毕业设计 基于Python手势识别的计算机视觉综合系统源码+部署文档+全部数据资料（优秀项目）.zip

# hand-gesture-recognition-using-mediapipe Estimate hand pose using MediaPipe (Python version).<br> This is a sample program that recognizes hand signs and finger gestures with a simple MLP using the detected key points. <br> ❗ _️**This is English Translated version of the [original repo](https://github.com/Kazuhito00/hand-gesture-recognition-using-mediapipe). All Content is translated to english along with comments and notebooks**_ ❗ <br>  This repository contains the following contents. * Sample program * Hand sign recognition model(TFLite) * Finger gesture recognition model(TFLite) * Learning data for hand sign recognition and notebook for learning * Learning data for finger gesture recognition and notebook for learning # Requirements * mediapipe 0.8.1 * OpenCV 3.4.2 or Later * Tensorflow 2.3.0 or Later<br>tf-nightly 2.5.0.dev or later (Only when creating a TFLite for an LSTM model) * scikit-learn 0.23.2 or Later (Only if you want to display the confusion matrix) * matplotlib 3.3.2 or Later (Only if you want to display the confusion matrix) # Demo Here's how to run the demo using your webcam. ```bash python app.py ``` The following options can be specified when running the demo. * --device<br>Specifying the camera device number (Default：0) * --width<br>Width at the time of camera capture (Default：960) * --height<br>Height at the time of camera capture (Default：540) * --use_static_image_mode<br>Whether to use static_image_mode option for MediaPipe inference (Default：Unspecified) * --min_detection_confidence<br> Detection confidence threshold (Default：0.5) * --min_tracking_confidence<br> Tracking confidence threshold (Default：0.5) # Directory <pre> │ app.py │ keypoint_classification.ipynb │ point_history_classification.ipynb │ ├─model │ ├─keypoint_classifier │ │ │ keypoint.csv │ │ │ keypoint_classifier.hdf5 │ │ │ keypoint_classifier.py │ │ │ keypoint_classifier.tflite │ │ └─ keypoint_classifier_label.csv │ │ │ └─point_history_classifier │ │ point_history.csv │ │ point_history_classifier.hdf5 │ │ point_history_classifier.py │ │ point_history_classifier.tflite │ └─ point_history_classifier_label.csv │ └─utils └─cvfpscalc.py </pre> ### app.py This is a sample program for inference.<br> In addition, learning data (key points) for hand sign recognition,<br> You can also collect training data (index finger coordinate history) for finger gesture recognition. ### keypoint_classification.ipynb This is a model training script for hand sign recognition. ### point_history_classification.ipynb This is a model training script for finger gesture recognition. ### model/keypoint_classifier This directory stores files related to hand sign recognition.<br> The following files are stored. * Training data(keypoint.csv) * Trained model(keypoint_classifier.tflite) * Label data(keypoint_classifier_label.csv) * Inference module(keypoint_classifier.py) ### model/point_history_classifier This directory stores files related to finger gesture recognition.<br> The following files are stored. * Training data(point_history.csv) * Trained model(point_history_classifier.tflite) * Label data(point_history_classifier_label.csv) * Inference module(point_history_classifier.py) ### utils/cvfpscalc.py This is a module for FPS measurement. # Training Hand sign recognition and finger gesture recognition can add and change training data and retrain the model. ### Hand sign recognition training #### 1.Learning data collection Press "k" to enter the mode to save key points（displayed as 「MODE:Logging Key Point」）<br> <img src="https://user-images.githubusercontent.com/37477845/102235423-aa6cb680-3f35-11eb-8ebd-5d823e211447.jpg" width="60%"><br><br> If you press "0" to "9", the key points will be added to "model/keypoint_classifier/keypoint.csv" as shown below.