#![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,
)]
//! # Object Detection
use crate::{mod_prelude::*, core, sys, types};
pub mod prelude {
	pub use { super::HOG, super::CascadeClassifier };
}

/// Cascade classifier class used for object detection. Supports HAAR and LBP cascades. :
/// 
/// 
/// Note:
///    *   A cascade classifier example can be found at
///        opencv_source_code/samples/gpu/cascadeclassifier.cpp
///    *   A Nvidea API specific cascade classifier example can be found at
///        opencv_source_code/samples/gpu/cascadeclassifier_nvidia_api.cpp
pub trait CascadeClassifier: core::AlgorithmTrait {
	fn as_raw_CascadeClassifier(&self) -> *const c_void;
	fn as_raw_mut_CascadeClassifier(&mut self) -> *mut c_void;

	/// Maximum possible object size. Objects larger than that are ignored. Used for
	/// second signature and supported only for LBP cascades.
	fn set_max_object_size(&mut self, max_object_size: core::Size) -> Result<()> {
		unsafe { sys::cv_cuda_CascadeClassifier_setMaxObjectSize_Size(self.as_raw_mut_CascadeClassifier(), max_object_size.opencv_as_extern()) }.into_result()
	}
	
	fn get_max_object_size(&self) -> Result<core::Size> {
		unsafe { sys::cv_cuda_CascadeClassifier_getMaxObjectSize_const(self.as_raw_CascadeClassifier()) }.into_result()
	}
	
	/// Minimum possible object size. Objects smaller than that are ignored.
	fn set_min_object_size(&mut self, min_size: core::Size) -> Result<()> {
		unsafe { sys::cv_cuda_CascadeClassifier_setMinObjectSize_Size(self.as_raw_mut_CascadeClassifier(), min_size.opencv_as_extern()) }.into_result()
	}
	
	fn get_min_object_size(&self) -> Result<core::Size> {
		unsafe { sys::cv_cuda_CascadeClassifier_getMinObjectSize_const(self.as_raw_CascadeClassifier()) }.into_result()
	}
	
	/// Parameter specifying how much the image size is reduced at each image scale.
	fn set_scale_factor(&mut self, scale_factor: f64) -> Result<()> {
		unsafe { sys::cv_cuda_CascadeClassifier_setScaleFactor_double(self.as_raw_mut_CascadeClassifier(), scale_factor) }.into_result()
	}
	
	fn get_scale_factor(&self) -> Result<f64> {
		unsafe { sys::cv_cuda_CascadeClassifier_getScaleFactor_const(self.as_raw_CascadeClassifier()) }.into_result()
	}
	
	/// Parameter specifying how many neighbors each candidate rectangle should have
	/// to retain it.
	fn set_min_neighbors(&mut self, min_neighbors: i32) -> Result<()> {
		unsafe { sys::cv_cuda_CascadeClassifier_setMinNeighbors_int(self.as_raw_mut_CascadeClassifier(), min_neighbors) }.into_result()
	}
	
	fn get_min_neighbors(&self) -> Result<i32> {
		unsafe { sys::cv_cuda_CascadeClassifier_getMinNeighbors_const(self.as_raw_CascadeClassifier()) }.into_result()
	}
	
	fn set_find_largest_object(&mut self, find_largest_object: bool) -> Result<()> {
		unsafe { sys::cv_cuda_CascadeClassifier_setFindLargestObject_bool(self.as_raw_mut_CascadeClassifier(), find_largest_object) }.into_result()
	}
	
	fn get_find_largest_object(&mut self) -> Result<bool> {
		unsafe { sys::cv_cuda_CascadeClassifier_getFindLargestObject(self.as_raw_mut_CascadeClassifier()) }.into_result()
	}
	
	fn set_max_num_objects(&mut self, max_num_objects: i32) -> Result<()> {
		unsafe { sys::cv_cuda_CascadeClassifier_setMaxNumObjects_int(self.as_raw_mut_CascadeClassifier(), max_num_objects) }.into_result()
	}
	
	fn get_max_num_objects(&self) -> Result<i32> {
		unsafe { sys::cv_cuda_CascadeClassifier_getMaxNumObjects_const(self.as_raw_CascadeClassifier()) }.into_result()
	}
	
	fn get_classifier_size(&self) -> Result<core::Size> {
		unsafe { sys::cv_cuda_CascadeClassifier_getClassifierSize_const(self.as_raw_CascadeClassifier()) }.into_result()
	}
	
