Module objdetect

Expand description

§Object Detection

§Cascade Classifier for Object Detection

The object detector described below has been initially proposed by Paul Viola Viola01 and improved by Rainer Lienhart Lienhart02 .

First, a classifier (namely a cascade of boosted classifiers working with haar-like features) is trained with a few hundred sample views of a particular object (i.e., a face or a car), called positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary images of the same size.

After a classifier is trained, it can be applied to a region of interest (of the same size as used during the training) in an input image. The classifier outputs a “1” if the region is likely to show the object (i.e., face/car), and “0” otherwise. To search for the object in the whole image one can move the search window across the image and check every location using the classifier. The classifier is designed so that it can be easily “resized” in order to be able to find the objects of interest at different sizes, which is more efficient than resizing the image itself. So, to find an object of an unknown size in the image the scan procedure should be done several times at different scales.

The word “cascade” in the classifier name means that the resultant classifier consists of several simpler classifiers (stages) that are applied subsequently to a region of interest until at some stage the candidate is rejected or all the stages are passed. The word “boosted” means that the classifiers at every stage of the cascade are complex themselves and they are built out of basic classifiers using one of four different boosting techniques (weighted voting). Currently Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic classifiers, and are calculated as described below. The current algorithm uses the following Haar-like features:

The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within the region of interest and the scale (this scale is not the same as the scale used at the detection stage, though these two scales are multiplied). For example, in the case of the third line feature (2c) the response is calculated as the difference between the sum of image pixels under the rectangle covering the whole feature (including the two white stripes and the black stripe in the middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to compensate for the differences in the size of areas. The sums of pixel values over a rectangular regions are calculated rapidly using integral images (see below and the integral description).

Check [tutorial_cascade_classifier] “the corresponding tutorial” for more details.

The following reference is for the detection part only. There is a separate application called opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.

Note: In the new C++ interface it is also possible to use LBP (local binary pattern) features in addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at https://github.com/SvHey/thesis/blob/master/Literature/ObjectDetection/violaJones_CVPR2001.pdf

§HOG (Histogram of Oriented Gradients) descriptor and object detector

§Barcode detection and decoding

§QRCode detection and encoding

§DNN-based face detection and recognition

Check [tutorial_dnn_face] “the corresponding tutorial” for more details.

§Common functions and classes

§ArUco markers and boards detection for robust camera pose estimation

   ArUco Marker Detection
   Square fiducial markers (also known as Augmented Reality Markers) are useful for easy,
   fast and robust camera pose estimation.

   The main functionality of ArucoDetector class is detection of markers in an image. If the markers are grouped
   as a board, then you can try to recover the missing markers with ArucoDetector::refineDetectedMarkers().
   ArUco markers can also be used for advanced chessboard corner finding. To do this, group the markers in the
   CharucoBoard and find the corners of the chessboard with the CharucoDetector::detectBoard().

   The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado [Aruco2014](https://docs.opencv.org/4.11.0/d0/de3/citelist.html#CITEREF_Aruco2014).

   Markers can also be detected based on the AprilTag 2 [wang2016iros](https://docs.opencv.org/4.11.0/d0/de3/citelist.html#CITEREF_wang2016iros) fiducial detection method.

§See also

Aruco2014 This code has been originally developed by Sergio Garrido-Jurado as a project for Google Summer of Code 2015 (GSoC 15).

Modules§

prelude

Structs§

ArucoDetector: The main functionality of ArucoDetector class is detection of markers in an image with detectMarkers() method.
BarcodeDetector
BaseCascadeClassifier
BaseCascadeClassifier_MaskGenerator
Board: Board of ArUco markers
CascadeClassifier: @example samples/cpp/facedetect.cpp This program demonstrates usage of the Cascade classifier class \image html Cascade_Classifier_Tutorial_Result_Haar.jpg “Sample screenshot” width=321 height=254
CharucoBoard: ChArUco board is a planar chessboard where the markers are placed inside the white squares of a chessboard.
CharucoDetector
CharucoParameters
DetectionBasedTracker
DetectionBasedTracker_ExtObject
DetectionBasedTracker_IDetector
DetectionBasedTracker_Parameters
DetectionROI: struct for detection region of interest (ROI)
DetectorParameters: struct DetectorParameters is used by ArucoDetector
Dictionary: Dictionary is a set of unique ArUco markers of the same size
FaceDetectorYN: DNN-based face detector
FaceRecognizerSF: DNN-based face recognizer
GraphicalCodeDetector
GridBoard: Planar board with grid arrangement of markers
HOGDescriptor: Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
QRCodeDetector
QRCodeDetectorAruco
QRCodeDetectorAruco_Params
QRCodeEncoder
QRCodeEncoder_Params: QR code encoder parameters.
RefineParameters: struct RefineParameters is used by ArucoDetector
SimilarRects: This class is used for grouping object candidates detected by Cascade Classifier, HOG etc.

