Misc. ./samples typos

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

samples/cpp/cloning_demo.cpp

@@ -15,7 +15,7 @@
 * 6- Texture Flattening
 
 * The program takes as input a source and a destination image (for 1-3 methods)
-* and ouputs the cloned image.
+* and outputs the cloned image.
 *
 * Download test images from opencv_extra folder @github.
 *

samples/cpp/cloning_gui.cpp

@@ -15,7 +15,7 @@
 * 6- Texture Flattening
 
 * The program takes as input a source and a destination image (for 1-3 methods)
-* and ouputs the cloned image.
+* and outputs the cloned image.
 
 * Step 1:
 * -> In the source image, select the region of interest by left click mouse button. A Polygon ROI will be created by left clicking mouse button.
@@ -447,7 +447,7 @@ int main()
     }
     else
     {
-        cout << "Wrong Option Choosen" << endl;
+        cout << "Wrong Option Chosen" << endl;
         exit(1);
     }
 

samples/cpp/cout_mat.cpp

@@ -17,7 +17,7 @@ static void help()
     << "\n------------------------------------------------------------------\n"
     << " This program shows the serial out capabilities of cv::Mat\n"
     << "That is, cv::Mat M(...); cout << M;  Now works.\n"
-    << "Output can be formated to OpenCV, matlab, python, numpy, csv and \n"
+    << "Output can be formatted to OpenCV, matlab, python, numpy, csv and \n"
     << "C styles Usage:\n"
     << "./cvout_sample\n"
     << "------------------------------------------------------------------\n\n"

samples/cpp/create_mask.cpp

@@ -5,7 +5,7 @@
 * Siddharth Kherada <siddharthkherada27[at]gmail[dot]com>
 *
 * This tutorial demonstrates how to make mask image (black and white).
-* The program takes as input a source image and ouputs its corresponding
+* The program takes as input a source image and outputs its corresponding
 * mask image.
 */
 

samples/cpp/delaunay2.cpp

@@ -7,8 +7,8 @@ using namespace std;
 
 static void help()
 {
-    cout << "\nThis program demostrates iterative construction of\n"
-           "delaunay triangulation and voronoi tesselation.\n"
+    cout << "\nThis program demonstrates iterative construction of\n"
+           "delaunay triangulation and voronoi tessellation.\n"
            "It draws a random set of points in an image and then delaunay triangulates them.\n"
            "Usage: \n"
            "./delaunay \n"

samples/cpp/detect_blob.cpp

@@ -118,7 +118,7 @@ int main(int argc, char *argv[])
     help();
 
 
-    // This descriptor are going to be detect and compute BLOBS with 6 differents params
+    // These descriptors are going to be detecting and computing BLOBS with 6 different params
     // Param for first BLOB detector we want all
     typeDesc.push_back("BLOB");    // see http://docs.opencv.org/trunk/d0/d7a/classcv_1_1SimpleBlobDetector.html
     pBLOB.push_back(pDefaultBLOB);

samples/cpp/filestorage.cpp

@@ -18,8 +18,8 @@ static void help(char** av)
   cout << "\nfilestorage_sample demonstrate the usage of the opencv serialization functionality.\n"
       << "usage:\n"
       <<  av[0] << " outputfile.yml.gz\n"
-      << "\n   outputfile above can have many different extenstions, see below."
-      << "\nThis program demonstrates the use of FileStorage for serialization, that is use << and >>  in OpenCV\n"
+      << "\n   outputfile above can have many different extensions, see below."
+      << "\nThis program demonstrates the use of FileStorage for serialization, that is in use << and >>  in OpenCV\n"
       << "For example, how to create a class and have it serialize, but also how to use it to read and write matrices.\n"
       << "FileStorage allows you to serialize to various formats specified by the file end type."
           << "\nYou should try using different file extensions.(e.g. yaml yml xml xml.gz yaml.gz etc...)\n" << endl;

samples/cpp/fitellipse.cpp

@@ -16,7 +16,7 @@
  *
  *
  *  Original Author:  Denis Burenkov
- *  AMS and Direct Methods Autor:  Jasper Shemilt
+ *  AMS and Direct Methods Author:  Jasper Shemilt
  *
  *
  ********************************************************************************/
@@ -219,8 +219,8 @@ int main( int argc, char** argv )
     return 0;
 }
 
