mirror of
https://github.com/opencv/opencv.git
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Merge remote-tracking branch 'origin/2.4' into merge-2.4
Conflicts: modules/imgproc/src/opencl/integral_sqrsum.cl modules/nonfree/doc/feature_detection.rst modules/nonfree/include/opencv2/nonfree/ocl.hpp modules/nonfree/src/surf_ocl.cpp modules/nonfree/test/test_features2d.cpp modules/ocl/doc/image_processing.rst modules/ocl/include/opencv2/ocl/ocl.hpp modules/ocl/perf/perf_imgproc.cpp modules/ocl/perf/perf_match_template.cpp modules/ocl/src/haar.cpp modules/ocl/src/imgproc.cpp modules/ocl/src/match_template.cpp modules/ocl/src/opencl/haarobjectdetect.cl modules/ocl/src/opencl/haarobjectdetect_scaled2.cl modules/ocl/test/test_imgproc.cpp
This commit is contained in:
Roman Donchenko
commit
f77c68e0fa
@ -213,7 +213,7 @@ foreach(__opttype OPT DBG)
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SET(OpenCV_EXTRA_LIBS_${__opttype} "")
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# CUDA
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if(OpenCV_CUDA_VERSION AND (CMAKE_CROSSCOMPILING OR (WIN32 AND NOT OpenCV_SHARED)))
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if(OpenCV_CUDA_VERSION)
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if(NOT CUDA_FOUND)
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find_package(CUDA ${OpenCV_CUDA_VERSION} EXACT REQUIRED)
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else()
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@ -222,32 +222,41 @@ foreach(__opttype OPT DBG)
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endif()
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endif()
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list(APPEND OpenCV_EXTRA_LIBS_${__opttype} ${CUDA_LIBRARIES})
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set(OpenCV_CUDA_LIBS_ABSPATH ${CUDA_LIBRARIES})
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if(${CUDA_VERSION} VERSION_LESS "5.5")
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list(APPEND OpenCV_EXTRA_LIBS_${__opttype} ${CUDA_npp_LIBRARY})
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list(APPEND OpenCV_CUDA_LIBS_ABSPATH ${CUDA_npp_LIBRARY})
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else()
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find_cuda_helper_libs(nppc)
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find_cuda_helper_libs(nppi)
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find_cuda_helper_libs(npps)
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list(APPEND OpenCV_EXTRA_LIBS_${__opttype} ${CUDA_nppc_LIBRARY} ${CUDA_nppi_LIBRARY} ${CUDA_npps_LIBRARY})
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list(APPEND OpenCV_CUDA_LIBS_ABSPATH ${CUDA_nppc_LIBRARY} ${CUDA_nppi_LIBRARY} ${CUDA_npps_LIBRARY})
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endif()
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if(OpenCV_USE_CUBLAS)
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list(APPEND OpenCV_EXTRA_LIBS_${__opttype} ${CUDA_CUBLAS_LIBRARIES})
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list(APPEND OpenCV_CUDA_LIBS_ABSPATH ${CUDA_CUBLAS_LIBRARIES})
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endif()
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if(OpenCV_USE_CUFFT)
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list(APPEND OpenCV_EXTRA_LIBS_${__opttype} ${CUDA_CUFFT_LIBRARIES})
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list(APPEND OpenCV_CUDA_LIBS_ABSPATH ${CUDA_CUFFT_LIBRARIES})
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endif()
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if(OpenCV_USE_NVCUVID)
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list(APPEND OpenCV_EXTRA_LIBS_${__opttype} ${CUDA_nvcuvid_LIBRARIES})
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list(APPEND OpenCV_CUDA_LIBS_ABSPATH ${CUDA_nvcuvid_LIBRARIES})
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endif()
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if(WIN32)
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list(APPEND OpenCV_EXTRA_LIBS_${__opttype} ${CUDA_nvcuvenc_LIBRARIES})
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list(APPEND OpenCV_CUDA_LIBS_ABSPATH ${CUDA_nvcuvenc_LIBRARIES})
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endif()
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set(OpenCV_CUDA_LIBS_RELPATH "")
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foreach(l ${OpenCV_CUDA_LIBS_ABSPATH})
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get_filename_component(_tmp ${l} PATH)
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list(APPEND OpenCV_CUDA_LIBS_RELPATH ${_tmp})
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endforeach()
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list(REMOVE_DUPLICATES OpenCV_CUDA_LIBS_RELPATH)
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link_directories(${OpenCV_CUDA_LIBS_RELPATH})
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endif()
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endforeach()
