Tutorials https://scikit-surgerycalibration.readthedocs.io/en/latest/tutorials/calibration.html seems to work much better using jupypter notebook, specially for this bit: "If you hold the chessboard in view of your camera, and run the below cell repeatedly, it will grab some frames."  But got few errors: 

      1 if number_of_views > 1:
      2 #     proj_err, params = calibrator.calibrate()
----> 3     proj_err, recon_err, params = calibrator.calibrate()
      4 
      5     print(f'Reprojection (2D) error is: {proj_err}')

ValueError: not enough values to unpack (expected 3, got 2)

---------------------------------------------------------------------------
error                                     Traceback (most recent call last)
/tmp/ipykernel_44007/3599310274.py in <module>
      9     # Draw detected points on frame
     10     for point in img_pts:
---> 11         frame = cv2.circle(frame, (point[0][0], point[0][1]), 5, (255, 0 ,0), -1)
     12 
     13 print(f"Detected {number_of_points} points")

error: OpenCV(4.6.0) :-1: error: (-5:Bad argument) in function 'circle'
> Overload resolution failed:
>  - Can't parse 'center'. Sequence item with index 0 has a wrong type
>  - Can't parse 'center'. Sequence item with index 0 has a wrong type

## Some useful commands to manage your conda env:
## LIST CONDA ENVS: conda list -n *VE # show list of installed packages
## UPDATE CONDA: conda update -n base -c defaults conda
## INSTALL CONDA EV: conda env create -f *VE.yml
## UPDATE CONDA ENV: conda env update -f *VE.yml --prune
## ACTIVATE CONDA ENV: conda activate *VE
## REMOVE CONDA ENV: conda remove -n *VE --all

name: scikit-surgerycalibrationVE
channels:
  - defaults
  - conda-forge #vtk; tox;
  - anaconda #coverage; scipy; pyserial 
  #- fastai #opencv-python-headless
dependencies:
  - python=3.7
  - jupyter
  #- cookiecutter>=1.7.3
  - numpy>=1.11
  #- vtk=8.1.2
  - matplotlib
  # - six>=1.10
  # - scipy>=1.7.3
  - tox>=3.26.0
  - pytest>=7.1.2
  - pylint>=2.14.5
  #- pyserial 
  - pip>=22.2.2
  - pip:
     #- scikit-surgeryvtk>=1.0.6
     #- scikit-surgeryutils>=1.2.0
     #- scikit-surgerycore>=0.1.7
     - scikit-surgeryimage>=0.10.1 
     - scikit-surgerycalibration
     # - ndicapi>=3.2.6 
     #- PySide2<5.15.0
     - opencv-contrib-python-headless<4.6 #==4.5.3.56 #<4.6 #4.5.5.64 installted with <4.6 #=4.5.3.56works 4.5.4.58 breaks. issue42 #>=4.2.0.32 # 4.6.0.66 current
     #- pylint-exit

FROM: def triangulate_points_hartley(...

# array shapes for input args _iter_triangulate_point_w_svd( [3, 4]; [3, 4]; [3, 1]; [3, 1] )
reconstructed_point = _iter_triangulate_point_w_svd(p1d, p2d, u1t, u2t)
#reconstructed_point = np.around(reconstructed_point, 3) #Truncate and discarding decimal digits (np.around)
print(f'\n {reconstructed_point}')

FROM: def triangulate_points_opencv(...
# array shapes for input args cv2.triangulatePoints( [3, 4]; [3, 4]; [2, 1]; [2, 1] )
reconstructed_point = cv2.triangulatePoints(p1mat, p2mat, u1t[:2], u2t[:2])
reconstructed_point /= reconstructed_point[3]  # Homogenize
#reconstructed_point = np.around(reconstructed_point, 3) #Truncate and discarding decimal digits (np.around)
print(f'\n {reconstructed_point}')

$ pytest -v -s tests/algorithms/test_triangulate.py #for individual tests

tests/algorithms/test_triangulate.py::test_triangulate_points_hartley
 [[  9.88256179]
 [-22.44358672]
 [127.9216597 ]]

 [[ 48.46473971]
 [-23.09607652]
 [119.95244623]]

 [[  6.94680311]
 [  1.97332699]
 [115.79217294]]

 [[ 44.77458535]
 [  1.76836361]
 [106.8097294 ]]

 Elapsed time for at.triangulate_points_hartley(): 2.426845 millisecs

 [[  9.88261104]
 [-22.44364881]
 [127.92238956]
 [  1.        ]]

