main.py

from scipy import misc
import numpy as np

from training import train_net
import inception
from inference import infere


def train(location='./train/'):
    """
    The training procedure is triggered here. OPTIONAL to run; everything that is required for testing the model
    must be saved to file (e.g., pickle) so that the test procedure can load, execute and report
    :param location: The location of the training data folder hierarchy
    :return: nothing
    """

    train_net(train_dir=location)


def test(queries=list(), location='./test'):
    """
    Test your system with the input. For each input, generate a list of IDs that is returned
    :param queries: list of image-IDs. Each element is assumed to be an entry in the test set. Hence, the image
    with id <id> is located on my computer at './test/pics/<id>.jpg'. Make sure this is the file you work with...
    :param location: The location of the test data folder hierarchy
    :return: a dictionary with keys equal to the images in the queries - list, and values a list of image-IDs
    retrieved for that input
    """

    # # ##### The following is an example implementation -- that would lead to 0 points  in the evaluation :-)
    # my_return_dict = {}
    #
    # # Load the dictionary with all training files. This is just to get a hold of which
    # # IDs are there; will choose randomly among them
    # training_labels = pickle.load(open('./train/pickle/combined.pickle', 'rb'))
    # training_labels = list(training_labels.keys())
    #
    # for query in queries:
    #
    #     # This is the image. Just opening if here for the fun of it; not used later
    #     image = Image.open(location + '/pics/' + query + '.jpg')
    #     image.show()
    #
    #     # Generate a random list of 50 entries
    #     cluster = [training_labels[random.randint(0, len(training_labels) - 1)] for idx in range(50)]
    #     my_return_dict[query] = cluster

    my_return_dict = {}

    # Inception
    inception.maybe_download()
    model = inception.Inception()

    for query in queries:

        # Data
        image_path = location + '/pics/' + query + '.jpg'
        image_data = (misc.imread(image_path)[:, 0:192, :3]).astype(np.float32)
        image_tranfer_values = model.transfer_values(image_path, image_data)

        # Inference
        my_return_dict[query] = infere(image_tranfer_values)

    return my_return_dict