Create predict_copy.py

predict_copy.py

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+from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Model
+import numpy as np
+from scipy.spatial.distance import euclidean
+from sklearn.metrics.pairwise import cosine_similarity
+# Load VGG16 model + higher level layers
+base_model = VGG16(weights='imagenet')
+model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)
+
+# Define data augmentation
+datagen = ImageDataGenerator(
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+def extract_features(img):
+    img = img.resize((224, 224))  # Ensure the image is resized to the input size expected by VGG16
+    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 augment_image(img):
+    x = image.img_to_array(img)
+    x = x.reshape((1,) + x.shape)  # Reshape to (1, height, width, channels)
+
+    # Generate batches of augmented images
+    augmented_images = []
+    for batch in datagen.flow(x, batch_size=1):
+        augmented_images.append(image.array_to_img(batch[0]))
+        if len(augmented_images) >= 5:  # Generate 5 augmented images
+            break
+    return augmented_images
+
+def extract_features_with_augmentation(img_path):
+    original_img = image.load_img(img_path)
+    augmented_images = augment_image(original_img)
+    
+    # Extract features from the original image
+    features = [extract_features(original_img)]
+    
+    # Extract features from augmented images
+    for aug_img in augmented_images:
+        features.append(extract_features(aug_img))
+    
+    return np.mean(features, axis=0)  # Return the average feature vector
+
+
+def extract_features_with_augmentation_cp(img_path):
+    pil_img = pil_img.resize((224, 224))  # (224, 224)
+    
+    # Convert the PIL image to a numpy array
+    
+    augmented_images = augment_image(pil_img)
+    
+    # Extract features from the original image
+    features = [extract_features(augmented_images)]
+    
+    # Extract features from augmented images
+    for aug_img in augmented_images:
+        features.append(extract_features(aug_img))
+    
+    return np.mean(features, axis=0)  # Return the average feature 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/rose3.jpg"
+
+    # Extract features
+    features1 = extract_features_with_augmentation(img_path1)
+    features2 = extract_features_with_augmentation(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"}')