opencv 0.94.4

pub mod dpm {
	//! # Deformable Part-based Models
	//!
	//! Discriminatively Trained Part Based Models for Object Detection
	//! ---------------------------------------------------------------
	//!
	//! The object detector described below has been initially proposed by P.F. Felzenszwalb in
	//! [Felzenszwalb2010a](https://docs.opencv.org/4.11.0/d0/de3/citelist.html#CITEREF_Felzenszwalb2010a) . It is based on a Dalal-Triggs detector that uses a single filter on histogram
	//! of oriented gradients (HOG) features to represent an object category. This detector uses a sliding
	//! window approach, where a filter is applied at all positions and scales of an image. The first
	//! innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a
	//! "root" filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated
	//! deformation models. The score of one of star models at a particular position and scale within an
	//! image is the score of the root filter at the given location plus the sum over parts of the maximum,
	//! over placements of that part, of the part filter score on its location minus a deformation cost
	//! easuring the deviation of the part from its ideal location relative to the root. Both root and part
	//! filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of
	//! a feature pyramid computed from the input image. Another improvement is a representation of the
	//! class of models by a mixture of star models. The score of a mixture model at a particular position
	//! and scale is the maximum over components, of the score of that component model at the given
	//! location.
	//!
	//! The detector was dramatically speeded-up with cascade algorithm proposed by P.F. Felzenszwalb in
	//! [Felzenszwalb2010b](https://docs.opencv.org/4.11.0/d0/de3/citelist.html#CITEREF_Felzenszwalb2010b) . The algorithm prunes partial hypotheses using thresholds on their scores.The
	//! basic idea of the algorithm is to use a hierarchy of models defined by an ordering of the original
	//! model's parts. For a model with (n+1) parts, including the root, a sequence of (n+1) models is
	//! obtained. The i-th model in this sequence is defined by the first i parts from the original model.
	//! Using this hierarchy, low scoring hypotheses can be pruned after looking at the best configuration
	//! of a subset of the parts. Hypotheses that score high under a weak model are evaluated further using
	//! a richer model.
	//!
	//! In OpenCV there is an C++ implementation of DPM cascade detector.
	use crate::mod_prelude::*;
	use crate::{core, sys, types};
	pub mod prelude {
		pub use super::{DPMDetectorTrait, DPMDetectorTraitConst, DPMDetector_ObjectDetectionTrait, DPMDetector_ObjectDetectionTraitConst};
	}

	/// This is a C++ abstract class, it provides external user API to work with DPM.
	pub struct DPMDetector {
		ptr: *mut c_void,
	}

	opencv_type_boxed! { DPMDetector }

	impl Drop for DPMDetector {
		#[inline]
		fn drop(&mut self) {
			unsafe { sys::cv_dpm_DPMDetector_delete(self.as_raw_mut_DPMDetector()) };
		}
	}

	unsafe impl Send for DPMDetector {}

	impl DPMDetector {
		/// Load the trained models from given .xml files and return cv::Ptr\<DPMDetector\>.
		/// ## Parameters
		/// * filenames: A set of filenames storing the trained detectors (models). Each file contains one
		/// model. See examples of such files here `/opencv_extra/testdata/cv/dpm/VOC2007_Cascade/`.
		/// * classNames: A set of trained models names. If it's empty then the name of each model will be
		/// constructed from the name of file containing the model. E.g. the model stored in
		/// "/home/user/cat.xml" will get the name "cat".
		///
		/// ## C++ default parameters
		/// * class_names: std::vector<std::string>()
		#[inline]
		pub fn create(filenames: &core::Vector<String>, class_names: &core::Vector<String>) -> Result<core::Ptr<crate::dpm::DPMDetector>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_create_const_vectorLstringGR_const_vectorLstringGR(filenames.as_raw_VectorOfString(), class_names.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::dpm::DPMDetector>::opencv_from_extern(ret) };
			Ok(ret)
		}