<br> 1st column: Pressed number (used as class ID), 2nd and subsequent columns: Key point coordinates<br> <img src="https://user-images.githubusercontent.com/37477845/102345725-28d26280-3fe1-11eb-9eeb-8c938e3f625b.png" width="80%"><br><br> The key point coordinates are the ones that have undergone the following preprocessing up to ④.<br> <img src="https://user-images.githubusercontent.com/37477845/102242918-ed328c80-3f3d-11eb-907c-61ba05678d54.png" width="80%"> <img src="https://user-images.githubusercontent.com/37477845/102244114-418a3c00-3f3f-11eb-8eef-f658e5aa2d0d.png" width="80%"><br><br> In the initial state, three types of learning data are included: open hand (class ID: 0), close hand (class ID: 1), and pointing (class ID: 2).<br> If necessary, add 3 or later, or delete the existing data of csv to prepare the training data.<br> <img src="https://user-images.githubusercontent.com/37477845/102348846-d0519400-3fe5-11eb-8789-2e7daec65751.jpg" width="25%">　<img src="https://user-images.githubusercontent.com/37477845/102348855-d2b3ee00-3fe5-11eb-9c6d-b8924092a6d8.jpg" width="25%">　<img src="https://user-images.githubusercontent.com/37477845/102348861-d3e51b00-3fe5-11eb-8b07-adc08a48a760.jpg" width="25%"> #### 2.Model training Open "[keypoint_classification.ipynb](keypoint_classification.ipynb)" in Jupyter Notebook and execute from top to bottom.<br> To change the number of training data classes, change the value of "NUM_CLASSES = 3" <br>and modify the label of "model/keypoint_classifier/keypoint_classifier_label.csv" as appropriate.<br><br> #### X.Model structure The image of the model prepared in "[keypoint_classification.ipynb](keypoint_classification.ipynb)" is as follows. <img src="https://user-images.githubusercontent.com/37477845/102246723-69c76a00-3f42-11eb-8a4b-7c6b032b7e71.png" width="50%"><br><br> ### Finger gesture recognition training #### 1.Learning data collection Press "h" to enter the mode to save the history of fingertip coordinates (displayed as "MODE:Logging Point History").<br> <img src="https://user-images.githubusercontent.com/37477845/102249074-4d78fc80-3f45-11eb-9c1b-3eb975798871.jpg" width="60%"><br><br> If you press "0" to "9", the key points will be added to "model/point_history_classifier/point_history.csv" as shown below.<br> 1st column: Pressed number (used as class ID), 2nd and subsequent columns: Coordinate history<br> <img src="https://user-images.githubusercontent.com/37477845/102345850-54ede380-3fe1-11eb-8d04-88e351445898.png" width="80%"><br><br> The key point coordinates are the ones that have undergone the following preprocessing up to ④.<br> <img src="https://user-images.githubusercontent.com/37477845/102244148-49e27700-3f3f-11eb-82e2-fc7de42b30fc.png" width="80%"><br><br> In the initial state, 4 types of learning data are included: stationary (class ID: 0), clockwise (class ID: 1), counterclockwise (class ID: 2), and moving (class ID: 4). <br> If necessary, add 5 or later, or delete the existing data of csv to prepare the training data.<br> <img src="https://user-images.githubusercontent.com/37477845/102350939-02b0c080-3fe9-11eb-94d8-54a3decdeebc.jpg" width="20%">　<img src="https://user-images.githubusercontent.com/37477845/102350945-05131a80-3fe9-11eb-904c-a1ec573a5c7d.jpg" width="20%">　<img src="https://user-images.githubusercontent.com/37477845/102350951-06444780-3fe9-11eb-98cc-91e352edc23c.jpg" width="20%">　<img src="https://user-images.githubusercontent.com/37477845/102350942-047a8400-3fe9-11eb-9103-dbf383e67bf5.jpg" width="20%"> #### 2.Model training Open "[point_history_classification.ipynb](point_history_classification.ipynb)" in Jupyter Notebook and execute from top to bottom.<br> To change the number of training data classes, change the value of "NUM_CLASSES = 4" and <br>modify the label of "model/point_history_classifier/point_history_classifier_label.