Day #318 - Image Comparison Techniques

Image is a set of points (vectors). We could use different techniques for image similarity comparison. Listed below some of out of box techniques. Dlib based comparison was earlier discussed.

• Haar, Extract Region

Comparison Techniques

• Euclidean distance
• Cosine distance
• Hamming distance
• Jaccard-Needham dissimilarity

Ref - Link

SSIM Approach Technique of structural similarity

	import cv2
	img1 = cv2.imread(r'D:\PetProject\image_comparison\4.jpg',0)
	face_cascade = cv2.CascadeClassifier(r'D:\PetProject\image_comparison\Haar.xml')
	#Perform Haar
	faces = face_cascade.detectMultiScale(img1, 1.3, 5)
	for (x,y,w,h) in faces:
	#Extract
	face1 = img1[y:y+h, x:x+w]
	#Resize
	face1 = cv2.resize(face1,(120,120), interpolation = cv2.INTER_CUBIC)
	img2 = cv2.imread(r'D:\PetProject\image_comparison\5.jpg',0)
	#Perform Haar
	#Extract
	#Resize
	faces = face_cascade.detectMultiScale(img2, 1.3, 5)
	for (x,y,w,h) in faces:
	face2 = img2[y:y+h, x:x+w]
	face2 = cv2.resize(face2,(120,120), interpolation = cv2.INTER_CUBIC)
	cv2.imshow('face1 1',face1)
	cv2.waitKey(0)
	cv2.imshow('face2 2',face2)
	cv2.waitKey(0)
	#Euclidean Distance
	import scipy.spatial.distance as dist
	import numpy as np
	#Convert to 1D
	img1_data= face1.reshape(-1)
	#Normalize
	img1_data= img1_data/255.0
	img2_data= face2.reshape(-1)
	img2_data = img2_data/255.0
	print(len(img1_data))
	print(len(img2_data))
	#Computes the Euclidean distance between two 1-D arrays
	print(dist.euclidean(img1_data,img2_data))
	#Compute the Cosine distance between 1-D arrays.
	#A.B= A.B/(|A||B|)
	print(dist.cosine(img1_data,img2_data))
	#Compute the Hamming distance between two 1-D arrays
	#Hamming distance is the number of bit positions in which the two bits are different
	print(dist.hamming(img1_data,img2_data))
	#Compute the Jaccard-Needham dissimilarity between two boolean 1-D array
	#Jaccard distance, which measures dissimilarity between sample sets
	print(dist.jaccard(img1_data,img2_data))
	cv2.destroyAllWindows()

view raw Image_Comparison_Techniques.py hosted with ❤ by GitHub

Happy Learning!!!