import numpy as np
import cv2
from matplotlib import pyplot as plt

img1 = cv2.imread('2015-05-05 22_24_15.870494.jpeg',0)          # queryImage
img2 = cv2.imread('2015-05-05 22_24_15.870494.jpeg',0) # trainImage

# Initiate SIFT detector
sift = cv2.SIFT()
 
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
 
# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
 
search_params = dict(checks=50)   # 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 xrange(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)
 
# errors if the line below is not commented out
#img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
#img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10], flags=2)
 
# what is the best way to quantify the how strong the match is?
count_matches = 0
for i in range(len(matches)):
	if matchesMask[i] == [1,0]:
		count_matches += 1
print count_matches
draw_params = dict(matchColor = (0,255,0),
                   singlePointColor = (255,0,0),
                   matchesMask = matchesMask,
                   flags = 0)

img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)

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