Misc. ./apps ./doc ./platoforms typos

Found via `codespell -q 3 --skip="./3rdparty" -I ../opencv-whitelist.txt`

doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown

@@ -82,7 +82,7 @@ Non-maximum Suppression.
 
 It is several times faster than other existing corner detectors.
 
-But it is not robust to high levels of noise. It is dependant on a threshold.
+But it is not robust to high levels of noise. It is dependent on a threshold.
 
 FAST Feature Detector in OpenCV
 -------------------------------

doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown

@@ -25,7 +25,7 @@ used.
 Second param is boolean variable, crossCheck which is false by default. If it is true, Matcher
 returns only those matches with value (i,j) such that i-th descriptor in set A has j-th descriptor
 in set B as the best match and vice-versa. That is, the two features in both sets should match each
-other. It provides consistant result, and is a good alternative to ratio test proposed by D.Lowe in
+other. It provides consistent result, and is a good alternative to ratio test proposed by D.Lowe in
 SIFT paper.
 
 Once it is created, two important methods are *BFMatcher.match()* and *BFMatcher.knnMatch()*. First
@@ -164,7 +164,7 @@ Second dictionary is the SearchParams. It specifies the number of times the tree
 should be recursively traversed. Higher values gives better precision, but also takes more time. If
 you want to change the value, pass search_params = dict(checks=100).
 
-With these informations, we are good to go.
+With this information, we are good to go.
 @code{.py}
 import numpy as np
 import cv2 as cv

doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown

@@ -39,7 +39,7 @@ grayscale image. Then you specify number of corners you want to find. Then you s
 level, which is a value between 0-1, which denotes the minimum quality of corner below which
 everyone is rejected. Then we provide the minimum euclidean distance between corners detected.
 
-With all these informations, the function finds corners in the image. All corners below quality
+With all this information, the function finds corners in the image. All corners below quality
 level are rejected. Then it sorts the remaining corners based on quality in the descending order.
 Then function takes first strongest corner, throws away all the nearby corners in the range of
 minimum distance and returns N strongest corners.

doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown

@@ -35,7 +35,7 @@ different scale. It is OK with small corner. But to detect larger corners we nee
 For this, scale-space filtering is used. In it, Laplacian of Gaussian is found for the image with
 various \f$\sigma\f$ values. LoG acts as a blob detector which detects blobs in various sizes due to
 change in \f$\sigma\f$. In short, \f$\sigma\f$ acts as a scaling parameter. For eg, in the above image,
-gaussian kernel with low \f$\sigma\f$ gives high value for small corner while guassian kernel with high
+gaussian kernel with low \f$\sigma\f$ gives high value for small corner while gaussian kernel with high
 \f$\sigma\f$ fits well for larger corner. So, we can find the local maxima across the scale and space
 which gives us a list of \f$(x,y,\sigma)\f$ values which means there is a potential keypoint at (x,y) at
 \f$\sigma\f$ scale.
@@ -66,7 +66,7 @@ the intensity at this extrema is less than a threshold value (0.03 as per the pa
 rejected. This threshold is called **contrastThreshold** in OpenCV
 
 DoG has higher response for edges, so edges also need to be removed. For this, a concept similar to
-Harris corner detector is used. They used a 2x2 Hessian matrix (H) to compute the pricipal
+Harris corner detector is used. They used a 2x2 Hessian matrix (H) to compute the principal
 curvature. We know from Harris corner detector that for edges, one eigen value is larger than the
 other. So here they used a simple function,
 
@@ -79,7 +79,7 @@ points.
 ### 3. Orientation Assignment
 
 Now an orientation is assigned to each keypoint to achieve invariance to image rotation. A
-neigbourhood is taken around the keypoint location depending on the scale, and the gradient
+neighbourhood is taken around the keypoint location depending on the scale, and the gradient
 magnitude and direction is calculated in that region. An orientation histogram with 36 bins covering
 360 degrees is created. (It is weighted by gradient magnitude and gaussian-weighted circular window
 with \f$\sigma\f$ equal to 1.5 times the scale of keypoint. The highest peak in the histogram is taken
@@ -89,7 +89,7 @@ with same location and scale, but different directions. It contribute to stabili
 ### 4. Keypoint Descriptor
 
 Now keypoint descriptor is created. A 16x16 neighbourhood around the keypoint is taken. It is
-devided into 16 sub-blocks of 4x4 size. For each sub-block, 8 bin orientation histogram is created.
+divided into 16 sub-blocks of 4x4 size. For each sub-block, 8 bin orientation histogram is created.
 So a total of 128 bin values are available. It is represented as a vector to form keypoint
 descriptor. In addition to this, several measures are taken to achieve robustness against
 illumination changes, rotation etc.

doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown

@@ -26,7 +26,7 @@ and location.
 ![image](images/surf_boxfilter.jpg)
 
 For orientation assignment, SURF uses wavelet responses in horizontal and vertical direction for a
-neighbourhood of size 6s. Adequate guassian weights are also applied to it. Then they are plotted in
+neighbourhood of size 6s. Adequate gaussian weights are also applied to it. Then they are plotted in
 a space as given in below image. The dominant orientation is estimated by calculating the sum of all
 responses within a sliding orientation window of angle 60 degrees. Interesting thing is that,
 wavelet response can be found out using integral images very easily at any scale. For many