mirror of
https://github.com/opencv/opencv.git
synced 2024-12-18 03:18:01 +08:00
142 lines
5.9 KiB
C++
142 lines
5.9 KiB
C++
// 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: <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>
|
|
* 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 input 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<Point>`)
|
|
* @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> impl;
|
|
#endif
|
|
};
|
|
|
|
//! @}
|
|
|
|
} // namespace segmentation
|
|
} // namespace cv
|
|
|
|
#endif // OPENCV_IMGPROC_SEGMENTATION_HPP
|