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from keras.applications.vgg16 import VGG16, preprocess_input
from keras.preprocessing import image
from keras.models import Model
import numpy as np
from scipy.spatial.distance import euclidean
from sklearn.metrics.pairwise import cosine_similarity
from PIL import Image
from keras.applications.efficientnet import EfficientNetB0
# Load VGG16 model + higher level layers
base_model = VGG16(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)
# Load EfficientNetB0 model + higher level layers
# base_model = EfficientNetB0(weights='imagenet')
# model = Model(inputs=base_model.input, outputs=base_model.get_layer('top_activation').output)
def extract_features_cp(pil_img: Image.Image) -> np.ndarray:
# Resize the image to the target size
pil_img = pil_img.resize((224, 224)) # (224, 224)
# Convert the PIL image to a numpy array
img_data = image.img_to_array(pil_img)
# Expand dimensions to match the input shape required by the model
img_data = np.expand_dims(img_data, axis=0)
# Preprocess the image data
img_data = preprocess_input(img_data)
# Predict the features using the model
features = model.predict(img_data)
# Return the features as a flattened array
return features.flatten()
def extract_features(img_path):
img = image.load_img(img_path, target_size=(224, 224)) # (224, 224)
img_data = image.img_to_array(img)
img_data = np.expand_dims(img_data, axis=0)
img_data = preprocess_input(img_data)
features = model.predict(img_data)
return features.flatten() # Flatten the features to a 1-D vector
def compare_features(features1, features2):
# Euclidean distance
euclidean_dist = euclidean(features1, features2)
# Cosine similarity
cos_sim = cosine_similarity([features1], [features2])[0][0]
return euclidean_dist, cos_sim
def predict_similarity(features1, features2, threshold=0.5):
_, cos_sim = compare_features(features1, features2)
similarity_score = cos_sim
# print(similarity_score)
if similarity_score > threshold:
return True
else:
return False
if __name__ == '__main__':
# Example usage
img_path1 = "D:/Downloads/image/rose.jpg"
img_path2 = "D:/Downloads/image/rose.jpg"
# Extract features
features1 = extract_features(img_path1)
features2 = extract_features(img_path2)
# Compare features
euclidean_dist, cos_sim = compare_features(features1, features2)
print(f'Euclidean Distance: {euclidean_dist}')
print(f'Cosine Similarity: {cos_sim}')
# Predict similarity
is_similar = predict_similarity(features1, features2, threshold=0.8)
print(f'Are the images similar? {"Yes" if is_similar else "No"}')
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