Surf_features_matching.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys , os , random
import cv, cv2
import numpy as np
from numpy.lib.stride_tricks import as_strided

SURF_THRESHOLD = 1000
SURF_MATCH_THRESHOLD = 0.4

INLIER_DISTANCE_THRESHOLD = 50
NUMBER_OF_INLIERS_THRESHOLD = 0.4

def SURFdetector ( image ):
	global SURF_THRESHOLD
	detector = cv2.SURF(SURF_THRESHOLD, 10, 10)
	keypoints, descriptors = detector.detectAndCompute(image, None)
	return keypoints, descriptors

def SURFmatcher(keypoints_set , descriptors_set):
	keypoint_1 = keypoints_set[0]
	keypoint_2 = keypoints_set[1]
	descriptor_1 = descriptors_set[0]
	descriptor_2 = descriptors_set[1]

	diff = descriptor_2 - descriptor_1[:,None]
	squre_of_diff = diff ** 2
	sum_square_diff = squre_of_diff.sum(axis=-1)
	score = np.sqrt(sum_square_diff.min(axis=-1))
	matches = np.argmin(sum_square_diff,axis=-1)
	invalid_matches = score > SURF_MATCH_THRESHOLD 
	
	score[invalid_matches] = -1
	matches[invalid_matches] = -1

	return matches , score

def main ( ) :

	image =[]
	keypoints_set = []
	descriptors_set = []

	#loading images
	for i in range (len ( sys . argv ) - 1 ):
		filename = sys . argv [ i + 1 ]
		image.append(cv2.imread (filename))

	width = image[0].shape[1]
	height = image[0].shape[0]

	surf_features = np.zeros((height , len(image)*width  ,3) ,np.uint8)
	surf_matches = np.zeros((height , len(image)*width  ,3) ,np.uint8)
	
	for i in range(len(image)):

		keypoints, descriptors = SURFdetector( image[i])
		
		keypoints_set.append(keypoints)
		descriptors_set.append(descriptors)

		surf_features[0:height , i*width : (i + 1)*width , :] = image[i]
	
		for j in range ( len ( keypoints ) ) :
			x , y = keypoints[j].pt
			cv2.circle ( surf_features , ( (i*width) + int (x) , int (y) ) , 0 , (255 , 0 , 0) , 4)
	
	cv2.imwrite('surf_features.png', surf_features)
	surf_matches = surf_features

	matches, score = SURFmatcher(keypoints_set,descriptors_set)
	for i in range ( len ( matches ) ) :
		match = matches.item(i)
		if match != -1:

			cv2.line (surf_matches , (int(((keypoints_set[0][i]).pt)[0]) , int(((keypoints_set[0][i]).pt)[1])), \
				(int(((keypoints_set[1][match]).pt)[0])+width , int(((keypoints_set[1][match]).pt)[1])), \
				(255*( i%4) ,255*(( i+1)%4) , 255*(( i+2)%4) ) , 1 , cv2.CV_AA, 0)

	cv2.imwrite('surf_matches.png', surf_matches)



main()

The features detected by SURF(Speeded Up Robust Features) detector (directly used opencv library). And the matching of these features found by measuring sum of squared differences of the discriptor values associated with each features this will be called as match score between two features. Matching will be done between the features having minimum score. And if we found more than one close match then such matches will be ignored for finding reliable matched points.

Some sample results can be found here.. http://cannibal-eshafeeqe.blogspot.in/2014/03/surf-feature-detection-and-matching.html