QuickDraw的实现-由Google开发的在线游戏，结合AirGesture-一个简单的手势识别应用程序_Pytho.zip

<p align="center"> <h1 align="center">QuickDraw - AirGesture</h1> </p> [](https://github.com/uvipen/QuickDraw-AirGesture-tensorflow/stargazers) [](https://github.com/uvipen/QuickDraw-AirGesture-tensorflow/network) [](https://github.com/uvipen/QuickDraw-AirGesture-tensorflow/blob/master/LICENSE) ## Introduction Here is my python source code for QuickDraw - an online game developed by google, combined with AirGesture - a simple gesture recognition application. By using my code, you could: * **Run an app which you could draw in front of a camera with your hand (If you use laptop, your webcam will be used by default)** * **Run an app which you could draw on a canvas** ## Camera app In order to use this application, you only need to use your hand to draw in front of a camera/webcam. The middle point of your hand will be detected and highlighted by a red dot. When you are ready for drawing, you need to press **space** button to start drawing. When you want to stop drawing, press **space** button again. Below is the demo by running the sript **camera_app.py**: <p align="center"> <img src="demo/quickdraw_airgesture.gif" width=800><br/> <i>Camera app demo</i> </p> ## Drawing app The script and demo will be released soon ## Categories: The table below shows 18 categories my model used: | | | | | |-----------|:-----------:|:-----------:|:-----------:| | apple | book | bowtie | candle | | cloud | cup | door | envelope | |eyeglasses | hammer | hat | ice cream | | leaf | scissors | star | t-shirt | | pants | tree | | | ## Trained models You could find my trained model at **data/trained_models/** ## Docker For being convenient, I provide Dockerfile which could be used for running training phase as well as launching application Assume that docker image's name is qd_ag. You already clone this repository and cd into it. Build: `sudo docker build --network=host -t qd_ag .` Run: If you want to launch the application, first you need to run `xhost +` to turn off access control (if you only want to run the training, you could skip this step). Then you run: `sudo docker run --gpus all -it --rm --volume="path/to/your/data:/workspace/code/data -e DISPLAY=$DISPLAY --env="QT_X11_NO_MITSHM=1" -v /tmp/.X11-unix:/tmp/.X11-unix --device=/dev/video0:/dev/video0 qd_ag` Inside docker container, you could run **train.py** or **camera_app.py** scripts for training or launching app respectively. By default, the **camera_app.py** script will automatically generate a video capturing what you have done during the session, at **data/output.mp4** ## Experiments: For each class, I split the data to training and test sets with ratio of 8:2. The training/test loss/accuracy curves for the experiment are shown below: <img src="demo/loss_accuracy_curves.png" width="800">