	/// Detects objects of different sizes in the input image.
	/// 
	/// ## Parameters
	/// * image: Matrix of type CV_8U containing an image where objects should be detected.
	/// * objects: Buffer to store detected objects (rectangles).
	/// * stream: CUDA stream.
	/// 
	/// To get final array of detected objects use CascadeClassifier::convert method.
	/// 
	/// ```ignore
	///    Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(...);
	/// 
	///    Mat image_cpu = imread(...)
	///    GpuMat image_gpu(image_cpu);
	/// 
	///    GpuMat objbuf;
	///    cascade_gpu->detectMultiScale(image_gpu, objbuf);
	/// 
	///    std::vector<Rect> faces;
	///    cascade_gpu->convert(objbuf, faces);
	/// 
	///    for(int i = 0; i < detections_num; ++i)
	///        cv::rectangle(image_cpu, faces[i], Scalar(255));
	/// 
	///    imshow("Faces", image_cpu);
	/// ```
	/// ## See also
	/// CascadeClassifier::detectMultiScale
	/// 
	/// ## C++ default parameters
	/// * stream: Stream::Null()
	fn detect_multi_scale(&mut self, image: &dyn core::ToInputArray, objects: &mut dyn core::ToOutputArray, stream: &mut core::Stream) -> Result<()> {
		input_array_arg!(image);
		output_array_arg!(objects);
		unsafe { sys::cv_cuda_CascadeClassifier_detectMultiScale_const__InputArrayR_const__OutputArrayR_StreamR(self.as_raw_mut_CascadeClassifier(), image.as_raw__InputArray(), objects.as_raw__OutputArray(), stream.as_raw_mut_Stream()) }.into_result()
	}
	
	/// Converts objects array from internal representation to standard vector.
	/// 
	/// ## Parameters
	/// * gpu_objects: Objects array in internal representation.
	/// * objects: Resulting array.
	fn convert(&mut self, gpu_objects: &mut dyn core::ToOutputArray, objects: &mut core::Vector::<core::Rect>) -> Result<()> {
		output_array_arg!(gpu_objects);
		unsafe { sys::cv_cuda_CascadeClassifier_convert_const__OutputArrayR_vector_Rect_R(self.as_raw_mut_CascadeClassifier(), gpu_objects.as_raw__OutputArray(), objects.as_raw_mut_VectorOfRect()) }.into_result()
	}
	
}

impl dyn CascadeClassifier + '_ {
	/// Loads the classifier from a file. Cascade type is detected automatically by constructor parameter.
	/// 
	/// ## Parameters
	/// * filename: Name of the file from which the classifier is loaded. Only the old haar classifier
	/// (trained by the haar training application) and NVIDIA's nvbin are supported for HAAR and only new
	/// type of OpenCV XML cascade supported for LBP. The working haar models can be found at opencv_folder/data/haarcascades_cuda/
	pub fn create(filename: &str) -> Result<core::Ptr::<dyn crate::cudaobjdetect::CascadeClassifier>> {
		extern_container_arg!(filename);
		unsafe { sys::cv_cuda_CascadeClassifier_create_const_StringR(filename.opencv_as_extern()) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::cudaobjdetect::CascadeClassifier>::opencv_from_extern(r) } )
	}
	
	/// Loads the classifier from a file. Cascade type is detected automatically by constructor parameter.
	/// 
	/// ## Parameters
	/// * filename: Name of the file from which the classifier is loaded. Only the old haar classifier
	/// (trained by the haar training application) and NVIDIA's nvbin are supported for HAAR and only new
	/// type of OpenCV XML cascade supported for LBP. The working haar models can be found at opencv_folder/data/haarcascades_cuda/
	/// 
	/// ## Overloaded parameters
	pub fn create_1(file: &core::FileStorage) -> Result<core::Ptr::<dyn crate::cudaobjdetect::CascadeClassifier>> {
		unsafe { sys::cv_cuda_CascadeClassifier_create_const_FileStorageR(file.as_raw_FileStorage()) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::cudaobjdetect::CascadeClassifier>::opencv_from_extern(r) } )
	}
	
