From 0940573e8bc7a9b4355a6b112010802e2f70caa9 Mon Sep 17 00:00:00 2001 From: Vadim Pisarevsky Date: Thu, 7 Jul 2011 16:59:09 +0000 Subject: [PATCH] fixed a few more typos in the docs. --- .../camera_calibration_and_3d_reconstruction.rst | 2 +- modules/core/doc/clustering.rst | 6 ++--- modules/gpu/doc/object_detection.rst | 28 +++++++--------------- modules/highgui/doc/qt_new_functions.rst | 8 +++---- .../doc/reading_and_writing_images_and_video.rst | 5 ++++ modules/imgproc/doc/feature_detection.rst | 2 +- modules/ml/doc/boosting.rst | 4 ++-- 7 files changed, 23 insertions(+), 32 deletions(-) diff --git a/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst b/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst index 36ea2e8..d3d3332 100644 --- a/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst +++ b/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst @@ -1407,6 +1407,6 @@ The function computes the rectification transformations without knowing intrinsi .. [BouguetMCT] J.Y.Bouguet. MATLAB calibration tool. http://www.vision.caltech.edu/bouguetj/calib_doc/ -.. [Hartley99] Hartley, R.I., “Theory and Practice of Projective Rectification”. IJCV 35 2, pp 115-127 (1999) +.. [Hartley99] Hartley, R.I., Theory and Practice of Projective Rectification. IJCV 35 2, pp 115-127 (1999) .. [Zhang2000] Z. Zhang. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000. diff --git a/modules/core/doc/clustering.rst b/modules/core/doc/clustering.rst index 0f4734b..81bfb7a 100644 --- a/modules/core/doc/clustering.rst +++ b/modules/core/doc/clustering.rst @@ -58,9 +58,7 @@ partition ------------- Splits an element set into equivalency classes. -.. ocv:function:: template int - -.. ocv:function:: partition( const vector<_Tp>& vec, vector& labels, _EqPredicate predicate=_EqPredicate()) +.. ocv:function:: template int partition( const vector<_Tp>& vec, vector& labels, _EqPredicate predicate=_EqPredicate()) :param vec: Set of elements stored as a vector. @@ -76,4 +74,4 @@ http://en.wikipedia.org/wiki/Disjoint-set_data_structure . The function returns the number of equivalency classes. -.. [Arthur2007] Arthur and S. Vassilvitskii “k-means++: the advantages of careful seeding”, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007 +.. [Arthur2007] Arthur and S. Vassilvitskii. k-means++: the advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007 diff --git a/modules/gpu/doc/object_detection.rst b/modules/gpu/doc/object_detection.rst index 04571b2..43c20e2 100644 --- a/modules/gpu/doc/object_detection.rst +++ b/modules/gpu/doc/object_detection.rst @@ -67,12 +67,7 @@ Interfaces of all methods are kept similar to the ``CPU HOG`` descriptor and det gpu::HOGDescriptor::HOGDescriptor ------------------------------------- -.. ocv:function:: gpu::HOGDescriptor::HOGDescriptor(Size win_size=Size(64, 128), - Size block_size=Size(16, 16), Size block_stride=Size(8, 8), - Size cell_size=Size(8, 8), int nbins=9, - double win_sigma=DEFAULT_WIN_SIGMA, - double threshold_L2hys=0.2, bool gamma_correction=true, - int nlevels=DEFAULT_NLEVELS)??check the output?? +.. ocv:function:: gpu::HOGDescriptor::HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, double threshold_L2hys=0.2, bool gamma_correction=true, int nlevels=DEFAULT_NLEVELS) Creates the ``HOG`` descriptor and detector. @@ -114,7 +109,7 @@ gpu::HOGDescriptor::getBlockHistogramSize gpu::HOGDescriptor::setSVMDetector -------------------------------------- -.. ocv:function:: void gpu::HOGDescriptor::setSVMDetector(const vector\& detector) +.. ocv:function:: void gpu::HOGDescriptor::setSVMDetector(const vector& detector) Sets coefficients for the linear SVM classifier. @@ -146,9 +141,7 @@ gpu::HOGDescriptor::getPeopleDetector64x128 gpu::HOGDescriptor::detect ------------------------------ -.. ocv:function:: void gpu::HOGDescriptor::detect(const GpuMat\& img, - vector\& found_locations, double hit_threshold=0, - Size win_stride=Size(), Size padding=Size())??see output?? +.. ocv:function:: void gpu::HOGDescriptor::detect(const GpuMat& img, vector& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size()) Performs object detection