import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
from scipy import stats
def drawMatchesKnn_cv(img1_gray, kp1, img2_gray, kp2, goodMatch):
h1, w1 = img1_gray.shape[:2]
h2, w2 = img2_gray.shape[:2]
vis = np.zeros((max(h1, h2), w1 + w2, 3), np.uint8)
vis[:h1, :w1] = img1_gray
vis[:h2, w1:w1 + w2] = img2_gray
p1 = [kpp.queryIdx for kpp in goodMatch]
p2 = [kpp.trainIdx for kpp in goodMatch]
post1 = np.int32([kp1[pp].pt for pp in p1])
post2 = np.int32([kp2[pp].pt for pp in p2]) + (w1, 0)
for (x1, y1), (x2, y2) in zip(post1, post2):
cv.line(vis, (x1, y1), (x2, y2), (0, 0, 255))
cv.namedWindow("match", cv.WINDOW_NORMAL)
cv.imshow("match", vis)
img1_gray = cv.imread("D:\\test3.png", 0)
img2_gray = cv.imread("D:\\test4.png", 0)
img1_gray = cv.resize(img1_gray, (1800, 2400))
img2_gray = cv.resize(img2_gray, (1800, 2400))
sift = cv.xfeatures2d.SURF_create()
kp1, des1 = sift.detectAndCompute(img1_gray, None)
kp2, des2 = sift.detectAndCompute(img2_gray, None)
FLANN_INDEX_KDTREE = 0
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
searchParams = dict(checks=50)
flann = cv.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(des1, des2, k=2)
destinationx = []
destinationy = []
matchesMask = [[0, 0] for i in range(len(matches))]
for i, (m, n) in enumerate(matches):
if m.distance < 0.5 * n.distance:
matchesMask[i] = [1, 0]
pt1 = kp1[m.queryIdx].pt
pt2 = kp2[n.trainIdx].pt
destinationx.append(int(pt1[0]-pt2[0]))
destinationy.append(int(pt1[1]-pt2[1]))
print(i, pt1, pt2)
print(destinationx)
print(stats.mode(destinationx)[0][0])
print(destinationy)
print(stats.mode(destinationy)[0][0])