Spaces:
Build error
Build error
File size: 3,708 Bytes
f5689ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
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"}')
|