CS 3630 Lab 1 Boilerplate Modification

def main():
    img_clf = ImageClassifier()

    # Feature cache files.
    # If you modify img_clf.extract_image_features(),
    # change the filename or delete the cache files.
    train_cache = 'train_data_v1.dat'
    test_cache = 'test_data_v1.dat'

    # load images
    print("Loading training images...")
    (train_raw, train_labels) = img_clf.load_data_from_folder('./train/')
    print("Loading testing images...")
    (test_raw, test_labels) = img_clf.load_data_from_folder('./test/')

    # convert images into features
    try:
        with open(train_cache, 'rb') as train_data_file:
            print("Loading cached training image features...")
            train_data = pickle.load(train_data_file)
    except FileNotFoundError:
        print("Extracting training image features...")
        train_data = img_clf.extract_image_features(train_raw)
        with open('train_data_v1.dat', 'wb') as train_data_file:
            print("Caching training image features...")
            pickle.dump(train_data, train_data_file)

    try:
        with open(test_cache, 'rb') as test_data_file:
            print("Loading cached testing image features...")
            test_data = pickle.load(test_data_file)
    except FileNotFoundError:
        print("Extracting testing image features...")
        test_data = img_clf.extract_image_features(test_raw)
        with open(test_cache, 'wb') as test_data_file:
            print("Caching testing image features...")
            pickle.dump(test_data, test_data_file)

    # train model and test on training data
    print("Training classifier...")
    img_clf.train_classifier(train_data, train_labels)
    print("Predicting training image labels...")
    predicted_labels = img_clf.predict_labels(train_data)
    print("\nTraining results")
    print("=============================")
    print("Confusion Matrix:\n", metrics.confusion_matrix(train_labels, predicted_labels))
    print("Accuracy: ", metrics.accuracy_score(train_labels, predicted_labels))
    print("F1 score: ", metrics.f1_score(train_labels, predicted_labels, average='micro'))

    # test model
    print("Predicting testing image labels...")
    predicted_labels = img_clf.predict_labels(test_data)
    print("\nTest results")
    print("=============================")
    print("Confusion Matrix:\n", metrics.confusion_matrix(test_labels, predicted_labels))
    print("Accuracy: ", metrics.accuracy_score(test_labels, predicted_labels))
    print("F1 score: ", metrics.f1_score(test_labels, predicted_labels, average='micro'))