202 lines
8.4 KiB
Objective-C
202 lines
8.4 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/seeds.hpp"
|
|
#else
|
|
#define CV_EXPORTS
|
|
#endif
|
|
|
|
#import <Foundation/Foundation.h>
|
|
#import "Algorithm.h"
|
|
|
|
@class Mat;
|
|
|
|
|
|
|
|
NS_ASSUME_NONNULL_BEGIN
|
|
|
|
// C++: class SuperpixelSEEDS
|
|
/**
|
|
* Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels
|
|
* algorithm described in CITE: VBRV14 .
|
|
*
|
|
* The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy
|
|
* function that is based on color histograms and a boundary term, which is optional. The energy
|
|
* function encourages superpixels to be of the same color, and if the boundary term is activated, the
|
|
* superpixels have smooth boundaries and are of similar shape. In practice it starts from a regular
|
|
* grid of superpixels and moves the pixels or blocks of pixels at the boundaries to refine the
|
|
* solution. The algorithm runs in real-time using a single CPU.
|
|
*
|
|
* Member of `Ximgproc`
|
|
*/
|
|
CV_EXPORTS @interface SuperpixelSEEDS : Algorithm
|
|
|
|
|
|
#ifdef __cplusplus
|
|
@property(readonly)cv::Ptr<cv::ximgproc::SuperpixelSEEDS> nativePtrSuperpixelSEEDS;
|
|
#endif
|
|
|
|
#ifdef __cplusplus
|
|
- (instancetype)initWithNativePtr:(cv::Ptr<cv::ximgproc::SuperpixelSEEDS>)nativePtr;
|
|
+ (instancetype)fromNative:(cv::Ptr<cv::ximgproc::SuperpixelSEEDS>)nativePtr;
|
|
#endif
|
|
|
|
|
|
#pragma mark - Methods
|
|
|
|
|
|
//
|
|
// int cv::ximgproc::SuperpixelSEEDS::getNumberOfSuperpixels()
|
|
//
|
|
/**
|
|
* Calculates the superpixel segmentation on a given image stored in SuperpixelSEEDS object.
|
|
*
|
|
* The function computes the superpixels segmentation of an image with the parameters initialized
|
|
* with the function createSuperpixelSEEDS().
|
|
*/
|
|
- (int)getNumberOfSuperpixels NS_SWIFT_NAME(getNumberOfSuperpixels());
|
|
|
|
|
|
//
|
|
// void cv::ximgproc::SuperpixelSEEDS::iterate(Mat img, int num_iterations = 4)
|
|
//
|
|
/**
|
|
* Calculates the superpixel segmentation on a given image with the initialized
|
|
* parameters in the SuperpixelSEEDS object.
|
|
*
|
|
* This function can be called again for other images without the need of initializing the
|
|
* algorithm with createSuperpixelSEEDS(). This save the computational cost of allocating memory
|
|
* for all the structures of the algorithm.
|
|
*
|
|
* @param img Input image. Supported formats: CV_8U, CV_16U, CV_32F. Image size & number of
|
|
* channels must match with the initialized image size & channels with the function
|
|
* createSuperpixelSEEDS(). It should be in HSV or Lab color space. Lab is a bit better, but also
|
|
* slower.
|
|
*
|
|
* @param num_iterations Number of pixel level iterations. Higher number improves the result.
|
|
*
|
|
* The function computes the superpixels segmentation of an image with the parameters initialized
|
|
* with the function createSuperpixelSEEDS(). The algorithms starts from a grid of superpixels and
|
|
* then refines the boundaries by proposing updates of blocks of pixels that lie at the boundaries
|
|
* from large to smaller size, finalizing with proposing pixel updates. An illustrative example
|
|
* can be seen below.
|
|
*
|
|
* 
|
|
*/
|
|
- (void)iterate:(Mat*)img num_iterations:(int)num_iterations NS_SWIFT_NAME(iterate(img:num_iterations:));
|
|
|
|
/**
|
|
* Calculates the superpixel segmentation on a given image with the initialized
|
|
* parameters in the SuperpixelSEEDS object.
