In this tutorial you will learn about Python OpenCV | cv2.blur() method. OpenCV is a powerful open-source computer vision library used for various image processing tasks. OpenCV is used for object detection, image recognition, and more. cv2.blur() is a function in OpenCV used for image blurring or smoothing. It helps reduce noise and detail in an image, making it useful for tasks like preprocessing before feature extraction or object detection.
OpenCV:
OpenCV-Python is a library of Python bindings designed to solve computer vision problems. An open-source computer vision library is called OpenCV. Open CV provides a comprehensive set of tools and algorithms for image and video processing, analysis, and computer vision applications. OpenCV is cross-platform and can be used on various operating systems including Windows, macOS, and Linux, making it suitable for a wide range of operations, including blurring, filtering, object detection, facial recognition, and more.
cv2.blur():
This is specific function within the OpenCV library, particularly in the cv2 module (the Python bindings for OpenCV). The cv2.blur function is used to apply a simple averaging filter to an image, effectively blurring it. This function takes an input image and a kernel size as arguments and returns the blurred image.
Syntax:
cv2.blur(src, ksize[, dst[,anchor[, borderType]]])
For example:
Input:
import cv2
image = cv2.imread('input_image.jpg')
blurred_image = cv2.blur(image, (10, 10))
cv2.imshow('Original Image', image)
cv2.imshow('Blurred Image', blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
Overview :
OpenCV, along with functions like cv2.blur(), provides powerful tools and capabilities for image processing and computer vision tasks in Python applications, making it a popular choice for developers working in these domains.