Enums§

CornerRefineMethod
DetectionBasedTracker_ObjectStatus
FaceRecognizerSF_DisType: Definition of distance used for calculating the distance between two face features
HOGDescriptor_DescriptorStorageFormat
HOGDescriptor_HistogramNormType
PredefinedDictionaryType: Predefined markers dictionaries/sets
QRCodeEncoder_CorrectionLevel
QRCodeEncoder_ECIEncodings
QRCodeEncoder_EncodeMode

Constants§

CASCADE_DO_CANNY_PRUNING
CASCADE_DO_ROUGH_SEARCH
CASCADE_FIND_BIGGEST_OBJECT
CASCADE_SCALE_IMAGE
CORNER_REFINE_APRILTAG: Tag and corners detection based on the AprilTag 2 approach wang2016iros
CORNER_REFINE_CONTOUR: ArUco approach and refine the corners locations using the contour-points line fitting
CORNER_REFINE_NONE: Tag and corners detection based on the ArUco approach
CORNER_REFINE_SUBPIX: ArUco approach and refine the corners locations using corner subpixel accuracy
DICT_4X4_50: 4x4 bits, minimum hamming distance between any two codes = 4, 50 codes
DICT_4X4_100: 4x4 bits, minimum hamming distance between any two codes = 3, 100 codes
DICT_4X4_250: 4x4 bits, minimum hamming distance between any two codes = 3, 250 codes
DICT_4X4_1000: 4x4 bits, minimum hamming distance between any two codes = 2, 1000 codes
DICT_5X5_50: 5x5 bits, minimum hamming distance between any two codes = 8, 50 codes
DICT_5X5_100: 5x5 bits, minimum hamming distance between any two codes = 7, 100 codes
DICT_5X5_250: 5x5 bits, minimum hamming distance between any two codes = 6, 250 codes
DICT_5X5_1000: 5x5 bits, minimum hamming distance between any two codes = 5, 1000 codes
DICT_6X6_50: 6x6 bits, minimum hamming distance between any two codes = 13, 50 codes
DICT_6X6_100: 6x6 bits, minimum hamming distance between any two codes = 12, 100 codes
DICT_6X6_250: 6x6 bits, minimum hamming distance between any two codes = 11, 250 codes
DICT_6X6_1000: 6x6 bits, minimum hamming distance between any two codes = 9, 1000 codes
DICT_7X7_50: 7x7 bits, minimum hamming distance between any two codes = 19, 50 codes
DICT_7X7_100: 7x7 bits, minimum hamming distance between any two codes = 18, 100 codes
DICT_7X7_250: 7x7 bits, minimum hamming distance between any two codes = 17, 250 codes
DICT_7X7_1000: 7x7 bits, minimum hamming distance between any two codes = 14, 1000 codes
DICT_APRILTAG_16h5: 4x4 bits, minimum hamming distance between any two codes = 5, 30 codes
DICT_APRILTAG_25h9: 5x5 bits, minimum hamming distance between any two codes = 9, 35 codes
DICT_APRILTAG_36h10: 6x6 bits, minimum hamming distance between any two codes = 10, 2320 codes
DICT_APRILTAG_36h11: 6x6 bits, minimum hamming distance between any two codes = 11, 587 codes
DICT_ARUCO_MIP_36h12: 6x6 bits, minimum hamming distance between any two codes = 12, 250 codes
DICT_ARUCO_ORIGINAL: 6x6 bits, minimum hamming distance between any two codes = 3, 1024 codes
DetectionBasedTracker_DETECTED
DetectionBasedTracker_DETECTED_NOT_SHOWN_YET
DetectionBasedTracker_DETECTED_TEMPORARY_LOST
DetectionBasedTracker_WRONG_OBJECT
FaceRecognizerSF_FR_COSINE
FaceRecognizerSF_FR_NORM_L2
HOGDescriptor_DEFAULT_NLEVELS: Default nlevels value.
HOGDescriptor_DESCR_FORMAT_COL_BY_COL
HOGDescriptor_DESCR_FORMAT_ROW_BY_ROW
HOGDescriptor_L2Hys: Default histogramNormType
QRCodeEncoder_CORRECT_LEVEL_H
QRCodeEncoder_CORRECT_LEVEL_L
QRCodeEncoder_CORRECT_LEVEL_M
QRCodeEncoder_CORRECT_LEVEL_Q
QRCodeEncoder_ECI_UTF8
QRCodeEncoder_MODE_ALPHANUMERIC
QRCodeEncoder_MODE_AUTO
QRCodeEncoder_MODE_BYTE
QRCodeEncoder_MODE_ECI
QRCodeEncoder_MODE_KANJI
QRCodeEncoder_MODE_NUMERIC
QRCodeEncoder_MODE_STRUCTURED_APPEND