		/// Load the trained models from given .xml files and return cv::Ptr\<DPMDetector\>.
		/// ## Parameters
		/// * filenames: A set of filenames storing the trained detectors (models). Each file contains one
		/// model. See examples of such files here `/opencv_extra/testdata/cv/dpm/VOC2007_Cascade/`.
		/// * classNames: A set of trained models names. If it's empty then the name of each model will be
		/// constructed from the name of file containing the model. E.g. the model stored in
		/// "/home/user/cat.xml" will get the name "cat".
		///
		/// ## Note
		/// This alternative version of [DPMDetector::create] function uses the following default values for its arguments:
		/// * class_names: std::vector<std::string>()
		#[inline]
		pub fn create_def(filenames: &core::Vector<String>) -> Result<core::Ptr<crate::dpm::DPMDetector>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_create_const_vectorLstringGR(filenames.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::dpm::DPMDetector>::opencv_from_extern(ret) };
			Ok(ret)
		}

	}

	/// Constant methods for [crate::dpm::DPMDetector]
	pub trait DPMDetectorTraitConst {
		fn as_raw_DPMDetector(&self) -> *const c_void;

		#[inline]
		fn is_empty(&self) -> Result<bool> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_isEmpty_const(self.as_raw_DPMDetector(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Return the class (model) names that were passed in constructor or method load or extracted from
		/// models filenames in those methods.
		#[inline]
		fn get_class_names(&self) -> Result<core::Vector<String>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_getClassNames_const(self.as_raw_DPMDetector(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Vector::<String>::opencv_from_extern(ret) };
			Ok(ret)
		}

		/// Return a count of loaded models (classes).
		#[inline]
		fn get_class_count(&self) -> Result<size_t> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_getClassCount_const(self.as_raw_DPMDetector(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	/// Mutable methods for [crate::dpm::DPMDetector]
	pub trait DPMDetectorTrait: crate::dpm::DPMDetectorTraitConst {
		fn as_raw_mut_DPMDetector(&mut self) -> *mut c_void;

		/// Find rectangular regions in the given image that are likely to contain objects of loaded classes
		/// (models) and corresponding confidence levels.
		/// ## Parameters
		/// * image: An image.
		/// * objects: The detections: rectangulars, scores and class IDs.
		#[inline]
		fn detect(&mut self, image: &mut impl core::MatTrait, objects: &mut core::Vector<crate::dpm::DPMDetector_ObjectDetection>) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_detect_MatR_vectorLObjectDetectionGR(self.as_raw_mut_DPMDetector(), image.as_raw_mut_Mat(), objects.as_raw_mut_VectorOfDPMDetector_ObjectDetection(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	impl std::fmt::Debug for DPMDetector {
		#[inline]
		fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
			f.debug_struct("DPMDetector")
				.finish()
		}
	}

	impl crate::dpm::DPMDetectorTraitConst for DPMDetector {
		#[inline] fn as_raw_DPMDetector(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::dpm::DPMDetectorTrait for DPMDetector {
		#[inline] fn as_raw_mut_DPMDetector(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { DPMDetector, crate::dpm::DPMDetectorTraitConst, as_raw_DPMDetector, crate::dpm::DPMDetectorTrait, as_raw_mut_DPMDetector }

	pub struct DPMDetector_ObjectDetection {
		ptr: *mut c_void,
	}

	opencv_type_boxed! { DPMDetector_ObjectDetection }

	impl Drop for DPMDetector_ObjectDetection {
		#[inline]
		fn drop(&mut self) {
			unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_delete(self.as_raw_mut_DPMDetector_ObjectDetection()) };
		}
	}

	unsafe impl Send for DPMDetector_ObjectDetection {}

	impl DPMDetector_ObjectDetection {
		#[inline]
		pub fn default() -> Result<crate::dpm::DPMDetector_ObjectDetection> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_ObjectDetection(ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { crate::dpm::DPMDetector_ObjectDetection::opencv_from_extern(ret) };
			Ok(ret)
		}

