Image_Feature_Extractor_V2.py

import tensorflow as tf
from keras.preprocessing import image
from keras.applications.resnet50 import ResNet50
from keras.applications.resnet50 import preprocess_input
from keras.models import Model

# load the ResNet50 Model
feature_extractor = ResNet50(weights='imagenet', include_top=False)
feature_extractor_new = Model(feature_extractor.input, feature_extractor.layers[-2].output)
feature_extractor_new.summary()

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 = feature_extractor_new.predict(img_data)
    feature_reshaped = np.array(feature).flatten()