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@ -321,6 +321,8 @@ extlinks = {
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'calib3d' : ('http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#%s', None ),
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'feature2d' : ('http://docs.opencv.org/modules/imgproc/doc/feature_detection.html#%s', None ),
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'imgproc_geometric' : ('http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html#%s', None ),
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'miscellaneous_transformations' : ('http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html#%s', None),
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'user_interface' : ('http://docs.opencv.org/modules/highgui/doc/user_interface.html#%s', None),
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# 'opencv_group' : ('http://answers.opencv.org/%s', None),
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'opencv_qa' : ('http://answers.opencv.org/%s', None),
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@ -92,7 +92,7 @@ We'll use the plain C [] operator to access pixels. Because we need to access mu
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}
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}
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On the borders of the image the upper notation results inexistent pixel locations (like minus one - minus one). In these points our formula is undefined. A simple solution is to not apply the mask in these points and, for example, set the pixels on the borders to zeros:
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On the borders of the image the upper notation results inexistent pixel locations (like minus one - minus one). In these points our formula is undefined. A simple solution is to not apply the kernel in these points and, for example, set the pixels on the borders to zeros:
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.. code-block:: cpp
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@ -45,7 +45,7 @@ All the above objects, in the end, point to the same single data matrix. Their h
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:linenos:
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Mat D (A, Rect(10, 10, 100, 100) ); // using a rectangle
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Mat E = A(Range:all(), Range(1,3)); // using row and column boundaries
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Mat E = A(Range::all(), Range(1,3)); // using row and column boundaries
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Now you may ask if the matrix itself may belong to multiple *Mat* objects who takes responsibility for cleaning it up when it's no longer needed. The short answer is: the last object that used it. This is handled by using a reference counting mechanism. Whenever somebody copies a header of a *Mat* object, a counter is increased for the matrix. Whenever a header is cleaned this counter is decreased. When the counter reaches zero the matrix too is freed. Sometimes you will want to copy the matrix itself too, so OpenCV provides the :basicstructures:`clone() <mat-clone>` and :basicstructures:`copyTo() <mat-copyto>` functions.
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@ -86,7 +86,7 @@ Each of the building components has their own valid domains. This leads to the d
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Creating a *Mat* object explicitly
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==================================
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In the :ref:`Load_Save_Image` tutorial you have already learned how to write a matrix to an image file by using the :readWriteImageVideo:` imwrite() <imwrite>` function. However, for debugging purposes it's much more convenient to see the actual values. You can do this using the << operator of *Mat*. Be aware that this only works for two dimensional matrices.
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In the :ref:`Load_Save_Image` tutorial you have already learned how to write a matrix to an image file by using the :readwriteimagevideo:`imwrite() <imwrite>` function. However, for debugging purposes it's much more convenient to see the actual values. You can do this using the << operator of *Mat*. Be aware that this only works for two dimensional matrices.
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Although *Mat* works really well as an image container, it is also a general matrix class. Therefore, it is possible to create and manipulate multidimensional matrices. You can create a Mat object in multiple ways:
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@ -5,7 +5,7 @@ Load, Modify, and Save an Image
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.. note::
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We assume that by now you know how to load an image using :imread:`imread <>` and to display it in a window (using :imshow:`imshow <>`). Read the :ref:`Display_Image` tutorial otherwise.
