Intelligent Scissors Example

From 3eea3dd46ba0f5534306272e773579bc81b64db7 Mon Sep 17 00:00:00 2001 From: Alexander Alekhin Date: Wed, 16 Dec 2020 00:53:52 +0000 Subject: [PATCH] imgproc: add IntelligentScissors --- .../js_assets/js_intelligent_scissors.html | 127 ++++ .../js_intelligent_scissors.markdown | 14 + .../js_table_of_contents_imgproc.markdown | 4 + doc/opencv.bib | 7 + modules/imgproc/include/opencv2/imgproc.hpp | 19 +- .../include/opencv2/imgproc/segmentation.hpp | 141 ++++ modules/imgproc/src/intelligent_scissors.cpp | 772 +++++++++++++++++++++ modules/imgproc/test/test_intelligent_scissors.cpp | 467 +++++++++++++ modules/js/src/core_bindings.cpp | 2 + modules/js/test/test_imgproc.js | 23 + platforms/js/opencv_js.config.py | 17 +- samples/cpp/CMakeLists.txt | 13 +- .../snippets/imgproc_segmentation.cpp | 35 + samples/samples_utils.cmake | 3 + 14 files changed, 1637 insertions(+), 7 deletions(-) create mode 100644 doc/js_tutorials/js_assets/js_intelligent_scissors.html create mode 100644 doc/js_tutorials/js_imgproc/js_intelligent_scissors/js_intelligent_scissors.markdown create mode 100644 modules/imgproc/include/opencv2/imgproc/segmentation.hpp create mode 100644 modules/imgproc/src/intelligent_scissors.cpp create mode 100644 modules/imgproc/test/test_intelligent_scissors.cpp create mode 100644 samples/cpp/tutorial_code/snippets/imgproc_segmentation.cpp diff --git a/doc/js_tutorials/js_assets/js_intelligent_scissors.html b/doc/js_tutorials/js_assets/js_intelligent_scissors.html new file mode 100644 index 0000000..1782dc6 --- /dev/null +++ b/doc/js_tutorials/js_assets/js_intelligent_scissors.html @@ -0,0 +1,127 @@ + + + + +Intelligent Scissors Example + + + +

Intelligent Scissors Example

+ Click Start button to launch the code below.
+ Then click on image to pick source point. After that you can hover mouse pointer over canvas to specify target point candidate.
+ You can change the code in the <textarea> to investigate more. You can choose another image (need to "Stop" first). +

+

canvasInput

+ + + + + + + + + diff --git a/doc/js_tutorials/js_imgproc/js_intelligent_scissors/js_intelligent_scissors.markdown b/doc/js_tutorials/js_imgproc/js_intelligent_scissors/js_intelligent_scissors.markdown new file mode 100644 index 0000000..97ffca5 --- /dev/null +++ b/doc/js_tutorials/js_imgproc/js_intelligent_scissors/js_intelligent_scissors.markdown @@ -0,0 +1,14 @@ +Intelligent Scissors Demo {#tutorial_js_intelligent_scissors} +========================= + +Goal +---- + +- Here you can check how to use IntelligentScissors tool for image segmentation task. +- Available methods and parameters: @ref cv::segmentation::IntelligentScissorsMB + +\htmlonly + +\endhtmlonly diff --git a/doc/js_tutorials/js_imgproc/js_table_of_contents_imgproc.markdown b/doc/js_tutorials/js_imgproc/js_table_of_contents_imgproc.markdown index 3bb809b..b06eb95 100644 --- a/doc/js_tutorials/js_imgproc/js_table_of_contents_imgproc.markdown +++ b/doc/js_tutorials/js_imgproc/js_table_of_contents_imgproc.markdown @@ -77,3 +77,7 @@ Image Processing {#tutorial_js_table_of_contents_imgproc} - @subpage tutorial_js_imgproc_camera Learn image processing for video capture. + +- @subpage tutorial_js_intelligent_scissors + + Learn how to use IntelligentScissors tool for image segmentation task. diff --git a/doc/opencv.bib b/doc/opencv.bib index 6212ea5..d3d6f78 100644 --- a/doc/opencv.bib +++ b/doc/opencv.bib @@ -768,6 +768,13 @@ pages = {432--441}, publisher = {Springer} } +@INPROCEEDINGS{Mortensen95intelligentscissors, + author = {Eric N. Mortensen and William A. Barrett}, + title = {Intelligent Scissors for Image Composition}, + booktitle = {In Computer Graphics, SIGGRAPH Proceedings}, + year = {1995}, + pages = {191--198} +} @inproceedings{Muja2009, author = {Muja, Marius and Lowe, David G}, title = {Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration}, diff --git a/modules/imgproc/include/opencv2/imgproc.hpp b/modules/imgproc/include/opencv2/imgproc.hpp index c607a18..533c223 100644 --- a/modules/imgproc/include/opencv2/imgproc.hpp +++ b/modules/imgproc/include/opencv2/imgproc.hpp @@ -185,6 +185,7 @@ location of points on the plane, building special graphs (such as NNG,RNG), and @defgroup imgproc_motion Motion Analysis and Object Tracking @defgroup imgproc_feature Feature Detection @defgroup imgproc_object Object Detection + @defgroup imgproc_segmentation Image Segmentation @defgroup imgproc_c C API @defgroup imgproc_hal Hardware Acceleration Layer @{ @@ -3227,6 +3228,9 @@ CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature //! @} imgproc_hist +//! @addtogroup imgproc_segmentation +//! @{ + /** @example samples/cpp/watershed.cpp An example using the watershed algorithm */ @@ -3254,11 +3258,11 @@ function. size as image . @sa findContours - -@ingroup imgproc_misc */ CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers ); +//! @} imgproc_segmentation + //! @addtogroup imgproc_filter //! @{ @@ -3304,7 +3308,7 @@ CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst, //! @} -//! @addtogroup imgproc_misc +//! @addtogroup imgproc_segmentation //! @{ /** @example samples/cpp/grabcut.cpp @@ -3334,6 +3338,11 @@ CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect, InputOutputArray bgdModel, InputOutputArray fgdModel, int iterCount, int mode = GC_EVAL ); +//! @} imgproc_segmentation + +//! @addtogroup imgproc_misc +//! @{ + /** @example samples/cpp/distrans.cpp An example on using the distance transform */ @@ -4876,4 +4885,8 @@ Point LineIterator::pos() const } // cv + +#include "./imgproc/segmentation.hpp" + + #endif diff --git a/modules/imgproc/include/opencv2/imgproc/segmentation.hpp b/modules/imgproc/include/opencv2/imgproc/segmentation.hpp