#![allow(
	unused_parens,
	clippy::excessive_precision,
	clippy::missing_safety_doc,
	clippy::not_unsafe_ptr_arg_deref,
	clippy::should_implement_trait,
	clippy::too_many_arguments,
	clippy::unused_unit,
)]
//! # Hierarchical Feature Selection for Efficient Image Segmentation
//! 
//! The opencv hfs module contains an efficient algorithm to segment an image.
//! This module is implemented based on the paper Hierarchical Feature Selection for Efficient
//! Image Segmentation, ECCV 2016. The original project was developed by
//! Yun Liu(https://github.com/yun-liu/hfs).
//! 
//! 
//! Introduction to Hierarchical Feature Selection
//! ----------------------------------------------
//! 
//! 
//! This algorithm is executed in 3 stages:
//! 
//! In the first stage, the algorithm uses SLIC (simple linear iterative clustering) algorithm
//! to obtain the superpixel of the input image.
//! 
//! In the second stage, the algorithm view each superpixel as a node in the graph.
//! It will calculate a feature vector for each edge of the graph. It then calculates a weight
//! for each edge based on the feature vector and trained SVM parameters. After obtaining
//! weight for each edge, it will exploit  EGB (Efficient Graph-based Image Segmentation)
//! algorithm to merge some nodes in the graph thus obtaining a coarser segmentation
//! After these operations, a post process will be executed to merge regions that are smaller
//! then a specific number of pixels into their nearby region.
//! 
//! In the third stage, the algorithm exploits the similar mechanism to further merge
//! the small regions obtained in the second stage into even coarser segmentation.
//! 
//! After these three stages, we can obtain the final segmentation of the image.
//! For further details about the algorithm, please refer to the original paper:
//! Hierarchical Feature Selection for Efficient Image Segmentation, ECCV 2016
use crate::{mod_prelude::*, core, sys, types};
pub mod prelude {
	pub use { super::HfsSegmentConst, super::HfsSegment };
}

pub trait HfsSegmentConst: core::AlgorithmTraitConst {
	fn as_raw_HfsSegment(&self) -> *const c_void;

}

pub trait HfsSegment: core::AlgorithmTrait + crate::hfs::HfsSegmentConst {
	fn as_raw_mut_HfsSegment(&mut self) -> *mut c_void;

	/// @brief: set and get the parameter segEgbThresholdI.
	/// This parameter is used in the second stage mentioned above.
	/// It is a constant used to threshold weights of the edge when merging
	/// adjacent nodes when applying EGB algorithm. The segmentation result
	/// tends to have more regions remained if this value is large and vice versa.
	#[inline]
	fn set_seg_egb_threshold_i(&mut self, c: f32) -> Result<()> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_setSegEgbThresholdI_float(self.as_raw_mut_HfsSegment(), c) }.into_result()?;
		Ok(ret)
	}
	
	#[inline]
	fn get_seg_egb_threshold_i(&mut self) -> Result<f32> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_getSegEgbThresholdI(self.as_raw_mut_HfsSegment()) }.into_result()?;
		Ok(ret)
	}
	
	/// @brief: set and get the parameter minRegionSizeI.
	/// This parameter is used in the second stage
	/// mentioned above. After the EGB segmentation, regions that have fewer
	/// pixels then this parameter will be merged into it's adjacent region.
	#[inline]
	fn set_min_region_size_i(&mut self, n: i32) -> Result<()> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_setMinRegionSizeI_int(self.as_raw_mut_HfsSegment(), n) }.into_result()?;
		Ok(ret)
	}
	
	#[inline]
	fn get_min_region_size_i(&mut self) -> Result<i32> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_getMinRegionSizeI(self.as_raw_mut_HfsSegment()) }.into_result()?;
		Ok(ret)
	}
	
	/// @brief: set and get the parameter segEgbThresholdII.
	/// This parameter is used in the third stage
	/// mentioned above. It serves the same purpose as segEgbThresholdI.
	/// The segmentation result tends to have more regions remained if
	/// this value is large and vice versa.
	#[inline]
	fn set_seg_egb_threshold_ii(&mut self, c: f32) -> Result<()> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_setSegEgbThresholdII_float(self.as_raw_mut_HfsSegment(), c) }.into_result()?;
		Ok(ret)
	}
	
	#[inline]
	fn get_seg_egb_threshold_ii(&mut self) -> Result<f32> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_getSegEgbThresholdII(self.as_raw_mut_HfsSegment()) }.into_result()?;
		Ok(ret)
	}
	
	/// @brief: set and get the parameter minRegionSizeII.
	/// This parameter is used in the third stage
	/// mentioned above. It serves the same purpose as minRegionSizeI
	#[inline]
	fn set_min_region_size_ii(&mut self, n: i32) -> Result<()> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_setMinRegionSizeII_int(self.as_raw_mut_HfsSegment(), n) }.into_result()?;
		Ok(ret)
	}
	
	#[inline]
	fn get_min_region_size_ii(&mut self) -> Result<i32> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_getMinRegionSizeII(self.as_raw_mut_HfsSegment()) }.into_result()?;
		Ok(ret)
	}
	
