Basics and practice of image processing programming using OpenCV3.2

What is OpenCV?

OpenCV, short for Open Source Computer Vision Library, is a comprehensive open-source library.
It aids in computer vision tasks.
It comes with a wide range of tools that help in processing and manipulating images and videos.
Popular among developers and researchers, OpenCV is accessible and versatile.
The library supports multiple programming languages like C++, Python, and Java, making it suitable for various applications.

Why OpenCV 3.2?

OpenCV has been around for quite some time, constantly evolving with each version.
OpenCV 3.2 is notable for many reasons.
Released with several updates and improvements, this version brings more stability and new features.
For programmers keen on the simplicity of older interfaces but wanting newer functionality, OpenCV 3.2 is a valuable choice.
Compatibility with various operating systems and ease of integration has made it a favorite among enthusiasts and professionals.

Setting Up OpenCV 3.2

Before diving into programming with OpenCV, setting up your environment is crucial.
Let’s look at how to install OpenCV 3.2.

Installing OpenCV

First, decide the programming language you will use.
For Python, which is highly recommended for beginners, you can use pip, the package installer for Python.
Simply run:

“`
pip install opencv-python==3.2.0.0
“`

For those using C++, download the OpenCV 3.2 archive from the official website.
Extract the files and configure the build using CMake.
This process can vary slightly depending on your operating system.

Verify Installation

Regardless of the programming language, it’s essential to verify if OpenCV is correctly installed.
In Python, you can do this by opening a command prompt or terminal and typing:

“`python
import cv2
print(cv2.__version__)
“`

This should display 3.2.0, confirming successful installation.

Basic Concepts in Image Processing

Understanding image processing is the foundation for programming with OpenCV.
Let’s explore some key concepts.

Pixels and Color

An image is composed of small units called pixels.
Each pixel has color information, typically stored in RGB (Red, Green, Blue) values.
In RGB, each color channel can range from 0 to 255, allowing for a wide range of colors.

Grayscale Images

In some applications, color information isn’t necessary.
Grayscale images simplify processing by having only intensity information.
Each pixel’s value varies from 0 (black) to 255 (white).

Image Resolution

Image resolution refers to the number of pixels in an image.
Higher resolution results in more detail.
However, it also requires more processing power and memory.

Getting Started with OpenCV Programming

Now that you understand the basics, it’s time to get your hands dirty with some practical examples.

Reading and Displaying an Image

Let’s begin by reading and displaying an image using Python.

“`python
import cv2

# Read the image from a file
image = cv2.imread(‘path_to_your_image.jpg’)

# Display the image
cv2.imshow(‘Displayed Image’, image)

# Wait for a key press and close the window
cv2.waitKey(0)
cv2.destroyAllWindows()
“`

This snippet uses OpenCV’s imread() function to read an image and imshow() to display it.
Ensure the specified image file path is correct.

Converting to Grayscale

Converting an image to grayscale is another fundamental task.
It reduces complexity and is often a pre-processing step.

“`python
# Convert the image to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Display the grayscale image
cv2.imshow(‘Grayscale Image’, gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
“`

The function cvtColor() performs the conversion, making further processing easier.

Basic Image Manipulations

OpenCV offers various functions for manipulating images, such as resizing and rotating.

“`python
# Resize the image
resized_image = cv2.resize(image, (400, 400))

# Rotate the image 90 degrees
rotated_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)

# Display resized and rotated images
cv2.imshow(‘Resized Image’, resized_image)
cv2.imshow(‘Rotated Image’, rotated_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
“`

These functions are particularly useful when working with images of different sizes or orientations.

Advanced Image Processing Techniques

After mastering basics, delve into more complex techniques to augment your skills.

Edge Detection

Edge detection is vital for identifying object boundaries within images.
The Canny edge detector is commonly used in OpenCV.

“`python
# Perform edge detection
edges = cv2.Canny(gray_image, 100, 200)

# Display the edges
cv2.imshow(‘Edges’, edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
“`

This technique is particularly useful in object recognition and computer vision applications.

Face Detection

OpenCV simplifies tasks like face detection with pre-trained classifiers, such as the Haar Cascades.

“`python
# Load pre-trained classifier
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + ‘haarcascade_frontalface_default.xml’)

# Detect faces
faces = face_cascade.detectMultiScale(gray_image, 1.1, 4)

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

# Display the result
cv2.imshow(‘Detected Faces’, image)
cv2.waitKey(0)
cv2.destroyAllWindows()
“`

This advanced functionality demonstrates OpenCV’s power in real-world applications.

Conclusion

OpenCV is a versatile and powerful tool in the realm of image processing.
Once you grasp the basics of OpenCV 3.2, you can progress to complex tasks with ease.
The wide range of functionalities allows for creativity and problem-solving.
Harnessing this library unlocks opportunities for innovative solutions.
With practice, you will master image processing programming, translating ideas into reality.