Traffic.py

import cv2
import numpy as np
import os
import sys
import tensorflow as tf
from keras import layers

from sklearn.model_selection import train_test_split

EPOCHS = 10
IMG_WIDTH = 30
IMG_HEIGHT = 30
NUM_CATEGORIES = 43
TEST_SIZE = 0.4


def main():

    # Check command-line arguments
    if len(sys.argv) not in [2, 3]:
        sys.exit("Usage: python traffic.py data_directory [model.h5]")

    # Get image arrays and labels for all image files
    images, labels = load_data(sys.argv[1])

    # Split data into training and testing sets
    labels = tf.keras.utils.to_categorical(labels)
    x_train: object
    x_train, x_test, y_train, y_test = train_test_split(
        np.array(images), np.array(labels), test_size=TEST_SIZE
    )

    # Get a compiled neural network
    model = get_model()

    # Fit model on training data
    model.fit(x_train, y_train, epochs=EPOCHS)

    # Evaluate neural network performance
    model.evaluate(x_test,  y_test, verbose=2)

    # Save model to file
    if len(sys.argv) == 3:
        filename = sys.argv[2]
        model.save(filename)
        print(f"Model saved to {filename}.")


def load_data(data_dir):
    """
    Load image data from directory `data_dir`.

    Assume `data_dir` has one directory named after each category, numbered
    0 through NUM_CATEGORIES - 1. Inside each category directory will be some
    number of image files.

    Return tuple `(images, labels)`. `images` should be a list of all
    of the images in the data directory, where each image is formatted as a
    numpy ndarray with dimensions IMG_WIDTH x IMG_HEIGHT x 3. `labels` should
    be a list of integer labels, representing the categories for each of the
    corresponding `images`.
    """
    images = []
    labels = []
    for category in range(NUM_CATEGORIES):
        category_path = os.path.join(data_dir, str(category))
        if not os.path.isdir(category_path):
            continue

        for image_name in os.listdir(category_path):
            image_path = os.path.join(category_path, image_name)
            image = cv2.imread(image_path, cv2.IMREAD_COLOR)
            if image is None:
                continue
            image = cv2.resize(image, (IMG_WIDTH, IMG_HEIGHT))
            images.append(image)
            labels.append(category)

    print(f"Loaded {len(images)} images.")
    print(f"Loaded {len(labels)} labels.")
    return images, labels


# noinspection PyUnresolvedReferences
def get_model():
    """
    Returns a compiled convolutional neural network model. Assume that the
    `input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`.
    The output layer should have `NUM_CATEGORIES` units, one for each category.
    """
    model = tf.keras.Sequential([
        layers.Conv2D(
            32, (3, 3), activation='tanh', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)
        ),

        layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
        layers.Dropout(0.25),

        layers.Conv2D(
            32, (3, 3), activation='tanh', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)
        ),

        layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
        layers.Dropout(0.25),

        layers.Flatten(),

        layers.Dense(128, activation='tanh'),

        layers.Dropout(0.25),

        layers.Dense(NUM_CATEGORIES, activation='softmax')
    ])

    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy', 'precision', 'recall'])

    return model


if __name__ == "__main__":
    main()