import keras
import numpy as np
from keras.applications.vgg16 import preprocess_input, VGG16
from keras.preprocessing import image
from numpy.linalg import norm

keras.backend.set_image_data_format('channels_first')

def preprocess_image(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    img_data = image.img_to_array(img, data_format='channels_first')
    img_data = np.expand_dims(img_data, axis=0)
    img_data = preprocess_input(img_data)
    return img_data


class Similarity(object):
    def __init__(self, dest_file_path):
        self.model = VGG16(weights='imagenet', include_top=False)
        self.dest = preprocess_image(dest_file_path)

    def _feature(self, image):
        image_features = self.model.predict(image)
        image_features = image_features.reshape(7 * 7 * 512)
        return image_features

    def similarity(self, before_image):
        """
        capture를 통해 얻은 이미지와 목표 이미지간 유사도를 계산
        :return: 현재 이미지와 목표 이미지 유사도
        """
        before = self._feature(before_image)
        after = self._feature(self.dest)
        return np.dot(before, after) / (norm(before) * norm(after))
# image path
# image..