traffic

Specification

Complete the implementation of load_data and get_model in traffic.py

• The load_data function should accept as an argument data_dir, representing the path to a directory where the data is stored, and return image arrays and labels for each image in the data set
  • You may assume that data_dir will contain one directory named after each category, numbered 0 through NUM_CATEGORIES - 1. Inside each category directory will be some number of image files.
  • Use the OpenCV-Python module (cv2) to read each image as a numpy.ndarray (a numpy multidimensional array). To pass these images into a neural network, the images will need to be the same size, so be sure to resize each image to have width IMG_WIDTH and height IMG_HEIGHT
  • The function should return a tuple (images, labels). images should be a list of all of the images in the data set, where each image is represented as a numpy.ndarray of the appropriate size. labels should be a list of integers, representing the category number for each of the corresponding images in the images list
  • Your function should be platform-independent: that is to say, it should work regardless of operating system. Note that on macOS, the / character is used to separate path components, while the \ character is used on Windows. Use os.sep and os.path.join as needed instead of using your platform’s specific separator character
• The get_model function should return a compiled neural network model
  • You may assume that the input to the neural network will be of the shape (IMG_WIDTH, IMG_HEIGHT, 3) (that is, an array representing an image of width IMG_WIDTH, height IMG_HEIGHT, and 3 values for each pixel for red, green, and blue)
  • The output layer of the neural network should have NUM_CATEGORIES units, one for each of the traffic sign categories
  • The number of layers and the types of layers you include in between are up to you. You may wish to experiment with:
    • different numbers of convolutional and pooling layers
    • different numbers and sizes of filters for convolutional layers
    • different pool sizes for pooling layers
    • different numbers and sizes of hidden layers
    • dropout

Ultimately, much of this project is about exploring documentation and investigating different options in cv2 and tensorflow and seeing what results you get when you try them!

You should not modify anything else in traffic.py other than the functions the specification calls for you to implement, though you may write additional functions and/or import other Python standard library modules. You may also import numpy or pandas, if familiar with them, but you should not use any other third-party Python modules. You may modify the global variables defined at the top of the file to test your program with other values

Hints

• Check out the official Tensorflow Keras overview for some guidelines for the syntax of building neural network layers. You may find the lecture source code useful as well
• The OpenCV-Python documentation may prove helpful for reading images as arrays and then resizing them
• Once you’ve resized an image img, you can verify its dimensions by printing the value of img.shape. If you’ve resized the image correctly, its shape should be (30, 30, 3) (assuming IMG_WIDTH and IMG_HEIGHT are both 30)
• If you’d like to practice with a smaller data set, you can download the modified dataset that contains only 3 different types of road signs instead of 43 through the link in the data directory