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Merge branch 4.x
This commit is contained in:
Alexander Alekhin
@@ -185,6 +185,7 @@ location of points on the plane, building special graphs (such as NNG,RNG), and
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@defgroup imgproc_motion Motion Analysis and Object Tracking
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@defgroup imgproc_feature Feature Detection
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@defgroup imgproc_object Object Detection
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@defgroup imgproc_segmentation Image Segmentation
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@defgroup imgproc_c C API
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@defgroup imgproc_hal Hardware Acceleration Layer
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@{
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@@ -404,9 +405,13 @@ enum ConnectedComponentsTypes {
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//! connected components algorithm
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enum ConnectedComponentsAlgorithmsTypes {
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CCL_WU = 0, //!< SAUF @cite Wu2009 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
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CCL_DEFAULT = -1, //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
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CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
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CCL_DEFAULT = -1, //!< BBDT @cite Grana2010 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for both BBDT and SAUF.
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CCL_WU = 0, //!< SAUF @cite Wu2009 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for SAUF.
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CCL_GRANA = 1, //!< BBDT @cite Grana2010 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for both BBDT and SAUF.
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CCL_BOLELLI = 2, //!< Spaghetti @cite Bolelli2019 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity.
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CCL_SAUF = 3, //!< Same as CCL_WU. It is preferable to use the flag with the name of the algorithm (CCL_SAUF) rather than the one with the name of the first author (CCL_WU).
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CCL_BBDT = 4, //!< Same as CCL_GRANA. It is preferable to use the flag with the name of the algorithm (CCL_BBDT) rather than the one with the name of the first author (CCL_GRANA).
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CCL_SPAGHETTI = 5, //!< Same as CCL_BOLELLI. It is preferable to use the flag with the name of the algorithm (CCL_SPAGHETTI) rather than the one with the name of the first author (CCL_BOLELLI).
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};
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//! mode of the contour retrieval algorithm
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@@ -578,7 +583,7 @@ enum ColorConversionCodes {
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COLOR_YCrCb2BGR = 38,
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COLOR_YCrCb2RGB = 39,
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COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
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COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value) with H range 0..180 if 8 bit image, @ref color_convert_rgb_hsv "color conversions"
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COLOR_RGB2HSV = 41,
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COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
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@@ -586,27 +591,27 @@ enum ColorConversionCodes {
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COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
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COLOR_RGB2Luv = 51,
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COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
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COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation) with H range 0..180 if 8 bit image, @ref color_convert_rgb_hls "color conversions"
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COLOR_RGB2HLS = 53,
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COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR
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COLOR_HSV2BGR = 54, //!< backward conversions HSV to RGB/BGR with H range 0..180 if 8 bit image
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COLOR_HSV2RGB = 55,
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COLOR_Lab2BGR = 56,
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COLOR_Lab2RGB = 57,
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COLOR_Luv2BGR = 58,
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COLOR_Luv2RGB = 59,
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COLOR_HLS2BGR = 60,
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COLOR_HLS2BGR = 60, //!< backward conversions HLS to RGB/BGR with H range 0..180 if 8 bit image
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COLOR_HLS2RGB = 61,
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COLOR_BGR2HSV_FULL = 66,
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COLOR_BGR2HSV_FULL = 66, //!< convert RGB/BGR to HSV (hue saturation value) with H range 0..255 if 8 bit image, @ref color_convert_rgb_hsv "color conversions"
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COLOR_RGB2HSV_FULL = 67,
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COLOR_BGR2HLS_FULL = 68,
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COLOR_BGR2HLS_FULL = 68, //!< convert RGB/BGR to HLS (hue lightness saturation) with H range 0..255 if 8 bit image, @ref color_convert_rgb_hls "color conversions"
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COLOR_RGB2HLS_FULL = 69,
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COLOR_HSV2BGR_FULL = 70,
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COLOR_HSV2BGR_FULL = 70, //!< backward conversions HSV to RGB/BGR with H range 0..255 if 8 bit image
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COLOR_HSV2RGB_FULL = 71,
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COLOR_HLS2BGR_FULL = 72,
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COLOR_HLS2BGR_FULL = 72, //!< backward conversions HLS to RGB/BGR with H range 0..255 if 8 bit image
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COLOR_HLS2RGB_FULL = 73,
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COLOR_LBGR2Lab = 74,
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@@ -1923,6 +1928,38 @@ CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
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InputArray mask, int blockSize,
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int gradientSize, bool useHarrisDetector = false,
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double k = 0.04 );
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/** @brief Same as above, but returns also quality measure of the detected corners.
