potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 23:33:05 +04:00
Code Packages Releases Wiki Activity

Merge pull request #28652 from mvanhorn:osc/28651-fix-reprojection-error-rmse

Browse Source 
calib3d: fix reprojection error RMSE calculation in Python tutorial
This commit is contained in:
Alexander Smorkalov
2026-03-23 17:08:16 +03:00
committed by GitHub
parent 1610602884 5e4592440e
commit 45c1f9d803
1 changed files with 5 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
+5 -5
View File
@@ -206,17 +206,17 @@ Re-projection Error
Re-projection error gives a good estimation of just how exact the found parameters are. The closer the re-projection error is to zero, the more accurate the parameters we found are. Given the intrinsic, distortion, rotation and translation matrices,
we must first transform the object point to image point using **cv.projectPoints()**. Then, we can calculate
the absolute norm between what we got with our transformation and the corner finding algorithm. To
find the average error, we calculate the arithmetical mean of the errors calculated for all the
calibration images.
the norm between what we got with our transformation and the corner finding algorithm. To find the
RMSE (root mean squared error), we average the squared errors over all points and images, then take
the square root.
@code{.py}
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2)/len(imgpoints2)
error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2SQR) / len(imgpoints2)
mean_error += error
print( "total error: {}".format(mean_error/len(objpoints)) )
print( "total error: {}".format(np.sqrt(mean_error/len(objpoints))) )
@endcode
Exercises