455 lines
23 KiB
Objective-C
455 lines
23 KiB
Objective-C
//
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// This file is auto-generated. Please don't modify it!
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//
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#pragma once
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#ifdef __cplusplus
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//#import "opencv.hpp"
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#import "opencv2/objdetect.hpp"
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#else
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#define CV_EXPORTS
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#endif
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#import <Foundation/Foundation.h>
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@class DoubleVector;
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@class IntVector;
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@class Mat;
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@class Rect2i;
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@class Size2i;
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NS_ASSUME_NONNULL_BEGIN
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// C++: class CascadeClassifier
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/**
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* Cascade classifier class for object detection.
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*
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* Member of `Objdetect`
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*/
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CV_EXPORTS @interface CascadeClassifier : NSObject
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#ifdef __cplusplus
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@property(readonly)cv::Ptr<cv::CascadeClassifier> nativePtr;
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#endif
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#ifdef __cplusplus
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- (instancetype)initWithNativePtr:(cv::Ptr<cv::CascadeClassifier>)nativePtr;
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+ (instancetype)fromNative:(cv::Ptr<cv::CascadeClassifier>)nativePtr;
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#endif
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#pragma mark - Methods
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//
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// cv::CascadeClassifier::CascadeClassifier()
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//
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- (instancetype)init;
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//
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// cv::CascadeClassifier::CascadeClassifier(String filename)
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//
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/**
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* Loads a classifier from a file.
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*
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* @param filename Name of the file from which the classifier is loaded.
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*/
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- (instancetype)initWithFilename:(NSString*)filename;
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//
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// bool cv::CascadeClassifier::empty()
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//
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/**
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* Checks whether the classifier has been loaded.
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*/
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- (BOOL)empty NS_SWIFT_NAME(empty());
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//
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// bool cv::CascadeClassifier::load(String filename)
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//
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/**
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* Loads a classifier from a file.
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*
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* @param filename Name of the file from which the classifier is loaded. The file may contain an old
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* HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
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* traincascade application.
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*/
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- (BOOL)load:(NSString*)filename NS_SWIFT_NAME(load(filename:));
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//
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// bool cv::CascadeClassifier::read(FileNode node)
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//
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// Unknown type 'FileNode' (I), skipping the function
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//
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// void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
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//
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/**
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* Detects objects of different sizes in the input image. The detected objects are returned as a list
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* of rectangles.
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* @param flags Parameter with the same meaning for an old cascade as in the function
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* cvHaarDetectObjects. It is not used for a new cascade.
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* @param minSize Minimum possible object size. Objects smaller than that are ignored.
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* @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
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*/
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- (void)detectMultiScale:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags minSize:(Size2i*)minSize maxSize:(Size2i*)maxSize NS_SWIFT_NAME(detectMultiScale(image:objects:scaleFactor:minNeighbors:flags:minSize:maxSize:));
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/**
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* Detects objects of different sizes in the input image. The detected objects are returned as a list
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* of rectangles.
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* @param flags Parameter with the same meaning for an old cascade as in the function
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* cvHaarDetectObjects. It is not used for a new cascade.
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* @param minSize Minimum possible object size. Objects smaller than that are ignored.
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*/
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- (void)detectMultiScale:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags minSize:(Size2i*)minSize NS_SWIFT_NAME(detectMultiScale(image:objects:scaleFactor:minNeighbors:flags:minSize:));
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/**
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* Detects objects of different sizes in the input image. The detected objects are returned as a list
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* of rectangles.
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* @param flags Parameter with the same meaning for an old cascade as in the function
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags NS_SWIFT_NAME(detectMultiScale(image:objects:scaleFactor:minNeighbors:flags:));
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/**
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* Detects objects of different sizes in the input image. The detected objects are returned as a list
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* of rectangles.
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors NS_SWIFT_NAME(detectMultiScale(image:objects:scaleFactor:minNeighbors:));
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/**
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* Detects objects of different sizes in the input image. The detected objects are returned as a list
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* of rectangles.
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* to retain it.
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects scaleFactor:(double)scaleFactor NS_SWIFT_NAME(detectMultiScale(image:objects:scaleFactor:));
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/**
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* Detects objects of different sizes in the input image. The detected objects are returned as a list
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* of rectangles.
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* to retain it.
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects NS_SWIFT_NAME(detectMultiScale(image:objects:));
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//
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// void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, vector_int& numDetections, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
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//
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/**
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
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* of detections is the number of neighboring positively classified rectangles that were joined
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* together to form the object.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* @param flags Parameter with the same meaning for an old cascade as in the function
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* cvHaarDetectObjects. It is not used for a new cascade.
