Gesture Recognition:-
Gesture recognition is an active field of research that is constantly evolving. It is used to interpret hand and body movements such as waving, pointing, and touching. It can also be used to interpret facial expressions and body language.
Gesture recognition technology has a wide range of applications, ranging from gaming and entertainment to medical and industrial. It can be used in fields such as robotics, virtual reality, computer vision, and natural language processing.
With advancements in technology, the accuracy and speed of gesture recognition systems are constantly improving. Furthermore, new technologies such as machine learning and deep learning are being used to improve the accuracy and speed of gesture recognition systems.
What is MediaPipe?
MediaPipe is an open-source cross-platform framework for creating and deploying various types of media processing pipelines. It provides a collection of reusable components for building end-to-end solutions for tasks such as analyzing and understanding video and audio, extracting features from images and videos, and understanding the context of the content.
Hand Landmark Model Bundle
The hand landmark model bundle detects the keypoint localization of 21 hand-knuckle coordinates within the detected hand regions. The model was trained on approximately 30K real-world images and several rendered synthetic hand models imposed over various backgrounds. The definition of the 21 landmarks is given below.
Method to Make Hand Gesture Recognition Using AI
- Import necessary packages
- Initialize models
- Read frames from a webcam
- Detect hand key points
- Recognize hand gestures
- Guide to Gesture Recognition Using AI
Step 1. installation of mediapipe
Install media pipe library with pip environment of python.
Step 2. Import Libraries
Import necessary libraries and initialize the mediapipe holistic function to detect hand and face nodes.
MediaPipe Holistic
Live perception of simultaneous human pose, face landmarks, and hand tracking in real-time on mobile devices can enable various modern life applications: fitness and sports analysis, gesture control and sign language recognition augmented reality try-on and effects. MediaPipe already offers fast, accurate, separate solutions for these tasks. Combining them all in real-time into a semantically consistent end-to-end solution is a uniquely difficult problem requiring simultaneous inference of multiple, dependent neural networks.
Step 3. Give drawing specifications.
Step 4. Define holistic function of mediapipe
Now, open the holistic models as holistic variable to find out connection.
Step 5. Real time video capture.
Next, open the webcam to find out the connection. Also, put this real time video frames with a holistic process.
Step 6. Draw connection points of face landmarks.
Output:-
Step 7. Draw left & right hand.
Step 8: Draw pose connection.
Output:-
Conclusion:-
With this, we have come to the end of the hand gesture recognition project. In this project, we’ve built a hand gesture recognizer using OpenCV and Python. Besides, we used MediaPipe framework for detection and gesture recognition. Once done, you can test the model to verify its accuracy. The accuracy of the model can be improved by tuning the parameters of the model. Finally, the model can be deployed in a real-time application that can accurately recognize different hand gestures.
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