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@param image Input 8-bit or floating-point 32-bit, single-channel image.
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@param corners Output vector of detected corners.
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@param maxCorners Maximum number of corners to return. If there are more corners than are found,
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the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
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and all detected corners are returned.
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@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
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parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
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(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
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quality measure less than the product are rejected. For example, if the best corner has the
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quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
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less than 15 are rejected.
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@param minDistance Minimum possible Euclidean distance between the returned corners.
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@param mask Region of interest. If the image is not empty (it needs to have the type
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CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
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@param cornersQuality Output vector of quality measure of the detected corners.
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@param blockSize Size of an average block for computing a derivative covariation matrix over each
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pixel neighborhood. See cornerEigenValsAndVecs .
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@param gradientSize Aperture parameter for the Sobel operator used for derivatives computation.
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See cornerEigenValsAndVecs .
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@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
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or #cornerMinEigenVal.
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@param k Free parameter of the Harris detector.
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*/
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CV_EXPORTS CV_WRAP_AS(goodFeaturesToTrackWithQuality) void goodFeaturesToTrack(
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InputArray image, OutputArray corners,
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int maxCorners, double qualityLevel, double minDistance,
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InputArray mask, OutputArray cornersQuality, int blockSize = 3,
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int gradientSize = 3, bool useHarrisDetector = false, double k = 0.04);
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/** @example samples/cpp/tutorial_code/ImgTrans/houghlines.cpp
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An example using the Hough line detector
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@@ -3152,6 +3189,9 @@ CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature
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//! @} imgproc_hist
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//! @addtogroup imgproc_segmentation
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//! @{
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/** @example samples/cpp/watershed.cpp
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An example using the watershed algorithm
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*/
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@@ -3179,11 +3219,11 @@ function.
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size as image .
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@sa findContours
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@ingroup imgproc_misc
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*/
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CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
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//! @} imgproc_segmentation
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//! @addtogroup imgproc_filter
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//! @{
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@@ -3229,7 +3269,7 @@ CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
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//! @}
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//! @addtogroup imgproc_misc
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//! @addtogroup imgproc_segmentation
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//! @{
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/** @example samples/cpp/grabcut.cpp
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@@ -3259,6 +3299,11 @@ CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
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InputOutputArray bgdModel, InputOutputArray fgdModel,
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int iterCount, int mode = GC_EVAL );
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//! @} imgproc_segmentation
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//! @addtogroup imgproc_misc
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//! @{
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/** @example samples/cpp/distrans.cpp
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An example on using the distance transform
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*/
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@@ -3430,7 +3475,7 @@ CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask,
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//! @param weights1 It has a type of CV_32FC1 and the same size with src1.
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//! @param weights2 It has a type of CV_32FC1 and the same size with src1.
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//! @param dst It is created if it does not have the same size and type with src1.
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CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst);
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CV_EXPORTS_W void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst);
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//! @} imgproc_misc
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@@ -4908,4 +4953,8 @@ Point LineIterator::pos() const
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} // cv
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#include "./imgproc/segmentation.hpp"
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#endif
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@@ -0,0 +1,34 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef OPENCV_IMGPROC_BINDINGS_HPP
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#define OPENCV_IMGPROC_BINDINGS_HPP
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// This file contains special overloads for OpenCV bindings
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// No need to use these functions in C++ code.
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namespace cv {
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/** @brief Finds lines in a binary image using the standard Hough transform and get accumulator.
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*
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* @note This function is for bindings use only. Use original function in C++ code
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*
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* @sa HoughLines
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*/
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CV_WRAP static inline
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void HoughLinesWithAccumulator(
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InputArray image, OutputArray lines,
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double rho, double theta, int threshold,
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double srn = 0, double stn = 0,
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double min_theta = 0, double max_theta = CV_PI
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)
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{
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std::vector<Vec3f> lines_acc;
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HoughLines(image, lines_acc, rho, theta, threshold, srn, stn, min_theta, max_theta);
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Mat(lines_acc).copyTo(lines);
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}
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} // namespace
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#endif // OPENCV_IMGPROC_BINDINGS_HPP
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@@ -0,0 +1,141 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef OPENCV_IMGPROC_SEGMENTATION_HPP
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#define OPENCV_IMGPROC_SEGMENTATION_HPP
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#include "opencv2/imgproc.hpp"
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namespace cv {
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namespace segmentation {
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//! @addtogroup imgproc_segmentation
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//! @{
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/** @brief Intelligent Scissors image segmentation
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*
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* This class is used to find the path (contour) between two points
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* which can be used for image segmentation.
