VisioLab raises $11M to scale its AI-powered iPad checkout to stadiums, canteens, and campuses worldwide

Introduction

In this tutorial, you'll learn how to build a basic AI-powered food identification system similar to the technology used by VisioLab. This system can identify food items from images in under 10 seconds, just like the checkout system used in stadiums and campuses. We'll create a simplified version that demonstrates the core concepts of computer vision and image recognition using Python and a pre-trained model.

Prerequisites

Before starting this tutorial, you'll need:

• A computer with Python 3.7 or higher installed
• Basic understanding of Python programming
• Internet connection for downloading packages
• Optional: A webcam or camera for live testing

Step-by-Step Instructions

Step 1: Set Up Your Python Environment

First, we need to create a virtual environment to keep our project dependencies isolated. This prevents conflicts with other Python projects on your system.

1.1 Create a new directory for our project

mkdir food_identifier
 cd food_identifier

1.2 Create a virtual environment

python -m venv food_identifier_env

1.3 Activate the virtual environment

On Windows:

food_identifier_env\Scripts\activate

On macOS/Linux:

source food_identifier_env/bin/activate

Step 2: Install Required Packages

We'll need several Python packages to build our food identification system. These include OpenCV for image processing, TensorFlow for machine learning, and other utilities.

2.1 Install the required packages

pip install opencv-python tensorflow numpy pillow

2.2 Verify installation

pip list

You should see the installed packages including opencv-python, tensorflow, and others.

Step 3: Download and Prepare a Pre-trained Model

Instead of training our own model from scratch, we'll use a pre-trained model called MobileNet, which is already trained to recognize thousands of different objects including food items.

3.1 Create the main Python file

touch food_identifier.py

3.2 Import necessary libraries

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import MobileNet
from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions

Step 4: Create the Food Identification Function

This function will take an image as input and return the identified food item with confidence level.

4.1 Define the main function

def identify_food(image_path):
    # Load the pre-trained MobileNet model
    model = MobileNet(weights='imagenet', include_top=True)
    
    # Load and preprocess the image
    image = cv2.imread(image_path)
    image = cv2.resize(image, (224, 224))
    image = np.expand_dims(image, axis=0)
    image = preprocess_input(image)
    
    # Make prediction
    predictions = model.predict(image)
    decoded_predictions = decode_predictions(predictions, top=3)
    
    return decoded_predictions

Step 5: Test with Sample Images

Let's create a simple test to see if our system works with sample food images.

5.1 Add test functionality to our script

def main():
    # Test with a sample image
    image_path = 'sample_food.jpg'
    
    try:
        predictions = identify_food(image_path)
        print("Top 3 predictions:")
        for i, (imagenet_id, label, score) in enumerate(predictions[0]):
            print(f"{i+1}. {label}: {score:.2f}")
    except Exception as e:
        print(f"Error: {e}")

Step 6: Add Live Camera Support

To make this more like the real VisioLab system, let's add functionality to identify food items from a live camera feed.

6.1 Add live camera functionality

def live_identification():
    # Initialize camera
    cap = cv2.VideoCapture(0)
    
    print("Press 'q' to quit")
    
    while True:
        # Capture frame-by-frame
        ret, frame = cap.read()
        
        if not ret:
            break
        
        # Resize frame for processing
        resized_frame = cv2.resize(frame, (224, 224))
        resized_frame = np.expand_dims(resized_frame, axis=0)
        resized_frame = preprocess_input(resized_frame)
        
        # Make prediction
        model = MobileNet(weights='imagenet', include_top=True)
        predictions = model.predict(resized_frame)
        decoded_predictions = decode_predictions(predictions, top=1)
        
        # Display result
        label = decoded_predictions[0][0][1]
        confidence = decoded_predictions[0][0][2]
        
        cv2.putText(frame, f'{label}: {confidence:.2f}', (10, 30),
                   cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
        
        # Display the frame
        cv2.imshow('Food Identifier', frame)
        
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    
    cap.release()
    cv2.destroyAllWindows()

Step 7: Complete the Main Script

Now let's put everything together in our main script.

7.1 Complete the main script

if __name__ == '__main__':
    print("Food Identification System")
    
    # You can choose between test mode or live mode
    mode = input("Choose mode - 'test' or 'live': ")
    
    if mode.lower() == 'test':
        main()
    elif mode.lower() == 'live':
        live_identification()
    else:
        print("Invalid mode. Please choose 'test' or 'live'")

Step 8: Run Your Food Identifier

With everything set up, it's time to test our system.

8.1 Save your script

Save your complete code in the food_identifier.py file.

8.2 Run the script

python food_identifier.py

8.3 Test with sample images

For testing, you can download sample food images from the internet or take your own photos. Place them in the same directory as your script and run the program in 'test' mode.

8.4 Test with live camera

Run the program in 'live' mode to see how it identifies food items in real-time through your webcam.

Summary

In this tutorial, you've built a basic AI-powered food identification system using Python and pre-trained models. This system mimics the core technology used by VisioLab for their AI checkout systems. You learned how to:

• Set up a Python development environment
• Use pre-trained machine learning models for image recognition
• Process images for AI analysis
• Implement both static image testing and live camera functionality

While this is a simplified version, it demonstrates the fundamental principles behind advanced systems like those used in stadiums and campuses. The real VisioLab system would include additional features like barcode detection, payment processing, and integration with point-of-sale systems, but this foundation gives you a solid understanding of the computer vision aspect.