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)