Computer vision using OpenCV

September 20, 2022

Computer Vision using OpenCV

Since its initial release in 2000, OpenCV, or Open Source Computer Vision Library, has become increasingly popular among developers and researchers due to its flexibility, efficiency, and ease of use. It’s a powerful tool for building applications that require image and/or video analysis, robotics, machine-learning, and more. In this blog post, we’ll provide an introduction to computer vision using OpenCV in Python, exploring some of the library’s core features and capabilities.

Introduction to Computer Vision

Computer vision refers to the capability of computers to interpret visual data from the world and understanding its content, enabling them to detect, track, recognize, and analyze images or videos. Researchers and industry experts use this technology widely for applications such as facial recognition, gait analysis, self-driving cars, and others.

OpenCV Features and Capabilities

OpenCV is free software with BSD license that is optimized for performance and reliability. It is written in C++ and has bindings for many programming languages, including Python, Java, and C#. Its rich set of features and algorithms provides developers and researchers with powerful tools for creating applications such as image segmentation, object recognition, face detection, and more.

Installing OpenCV

Before we get started with coding, we need to install OpenCV. OpenCV can be easily installed using pip command. OpenCV provides the Python bindings using which the developer can access OpenCV library from python. Run the following command to install OpenCV:

pip install opencv-python

Reading and Displaying Images

The first step in any computer vision application is usually to load an image into memory. OpenCV provides the cv2.imread() function for loading images, and the cv2.imshow() function for displaying them. Here’s some code that loads an image and displays it using OpenCV:

import cv2

# Load an color image (in grayscale)
img = cv2.imread('sample.jpg',0)

# Show image
cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

###Image Manipulations

Image manipulation is an important aspect of computer vision, and OpenCV provides many functions for image processing and manipulation. Here are a few examples:

Resizing Images

Resizing images using openCV can be useful for image preprocessing tasks such as reducing noise or increasing sharpness. OpenCV provides the cv2.resize() function to handle image resizing. Here’s some code that uses this function:

import cv2

# Load an color image
img = cv2.imread('sample.jpg')

# Resizing the Image
resized_img = cv2.resize(img, None, fx=0.5, fy=0.5)
cv2.imshow('Resized Image',resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Object Detection

Detecting objects in images and video is a common use case for computer vision, and OpenCV provides some powerful tools and algorithms for object detection. One such algorithm is the Haar cascade classifier, which is useful for detecting faces in images.

import cv2

# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load an color image
img = cv2.imread('sample.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Detect faces using the cascade
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

# Draw rectangles around each detected face
for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display the resulting image
cv2.imshow('Detected Faces', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Conclusion

OpenCV is a powerful library for computer vision and image processing that provides a wide range of tools and algorithms. In this post, we’ve covered only a few of its features, but with this knowledge, you can start building your own applications using OpenCV. There are plenty of resources available online to further your understanding of the topic, and some of these are linked below. Thanks for reading!