new file mode 100644 index 0000000..26882f4 --- /dev/null +++ b/modules/imgproc/include/opencv2/imgproc/segmentation.hpp @@ -0,0 +1,141 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_IMGPROC_SEGMENTATION_HPP +#define OPENCV_IMGPROC_SEGMENTATION_HPP + +#include "opencv2/imgproc.hpp" + +namespace cv { + +namespace segmentation { + +//! @addtogroup imgproc_segmentation +//! @{ + + +/** @brief Intelligent Scissors image segmentation + * + * This class is used to find the path (contour) between two points + * which can be used for image segmentation. + * + * Usage example: + * @snippet snippets/imgproc_segmentation.cpp usage_example_intelligent_scissors + * + * Reference: "Intelligent Scissors for Image Composition" + * algorithm designed by Eric N. Mortensen and William A. Barrett, Brigham Young University + * @cite Mortensen95intelligentscissors + */ +class CV_EXPORTS_W_SIMPLE IntelligentScissorsMB +{ +public: + CV_WRAP + IntelligentScissorsMB(); + + /** @brief Specify weights of feature functions + * + * Consider keeping weights normalized (sum of weights equals to 1.0) + * Discrete dynamic programming (DP) goal is minimization of costs between pixels. + * + * @param weight_non_edge Specify cost of non-edge pixels (default: 0.43f) + * @param weight_gradient_direction Specify cost of gradient direction function (default: 0.43f) + * @param weight_gradient_magnitude Specify cost of gradient magnitude function (default: 0.14f) + */ + CV_WRAP + IntelligentScissorsMB& setWeights(float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude); + + /** @brief Specify gradient magnitude max value threshold + * + * Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article). + * Otherwize pixels with `gradient magnitude >= threshold` have zero cost. + * + * @note Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos). + * + * @param gradient_magnitude_threshold_max Specify gradient magnitude max value threshold (default: 0, disabled) + */ + CV_WRAP + IntelligentScissorsMB& setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max = 0.0f); + + /** @brief Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters + * + * This feature extractor is used by default according to article. + * + * Implementation has additional filtering for regions with low-amplitude noise. + * This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16). + * + * @note Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first). + * + * @note Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters(). + * + * @param gradient_magnitude_min_value Minimal gradient magnitude value for edge pixels (default: 0, check is disabled) + */ + CV_WRAP + IntelligentScissorsMB& setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value = 0.0f); + + /** @brief Switch edge feature extractor to use Canny edge detector + * + * @note "Laplacian Zero-Crossing" feature extractor is used by default (following to original article) + * + * @sa Canny + */ + CV_WRAP + IntelligentScissorsMB& setEdgeFeatureCannyParameters( + double threshold1, double threshold2, + int apertureSize = 3, bool L2gradient = false + ); + + /** @brief Specify input image and extract image features + * + * @param image input image. Type is #CV_8UC1 / #CV_8UC3 + */ + CV_WRAP + IntelligentScissorsMB& applyImage(InputArray image); + + /** @brief Specify custom features of imput image + * + * Customized advanced variant of applyImage() call. + * + * @param non_edge Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are `{0, 1}`. + * @param gradient_direction Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: `x^2 + y^2 == 1` + * @param gradient_magnitude Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range `[0, 1]`. + * @param image **Optional parameter**. Must be specified if subset of features is specified (non-specified features are calculated internally) + */ + CV_WRAP + IntelligentScissorsMB& applyImageFeatures( + InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude, + InputArray image = noArray() + ); + + /** @brief Prepares a map of optimal paths for the given source point on the image + * + * @note applyImage() / applyImageFeatures() must be called before this call + * + * @param sourcePt The source point used to find the paths + */ + CV_WRAP void buildMap(const Point& sourcePt); + + /** @brief Extracts optimal contour for the given target point on the image + * + * @note buildMap() must be called before this call + * + * @param targetPt The target point + * @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`) + * @param backward Flag to indicate reverse order of retrived pixels (use "true" value to fetch points from the target to the source point) + */ + CV_WRAP void getContour(const Point& targetPt, OutputArray contour, bool backward = false) const; + +#ifndef CV_DOXYGEN + struct Impl; + inline Impl* getImpl() const { return impl.get(); } +protected: + std::shared_ptr impl; +#endif +}; + +//! @} + +} // namespace segmentation +} // namespace cv + +#endif // OPENCV_IMGPROC_SEGMENTATION_HPP diff --git a/modules/imgproc/src/intelligent_scissors.cpp b/modules/imgproc/src/intelligent_scissors.cpp new file mode 100644 index 0000000..38acfd7 --- /dev/null +++ b/modules/imgproc/src/intelligent_scissors.cpp @@ -0,0 +1,772 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2020, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. + + +#include "precomp.hpp" +//#include "opencv2/imgproc/segmentation.hpp" + +#include + +#include // std::priority_queue + +namespace cv { +namespace segmentation { + +namespace { + +// 0 1 2 +// 3 x 4 +// 5 6 7 +static const int neighbors[8][2] = { + { -1, -1 }, + { 0, -1 }, + { 1, -1 }, + { -1, 0 }, + { 1, 0 }, + { -1, 1 }, + { 0, 1 }, + { 1, 1 }, +}; + +// encoded reverse direction +static const int neighbors_encode[8] = { + 7+1, 6+1, 5+1, + 4+1, 3+1, + 2+1, 1+1, 0+1 +}; + +#define ACOS_TABLE_SIZE 64 +// acos_table[x + ACOS_TABLE_SIZE] = acos(x / ACOS_TABLE_SIZE) / CV_PI (see local_cost) +// x = [ -ACOS_TABLE_SIZE .. ACOS_TABLE_SIZE ] +float* getAcosTable() +{ + constexpr int N = ACOS_TABLE_SIZE; + static bool initialized = false; + static float acos_table[2*N + 1] = { 0 }; + if (!initialized) + { + const float CV_PI_inv = static_cast(1.0 / CV_PI); + for (int i = -N; i <= N; i++) + { + acos_table[i + N] = acosf(i / (float)N) * CV_PI_inv; + } + initialized = true; + } + return acos_table; +} + +} // namespace anon + +struct IntelligentScissorsMB::Impl +{ + // proposed weights from the article (sum = 1.0) + float weight_non_edge = 0.43f; + float weight_gradient_direction = 0.43f; + float weight_gradient_magnitude = 0.14f; + + enum EdgeFeatureMode { + FEATURE_ZERO_CROSSING = 0, + FEATURE_CANNY + }; + EdgeFeatureMode edge_mode = FEATURE_ZERO_CROSSING; + + // FEATURE_ZERO_CROSSING + float edge_gradient_magnitude_min_value = 0.0f; + + // FEATURE_CANNY + double edge_canny_threshold1 = 10; + double edge_canny_threshold2 = 100; + int edge_canny_apertureSize = 3; + bool edge_canny_L2gradient = false; + + + float gradient_magnitude_threshold_max = 0.0f; // disabled thresholding + + int sobelKernelSize = 3; // 1 or 3 + int laplacianKernelSize = 3; // 1 or 3 + + // image features + Mat_ gradient_direction; //< I: normalized laplacian x/y components + Mat_ gradient_magnitude; //< Fg: gradient cost function + Mat_ non_edge_feature; //< Fz: zero-crossing function + + float weight_non_edge_compute = 0.0f; + + // encoded paths map (produced by `buildMap()`) + Mat_ optimalPathsMap; + + void resetFeatures_() + { + CV_TRACE_FUNCTION(); + + gradient_direction.release(); + gradient_magnitude.release(); + non_edge_feature.release(); + + weight_non_edge_compute = weight_non_edge; + + optimalPathsMap.release(); + } + + Size src_size; + Mat image_; + Mat grayscale_; + void initImage_(InputArray image) + { + CV_TRACE_FUNCTION(); + if (!image_.empty()) + return; + CV_CheckType(image.type(), image.type() == CV_8UC1 || image.type() == CV_8UC3 || image.type() == CV_8UC4, ""); + src_size = image.size(); + image_ = image.getMat(); + } + void initGrayscale_(InputArray image) + { + CV_TRACE_FUNCTION(); + if (!grayscale_.empty()) + return; + CV_Assert(!image.empty()); + CV_CheckType(image.type(), image.type() == CV_8UC1 || image.type() == CV_8UC3 || image.type() == CV_8UC4, ""); + src_size = image.size(); + if (image.channels() > 1) + cvtColor(image, grayscale_, COLOR_BGR2GRAY); + else + grayscale_ = image.getMat(); + } + Mat Ix_, Iy_; + void initImageDerives_(InputArray image) + { + CV_TRACE_FUNCTION(); + if (!Ix_.empty()) + return; + initGrayscale_(image); + Sobel(grayscale_, Ix_, CV_32FC1, 1, 0, sobelKernelSize); + Sobel(grayscale_, Iy_, CV_32FC1, 0, 1, sobelKernelSize); + } + Mat image_magnitude_; + void initImageMagnitude_(InputArray image) + { + CV_TRACE_FUNCTION(); + if (!image_magnitude_.empty()) + return; + initImageDerives_(image); + magnitude(Ix_, Iy_, image_magnitude_); + } + + void cleanupFeaturesTemporaryArrays_() + { + CV_TRACE_FUNCTION(); + image_.release(); + grayscale_.release(); + Ix_.release(); + Iy_.release(); + image_magnitude_.release(); + } + + Impl() + { + // nothing + CV_TRACE_FUNCTION(); + } + + void setWeights(float weight_non_edge_, float weight_gradient_direction_, float weight_gradient_magnitude_) + { + CV_TRACE_FUNCTION(); + + CV_CheckGE(weight_non_edge_, 0.0f, ""); + CV_CheckGE(weight_gradient_direction_, 0.0f, ""); + CV_CheckGE(weight_gradient_magnitude_, 0.0f, ""); + CV_CheckGE(weight_non_edge_ + weight_gradient_direction_ + weight_gradient_magnitude_, FLT_EPSILON, "Sum of weights must be greater than zero"); + weight_non_edge = weight_non_edge_; + weight_gradient_direction = weight_gradient_direction_; + weight_gradient_magnitude = weight_gradient_magnitude_; + resetFeatures_(); + } + + void setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max_) + { + CV_TRACE_FUNCTION(); + + CV_CheckGE(gradient_magnitude_threshold_max_, 0.0f, ""); + gradient_magnitude_threshold_max = gradient_magnitude_threshold_max_; + resetFeatures_(); + } + + void setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value_) + { + CV_TRACE_FUNCTION(); + + CV_CheckGE(gradient_magnitude_min_value_, 0.0f, ""); + edge_mode = FEATURE_ZERO_CROSSING; + edge_gradient_magnitude_min_value = gradient_magnitude_min_value_; + resetFeatures_(); + } + + void setEdgeFeatureCannyParameters( + double threshold1, double threshold2, + int apertureSize = 3, bool L2gradient = false + ) + { + CV_TRACE_FUNCTION(); + + CV_CheckGE(threshold1, 0.0, ""); + CV_CheckGE(threshold2, 0.0, ""); + edge_mode = FEATURE_CANNY; + edge_canny_threshold1 = threshold1; + edge_canny_threshold2 = threshold2; + edge_canny_apertureSize = apertureSize; + edge_canny_L2gradient = L2gradient; + resetFeatures_(); + } + + void applyImageFeatures( + InputArray non_edge, InputArray gradient_direction_, InputArray gradient_magnitude_, + InputArray image + ) + { + CV_TRACE_FUNCTION(); + + resetFeatures_(); + cleanupFeaturesTemporaryArrays_(); + + src_size = Size(0, 0); + if (!non_edge.empty()) + src_size = non_edge.size(); + if (!gradient_direction_.empty()) + { + Size gradient_direction_size = gradient_direction_.size(); + if (!src_size.empty()) + CV_CheckEQ(src_size, gradient_direction_size, ""); + else + src_size = gradient_direction_size; + } + if (!gradient_magnitude_.empty()) + { + Size gradient_magnitude_size = gradient_magnitude_.size(); + if (!src_size.empty()) + CV_CheckEQ(src_size, gradient_magnitude_size, ""); + else + src_size = gradient_magnitude_size; + } + if (!image.empty()) + { + Size image_size = image.size(); + if (!src_size.empty()) + CV_CheckEQ(src_size, image_size, ""); + else + src_size = image_size; + } + // src_size must be filled + CV_Assert(!src_size.empty()); + + if (!non_edge.empty()) + { + CV_CheckTypeEQ(non_edge.type(), CV_8UC1, ""); + non_edge_feature = non_edge.getMat(); + } + else + { + if (weight_non_edge == 0.0f) + { + non_edge_feature.create(src_size); + non_edge_feature.setTo(0); + } + else + { + if (image.empty()) + CV_Error(Error::StsBadArg, "Non-edge feature parameter is missing. Input image parameter is required to extract this feature"); + extractEdgeFeature_(image); + } + } + + if (!gradient_direction_.empty()) + { + CV_CheckTypeEQ(gradient_direction_.type(), CV_32FC2, ""); + gradient_direction = gradient_direction_.getMat(); + } + else + { + if (weight_gradient_direction == 0.0f) + { + gradient_direction.create(src_size); + gradient_direction.setTo(Scalar::all(0)); + } + else + { + if (image.empty()) + CV_Error(Error::StsBadArg, "Gradient direction feature parameter is missing. Input image parameter is required to extract this feature"); + extractGradientDirection_(image); + } + } + + if (!gradient_magnitude_.empty()) + { + CV_CheckTypeEQ(gradient_magnitude_.type(), CV_32FC1, ""); + gradient_magnitude = gradient_magnitude_.getMat(); + } + else + { + if (weight_gradient_magnitude == 0.0f) + { + gradient_magnitude.create(src_size); + gradient_magnitude.setTo(Scalar::all(0)); + } + else + { + if (image.empty()) + CV_Error(Error::StsBadArg, "Gradient magnitude feature parameter is missing. Input image parameter is required to extract this feature"); + extractGradientMagnitude_(image); + } + } + + cleanupFeaturesTemporaryArrays_(); + } + + + void extractEdgeFeature_(InputArray image) + { + CV_TRACE_FUNCTION(); + + if (edge_mode == FEATURE_CANNY) + { + CV_LOG_DEBUG(NULL, "Canny(" << edge_canny_threshold1 << ", " << edge_canny_threshold2 << ")"); + Mat img_canny; + Canny(image, img_canny, edge_canny_threshold1, edge_canny_threshold2, edge_canny_apertureSize, edge_canny_L2gradient); +#if 0 + threshold(img_canny, non_edge_feature, 254, 1, THRESH_BINARY_INV); +#else + // Canny result values are 0 or 255 + bitwise_not(img_canny, non_edge_feature); + weight_non_edge_compute = weight_non_edge * (1.0f / 255.0f); +#endif + } + else // if (edge_mode == FEATURE_ZERO_CROSSING) + { + initGrayscale_(image); + Mat_ laplacian; + Laplacian(grayscale_, laplacian, CV_16S, laplacianKernelSize); + Mat_ zero_crossing(src_size, 1); + + const size_t zstep = zero_crossing.step[0]; + for (int y = 0; y < src_size.height - 1; y++) + { + const short* row0 = laplacian.ptr(y); + const short* row1 = laplacian.ptr(y + 1); + uchar* zrow0 = zero_crossing.ptr(y); + //uchar* zrow1 = zero_crossing.ptr(y + 1); + for (int x = 0; x < src_size.width - 1; x++) + { + const int v = row0[x]; + const int neg_v = -v; + // - * 1 + // 2 3 4 + const int v1 = row0[x + 1]; + const int v2 = (x > 0) ? row1[x - 1] : v; + const int v3 = row1[x + 0]; + const int v4 = row1[x + 1]; + if (v < 0) + { + if (v1 > 0) + { + zrow0[x + ((v1 < neg_v) ? 1 : 0)] = 0; + } + if (v2 > 0) + { + zrow0[x + ((v2 < neg_v) ? (zstep - 1) : 0)] = 0; + } + if (v3 > 0) + { + zrow0[x + ((v3 < neg_v) ? (zstep + 0) : 0)] = 0; + } + if (v4 > 0) + { + zrow0[x + ((v4 < neg_v) ? (zstep + 1) : 0)] = 0; + } + } + else + { + if (v1 < 0) + { + zrow0[x + ((v1 > neg_v) ? 1 : 0)] = 0; + } + if (v2 < 0) + { + zrow0[x + ((v2 > neg_v) ? (zstep - 1) : 0)] = 0; + } + if (v3 < 0) + { + zrow0[x + ((v3 > neg_v) ? (zstep + 0) : 0)] = 0; + } + if (v4 < 0) + { + zrow0[x + ((v4 > neg_v) ? (zstep + 1) : 0)] = 0; + } + } + } + } + + if (edge_gradient_magnitude_min_value > 0) + { + initImageMagnitude_(image); + Mat mask = image_magnitude_ < edge_gradient_magnitude_min_value; + zero_crossing.setTo(1, mask); // reset low-amplitude noise + } + + non_edge_feature = zero_crossing; + } + } + + + void extractGradientDirection_(InputArray image) + { + CV_TRACE_FUNCTION(); + + initImageMagnitude_(image); // calls internally: initImageDerives_(image); + gradient_direction.create(src_size); + for (int y = 0; y < src_size.height; y++) + { + const float* magnutude_row = image_magnitude_.ptr(y); + const float* Ix_row = Ix_.ptr(y); + const float* Iy_row = Iy_.ptr(y); + Point2f* gradient_direction_row = gradient_direction.ptr(y); + for (int x = 0; x < src_size.width; x++) + { + const float m = magnutude_row[x]; + if (m > FLT_EPSILON) + { + float m_inv = 1.0f / m; + gradient_direction_row[x] = Point2f(Ix_row[x] * m_inv, Iy_row[x] * m_inv); + } + else + { + gradient_direction_row[x] = Point2f(0, 0); + } + } + } + } + + void extractGradientMagnitude_(InputArray image) + { + CV_TRACE_FUNCTION(); + + initImageMagnitude_(image); // calls internally: initImageDerives_(image); + Mat m; + double max_m = 0; + if (gradient_magnitude_threshold_max > 0) + { + threshold(image_magnitude_, m, gradient_magnitude_threshold_max, 0, THRESH_TRUNC); + max_m = gradient_magnitude_threshold_max; + } + else + { + m = image_magnitude_; + minMaxLoc(m, 0, &max_m); + } + if (max_m <= FLT_EPSILON) + { + CV_LOG_INFO(NULL, "IntelligentScissorsMB: input image gradient is almost zero") + gradient_magnitude.create(src_size); + gradient_magnitude.setTo(0); + } + else + { + m.convertTo(gradient_magnitude, CV_32F, -1.0 / max_m, 1.0); // normalize and inverse to range 0..1 + } + } + + void applyImage(InputArray image) + { + CV_TRACE_FUNCTION(); + + CV_CheckType(image.type(), image.type() == CV_8UC1 || image.type() == CV_8UC3 || image.type() == CV_8UC4, ""); + + resetFeatures_(); + cleanupFeaturesTemporaryArrays_(); + extractEdgeFeature_(image); + extractGradientDirection_(image); + extractGradientMagnitude_(image); + cleanupFeaturesTemporaryArrays_(); + } + + + // details: see section 3.1 of the article + const float* acos_table = getAcosTable(); + float local_cost(const Point& p, const Point& q) const + { + const bool isDiag = (p.x != q.x) && (p.y != q.y); + + float fG = gradient_magnitude.at(q); + + const Point2f diff((float)(q.x - p.x), (float)(q.y - p.y)); + + const Point2f Ip = gradient_direction(p); + const Point2f Iq = gradient_direction(q); + + const Point2f Dp(Ip.y, -Ip.x); // D(p) - 90 degrees clockwise + const Point2f Dq(Iq.y, -Iq.x); // D(q) - 90 degrees clockwise + + float dp = Dp.dot(diff); // dp(p, q) + float dq = Dq.dot(diff); // dq(p, q) + if (dp < 0) + { + dp = -dp; // ensure dp >= 0 + dq = -dq; + } + + const float sqrt2_inv = 0.7071067811865475f; // 1.0 / sqrt(2) + if (isDiag) + { + dp *= sqrt2_inv; // normalize length of (q - p) + dq *= sqrt2_inv; // normalize length of (q - p) + } + else + { + fG *= sqrt2_inv; + } + +#if 1 + int dp_i = cvFloor(dp * ACOS_TABLE_SIZE); // dp is in range 0..1 + dp_i = std::min(ACOS_TABLE_SIZE, std::max(0, dp_i)); + int dq_i = cvFloor(dq * ACOS_TABLE_SIZE); // dq is in range -1..1 + dq_i = std::min(ACOS_TABLE_SIZE, std::max(-ACOS_TABLE_SIZE, dq_i)); + const float fD = acos_table[dp_i + ACOS_TABLE_SIZE] + acos_table[dq_i + ACOS_TABLE_SIZE]; +#else + const float CV_PI_inv = static_cast(1.0 / CV_PI); + const float fD = (acosf(dp) + acosf(dq)) * CV_PI_inv; // TODO optimize acos calls (through tables) +#endif + + float cost = + weight_non_edge_compute * non_edge_feature.at(q) + + weight_gradient_direction * fD + + weight_gradient_magnitude * fG; + return cost; + } + + struct Pix + { + Point pt; + float cost; // NOTE: do not remove cost from here through replacing by cost(pt) map access + + inline bool operator > (const Pix &b) const + { + return cost > b.cost; + } + }; + + void buildMap(const Point& start_point) + { + CV_TRACE_FUNCTION(); + + CV_Assert(!src_size.empty()); + CV_Assert(!gradient_magnitude.empty() && "Features are missing. applyImage() must be called first"); + + CV_CheckGE(weight_non_edge + weight_gradient_direction + weight_gradient_magnitude, FLT_EPSILON, ""); + +#if 0 // debug + Rect wholeImage(0, 0, src_size.width, src_size.height); + Rect roi = Rect(start_point.x - 5, start_point.y - 5, 11, 11) & wholeImage; + std::cout << roi << std::endl; + std::cout << gradient_magnitude(roi) << std::endl; + std::cout << gradient_direction(roi) << std::endl; + std::cout << non_edge_feature(roi) << std::endl; +#endif + + optimalPathsMap.release(); + optimalPathsMap.create(src_size); + optimalPathsMap.setTo(0); // optimalPathsMap(start_point) = 0; + + // + // Section 3.2 + // Live-Wire 2-D DP graph search. + // + + Mat_ cost_map(src_size, FLT_MAX); // g(q) + Mat_ processed(src_size, (uchar)0); // e(q) + + // Note: std::vector is faster than std::deque + // TODO check std::set + std::priority_queue< Pix, std::vector, std::greater > L; + + cost_map(start_point) = 0; + L.emplace(Pix{ start_point, 0/*cost*/ }); + + while (!L.empty()) + { + Pix pix = L.top(); L.pop(); + Point q = pix.pt; // 'q' from the article + if (processed(q)) + continue; // already processed (with lower cost, see note below) + processed(q) = 1; +#if 1 + const float cost_q = pix.cost; +#else + const float cost_q = cost_map(q); + CV_Assert(cost_q == pix.cost); +#endif + for (int n = 0; n < 8; n++) // scan neighbours + { + Point r(q.x + neighbors[n][0], q.y + neighbors[n][1]); // 'r' from the article + if (r.x < 0 || r.x >= src_size.width || r.y < 0 || r.y >= src_size.height) + continue; // out of range + +#if !defined(__EMSCRIPTEN__) // slower in JS + float& cost_r = cost_map(r); + if (cost_r < cost_q) + continue; // already processed +#else + if (processed(r)) + continue; // already processed + + float& cost_r = cost_map(r); + CV_DbgCheckLE(cost_q, cost_r, "INTERNAL ERROR: sorted queue is corrupted"); +#endif + + float cost = cost_q + local_cost(q, r); // TODO(opt): compute partially until cost < cost_r + if (cost < cost_r) + { +#if 0 // avoid compiler warning + if (cost_r != FLT_MAX) + { + // In article the point 'r' is removed from the queue L + // to be re-inserted again with sorting against new optimized cost. + // We can do nothing, because "new point" will be placed before in the sorted queue. + // Old point will be skipped through "if (processed(q))" check above after processing of new optimal candidate. + // + // This approach leads to some performance impact, however it is much smaller than element removal from the sorted queue. + // So, do nothing. + } +#endif + cost_r = cost; + L.emplace(Pix{ r, cost }); + optimalPathsMap(r) = (uchar)neighbors_encode[n]; + } + } + } + } + + void getContour(const Point& target, OutputArray contour_, bool backward) + { + CV_TRACE_FUNCTION(); + + CV_Assert(!optimalPathsMap.empty() && "buildMap() must be called before getContour()"); + + const int cols = optimalPathsMap.cols; + const int rows = optimalPathsMap.rows; + + std::vector result; result.reserve(512); + + size_t loop_check = 4096; + Point pt = target; + for (size_t i = 0; i < (size_t)rows * cols; i++) // don't hang on invalid maps + { + CV_CheckLT(pt.x, cols, ""); + CV_CheckLT(pt.y, rows, ""); + result.push_back(pt); + int direction = (int)optimalPathsMap(pt); + if (direction == 0) + break; // stop, start point is reached + CV_CheckLT(direction, 9, "Map is invalid"); + Point next(pt.x + neighbors[direction - 1][0], pt.y + neighbors[direction - 1][1]); + pt = next; + + if (result.size() == loop_check) // optional sanity check of invalid maps with loops (don't eat huge amount of memory) + { + loop_check *= 4; // next limit for loop check + for (const auto& pt_check : result) + { + CV_CheckNE(pt_check, pt, "Map is invalid. Contour loop is detected"); + } + } + } + + if (backward) + { + _InputArray(result).copyTo(contour_); + } + else + { + const int N = (int)result.size(); + const int sz[1] = { N }; + contour_.create(1, sz, CV_32SC2); + Mat_ contour = contour_.getMat(); + for (int i = 0; i < N; i++) + { + contour.at(i) = result[N - (i + 1)]; + } + } + } +}; + + + +IntelligentScissorsMB::IntelligentScissorsMB() + : impl(std::make_shared()) +{ + // nothing +} + +IntelligentScissorsMB& IntelligentScissorsMB::setWeights(float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude) +{ + CV_DbgAssert(impl); + impl->setWeights(weight_non_edge, weight_gradient_direction, weight_gradient_magnitude); + return *this; +} + +IntelligentScissorsMB& IntelligentScissorsMB::setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max) +{ + CV_DbgAssert(impl); + impl->setGradientMagnitudeMaxLimit(gradient_magnitude_threshold_max); + return *this; +} + +IntelligentScissorsMB& IntelligentScissorsMB::setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value) +{ + CV_DbgAssert(impl); + impl->setEdgeFeatureZeroCrossingParameters(gradient_magnitude_min_value); + return *this; +} + +IntelligentScissorsMB& IntelligentScissorsMB::setEdgeFeatureCannyParameters( + double threshold1, double threshold2, + int apertureSize, bool L2gradient +) +{ + CV_DbgAssert(impl); + impl->setEdgeFeatureCannyParameters(threshold1, threshold2, apertureSize, L2gradient); + return *this; +} + +IntelligentScissorsMB& IntelligentScissorsMB::applyImage(InputArray image) +{ + CV_DbgAssert(impl); + impl->applyImage(image); + return *this; +} + +IntelligentScissorsMB& IntelligentScissorsMB::applyImageFeatures( + InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude, + InputArray image +) +{ + CV_DbgAssert(impl); + impl->applyImageFeatures(non_edge, gradient_direction, gradient_magnitude, image); + return *this; +} + +void IntelligentScissorsMB::buildMap(const Point& pt) +{ + CV_DbgAssert(impl); + impl->buildMap(pt); +} + +void IntelligentScissorsMB::getContour(const Point& target, OutputArray contour, bool backward) const +{ + CV_DbgAssert(impl); + impl->getContour(target, contour, backward); +} + +}} // namespace diff --git a/modules/imgproc/test/test_intelligent_scissors.cpp b/modules/imgproc/test/test_intelligent_scissors.cpp new file mode 100644 index 0000000..c6b51fd --- /dev/null +++ b/modules/imgproc/test/test_intelligent_scissors.cpp @@ -0,0 +1,467 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "test_precomp.hpp" +//#include "opencv2/imgproc/segmentation.hpp" + +namespace opencv_test { namespace { + + +Mat getTestImageGray() +{ + static Mat m; + if (m.empty()) + { + m = imread(findDataFile("shared/lena.png"), IMREAD_GRAYSCALE); + } + return m.clone(); +} + +Mat getTestImageColor() +{ + static Mat m; + if (m.empty()) + { + m = imread(findDataFile("shared/lena.png"), IMREAD_COLOR); + } + return m.clone(); +} + +Mat getTestImage1() +{ + static Mat m; + if (m.empty()) + { + m.create(Size(200, 100), CV_8UC1); + m.setTo(Scalar::all(128)); + Rect roi(50, 30, 100, 40); + m(roi).setTo(Scalar::all(0)); +#if 0 + imshow("image", m); + waitKey(); +#endif + } + return m.clone(); +} + +Mat getTestImage2() +{ + static Mat m; + if (m.empty()) + { + m.create(Size(200, 100), CV_8UC1); + m.setTo(Scalar::all(128)); + Rect roi(40, 30, 100, 40); + m(roi).setTo(Scalar::all(255)); +#if 0 + imshow("image", m); + waitKey(); +#endif + } + return m.clone(); +} + +Mat getTestImage3() +{ + static Mat m; + if (m.empty()) + { + m.create(Size(200, 100), CV_8UC1); + m.setTo(Scalar::all(128)); + Scalar color(0,0,0,0); + line(m, Point(30, 50), Point(50, 50), color, 1); + line(m, Point(50, 50), Point(80, 30), color, 1); + line(m, Point(150, 50), Point(80, 30), color, 1); + line(m, Point(150, 50), Point(180, 50), color, 1); + + line(m, Point(80, 10), Point(80, 90), Scalar::all(200), 1); + line(m, Point(100, 10), Point(100, 90), Scalar::all(200), 1); + line(m, Point(120, 10), Point(120, 90), Scalar::all(200), 1); +#if 0 + imshow("image", m); + waitKey(); +#endif + } + return m.clone(); +} + +Mat getTestImage4() +{ + static Mat m; + if (m.empty()) + { + m.create(Size(200, 100), CV_8UC1); + for (int y = 0; y < m.rows; y++) + { + for (int x = 0; x < m.cols; x++) + { + float dx = (float)(x - 100); + float dy = (float)(y - 100); + float d = sqrtf(dx * dx + dy * dy); + m.at(y, x) = saturate_cast(100 + 100 * sin(d / 10 * CV_PI)); + } + } +#if 0 + imshow("image", m); + waitKey(); +#endif + } + return m.clone(); +} + +Mat getTestImage5() +{ + static Mat m; + if (m.empty()) + { + m.create(Size(200, 100), CV_8UC1); + for (int y = 0; y < m.rows; y++) + { + for (int x = 0; x < m.cols; x++) + { + float dx = (float)(x - 100); + float dy = (float)(y - 100); + float d = sqrtf(dx * dx + dy * dy); + m.at(y, x) = saturate_cast(x / 2 + 100 * sin(d / 10 * CV_PI)); + } + } +#if 0 + imshow("image", m); + waitKey(); +#endif + } + return m.clone(); +} + +void show(const Mat& img, const std::vector pts) +{ + if (cvtest::debugLevel >= 10) + { + Mat dst = img.clone(); + std::vector< std::vector > contours; + contours.push_back(pts); + polylines(dst, contours, false, Scalar::all(255)); + imshow("dst", dst); + waitKey(); + } +} + +TEST(Imgproc_IntelligentScissorsMB, rect) +{ + segmentation::IntelligentScissorsMB tool; + + tool.applyImage(getTestImage1()); + + Point source_point(50, 30); + tool.buildMap(source_point); + + Point target_point(100, 30); + std::vector pts; + tool.getContour(target_point, pts); + + tool.applyImage(getTestImage2()); + + tool.buildMap(source_point); + + std::vector