Aligning 2 images using affnet about affnet HOT 8 CLOSED

def LAF2pts(LAF, n_pts = 50):
    a = np.linspace(0, 2*np.pi, n_pts);
    x = [0]
    x.extend(list(np.sin(a)))
    x = np.array(x).reshape(1,-1)
    y = [0]
    y.extend(list(np.cos(a)))
    y = np.array(y).reshape(1,-1)
    HLAF = np.concatenate([LAF, np.array([0,0,1]).reshape(1,3)])
    H_pts =np.concatenate([x,y,np.ones(x.shape)])
    H_pts_out = np.transpose(np.matmul(HLAF, H_pts))
    H_pts_out[:,0] = H_pts_out[:,0] / H_pts_out[:, 2]
    H_pts_out[:,1] = H_pts_out[:,1] / H_pts_out[:, 2]
    return H_pts_out[:,0:2]```

def LAF2pts(LAF, n_pts = 50):
    a = np.linspace(0, 2*np.pi, n_pts);
    x = [0]
    x.extend(list(np.sin(a)))
    x = np.array(x).reshape(1,-1)
    y = [0]
    y.extend(list(np.cos(a)))
    y = np.array(y).reshape(1,-1)
    HLAF = np.concatenate([LAF, np.array([0,0,1]).reshape(1,3)])
    H_pts =np.concatenate([x,y,np.ones(x.shape)])
    H_pts_out = np.transpose(np.matmul(HLAF, H_pts))
    H_pts_out[:,0] = H_pts_out[:,0] / H_pts_out[:, 2]
    H_pts_out[:,1] = H_pts_out[:,1] / H_pts_out[:, 2]
    return H_pts_out[:,0:2]

keypoints1  = list(map(lambda x:cv2.KeyPoint(x=x[0],y=x[1], _size = 2),LAFs1.numpy()[:,:,2]))
keypoints2 = list(map(lambda x:cv2.KeyPoint(x=x[0],y=x[1], _size = 2),LAFs2.numpy()[:,:,2]))

# Draw top matches
imMatches = cv2.drawMatches(im1,keypoints1, im2,keypoints2 , matches, None)
cv2.imwrite("matches.jpg", imMatches)
#cv2_imshow(imMatches)
# Extract location of good matches
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)

for i, match in enumerate(matches):
  points1[i, :] = keypoints1[match.queryIdx].pt
  points2[i, :] = keypoints2[match.trainIdx].pt

# Find homography
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
print(img2.shape)
# Use homography
height, width, channels = im2.shape
im1Reg = cv2.warpPerspective(im2, h, (width, height))