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We assume that by now you know how to load an image using :readwriteimagevideo:`imread <imread>` and to display it in a window (using :user_interface:`imshow <imshow>`). Read the :ref:`Display_Image` tutorial otherwise.
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Goals
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======
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@ -14,9 +14,9 @@ In this tutorial you will learn how to:
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.. container:: enumeratevisibleitemswithsquare
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* Load an image using :imread:`imread <>`
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* Transform an image from BGR to Grayscale format by using :cvt_color:`cvtColor <>`
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* Save your transformed image in a file on disk (using :imwrite:`imwrite <>`)
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* Load an image using :readwriteimagevideo:`imread <imread>`
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* Transform an image from BGR to Grayscale format by using :miscellaneous_transformations:`cvtColor <cvtcolor>`
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* Save your transformed image in a file on disk (using :readwriteimagevideo:`imwrite <imwrite>`)
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Code
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======
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@ -62,10 +62,7 @@ Here it is:
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Explanation
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============
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#. We begin by:
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* Creating a Mat object to store the image information
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* Load an image using :imread:`imread <>`, located in the path given by *imageName*. Fort this example, assume you are loading a RGB image.
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#. We begin by loading an image using :readwriteimagevideo:`imread <imread>`, located in the path given by *imageName*. For this example, assume you are loading a RGB image.
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#. Now we are going to convert our image from BGR to Grayscale format. OpenCV has a really nice function to do this kind of transformations:
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@ -73,15 +70,15 @@ Explanation
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cvtColor( image, gray_image, CV_BGR2GRAY );
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As you can see, :cvt_color:`cvtColor <>` takes as arguments:
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As you can see, :miscellaneous_transformations:`cvtColor <cvtcolor>` takes as arguments:
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.. container:: enumeratevisibleitemswithsquare
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* a source image (*image*)
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* a destination image (*gray_image*), in which we will save the converted image.
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* an additional parameter that indicates what kind of transformation will be performed. In this case we use **CV_BGR2GRAY** (because of :imread:`imread <>` has BGR default channel order in case of color images).
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* an additional parameter that indicates what kind of transformation will be performed. In this case we use **CV_BGR2GRAY** (because of :readwriteimagevideo:`imread <imread>` has BGR default channel order in case of color images).
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#. So now we have our new *gray_image* and want to save it on disk (otherwise it will get lost after the program ends). To save it, we will use a function analagous to :imread:`imread <>`: :imwrite:`imwrite <>`
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#. So now we have our new *gray_image* and want to save it on disk (otherwise it will get lost after the program ends). To save it, we will use a function analagous to :readwriteimagevideo:`imread <imread>`: :readwriteimagevideo:`imwrite <imwrite>`
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.. code-block:: cpp
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@ -1387,7 +1387,7 @@ description rewritten using
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IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3);
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IplImage gray_img_hdr, *gray_img;
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gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0);
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gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0);
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...
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@ -1395,6 +1395,18 @@ description rewritten using
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int size[] = { 2, 2, 2 };
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CvMatND* mat = cvCreateMatND(3, size, CV_32F);
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CvMat row_header, *row;
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row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0);
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..
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In C, the header file for this function includes a convenient macro ``cvReshapeND`` that does away with the ``sizeof_header`` parameter. So, the lines containing the call to ``cvReshapeMatND`` in the examples may be replaced as follow:
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::
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gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0);
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...
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row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0);
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..
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@ -69,7 +69,7 @@ Computes the descriptors for a set of keypoints detected in an image (first vari
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:param keypoints: Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: ``SIFT`` duplicates keypoint with several dominant orientations (for each orientation).
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:param descriptors: Computed descriptors. In the second variant of the method ``descriptors[i]`` are descriptors computed for a ``keypoints[i]`. Row ``j`` is the ``keypoints`` (or ``keypoints[i]``) is the descriptor for keypoint ``j``-th keypoint.
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:param descriptors: Computed descriptors. In the second variant of the method ``descriptors[i]`` are descriptors computed for a ``keypoints[i]``. Row ``j`` is the ``keypoints`` (or ``keypoints[i]``) is the descriptor for keypoint ``j``-th keypoint.
