img1 = cv2.imread('map1.png')
img2 = cv2.imread('map2.png')

img1 = cv2.resize(img1, (640, 480))
img2 = cv2.resize(img2, (640, 480))

input = np.hstack((img1, img2))
cv2.imshow('input', input)
cv2.waitKey(0)

def get_homo(img1, img2):
? ? # 1實(shí)現(xiàn)
? ? sift = cv2.xfeatures2d.SIFT_create()
? ? # 2實(shí)現(xiàn)
? ? k1, p1 = sift.detectAndCompute(img1, None)
? ? k2, p2 = sift.detectAndCompute(img2, None)
? ? # 3實(shí)現(xiàn)
? ? bf = cv2.BFMatcher()
? ? # 4實(shí)現(xiàn)
? ? matches = bf.knnMatch(p1, p2, k=2)
? ? # 5實(shí)現(xiàn)
? ? good = []
? ? for m1, m2 in matches:
? ? ? ? if m1.distance < 0.8 * m2.distance:
? ? ? ? ? ? good.append(m1)
? ? # 6實(shí)現(xiàn)
? ? if len(good) > 8:
? ? ? ? img1_pts = []
? ? ? ? img2_pts = []
? ? ? ? for m in good:
? ? ? ? ? ? img1_pts.append(k1[m.queryIdx].pt)
? ? ? ? ? ? img2_pts.append(k2[m.trainIdx].pt)
? ? ? ? img1_pts = np.float32(img1_pts).reshape(-1, 1, 2)
? ? ? ? img2_pts = np.float32(img2_pts).reshape(-1, 1, 2)
? ? ? ? H, mask = cv2.findHomography(img1_pts, img2_pts, cv2.RANSAC, 5.0)
? ? ? ? return H
? ? else:
? ? ? ? print('piints is not enough 8!')
? ? ? ? exit()

def stitch_img(img1, img2, H):
? ? # 1實(shí)現(xiàn)
? ? h1, w1 = img1.shape[:2]
? ? h2, w2 = img2.shape[:2]
? ? img1_point = np.float32([[0,0], [0,h1], [w1,h1], [w1,0]]).reshape(-1, 1, 2)
? ? img2_point = np.float32([[0,0], [0,h2], [w2,h2], [w2,0]]).reshape(-1, 1, 2)
? ? # 2實(shí)現(xiàn)
? ? img1_trans = cv2.perspectiveTransform(img1_point, H)
? ? # 將img1變換后的角點(diǎn)與img2原來(lái)的角點(diǎn)做拼接
? ? result_point = np.concatenate((img2_point, img1_trans), axis=0)
? ? # 獲得拼接后圖像x,y的最小值
? ? [x_min, y_min] = np.int32(result_point.min(axis=0).ravel()-0.5)
? ? # 獲得拼接后圖像x,y的最大值
? ? [x_max, y_max] = np.int32(result_point.max(axis=0).ravel()+0.5)
? ? # 平移距離
? ? trans_dist = [-x_min, -y_min]
? ? # 構(gòu)建一個(gè)齊次平移矩陣
? ? trans_array = np.array([[1, 0, trans_dist[0]],
? ? ? ? ? ? ? ? ? ? ? ? ? ? [0, 1, trans_dist[1]],
? ? ? ? ? ? ? ? ? ? ? ? ? ? [0, 0, 1]])
? ? # 平移和單應(yīng)性變換
? ? res_img = cv2.warpPerspective(img1, trans_array.dot(H), (x_max-x_min, y_max-y_min))
? ? # 3實(shí)現(xiàn)
? ? res_img[trans_dist[1]:trans_dist[1]+h2,
? ? ? ? ? ? trans_dist[0]:trans_dist[0]+w2] = img2
? ? return res_img

H = get_homo(img1, img2)
res_img = stitch_img(img1, img2, H)
# 4實(shí)現(xiàn)
cv2.imshow('result', res_img)
cv2.waitKey(0)?