Health/Assets/OpenCVForUnity/Plugins/iOS/opencv2.framework/Headers/ScanSegment.h

122 lines
4.0 KiB
Objective-C

//
// This file is auto-generated. Please don't modify it!
//
#pragma once
#ifdef __cplusplus
//#import "opencv.hpp"
#import "opencv2/ximgproc.hpp"
#import "opencv2/ximgproc/scansegment.hpp"
#else
#define CV_EXPORTS
#endif
#import <Foundation/Foundation.h>
#import "Algorithm.h"
@class Mat;
NS_ASSUME_NONNULL_BEGIN
// C++: class ScanSegment
/**
* Class implementing the F-DBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels
* algorithm by Loke SC, et al. CITE: loke2021accelerated for original paper.
*
* The algorithm uses a parallelised DBSCAN cluster search that is resistant to noise, competitive in segmentation quality, and faster than
* existing superpixel segmentation methods. When tested on the Berkeley Segmentation Dataset, the average processing speed is 175 frames/s
* with a Boundary Recall of 0.797 and an Achievable Segmentation Accuracy of 0.944. The computational complexity is quadratic O(n2) and
* more suited to smaller images, but can still process a 2MP colour image faster than the SEEDS algorithm in OpenCV. The output is deterministic
* when the number of processing threads is fixed, and requires the source image to be in Lab colour format.
*
* Member of `Ximgproc`
*/
CV_EXPORTS @interface ScanSegment : Algorithm
#ifdef __cplusplus
@property(readonly)cv::Ptr<cv::ximgproc::ScanSegment> nativePtrScanSegment;
#endif
#ifdef __cplusplus
- (instancetype)initWithNativePtr:(cv::Ptr<cv::ximgproc::ScanSegment>)nativePtr;
+ (instancetype)fromNative:(cv::Ptr<cv::ximgproc::ScanSegment>)nativePtr;
#endif
#pragma mark - Methods
//
// int cv::ximgproc::ScanSegment::getNumberOfSuperpixels()
//
/**
* Returns the actual superpixel segmentation from the last image processed using iterate.
*
* Returns zero if no image has been processed.
*/
- (int)getNumberOfSuperpixels NS_SWIFT_NAME(getNumberOfSuperpixels());
//
// void cv::ximgproc::ScanSegment::iterate(Mat img)
//
/**
* Calculates the superpixel segmentation on a given image with the initialized
* parameters in the ScanSegment object.
*
* This function can be called again for other images without the need of initializing the algorithm with createScanSegment().
* This save the computational cost of allocating memory for all the structures of the algorithm.
*
* @param img Input image. Supported format: CV_8UC3. Image size must match with the initialized
* image size with the function createScanSegment(). It MUST be in Lab color space.
*/
- (void)iterate:(Mat*)img NS_SWIFT_NAME(iterate(img:));
//
// void cv::ximgproc::ScanSegment::getLabels(Mat& labels_out)
//
/**
* Returns the segmentation labeling of the image.
*
* Each label represents a superpixel, and each pixel is assigned to one superpixel label.
*
* @param labels_out Return: A CV_32UC1 integer array containing the labels of the superpixel
* segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
*/
- (void)getLabels:(Mat*)labels_out NS_SWIFT_NAME(getLabels(labels_out:));
//
// void cv::ximgproc::ScanSegment::getLabelContourMask(Mat& image, bool thick_line = false)
//
/**
* Returns the mask of the superpixel segmentation stored in the ScanSegment object.
*
* The function return the boundaries of the superpixel segmentation.
*
* @param image Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.
* @param thick_line If false, the border is only one pixel wide, otherwise all pixels at the border are masked.
*/
- (void)getLabelContourMask:(Mat*)image thick_line:(BOOL)thick_line NS_SWIFT_NAME(getLabelContourMask(image:thick_line:));
/**
* Returns the mask of the superpixel segmentation stored in the ScanSegment object.
*
* The function return the boundaries of the superpixel segmentation.
*
* @param image Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.
*/
- (void)getLabelContourMask:(Mat*)image NS_SWIFT_NAME(getLabelContourMask(image:));
@end
NS_ASSUME_NONNULL_END