Applications of Computer Vision in Real World

Tutorial 5 of 5

1. Introduction

1.1 Tutorial's goal

This tutorial aims to introduce computer vision's practical applications in various fields like healthcare, automotive, security, and more.

1.2 What will you learn

By the end of this tutorial, you will understand how computer vision is used in real-world applications and get hands-on experience with coding examples.

1.3 Prerequisites

Basic knowledge of Python programming and a general understanding of computer vision concepts are recommended.

2. Step-by-Step Guide

2.1 Computer Vision

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world.

2.2 Applications of Computer Vision

2.2.1 Healthcare

Computer vision is used for medical image analysis, including detecting diseases in X-rays and MRIs.

2.2.2 Automotive

Self-driving cars use computer vision to perceive their environment and make decisions.

2.2.3 Security

Computer vision is used in facial recognition systems for security purposes.

3. Code Examples

3.1 Example: Detecting edges in an image (Healthcare Application)

This is a simple example of how computer vision can be used to detect edges in an image, which is a common task in medical imaging.

#importing libraries
import cv2
import numpy as np

# Load the image
img = cv2.imread('image.jpg', 0)

# Detect edges in the image
edges = cv2.Canny(img, 100, 200)

# Display the original image and the edges side by side
cv2.imshow('Original Image', img)
cv2.imshow('Edge Image', edges)

cv2.waitKey(0)
cv2.destroyAllWindows()

In this example, we first import the necessary libraries (cv2 for OpenCV and numpy). We then load the image, and use the Canny function to detect the edges in the image. Finally, we display the original image and the image with detected edges side by side.

4. Summary

In this tutorial, we introduced computer vision and its applications in various fields. We also went through a practical example of edge detection in an image.

5. Practice Exercises

5.1 Exercise 1: Object Detection

Try to implement a basic object detection algorithm using the Haar cascades method.

5.2 Exercise 2: Facial Recognition

Implement a basic facial recognition system using the LBPH (Local Binary Pattern Histogram) method.

6. Next Steps

Continue to explore more advanced topics in computer vision, such as deep learning for image classification and object detection.