traffic.py

"""
Epoch 1/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 4s 7ms/step - accuracy: 0.4295 - loss: 25.8755
Epoch 2/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.9064 - loss: 0.4020
Epoch 3/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.9371 - loss: 0.2649
Epoch 4/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 4s 7ms/step - accuracy: 0.9484 - loss: 0.2302
Epoch 5/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.9597 - loss: 0.1605
Epoch 6/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 4s 7ms/step - accuracy: 0.9661 - loss: 0.1407
Epoch 7/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 5s 10ms/step - accuracy: 0.9649 - loss: 0.1498
Epoch 8/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 7s 14ms/step - accuracy: 0.9623 - loss: 0.1953
Epoch 9/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 6s 12ms/step - accuracy: 0.9676 - loss: 0.1541
Epoch 10/10
500/500 ━━━━━━━━━━━━━━━━━━━━ 6s 12ms/step - accuracy: 0.9777 - loss: 0.0983
333/333 - 1s - 4ms/step - accuracy: 0.9359 - loss: 0.5395
"""


import cv2
import numpy as np
import os
import sys
import tensorflow as tf
from pathlib import Path
import pandas as pd
import keras

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, 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()

    # Train neural network
    model.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["accuracy"]
    )



    # 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 root, dirs, files in os.walk(data_dir):
        for file in files:
            if file.endswith(".ppm"):
                image_path = os.path.join(root, file)
                img = cv2.imread(image_path)
                img = cv2.resize(img, (IMG_WIDTH, IMG_HEIGHT), interpolation=cv2.INTER_AREA)
                images.append(img)
                labels.append(int(os.path.basename(root)))

    return images, labels



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.models.Sequential([

        # Convolutional layer. Learn 32 filters using a 3x3 kernel
        tf.keras.layers.Conv2D(32, (3,3), activation="relu"),

        # Max-pooling layer, using 2x2 pool size
        tf.keras.layers.MaxPooling2D(pool_size=(2,2)),

        tf.keras.layers.Flatten(),

        # Add a hidden layer with dropout
        #tf.keras.layers.Dense(128, activation="relu"),
        #tf.keras.layers.Dropout(0.5),

    # Add an output layer with output units for all 10 digits
    tf.keras.layers.Dense(NUM_CATEGORIES, activation="softmax")

    ])

    return model


if __name__ == "__main__":
    main()