darknet_2_5_python/include/opencv2/ximgproc/seeds.hpp

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#ifndef __OPENCV_SEEDS_HPP__
#define __OPENCV_SEEDS_HPP__
#ifdef __cplusplus

#include <opencv2/core.hpp>

namespace cv
{
namespace ximgproc
{

//! @addtogroup ximgproc_superpixel
//! @{

/** @brief 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.
 */
class CV_EXPORTS_W SuperpixelSEEDS : public Algorithm
{
public:

    /** @brief 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().
     */
    CV_WRAP virtual int getNumberOfSuperpixels() = 0;

    /** @brief 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)
     */
    CV_WRAP virtual void iterate(InputArray img, int num_iterations=4) = 0;

    /** @brief 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()].
     */
    CV_WRAP virtual void getLabels(OutputArray labels_out) = 0;

    /** @brief 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)
     */
    CV_WRAP virtual void getLabelContourMask(OutputArray image, bool thick_line = false) = 0;

    virtual ~SuperpixelSEEDS() {}
};

/** @brief Initializes a SuperpixelSEEDS object.

@param image_width Image width.
@param image_height Image height.
@param image_channels Number of channels of the image.
@param num_superpixels Desired number of superpixels. Note that the actual number may be smaller
due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to
get the actual number.
@param num_levels Number of block levels. The more levels, the more accurate is the segmentation,
but needs more memory and CPU time.
@param prior enable 3x3 shape smoothing term if \>0. A larger value leads to smoother shapes. prior
must be in the range [0, 5].
@param histogram_bins Number of histogram bins.
@param double_step If true, iterate each block level twice for higher accuracy.

The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of
the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS
superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and
double_step.

The number of levels in num_levels defines the amount of block levels that the algorithm use in the
optimization. The initialization is a grid, in which the superpixels are equally distributed through
the width and the height of the image. The larger blocks correspond to the superpixel size, and the
levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels,
recursively until the smaller block level. An example of initialization of 4 block levels is
illustrated in the following figure.

![image](pics/superpixels_blocks.png)
 */
CV_EXPORTS_W Ptr<SuperpixelSEEDS> createSuperpixelSEEDS(
    int image_width, int image_height, int image_channels,
    int num_superpixels, int num_levels, int prior = 2,
    int histogram_bins=5, bool double_step = false);

//! @}

}
}
#endif
#endif