// // 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 #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 nativePtrSuperpixelSEEDS; #endif #ifdef __cplusplus - (instancetype)initWithNativePtr:(cv::Ptr)nativePtr; + (instancetype)fromNative:(cv::Ptr)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. * * ![image](pics/superpixels_blocks2.png) */ - (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. * * ![image](pics/superpixels_blocks2.png) */ - (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. * * ![image](pics/superpixels_demo.png) */ - (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. * * ![image](pics/superpixels_demo.png) */ - (void)getLabelContourMask:(Mat*)image NS_SWIFT_NAME(getLabelContourMask(image:)); @end NS_ASSUME_NONNULL_END