基于tinyML在arduino芯片上实现的篮球投篮手势鉴别.zip（毕设/课设/竞赛/实训/项目开发）

# Gesture Recognition Magic Wand Training Scripts ## Introduction The scripts in this directory can be used to train a TensorFlow model that classifies gestures based on accelerometer data. The code uses Python 3.7 and TensorFlow 2.0. The resulting model is less than 20KB in size. The following document contains instructions on using the scripts to train a model, and capturing your own training data. This project was inspired by the [Gesture Recognition Magic Wand](https://github.com/jewang/gesture-demo) project by Jennifer Wang. ## Training ### Dataset Three magic gestures were chosen, and data were collected from 7 different people. Some random long movement sequences were collected and divided into shorter pieces, which made up "negative" data along with some other automatically generated random data. The dataset can be downloaded from the following URL: [download.tensorflow.org/models/tflite/magic_wand/data.tar.gz](http://download.tensorflow.org/models/tflite/magic_wand/data.tar.gz) ### Training in Colab The following [Google Colaboratory](https://colab.research.google.com) notebook demonstrates how to train the model. It's the easiest way to get started: <table class="tfo-notebook-buttons" align="left"> <td> <a target="_blank" href="https://colab.research.google.com/github/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples/magic_wand/train/train_magic_wand_model.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a> </td> <td> <a target="_blank" href="https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples/magic_wand/train/train_magic_wand_model.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a> </td> </table> If you'd prefer to run the scripts locally, use the following instructions. ### Running the scripts Use the following command to install the required dependencies: ```shell pip install -r requirements.txt ``` There are two ways to train the model: - Random data split, which mixes different people's data together and randomly splits them into training, validation, and test sets - Person data split, which splits the data by person #### Random data split Using a random split results in higher training accuracy than a person split, but inferior performance on new data. ```shell $ python data_prepare.py $ python data_split.py $ python train.py --model CNN --person false ``` #### Person data split Using a person data split results in lower training accuracy but better performance on new data. ```shell $ python data_prepare.py $ python data_split_person.py $ python train.py --model CNN --person true ``` #### Model type In the `--model` argument, you can provide `CNN` or `LSTM`. The CNN model has a smaller size and lower latency. ## Collecting new data To obtain new training data using the [SparkFun Edge development board](https://sparkfun.com/products/15170), you can modify one of the examples in the [SparkFun Edge BSP](https://github.com/sparkfun/SparkFun_Edge_BSP) and deploy it using the Ambiq SDK. ### Install the Ambiq SDK and SparkFun Edge BSP Follow SparkFun's [Using SparkFun Edge Board with Ambiq Apollo3 SDK](https://learn.sparkfun.com/tutorials/using-sparkfun-edge-board-with-ambiq-apollo3-sdk/all) guide to set up the Ambiq SDK and SparkFun Edge BSP. #### Modify the example code First, `cd` into `AmbiqSuite-Rel2.2.0/boards/SparkFun_Edge_BSP/examples/example1_edge_test`. ##### Modify `src/tf_adc/tf_adc.c` Add `true` in line 62 as the second parameter of function `am_hal_adc_samples_read`. ##### Modify `src/main.c` Add the line below in `int main(void)`, just before the line `while(1)`: ```cc am_util_stdio_printf("-,-,-\r\n"); ``` Change the following lines in `while(1){...}` ```cc am_util_stdio_printf("Acc [mg] %04.2f x, %04.2f y, %04.2f z, Temp [deg C] %04.2f, MIC0 [counts / 2^14] %d\r\n", acceleration_mg[0], acceleration_mg[1], acceleration_mg[2], temperature_degC, (audioSample) ); ``` to: ```cc am_util_stdio_printf("%04.2f,%04.2f,%04.2f\r\n", acceleration_mg[0], acceleration_mg[1], acceleration_mg[2]); ``` #### Flash the binary Follow the instructions in [SparkFun's guide](https://learn.sparkfun.com/tutorials/using-sparkfun-edge-board-with-ambiq-apollo3-sdk/all#example-applications) to flash the binary to the device. #### Collect accelerometer data First, in a new terminal window, run the following command to begin logging output to `output.txt`: ```shell $ script output.txt ``` Next, in the same window, use `screen` to connect to the device: ```shell $ screen ${DEVICENAME} 115200 ``` Output information collected from accelerometer sensor will be shown on the screen and saved in `output.txt`, in the format of "x,y,z" per line. Press the `RST` button to start capturing a new gesture, then press Button 14 when it ends. New data will begin with a line "-,-,-". To exit `screen`, hit +Ctrl\\+A+, immediately followed by the +K+ key, then hit the +Y+ key. Then run ```shell $ exit ``` to stop logging data. Data will be saved in `output.txt`. For compatibility with the training scripts, change the file name to include person's name and the gesture name, in the following format: ``` output_{gesture_name}_{person_name}.txt ``` #### Edit and run the scripts Edit the following files to include your new gesture names (replacing "wing", "ring", and "slope") - `data_load.py` - `data_prepare.py` - `data_split.py` Edit the following files to include your new person names (replacing "hyw", "shiyun", "tangsy", "dengyl", "jiangyh", "xunkai", "lsj", "pengxl", "liucx", and "zhangxy"): - `data_prepare.py` - `data_split_person.py` Finally, run the commands described earlier to train a new model.