	/// @brief: set and get the parameter spatialWeight.
	/// This parameter is used in the first stage
	/// mentioned above(the SLIC stage). It describes how important is the role
	/// of position when calculating the distance between each pixel and it's
	/// center. The exact formula to calculate the distance is
	/// ![inline formula](https://latex.codecogs.com/png.latex?colorDistance%20%2B%20spatialWeight%20%5Ctimes%20spatialDistance).
	/// The segmentation result tends to have more local consistency
	/// if this value is larger.
	#[inline]
	fn set_spatial_weight(&mut self, w: f32) -> Result<()> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_setSpatialWeight_float(self.as_raw_mut_HfsSegment(), w) }.into_result()?;
		Ok(ret)
	}
	
	#[inline]
	fn get_spatial_weight(&mut self) -> Result<f32> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_getSpatialWeight(self.as_raw_mut_HfsSegment()) }.into_result()?;
		Ok(ret)
	}
	
	/// @brief: set and get the parameter slicSpixelSize.
	/// This parameter is used in the first stage mentioned
	/// above(the SLIC stage). It describes the size of each
	/// superpixel when initializing SLIC. Every superpixel
	/// approximately has ![inline formula](https://latex.codecogs.com/png.latex?slicSpixelSize%20%5Ctimes%20slicSpixelSize)
	/// pixels in the beginning.
	#[inline]
	fn set_slic_spixel_size(&mut self, n: i32) -> Result<()> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_setSlicSpixelSize_int(self.as_raw_mut_HfsSegment(), n) }.into_result()?;
		Ok(ret)
	}
	
	#[inline]
	fn get_slic_spixel_size(&mut self) -> Result<i32> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_getSlicSpixelSize(self.as_raw_mut_HfsSegment()) }.into_result()?;
		Ok(ret)
	}
	
	/// @brief: set and get the parameter numSlicIter.
	/// This parameter is used in the first stage. It
	/// describes how many iteration to perform when executing SLIC.
	#[inline]
	fn set_num_slic_iter(&mut self, n: i32) -> Result<()> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_setNumSlicIter_int(self.as_raw_mut_HfsSegment(), n) }.into_result()?;
		Ok(ret)
	}
	
	#[inline]
	fn get_num_slic_iter(&mut self) -> Result<i32> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_getNumSlicIter(self.as_raw_mut_HfsSegment()) }.into_result()?;
		Ok(ret)
	}
	
	/// do segmentation gpu
	/// ## Parameters
	/// * src: : the input image
	/// * ifDraw: : if draw the image in the returned Mat. if this parameter is false,
	/// then the content of the returned Mat is a matrix of index, describing the region
	/// each pixel belongs to. And it's data type is CV_16U. If this parameter is true,
	/// then the returned Mat is a segmented picture, and color of each region is the
	/// average color of all pixels in that region. And it's data type is the same as
	/// the input image
	/// 
	/// ## C++ default parameters
	/// * if_draw: true
	#[inline]
	fn perform_segment_gpu(&mut self, src: &dyn core::ToInputArray, if_draw: bool) -> Result<core::Mat> {
		input_array_arg!(src);
		let ret = unsafe { sys::cv_hfs_HfsSegment_performSegmentGpu_const__InputArrayR_bool(self.as_raw_mut_HfsSegment(), src.as_raw__InputArray(), if_draw) }.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}
	
	/// do segmentation with cpu
	/// This method is only implemented for reference.
	/// It is highly NOT recommanded to use it.
	/// 
	/// ## C++ default parameters
	/// * if_draw: true
	#[inline]
	fn perform_segment_cpu(&mut self, src: &dyn core::ToInputArray, if_draw: bool) -> Result<core::Mat> {
		input_array_arg!(src);
		let ret = unsafe { sys::cv_hfs_HfsSegment_performSegmentCpu_const__InputArrayR_bool(self.as_raw_mut_HfsSegment(), src.as_raw__InputArray(), if_draw) }.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}
	
}

impl dyn HfsSegment + '_ {
	/// @brief: create a hfs object
	/// ## Parameters
	/// * height: : the height of the input image
	/// * width: : the width of the input image
	/// * segEgbThresholdI: : parameter segEgbThresholdI
	/// * minRegionSizeI: : parameter minRegionSizeI
	/// * segEgbThresholdII: : parameter segEgbThresholdII
	/// * minRegionSizeII: : parameter minRegionSizeII
	/// * spatialWeight: : parameter spatialWeight
	/// * slicSpixelSize: : parameter slicSpixelSize
	/// * numSlicIter: : parameter numSlicIter
	/// 
	/// ## C++ default parameters
	/// * seg_egb_threshold_i: 0.08f
	/// * min_region_size_i: 100
	/// * seg_egb_threshold_ii: 0.28f
	/// * min_region_size_ii: 200
	/// * spatial_weight: 0.6f
	/// * slic_spixel_size: 8
	/// * num_slic_iter: 5
	#[inline]
	pub fn create(height: i32, width: i32, seg_egb_threshold_i: f32, min_region_size_i: i32, seg_egb_threshold_ii: f32, min_region_size_ii: i32, spatial_weight: f32, slic_spixel_size: i32, num_slic_iter: i32) -> Result<core::Ptr<dyn crate::hfs::HfsSegment>> {
		let ret = unsafe { sys::cv_hfs_HfsSegment_create_int_int_float_int_float_int_float_int_int(height, width, seg_egb_threshold_i, min_region_size_i, seg_egb_threshold_ii, min_region_size_ii, spatial_weight, slic_spixel_size, num_slic_iter) }.into_result()?;
		let ret = unsafe { core::Ptr::<dyn crate::hfs::HfsSegment>::opencv_from_extern(ret) };
		Ok(ret)
	}
	
}