Putting it all together

To run our app, we will need to execute the main function routine (in chapter6.py). This loads the data, trains the classifier, evaluates its performance, and visualizes the result:

1. First, we need to import all the relevant modules and set up the main function, as follows:

import cv2
import numpy as np
import matplotlib.pyplot as plt

from data.gtsrb import load_training_data
from data.gtsrb import load_test_data
from data.process import grayscale_featurize, hog_featurize

2. Then, the goal is to compare classification performance across feature extraction methods. This includes running the task using a list of different feature extraction approaches. So, we first load the data, and repeat the process for each of the featurizing functions, as follows:

def main(labels):
    train_data, train_labels = load_training_data(labels)
    test_data, test_labels = load_test_data...