Image_Feature_Extractor.py

import tensorflow as tf
from keras.preprocessing import image

import numpy as np

# function to extract features from image
def extract_image_features():
    
    model = tf.keras.models.Sequential()
    
    # adding first layers of convolution and pooling layers to network
    model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), input_shape=(90,90,3), padding="VALID", activation="relu"))
    model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation="relu"))
    model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
    
    # adding second layers of convolution and pooling layers to network
    model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), padding="VALID", activation="relu"))
    model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu"))
    model.add(tf.keras.layers.AveragePooling2D(pool_size=2, strides=1))
    
    # flattening the output using flatten layer, since the input to neural net has to be flat
    model.add(tf.keras.layers.Flatten())
    
    # model summary
    model.summary()
    
    return model

for file in os.listdir(image_path):
    path = image_path + "//" + file
    img = image.load_img(path, target_size=(90, 90))
    img_data = image.img_to_array(img)
    img_data = np.expand_dims(img_data, axis=0)
    img_data = preprocess_input(img_data)
    
    feature = extract_image_features.predict(img_data)
    feature = np.reshape(feature, feature.shape[1])