OpenCV image processing using Python

from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import matplotlib.pyplot as plt
from google.colab.patches import cv2_imshow

mg = cv2.imread(r'/content/parrot.jpg',cv2.IMREAD_UNCHANGED)
height = img.shape[0]  
width = img.shape[1]  
channels = img.shape[2]  
size1 = img.size
cv2_imshow(img)
print('Image Height       : ',height)  
print('Image Width        : ',width)  
print('Number of Channels : ',channels)  
print('Image Size  :', size1)

pixel = img[100,100]
pixel1 = img[200,200]
pixel_diff= pixel1-pixel  
print("The difference between the two pixels is :",pixel_diff)

print("Part A : Negative of the image")
plt.imshow(img)
plt.show()
# negative transformed image
color = ('b', 'g', 'r')    
plt.show()

print("Part B : Power Law ")
img = cv2.imread('/content/parrot.jpg', cv2.IMREAD_UNCHANGED)
gamma_two_point_two = np.array(255*(img/255)**2.2,dtype='uint8')

# Similarly, Apply Gamma=0.4 
gamma_point_four = np.array(255*(img/255)**0.4,dtype='uint8')

# Display the images in subplots
img3 = cv2.hconcat([gamma_two_point_two,gamma_point_four])
cv2_imshow(img3)

print("Part C : Gray-level slicing, Contrast stretching")
img = cv2.imread('/content/parrot.jpg', cv2.IMREAD_UNCHANGED)
def pixelVal(pix, r1, s1, r2, s2):
    if (0 <= pix and pix <= r1):
        return (s1 / r1)*pix
    elif (r1 < pix and pix <= r2):
        return ((s2 - s1)/(r2 - r1)) * (pix - r1) + s1
    else:
        return ((255 - s2)/(255 - r2)) * (pix - r2) + s2
r1 = 70
s1 = 0
r2 = 140
s2 = 255
pixelVal_vec = np.vectorize(pixelVal)

# Apply contrast stretching.
contrast_stretched = pixelVal_vec(img, r1, s1, r2, s2)

print("Constrat Strethcing :")
# Save edited image.
cv2_imshow(contrast_stretched)

class pointProcessing:
 def slicedGreyScale(self,image):
 # T1 and T2 Represent Lower and Upper Threshold Value
  T1 = 100
  T2 = 200
  h, w, c = img.shape
  img_thresh_back = np.zeros((h,w), dtype=np.uint8)
  for i in range(h):
    for j in range(w):
      if (T1 < image[i,j] and image[i,j] < T2):
        img_thresh_back[i,j]= 255
      else:
        img_thresh_back[i,j]= image[i,j]
  cv2_imshow(img_thresh_back)
pointObj= pointProcessing()
pointObj.slicedGreyScale(img)

#Nearest neighbor Interpolation Using cv2.resize()Python
near_img = cv2.resize(img,None, fx = 2, fy = 2, interpolation = cv2.INTER_NEAREST)
cv2_imshow(near_img)

# Bilinear Interpolation 
bilinear_img = cv2.resize(img,None, fx = .5, fy = .5, interpolation = cv2.INTER_LINEAR)
cv2_imshow(bilinear_img)

print("A : Addition and Division :")
img1 = cv2.imread('/content/parrot.jpg')
img2 = cv2.imread('/content/bg.jpg')
dst = cv2.addWeighted(img1,0.3,img2,0.7,0)
#Div
div = cv2.divide(img1, img2)
AddDiv = cv2.hconcat([dst,div])
cv2_imshow(AddDiv)

print("B : Xor and Not Operations :")
#XOR function
bitwiseXor = cv2.bitwise_xor(img1, img2)
#NOT function
bitwiseNot = cv2.bitwise_not(img1)
#concat
img5 = cv2.hconcat([bitwiseXor,bitwiseNot])
cv2_imshow(img5)

print("C : Geometric Operations :")
print("Rotation and Affine Translation :")
#Rotation
image = cv2.rotate(img1, cv2.cv2.ROTATE_90_CLOCKWISE)
cv2_imshow(image)
#Affine Translation
srcTri = np.array( [[0, 0], [img1.shape[1] - 1, 0], [0, img1.shape[0] - 1]] ).astype(np.float32)
dstTri = np.array( [[0, img1.shape[1]*0.33], [img1.shape[1]*0.85, img1.shape[0]*0.25], [img1.shape[1]*0.15, img1.shape[0]*0.7]] ).astype(np.float32)
warp_mat = cv2.getAffineTransform(srcTri, dstTri)
warp_dst = cv2.warpAffine(img1, warp_mat, (img1.shape[1], img1.shape[0]))
# Rotating the image after Warp
center = (warp_dst.shape[1]//2, warp_dst.shape[0]//2)
angle = -50
scale = 0.6
rot_mat = cv2.getRotationMatrix2D( center, angle, scale )
warp_rotate_dst = cv2.warpAffine(warp_dst, rot_mat, (warp_dst.shape[1], warp_dst.shape[0]))
cv2_imshow(warp_dst)

print("D : Mean, Variance :")
#Mean of img1 and img2 
img7 = (img1+img2) * 0.5;
cv2_imshow(img7)
#Variance

print("E : Image interpolation : Down Sampling")
ds = cv2.pyrDown(img1)
cv2_imshow(ds)