}
/// The class implements Histogram of Oriented Gradients ([Dalal2005](https://docs.opencv.org/4.5.3/d0/de3/citelist.html#CITEREF_Dalal2005)) object detector.
/// 
/// 
/// Note:
///    *   An example applying the HOG descriptor for people detection can be found at
///        opencv_source_code/samples/cpp/peopledetect.cpp
///    *   A CUDA example applying the HOG descriptor for people detection can be found at
///        opencv_source_code/samples/gpu/hog.cpp
///    *   (Python) An example applying the HOG descriptor for people detection can be found at
///        opencv_source_code/samples/python/peopledetect.py
pub trait HOG: core::AlgorithmTrait {
	fn as_raw_HOG(&self) -> *const c_void;
	fn as_raw_mut_HOG(&mut self) -> *mut c_void;

	/// Gaussian smoothing window parameter.
	fn set_win_sigma(&mut self, win_sigma: f64) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setWinSigma_double(self.as_raw_mut_HOG(), win_sigma) }.into_result()
	}
	
	fn get_win_sigma(&self) -> Result<f64> {
		unsafe { sys::cv_cuda_HOG_getWinSigma_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// L2-Hys normalization method shrinkage.
	fn set_l2_hys_threshold(&mut self, threshold_l2hys: f64) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setL2HysThreshold_double(self.as_raw_mut_HOG(), threshold_l2hys) }.into_result()
	}
	
	fn get_l2_hys_threshold(&self) -> Result<f64> {
		unsafe { sys::cv_cuda_HOG_getL2HysThreshold_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Flag to specify whether the gamma correction preprocessing is required or not.
	fn set_gamma_correction(&mut self, gamma_correction: bool) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setGammaCorrection_bool(self.as_raw_mut_HOG(), gamma_correction) }.into_result()
	}
	
	fn get_gamma_correction(&self) -> Result<bool> {
		unsafe { sys::cv_cuda_HOG_getGammaCorrection_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Maximum number of detection window increases.
	fn set_num_levels(&mut self, nlevels: i32) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setNumLevels_int(self.as_raw_mut_HOG(), nlevels) }.into_result()
	}
	
	fn get_num_levels(&self) -> Result<i32> {
		unsafe { sys::cv_cuda_HOG_getNumLevels_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Threshold for the distance between features and SVM classifying plane.
	/// Usually it is 0 and should be specified in the detector coefficients (as the last free
	/// coefficient). But if the free coefficient is omitted (which is allowed), you can specify it
	/// manually here.
	fn set_hit_threshold(&mut self, hit_threshold: f64) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setHitThreshold_double(self.as_raw_mut_HOG(), hit_threshold) }.into_result()
	}
	
	fn get_hit_threshold(&self) -> Result<f64> {
		unsafe { sys::cv_cuda_HOG_getHitThreshold_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Window stride. It must be a multiple of block stride.
	fn set_win_stride(&mut self, win_stride: core::Size) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setWinStride_Size(self.as_raw_mut_HOG(), win_stride.opencv_as_extern()) }.into_result()
	}
	
	fn get_win_stride(&self) -> Result<core::Size> {
		unsafe { sys::cv_cuda_HOG_getWinStride_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Coefficient of the detection window increase.
	fn set_scale_factor(&mut self, scale0: f64) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setScaleFactor_double(self.as_raw_mut_HOG(), scale0) }.into_result()
	}
	
	fn get_scale_factor(&self) -> Result<f64> {
		unsafe { sys::cv_cuda_HOG_getScaleFactor_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Coefficient to regulate the similarity threshold. When detected, some
	/// objects can be covered by many rectangles. 0 means not to perform grouping.
	/// See groupRectangles.
	fn set_group_threshold(&mut self, group_threshold: i32) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setGroupThreshold_int(self.as_raw_mut_HOG(), group_threshold) }.into_result()
	}
	
