>>> 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
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,
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)
>>> 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.
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?