ritvikmath

#create a triangle mask by picking all pixels below a given line
triangle_mask = (y > 400 + n - x)

#set the triangle to be cyan
copyImg[triangle_mask] = [0,255,255]

#show image
plt.imshow(copyImg)

ritvikmath

#create a square mask around the top right of the image
square_mask = (x<200)&(x>100)&(y<600)&(y>500)

#make the square red
copyImg[square_mask] = [255,0,0]

#show image
plt.imshow(copyImg)

ritvikmath

#operate on a copy of the image
copyImg = img.copy()

#get the x and y center points of our image
center_x = n/2
center_y = m/2

#create a circle mask which is centered in the middle of the image, and with radius 100
circle_mask = (x-center_x)**2 + (y-center_y)**2 <= 100**2

ritvikmath

import numpy as np
import matplotlib.pyplot as plt

#read the image
img = plt.imread('mouse.jpg')

#show the image
plt.imshow(img)

ritvikmath

#create a copy of my grey image and just get first layer (because all layers are same)
copyImg = greyImage.copy()[:,:,0]

#get number of rows, columns in our image
numRows,numCols = copyImg.shape

#define how big we want our blurring box to be, the bigger the blurrier
boxSize = 31

#get half the size of the box 

ritvikmath

#create a copy of my image
copyImg = img.copy()

#reverse all the rows in my image
hFlipImg = copyImg[:,::-1]

#show the image
plt.imshow(hFlipImg)

ritvikmath

#create a copy of my image
copyImg = img.copy()

#reverse all the rows in my image
vFlipImg = copyImg[::-1]

#show the image
plt.imshow(vFlipImg)

ritvikmath

#create a copy of my image
copyImg = img.copy()

#get the average of each pixel
#we need axis=2 so that we take averages accross the 3rd axis (the color axis) rather than accross the rows or columns
average_pixel_values = np.mean(copyImg, axis=2, keepdims=True)

#create the grey image by "stacking" three copies of these grey values together
greyImage = np.concatenate([average_pixel_values]*3, axis=2)

ritvikmath

import matplotlib.pyplot as plt
import numpy as np

#read my image from file
img = plt.imread('baby_yoda.jpg')

#show my image
plt.imshow(img)

ritvikmath

#initialize tf-idf dictionary for secret speech
tfidf_dict_secret = {}

#initalize bush and obama scores to zero
bush_score = 0
obama_score = 0

#for each phrase in the secret TF dictionary...
for phrase, tf in d_secret.items():
    #get the importance of this phrase in the bush tf-idf dictionary