carNotCar.py

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

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)
        
# 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_image = mpimg.imread(car_list[0])
    # Define a key "image_shape" and store the test image shape 3-tuple
    data_dict["image_shape"] = test_image.shape
    # Define a key "data_type" and store the data type of the test image.
    data_dict["data_type"] = test_image.dtype
    # Return data_dict
    return data_dict
    
data_info = data_look(cars, notcars)

print('Your function returned a count of', 
      data_info["n_cars"], ' cars and', 
      data_info["n_notcars"], ' non-cars')
print('of size: ',data_info["image_shape"], ' and data type:', 
      data_info["data_type"])
# 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')