import numpy as np
import cv2
def sift_kp(image):
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d_SIFT.create()
kp, des = sift.detectAndCompute(gray_image, None)
kp_image = cv2.drawKeypoints(gray_image, kp, None)
return kp_image, kp, des
def get_good_match(des1, des2):
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append(m)
return good
def surf_kp(image):
'''SIFT(surf)特征点检测(速度比sift快),精度差了很多'''
height, width = image.shape[:2]
size = (int(width * 0.2), int(height * 0.2))
shrink = cv2.resize(image, size, interpolation=cv2.INTER_AREA)
gray_image = cv2.cvtColor(shrink, cv2.COLOR_BGR2GRAY)
surf = cv2.xfeatures2d_SURF.create()
kp, des = surf.detectAndCompute(gray_image, None)
kp_image = cv2.drawKeypoints(gray_image, kp, None)
return kp_image, kp, des
def siftImageAlignment(img1, img2):
_, kp1, des1 = sift_kp(img1)
_, kp2, des2 = sift_kp(img2)
goodMatch = get_good_match(des1, des2)
if len(goodMatch) > 4:
ptsA = np.float32([kp1[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2)
ptsB = np.float32([kp2[m.trainIdx].pt for m in goodMatch]).reshape(-1, 1, 2)
ransacReprojThreshold = 4
H, status = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, ransacReprojThreshold);
imgOut = cv2.warpPerspective(img2, H, (img1.shape[1], img1.shape[0]),
flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
return imgOut, H, status
img1 = cv2.imread("/home/laoer/五一前解决/船只与水/122/1-00000122.jpg")
img2 = cv2.imread("/home/laoer/五一前解决/船只与水/122/2-00000122.jpg")
img3 = cv2.imread("/home/laoer/五一前解决/船只与水/122/3-00000122.jpg")
result1,_,_ = siftImageAlignment(img1,img2)
result2,_,_ = siftImageAlignment(img1,img3)
allImg = cv2.merge([img1[:,:,0],result1[:,:,0],result2[:,:,0]])
cv2.namedWindow('Result',cv2.WINDOW_NORMAL)
cv2.imshow('Result',allImg)
cv2.waitKey(0)