Once we know how to detect edges, the next task is to detect features. Many edges combine to form features. Feature extraction is the process of recognizing visual patterns in an image and extracting any discriminating local features that match with the image of an unknown object. Before performing feature extraction, it is important to understand the image histogram. An image histogram is the distribution of the color intensity of the image.
An image feature matches with the test image if the histograms are similar. The following is the Python code used to create an image histogram of the car:
import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.pyplot as plt
from PIL import Image
image = Image.open('../car.png')
plt.imshow(image)
image_arr = np.asarray(image) # convert image to numpy array
image_arr...