 [[ 48.46887878]
 [-23.09824868]
 [119.96328148]
 [  1.        ]]

 [[  6.94682563]
 [  1.97329098]
 [115.79279036]
 [  1.        ]]

 [[ 44.77479814]
 [  1.76837639]
 [106.81026738]
 [  1.        ]]

 Elapsed time for at.triangulate_points_opencv(): 0.91077 millisecs

 rms_hartley: 1.2954729076604021 and test (rms_hartley < 1.5)

 rms_hartley_opencv:  1.3009661570992705 and test (rms_hartley < 1.5)

test_charuco_plus_chessboard.py
test_chessboard_calibration.py
test_hand_eye.py
test_precalib.py

==================================================================================================== FAILURES =====================================================================================================
_____________________________________________________________________________________________ test_charuco_dataset_B ______________________________________________________________________________________________

    def test_charuco_dataset_B():
    
        calib_dir = 'tests/data/precalib/data_moved_scope'
        calib_driver = get_calib_driver(calib_dir)
    
        stereo_reproj_err, stereo_recon_err, _ = \
            calib_driver.calibrate()
    
        tracked_reproj_err, tracked_recon_err, _ = \
            calib_driver.handeye_calibration()
    
        print(stereo_reproj_err, stereo_recon_err, tracked_reproj_err, tracked_recon_err)
>       assert stereo_reproj_err < 1
E       assert 7.116922375480872 < 1

tests/video/test_precalib.py:92: AssertionError
_______________________________________________________________________________________________ test_precalbration ________________________________________________________________________________________________

    def test_precalbration():
        """ Use intrinsics from A to calibration B, currently failing. """
        left_intrinsics = np.loadtxt('tests/data/precalib/precalib_base_data/calib.left.intrinsics.txt')
        left_distortion = np.loadtxt('tests/data/precalib/precalib_base_data/calib.left.distortion.txt')
        right_intrinsics = np.loadtxt('tests/data/precalib/precalib_base_data/calib.right.intrinsics.txt')
        right_distortion = np.loadtxt('tests/data/precalib/precalib_base_data/calib.right.distortion.txt')
        l2r = np.loadtxt('tests/data/precalib/precalib_base_data/calib.l2r.txt')
        l2r_rmat = l2r[0:3, 0:3]
        l2r_tvec = l2r[0:3, 3]
    
        calib_dir = 'tests/data/precalib/data_moved_scope'
        calib_driver = get_calib_driver(calib_dir)
    
        stereo_reproj_err, stereo_recon_err, _ = \
            calib_driver.calibrate(
                override_left_intrinsics=left_intrinsics,
                override_left_distortion=left_distortion,
                override_right_intrinsics=right_intrinsics,
                override_right_distortion=right_distortion,
                override_l2r_rmat=l2r_rmat,
                override_l2r_tvec=l2r_tvec)
    
        tracked_reproj_err, tracked_recon_err, _ = \
            calib_driver.handeye_calibration()
    
        print(stereo_reproj_err, stereo_recon_err, tracked_reproj_err, tracked_recon_err)
>       assert stereo_reproj_err < 4.5
E       assert 34.74794862170866 < 4.5

tests/video/test_precalib.py:124: AssertionError
================================================================================================ warnings s

    for j, _ in enumerate(left_images):
        left_undistorted = cv2.undistort(
            left_images[j],
            left_camera_matrix,
            left_distortion_coeffs,
            left_camera_matrix
        )
        left_ids, left_obj_pts, left_img_pts = \
            left_point_detector.get_points(left_undistorted)

        if left_ids is not None \
                and left_ids.shape[0] >= minimum_points_per_frame \
                and right_ids is not None \
                and right_ids.shape[0] >= minimum_points_per_frame:

            left_common_points = match_points_by_id(left_ids,
                                                    left_img_pts,
                                                    reference_ids,
                                                    reference_image_points)
            left_homography, _ = \
                cv2.findHomography(left_common_points[0:, 0:2],
                                   left_common_points[0:, 2:4])
            left_warped = cv2.warpPerspective(left_undistorted,
                                              left_homography,
                                              reference_image_size,)

            left_ids, left_obj_pts, left_img_pts = \
                left_point_detector.get_points(left_warped)

_, l_c, l_d, l_rvecs, l_tvecs \
    = cv2.calibrateCamera(left_object_points,
                          left_image_points,
                          image_size,
                          None, None)
# Convert tuples to lists for later editing
l_rvecs = list(l_rvecs)
l_tvecs = list(l_tvecs)