	fn get_group_threshold(&self) -> Result<i32> {
		unsafe { sys::cv_cuda_HOG_getGroupThreshold_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Descriptor storage format:
	/// - **DESCR_FORMAT_ROW_BY_ROW** - Row-major order.
	/// - **DESCR_FORMAT_COL_BY_COL** - Column-major order.
	fn set_descriptor_format(&mut self, descr_format: crate::objdetect::HOGDescriptor_DescriptorStorageFormat) -> Result<()> {
		unsafe { sys::cv_cuda_HOG_setDescriptorFormat_DescriptorStorageFormat(self.as_raw_mut_HOG(), descr_format) }.into_result()
	}
	
	fn get_descriptor_format(&self) -> Result<crate::objdetect::HOGDescriptor_DescriptorStorageFormat> {
		unsafe { sys::cv_cuda_HOG_getDescriptorFormat_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Returns the number of coefficients required for the classification.
	fn get_descriptor_size(&self) -> Result<size_t> {
		unsafe { sys::cv_cuda_HOG_getDescriptorSize_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Returns the block histogram size.
	fn get_block_histogram_size(&self) -> Result<size_t> {
		unsafe { sys::cv_cuda_HOG_getBlockHistogramSize_const(self.as_raw_HOG()) }.into_result()
	}
	
	/// Sets coefficients for the linear SVM classifier.
	fn set_svm_detector(&mut self, detector: &dyn core::ToInputArray) -> Result<()> {
		input_array_arg!(detector);
		unsafe { sys::cv_cuda_HOG_setSVMDetector_const__InputArrayR(self.as_raw_mut_HOG(), detector.as_raw__InputArray()) }.into_result()
	}
	
	/// Returns coefficients of the classifier trained for people detection.
	fn get_default_people_detector(&self) -> Result<core::Mat> {
		unsafe { sys::cv_cuda_HOG_getDefaultPeopleDetector_const(self.as_raw_HOG()) }.into_result().map(|r| unsafe { core::Mat::opencv_from_extern(r) } )
	}
	
	/// Performs object detection without a multi-scale window.
	/// 
	/// ## Parameters
	/// * img: Source image. CV_8UC1 and CV_8UC4 types are supported for now.
	/// * found_locations: Left-top corner points of detected objects boundaries.
	/// * confidences: Optional output array for confidences.
	/// 
	/// ## C++ default parameters
	/// * confidences: NULL
	fn detect(&mut self, img: &dyn core::ToInputArray, found_locations: &mut core::Vector::<core::Point>, confidences: &mut core::Vector::<f64>) -> Result<()> {
		input_array_arg!(img);
		unsafe { sys::cv_cuda_HOG_detect_const__InputArrayR_vector_Point_R_vector_double_X(self.as_raw_mut_HOG(), img.as_raw__InputArray(), found_locations.as_raw_mut_VectorOfPoint(), confidences.as_raw_mut_VectorOff64()) }.into_result()
	}
	
	fn detect_1(&mut self, img: &dyn core::ToInputArray, found_locations: &mut core::Vector::<core::Point>, confidences: &mut core::Vector::<f64>) -> Result<()> {
		input_array_arg!(img);
		unsafe { sys::cv_cuda_HOG_detect_const__InputArrayR_vector_Point_R_vector_double_R(self.as_raw_mut_HOG(), img.as_raw__InputArray(), found_locations.as_raw_mut_VectorOfPoint(), confidences.as_raw_mut_VectorOff64()) }.into_result()
	}
	
	/// Performs object detection without a multi-scale window.
	/// 
	/// ## Parameters
	/// * img: Source image. CV_8UC1 and CV_8UC4 types are supported for now.
	/// * found_locations: Left-top corner points of detected objects boundaries.
	fn detect_without_conf(&mut self, img: &dyn core::ToInputArray, found_locations: &mut core::Vector::<core::Point>) -> Result<()> {
		input_array_arg!(img);
		unsafe { sys::cv_cuda_HOG_detectWithoutConf_const__InputArrayR_vector_Point_R(self.as_raw_mut_HOG(), img.as_raw__InputArray(), found_locations.as_raw_mut_VectorOfPoint()) }.into_result()
	}
	
