Lecture

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

Here's how to convert an image to grayscale.

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.

Mission
0 / 1

Which of the following is the correct code to import OpenCV?

import cv2

import opencv

import openCV2

import cv

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