		/// ## C++ default parameters
		/// * class_id: -1
		#[inline]
		pub fn new(rect: core::Rect, score: f32, class_id: i32) -> Result<crate::dpm::DPMDetector_ObjectDetection> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_ObjectDetection_const_RectR_float_int(&rect, score, class_id, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { crate::dpm::DPMDetector_ObjectDetection::opencv_from_extern(ret) };
			Ok(ret)
		}

		/// ## Note
		/// This alternative version of [new] function uses the following default values for its arguments:
		/// * class_id: -1
		#[inline]
		pub fn new_def(rect: core::Rect, score: f32) -> Result<crate::dpm::DPMDetector_ObjectDetection> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_ObjectDetection_const_RectR_float(&rect, score, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { crate::dpm::DPMDetector_ObjectDetection::opencv_from_extern(ret) };
			Ok(ret)
		}

	}

	/// Constant methods for [crate::dpm::DPMDetector_ObjectDetection]
	pub trait DPMDetector_ObjectDetectionTraitConst {
		fn as_raw_DPMDetector_ObjectDetection(&self) -> *const c_void;

		#[inline]
		fn rect(&self) -> core::Rect {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_propRect_const(self.as_raw_DPMDetector_ObjectDetection(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			ret
		}

		#[inline]
		fn score(&self) -> f32 {
			let ret = unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_propScore_const(self.as_raw_DPMDetector_ObjectDetection()) };
			ret
		}

		#[inline]
		fn class_id(&self) -> i32 {
			let ret = unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_propClassID_const(self.as_raw_DPMDetector_ObjectDetection()) };
			ret
		}

	}

	/// Mutable methods for [crate::dpm::DPMDetector_ObjectDetection]
	pub trait DPMDetector_ObjectDetectionTrait: crate::dpm::DPMDetector_ObjectDetectionTraitConst {
		fn as_raw_mut_DPMDetector_ObjectDetection(&mut self) -> *mut c_void;

		#[inline]
		fn set_rect(&mut self, val: core::Rect) {
			let ret = unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_propRect_const_Rect(self.as_raw_mut_DPMDetector_ObjectDetection(), &val) };
			ret
		}

		#[inline]
		fn set_score(&mut self, val: f32) {
			let ret = unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_propScore_const_float(self.as_raw_mut_DPMDetector_ObjectDetection(), val) };
			ret
		}

		#[inline]
		fn set_class_id(&mut self, val: i32) {
			let ret = unsafe { sys::cv_dpm_DPMDetector_ObjectDetection_propClassID_const_int(self.as_raw_mut_DPMDetector_ObjectDetection(), val) };
			ret
		}

	}

	impl std::fmt::Debug for DPMDetector_ObjectDetection {
		#[inline]
		fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
			f.debug_struct("DPMDetector_ObjectDetection")
				.field("rect", &crate::dpm::DPMDetector_ObjectDetectionTraitConst::rect(self))
				.field("score", &crate::dpm::DPMDetector_ObjectDetectionTraitConst::score(self))
				.field("class_id", &crate::dpm::DPMDetector_ObjectDetectionTraitConst::class_id(self))
				.finish()
		}
	}

	impl crate::dpm::DPMDetector_ObjectDetectionTraitConst for DPMDetector_ObjectDetection {
		#[inline] fn as_raw_DPMDetector_ObjectDetection(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::dpm::DPMDetector_ObjectDetectionTrait for DPMDetector_ObjectDetection {
		#[inline] fn as_raw_mut_DPMDetector_ObjectDetection(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { DPMDetector_ObjectDetection, crate::dpm::DPMDetector_ObjectDetectionTraitConst, as_raw_DPMDetector_ObjectDetection, crate::dpm::DPMDetector_ObjectDetectionTrait, as_raw_mut_DPMDetector_ObjectDetection }

}