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* @param minSize Minimum possible object size. Objects smaller than that are ignored.
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* @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
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*/
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- (void)detectMultiScale2:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects numDetections:(IntVector*)numDetections scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags minSize:(Size2i*)minSize maxSize:(Size2i*)maxSize NS_SWIFT_NAME(detectMultiScale(image:objects:numDetections:scaleFactor:minNeighbors:flags:minSize:maxSize:));
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/**
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
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* of detections is the number of neighboring positively classified rectangles that were joined
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* together to form the object.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* @param flags Parameter with the same meaning for an old cascade as in the function
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* cvHaarDetectObjects. It is not used for a new cascade.
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* @param minSize Minimum possible object size. Objects smaller than that are ignored.
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*/
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- (void)detectMultiScale2:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects numDetections:(IntVector*)numDetections scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags minSize:(Size2i*)minSize NS_SWIFT_NAME(detectMultiScale(image:objects:numDetections:scaleFactor:minNeighbors:flags:minSize:));
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/**
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
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* of detections is the number of neighboring positively classified rectangles that were joined
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* together to form the object.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* @param flags Parameter with the same meaning for an old cascade as in the function
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale2:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects numDetections:(IntVector*)numDetections scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags NS_SWIFT_NAME(detectMultiScale(image:objects:numDetections:scaleFactor:minNeighbors:flags:));
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/**
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
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* of detections is the number of neighboring positively classified rectangles that were joined
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* together to form the object.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
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* to retain it.
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale2:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects numDetections:(IntVector*)numDetections scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors NS_SWIFT_NAME(detectMultiScale(image:objects:numDetections:scaleFactor:minNeighbors:));
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/**
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
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* of detections is the number of neighboring positively classified rectangles that were joined
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* together to form the object.
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* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
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* to retain it.
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale2:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects numDetections:(IntVector*)numDetections scaleFactor:(double)scaleFactor NS_SWIFT_NAME(detectMultiScale(image:objects:numDetections:scaleFactor:));
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/**
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*
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* @param image Matrix of the type CV_8U containing an image where objects are detected.
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* @param objects Vector of rectangles where each rectangle contains the detected object, the
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* rectangles may be partially outside the original image.
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* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
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* of detections is the number of neighboring positively classified rectangles that were joined
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* together to form the object.
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* to retain it.
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* cvHaarDetectObjects. It is not used for a new cascade.
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*/
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- (void)detectMultiScale2:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects numDetections:(IntVector*)numDetections NS_SWIFT_NAME(detectMultiScale(image:objects:numDetections:));
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//
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// void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, vector_int& rejectLevels, vector_double& levelWeights, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size(), bool outputRejectLevels = false)
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//
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/**
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*
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* This function allows you to retrieve the final stage decision certainty of classification.
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* For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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* For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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* This value can then be used to separate strong from weaker classifications.
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*
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* A code sample on how to use it efficiently can be found below:
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*
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* Mat img;
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* vector<double> weights;
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* vector<int> levels;
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* vector<Rect> detections;
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* CascadeClassifier model("/path/to/your/model.xml");
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* model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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* cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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*
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*/
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- (void)detectMultiScale3:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects rejectLevels:(IntVector*)rejectLevels levelWeights:(DoubleVector*)levelWeights scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags minSize:(Size2i*)minSize maxSize:(Size2i*)maxSize outputRejectLevels:(BOOL)outputRejectLevels NS_SWIFT_NAME(detectMultiScale(image:objects:rejectLevels:levelWeights:scaleFactor:minNeighbors:flags:minSize:maxSize:outputRejectLevels:));
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/**
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*
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* This function allows you to retrieve the final stage decision certainty of classification.
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* For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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* For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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* This value can then be used to separate strong from weaker classifications.
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*
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* A code sample on how to use it efficiently can be found below:
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*
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* Mat img;
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* vector<double> weights;
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* vector<int> levels;
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* vector<Rect> detections;
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* CascadeClassifier model("/path/to/your/model.xml");
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* model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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* cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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*
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*/
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- (void)detectMultiScale3:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects rejectLevels:(IntVector*)rejectLevels levelWeights:(DoubleVector*)levelWeights scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags minSize:(Size2i*)minSize maxSize:(Size2i*)maxSize NS_SWIFT_NAME(detectMultiScale(image:objects:rejectLevels:levelWeights:scaleFactor:minNeighbors:flags:minSize:maxSize:));
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/**
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*
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* This function allows you to retrieve the final stage decision certainty of classification.
