Basic Concepts

Introduction

Image processing is a crucial aspect of various fields, including computer vision, machine learning, digital media, and more The tasks of reading, displaying, and saving images or frames from videos are fundamental. Below is a description of how to perform these tasks using popular libraries like OpenCV and Pillow (PIL) for images, and OpenCV for video processing. 

Description 

1.Reading an Image: Use cv2.imread() to load an image file into memory as a NumPy array for processing.

2.Displaying an Image: Display the loaded image with cv2.imshow(), pause with cv2.waitKey(), and close windows using cv2.destroyAllWindows().

3.Saving an Image: After processing, save the modified image back to a file for storage or further use with cv2.imwrite().

Converting an RGB image to grayscale reduces it from three color channels to a single channel representing light intensity, simplifying processing. This technique enhances efficiency by focusing only on brightness values, removing complex color data while preserving essential details. 

This process involves altering the color of the image’s center pixel, demonstrating the technique of modifying individual pixel values within an image. 

This process involves changing the color of a specific image region, such as a 20x20 pixel area in the top-left corner, to blue by setting each pixel's RGB value to (255, 0, 0). Using OpenCV, the image is loaded as an array, and the region can be updated with new colors through array slicing. 

This task involves overlaying text on an image, which is useful for adding annotations or watermarks. 

This process includes drawing a circle on an image and placing text within it, which can help highlight or label specific parts of the image. This approach is particularly useful for emphasizing certain areas and providing clear annotations. 

This process involves converting an RGB image into the YCbCr color space, which is frequently used in applications like video compression and broadcasting.