|
|
*
|
|
* This function can be called again for other images without the need of initializing the
|
|
* algorithm with createSuperpixelSEEDS(). This save the computational cost of allocating memory
|
|
* for all the structures of the algorithm.
|
|
*
|
|
* @param img Input image. Supported formats: CV_8U, CV_16U, CV_32F. Image size & number of
|
|
* channels must match with the initialized image size & channels with the function
|
|
* createSuperpixelSEEDS(). It should be in HSV or Lab color space. Lab is a bit better, but also
|
|
* slower.
|
|
*
|
|
*
|
|
* The function computes the superpixels segmentation of an image with the parameters initialized
|
|
* with the function createSuperpixelSEEDS(). The algorithms starts from a grid of superpixels and
|
|
* then refines the boundaries by proposing updates of blocks of pixels that lie at the boundaries
|
|
* from large to smaller size, finalizing with proposing pixel updates. An illustrative example
|
|
* can be seen below.
|
|
*
|
|
* 
|
|
*/
|
|
- (void)iterate:(Mat*)img NS_SWIFT_NAME(iterate(img:));
|
|
|
|
|
|
//
|
|
// void cv::ximgproc::SuperpixelSEEDS::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()].
|
|
*
|
|
* The function returns an image with ssthe 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::SuperpixelSEEDS::getLabelContourMask(Mat& image, bool thick_line = false)
|
|
//
|
|
/**
|
|
* Returns the mask of the superpixel segmentation stored in SuperpixelSEEDS object.
|
|
*
|
|
* @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.
|
|
*
|
|
* The function return the boundaries of the superpixel segmentation.
|
|
*
|
|
* NOTE:
|
|
* - (Python) A demo on how to generate superpixels in images from the webcam can be found at
|
|
* opencv_source_code/samples/python2/seeds.py
|
|
* - (cpp) A demo on how to generate superpixels in images from the webcam can be found at
|
|
* opencv_source_code/modules/ximgproc/samples/seeds.cpp. By adding a file image as a command
|
|
* line argument, the static image will be used instead of the webcam.
|
|
* - It will show a window with the video from the webcam with the superpixel boundaries marked
|
|
* in red (see below). Use Space to switch between different output modes. At the top of the
|
|
* window there are 4 sliders, from which the user can change on-the-fly the number of
|
|
* superpixels, the number of block levels, the strength of the boundary prior term to modify
|
|
* the shape, and the number of iterations at pixel level. This is useful to play with the
|
|
* parameters and set them to the user convenience. In the console the frame-rate of the
|
|
* algorithm is indicated.
|
|
*
|
|
* 
|
|
*/
|
|
- (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 SuperpixelSEEDS object.
|
|
*
|
|
* @param image Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border,
|
|
* and 0 otherwise.
|
|
*
|
|
* are masked.
|
|
*
|
|
* The function return the boundaries of the superpixel segmentation.
|
|
*
|
|
* NOTE:
|
|
* - (Python) A demo on how to generate superpixels in images from the webcam can be found at
|
|
* opencv_source_code/samples/python2/seeds.py
|
|
* - (cpp) A demo on how to generate superpixels in images from the webcam can be found at
|
|
* opencv_source_code/modules/ximgproc/samples/seeds.cpp. By adding a file image as a command
|
|
* line argument, the static image will be used instead of the webcam.
|
|
* - It will show a window with the video from the webcam with the superpixel boundaries marked
|
|
* in red (see below). Use Space to switch between different output modes. At the top of the
|
|
* window there are 4 sliders, from which the user can change on-the-fly the number of
|
|
* superpixels, the number of block levels, the strength of the boundary prior term to modify
|
|
* the shape, and the number of iterations at pixel level. This is useful to play with the
|
|
* parameters and set them to the user convenience. In the console the frame-rate of the
|
|
* algorithm is indicated.
|
|
*
|
|
* 
|
|
*/
|
|
- (void)getLabelContourMask:(Mat*)image NS_SWIFT_NAME(getLabelContourMask(image:));
|
|
|
|
|
|
|
|
@end
|
|
|
|
NS_ASSUME_NONNULL_END
|
|
|
|
|