ResNet50_similarity_Xing.py

# -*- coding: utf-8 -*-
"""
Created on Mon Mar 30 12:46:51 2020
@author: x.liang@greenwich.ac.uk
Image Similarity using ResNet50
"""
import os
import numpy as np
from resnet50 import ResNet50
#from keras.models import Model
from keras.layers import Input
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
from sklearn.metrics.pairwise import cosine_similarity
#from scipy.spatial import distance

'''
def get_feature_vector(img):
 img1 = cv2.resize(img, (224, 224))
 feature_vector = feature_model.predict(img1.reshape(1, 224, 224, 3))
 return feature_vector
'''
# avg_pool (AveragePooling2D) output shape: (None, 1, 1, 2048)
# Latest Keras version causing no 'flatten_1' issue; output shape:(None,2048) 
def get_feature_vector_fromPIL(img):
 feature_vector = feature_model.predict(img)
 a, b, c, n = feature_vector.shape
 feature_vector= feature_vector.reshape(b,n)
 return feature_vector

def calculate_similarity_cosine(vector1, vector2):
 #return 1 - distance.cosine(vector1, vector2)
 return cosine_similarity(vector1, vector2)

# This distance can be in range of [0,∞]. And this distance is converted to a [0,1]
def calculate_similarity_euclidean(vector1, vector2):
 return 1/(1 + np.linalg.norm(vector1- vector2))  

# Use ResNet-50 model as an image feature extractor
image_input = Input(shape=(224, 224, 3))
feature_model = ResNet50(input_tensor=image_input, include_top=False,weights='imagenet')

# Load images in the images folder into array
cwd_path = os.getcwd()
data_path =cwd_path + '/images'
data_dir_list = os.listdir(data_path)

img_data_list=[]
for dataset in data_dir_list:

		img_path = data_path + '/'+ dataset
		img = image.load_img(img_path, target_size=(224, 224))
		x = image.img_to_array(img)
		x = np.expand_dims(x, axis=0)
		x = preprocess_input(x)
		img_data_list.append(x)

#vector_resnet = get_feature_vector_fromPIL(img_data_list[6])

# Cacluate euclidean similarity: range from [0, 1], that is, [completedly different, same]
image_similarity_euclidean = calculate_similarity_euclidean(get_feature_vector_fromPIL(img_data_list[11]), get_feature_vector_fromPIL(img_data_list[31]))
# Caculate cosine similarity: [-1,1], that is, [completedly different, same]
image_similarity_cosine = calculate_similarity_cosine(get_feature_vector_fromPIL(img_data_list[11]), get_feature_vector_fromPIL(img_data_list[31]))

print('ResNet50 image similarity_euclidean: ',image_similarity_euclidean)
print('ResNet50 image similarity_cosine: {:.2f}%'.format(image_similarity_cosine[0][0]*100))