	/// Performs object detection with a multi-scale window.
	/// 
	/// ## Parameters
	/// * img: Source image. See cuda::HOGDescriptor::detect for type limitations.
	/// * found_locations: Detected objects boundaries.
	/// * confidences: Optional output array for confidences.
	/// 
	/// ## C++ default parameters
	/// * confidences: NULL
	fn detect_multi_scale(&mut self, img: &dyn core::ToInputArray, found_locations: &mut core::Vector::<core::Rect>, confidences: &mut core::Vector::<f64>) -> Result<()> {
		input_array_arg!(img);
		unsafe { sys::cv_cuda_HOG_detectMultiScale_const__InputArrayR_vector_Rect_R_vector_double_X(self.as_raw_mut_HOG(), img.as_raw__InputArray(), found_locations.as_raw_mut_VectorOfRect(), confidences.as_raw_mut_VectorOff64()) }.into_result()
	}
	
	fn detect_multi_scale_1(&mut self, img: &dyn core::ToInputArray, found_locations: &mut core::Vector::<core::Rect>, confidences: &mut core::Vector::<f64>) -> Result<()> {
		input_array_arg!(img);
		unsafe { sys::cv_cuda_HOG_detectMultiScale_const__InputArrayR_vector_Rect_R_vector_double_R(self.as_raw_mut_HOG(), img.as_raw__InputArray(), found_locations.as_raw_mut_VectorOfRect(), confidences.as_raw_mut_VectorOff64()) }.into_result()
	}
	
	/// Performs object detection with a multi-scale window.
	/// 
	/// ## Parameters
	/// * img: Source image. See cuda::HOGDescriptor::detect for type limitations.
	/// * found_locations: Detected objects boundaries.
	fn detect_multi_scale_without_conf(&mut self, img: &dyn core::ToInputArray, found_locations: &mut core::Vector::<core::Rect>) -> Result<()> {
		input_array_arg!(img);
		unsafe { sys::cv_cuda_HOG_detectMultiScaleWithoutConf_const__InputArrayR_vector_Rect_R(self.as_raw_mut_HOG(), img.as_raw__InputArray(), found_locations.as_raw_mut_VectorOfRect()) }.into_result()
	}
	
	/// Returns block descriptors computed for the whole image.
	/// 
	/// ## Parameters
	/// * img: Source image. See cuda::HOGDescriptor::detect for type limitations.
	/// * descriptors: 2D array of descriptors.
	/// * stream: CUDA stream.
	/// 
	/// ## C++ default parameters
	/// * stream: Stream::Null()
	fn compute(&mut self, img: &dyn core::ToInputArray, descriptors: &mut dyn core::ToOutputArray, stream: &mut core::Stream) -> Result<()> {
		input_array_arg!(img);
		output_array_arg!(descriptors);
		unsafe { sys::cv_cuda_HOG_compute_const__InputArrayR_const__OutputArrayR_StreamR(self.as_raw_mut_HOG(), img.as_raw__InputArray(), descriptors.as_raw__OutputArray(), stream.as_raw_mut_Stream()) }.into_result()
	}
	
}

impl dyn HOG + '_ {
	/// Creates the HOG descriptor and detector.
	/// 
	/// ## Parameters
	/// * win_size: Detection window size. Align to block size and block stride.
	/// * block_size: Block size in pixels. Align to cell size. Only (16,16) is supported for now.
	/// * block_stride: Block stride. It must be a multiple of cell size.
	/// * cell_size: Cell size. Only (8, 8) is supported for now.
	/// * nbins: Number of bins. Only 9 bins per cell are supported for now.
	/// 
	/// ## C++ default parameters
	/// * win_size: Size(64,128)
	/// * block_size: Size(16,16)
	/// * block_stride: Size(8,8)
	/// * cell_size: Size(8,8)
	/// * nbins: 9
	pub fn create(win_size: core::Size, block_size: core::Size, block_stride: core::Size, cell_size: core::Size, nbins: i32) -> Result<core::Ptr::<dyn crate::cudaobjdetect::HOG>> {
		unsafe { sys::cv_cuda_HOG_create_Size_Size_Size_Size_int(win_size.opencv_as_extern(), block_size.opencv_as_extern(), block_stride.opencv_as_extern(), cell_size.opencv_as_extern(), nbins) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::cudaobjdetect::HOG>::opencv_from_extern(r) } )
	}
	
}