sign-detection

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Sign detection for visually impaired: accurate, real-time, diverse signs, user-friendly, adaptable to environments.

sign-detection 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# hand-gesture-recognition-using-mediapipe
Estimate hand pose using MediaPipe (Python version). This is a sample 
program that recognizes hand signs and finger gestures with a simple MLP using the detected key points.
 ❗ _️**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**_ ❗
 
![mqlrf-s6x16](https://user-images.githubusercontent.com/37477845/102222442-c452cd00-3f26-11eb-93ec-c387c98231be.gif)

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 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 Specifying the camera device number (Default：0)
* --width Width at the time of camera capture (Default：960)
* --height Height at the time of camera capture (Default：540)
* --use_static_image_mode Whether to use static_image_mode option for MediaPipe inference (Default：Unspecified)
* --min_detection_confidence 
Detection confidence threshold (Default：0.5)
* --min_tracking_confidence 
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. 
In addition, learning data (key points) for hand sign recognition, 
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. 
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. 
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」） 
<img src="https://user-images.githubusercontent.com/37477845/102235423-aa6cb680-3f35-11eb-8ebd-5d823e211447.jpg" width="60%"> 
If you press "0" to "9", the key points will be added to "model/keypoint_classifier/keypoint.csv" as shown below. 
1st column: Pressed number (used as class ID), 2nd and subsequent columns: Key point coordinates 
<img src="https://user-images.githubusercontent.com/37477845/102345725-28d26280-3fe1-11eb-9eeb-8c938e3f625b.png" width="80%"> 
The key point coordinates are the ones that have undergone the following preprocessing up to ④. 
<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%"> 
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). 
If necessary, add 3 or later, or delete the existing data of csv to prepare the training data. 
<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. 
To change the number of training data classes, change the value of "NUM_CLASSES = 3" and modify the label of "model/keypoint_classifier/keypoint_classifier_label.csv" as appropriate.

#### 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%">

### 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"). 
<img src="https://user-images.githubusercontent.com/37477845/102249074-4d78fc80-3f45-11eb-9c1b-3eb975798871.jpg" width="60%"> 
If you press "0" to "9", the key points will be added to "model/point_history_classifier/point_history.csv" as shown below. 
1st column: Pressed number (used as class ID), 2nd and subsequent columns: Coordinate history 
<img src="https://user-images.githubusercontent.com/37477845/102345850-54ede380-3fe1-11eb-8d04-88e351445898.png" width="80%"> 
The key point coordinates are the ones that have undergone the following preprocessing up to ④. 
<img src="https://user-images.githubusercontent.com/37477845/102244148-49e27700-3f3f-11eb-82e2-fc7de42b30fc.png" width="80%"> 
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). 
If necessary, add 5 or later, or delete the existing data of csv to prepare the training data. 
<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. 
To change the number of training data classes, change the value of "NUM_CLASSES = 4" and modify the label of "model/point_history_classifier/point_history_classifier_label.csv" as appropriate.

#### X.Model structure
The image of the model prepared in "[point_history_classification.ipynb](point_history_classification.ipynb)" is as follows.
<img src="https://user-images.githubusercontent.com/37477845/102246771-7481ff00-3f42-11eb-8ddf-9e3cc30c5816.png" width="50%"> 
The model using "LSTM" is as follows. Please change "use_lstm = False" to "True" when using (tf-nightly required (as of 2020/12/16)) 
<img src="https://user-images.githubusercontent.com/37477845/102246817-8368b180-3f42-11eb-9851-23a7b12467aa.png" width="60%">

# Reference
* [MediaPipe](https://mediapipe.dev/)

# Author
Kazuhito Takahashi(https://twitter.com/KzhtTkhs)

# Translation and other improvements
Nikita Kiselov(https://github.com/kinivi)
 
# License 
hand-gesture-recognition-using-mediapipe is under [Apache v2 license](LICENSE).

sign-detection

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