without a multi-scale window. @@ -166,10 +159,7 @@ gpu::HOGDescriptor::detect gpu::HOGDescriptor::detectMultiScale ---------------------------------------- -.. ocv:function:: void gpu::HOGDescriptor::detectMultiScale(const GpuMat\& img, - vector\& found_locations, double hit_threshold=0, - Size win_stride=Size(), Size padding=Size(), - double scale0=1.05, int group_threshold=2)??the same?? +.. ocv:function:: void gpu::HOGDescriptor::detectMultiScale(const GpuMat& img, vector& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size(), double scale0=1.05, int group_threshold=2) Performs object detection with a multi-scale window. @@ -191,9 +181,7 @@ gpu::HOGDescriptor::detectMultiScale gpu::HOGDescriptor::getDescriptors -------------------------------------- -.. ocv:function:: void gpu::HOGDescriptor::getDescriptors(const GpuMat\& img, - Size win_stride, GpuMat\& descriptors, - int descr_format=DESCR_FORMAT_COL_BY_COL)?? the same?? +.. ocv:function:: void gpu::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL) Returns block descriptors computed for the whole image. The function is mainly used to learn the classifier. @@ -247,7 +235,7 @@ Cascade classifier class used for object detection. gpu::CascadeClassifier_GPU::CascadeClassifier_GPU ----------------------------------------------------- -.. ocv:function:: gpu::CascadeClassifier_GPU(const string\& filename) +.. ocv:function:: gpu::CascadeClassifier_GPU(const string& filename) Loads the classifier from a file. @@ -269,7 +257,7 @@ gpu::CascadeClassifier_GPU::empty gpu::CascadeClassifier_GPU::load ------------------------------------ -.. ocv:function:: bool gpu::CascadeClassifier_GPU::load(const string\& filename) +.. ocv:function:: bool gpu::CascadeClassifier_GPU::load(const string& filename) Loads the classifier from a file. The previous content is destroyed. @@ -287,7 +275,7 @@ gpu::CascadeClassifier_GPU::release gpu::CascadeClassifier_GPU::detectMultiScale ------------------------------------------------ -.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat\& image, GpuMat\& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size()) +.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size()) Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. diff --git a/modules/highgui/doc/qt_new_functions.rst b/modules/highgui/doc/qt_new_functions.rst index 534b9bf..8fcb050 100644 --- a/modules/highgui/doc/qt_new_functions.rst +++ b/modules/highgui/doc/qt_new_functions.rst @@ -230,9 +230,9 @@ createOpenGLCallback ------------------------ Creates a callback function called to draw OpenGL on top the the image display by ``windowname``. -.. ocv:function:: void createOpenGLCallback( const string& window_name, OpenGLCallback callbackOpenGL, void* userdata CV_DEFAULT(NULL), double angle CV_DEFAULT(-1), double zmin CV_DEFAULT(-1), double zmax CV_DEFAULT(-1) +.. ocv:function:: void createOpenGLCallback( const string& window_name, OpenGLCallback callbackOpenGL, void* userdata =NULL, double angle=-1, double zmin=-1, double zmax=-1) -.. ocv:cfunction:: void cvCreateOpenGLCallback( const char* windowName, CvOpenGLCallback callbackOpenGL, void* userdata=NULL, double angle=-1, double zmin=-1, double zmax=-1 +.. ocv:cfunction:: void cvCreateOpenGLCallback( const char* windowName, CvOpenGLCallback callbackOpenGL, void* userdata=NULL, double angle=-1, double zmin=-1, double zmax=-1 ) :param window_name: Name of the window. @@ -308,9 +308,9 @@ createButton ---------------- Attaches a button to the control panel. -.. ocv:function:: createButton( const string& button_name CV_DEFAULT(NULL),ButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL), int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0)) +.. ocv:function:: createButton( const string& button_name=NULL, ButtonCallback on_change=NULL, void* userdata=NULL, int button_type=CV_PUSH_BUTTON, int initial_button_state=0 ) -.. ocv:cfunction:: cvCreateButton( const char* buttonName=NULL, CvButtonCallback onChange=NULL, void* userdata=NULL, int buttonType=CV_PUSH_BUTTON, int initialButtonState=0 +.. ocv:cfunction:: cvCreateButton( const char* buttonName=NULL, CvButtonCallback