Sign language detection with Python and Scikit Learn | Landmark detection | Computer vision tutorial

Introduction

In this tutorial, we will learn how to detect hand signs using Python along with key libraries such as Mediapipe, OpenCV, and Scikit-Learn. This project is an excellent introduction to computer vision and machine learning, particularly in the area of sign language detection. By the end of this guide, you'll have a working model to recognize specific hand signs.

Step 1: Data Collection

Before creating a model, we need to gather data.

• Use a camera to capture images of different hand signs.
• Ensure to include a variety of signs and angles to improve model accuracy.
• Aim for at least 1000 images for each sign to create a robust dataset.

Tip: Save images in separate folders named after the corresponding sign for easy categorization.

Step 2: Data Processing

Once you have collected your data, it's essential to process it before feeding it into the model.

• Use OpenCV to read images from your dataset.
• Resize images to a uniform size (e.g., 224x224 pixels) for consistency.

Here’s a sample code snippet to read and resize images:

import cv2
import os

def load_and_preprocess_images(folder_path)
    images = []
    labels = []
    for label in os.listdir(folder_path)
        for image_name in os.listdir(os.path.join(folder_path, label))
            image_path = os.path.join(folder_path, label, image_name)
            image = cv2.imread(image_path)
            image = cv2.resize(image, (224, 224))
            images.append(image)
            labels.append(label)
    return images, labels

• Normalize pixel values to a range between 0 and 1 to enhance model training.

Step 3: Landmark Detection

Utilize Mediapipe for landmark detection to improve the model's ability to identify hand signs.

• Import the Mediapipe library and initialize the hands module.
• Process the images to detect hand landmarks, which will act as features for the model.

Here’s an example of how to detect landmarks:

import mediapipe as mp

mp_hands = mp.solutions.hands
hands = mp_hands.Hands()

def detect_landmarks(image)
    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    if results.multi_hand_landmarks
        for hand_landmarks in results.multi_hand_landmarks
            # Extract landmarks for each hand
            pass
    return landmarks

Step 4: Train Model

With processed data and landmarks, it’s time to train your model.

• Split your dataset into training and testing sets (e.g., 80% train, 20% test).
• Use Scikit-Learn to create a classifier, such as a Random Forest or SVM.

Sample training code:

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)
model = RandomForestClassifier()
model.fit(X_train, y_train)

Step 5: Test Model

After training, you can evaluate your model's performance.

• Use the testing set to check the model's accuracy.
• Display the confusion matrix to understand misclassifications.

Here is how you can evaluate your model:

from sklearn.metrics import accuracy_score, confusion_matrix

y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))

Conclusion

In this tutorial, we've covered the essential steps needed to detect hand signs using Python and various libraries. You learned how to collect and process data, detect landmarks, train a model, and evaluate its performance.

Next steps could involve:

• Fine-tuning your model for better accuracy.
• Exploring deep learning models for potentially improved results.
• Implementing real-time sign language detection using a webcam.

For the full source code and additional details, you can check the project repository here.