feature_match.py

import numpy as np
import cv2
from matplotlib import pyplot as plt
from sklearn.cluster import KMeans
import argparse
import utils

source = 'data/train/D_53277.jpg'
target = 'data/train/D_53280.jpg'

img1 = cv2.imread(source, 0)  # queryImage
img2 = cv2.imread(target, 0)  # trainImage

# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

# Apply ratio test
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append([m])

print("good,len", len(good))

# cv2.drawMatchesKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)

plt.imshow(img3), plt.show()