Assignment 1.1: Image Transformation in Python

 

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_GRAYSCALE)

type(img)

img.dtype

img.shape

img[23,100] = 200

plt.imshow(img,cmap='gray')

cv2.imshow('Gray',img)

cv2.waitKey(0)

cv2.destroyAllWindows()

img[500:700,500:600] = 255

cv2.imshow('Gray',img)

cv2.waitKey(0)

cv2.destroyAllWindows()

cv2.imwrite(r'albert-einstein_gray_opencv.jpg',img)

RGB Images

cim = plt.imread(r'tulips.jpg')

plt.imshow(cim)

cim.shape

type(cim)

cim.dtype

# Solution to Quiz 03_06

rgb = np.zeros((100,150,3), dtype='uint8')

rgb[:,0:50,0] = 255 # red

rgb[:,50:100,1] = 255 #green

rgb[:,100:150,2] = 255 #blue

 

plt.imshow(rgb)

cv2.imshow('RGB',rgb)

cv2.waitKey(0)

cv2.destroyAllWindows()

OpenCV reads as BGR

cim = cv2.imread(r'tulips.jpg')

cim = cim[:,:,::-1]

R = cim[:,:,0]

G = cim[:,:,1]

B = cim[:,:,2]

plt.figure(1)

plt.subplot(231)

plt.imshow(cim)

plt.xticks([])

plt.yticks([])

plt.subplot(232)

plt.imshow(cim)

plt.xticks([])

plt.yticks([])

plt.subplot(233)

plt.imshow(cim)

plt.xticks([])

plt.yticks([])

plt.subplot(234)

plt.imshow(R,cmap='gray')

plt.xticks([])

plt.yticks([])

plt.title('Red')

plt.subplot(235)

plt.imshow(G,cmap='gray')

plt.xticks([])

plt.yticks([])

plt.title('Green')

plt.subplot(236)

plt.imshow(B,cmap='gray')

plt.xticks([])

plt.yticks([])

plt.title('Blue')

plt.show()

R[100:400,100:400] = 100

G[100:400,100:400] = 50

B[100:400,100:400] = 70

cim[:,:,0] = R

cim[:,:,1] = G

cim[:,:,2] = B

plt.figure(1)

plt.subplot(231)

plt.imshow(cim)

plt.xticks([])

plt.yticks([])

plt.subplot(232)

plt.imshow(cim)

plt.xticks([])

plt.yticks([])

plt.subplot(233)

plt.imshow(cim)

plt.xticks([])

plt.yticks([])

plt.subplot(234)

plt.imshow(R,cmap='gray')

plt.xticks([])

plt.yticks([])

plt.title('Red')

plt.subplot(235)

plt.imshow(G,cmap='gray')

plt.xticks([])

plt.yticks([])

plt.title('Green')

plt.subplot(236)

plt.imshow(B,cmap='gray')

plt.xticks([])

plt.yticks([])

plt.title('Blue')

plt.show()

RGB to HSV conversion

def f_rgb_to_hsv(r, g, b,scaleFactor): 

    r, g, b = r / 255.0, g / 255.0, b / 255.0

    cmax = max(r, g, b)    # maximum of r, g, b 

    cmin = min(r, g, b)    # minimum of r, g, b 

    diff = cmax-cmin       # diff of cmax and cmin. 

    if cmax == cmin:  

        h = 0

    elif cmax == r:  

        h = (60 * ((g - b) / diff) + 0) % 360

    elif cmax == g: 

        h = (60 * ((b - r) / diff) + 120) % 360

    elif cmax == b: 

        h = (60 * ((r - g) / diff) + 240) % 360

   

    if h < 0:

        h = h + 360

    if cmax == 0: 

        s = 0

    else: 

        s = (diff / cmax) * scaleFactor

    v = cmax * scaleFactor

    return h, s, v 

print(f_rgb_to_hsv(100,200,50,100))

im = cv2.imread(r'tulips.jpg')

HsvIm = cv2.cvtColor(im,cv2.COLOR_BGR2HSV)

cv2.cvtColor(im,cv2.COLOR_HSV2BGR)

type(HsvIm)

HsvIm.shape

img = cv2.imread(r'tulips.jpg')

plt.imshow(img[:,:,::-1])

hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

lr = np.array([0,120,70])

ur = np.array([8,255,255])

mask1 = cv2.inRange(hsv,lr,ur)

type(mask1)

mask1.dtype

mask1.shape

lr = np.array([170,120,70])

ur = np.array([180,255,255])

mask2 = cv2.inRange(hsv,lr,ur)

np.unique(mask2)

np.unique(mask1)

mask = mask1 | mask2

np.unique(mask)

res = cv2.bitwise_and(img,img,mask=mask)

plt.figure(1)

plt.subplot(121)

plt.imshow(img[:,:,::-1])

plt.subplot(122)

plt.imshow(res[:,:,::-1])

plt.show()

# solution to quiz

lr = np.array([20,100,100])

ur = np.array([30,255,255])

yellow_mask = cv2.inRange(hsv,lr,ur)

res = cv2.bitwise_and(img,img,mask=yellow_mask)

plt.figure(1)

plt.subplot(121)

plt.imshow(img[:,:,::-1])

plt.subplot(122)

plt.imshow(res[:,:,::-1])

plt.show()