tree-ind / dl-gesture-recognition Goto Github PK

This project uses front-end separation, and the client has the following three forms of implementation:

• Manual gesture recognition, which means that the user determines the segmentation of continuous gestures
• Dynamic gesture recognition based on frame difference method
• Dynamic gesture recognition based on object tracking

Note: No server code is currently provided.

Python3.6, OpenCV3.4 + opencv_contrib

$ pip install opencv-python
$ pip install opencv-contrib-python
$ pip install pillow
$ pip install requests

• server-address: gesture recognition server address

$ python run_manual.py -s [server-address]

Interactive mode: press the keyboard s key before each action, and press the s key again after the action is complete to complete the recognition

you can choose the Background Subtraction Methods

• method: knn or mog
• threshold: The sum of the length and width of the contour identified by the algorithm is greater than the threshold is considered to be the hand

$ python run_frameDifferent -s [server-address] --method [method] --threshold [threshold]

GPU support is required to run this version, we tested on Ubuntu 16.04 + cuda9.0 + cudnn7.0.5 + tensorflow1.6. You need to install tensorflow1.6-gpu extra and darkflow, You can download darkflow from here.

$ pip install tensorflow-gpu
$ pip install Cython
$ cd darkflow
$ pip install .

# Check whether the installation is complete
$ flow --h

and then download the weight file and configuration file, and place them in the model folder and cfg folder respectively. Finally, run

$ python run_objectDetection.py -s [server-address]