helpers.py

# Helper functions

import os
import glob # library for loading images from a directory
import matplotlib.image as mpimg

import cv2



# This function loads in images and their labels and places them in a list
# The list contains all images and their associated labels
# For example, after data is loaded, im_list[0][:] will be the first image-label pair in the list
def load_dataset(image_dir):
    
    # Populate this empty image list
    im_list = []
    image_types = ["day", "night"]
    
    # Iterate through each color folder
    for im_type in image_types:
        
        # Iterate through each image file in each image_type folder
        # glob reads in any image with the extension "image_dir/im_type/*"
        for file in glob.glob(os.path.join(image_dir, im_type, "*")):
            
            # Read in the image
            im = mpimg.imread(file)
            
            # Check if the image exists/if it's been correctly read-in
            if not im is None:
                # Append the image, and it's type (red, green, yellow) to the image list
                im_list.append((im, im_type))
    
    return im_list



## Standardize the input images
# Resize each image to the desired input size: 600x1100px (hxw).

## Standardize the output
# With each loaded image, we also specify the expected output.
# For this, we use binary numerical values 0/1 = night/day.


# This function should take in an RGB image and return a new, standardized version
# 600 height x 1100 width image size (px x px)
def standardize_input(image):
    
    # Resize image and pre-process so that all "standard" images are the same size
    standard_im = cv2.resize(image, (1100, 600))
    
    return standard_im


# Examples:
# encode("day") should return: 1
# encode("night") should return: 0
def encode(label):
    
    numerical_val = 0
    if(label == 'day'):
        numerical_val = 1
    # else it is night and can stay 0
    
    return numerical_val

# using both functions above, standardize the input images and output labels
def standardize(image_list):
    
    # Empty image data array
    standard_list = []
    
    # Iterate through all the image-label pairs
    for item in image_list:
        image = item[0]
        label = item[1]
        
        # Standardize the image
        standardized_im = standardize_input(image)
        
        # Create a numerical label
        binary_label = encode(label)
        
        # Append the image, and it's one hot encoded label to the full, processed list of image data
        standard_list.append((standardized_im, binary_label))
    
    return standard_list