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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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Alexander Alekhin
2020-11-27 14:06:34 +00:00
parent 23be514886 7c78c59e64
commit 2155296a13
12 changed files with 397 additions and 111 deletions
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doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
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@@ -209,7 +209,7 @@ find the average error, we calculate the arithmetical mean of the errors calcula
calibration images.
@code{.py}
mean_error = 0
for i in xrange(len(objpoints)):
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)
mean_error += error
doc/tutorials/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.markdown
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@@ -32,7 +32,7 @@ automatically available with the platform (e.g. APPLE GCD) but chances are that
have access to a parallel framework either directly or by enabling the option in CMake and rebuild the library.
The second (weak) precondition is more related to the task you want to achieve as not all computations
are suitable / can be adatapted to be run in a parallel way. To remain simple, tasks that can be split
are suitable / can be adapted to be run in a parallel way. To remain simple, tasks that can be split
into multiple elementary operations with no memory dependency (no possible race condition) are easily
parallelizable. Computer vision processing are often easily parallelizable as most of the time the processing of
one pixel does not depend to the state of other pixels.