FIx misc. source and comment typos

Found via `codespell -q 3 -S ./3rdparty,./modules -L amin,ang,atleast,dof,endwhile,hist,uint`
2019-08-14 13:33:49 -04:00
parent dbf8e22fad
commit 32aba5e64b
28 changed files with 36 additions and 36 deletions
@@ -253,8 +253,8 @@ Here is explained in detail the code for the real time application:
    @code{.cpp}
    RobustMatcher rmatcher;                                                          // instantiate RobustMatcher

-    cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints);       // instatiate ORB feature detector
-    cv::DescriptorExtractor * extractor = new cv::OrbDescriptorExtractor();          // instatiate ORB descriptor extractor
+    cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints);       // instantiate ORB feature detector
+    cv::DescriptorExtractor * extractor = new cv::OrbDescriptorExtractor();          // instantiate ORB descriptor extractor

    rmatcher.setFeatureDetector(detector);                                           // set feature detector
    rmatcher.setDescriptorExtractor(extractor);                                      // set descriptor extractor
@@ -29,8 +29,8 @@ This distance is equivalent to count the number of different elements for binary

 To filter the matches, Lowe proposed in @cite Lowe:2004:DIF:993451.996342 to use a distance ratio test to try to eliminate false matches.
 The distance ratio between the two nearest matches of a considered keypoint is computed and it is a good match when this value is below
-a thresold. Indeed, this ratio allows helping to discriminate between ambiguous matches (distance ratio between the two nearest neighbors is
-close to one) and well discriminated matches. The figure below from the SIFT paper illustrates the probability that a match is correct
+a threshold. Indeed, this ratio allows helping to discriminate between ambiguous matches (distance ratio between the two nearest neighbors
+is close to one) and well discriminated matches. The figure below from the SIFT paper illustrates the probability that a match is correct
 based on the nearest-neighbor distance ratio test.

 ![](images/Feature_FlannMatcher_Lowe_ratio_test.png)
@@ -39,7 +39,7 @@ With G-API, we can define it as follows:
 It is important to understand that the new G-API based version of
 calcGST() will just produce a compute graph, in contrast to its
 original version, which actually calculates the values. This is a
-principial difference -- G-API based functions like this are used to
+principal difference -- G-API based functions like this are used to
 construct graphs, not to process the actual data.

 Let's start implementing calcGST() with calculation of \f$J\f$
@@ -186,7 +186,7 @@ is also OpenCV-based since it fallbacks to OpenCV functions inside.

 On GNU/Linux, application memory footprint can be profiled with
 [Valgrind](http://valgrind.org/). On Debian/Ubuntu systems it can be
-installed like this (assuming you have administrator priveleges):
+installed like this (assuming you have administrator privileges):

    $ sudo apt-get install valgrind massif-visualizer

@@ -239,10 +239,10 @@ consumption is because the default naive OpenCV-based backend is used to
 execute this graph. This backend serves mostly for quick prototyping
 and debugging algorithms before offload/further optimization.

-This backend doesn't utilize any complex memory mamagement strategies yet
+This backend doesn't utilize any complex memory management strategies yet
 since it is not its point at the moment. In the following chapter,
 we'll learn about Fluid backend and see how the same G-API code can
-run in a completely different model (and the footprint shrinked to a
+run in a completely different model (and the footprint shrunk to a
 number of kilobytes).

 # Backends and kernels {#gapi_anisotropic_backends}
@@ -298,7 +298,7 @@ as a _graph compilation option_:

@snippet cpp/tutorial_code/gapi/porting_anisotropic_image_segmentation/porting_anisotropic_image_segmentation_gapi_fluid.cpp kernel_pkg_use

-Traditional OpenCV is logically divided into modules, whith every
+Traditional OpenCV is logically divided into modules, with every
 module providing a set of functions. In G-API, there are also
 "modules" which are represented as kernel packages provided by a
 particular backend. In this example, we pass Fluid kernel packages to
@@ -375,7 +375,7 @@ left side of the dump) is easily noticeable.
 The visualization reflects how G-API deals with mixed graphs, also
 called _heterogeneous_ graphs. The majority of operations in this
 graph are implemented with Fluid backend, but Box filters are executed
-by the OpenCV backend. One can easily see that the graph is partioned
+by the OpenCV backend. One can easily see that the graph is partitioned
 (with rectangles). G-API groups connected operations based on their
 affinity, forming _subgraphs_ (or _islands_ in G-API terminology), and
 our top-level graph becomes a composition of multiple smaller
@@ -15,7 +15,7 @@ The primary objectives for this tutorial:

