FIx misc. source and comment typos

Found via `codespell -q 3 -S ./3rdparty,./modules -L amin,ang,atleast,dof,endwhile,hist,uint`

backporting of commit: 32aba5e64b
pull/15309/head
luz.paz 5 years ago committed by Alexander Alekhin
parent 13ecd5bb25
commit 57ccf14952
  1. 4
      apps/createsamples/utility.cpp
  2. 2
      cmake/FindCUDA/run_nvcc.cmake
  3. 2
      cmake/OpenCVCompilerOptimizations.cmake
  4. 2
      cmake/OpenCVDetectApacheAnt.cmake
  5. 2
      cmake/OpenCVDetectInferenceEngine.cmake
  6. 2
      cmake/OpenCVDetectPython.cmake
  7. 2
      cmake/OpenCVUtils.cmake
  8. 2
      cmake/android/android_ant_projects.cmake
  9. 2
      doc/js_tutorials/js_video/js_lucas_kanade/js_lucas_kanade.markdown
  10. 4
      doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown
  11. 4
      doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.markdown
  12. 2
      doc/tutorials/imgcodecs/raster-gdal/raster_io_gdal.markdown
  13. 2
      doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown
  14. 2
      doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md
  15. 2
      doc/tutorials/introduction/transition_guide/transition_guide.markdown
  16. 2
      doc/tutorials/ios/video_processing/video_processing.markdown
  17. 2
      doc/tutorials/ml/non_linear_svms/non_linear_svms.markdown
  18. 2
      doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown
  19. 2
      doc/tutorials/video/optical_flow/optical_flow.markdown
  20. 2
      samples/cpp/stitching_detailed.cpp
  21. 2
      samples/cpp/videocapture_gphoto2_autofocus.cpp
  22. 2
      samples/directx/d3d11_interop.cpp
  23. 2
      samples/dnn/face_detector/how_to_train_face_detector.txt
  24. 2
      samples/dnn/openpose.py
  25. 2
      samples/opencl/opencl-opencv-interop.cpp
  26. 2
      samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py
  27. 2
      samples/python/tutorial_code/objectDetection/cascade_classifier/objectDetection.py

@ -895,7 +895,7 @@ void icvGetNextFromBackgroundData( CvBackgroundData* data,
* #pragma omp parallel
* {
* ...
* icvGetBackgourndImage( cvbgdata, cvbgreader, img );
* icvGetBackgroundImage( cvbgdata, cvbgreader, img );
* ...
* }
* ...
@ -990,7 +990,7 @@ static int icvInitBackgroundReaders( const char* filename, Size winsize )
/*
* icvDestroyBackgroundReaders
*
* Finish backgournd reading process
* Finish background reading process
*/
static
void icvDestroyBackgroundReaders()

@ -136,7 +136,7 @@ macro(cuda_execute_process status command)
# copy and paste a runnable command line.
set(cuda_execute_process_string)
foreach(arg ${ARGN})
# If there are quotes, excape them, so they come through.
# If there are quotes, escape them, so they come through.
string(REPLACE "\"" "\\\"" arg ${arg})
# Args with spaces need quotes around them to get them to be parsed as a single argument.
if(arg MATCHES " ")

@ -854,7 +854,7 @@ macro(__ocv_add_dispatched_file filename target_src_var src_directory dst_direct
if(";${CPU_DISPATCH_FINAL};" MATCHES "${OPT}" OR __CPU_DISPATCH_INCLUDE_ALL)
if(EXISTS "${src_directory}/${filename}.${OPT_LOWER}.cpp")
message(STATUS "Using overrided ${OPT} source: ${src_directory}/${filename}.${OPT_LOWER}.cpp")
message(STATUS "Using overridden ${OPT} source: ${src_directory}/${filename}.${OPT_LOWER}.cpp")
else()
list(APPEND ${target_src_var} "${__file}")
endif()

@ -27,7 +27,7 @@ if(ANT_EXECUTABLE)
unset(ANT_EXECUTABLE CACHE)
else()
string(REGEX MATCH "[0-9]+.[0-9]+.[0-9]+" ANT_VERSION "${ANT_VERSION_FULL}")
set(ANT_VERSION "${ANT_VERSION}" CACHE INTERNAL "Detected ant vesion")
set(ANT_VERSION "${ANT_VERSION}" CACHE INTERNAL "Detected ant version")
message(STATUS "Found apache ant: ${ANT_EXECUTABLE} (${ANT_VERSION})")
endif()

@ -5,7 +5,7 @@
#
# Detect parameters:
# 1. Native cmake IE package:
# - enironment variable InferenceEngine_DIR is set to location of cmake module
# - environment variable InferenceEngine_DIR is set to location of cmake module
# 2. Custom location:
# - INF_ENGINE_INCLUDE_DIRS - headers search location
# - INF_ENGINE_LIB_DIRS - library search location

