def index_images(folder, features_path, mapping_path, model, glove_path):
print("Now indexing images...")
word_vectors = utils.load_glove_vectors(glove_path)
_, _, paths = utils.load_paired_img_wrd(folder=folder,
word_vectors=word_vectors)
images_features, file_index = utils.generate_features(paths, model)
utils.save_features(features_path, images_features, mapping_path,
file_index)
return images_features, file_index
def index_images(folder, features_path, mapping_path, model, features_from_new_model_boolean, glove_path):
print ("Now indexing images...")
# Use word vectors if leveraging the new model
if features_from_new_model_boolean:
word_vectors=vector_search.load_glove_vectors(glove_path)
else:
word_vectors=[]
# Use utiliy function
_, _, paths = load_paired_img_wrd(
folder=folder,
word_vectors=word_vectors,
use_word_vectors=features_from_new_model_boolean)
images_features, file_index = vector_search.generate_features(paths, model)
vector_search.save_features(features_path, images_features, mapping_path, file_index)
return images_features, file_index
def load_images_vectors_paths(glove_model_path, data_path):
word_vectors = vector_search.load_glove_vectors(glove_model_path)
images, vectors, image_paths = load_paired_img_wrd(data_path, word_vectors)
return images, vectors, image_paths, word_vectors
default=50)
return par
if __name__ == "__main__":
parser = build_parser()
options = parser.parse_args()
model_save_path = options.model_save_path
checkpoint_path = options.checkpoint_path
glove_path = options.glove_path
dataset_path = options.dataset_path
num_epochs = options.num_epochs
word_vectors = vector_search.load_glove_vectors(glove_path)
images, vectors, image_paths = load_paired_img_wrd(dataset_path,
word_vectors)
x, y = shuffle(images, vectors, random_state=2)
X_train, X_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=2)
checkpointer = ModelCheckpoint(filepath=checkpoint_path,
verbose=1,
save_best_only=True)
custom_model = vector_search.setup_custon_model()
custom_model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=num_epochs,
batch_size=32,
def index_images(folder, features_path, mapping_path, model):
_, _, paths = load_paired_img_wrd(folder, [], use_word_vectors=False)
images_features, file_index = vector_search.generate_features(paths, model)
vector_search.save_features(features_path, images_features, mapping_path,
file_index)
return images_features, file_index
