# Wrong rank in Fundamental Matrix

Hi guys,

I'm using the OpenCV for Python3 and, based on the Mastering OpenCV Book, try to compute the epipoles from many images (Structure from Motion algorithm).

In many books, they say which Fundamental Matrix has rank 2. But, the OpenCV function returns a rank 3 matrix.

How can I make this right?

```
orb = cv2.ORB_create()
# find the keypoints and descriptors with ORB
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
# Match descriptors.
matches = bf.match(des1,des2)
# Sort them in the order of their distance.
matches = sorted(matches, key = lambda x:x.distance)
pts1 = []
pts2 = []
for m in matches:
pts2.append(kp2[m.trainIdx].pt)
pts1.append(kp1[m.queryIdx].pt)
F, mask = cv2.findFundamentalMat(pts1, pt2,cv2.FM_RANSAC)
pts1 = match['leftPts'][mask.ravel()==1]
pts2 = match['rightPts'][mask.ravel()==1]
# F is the Fundamental Matrix
```

From that code, the output are like

```
Processing image 0 and image 1
rank of F: 3
Processing image 0 and image 2
rank of F: 3
Processing image 0 and image 3
rank of F: 3
Processing image 0 and image 4
rank of F: 2
[...]
```

Someone could help me? Someone have any functional code for SfM using OpenCV? Thanks in advance.

Can you show one example of a Fundamentalmatrix and how do you determine the rank?

[[-2.29390496e-08 8.55007340e-07 -1.07108724e-03] [ 2.87605686e-08 -2.30601171e-06 2.86179300e-03] [ 2.94004722e-05 -7.92971149e-04 1.00000000e+00]] Rank of F: 3

[[ 1.95163442e-08 1.34608880e-07 -3.20936720e-04] [ 4.76676164e-08 5.75973164e-07 -1.02011608e-03] [-5.95314437e-05 -4.32612338e-04 1.00000000e+00]] Rank of F: 3

computed by numpy.linalg.matrix_rank function