preprocessing.py

## This preprocessing.py helps to pre-process the images for classification, including normalization, one-hot-encode, train-test splitting.


import pickle
import check_data
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer


def _load_label_names():
    """
    Load the label names from file
    """
    return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']


def load_cfar10_batch(cifar10_dataset_folder_path, batch_id):
    """
    Load a batch of the dataset
    """
    with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:
        batch = pickle.load(file, encoding='latin1')

    features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
    labels = batch['labels']

    return features, labels


def display_stats(cifar10_dataset_folder_path, batch_id, sample_id):
    """
    Display Stats of the the dataset
    """
    batch_ids = list(range(1, 6))

    if batch_id not in batch_ids:
        print('Batch Id out of Range. Possible Batch Ids: {}'.format(batch_ids))
        return None

    features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_id)

    if not (0 <= sample_id < len(features)):
        print('{} samples in batch {}.  {} is out of range.'.format(len(features), batch_id, sample_id))
        return None

    print('\nStats of batch {}:'.format(batch_id))
    print('Samples: {}'.format(len(features)))
    print('Label Counts: {}'.format(dict(zip(*np.unique(labels, return_counts=True)))))
    print('First 20 Labels: {}'.format(labels[:20]))

    sample_image = features[sample_id]
    sample_label = labels[sample_id]
    label_names = _load_label_names()

    print('\nExample of Image {}:'.format(sample_id))
    print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max()))
    print('Image - Shape: {}'.format(sample_image.shape))
    print('Label - Label Id: {} Name: {}'.format(sample_label, label_names[sample_label]))
    plt.axis('off')
    plt.imshow(sample_image)


def _preprocess_and_save(normalize, one_hot_encode, features, labels, filename):
    """
    Preprocess data and save it to file
    """
    features = normalize(features)
    labels = one_hot_encode(labels)

    pickle.dump((features, labels), open(filename, 'wb'))


def preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode):
    """
    Preprocess Training and Validation Data
    """
    n_batches = 5
    valid_features = []
    valid_labels = []

    for batch_i in range(1, n_batches + 1):
        features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i)
        validation_count = int(len(features) * 0.1)

        # Prprocess and save a batch of training data
        _preprocess_and_save(
            normalize,
            one_hot_encode,
            features[:-validation_count],
            labels[:-validation_count],
            'preprocess_batch_' + str(batch_i) + '.p')

        # Use a portion of training batch for validation
        valid_features.extend(features[-validation_count:])
        valid_labels.extend(labels[-validation_count:])

    # Preprocess and Save all validation data
    _preprocess_and_save(
        normalize,
        one_hot_encode,
        np.array(valid_features),
        np.array(valid_labels),
        'preprocess_validation.p')

    with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:
        batch = pickle.load(file, encoding='latin1')

    # load the test data
    test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
    test_labels = batch['labels']

    # Preprocess and Save all test data
    _preprocess_and_save(
        normalize,
        one_hot_encode,
        np.array(test_features),
        np.array(test_labels),
        'preprocess_test.p')


def batch_features_labels(features, labels, batch_size):
    """
    Split features and labels into batches
    """
    for start in range(0, len(features), batch_size):
        end = min(start + batch_size, len(features))
        yield features[start:end], labels[start:end]


def load_preprocess_training_batch(batch_id, batch_size):
    """
    Load the Preprocessed Training data and return them in batches of <batch_size> or less
    """
    filename = 'preprocess_batch_' + str(batch_id) + '.p'
    features, labels = pickle.load(open(filename, mode='rb'))

    # Return the training data in batches of size <batch_size> or less
    return batch_features_labels(features, labels, batch_size)


def display_image_predictions(features, labels, predictions):
    n_classes = 10
    label_names = _load_label_names()
    label_binarizer = LabelBinarizer()
    label_binarizer.fit(range(n_classes))
    label_ids = label_binarizer.inverse_transform(np.array(labels))

    fig, axies = plt.subplots(nrows=4, ncols=2)
    fig.tight_layout()
    fig.suptitle('Softmax Predictions', fontsize=20, y=1.1)

    n_predictions = 3
    margin = 0.05
    ind = np.arange(n_predictions)
    width = (1. - 2. * margin) / n_predictions

    for image_i, (feature, label_id, pred_indicies, pred_values) in enumerate(zip(features, label_ids, predictions.indices, predictions.values)):
        pred_names = [label_names[pred_i] for pred_i in pred_indicies]
        correct_name = label_names[label_id]

        axies[image_i][0].imshow(feature)
        axies[image_i][0].set_title(correct_name)
        axies[image_i][0].set_axis_off()

        axies[image_i][1].barh(ind + margin, pred_values[::-1], width)
        axies[image_i][1].set_yticks(ind + margin)
        axies[image_i][1].set_yticklabels(pred_names[::-1])
        axies[image_i][1].set_xticks([0, 0.5, 1.0])

def normalize(x):
    """
    Normalize a list of sample image data in the range of 0 to 1
    : x: List of image data.
    : return: Numpy array of normalize data
    """
    x_normed = (x - np.min(x)) / (np.max(x) - np.min(x))

    return x_normed


# One-hot encode
def one_hot_encode(x):
    """
    One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
    : x: List of sample Labels
    : return: Numpy array of one-hot encoded labels
    """
    y = np.zeros((len(x), np.max(x) + 1))

    for i in range(len(x)):
        y[i, x[i]] = 1

    return y


# Preprocess all the data and save it

# Preprocess Training, Validation, and Testing Data
data_path = check_data.cifar10_dataset_folder_path
preprocess_and_save_data(data_path, normalize, one_hot_encode)