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Python3 : AttributeError: module 'cv2.cv2' has no attribute 'xfeatures2d'

I'm trying to execute a example open source program. But, getting error in library. Using OpevCV 3.4.0.

Issue :

3Dreconstruction(master)$ python3 example.py Traceback (most recent call last): File "example.py", line 90, in <module> points1, points2, intrinsic = dino() File "example.py", line 68, in dino pts1, pts2 = features.find_correspondence_points(img1, img2) File "/home/aximsoft/Computer Vision/Existing Examples/3Dreconstruction/features.py", line 6, in find_correspondence_points sift = cv2.xfeatures2d.SIFT_create() AttributeError: module 'cv2.cv2' has no attribute 'xfeatures2d'

Source Code :Open Source Link

example.py

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import cv2

from camera import Camera
import structure
import processor
import features

# Download images from http://www.robots.ox.ac.uk/~vgg/data/data-mview.html

def house():
    input_path = 'imgs/house/'
    camera_filepath = 'imgs/house/3D/house.00{0}.P'

    cameras = [Camera(processor.read_matrix(camera_filepath.format(i)))
            for i in range(9)]
    [c.factor() for c in cameras]

    points3d = processor.read_matrix(input_path + '3D/house.p3d').T  # 3 x n
    points4d = np.vstack((points3d, np.ones(points3d.shape[1])))  # 4 x n
    points2d = [processor.read_matrix(
        input_path + '2D/house.00' + str(i) + '.corners') for i in range(9)]

    index1 = 2
    index2 = 4
    img1 = cv2.imread(input_path + 'house.00' + str(index1) + '.pgm')  # left image
    img2 = cv2.imread(input_path + 'house.00' + str(index2) + '.pgm')

    # fig = plt.figure()
    # ax = fig.gca(projection='3d')
    # ax.plot(points3d[0], points3d[1], points3d[2], 'b.')
    # ax.set_xlabel('x axis')
    # ax.set_ylabel('y axis')
    # ax.set_zlabel('z axis')
    # ax.view_init(elev=135, azim=90)

    # x = cameras[index2].project(points4d)
    # plt.figure()
    # plt.plot(x[0], x[1], 'b.')
    # plt.show()

    corner_indexes = processor.read_matrix(
        input_path + '2D/house.nview-corners', np.int)
    corner_indexes1 = corner_indexes[:, index1]
    corner_indexes2 = corner_indexes[:, index2]
    intersect_indexes = np.intersect1d(np.nonzero(
        [corner_indexes1 != -1]), np.nonzero([corner_indexes2 != -1]))
    corner_indexes1 = corner_indexes1[intersect_indexes]
    corner_indexes2 = corner_indexes2[intersect_indexes]
    points1 = processor.cart2hom(points2d[index1][corner_indexes1].T)
    points2 = processor.cart2hom(points2d[index2][corner_indexes2].T)

    height, width, ch = img1.shape
    intrinsic = np.array([  # for imgs/house
        [2362.12, 0, width / 2],
        [0, 2366.12, height / 2],
        [0, 0, 1]])

    return points1, points2, intrinsic


def dino():
    # Dino
    img1 = cv2.imread('imgs/dinos/viff.003.ppm')
    img2 = cv2.imread('imgs/dinos/viff.001.ppm')
    pts1, pts2 = features.find_correspondence_points(img1, img2)
    points1 = processor.cart2hom(pts1)
    points2 = processor.cart2hom(pts2)

    fig, ax = plt.subplots(1, 2)
    ax[0].autoscale_view('tight')
    ax[0].imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
    ax[0].plot(points1[0], points1[1], 'r.')
    ax[1].autoscale_view('tight')
    ax[1].imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
    ax[1].plot(points2[0], points2[1], 'r.')
    fig.show()

    height, width, ch = img1.shape
    intrinsic = np.array([  # for dino
        [2360, 0, width / 2],
        [0, 2360, height / 2],
        [0, 0, 1]])

    return points1, points2, intrinsic


points1, points2, intrinsic = dino()

# Calculate essential matrix with 2d points.
# Result will be up to a scale
# First, normalize points
points1n = np.dot(np.linalg.inv(intrinsic), points1)
points2n = np.dot(np.linalg.inv(intrinsic), points2)
E = structure.compute_essential_normalized(points1n, points2n)
print('Computed essential matrix:', (-E / E[0][1]))

# Given we are at camera 1, calculate the parameters for camera 2
# Using the essential matrix returns 4 possible camera paramters
P1 = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
P2s = structure.compute_P_from_essential(E)

ind = -1
for i, P2 in enumerate(P2s):
    # Find the correct camera parameters
    d1 = structure.reconstruct_one_point(
        points1n[:, 0], points2n[:, 0], P1, P2)

    # Convert P2 from camera view to world view
    P2_homogenous = np.linalg.inv(np.vstack([P2, [0, 0, 0, 1]]))
    d2 = np.dot(P2_homogenous[:3, :4], d1)

    if d1[2] > 0 and d2[2] > 0:
        ind = i

P2 = np.linalg.inv(np.vstack([P2s[ind], [0, 0, 0, 1]]))[:3, :4]
#tripoints3d = structure.reconstruct_points(points1n, points2n, P1, P2)
tripoints3d = structure.linear_triangulation(points1n, points2n, P1, P2)

fig = plt.figure()
fig.suptitle('3D reconstructed', fontsize=16)
ax = fig.gca(projection='3d')
ax.plot(tripoints3d[0], tripoints3d[1], tripoints3d[2], 'b.')
ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
ax.view_init(elev=135, azim=90)
plt.show()

features.py

import cv2
import numpy as np


def find_correspondence_points(img1, img2):
    sift = cv2.xfeatures2d.SIFT_create()

    # find the keypoints and descriptors with SIFT
    kp1, des1 = sift.detectAndCompute(
        cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY), None)
    kp2, des2 = sift.detectAndCompute(
        cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY), None)

    # Find point matches
    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)

    # Apply Lowe's SIFT matching ratio test
    good = []
    for m, n in matches:
        if m.distance < 0.8 * n.distance:
            good.append(m)

    src_pts = np.asarray([kp1[m.queryIdx].pt for m in good])
    dst_pts = np.asarray([kp2[m.trainIdx].pt for m in good])

    # Constrain matches to fit homography
    retval, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 100.0)
    mask = mask.ravel()

    # We select only inlier points
    pts1 = src_pts[mask == 1]
    pts2 = dst_pts[mask == 1]

    return pts1.T, pts2.T