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*
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* Usage example:
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* @snippet snippets/imgproc_segmentation.cpp usage_example_intelligent_scissors
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*
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* Reference: <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.3811&rep=rep1&type=pdf">"Intelligent Scissors for Image Composition"</a>
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* algorithm designed by Eric N. Mortensen and William A. Barrett, Brigham Young University
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* @cite Mortensen95intelligentscissors
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*/
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class CV_EXPORTS_W_SIMPLE IntelligentScissorsMB
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{
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public:
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CV_WRAP
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IntelligentScissorsMB();
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/** @brief Specify weights of feature functions
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*
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* Consider keeping weights normalized (sum of weights equals to 1.0)
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* Discrete dynamic programming (DP) goal is minimization of costs between pixels.
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*
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* @param weight_non_edge Specify cost of non-edge pixels (default: 0.43f)
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* @param weight_gradient_direction Specify cost of gradient direction function (default: 0.43f)
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* @param weight_gradient_magnitude Specify cost of gradient magnitude function (default: 0.14f)
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*/
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CV_WRAP
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IntelligentScissorsMB& setWeights(float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude);
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/** @brief Specify gradient magnitude max value threshold
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*
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* Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article).
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* Otherwize pixels with `gradient magnitude >= threshold` have zero cost.
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*
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* @note Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos).
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*
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* @param gradient_magnitude_threshold_max Specify gradient magnitude max value threshold (default: 0, disabled)
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*/
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CV_WRAP
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IntelligentScissorsMB& setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max = 0.0f);
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/** @brief Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters
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*
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* This feature extractor is used by default according to article.
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*
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* Implementation has additional filtering for regions with low-amplitude noise.
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* This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16).
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*
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* @note Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first).
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*
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* @note Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters().
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*
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* @param gradient_magnitude_min_value Minimal gradient magnitude value for edge pixels (default: 0, check is disabled)
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*/
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CV_WRAP
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IntelligentScissorsMB& setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value = 0.0f);
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/** @brief Switch edge feature extractor to use Canny edge detector
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*
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* @note "Laplacian Zero-Crossing" feature extractor is used by default (following to original article)
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*
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* @sa Canny
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*/
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CV_WRAP
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IntelligentScissorsMB& setEdgeFeatureCannyParameters(
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double threshold1, double threshold2,
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int apertureSize = 3, bool L2gradient = false
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);
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/** @brief Specify input image and extract image features
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*
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* @param image input image. Type is #CV_8UC1 / #CV_8UC3
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*/
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CV_WRAP
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IntelligentScissorsMB& applyImage(InputArray image);
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/** @brief Specify custom features of imput image
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*
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* Customized advanced variant of applyImage() call.
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*
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* @param non_edge Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are `{0, 1}`.
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* @param gradient_direction Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: `x^2 + y^2 == 1`
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* @param gradient_magnitude Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range `[0, 1]`.
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* @param image **Optional parameter**. Must be specified if subset of features is specified (non-specified features are calculated internally)
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*/
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CV_WRAP
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IntelligentScissorsMB& applyImageFeatures(
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InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude,
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InputArray image = noArray()
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);
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/** @brief Prepares a map of optimal paths for the given source point on the image
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*
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* @note applyImage() / applyImageFeatures() must be called before this call
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*
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* @param sourcePt The source point used to find the paths
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*/
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CV_WRAP void buildMap(const Point& sourcePt);
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/** @brief Extracts optimal contour for the given target point on the image
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*
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* @note buildMap() must be called before this call
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*
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* @param targetPt The target point
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* @param[out] contour The list of pixels which contains optimal path between the source and the target points of the image. Type is CV_32SC2 (compatible with `std::vector<Point>`)
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* @param backward Flag to indicate reverse order of retrived pixels (use "true" value to fetch points from the target to the source point)
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*/
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CV_WRAP void getContour(const Point& targetPt, OutputArray contour, bool backward = false) const;
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#ifndef CV_DOXYGEN
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struct Impl;
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inline Impl* getImpl() const { return impl.get(); }
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protected:
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std::shared_ptr<Impl> impl;
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#endif
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};
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//! @}
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} // namespace segmentation
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} // namespace cv
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#endif // OPENCV_IMGPROC_SEGMENTATION_HPP
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