Fix spelling typos

Author: bwignall

apps/createsamples/utility.cpp

@@ -1078,8 +1078,8 @@ void cvCreateTrainingSamples( const char* filename,
                 icvPlaceDistortedSample( sample, inverse, maxintensitydev,
                     maxxangle, maxyangle, maxzangle,
                     0   /* nonzero means placing image without cut offs */,
-                    0.0 /* nozero adds random shifting                  */,
-                    0.0 /* nozero adds random scaling                   */,
+                    0.0 /* nonzero adds random shifting                  */,
+                    0.0 /* nonzero adds random scaling                   */,
                     &data );
 
                 if( showsamples )

apps/traincascade/HOGfeatures.h

@@ -45,7 +45,7 @@ class CvHOGEvaluator : public CvFeatureEvaluator
     };
     std::vector<Feature> features;
 
-    cv::Mat normSum; //for nomalization calculation (L1 or L2)
+    cv::Mat normSum; //for normalization calculation (L1 or L2)
     std::vector<cv::Mat> hist;
 };
 
@@ -70,7 +70,7 @@ inline float CvHOGEvaluator::Feature::calc( const std::vector<cv::Mat>& _hists,
 
     const float *pnormSum = _normSum.ptr<float>((int)y);
     normFactor = (float)(pnormSum[fastRect[0].p0] - pnormSum[fastRect[1].p1] - pnormSum[fastRect[2].p2] + pnormSum[fastRect[3].p3]);
-    res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which apper due to floating precision
+    res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which appear due to floating precision
 
     return res;
 }

doc/js_tutorials/js_imgproc/js_contours/js_contours_hierarchy/js_contours_hierarchy.markdown

@@ -145,7 +145,7 @@ no child, parent is contour-3. So array is [-1,-1,-1,3].
 And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
 hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.
 
-For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
+For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
 result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
 give the hierarchy order.
 

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -17,7 +17,7 @@ In short, we found locations of some parts of an object in another cluttered ima
 is sufficient to find the object exactly on the trainImage.
 
 For that, we can use a function from calib3d module, ie **cv.findHomography()**. If we pass the set
-of points from both the images, it will find the perpective transformation of that object. Then we
+of points from both the images, it will find the perspective transformation of that object. Then we
 can use **cv.perspectiveTransform()** to find the object. It needs atleast four correct points to
 find the transformation.
 
@@ -68,7 +68,7 @@ Now we set a condition that atleast 10 matches (defined by MIN_MATCH_COUNT) are
 find the object. Otherwise simply show a message saying not enough matches are present.
 
 If enough matches are found, we extract the locations of matched keypoints in both the images. They
-are passed to find the perpective transformation. Once we get this 3x3 transformation matrix, we use
+are passed to find the perspective transformation. Once we get this 3x3 transformation matrix, we use
 it to transform the corners of queryImage to corresponding points in trainImage. Then we draw it.
 @code{.py}
 if len(good)>MIN_MATCH_COUNT:

doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown

@@ -28,7 +28,7 @@ If it is a greater than a threshold value, it is considered as a corner. If we p
 ![image](images/shitomasi_space.png)
 
 From the figure, you can see that only when \f$\lambda_1\f$ and \f$\lambda_2\f$ are above a minimum value,
-\f$\lambda_{min}\f$, it is conidered as a corner(green region).
+\f$\lambda_{min}\f$, it is considered as a corner(green region).
 
 Code
 ----

doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown

@@ -144,7 +144,7 @@ cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
 ### 7.b. Rotated Rectangle
 
 Here, bounding rectangle is drawn with minimum area, so it considers the rotation also. The function
-used is **cv.minAreaRect()**. It returns a Box2D structure which contains following detals - (
+used is **cv.minAreaRect()**. It returns a Box2D structure which contains following details - (
 center (x,y), (width, height), angle of rotation ). But to draw this rectangle, we need 4 corners of
 the rectangle. It is obtained by the function **cv.boxPoints()**
 @code{.py}

doc/py_tutorials/py_imgproc/py_contours/py_contours_hierarchy/py_contours_hierarchy.markdown

@@ -185,7 +185,7 @@ array([[[ 3, -1,  1, -1],
 And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
 hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.
 
-For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
+For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
 result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
 give the hierarchy order.
 

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

@@ -381,7 +381,7 @@ Here is explained in detail the code for the real time application:
     as not, there are false correspondences or also called *outliers*. The [Random Sample
     Consensus](http://en.wikipedia.org/wiki/RANSAC) or *Ransac* is a non-deterministic iterative
     method which estimate parameters of a mathematical model from observed data producing an
-    approximate result as the number of iterations increase. After appyling *Ransac* all the *outliers*
+    approximate result as the number of iterations increase. After applying *Ransac* all the *outliers*
     will be eliminated to then estimate the camera pose with a certain probability to obtain a good
     solution.
 

doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown

@@ -153,7 +153,7 @@ file name before running the application, e.g.:
 
     $ GRAPH_DUMP_PATH=segm.dot ./bin/example_tutorial_porting_anisotropic_image_segmentation_gapi
 
-Now this file can be visalized with a `dot` command like this:
+Now this file can be visualized with a `dot` command like this:
 
     $ dot segm.dot -Tpng -o segm.png
 
@@ -368,7 +368,7 @@ visualization like this:
 
 ![Anisotropic image segmentation graph with OpenCV & Fluid kernels](pics/segm_fluid.gif)
 
-This graph doesn't differ structually from its previous version (in
+This graph doesn't differ structurally from its previous version (in
 terms of operations and data objects), though a changed layout (on the
 left side of the dump) is easily noticeable.
 

doc/tutorials/gapi/face_beautification/face_beautification.markdown

@@ -427,7 +427,7 @@ the ROI, which will lead to accuracy improvement.
 Unfortunately, another problem occurs if we do that:
 if the rectangular ROI is near the border, a describing square will probably go
 out of the frame --- that leads to errors of the landmarks detector.
-To aviod such a mistake, we have to implement an algorithm that, firstly,
+To avoid such a mistake, we have to implement an algorithm that, firstly,
 describes every rectangle by a square, then counts the farthest coordinates
 turned up to be outside of the frame and, finally, pads the source image by
 borders (e.g. single-colored) with the size counted. It will be safe to take