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doc: fix misused "see also" doxygen command

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Alexander Alekhin
2020-03-21 00:25:49 +00:00
parent 183e4d50ed
commit 2fb1d9d02e
9 changed files with 14 additions and 13 deletions
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doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
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@@ -83,7 +83,7 @@ use 7x6 grid. (Normally a chess board has 8x8 squares and 7x7 internal corners).
corner points and retval which will be True if pattern is obtained. These corners will be placed in
an order (from left-to-right, top-to-bottom)
@sa This function may not be able to find the required pattern in all the images. So, one good option
@note This function may not be able to find the required pattern in all the images. So, one good option
is to write the code such that, it starts the camera and check each frame for required pattern. Once
the pattern is obtained, find the corners and store it in a list. Also, provide some interval before
reading next frame so that we can adjust our chess board in different direction. Continue this
@@ -91,7 +91,7 @@ process until the required number of good patterns are obtained. Even in the exa
are not sure how many images out of the 14 given are good. Thus, we must read all the images and take only the good
ones.
@sa Instead of chess board, we can alternatively use a circular grid. In this case, we must use the function
@note Instead of chess board, we can alternatively use a circular grid. In this case, we must use the function
**cv.findCirclesGrid()** to find the pattern. Fewer images are sufficient to perform camera calibration using a circular grid.
Once we find the corners, we can increase their accuracy using **cv.cornerSubPix()**. We can also
doc/py_tutorials/py_gui/py_image_display/py_image_display.markdown
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@@ -132,7 +132,7 @@ A screen-shot of the window will look like this :
![image](images/matplotlib_screenshot.jpg)
@sa Plenty of plotting options are available in Matplotlib. Please refer to Matplotlib docs for more
@note Plenty of plotting options are available in Matplotlib. Please refer to Matplotlib docs for more
details. Some, we will see on the way.
__warning__
doc/py_tutorials/py_imgproc/py_contours/py_contours_more_functions/py_contours_more_functions.markdown
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@@ -113,7 +113,7 @@ I got following results:
See, even image rotation doesn't affect much on this comparison.
@sa [Hu-Moments](http://en.wikipedia.org/wiki/Image_moment#Rotation_invariant_moments) are seven
@note [Hu-Moments](http://en.wikipedia.org/wiki/Image_moment#Rotation_invariant_moments) are seven
moments invariant to translation, rotation and scale. Seventh one is skew-invariant. Those values
can be found using **cv.HuMoments()** function.
doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown
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@@ -94,7 +94,7 @@ hist is same as we calculated before. But bins will have 257 elements, because N
as 0-0.99, 1-1.99, 2-2.99 etc. So final range would be 255-255.99. To represent that, they also add
256 at end of bins. But we don't need that 256. Upto 255 is sufficient.
@sa Numpy has another function, **np.bincount()** which is much faster than (around 10X)
@note Numpy has another function, **np.bincount()** which is much faster than (around 10X)
np.histogram(). So for one-dimensional histograms, you can better try that. Don't forget to set
minlength = 256 in np.bincount. For example, hist = np.bincount(img.ravel(),minlength=256)