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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"}') | |