Day #111 - OpenCV3 Feature Matching

On Windows platform performed the following installations

pip install opencv-python
pip install opencv-contrib-python

	#pip install opencv-python --upgrade
	#pip install opencv-contrib-python --upgrade
	#pip show opencv-python
	#pip show opencv-contrib-python
	#pip install opencv-contrib-python==3.3.0.9
	import numpy as np
	import cv2
	import matplotlib.pyplot as plt
	img1 = cv2.imread('B4.jpg',0) # queryImage
	img2 = cv2.imread('SKU.jpg',0) # trainImage
	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 < 0.7*n.distance:
	good.append([m])
	img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2)
	plt.imshow(img3),plt.show()
	print('Number of matches - SIFT')
	print(len(good))
	# FLANN parameters
	FLANN_INDEX_KDTREE = 1
	index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
	search_params = dict(checks=100) # or pass empty dictionary
	flann = cv2.FlannBasedMatcher(index_params,search_params)
	matches = flann.knnMatch(des1,des2,k=2)
	# Need to draw only good matches, so create a mask
	matchesMask = [[0,0] for i in range(len(matches))]
	# ratio test as per Lowe's paper
	for i,(m,n) in enumerate(matches):
	if m.distance < 0.7*n.distance:
	matchesMask[i]=[1,0]
	draw_params = dict(matchColor = (0,255,0),
	singlePointColor = (255,0,0),
	matchesMask = matchesMask,
	flags = 0)
	print('Number of matches - FLANN')
	print(len(draw_params))
	img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
	#plt.imshow(img3,),plt.show()
	# Initiate ORB detector
	orb = cv2.ORB_create()
	# find the keypoints and descriptors with ORB
	kp1, des1 = orb.detectAndCompute(img1,None)
	kp2, des2 = orb.detectAndCompute(img2,None)
	# create BFMatcher object
	bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
	# Match descriptors.
	matches = bf.match(des1,des2)
	# Sort them in the order of their distance.
	matches = sorted(matches, key = lambda x:x.distance)
	print('Number of matches - ORB')
	print(len(matches))
	#Modified Original code with Minor Changes
	#https://docs.opencv.org/3.3.0/dc/dc3/tutorial_py_matcher.html

view raw OpebCV3_FeatureMatching.py hosted with ❤ by GitHub

Happy Learning!!!