-// Define trackbar callback functon. This function find contours,
-// draw it and approximate it by ellipses.
+// Define trackbar callback function. This function finds contours,
+// draws them, and approximates by ellipses.
 void processImage(int /*h*/, void*)
 {
     RotatedRect box, boxAMS, boxDirect;

samples/cpp/image_alignment.cpp

@@ -60,7 +60,7 @@ const std::string keys =
 static void help(void)
 {
 
-    cout << "\nThis file demostrates the use of the ECC image alignment algorithm. When one image"
+    cout << "\nThis file demonstrates the use of the ECC image alignment algorithm. When one image"
         " is given, the template image is artificially formed by a random warp. When both images"
         " are given, the initialization of the warp by command line parsing is possible. "
         "If inputWarp is missing, the identity transformation initializes the algorithm. \n" << endl;

samples/cpp/intelperc_capture.cpp

@@ -36,7 +36,7 @@ static void printUsage(const char *arg0)
     cout << "   -isp=IDX,       set profile index of the image stream" << endl;
     cout << "   -dsp=IDX,       set profile index of the depth stream" << endl;
     cout << "   -ir,            show data from IR stream" << endl;
-    cout << "   -imb=VAL,       set brighness value for a image stream" << endl;
+    cout << "   -imb=VAL,       set brightness value for an image stream" << endl;
     cout << "   -imc=VAL,       set contrast value for a image stream" << endl;
     cout << "   -pts,           print frame index and frame time" << endl;
     cout << "   --show-closed,  print frame index and frame time" << endl;
@@ -307,7 +307,7 @@ int main(int argc, char* argv[])
         return 0;
     }
 
-    //Setup additional properies only after set profile of the stream
+    //Setup additional properties only after set profile of the stream
     if ( (-10000.0 < g_imageBrightness) && (g_imageBrightness < 10000.0))
         capture.set(CAP_INTELPERC_IMAGE_GENERATOR | CAP_PROP_BRIGHTNESS, g_imageBrightness);
     if ( (0 < g_imageContrast) && (g_imageContrast < 10000.0))

samples/cpp/matchmethod_orb_akaze_brisk.cpp

@@ -164,7 +164,7 @@ int main(int argc, char *argv[])
     }
     int i=0;
     cout << "Cumulative distance between keypoint match for different algorithm and feature detector \n\t";
-    cout << "We cannot say which is the best but we can say results are differents! \n\t";
+    cout << "We cannot say which is the best but we can say results are different! \n\t";
     for (vector<String>::iterator itMatcher = typeAlgoMatch.begin(); itMatcher != typeAlgoMatch.end(); ++itMatcher)
     {
         cout<<*itMatcher<<"\t";

samples/cpp/select3dobj.cpp

@@ -1,7 +1,7 @@
 /*
  *
  * select3obj.cpp With a calibration chessboard on a table, mark an object in a 3D box and
- *                track that object in all subseqent frames as long as the camera can see
+ *                track that object in all subsequent frames as long as the camera can see
  *                the chessboard. Also segments the object using the box projection. This
  *                program is useful for collecting large datasets of many views of an object
  *                on a table.
@@ -42,11 +42,11 @@ const char* helphelp =
 "\n"
 "Using a camera's intrinsics (from calibrating a camera -- see calibration.cpp) and an\n"
 "image of the object sitting on a planar surface with a calibration pattern of\n"
-"(board_width x board_height) on the surface, we draw a 3D box aroung the object. From\n"
+"(board_width x board_height) on the surface, we draw a 3D box around the object. From\n"
 "then on, we can move a camera and as long as it sees the chessboard calibration pattern,\n"
-"it will store a mask of where the object is. We get succesive images using <output_prefix>\n"
+"it will store a mask of where the object is. We get successive images using <output_prefix>\n"
 "of the segmentation mask containing the object. This makes creating training sets easy.\n"
-"It is best of the chessboard is odd x even in dimensions to avoid amiguous poses.\n"
+"It is best if the chessboard is odd x even in dimensions to avoid ambiguous poses.\n"
 "\n"
 "The actions one can use while the program is running are:\n"
 "\n"

