Image_Processing

Gaussian Blur is a widely used image processing technique that reduces image noise and detail by averaging the pixel values in a Gaussian kernel. This makes the image smoother and less noisy while preserving edges better than other types of blurring. Why Gaussian Blur? Key Function: cv2.GaussianBlur() Syntax: cv2.GaussianBlur(src, ksize, sigmaX, dst=None, sigmaY=0, borderType=cv2.BORDER_DEFAULT) Parameters: […]

Resizing an image involves changing its dimensions (width and height) to the desired size. OpenCV provides the cv2.resize() function to perform this operation efficiently. Syntax: cv2.resize(src, dsize, fx=0, fy=0, interpolation=cv2.INTER_LINEAR) Parameters: Example Code: How Resizing Works: Scaling with fx and fy: Instead of providing exact dimensions, scaling factors can be used: # Scale the image

Grayscale images simplify processing by reducing color complexity. A grayscale image contains shades of gray, where each pixel’s intensity ranges from 0 (black) to 255 (white). OpenCV provides the cv2.cvtColor() function to convert a color image to grayscale. Step 1: Read the Image image = cv2.imread(‘example.jpg’) Step 2: Convert to Grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

OpenCV (Open Source Computer Vision Library) is a powerful library for real-time computer vision and image processing. With Python bindings, OpenCV enables developers to perform a wide range of image processing tasks efficiently and effectively. Key Features of OpenCV Installing OpenCV To use OpenCV, install it using pip: Reading and Displaying an Image import cv2:This

Digital Image Processing refers to the manipulation and analysis of digital images using computational algorithms to enhance their quality, extract meaningful information, or prepare them for various applications. It plays a vital role in fields like computer vision, artificial intelligence, medical imaging, and multimedia. What is a Digital Image?A digital image is represented as a