E material for reference for TA 2 Exam

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E material for reference for TA 2 Exam

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Assignment 1.2 - Transformation

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Images:  https://drive.google.com/drive/folders/1yCfFEXnEinsWBNejbDgEsziCHdESol1D?usp=share_link Code import numpy as np import matplotlib.pyplot as plt import cv2 grayImage = r'albert-einstein_gray.jpg' colourImage = r'tulips.jpg' I_gray = cv2.imread(grayImage,cv2.IMREAD_GRAYSCALE) I_BGR = cv2.imread(colourImage) plt.imshow(I_gray,cmap='gray') plt.imshow(I_BGR[:,:,::-1]) #scaling I_gray_resized = cv2.resize(src=I_gray,fx=2,fy=2,dsize=None) plt.imshow(I_gray_resized,cmap='gray') # plt.xticks([]) # plt.yticks([]) I_gray_resized.shape I_gray.shape I_BGR_resized = cv2.resize(src=I_BGR,fx=0.5,fy=2,dsize=None) plt.imshow(I_BGR_resized[:,:,::-1]) #Rotation  image = cv2.rotate(I_gray , cv2.ROTATE_90_CLOCKWISE) plt.imshow(I_gray_rot,cmap='gray') # access height and width of the image height, width = I_gray.shape # define center of rotation cr = (width/2,height/2) # get the rotation matrix M = cv2.getRotationMatrix2D(cr,30,1) # apply warpAffine() method t
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Assignment 4: Canny Edge Detection

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Image Link:  https://drive.google.com/drive/folders/1USa7N0JCIeGGhZhkKP18-QuziOzh8riX?usp=share_link Canny Edge Detection Algorithm Basic Code: import cv2 import matplotlib.pyplot as plt import numpy as np imPth = r ' c m.jpg' img = plt.imread(imPth) plt.imshow(img, cmap = 'gray' ) E = cv2.Canny(img, 150 , 250 ) plt.subplot( 121 ) plt.imshow(img, cmap = 'gray' ) plt.axis( 'off' ) plt.subplot( 122 ) plt.imshow(E, cmap = 'gray' ) plt.axis( 'off' ) plt.show() Gaussian filter x = 0 y = - 1 sigma = 1 v = ( 1 /( 2 *np.pi*(sigma** 2 )))*np.exp(-(x** 2 + y** 2 )/( 2 *(sigma** 2 ))) v def f_getGaussianFilter ( s , sigma ):     maxX = s// 2     minX = -maxX     minY = minX     maxY = maxX     G = np.zeros((s,s))     for x in range (minX,maxX+ 1 ):         for y in range (minY,maxY+ 1 ):             v = ( 1 /( 2 *np.pi*(sigma** 2 )))*np.exp(-(x** 2 + y** 2 )/( 2 *(sigma** 2 )))             G[x-minX,y-minY] = v     return G G = f_getGaus
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Assignment 1.1: Image Transformation in Python

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  Images:  https://drive.google.com/drive/folders/1yCfFEXnEinsWBNejbDgEsziCHdESol1D?usp=share_link Code:   import numpy as np import matplotlib.pyplot as plt im = np.arange(256) im = im[np.newaxis,:] im = np.repeat(im,100,axis=0) plt.imshow(im,cmap='gray') Processing Grayscale images im = plt.imread(r'albert-einstein_gray.jpg') type(im) im.shape im.dtype plt.imshow(im,cmap='gray') im im[23,300] im2 = im.copy() im2[23,100] = 200 im2[23:100,40:100] = 255 plt.imshow(im2,cmap='gray') im2[300:400,40:100] = 0 plt.imshow(im2,cmap='gray') # Quiz Solution im = plt.imread(r'albert-einstein_gray.jpg') im3 = im.copy() im3[330:430,300:400] = 255 #for left eye im3[330:430,440:540] = 255 #for right eye plt.imshow(im3, cmap='gray') plt.imsave(r'albert-einstein_gray_modified.jpg',im2,cmap='gray') # pip install opencv-contrib-python Grayscale Images in OpenCV import cv2 img = cv2.imread(r'albert-einstein_gray.jpg',cv2.IMREAD
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