Question

In simple python code

1) Write a function to convert the image to grayscale.

2Write a function to convert an image to black and white

3)Sepia Tone images are those brownish colored images that may remind you of times past. The formula for creating a sepia tone is as follows:

newR = (R × 0.393 + G × 0.769 + B × 0.189)

newG = (R × 0.349 + G × 0.686 + B × 0.168)

newB = (R × 0.272 + G × 0.534 + B × 0.131)

Write a function to convert an image to sepia tone. Remember that rgb values must be integers between 0 and 255.

4Write a function to uniformly enlarge an image by a factor of 2 (double the size).

5) After you have scaled an image too much it looks blocky. One way of reducing the blockiness of the image is to replace each pixel with the average values of the pixels around it. This has the effect of smoothing out the changes in color. Write a function that takes an image as a parameter and smooths the image. Your function should return a new image that is the same as the old but smoothed.

6 Write a function, is_prime, that takes a single integer argument and returns True when the argument is a prime number and False otherwise.

Answer 1

1) Write a function to convert the image to grayscale.

`

original_pixels = []

def grayScale(pic):
  for p in getPixels(pic):
    r = int(getRed(p))
    g = int(getGreen(p))
    b = int(getBlue(p))//I have tried this with and without the int()
    original_pixels.append((r, g, b))
    new = (r + g + b)/3
    color= makeColor(new,new,new)
    setColor(p, color)

`

2) Write a function to convert an image to black and white

`

import cv2
import numpy as np
img1 = cv2.imread('opencvlogo.png')
row,col,ch = img1.shape
g = [ ]  #the list in which we will stuff single grayscale pixel value inplace of 3 RBG values
#this function converts each RGB pixel value into single Grayscale pixel value and appends that value to list 'g'
def rgb2gray(Img):
    global g
    row,col,CHANNEL = Img.shape
    for i in range(row) :
        for j in range(col):
        a =      (   Img[i,j,0]*0.07  +  Img[i,j,1]*0.72 +    Img[i,j,2] *0.21   ) #the algorithm i used id , G =  B*0.07 + G*0.72 + R* 0.21
                                                                                   #I found it online
        g.append(a)
rgb2gray(img1)  #convert the img1 into grayscale
gr = np.array(g)  #convert the list 'g' containing grayscale pixel values into numpy array
cv2.imwrite("test1.png" , gr.reshape(row,col)) #save the image file as test1.jpg

`

4)

`

p = img.getPixel(col/factor,row/factor)
    newImage.setPixel(col,row,p)

`

5)

`

def is_prime(x):
    # only positive numbers are allowed and the smallest prime number is 2 
    if (x > 1):
        # since the smallest prime number is 2, we start the divisor at 3
        divisor = 2

        # because the submit number can always be divided by itself we can use the
        # range function to set the correct range
        for i in range(divisor,x):
            if (x % i) == 0:
                return False
    else:
        return False

    return True

print(is_prime(3))

`