using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
namespace OpenCVForUnity.XimgprocModule
{
// 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.
*/
public class SuperpixelSEEDS : Algorithm
{
protected override void Dispose(bool disposing)
{
try
{
if (disposing)
{
}
if (IsEnabledDispose)
{
if (nativeObj != IntPtr.Zero)
ximgproc_SuperpixelSEEDS_delete(nativeObj);
nativeObj = IntPtr.Zero;
}
}
finally
{
base.Dispose(disposing);
}
}
protected internal SuperpixelSEEDS(IntPtr addr) : base(addr) { }
// internal usage only
public static new SuperpixelSEEDS __fromPtr__(IntPtr addr) { return new SuperpixelSEEDS(addr); }
//
// C++: 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().
* return automatically generated
*/
public int getNumberOfSuperpixels()
{
ThrowIfDisposed();
return ximgproc_SuperpixelSEEDS_getNumberOfSuperpixels_10(nativeObj);
}
//
// C++: 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.
*
* 
*/
public void iterate(Mat img, int num_iterations)
{
ThrowIfDisposed();
if (img != null) img.ThrowIfDisposed();
ximgproc_SuperpixelSEEDS_iterate_10(nativeObj, img.nativeObj, 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.
*
* 
*/
public void iterate(Mat img)
{
ThrowIfDisposed();
if (img != null) img.ThrowIfDisposed();
ximgproc_SuperpixelSEEDS_iterate_11(nativeObj, img.nativeObj);
}
//
// C++: 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()].
*/
public void getLabels(Mat labels_out)
{
ThrowIfDisposed();
if (labels_out != null) labels_out.ThrowIfDisposed();
ximgproc_SuperpixelSEEDS_getLabels_10(nativeObj, labels_out.nativeObj);
}
//
// C++: 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.
*
*
*
* 
*
*
*/
public void getLabelContourMask(Mat image, bool thick_line)
{
ThrowIfDisposed();
if (image != null) image.ThrowIfDisposed();
ximgproc_SuperpixelSEEDS_getLabelContourMask_10(nativeObj, image.nativeObj, 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.
*
*
*
* 
*
*
*/
public void getLabelContourMask(Mat image)
{
ThrowIfDisposed();
if (image != null) image.ThrowIfDisposed();
ximgproc_SuperpixelSEEDS_getLabelContourMask_11(nativeObj, image.nativeObj);
}
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
const string LIBNAME = "__Internal";
#else
const string LIBNAME = "opencvforunity";
#endif
// C++: int cv::ximgproc::SuperpixelSEEDS::getNumberOfSuperpixels()
[DllImport(LIBNAME)]
private static extern int ximgproc_SuperpixelSEEDS_getNumberOfSuperpixels_10(IntPtr nativeObj);
// C++: void cv::ximgproc::SuperpixelSEEDS::iterate(Mat img, int num_iterations = 4)
[DllImport(LIBNAME)]
private static extern void ximgproc_SuperpixelSEEDS_iterate_10(IntPtr nativeObj, IntPtr img_nativeObj, int num_iterations);
[DllImport(LIBNAME)]
private static extern void ximgproc_SuperpixelSEEDS_iterate_11(IntPtr nativeObj, IntPtr img_nativeObj);
// C++: void cv::ximgproc::SuperpixelSEEDS::getLabels(Mat& labels_out)
[DllImport(LIBNAME)]
private static extern void ximgproc_SuperpixelSEEDS_getLabels_10(IntPtr nativeObj, IntPtr labels_out_nativeObj);
// C++: void cv::ximgproc::SuperpixelSEEDS::getLabelContourMask(Mat& image, bool thick_line = false)
[DllImport(LIBNAME)]
private static extern void ximgproc_SuperpixelSEEDS_getLabelContourMask_10(IntPtr nativeObj, IntPtr image_nativeObj, [MarshalAs(UnmanagedType.U1)] bool thick_line);
[DllImport(LIBNAME)]
private static extern void ximgproc_SuperpixelSEEDS_getLabelContourMask_11(IntPtr nativeObj, IntPtr image_nativeObj);
// native support for java finalize()
[DllImport(LIBNAME)]
private static extern void ximgproc_SuperpixelSEEDS_delete(IntPtr nativeObj);
}
}