from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import Model
import numpy as np
# See https://keras.io/api/applications/ for details
class FeatureExtractor:
def __init__(self):
base_model = VGG16(weights='imagenet')
self.model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)
def extract(self, img):
"""
Extract a deep feature from an input image
Args:
img: from PIL.Image.open(path) or tensorflow.keras.preprocessing.image.load_img(path)
Returns:
feature (np.ndarray): deep feature with the shape=(4096, )
"""
img = img.resize((224, 224)) # VGG must take a 224x224 img as an input
img = img.convert('RGB') # Make sure img is color
x = image.img_to_array(img) # To np.array. Height x Width x Channel. dtype=float32
x = np.expand_dims(x, axis=0) # (H, W, C)->(1, H, W, C), where the first elem is the number of img
x = preprocess_input(x) # Subtracting avg values for each pixel
feature = self.model.predict(x)[0] # (1, 4096) -> (4096, )
return feature / np.linalg.norm(feature) # Normalize