onChange=NULL, void* userdata=NULL, int buttonType=CV_PUSH_BUTTON, int initialButtonState=0 ) :param button_name: Name of the button. diff --git a/modules/highgui/doc/reading_and_writing_images_and_video.rst b/modules/highgui/doc/reading_and_writing_images_and_video.rst index 1af534a..9878d3c 100644 --- a/modules/highgui/doc/reading_and_writing_images_and_video.rst +++ b/modules/highgui/doc/reading_and_writing_images_and_video.rst @@ -266,6 +266,7 @@ VideoCapture::read Grabs, decodes and returns the next video frame. .. ocv:function:: VideoCapture& VideoCapture::operator >> (Mat& image) + .. ocv:function:: bool VideoCapture::read(Mat& image) .. ocv:pyfunction:: cv2.VideoCapture.read([image]) -> successFlag, image @@ -288,6 +289,7 @@ Returns the specified ``VideoCapture`` property .. ocv:pyfunction:: cv2.VideoCapture.get(propId) -> retval .. ocv:cfunction:: double cvGetCaptureProperty( CvCapture* capture, int propId ) + .. ocv:pyoldfunction:: cv.GetCaptureProperty(capture, propId)->double @@ -343,6 +345,7 @@ Sets a property in the ``VideoCapture``. .. ocv:pyfunction:: cv2.VideoCapture.set(propId, value) -> retval .. ocv:cfunction:: int cvSetCaptureProperty( CvCapture* capture, int propId, double value ) + .. ocv:pyoldfunction:: cv.SetCaptureProperty(capture, propId, value)->None :param propId: Property identifier. It can be one of the following: @@ -402,6 +405,7 @@ VideoWriter::VideoWriter VideoWriter constructors .. ocv:function:: VideoWriter::VideoWriter() + .. ocv:function:: VideoWriter::VideoWriter(const string& filename, int fourcc, double fps, Size frameSize, bool isColor=true) .. ocv:pyfunction:: cv2.VideoWriter([filename, fourcc, fps, frameSize[, isColor]]) -> @@ -461,6 +465,7 @@ VideoWriter::write Writes the next video frame .. ocv:function:: VideoWriter& VideoWriter::operator << (const Mat& image) + .. ocv:function:: void VideoWriter::write(const Mat& image) .. ocv:pyfunction:: cv2.VideoWriter.write(image) -> None diff --git a/modules/imgproc/doc/feature_detection.rst b/modules/imgproc/doc/feature_detection.rst index 58bfe67..21189d4 100644 --- a/modules/imgproc/doc/feature_detection.rst +++ b/modules/imgproc/doc/feature_detection.rst @@ -222,7 +222,7 @@ Determines strong corners on an image. .. ocv:pyfunction:: cv2.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners -.. ocv:cfunction:: void cvGoodFeaturesToTrack( const CvArr* image, CvArr* eigImage, CvArr* tempImage CvPoint2D32f* corners, int* cornerCount, double qualityLevel, double minDistance, const CvArr* mask=NULL, int blockSize=3, int useHarris=0, double k=0.04 ) +.. ocv:cfunction:: void cvGoodFeaturesToTrack( const CvArr* image, CvArr* eigImage, CvArr* tempImage, CvPoint2D32f* corners, int* cornerCount, double qualityLevel, double minDistance, const CvArr* mask=NULL, int blockSize=3, int useHarris=0, double k=0.04 ) .. ocv:pyoldfunction:: cv.GoodFeaturesToTrack(image, eigImage, tempImage, cornerCount, qualityLevel, minDistance, mask=None, blockSize=3, useHarris=0, k=0.04)-> corners diff --git a/modules/ml/doc/boosting.rst b/modules/ml/doc/boosting.rst index 48e25d9..a8d368d 100644 --- a/modules/ml/doc/boosting.rst +++ b/modules/ml/doc/boosting.rst @@ -59,9 +59,9 @@ To reduce computation time for boosted models without substantially losing accur **all** training examples are recomputed at each training iteration. Examples deleted at a particular iteration may be used again for learning some of the weak classifiers further [FHT98]_. -.. _HTF01: [HTF01] Hastie, T., Tibshirani, R., Friedman, J. H. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics*. 2001. +.. [HTF01] Hastie, T., Tibshirani, R., Friedman, J. H. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics*. 2001. -.. _FHT98: [FHT98] Friedman, J. H., Hastie, T. and Tibshirani, R. Additive Logistic Regression: a Statistical View of Boosting. Technical Report, Dept. of Statistics*, Stanford University, 1998. +.. [FHT98] Friedman, J. H., Hastie, T. and Tibshirani, R. Additive Logistic Regression: a Statistical View of Boosting. Technical Report, Dept. of Statistics*, Stanford University, 1998. CvBoostParams ------------- -- 2.7.4