, haven't tested on other OS's).
2. To run the desktop demo via webcam on CPU run

# This compiles and builds the demo
# It should print:
#Target //mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu up-to-date:
# bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_cpu
#INFO: Build completed successfully, 12517 total actions
$ bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 \
    mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu


# This will open up your webcam as long as it is connected and on
# Any errors is likely due to your webcam being not accessible
$ GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_cpu \
    --calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt

3. To run on GPU on desktop run

# Video from webcam running on desktop GPU
# This works only for linux currently
$ bazel build -c opt --copt -DMESA_EGL_NO_X11_HEADERS \
    mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu

# It should print:
# Target //mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu up-to-date:
#  bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_gpu
#INFO: Build completed successfully, 22455 total actions

# This will open up your webcam as long as it is connected and on
# Any errors is likely due to your webcam being not accessible,
# or GPU drivers not setup properly.
$ GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_gpu \
    --calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt

Custom hand gesture recognition calculator built with mediapipe

• 
• GestureRecognizer calulator
• It currently uses finger state to detect signs i.e bent, straight relative to thumb
• Currently working on using weighted matching
• To view graph in visualizer, upload hand_landmark_cpu.pbtxt to MediaPipe Visualizer.

Android

• Android build can be found here

Roadmap

• Implement a robust similarity matching function(Working on a weighted matching function)
• Hook up Travis CI for remote builds and Continuous integration
• Sign/Gesture preseting and storage
• Port to mobile platforms(Android and iOS)