yangqing-yq/openvino_hand_tracker

Hand tracking with OpenVINO

Running Google Mediapipe Hand Tracking models on OpenVINO.

There is a version for DepthAI there : depthai_hand_tracker

Install

You just need to have OpenVINO installed on your computer and to clone/download this repository.

Note that the models were generated using OpenVINO 2021.2.

Run

To use default webcam camera as input :

python3 HandTracker.py

To use a file (video or image) as input :

python3 HandTracker.py -i filename

To enable gesture recognition :

python3 HandTracker.py -g

By default, the inferences are run on the CPU. For each model, you can choose the device where to run the model. For instance, if you want to run both models on a NCS2 :

python3 HandTracker.py --pd_device MYRIAD --lm_device MYRIAD

To run only the palm detection model (without hand landmarks), use --no_lm argument. Of course, gesture recognition is not possible in this mode.

Use keypress between 1 and 7 to enable/disable the display of hand features (palm bounding box, palm landmarks, hand landmarks, handedness, gesture,...), spacebar to pause, Esc to exit.

The models

You can find the models palm_detector.blob and hand_landmark.blob under the 'models' directory, but below I describe how to get the files.

1. Clone this github repository in a local directory (DEST_DIR)
2. In DEST_DIR/models directory, download the source tflite models from Mediapipe:

• Palm Detection model
• Hand Landmarks model

3. Install the amazing PINTO's tflite2tensorflow tool. Use the docker installation which includes many packages including a recent version of Openvino.
4. From DEST_DIR, run the tflite2tensorflow container: ./docker_tflite2tensorflow.sh
5. From the running container:

cd workdir/models
./convert_models.sh

The convert_models.sh converts the tflite models in tensorflow (.pb), then converts the pb file into Openvino IR format (.xml and .bin). By default, the precision used is FP32. To generate in FP16 precision, run ./convert_models.sh FP16

Explanation about the Model Optimizer params : The frames read by OpenCV are BGR [0, 255] frames. The original tflite palm detection model is expecting RGB [-1, 1] frames. --reverse_input_channels converts BGR to RGB. --mean_values [127.5,127.5,127.5] --scale_values [127.5,127.5,127.5] normalizes the frames between [-1, 1]. The original hand landmark model is expecting RGB [0, 1] frames. Therefore, the following arguments are used --reverse_input_channels --scale_values [255.0, 255.0, 255.0]

IR models vs tflite models The palm detection OpenVINO IR does not exactly give the same results as the tflite version, because the tflite ResizeBilinear instruction is converted into IR Interpolate-1. Yet the difference is almost imperceptible thanks to the great help of PINTO (see issue ).