samples/cpp/shape_example.cpp

@@ -16,7 +16,7 @@ using namespace cv;
 static void help()
 {
     printf("\n"
-            "This program demonstrates a method for shape comparisson based on Shape Context\n"
+            "This program demonstrates a method for shape comparison based on Shape Context\n"
             "You should run the program providing a number between 1 and 20 for selecting an image in the folder ../data/shape_sample.\n"
             "Call\n"
             "./shape_example [number between 1 and 20, 1 default]\n\n");

samples/cpp/travelsalesman.cpp

@@ -18,7 +18,7 @@ public:
     }
     /** Give energy value for a state of system.*/
     double energy() const;
-    /** Function which change the state of system (random pertubation).*/
+    /** Function which change the state of system (random perturbation).*/
     void changeState();
     /** Function to reverse to the previous state.*/
     void reverseState();

samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp

@@ -91,7 +91,7 @@ int main(int argc, char** argv)
     char key = 0;
     while(key != 'q' && key != 'Q')
     {
-        // those paramaters cannot be =0
+        // those parameters cannot be =0
         // so we must check here
         cannyThreshold = std::max(cannyThreshold, 1);
         accumulatorThreshold = std::max(accumulatorThreshold, 1);

samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp

@@ -96,7 +96,7 @@ void goodFeaturesToTrack_Demo( int, void* )
   namedWindow( source_window, WINDOW_AUTOSIZE );
   imshow( source_window, copy );
 
-  /// Set the neeed parameters to find the refined corners
+  /// Set the needed parameters to find the refined corners
   Size winSize = Size( 5, 5 );
   Size zeroZone = Size( -1, -1 );
   TermCriteria criteria = TermCriteria( TermCriteria::EPS + TermCriteria::COUNT, 40, 0.001 );

samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_detection.cpp

@@ -84,7 +84,7 @@ int main(int argc, char *argv[])
       "{keypoints k   |2000  | number of keypoints to detect        }"
       "{ratio r       |0.7   | threshold for ratio test             }"
       "{iterations it |500   | RANSAC maximum iterations count      }"
-      "{error e       |2.0   | RANSAC reprojection errror           }"
+      "{error e       |2.0   | RANSAC reprojection error            }"
       "{confidence c  |0.95  | RANSAC confidence                    }"
       "{inliers in    |30    | minimum inliers for Kalman update    }"
       "{method  pnp   |0     | PnP method: (0) ITERATIVE - (1) EPNP - (2) P3P - (3) DLS}"

samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp

@@ -13,7 +13,7 @@ static void help( char* progName)
 {
     cout << endl << progName
         << " shows how to use cv::Mat and IplImages together (converting back and forth)." << endl
-        << "Also contains example for image read, spliting the planes, merging back and "  << endl
+        << "Also contains example for image read, splitting the planes, merging back and " << endl
         << " color conversion, plus iterating through pixels. "                            << endl
         << "Usage:" << endl
         << progName << " [image-name Default: ../data/lena.jpg]"                   << endl << endl;

samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp

@@ -9,14 +9,14 @@ using namespace cv;
 static void help()
 {
     cout
-    << "\n--------------------------------------------------------------------------" << endl
+    << "\n---------------------------------------------------------------------------" << endl
     << "This program shows how to create matrices(cv::Mat) in OpenCV and its serial"
     << " out capabilities"                                                             << endl
     << "That is, cv::Mat M(...); M.create and cout << M. "                             << endl
-    << "Shows how output can be formated to OpenCV, python, numpy, csv and C styles." << endl
+    << "Shows how output can be formatted to OpenCV, python, numpy, csv and C styles." << endl
     << "Usage:"                                                                        << endl
     << "./mat_the_basic_image_container"                                               << endl
-    << "--------------------------------------------------------------------------"   << endl
+    << "-----------------------------------------------------------------------------" << endl
     << endl;
 }
 
@@ -76,7 +76,7 @@ int main(int,char**)
     randu(R, Scalar::all(0), Scalar::all(255));
     //! [random]
 
-    // Demonstrate the output formating options
+    // Demonstrate the output formatting options
     //! [out-default]
     cout << "R (default) = " << endl <<        R           << endl << endl;
     //! [out-default]

samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp

@@ -15,7 +15,7 @@
 * 6- Texture Flattening
 
 * The program takes as input a source and a destination image (for 1-3 methods)
-* and ouputs the cloned image.
+* and outputs the cloned image.
 *
 * Download test images from opencv_extra folder @github.
 *

samples/cpp/tutorial_code/photo/seamless_cloning/cloning_gui.cpp

@@ -15,7 +15,7 @@
 * 6- Texture Flattening
 
 * The program takes as input a source and a destination image (for 1-3 methods)
-* and ouputs the cloned image.
+* and outputs the cloned image.
 
 * Step 1:
 * -> In the source image, select the region of interest by left click mouse button. A Polygon ROI will be created by left clicking mouse button.
@@ -446,7 +446,7 @@ int main()
     }
     else
     {
-        cout << "Wrong Option Choosen" << endl;
+        cout << "Wrong Option Chosen" << endl;
         exit(0);
     }
 

samples/cpp/videostab.cpp

@@ -131,7 +131,7 @@ void printHelp()
             "  --mi-dist-thresh=<float_number>\n"
             "      Estimated flow distance threshold for motion inpainting. The default is 5.0.\n\n"
             "  -ci=, --color-inpaint=(no|average|ns|telea)\n"
-            "      Do color inpainting. The defailt is no.\n"
+            "      Do color inpainting. The default is no.\n"
             "  --ci-radius=<float_number>\n"
             "      Set color inpainting radius (for ns and telea options only).\n"
             "      The default is 2.0\n\n"
@@ -163,9 +163,9 @@ void printHelp()
             "  -gpu=(yes|no)\n"
             "      Use CUDA optimization whenever possible. The default is no.\n\n"
             "  -o=, --output=(no|<file_path>)\n"
-            "      Set output file path explicitely. The default is stabilized.avi.\n"
+            "      Set output file path explicitly. The default is stabilized.avi.\n"
             "  --fps=(<float_number>|auto)\n"
-            "      Set output video FPS explicitely. By default the source FPS is used (auto).\n"
+            "      Set output video FPS explicitly. By default the source FPS is used (auto).\n"
             "  -q, --quiet\n"
             "      Don't show output video frames.\n\n"
             "  -h, --help\n"
@@ -487,7 +487,7 @@ int main(int argc, const char **argv)
             stabilizer->setDeblurer(deblurer);
         }
 
-        // set up trimming paramters
+        // set up trimming parameters
         stabilizer->setTrimRatio(argf("trim-ratio"));
         stabilizer->setCorrectionForInclusion(arg("incl-constr") == "yes");
 

samples/dnn/face_detector/how_to_train_face_detector.txt

@@ -1,6 +1,6 @@
 This is a brief description of training process which has been used to get res10_300x300_ssd_iter_140000.caffemodel.
 The model was created with SSD framework using ResNet-10 like architecture as a backbone. Channels count in ResNet-10 convolution layers was significantly dropped (2x- or 4x- fewer channels).
-The model was trained in Caffe framework on some huge and avaliable online dataset.
+The model was trained in Caffe framework on some huge and available online dataset.
 
