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similarity.py
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similarity.py
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from HOG import HOG
import matplotlib.pyplot as plt
import numpy as np
def euclidian_metric(x,y):
'''Computes euclidian metric between x and y'''
return np.linalg.norm(x-y)
def cosine_similarity(x,y):
'''Computes cosine similarity between x and y'''
norm = np.linalg.norm(x) * np.linalg.norm(y)
if norm == 0:
return 0
else:
return np.dot(x,np.transpose(y)) / norm
def similarity(img1, img2, w, h, nb_bins, metric):
'''Computes similarity between image 1 and image 2 by applying the given metric \
to HOG representations of image 1 and 2. HOG representations are computed over \
cells with size w x h and with nb_bins bins. Returns a float in [0,1]'''
# Computation of HOG representations of img1 and img2
hog_similar_1 = HOG(img1,w,h,nb_bins,False)
hog_similar_2 = HOG(img2,w,h,nb_bins,False)
I,J,k = np.shape(hog_similar_1)
metric_values = np.zeros((I,J))
# Computation of similarity
concatened_hog1 = []
concatened_hog2 = []
for i in range(I):
for j in range(J):
concatened_hog1 = np.concatenate(( concatened_hog1 , hog_similar_1[i][j] ))
concatened_hog2 = np.concatenate(( concatened_hog2 , hog_similar_2[i][j] ))
return metric(concatened_hog1,concatened_hog2) # This is similarity value
# Examples
if __name__=="__main__":
from skimage import io
img_similar = io.imread('HOG 2/hog_similar2.bmp')
img_similar_1 = img_similar[22:,0:64]
img_similar_2 = img_similar[22:,90:154]
img_different = io.imread('HOG 2/hog_different2.bmp')
img_different_1 = img_different[22:,0:64]
img_different_2 = img_different[22:,90:154]
print("Similarity of hog_similar.bmp objects :")
print("-> For w=64 , h=128 , nb_bins=12 ")
print(similarity(img_similar_1,img_similar_2,64,128,12,cosine_similarity))
print("-> For w=16 , h=16 , nb_bins=9 ")
print(similarity(img_similar_1,img_similar_2,16,16,9,cosine_similarity))
print("Similarity of hog_different.bmp objects :")
print("-> For w=64 , h=128 , nb_bins=12 ")
print(similarity(img_different_1,img_different_2,64,128,12,cosine_similarity))
print("-> For w=16 , h=16 , nb_bins=9 ")
print(similarity(img_different_1,img_different_2,16,16,9,cosine_similarity))