Binary robust independent elementary features (BRIEF)

Even though we have FAST to quickly detect the keypoints, we still have to use SIFT or SURF to compute the descriptors. We need a way to quickly compute the descriptors as well. This is where BRIEF comes into the picture. BRIEF is a method for extracting feature descriptors. It cannot detect the keypoints by itself, so we need to use it in conjunction with a keypoint detector. The good thing about BRIEF is that it's compact and fast.

Consider the following image:

BRIEF takes the list of input keypoints and outputs an updated list. So, if you run BRIEF on this image, you will see something like this:

The following is the code:

import cv2 
import numpy as np 

input_image = cv2.imread('images/house.jpg') 
gray_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)

# Initiate FAST detector 
fast = cv2.FastFeatureDetector_create() 

# Initiate BRIEF extractor, before opencv 3.0.0 use cv2.DescriptorExtractor_create("BRIEF")
brief = cv2.xfeatures2d...