https://github.com/shouryan01/train-own-gesture-detector-ml-model
Train your own ML model to detect hand gestures!
https://github.com/shouryan01/train-own-gesture-detector-ml-model
mediapipe opencv pandas sklearn tensorflow-lite
Last synced: 2 months ago
JSON representation
Train your own ML model to detect hand gestures!
- Host: GitHub
- URL: https://github.com/shouryan01/train-own-gesture-detector-ml-model
- Owner: shouryan01
- License: mit
- Created: 2023-06-11T11:19:09.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-11T15:56:16.000Z (about 3 years ago)
- Last Synced: 2025-03-22T23:11:39.834Z (about 1 year ago)
- Topics: mediapipe, opencv, pandas, sklearn, tensorflow-lite
- Language: Python
- Homepage:
- Size: 25.4 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Train Your Own ML Hand Gesture Detection Model!
Demo video: https://youtu.be/yboY9NyaT8s
Collecting Data
Testing Model
## Why did I make this?
Google provides a [hand detection model]([url](https://developers.google.com/mediapipe/solutions/vision/gesture_recognizer#hand_landmark_model_bundle)) through [mediapipe](https://developers.google.com/mediapipe) but it only recognizes 7 gestures:
```
0 - Unrecognized gesture, label: Unknown
1 - ✊ Closed fist, label: Closed_Fist
2 - ✋ Open palm, label: Open_Palm
3 - ☝️ Pointing up, label: Pointing_Up
4 - 👎 Thumbs down, label: Thumb_Down
5 - 👍 Thumbs up, label: Thumb_Up
6 - ✌️ Victory, label: Victory
7 - 🤟 Love, label: ILoveYou
```
They do provide documentation on how to train a custom model (https://developers.google.com/mediapipe/solutions/vision/gesture_recognizer/customize) but for that you need access to a dataset of images. It is not always easy or possible to have access to the exact dataset you need. Plus, the documentation is long and annoying to follow, and is not the easiest to follow along with.
That's why I created this repository. You can create a dataset using just your webcam, train a model and start using it right away!
## Dependencies:
There are some libraries that are required for this program to run. All the requirements are listed in **requirements.txt**. You're welcome to install them one by one, or you can simply run to install all of them at once.
```
pip install -r requirements.txt
```
## There are three easy steps to create your model:
1. In **classes_and_constants.py**, fill out the constants you want to use,
```
num_hands - number of hands that you want the program to detect as a class (default is 1)
num_class - number of different hand classes to distinguish between
num_instance - training dataset size for each class (higher is more accurate, default is 50)
break_time - a time delay between taking pictures of different hand classes (default is 2s)
You must also define the names of your various classes. In this demo, I am using 'ROCK', 'PAPER', 'SCISSOR' but you can name this anything you want! Just make sure to add it to the list in the same classes_and_constants.py
```
2. Run generate_data.py
- After all samples have been taken, please take your hand out of the frame of the camera so as to not contaminate other classes with bad data.
3. Run train_model.py
## For your convenience, a **demo.py** file is provided to test out your new model!
Simply run this file to see your predictions in action.