 1. Prepare training tools
 You need to use "ssd" branch from this repository https://github.com/weiliu89/caffe/tree/ssd . Checkout this branch and built it (see instructions in repo's README)

samples/dnn/mobilenet_ssd_python.py

@@ -16,7 +16,7 @@ try:
     import cv2 as cv
 except ImportError:
     raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
+                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
 
 inWidth = 300
 inHeight = 300

samples/dnn/resnet_ssd_face_python.py

@@ -5,7 +5,7 @@ try:
     import cv2 as cv
 except ImportError:
     raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
-                      'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
+                      'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
 
 from cv2 import dnn
 

samples/gpu/driver_api_multi.cpp

@@ -1,4 +1,4 @@
-/* This sample demonstrates the way you can perform independed tasks
+/* This sample demonstrates the way you can perform independent tasks
    on the different GPUs */
 
 // Disable some warnings which are caused with CUDA headers

samples/gpu/generalized_hough.cpp

@@ -32,8 +32,8 @@ int main(int argc, const char* argv[])
         "{ minDist        | 100       | minimum distance between the centers of the detected objects }"
         "{ levels         | 360       | R-Table levels }"
         "{ votesThreshold | 30        | the accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected }"
-        "{ angleThresh    | 10000     | angle votes treshold }"
-        "{ scaleThresh    | 1000      | scale votes treshold }"
+        "{ angleThresh    | 10000     | angle votes threshold }"
+        "{ scaleThresh    | 1000      | scale votes threshold }"
         "{ posThresh      | 100       | position votes threshold }"
         "{ dp             | 2         | inverse ratio of the accumulator resolution to the image resolution }"
         "{ minScale       | 0.5       | minimal scale to detect }"
@@ -46,7 +46,7 @@ int main(int argc, const char* argv[])
         "{ help h ?       |           | print help message }"
     );
 
-    cmd.about("This program demonstrates arbitary object finding with the Generalized Hough transform.");
+    cmd.about("This program demonstrates arbitrary object finding with the Generalized Hough transform.");
 
     if (cmd.has("help"))
     {

samples/gpu/multi.cpp

@@ -1,4 +1,4 @@
-/* This sample demonstrates the way you can perform independed tasks
+/* This sample demonstrates the way you can perform independent tasks
    on the different GPUs */
 
 // Disable some warnings which are caused with CUDA headers

samples/gpu/stereo_multi.cpp

@@ -371,7 +371,7 @@ int main(int argc, char** argv)
         DeviceInfo devInfo(i);
         if (!devInfo.isCompatible())
         {
-            cerr << "CUDA module was't built for GPU #" << i << " ("
+            cerr << "CUDA module wasn't built for GPU #" << i << " ("
                  << devInfo.name() << ", CC " << devInfo.majorVersion()
                  << devInfo.minorVersion() << endl;
             return -1;

samples/python/mouse_and_match.py

@@ -67,7 +67,7 @@ if __name__ == '__main__':
     cv.setMouseCallback("gray", onmouse)
     '''Loop through all the images in the directory'''
     for infile in glob.glob( os.path.join(path, '*.*') ):
-        ext = os.path.splitext(infile)[1][1:] #get the filename extenstion
+        ext = os.path.splitext(infile)[1][1:] #get the filename extension
         if ext == "png" or ext == "jpg" or ext == "bmp" or ext == "tiff" or ext == "pbm":
             print(infile)
 

samples/python/plane_ar.py

@@ -5,7 +5,7 @@ Planar augmented reality
 ==================
 
 This sample shows an example of augmented reality overlay over a planar object
-tracked by PlaneTracker from plane_tracker.py. solvePnP funciton is used to
+tracked by PlaneTracker from plane_tracker.py. solvePnP function is used to
 estimate the tracked object location in 3d space.
 
 video: http://www.youtube.com/watch?v=pzVbhxx6aog

samples/python/video.py

@@ -8,7 +8,7 @@ frames from a camera of a movie file. Also the sample provides
 an example of procedural video generation by an object, mimicking
 the VideoCapture interface (see Chess class).
 