 -   How to use OpenCV [imread](@ref imread) to load satellite imagery.
 -   How to use OpenCV [imread](@ref imread) to load SRTM Digital Elevation Models
-   Given the corner coordinates of both the image and DEM, correllate the elevation data to the
+-   Given the corner coordinates of both the image and DEM, correlate the elevation data to the
    image to find elevations for each pixel.
 -   Show a basic, easy-to-implement example of a terrain heat map.
 -   Show a basic use of DEM data coupled with ortho-rectified imagery.
@@ -157,7 +157,7 @@ already known by now.
    -   *src*: Source image
    -   *dst*: Destination image
    -   *Size(w, h)*: The size of the kernel to be used (the neighbors to be considered). \f$w\f$ and
-        \f$h\f$ have to be odd and positive numbers otherwise thi size will be calculated using the
+        \f$h\f$ have to be odd and positive numbers otherwise the size will be calculated using the
        \f$\sigma_{x}\f$ and \f$\sigma_{y}\f$ arguments.
    -   \f$\sigma_{x}\f$: The standard deviation in x. Writing \f$0\f$ implies that \f$\sigma_{x}\f$ is
        calculated using kernel size.
@@ -30,7 +30,7 @@ Two of the most basic morphological operations are dilation and erosion. Dilatio

    ![Dilation on a Grayscale Image](images/morph6.gif)

-   __Erosion__: The vise versa applies for the erosion operation. The value of the output pixel is the <b><em>minimum</em></b> value of all the pixels that fall within the structuring element's size and shape. Look the at the example figures below:
+-   __Erosion__: The vice versa applies for the erosion operation. The value of the output pixel is the <b><em>minimum</em></b> value of all the pixels that fall within the structuring element's size and shape. Look the at the example figures below:

    ![Erosion on a Binary Image](images/morph211.png)

@@ -189,7 +189,7 @@ brief->compute(gray, query_kpts, query_desc); //Compute brief descriptors at eac

 OpenCL {#tutorial_transition_hints_opencl}
 ------
-All specialized `ocl` implemetations has been hidden behind general C++ algorithm interface. Now the function execution path can be selected dynamically at runtime: CPU or OpenCL; this mechanism is also called "Transparent API".
+All specialized `ocl` implementations has been hidden behind general C++ algorithm interface. Now the function execution path can be selected dynamically at runtime: CPU or OpenCL; this mechanism is also called "Transparent API".

 New class cv::UMat is intended to hide data exchange with OpenCL device in a convenient way.

@@ -101,7 +101,7 @@ using namespace cv;
 }
@endcode
 In this case, we initialize the camera and provide the imageView as a target for rendering each
-frame. CvVideoCamera is basically a wrapper around AVFoundation, so we provie as properties some of
+frame. CvVideoCamera is basically a wrapper around AVFoundation, so we provide as properties some of
 the AVFoundation camera options. For example we want to use the front camera, set the video size to
 352x288 and a video orientation (the video camera normally outputs in landscape mode, which results
 in transposed data when you design a portrait application).
@@ -13,7 +13,7 @@ In this tutorial you will learn how to:
 Motivation
 ----------

-Why is it interesting to extend the SVM optimation problem in order to handle non-linearly separable
+Why is it interesting to extend the SVM optimization problem in order to handle non-linearly separable
 training data? Most of the applications in which SVMs are used in computer vision require a more
 powerful tool than a simple linear classifier. This stems from the fact that in these tasks __the
 training data can be rarely separated using an hyperplane__.
@@ -113,7 +113,7 @@ This tutorial code's is shown lines below. You can also download it from
 Result
 ------

-#  Here is the result of running the code above and using as input the video stream of a build-in
+-#  Here is the result of running the code above and using as input the video stream of a built-in
    webcam:

    ![](images/Cascade_Classifier_Tutorial_Result_Haar.jpg)
@@ -15,7 +15,7 @@ Optical Flow
 ------------

 Optical flow is the pattern of apparent motion of image objects between two consecutive frames
-caused by the movemement of object or camera. It is 2D vector field where each vector is a
+caused by the movement of object or camera. It is 2D vector field where each vector is a
 displacement vector showing the movement of points from first frame to second. Consider the image
 below (Image Courtesy: [Wikipedia article on Optical Flow](http://en.wikipedia.org/wiki/Optical_flow)).