pts2; + tool.getContour(target_point, pts2, true/*backward*/); + + EXPECT_EQ(pts.size(), pts2.size()); +} + +TEST(Imgproc_IntelligentScissorsMB, lines) +{ + segmentation::IntelligentScissorsMB tool; + Mat image = getTestImage3(); + tool.applyImage(image); + + Point source_point(30, 50); + tool.buildMap(source_point); + + Point target_point(150, 50); + std::vector pts; + tool.getContour(target_point, pts); + + EXPECT_EQ((size_t)121, pts.size()); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, circles) +{ + segmentation::IntelligentScissorsMB tool; + tool.setGradientMagnitudeMaxLimit(10); + + Mat image = getTestImage4(); + tool.applyImage(image); + + Point source_point(50, 50); + tool.buildMap(source_point); + + Point target_point(150, 50); + std::vector pts; + tool.getContour(target_point, pts); + + EXPECT_EQ((size_t)101, pts.size()); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, circles_gradient) +{ + segmentation::IntelligentScissorsMB tool; + Mat image = getTestImage5(); + tool.applyImage(image); + + Point source_point(50, 50); + tool.buildMap(source_point); + + Point target_point(150, 50); + std::vector pts; + tool.getContour(target_point, pts); + + EXPECT_EQ((size_t)101, pts.size()); + show(image, pts); +} + +#define PTS_SIZE_EPS 2 + +TEST(Imgproc_IntelligentScissorsMB, grayscale) +{ + segmentation::IntelligentScissorsMB tool; + + Mat image = getTestImageGray(); + tool.applyImage(image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 206; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_1_0_0_zerro_crossing_with_limit) +{ + segmentation::IntelligentScissorsMB tool; + tool.setEdgeFeatureZeroCrossingParameters(64); + tool.setWeights(1.0f, 0.0f, 0.0f); + + Mat image = getTestImageGray(); + tool.applyImage(image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 207; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_1_0_0_canny) +{ + segmentation::IntelligentScissorsMB tool; + tool.setEdgeFeatureCannyParameters(50, 100); + tool.setWeights(1.0f, 0.0f, 0.0f); + + Mat image = getTestImageGray(); + tool.applyImage(image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 201; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_0_1_0) +{ + segmentation::IntelligentScissorsMB tool; + tool.setWeights(0.0f, 1.0f, 0.0f); + + Mat image = getTestImageGray(); + tool.applyImage(image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 166; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_0_0_1) +{ + segmentation::IntelligentScissorsMB tool; + tool.setWeights(0.0f, 0.0f, 1.0f); + + Mat image = getTestImageGray(); + tool.applyImage(image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 197; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, color) +{ + segmentation::IntelligentScissorsMB tool; + + Mat image = getTestImageColor(); + tool.applyImage(image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 205; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, color_canny) +{ + segmentation::IntelligentScissorsMB tool; + tool.setEdgeFeatureCannyParameters(32, 100); + + Mat image = getTestImageColor(); + tool.applyImage(image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 200; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + + +TEST(Imgproc_IntelligentScissorsMB, color_custom_features_invalid) +{ + segmentation::IntelligentScissorsMB tool; + ASSERT_ANY_THROW(tool.applyImageFeatures(noArray(), noArray(), noArray())); +} + +TEST(Imgproc_IntelligentScissorsMB, color_custom_features_edge) +{ + segmentation::IntelligentScissorsMB tool; + + Mat image = getTestImageColor(); + + Mat canny_edges; + Canny(image, canny_edges, 32, 100, 5); + Mat binary_edge_feature; + cv::threshold(canny_edges, binary_edge_feature, 254, 1, THRESH_BINARY_INV); + tool.applyImageFeatures(binary_edge_feature, noArray(), noArray(), image); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 201; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, color_custom_features_all) +{ + segmentation::IntelligentScissorsMB tool; + + tool.setWeights(0.9f, 0.0f, 0.1f); + + Mat image = getTestImageColor(); + + Mat canny_edges; + Canny(image, canny_edges, 50, 100, 5); + Mat binary_edge_feature; // 0, 1 values + cv::threshold(canny_edges, binary_edge_feature, 254, 1, THRESH_BINARY_INV); + + Mat_ gradient_direction(image.size(), Point2f(0, 0)); // normalized + Mat_ gradient_magnitude(image.size(), 0); // cost function + tool.applyImageFeatures(binary_edge_feature, gradient_direction, gradient_magnitude); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 201; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + +TEST(Imgproc_IntelligentScissorsMB, color_custom_features_edge_magnitude) +{ + segmentation::IntelligentScissorsMB tool; + + tool.setWeights(0.9f, 0.0f, 0.1f); + + Mat image = getTestImageColor(); + + Mat canny_edges; + Canny(image, canny_edges, 50, 100, 5); + Mat binary_edge_feature; // 0, 1 values + cv::threshold(canny_edges, binary_edge_feature, 254, 1, THRESH_BINARY_INV); + + Mat_ gradient_magnitude(image.size(), 0); // cost function + tool.applyImageFeatures(binary_edge_feature, noArray(), gradient_magnitude); + + Point source_point(275, 63); + tool.buildMap(source_point); + + Point target_point(413, 155); + std::vector pts; + tool.getContour(target_point, pts); + + size_t gold = 201; + EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS); + EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS); + show(image, pts); +} + + +}} // namespace diff --git a/modules/js/src/core_bindings.cpp b/modules/js/src/core_bindings.cpp index c660253..4409778 100644 --- a/modules/js/src/core_bindings.cpp +++ b/modules/js/src/core_bindings.cpp @@ -87,6 +87,8 @@ namespace hal { using namespace emscripten; using namespace cv; +using