USC Professor Itti gives a talk on the JeVois camera machine vision YOLO RSSC robotics

Introduction

This tutorial provides a comprehensive guide based on a presentation by USC Professor Itti about the JeVois camera and its applications in machine vision, machine learning, and robotics. The JeVois camera is a powerful tool for real-time visual processing, making it relevant for robotics enthusiasts, developers, and researchers interested in integrating advanced vision capabilities into their projects.

Step 1: Understanding the JeVois Camera

• Overview: The JeVois camera is a compact, intelligent camera designed for machine vision tasks.
• Key Features:
  • Real-time processing: Capable of processing images and videos on the fly.
  • Machine Learning Integration: Supports various machine learning algorithms, including YOLO (You Only Look Once) for object detection.
  • Open-source: The firmware is open-source, allowing for customization and experimentation.

Step 2: Setting Up Your JeVois Camera

• What You Need:
  • A JeVois camera unit
  • A compatible computer or microcontroller (like Raspberry Pi)
  • USB or serial connectors for communication
• Installation Steps:
  1. Connect the JeVois camera to your computer or microcontroller.
  2. Install necessary drivers if prompted (refer to the official documentation on jevois.org).
  3. Ensure the camera is powered on and recognized by your system.

Step 3: Installing Software and Libraries

• Required Software:
  • JeVois software package
  • Machine learning libraries (e.g., OpenCV)
• Installation Steps:
  1. Download the JeVois software from jevois.org.
  2. Install the software using your system’s package manager or by following the installation instructions provided on the website.
  3. Install OpenCV for image processing capabilities, if not already installed.

Step 4: Configuring the Camera for Machine Vision Tasks

• Settings to Adjust:
  • Resolution and frame rate for optimal performance
  • Specific modes for object detection and tracking
• Configuration Steps:
  1. Access the camera’s configuration interface through a web browser or command line.
  2. Adjust the parameters according to the requirements of your project (e.g., higher resolution for detailed detection).
  3. Save the settings and restart the camera if necessary.

Step 5: Implementing YOLO for Object Detection

• What is YOLO: YOLO (You Only Look Once) is a popular algorithm for real-time object detection.
• Implementation Steps:
  1. Load a pre-trained YOLO model or train your own model using relevant datasets.
  2. Use the following code snippet to implement YOLO with the JeVois camera:

  import cv2
  import numpy as np
  
  # Load YOLO
  net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
  layer_names = net.getLayerNames()
  output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
  
  # Start capturing video
  cap = cv2.VideoCapture(0)
  
  while True:
      _, frame = cap.read()
      height, width, channels = frame.shape
  
      # Detecting objects
      blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
      net.setInput(blob)
      outs = net.forward(output_layers)
  
      # Process the results (draw boxes, labels, etc.)
      # (Add your processing code here)
  
      cv2.imshow("Image", frame)
      if cv2.waitKey(1) & 0xFF == ord('q'):
          break
  
  cap.release()
  cv2.destroyAllWindows()
  

  3. Test the implementation and adjust parameters as needed.

Conclusion

Throughout this tutorial, we explored the JeVois camera and its capabilities in machine vision and robotics. We covered setup, software installation, configuration, and the implementation of YOLO for object detection. As a next step, users can experiment with different machine learning models, adjust camera settings, and integrate the camera into more complex robotic systems. For further exploration, refer to the JeVois documentation and community forums for support and project ideas.