我正在学习本教程，并尝试使用使用cv2.stereoCalibrate获得的基本矩阵(Fmat)在立体图像对上绘制线条。我正在尝试使用我导入的Fmat.npy，而不是cv.findFundamentalMat和cv.FM_RANSAC。但是，两次代码尝试都会产生类似的值错误。
代码如下：

# 1. Detect keypoints and their descriptors

# Based on: https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html

# Initiate SIFT detector

sift = cv.SIFT_create()

# find the keypoints and descriptors with SIFT

kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# Visualize keypoints

imgSift = cv.drawKeypoints(
    img1, kp1, None, flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv.imshow("SIFT Keypoints", imgSift)

# Match keypoints in both images

# Based on: https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html

FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)   # or pass empty dictionary
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)

# Keep good matches: calculate distinctive image features

# Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94

# https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf

matchesMask = [[0, 0] for i in range(len(matches))]
good = []
pts1 = []
pts2 = []

for i, (m, n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        # Keep this keypoint pair
        matchesMask[i] = [1, 0]
        good.append(m)
        pts2.append(kp2[m.trainIdx].pt)
        pts1.append(kp1[m.queryIdx].pt)

# Draw the keypoint matches between both pictures

# Still based on: https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html

draw_params = dict(matchColor=(0, 255, 0),
                   singlePointColor=(255, 0, 0),
                   matchesMask=matchesMask[300:500],
                   flags=cv.DrawMatchesFlags_DEFAULT)

keypoint_matches = cv.drawMatchesKnn(
    img1, kp1, img2, kp2, matches[300:500], None,**draw_params)
cv.imshow("Keypoint matches", keypoint_matches)

# ------------------------------------------------------------

# STEREO RECTIFICATION

Fmat = np.load('Fmat.npy') # Load fundamental matrix

# Calculate the fundamental matrix for the cameras

# https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html

pts1 = np.int32(pts1)
pts2 = np.int32(pts2)
fundamental_matrix, inliers = cv.findFundamentalMat(pts1, pts2, cv.FM_RANSAC)  #Fmat

# We select only inlier points

pts1 = pts1[inliers.ravel() == 1]
pts2 = pts2[inliers.ravel() == 1]

# Visualize epilines

# Adapted from: https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html

def drawlines(img1src, img2src, lines, pts1src, pts2src):
    ''' img1 - image on which we draw the epilines for the points in img2
        lines - corresponding epilines '''
    r, c = img1src.shape
    img1color = cv.cvtColor(img1src, cv.COLOR_GRAY2BGR)
    img2color = cv.cvtColor(img2src, cv.COLOR_GRAY2BGR)
    # Edit: use the same random seed so that two images are comparable!
    np.random.seed(0)
    for r, pt1, pt2 in zip(lines, pts1src, pts2src):
        color = tuple(np.random.randint(0, 255, 3).tolist())
        x0, y0 = map(int, [0, -r[2]/r[1]])
        x1, y1 = map(int, [c, -(r[2]+r[0]*c)/r[1]])
        img1color = cv.line(img1color, (x0, y0), (x1, y1), color, 1)
        img1color = cv.circle(img1color, tuple(pt1), 5, color, -1)
        img2color = cv.circle(img2color, tuple(pt2), 5, color, -1)
    return img1color, img2color

# Find epilines corresponding to points in right image (second image) and

# drawing its lines on left image

lines1 = cv.computeCorrespondEpilines(
    pts2.reshape(-1, 1, 2), 2, fundamental_matrix)
lines1 = lines1.reshape(-1, 3)
img5, img6 = drawlines(img1, img2, lines1, pts1, pts2)

# Find epilines corresponding to points in left image (first image) and

# drawing its lines on right image

lines2 = cv.computeCorrespondEpilines(
    pts1.reshape(-1, 1, 2), 1, fundamental_matrix)
lines2 = lines2.reshape(-1, 3)
img3, img4 = drawlines(img2, img1, lines2, pts2, pts1)

plt.subplot(121), plt.imshow(img5)
plt.subplot(122), plt.imshow(img3)
plt.suptitle("Epilines in both images")
plt.show()

当我使用fundamental_matrix, inliers = cv.findFundamentalMat(pts1, pts2, cv.FM_RANSAC)行运行上面的代码时，如本教程中所提供的，返回以下错误：

Traceback (most recent call last):
  File "C:\Users\xxx\stereo-camera\featureMatching.py", line 103, in <module>
    img5, img6 = drawlines(img1, img2, lines1, pts1, pts2)
  File "C:\Users\xxx\stereo-camera\featureMatching.py", line 83, in drawlines
    r, c = img1src.shape
ValueError: too many values to unpack (expected 2)

当我运行代码并将行更改为fundamental_matrix, inliers = Fmat以获取我的基本矩阵时，我得到以下错误：

Traceback (most recent call last):
  File "C:\Users\xxx\stereo-camera\featureMatching.py", line 71, in <module>
    fundamental_matrix, inliers = Fmat
ValueError: too many values to unpack (expected 2)

每种情况下都出了什么问题，我需要做什么才能使用Fmat.npy文件产生所需的eplines结果？