import cv2
import numpy as np

SOURCE_IMAGE1='../lisbon1.jpg'
SOURCE_IMAGE2='../lisbon2.jpg'

OUTPUT_IMAGE1='../keypoints_lisbon1.jpg'
OUTPUT_IMAGE2='../keypoints_lisbon2.jpg'

MATCHING_IMAGE='flann_matching_lisbon1_lisbon2.jpg'

## képek beolvasása
img1 = cv2.imread(SOURCE_IMAGE1);
img2 = cv2.imread(SOURCE_IMAGE2);
h1,w1 = img1.shape[:2]
h2,w2 = img2.shape[:2]
border_top_bottom = int(h1/2)
print(str(border_top_bottom))
border_left_right = int(w1/2)
#img1_pad = cv2.copyMakeBorder(img1, border_top_bottom, border_top_bottom, border_left_right, border_left_right, cv2.BORDER_CONSTANT, value=[0,0,0] )
#img2_pad = cv2.copyMakeBorder(img2, border_top_bottom, border_top_bottom, border_left_right, border_left_right, cv2.BORDER_CONSTANT, value=[0,0,0] )


## a képet szürkeárnyalatossá konvertáljuk
gray_img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray_img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

## jellemzőpontok detektálása 
surf = cv2.xfeatures2d.SURF_create()
keypoints1 = surf.detect(gray_img1, None)
keypoints2 = surf.detect(gray_img2, None)

## kulcspont leírók számítása
keypoints1, descriptors1 = surf.compute(gray_img1, keypoints1)
keypoints2, descriptors2 = surf.compute(gray_img2, keypoints2)

## pontpárok keresése
# FLANN parameterek
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(descriptors1,descriptors2,k=2) ## ez kNN-alapú, 
                                                        ## minden pontnak két lehetséges párja lehet

# csak a jó párosításokat tároljuk el, amelyek átmentek a Lowe-teszten
good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)

points1 = []
points2 = []

#goodKeypoints1 = np.float32([ keypoints1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
#goodKeypoints2 = np.float32([ keypoints2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
for m in good:
	points1.append(keypoints1[m.queryIdx].pt)
	points2.append(keypoints2[m.trainIdx].pt)
points1, points2 = np.float32((points1, points2))
#points1 = cv2.KeyPoint_convert(goodKeypoints1)
#points2 = cv2.KeyPoint_convert(goodKeypoints2)

H, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
matchesMask = mask.ravel().tolist() # azok a pontok, amelyek szerepelnek a párosításban
img2_size = (img2.shape[1], img2.shape[0])
img1_size = (img1.shape[1], img1.shape[0])
deformedImg2 = cv2.warpPerspective(img1, H, img1_size )
img2_copy = img2

for x in range(0, img1.shape[1]):
	for y in range(0, img1.shape[0]):
		if (np.array_equal(deformedImg2[y,x],np.array([0,0,0]))):
			img2_copy[y,x] = img2[y,x]
		else:
			img2_copy[y,x] = deformedImg2[y,x]
		

cv2.imwrite("deformed2.png", img2_copy)