@ -249,7 +249,7 @@ if(NOT ${found})
# Export return values
set(${found} "${_found}" CACHE INTERNAL "")
set(${executable} "${_executable}" CACHE FILEPATH "Path to Python interpretor")
set(${executable} "${_executable}" CACHE FILEPATH "Path to Python interpreter")
set(${version_string} "${_version_string}" CACHE INTERNAL "")
set(${version_major} "${_version_major}" CACHE INTERNAL "")
set(${version_minor} "${_version_minor}" CACHE INTERNAL "")

@ -782,7 +782,7 @@ macro(ocv_check_modules define)
if(pkgcfg_lib_${define}_${_lib})
list(APPEND _libs "${pkgcfg_lib_${define}_${_lib}}")
else()
message(WARNING "ocv_check_modules(${define}): can't find library '${_lib}'. Specify 'pkgcfg_lib_${define}_${_lib}' manualy")
message(WARNING "ocv_check_modules(${define}): can't find library '${_lib}'. Specify 'pkgcfg_lib_${define}_${_lib}' manually")
list(APPEND _libs "${_lib}")
endif()
else()

@ -49,7 +49,7 @@ macro(android_get_compatible_target VAR)
list(GET ANDROID_SDK_TARGETS 0 __lvl)
string(REGEX MATCH "[0-9]+$" __lvl "${__lvl}")
#find minimal level mathing to all provided levels
#find minimal level matching to all provided levels
foreach(lvl ${ARGN})
string(REGEX MATCH "[0-9]+$" __level "${lvl}")
if(__level GREATER __lvl)

@ -13,7 +13,7 @@ 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)).

@ -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)

@ -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)

@ -191,7 +191,7 @@ 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)).

@ -581,7 +581,7 @@ int main(int argc, char* argv[])
vector<Size> sizes(num_images);
vector<UMat> masks(num_images);
// Preapre images masks
// Prepare images masks
for (int i = 0; i < num_images; ++i)
{
masks[i].create(images[i].size(), CV_8U);

@ -41,7 +41,7 @@ const int MAX_FOCUS_STEP = 32767;
const int FOCUS_DIRECTION_INFTY = 1;
const int DEFAULT_BREAK_LIMIT = 5;
const int DEFAULT_OUTPUT_FPS = 20;
const double epsylon = 0.0005; // compression, noice, etc.
const double epsylon = 0.0005; // compression, noise, etc.
struct Args_t
{

@ -83,7 +83,7 @@ public:
r = m_pD3D11SwapChain->GetBuffer(0, __uuidof(ID3D11Texture2D), (LPVOID*)&m_pBackBuffer);
if (FAILED(r))
{
throw std::runtime_error("GetBufer() failed!");
throw std::runtime_error("GetBuffer() failed!");
}
r = m_pD3D11Dev->CreateRenderTargetView(m_pBackBuffer, NULL, &m_pRenderTarget);

@ -67,7 +67,7 @@ You need to prepare 2 LMDB databases: one for training images, one for validatio
3. Train your detector
For training you need to have 3 files: train.prototxt, test.prototxt and solver.prototxt. You can find these files in the same directory as for this readme.
Also you need to edit train.prototxt and test.prototxt to replace paths for your LMDB databases to actual databases you've crated in step 2.
Also you need to edit train.prototxt and test.prototxt to replace paths for your LMDB databases to actual databases you've created in step 2.
Now all is done for launch training process.
Execute next lines in Terminal:

@ -88,7 +88,7 @@ while cv.waitKey(1) < 0:
points = []
for i in range(len(BODY_PARTS)):
# Slice heatmap of corresponging body's part.
# Slice heatmap of corresponding body's part.
heatMap = out[0, i, :, :]
# Originally, we try to find all the local maximums. To simplify a sample

@ -703,7 +703,7 @@ int App::process_frame_with_open_cl(cv::Mat& frame, bool use_buffer, cl_mem* mem
if (0 == mem || 0 == m_img_src)
{
// allocate/delete cl memory objects every frame for the simplicity.
// in real applicaton more efficient pipeline can be built.
// in real application more efficient pipeline can be built.
if (use_buffer)
{

@ -66,7 +66,7 @@ def on_high_V_thresh_trackbar(val):
cv.setTrackbarPos(high_V_name, window_detection_name, high_V)
parser = argparse.ArgumentParser(description='Code for Thresholding Operations using inRange tutorial.')
parser.add_argument('--camera', help='Camera devide number.', default=0, type=int)
parser.add_argument('--camera', help='Camera divide number.', default=0, type=int)
args = parser.parse_args()
## [cap]

@ -25,7 +25,7 @@ def detectAndDisplay(frame):
parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
parser.add_argument('--face_cascade', help='Path to face cascade.', default='data/haarcascades/haarcascade_frontalface_alt.xml')
parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
parser.add_argument('--camera', help='Camera devide number.', type=int, default=0)
parser.add_argument('--camera', help='Camera divide number.', type=int, default=0)
args = parser.parse_args()
face_cascade_name = args.face_cascade

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