-'create_capture' is a convinience function for capture creation,
+'create_capture' is a convenience function for capture creation,
 falling back to procedural video in case of error.
 
 Usage:

samples/tapi/hog.cpp

@@ -223,7 +223,7 @@ void App::run()
 
             if (output!="" && write_once)
             {
-                if (img_source!="")     // wirte image
+                if (img_source!="")     // write image
                 {
                     write_once = false;
                     imwrite(output, img_to_show);

samples/winrt/ImageManipulations/AdvancedCapture.xaml.cpp

@@ -295,7 +295,7 @@ void AdvancedCapture::AddEffectToImageStream()
         Windows::Media::MediaProperties::IMediaEncodingProperties ^props = mediaCapture->VideoDeviceController->GetMediaStreamProperties(Windows::Media::Capture::MediaStreamType::Photo);
         if(props->Type->Equals("Image"))
         {
-            //Switch to a video media type instead since we cant add an effect to a image media type
+            //Switch to a video media type instead since we can't add an effect to an image media type
             Windows::Foundation::Collections::IVectorView<Windows::Media::MediaProperties::IMediaEncodingProperties^>^ supportedPropsList = mediaCapture->VideoDeviceController->GetAvailableMediaStreamProperties(Windows::Media::Capture::MediaStreamType::Photo);
             {
                 unsigned int i = 0;
@@ -565,7 +565,7 @@ void SDKSample::MediaCapture::AdvancedCapture::Button_Click(Platform::Object^ se
                     {
                         Windows::Media::MediaProperties::IMediaEncodingProperties ^props = mediaCapture->VideoDeviceController->GetMediaStreamProperties(Windows::Media::Capture::MediaStreamType::VideoRecord);
                         Windows::Media::MediaProperties::VideoEncodingProperties ^videoEncodingProperties  = static_cast<Windows::Media::MediaProperties::VideoEncodingProperties ^>(props);
-                        if(!videoEncodingProperties->Subtype->Equals("H264")) //Cant add an effect to an H264 stream
+                        if(!videoEncodingProperties->Subtype->Equals("H264")) //Can't add an effect to an H264 stream
                         {
                             task<void>(mediaCapture->AddEffectAsync(Windows::Media::Capture::MediaStreamType::VideoRecord,"OcvTransform.OcvImageManipulations", nullptr)).then([this](task<void> effectTask2)
                             {

samples/winrt/ImageManipulations/MainPage.xaml

@@ -61,7 +61,7 @@
                     <Grid Grid.Row="1">
 
                         <!-- All XAML in this section is purely for design time so you can see sample content in the designer. -->
-                        <!-- This will be repaced at runtime by live content.                                                  -->
+                        <!-- This will be replaced at runtime by live content.                                                 -->
                         <Grid>
                             <Grid.RowDefinitions>
                                 <RowDefinition Height="Auto"/>

samples/winrt/ImageManipulations/common/suspensionmanager.cpp

@@ -244,7 +244,7 @@ task<void> SuspensionManager::SaveAsync(void)
 /// state, which in turn gives their active <see cref="Page"/> an opportunity restore its
 /// state.
 /// </summary>
-/// <param name="version">A version identifer compared to the session state to prevent
+/// <param name="version">A version identifier compared to the session state to prevent
 /// incompatible versions of session state from reaching app code.  Saved state with a
 /// different version will be ignored, resulting in an empty <see cref="SessionState"/>
 /// dictionary.</param>