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.

OpenCV can be installed with the following command:

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pip install opencv-python

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

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import cv2

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.)

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

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

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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.

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

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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.

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

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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.