import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import os
#from skimage.feature import hog
#from skimage import color, exposure
# images are divided up into vehicles and non-vehicles

os.chdir('./objects')

# Define a function to return some characteristics of the dataset
def data_look(car_list, notcar_list):
    data_dict = {}
    # Define a key in data_dict "n_cars" and store the number of car images
    data_dict["n_cars"] = len(car_list)
    # Define a key "n_notcars" and store the number of notcar images
    data_dict["n_notcars"] = len(notcar_list)
    # Read in a test image, either car or notcar
    test_img = mpimg.imread(car_list[0])
    # Define a key "image_shape" and store the test image shape 3-tuple
    data_dict["image_shape"] = test_img.shape
    # Define a key "data_type" and store the data type of the test image.
    data_dict["data_type"] = test_img.dtype
    # Return data_dict
    return data_dict

def main():

    images = glob.glob('*.jpeg')
    cars = []
    notcars = []

    for image in images:
        if 'image' in image or 'extra' in image:
            notcars.append(image)
        else:
            cars.append(image)

    data_info = data_look(cars, notcars)
    # Just for fun choose random car / not-car indices and plot example images
    car_ind = np.random.randint(0, len(cars))
    notcar_ind = np.random.randint(0, len(notcars))

    # Read in car / not-car images
    car_image = mpimg.imread(cars[car_ind])
    notcar_image = mpimg.imread(notcars[notcar_ind])

    # Plot the examples

    fig = plt.figure()
    plt.subplot(121)
    plt.imshow(car_image)
    plt.title('Example Car Image')
    plt.subplot(122)
    plt.imshow(notcar_image)
    plt.title('Example Not-car Image')
    plt.show()


if __name__ == '__main__':
    main()

Add scripts which define a function to return some characteristics of the dataset
feat: Add scripts which define a function to return some characteristics of the dataset