namespace cv::segmentation; // FIXIT + #ifdef HAVE_OPENCV_DNN using namespace cv::dnn; #endif diff --git a/modules/js/test/test_imgproc.js b/modules/js/test/test_imgproc.js index 9ba5cd4..ada315b 100644 --- a/modules/js/test/test_imgproc.js +++ b/modules/js/test/test_imgproc.js @@ -977,3 +977,26 @@ QUnit.test('warpPolar', function(assert) { 96, 83, 64, 45, 32 ]); }); + + +QUnit.test('IntelligentScissorsMB', function(assert) { + const lines = new cv.Mat(50, 100, cv.CV_8U, new cv.Scalar(0)); + lines.row(10).setTo(new cv.Scalar(255)); + assert.ok(lines instanceof cv.Mat); + + let tool = new cv.segmentation_IntelligentScissorsMB(); + tool.applyImage(lines); + assert.ok(lines instanceof cv.Mat); + lines.delete(); + + tool.buildMap(new cv.Point(10, 10)); + + let contour = new cv.Mat(); + tool.getContour(new cv.Point(50, 10), contour); + assert.equal(contour.type(), cv.CV_32SC2); + assert.ok(contour.total() == 41, contour.total()); + + tool.getContour(new cv.Point(80, 10), contour); + assert.equal(contour.type(), cv.CV_32SC2); + assert.ok(contour.total() == 71, contour.total()); +}); diff --git a/platforms/js/opencv_js.config.py b/platforms/js/opencv_js.config.py index 6e4677f..2bcc7de 100644 --- a/platforms/js/opencv_js.config.py +++ b/platforms/js/opencv_js.config.py @@ -18,8 +18,21 @@ imgproc = {'': ['Canny', 'GaussianBlur', 'Laplacian', 'HoughLines', 'HoughLinesP 'matchShapes', 'matchTemplate','medianBlur', 'minAreaRect', 'minEnclosingCircle', 'moments', 'morphologyEx', \ 'pointPolygonTest', 'putText','pyrDown','pyrUp','rectangle','remap', 'resize','sepFilter2D','threshold', \ 'undistort','warpAffine','warpPerspective','warpPolar','watershed', \ - 'fillPoly', 'fillConvexPoly'], - 'CLAHE': ['apply', 'collectGarbage', 'getClipLimit', 'getTilesGridSize', 'setClipLimit', 'setTilesGridSize']} + 'fillPoly', 'fillConvexPoly', 'polylines', + ], + 'CLAHE': ['apply', 'collectGarbage', 'getClipLimit', 'getTilesGridSize', 'setClipLimit', 'setTilesGridSize'], + 'segmentation_IntelligentScissorsMB': [ + 'IntelligentScissorsMB', + 'setWeights', + 'setGradientMagnitudeMaxLimit', + 'setEdgeFeatureZeroCrossingParameters', + 'setEdgeFeatureCannyParameters', + 'applyImage', + 'applyImageFeatures', + 'buildMap', + 'getContour' + ], +} objdetect = {'': ['groupRectangles'], 'HOGDescriptor': ['load', 'HOGDescriptor', 'getDefaultPeopleDetector', 'getDaimlerPeopleDetector', 'setSVMDetector', 'detectMultiScale'], diff --git a/samples/cpp/CMakeLists.txt b/samples/cpp/CMakeLists.txt index 14ab614..5f40dba 100644 --- a/samples/cpp/CMakeLists.txt +++ b/samples/cpp/CMakeLists.txt @@ -23,6 +23,9 @@ if(NOT BUILD_EXAMPLES OR NOT OCV_DEPENDENCIES_FOUND) return() endif() +set(DEPS_example_snippet_imgproc_segmentation opencv_core opencv_imgproc) +set(DEPS_example_cpp_intelligent_scissors opencv_core opencv_imgproc opencv_imgcodecs opencv_highgui) + project(cpp_samples) ocv_include_modules_recurse(${OPENCV_CPP_SAMPLES_REQUIRED_DEPS}) file(GLOB_RECURSE cpp_samples RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.cpp) @@ -32,11 +35,17 @@ endif() ocv_list_filterout(cpp_samples "real_time_pose_estimation/") foreach(sample_filename ${cpp_samples}) set(package "cpp") - if(sample_filename MATCHES "tutorial_code") + if(sample_filename MATCHES "tutorial_code/snippet") + set(package "snippet") + elseif(sample_filename MATCHES "tutorial_code") set(package "tutorial") endif() ocv_define_sample(tgt ${sample_filename} ${package}) - ocv_target_link_libraries(${tgt} PRIVATE ${OPENCV_LINKER_LIBS} ${OPENCV_CPP_SAMPLES_REQUIRED_DEPS}) + set(deps ${OPENCV_CPP_SAMPLES_REQUIRED_DEPS}) + if(DEFINED DEPS_${tgt}) + set(deps ${DEPS_${tgt}}) + endif() + ocv_target_link_libraries(${tgt} PRIVATE ${OPENCV_LINKER_LIBS} ${deps}) if(sample_filename MATCHES "/gpu/" AND HAVE_opencv_cudaarithm AND HAVE_opencv_cuda_filters) ocv_target_link_libraries(${tgt} PRIVATE opencv_cudaarithm opencv_cudafilters) endif() diff --git a/samples/cpp/tutorial_code/snippets/imgproc_segmentation.cpp b/samples/cpp/tutorial_code/snippets/imgproc_segmentation.cpp new file mode 100644 index 0000000..b81ba34 --- /dev/null +++ b/samples/cpp/tutorial_code/snippets/imgproc_segmentation.cpp @@ -0,0 +1,35 @@ +#include "opencv2/imgproc.hpp" +#include "opencv2/imgproc/segmentation.hpp" + +using namespace cv; + +static +void usage_example_intelligent_scissors() +{ + Mat image(Size(1920, 1080), CV_8UC3, Scalar::all(128)); + + //! [usage_example_intelligent_scissors] + segmentation::IntelligentScissorsMB tool; + tool.setEdgeFeatureCannyParameters(16, 100) // using Canny() as edge feature extractor + .setGradientMagnitudeMaxLimit(200); + + // calculate image features + tool.applyImage(image); + + // calculate map for specified source point + Point source_point(200, 100); + tool.buildMap(source_point); + + // fast fetching of contours + // for specified target point and the pre-calculated map (stored internally) + Point target_point(400, 300); + std::vector pts; + tool.getContour(target_point, pts); + //! [usage_example_intelligent_scissors] +} + +int main() +{ + usage_example_intelligent_scissors(); + return 0; +} diff --git a/samples/samples_utils.cmake b/samples/samples_utils.cmake index 9459099..2780b47 100644 --- a/samples/samples_utils.cmake +++ b/samples/samples_utils.cmake @@ -4,6 +4,9 @@ function(ocv_define_sample out_target source sub) get_filename_component(name "${source}" NAME_WE) set(the_target "example_${sub}_${name}") + if(OPENCV_DUMP_EXAMPLE_TARGET) + message(STATUS "Example: ${the_target} (${source})") + endif() add_executable(${the_target} "${source}") if(TARGET Threads::Threads AND NOT OPENCV_EXAMPLES_DISABLE_THREADS) target_link_libraries(${the_target} PRIVATE Threads::Threads) -- 2.7.4