Image Processing in Python: OpenCV Example Code

Image processing is a branch of computer science and engineering that deals with the analysis, manipulation, and enhancement of digital images. It involves the use of various mathematical and algorithmic techniques to process digital images, with the aim of improving their quality, extracting useful information, or converting them into a more suitable format for further use.

Image processing has many practical applications, including:

Medical imaging: for diagnosing and treating medical conditions
Surveillance and security: for detecting and tracking objects and people
Robotics: for object recognition and tracking
Entertainment: for special effects in movies and video games
Remote sensing: for analyzing satellite and aerial images
Industrial inspection: for detecting defects in products and machinery
Computer vision: for understanding and interpreting visual information by machines

Some of the common tasks in image processing include:

Image filtering: removing noise and enhancing features in an image
Image segmentation: dividing an image into regions that correspond to different objects or parts of the image
Image compression: reducing the file size of an image while retaining its quality
Image restoration: restoring degraded or damaged images to their original quality
Object recognition: detecting and recognizing objects in an image
Image registration: aligning multiple images of the same scene to create a composite image

Image processing is an important field with many practical applications, and it continues to evolve with the development of new techniques and algorithms.

Image Processing with Python

Image processing with Python involves performing various operations and algorithms on digital images using Python programming language and its libraries. Python offers many image processing libraries, including OpenCV, PIL (Python Imaging Library), scikit-image, and more.

Here’s a list of some of the basic image processing operations that you can perform with Python:

Reading and displaying an image
Converting an image to grayscale
Resizing an image
Applying filters to an image
Edge detection
Image segmentation
Object recognition

OpenCV Python Library

To start with, you need to install a Python package called “OpenCV“. OpenCV is a library of programming functions mainly aimed at real-time computer vision. It is one of the most widely used libraries for image processing in Python.

You can install OpenCV using the following command:

pip install opencv-python

Once you have installed OpenCV, you can start with some basic image processing tasks.

Reading and displaying an image

The first task is to read an image and display it on the screen. You can use the cv2.imread() function to read an image and the cv2.imshow() function to display it.

Here’s the code to read and display an image:

import cv2

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

# Display the image
cv2.imshow('image', img)

# Wait for the user to press a key
cv2.waitKey(0)

# Close all windows
cv2.destroyAllWindows()

White cat with different color eyes hides behind a green plant. Turkish angora eats peace lily green leaves in living room.

In this code, cv2.imread('image.jpg') reads the image “image.jpg” from the current directory, and cv2.imshow('image', img) displays it on the screen. cv2.waitKey(0) waits for the user to press a key, and cv2.destroyAllWindows() closes all windows.

Converting an image to grayscale

The next task is to convert an image to grayscale. Grayscale images are simpler to process than color images, and many image processing algorithms work only with grayscale images.

Here’s the code to convert an image to grayscale:

import cv2

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

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

# Display the grayscale image
cv2.imshow('grayscale', gray)

# Wait for the user to press a key
cv2.waitKey(0)

# Close all windows
cv2.destroyAllWindows()

In this code, cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) converts the color image to grayscale.

Resizing an image

The next task is to resize an image. Resizing an image is useful when you want to reduce the size of an image or when you want to increase the size of an image.

Here’s the code to resize an image:

import cv2

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

# Resize the image
resized = cv2.resize(img, (500, 500))

# Display the resized image
cv2.imshow('resized', resized)

# Wait for the user to press a key
cv2.waitKey(0)

# Close all windows
cv2.destroyAllWindows()

In this code, cv2.resize(img, (500, 500)) resizes the image to 500×500 pixels.

Applying filters to an image

The next task is to apply filters to an image. Filters are used to remove noise from an image or to enhance certain features of an image.

Here’s the code to apply a filter to an image:

import cv2

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

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

# Apply a Gaussian blur filter
blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# Display the filtered image
cv2.imshow('blurred', blurred)

# Wait for the user to press a key
cv2.waitKey(0)

# Close all windows
cv2.destroyAllWindows()

In this code, cv2.GaussianBlur(gray, (5, 5), 0) applies a Gaussian blur filter to the grayscale image. The (5, 5) parameter specifies the kernel size of the filter, and 0 specifies the standard deviation of the Gaussian function.

Edge detection

Edge detection is a popular task in image processing that involves detecting the edges of objects in an image. There are many different techniques and algorithms for edge detection, but one of the most popular ones is the Canny edge detection algorithm.

Here’s how you can perform edge detection using OpenCV and the Canny algorithm:

import cv2

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

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

# Apply Gaussian blur to the grayscale image
blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# Apply Canny edge detection to the blurred image
edges = cv2.Canny(blurred, 50, 150)

# Display the edges
cv2.imshow('edges', edges)

# Wait for the user to press a key
cv2.waitKey(0)

# Close all windows
cv2.destroyAllWindows()

Edge detection (white cat behind a plant)

In this code, cv2.Canny(blurred, 50, 150) applies the Canny edge detection algorithm to the blurred image. The 50 and 150 parameters specify the lower and upper thresholds for the edges. Pixels with gradient values below the lower threshold are discarded, and pixels with gradient values above the upper threshold are considered edges. Pixels with gradient values between the two thresholds are considered edges only if they are connected to pixels with gradient values above the upper threshold.

You can adjust the values of the lower and upper thresholds to fine-tune the edge detection. A higher lower threshold will result in fewer edges being detected, while a lower upper threshold will result in more edges being detected.

These are some of the basic tasks that you can perform with image processing in Python using OpenCV. There are many more advanced tasks that you can perform, such as image segmentation and object recognition, but these require more advanced techniques and algorithms.