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DescriptorExtractor::create
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@ -249,7 +249,7 @@ Brute-force matcher constructor.
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:param normType: One of ``NORM_L1``, ``NORM_L2``, ``NORM_HAMMING``, ``NORM_HAMMING2``. ``L1`` and ``L2`` norms are preferable choices for SIFT and SURF descriptors, ``NORM_HAMMING`` should be used with ORB, BRISK and BRIEF, ``NORM_HAMMING2`` should be used with ORB when ``WTA_K==3`` or ``4`` (see ORB::ORB constructor description).
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:param crossCheck: If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If ``crossCheck==true``, then the ``knnMatch()`` method with ``k=1`` will only return pairs ``(i,j)`` such that for ``i-th`` query descriptor the ``j-th`` descriptor in the matcher's collection is the nearest and vice versa, i.e. the ``BFMathcher`` will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
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:param crossCheck: If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If ``crossCheck==true``, then the ``knnMatch()`` method with ``k=1`` will only return pairs ``(i,j)`` such that for ``i-th`` query descriptor the ``j-th`` descriptor in the matcher's collection is the nearest and vice versa, i.e. the ``BFMatcher`` will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
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FlannBasedMatcher
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@ -1309,6 +1309,8 @@ bool CvVideoWriter_AVFoundation::writeFrame(const IplImage* iplimage) {
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}
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//cleanup
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CFRelease(cfData);
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CVPixelBufferRelease(pixelBuffer);
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CGImageRelease(cgImage);
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CGDataProviderRelease(provider);
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CGColorSpaceRelease(colorSpace);
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@ -288,7 +288,7 @@ public class JavaCameraView extends CameraBridgeViewBase implements PreviewCallb
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}
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public Mat rgba() {
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Imgproc.cvtColor(mYuvFrameData, mRgba, Imgproc.COLOR_YUV2BGR_NV12, 4);
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Imgproc.cvtColor(mYuvFrameData, mRgba, Imgproc.COLOR_YUV2RGBA_NV21, 4);
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return mRgba;
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}
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@ -16,9 +16,7 @@ PERF_TEST_P(surf, detect, testing::Values(SURF_IMAGES))
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{
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string filename = getDataPath(GetParam());
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Mat frame = imread(filename, IMREAD_GRAYSCALE);
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if (frame.empty())
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FAIL() << "Unable to load source image " << filename;
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ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
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Mat mask;
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declare.in(frame).time(90);
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@ -34,9 +32,7 @@ PERF_TEST_P(surf, extract, testing::Values(SURF_IMAGES))
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{
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string filename = getDataPath(GetParam());
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Mat frame = imread(filename, IMREAD_GRAYSCALE);
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if (frame.empty())
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FAIL() << "Unable to load source image " << filename;
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ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
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Mat mask;
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declare.in(frame).time(90);
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@ -55,9 +51,7 @@ PERF_TEST_P(surf, full, testing::Values(SURF_IMAGES))
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{
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string filename = getDataPath(GetParam());
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Mat frame = imread(filename, IMREAD_GRAYSCALE);
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if (frame.empty())
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FAIL() << "Unable to load source image " << filename;
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ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
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Mat mask;
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declare.in(frame).time(90);
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@ -426,7 +426,7 @@ class table(object):
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if r == 0:
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css = css[:-1] + "border-top:2px solid #6678B1;\""
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out.write(" <td%s%s>\n" % (attr, css))
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if th is not None:
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if td is not None:
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out.write(" %s\n" % htmlEncode(td.text))
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out.write(" </td>\n")
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i += colspan
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@ -16,7 +16,7 @@ USAGE
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--feature - Feature to use. Can be sift, surf, orb or brisk. Append '-flann'
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to feature name to use Flann-based matcher instead bruteforce.
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Press left mouse button on a feature point to see its mathcing point.
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Press left mouse button on a feature point to see its matching point.
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'''
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import numpy as np
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