Hand Gesture Recognition in python

Hand Gesture Recognition in python

As a final year project, you can develop a gesture recognition interface that will help you enable direct communication between humans and machines. Traditional methods, such as keyboards and mice, can limit communication between humans and machines. So, this project used machine learning techniques and infrared information to detect and classify hand gestures. The system consists of several stages: hand detection, gesture segmentation, feature extraction, gesture classification using five pre-trained convolutional neural network models and vision transformer, and human-machine interface design. To build this system, you can use several machine learning algorithms like support vector machine, K nearest neighbor, decision tree, and neural networks to recognize gestures. This article also provides source code to make this system easy to use.

The Basics of Hand Gestures

Let’s learn some simple things about hand gestures first. We use our hands to show many things by moving them, making different shapes, and putting them in different places. Machines can understand what we want to do by looking at these gestures.

Applications of Hand Gesture Recognition

There are many different and amazing things you can do with hand gestures recognition system. You can make your computer or smart home things do what you want by just moving your hand. Hand gestures can also help people get better after being sick or hurt, and make cars more fun and easy to use.

Hand Gesture Recognition in python

To make Hand Gesture Recognition in python follow these steps.

Step 1: Install Required libraries

Install following libraries by typing these commands in terminal

				pip install opencv-python

				pip install mediapipe


Step 2: Import required libraries

Create a new python file in VS Code and  start typing this code

				import cv2
import mediapipe as mp

Step 3: Initialize MediaPipe Hands module

				mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands


Step 4: Set up webcam capture:

				cap = cv2.VideoCapture(0)


Step 5: Create a loop for real-time processing

				with mp_hands.Hands(
    min_tracking_confidence=0.5) as hands:


Step 6: Inside the loop, read frames from the webcam

				success, image = cap.read()


Step 7: Convert and flip the image:

				image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)


Step 8: Process the image using MediaPipe Hands

				results = hands.process(image)


Step 9: Draw hand landmarks on the image

				if results.multi_hand_landmarks:
    for hand_landmarks in results.multi_hand_landmarks:
            image, hand_landmarks, mp_hands.HAND_CONNECTIONS)


Step 10: Display the annotated image:

				cv2.imshow('MediaPipe Hands', image)


Step 11: Break the loop if the 'Esc' key is pressed

				if cv2.waitKey(5) & 0xFF == 27:


Step 12: Release the webcam and close the window when done




Hand Gesture Recognition in python

You did it! You learned a lot about hand gesture recognition in Python. You learned some simple things and also how to make it work in real time. You know a lot about this cool technology that can do amazing things. When you work on your own things, don’t forget that you can do anything you want.

Absolutely! Hand gesture recognition is widely used in virtual reality to enhance user interaction and create immersive experiences.

While it may seem complex at first, with the right resources and dedication, beginners can grasp the fundamentals and gradually master the techniques.

Hand gesture recognition is valuable in healthcare for applications such as rehabilitation exercises, where patients can interact with virtual environments to aid in their recovery.

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