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

#Import Images
# This is where you can add your own images if you want
img1 = cv2.imread('RedShirt2.jpg',0)
img2 = cv2.imread('instructablesRobot.jpg',0)

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

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


FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)

flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
FLANN_INDEX_KDTREE = 0
MIN_MATCH_COUNT = 10
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1, des2, k=2)

# store all the good matches as per Lowe's ratio test.
good = []
for m, n in matches:
    if m.distance < 0.6 * n.distance:
        good.append(m)

if len(good) > MIN_MATCH_COUNT:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    matchesMask = mask.ravel().tolist()

else:
    print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))
    matchesMask = None

draw_params = dict(matchColor=(0, 255, 0), # draw matches in green color
                   singlePointColor=None,
                   matchesMask=matchesMask, # draw only inliers
                   flags=2)

img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
img4=cv2.drawKeypoints(img1,kp1[1:20],None,(255,0,0),4)
img5=cv2.drawKeypoints(img2,kp2[1:20],None,(255,0,0),4)
plt.imshow(img4),plt.show()
plt.imshow(img5),plt.show()
plt.imshow(img3,),plt.show()