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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"}') | |