Grayscale gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)įind canny edges edged=cv2.Canny(gray,30,200) cv2.imshow('canny edges',edged) cv2.waitKey(0)įinding contours #use a copy of your image, e.g. Let’s load a simple image with 3 black squares image=cv2.imread('squares.jpg') cv2.imshow('input image',image) cv2.waitKey(0) Let’s know how to implement contours in opencv, by extracting contours of squares. And, here we will use image segmentation technique called contours to extract the parts of an image.Īnd they have very much broad field of application from the real world image analysis to medical image analysis such as in MRI’s Whereas the contours are the continuous lines or curves that bound or cover the full boundary of an object in an image. Image segmentation is a process by which we partition images into different regions. Filtering the blobs – counting circles and ellipses.Identifying Shapes (circle, rectangle, triangle, square, star).Approximating contours and finding their convex hull.The operations we are going to perform are listed below:

In this tutorial we will learn that how to do image segmentation using OpenCV.

And Raspberry Pi with OpenCV and attached camera can be used to create many real-time image processing applications like Face detection, face lock, object tracking, car number plate detection, Home security system etc. So it can be easily installed in Raspberry Pi with Python and Linux environment. As we know, OpenCV is Open Source Commuter Vision Library which has C++, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. In the previous tutorials, we have used OpenCV for basic image processing and done some advance image editing operations.

Inside out makemkv segment map