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* For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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* For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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* This value can then be used to separate strong from weaker classifications.
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*
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* A code sample on how to use it efficiently can be found below:
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*
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* Mat img;
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* vector<double> weights;
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* vector<int> levels;
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* vector<Rect> detections;
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* CascadeClassifier model("/path/to/your/model.xml");
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* model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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* cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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*
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*/
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- (void)detectMultiScale3:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects rejectLevels:(IntVector*)rejectLevels levelWeights:(DoubleVector*)levelWeights scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags minSize:(Size2i*)minSize NS_SWIFT_NAME(detectMultiScale(image:objects:rejectLevels:levelWeights:scaleFactor:minNeighbors:flags:minSize:));
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/**
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*
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* This function allows you to retrieve the final stage decision certainty of classification.
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* For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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* For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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* This value can then be used to separate strong from weaker classifications.
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*
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* A code sample on how to use it efficiently can be found below:
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*
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* Mat img;
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* vector<double> weights;
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* vector<int> levels;
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* vector<Rect> detections;
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* CascadeClassifier model("/path/to/your/model.xml");
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* model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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* cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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*
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*/
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- (void)detectMultiScale3:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects rejectLevels:(IntVector*)rejectLevels levelWeights:(DoubleVector*)levelWeights scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors flags:(int)flags NS_SWIFT_NAME(detectMultiScale(image:objects:rejectLevels:levelWeights:scaleFactor:minNeighbors:flags:));
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/**
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*
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* This function allows you to retrieve the final stage decision certainty of classification.
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* For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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* For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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* This value can then be used to separate strong from weaker classifications.
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*
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* A code sample on how to use it efficiently can be found below:
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*
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* Mat img;
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* vector<double> weights;
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* vector<int> levels;
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* vector<Rect> detections;
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* CascadeClassifier model("/path/to/your/model.xml");
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* model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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* cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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*
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*/
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- (void)detectMultiScale3:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects rejectLevels:(IntVector*)rejectLevels levelWeights:(DoubleVector*)levelWeights scaleFactor:(double)scaleFactor minNeighbors:(int)minNeighbors NS_SWIFT_NAME(detectMultiScale(image:objects:rejectLevels:levelWeights:scaleFactor:minNeighbors:));
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/**
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*
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* This function allows you to retrieve the final stage decision certainty of classification.
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* For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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* For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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* This value can then be used to separate strong from weaker classifications.
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*
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* A code sample on how to use it efficiently can be found below:
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*
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* Mat img;
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* vector<double> weights;
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* vector<int> levels;
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* vector<Rect> detections;
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* CascadeClassifier model("/path/to/your/model.xml");
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* model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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* cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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*
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*/
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- (void)detectMultiScale3:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects rejectLevels:(IntVector*)rejectLevels levelWeights:(DoubleVector*)levelWeights scaleFactor:(double)scaleFactor NS_SWIFT_NAME(detectMultiScale(image:objects:rejectLevels:levelWeights:scaleFactor:));
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/**
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*
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* This function allows you to retrieve the final stage decision certainty of classification.
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* For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
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* For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
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* This value can then be used to separate strong from weaker classifications.
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*
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* A code sample on how to use it efficiently can be found below:
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*
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* Mat img;
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* vector<double> weights;
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* vector<int> levels;
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* vector<Rect> detections;
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* CascadeClassifier model("/path/to/your/model.xml");
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* model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
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* cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
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*
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*/
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- (void)detectMultiScale3:(Mat*)image objects:(NSMutableArray<Rect2i*>*)objects rejectLevels:(IntVector*)rejectLevels levelWeights:(DoubleVector*)levelWeights NS_SWIFT_NAME(detectMultiScale(image:objects:rejectLevels:levelWeights:));
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//
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// bool cv::CascadeClassifier::isOldFormatCascade()
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//
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- (BOOL)isOldFormatCascade NS_SWIFT_NAME(isOldFormatCascade());
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//
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// Size cv::CascadeClassifier::getOriginalWindowSize()
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//
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- (Size2i*)getOriginalWindowSize NS_SWIFT_NAME(getOriginalWindowSize());
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//
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// int cv::CascadeClassifier::getFeatureType()
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//
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- (int)getFeatureType NS_SWIFT_NAME(getFeatureType());
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//
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// static bool cv::CascadeClassifier::convert(String oldcascade, String newcascade)
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//
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+ (BOOL)convert:(NSString*)oldcascade newcascade:(NSString*)newcascade NS_SWIFT_NAME(convert(oldcascade:newcascade:));
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@end
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NS_ASSUME_NONNULL_END
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