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Practice

Python Library for Computer Vision, OpenCV

OpenCV (Open Source Computer Vision Library) is one of the most widely-used open-source libraries for computer vision.

It offers various functionalities, including image processing, feature extraction, video transformation, and machine learning.


Installing OpenCV​

OpenCV can be installed with the following command:

Installing OpenCV
pip install opencv-python

It's important to note that to use OpenCV in Python code, you need to import the cv2 package.

Example of Importing OpenCV
import cv2

Why is OpenCV widely used?​

OpenCV is one of the most popular libraries in the field of computer vision.

Its key advantages include:

  • Offers image and video processing functions (filtering, transformation, feature extraction, etc.)

  • Supports various formats (JPG, PNG, BMP, TIFF, etc.)

  • Provides hardware acceleration features (supports GPU)

  • Supports multiple languages like C++, Python, Java

  • Can be integrated with machine learning and deep learning (usable with TensorFlow, PyTorch, etc.)


Basic Usage of OpenCV​

Let's explore the basic ways to process images and videos using the OpenCV library.


1. Loading an Image​

To load an image, use the cv2.imread() method.

Example of Loading an Image
import cv2

# Load the image
image = cv2.imread("sample.jpg")

# Display the image in a window
cv2.imshow("Sample Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Use cv2.imread() to load an image, and cv2.imshow() to display it on the screen.


2. Resizing an Image​

To adjust the size of an image, use the cv2.resize() method.

Example of Resizing an Image
resized_image = cv2.resize(image, (300, 300))
cv2.imshow("Resized Image", resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

The code above resizes the image to 300x300 and displays it.


3. Converting to Grayscale​

To convert an image to grayscale, use the following code:

Example of Grayscale Conversion
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Grayscale Image", gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

You can now convert a color image to a grayscale image.


Using OpenCV makes implementing image processing and computer vision tasks straightforward.

In the next lessons, we'll cover advanced features like edge detection and object detection.

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