VGG16_similarity_Xing.py

# -*- coding: utf-8 -*-
"""
x.liang@greenwich.ac.uk
25th March, 2020
Image Similarity using VGG16
"""
import os
import numpy as np
from keras.layers import Input
from keras.models import Model
from vgg16 import VGG16
from keras.utils import image_utils as 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
'''

# fc2(Dense)output shape: (None, 4096) 
def get_feature_vector_fromPIL(img):
 feature_vector = feature_model.predict(img)
 assert(feature_vector.shape == (1,4096))
 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 distance.euclidean(vector1, vector2)     #distance.euclidean is slower
 return 1/(1+np.linalg.norm(vector1 - vector2))   #np.linalg.norm is faster

def SIM(a,b):
	image_input = Input(shape=(224, 224, 3))
	model = VGG16(input_tensor=image_input, include_top=True,weights='imagenet')
	layer_name = 'fc2'
	feature_model = Model(inputs=model.input,outputs=model.get_layer(layer_name).output) 
	image_similarity_cosine = calculate_similarity_cosine(get_feature_vector_fromPIL(a),get_feature_vector_fromPIL(b))
	image_similarity_euclidean = calculate_similarity_euclidean(get_feature_vector_fromPIL(a),get_feature_vector_fromPIL(b))
	print('VGG16 image similarity_euclidean:',image_similarity_euclidean)
	print("VGG16 image similarity_cosine: {:.2f}%".format(image_similarity_cosine[0][0]*100))
# 首先定义一个类，要有__init__
class jisuanxiangsidu:
	def __init__(self):
		image_input = Input(shape=(224, 224, 3))
		model = VGG16(input_tensor=image_input, include_top=True,weights='imagenet')
		layer_name = 'fc2'
		feature_model = Model(inputs=model.input,outputs=model.get_layer(layer_name).output) 
	def begin(a,b):
		image_similarity_cosine = calculate_similarity_cosine(get_feature_vector_fromPIL(a),get_feature_vector_fromPIL(b))
		image_similarity_euclidean = calculate_similarity_euclidean(get_feature_vector_fromPIL(a),get_feature_vector_fromPIL(b))
		print('VGG16 image similarity_euclidean:',image_similarity_euclidean)
		print("VGG16 image similarity_cosine: {:.2f}%".format(image_similarity_cosine[0][0]*100))

# Use VGG16 model as an image feature extractor 
image_input = Input(shape=(224, 224, 3))
model = VGG16(input_tensor=image_input, include_top=True,weights='imagenet')
layer_name = 'fc2'
feature_model = Model(inputs=model.input,outputs=model.get_layer(layer_name).output)

# 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
		print(img_path)
		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_VGG16 =get_feature_vector_fromPIL(img_data_list[6])

# Caculate cosine similarity: [-1,1], that is, [completedly different,same]
image_similarity_cosine = calculate_similarity_cosine(get_feature_vector_fromPIL(img_data_list[1]), get_feature_vector_fromPIL(img_data_list[2]))
# 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[1]), get_feature_vector_fromPIL(img_data_list[2]))

print('VGG16 image similarity_euclidean:',image_similarity_euclidean)
print("VGG16 image similarity_cosine: {:.2f}%".format(image_similarity_cosine[0][0]*100))