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doc: fix typo in py_tutorials

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Pranit Bauva 2017-10-01 18:46:58 +05:30
parent 9365817bfd
commit d3e3d0996c
4 changed files with 5 additions and 5 deletions
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2
doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown
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@ -130,7 +130,7 @@ Or
>>> b = img[:,:,0]
@endcode
Suppose, you want to make all the red pixels to zero, you need not split like this and put it equal
to zero. You can simply use Numpy indexing, and that is more faster.
to zero. You can simply use Numpy indexing, and that is faster.
@code{.py}
>>> img[:,:,2] = 0
@endcode

2
doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown
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@ -140,7 +140,7 @@ FLANN based Matcher
FLANN stands for Fast Library for Approximate Nearest Neighbors. It contains a collection of
algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional
features. It works more faster than BFMatcher for large datasets. We will see the second example
features. It works faster than BFMatcher for large datasets. We will see the second example
with FLANN based matcher.
For FLANN based matcher, we need to pass two dictionaries which specifies the algorithm to be used,

4
doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown
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@ -34,7 +34,7 @@ applications, rotation invariance is not required, so no need of finding this or
speeds up the process. SURF provides such a functionality called Upright-SURF or U-SURF. It improves
speed and is robust upto \f$\pm 15^{\circ}\f$. OpenCV supports both, depending upon the flag,
**upright**. If it is 0, orientation is calculated. If it is 1, orientation is not calculated and it
is more faster.
is faster.
![image](images/surf_orientation.jpg)
@ -130,7 +130,7 @@ False
>>> plt.imshow(img2),plt.show()
@endcode
See the results below. All the orientations are shown in same direction. It is more faster than
See the results below. All the orientations are shown in same direction. It is faster than
previous. If you are working on cases where orientation is not a problem (like panorama stitching)
etc, this is better.

2
doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown
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@ -99,7 +99,7 @@ as 0-0.99, 1-1.99, 2-2.99 etc. So final range would be 255-255.99. To represent
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)
@note OpenCV function is more faster than (around 40X) than np.histogram(). So stick with OpenCV
@note OpenCV function is faster than (around 40X) than np.histogram(). So stick with OpenCV
function.
Now we should plot histograms, but how?