Question

1. Image Class: Computer Vision (Allegro Application)                        (10 marks) Computer Vision is the process of analyzing input images to detect, track, recognize, and react to objects. Edge Detection is a common task in computer vision where the program detects the boundary of an object. This boundary is then used to identify the shape of the object and eventually recognize the object.

Consider the Imager class in Module 3. Create a new class based on Imager called Imager2 with three new functions:

• Gray. Convert image to gray scale assuming the image is colour (RGB values are equal)
• BW. Convert image to black-and-white assuming image is gray (pixels turn to black if gray value less than 100, otherwise white)
• Edge. Detect edge points as pixels that are different from their neighbours, and then set them as black. Other pixels turn to white. Assuming the image is black- and-white.

Answer 1

import cv2
#greyscale
def grayscale():

    img=cv2.imread('modern.jpg',0)

    cv2.imshow('grayscale',img)
    cv2.waitKey()
    cv2.destroyAllWindows()
#blackandwhite
def blackandwhite():
    originalImg = cv2.imread('modern.jpg')
    gray = cv2.cvtColor(originalImg, cv2.COLOR_BGR2GRAY)

    (thresh, blackAndWhiteImage) = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

    cv2.imshow('Black white image', blackAndWhiteImage)

    cv2.waitKey(0)
    cv2.destroyAllWindows()
#edgedetection
def edgedetection():
    image = cv2.imread('modern.jpg',0)


    cany = cv2.Canny(image, 50, 120)
    cv2.imshow('Canny', cany)
    cv2.waitKey(0)
    cv2.destroyAllWindows()