Traits§

ArucoDetectorTrait: Mutable methods for crate::objdetect::ArucoDetector
ArucoDetectorTraitConst: Constant methods for crate::objdetect::ArucoDetector
BarcodeDetectorTrait: Mutable methods for crate::objdetect::BarcodeDetector
BarcodeDetectorTraitConst: Constant methods for crate::objdetect::BarcodeDetector
BaseCascadeClassifierTrait: Mutable methods for crate::objdetect::BaseCascadeClassifier
BaseCascadeClassifierTraitConst: Constant methods for crate::objdetect::BaseCascadeClassifier
BaseCascadeClassifier_MaskGeneratorTrait: Mutable methods for crate::objdetect::BaseCascadeClassifier_MaskGenerator
BaseCascadeClassifier_MaskGeneratorTraitConst: Constant methods for crate::objdetect::BaseCascadeClassifier_MaskGenerator
BoardTrait: Mutable methods for crate::objdetect::Board
BoardTraitConst: Constant methods for crate::objdetect::Board
CascadeClassifierTrait: Mutable methods for crate::objdetect::CascadeClassifier
CascadeClassifierTraitConst: Constant methods for crate::objdetect::CascadeClassifier
CharucoBoardTrait: Mutable methods for crate::objdetect::CharucoBoard
CharucoBoardTraitConst: Constant methods for crate::objdetect::CharucoBoard
CharucoDetectorTrait: Mutable methods for crate::objdetect::CharucoDetector
CharucoDetectorTraitConst: Constant methods for crate::objdetect::CharucoDetector
CharucoParametersTrait: Mutable methods for crate::objdetect::CharucoParameters
CharucoParametersTraitConst: Constant methods for crate::objdetect::CharucoParameters
DetectionBasedTrackerTrait: Mutable methods for crate::objdetect::DetectionBasedTracker
DetectionBasedTrackerTraitConst: Constant methods for crate::objdetect::DetectionBasedTracker
DetectionBasedTracker_ExtObjectTrait: Mutable methods for crate::objdetect::DetectionBasedTracker_ExtObject
DetectionBasedTracker_ExtObjectTraitConst: Constant methods for crate::objdetect::DetectionBasedTracker_ExtObject
DetectionBasedTracker_IDetectorTrait: Mutable methods for crate::objdetect::DetectionBasedTracker_IDetector
DetectionBasedTracker_IDetectorTraitConst: Constant methods for crate::objdetect::DetectionBasedTracker_IDetector
DetectionBasedTracker_ParametersTrait: Mutable methods for crate::objdetect::DetectionBasedTracker_Parameters
DetectionBasedTracker_ParametersTraitConst: Constant methods for crate::objdetect::DetectionBasedTracker_Parameters
DetectionROITrait: Mutable methods for crate::objdetect::DetectionROI
DetectionROITraitConst: Constant methods for crate::objdetect::DetectionROI
DetectorParametersTrait: Mutable methods for crate::objdetect::DetectorParameters
DetectorParametersTraitConst: Constant methods for crate::objdetect::DetectorParameters
DictionaryTrait: Mutable methods for crate::objdetect::Dictionary
DictionaryTraitConst: Constant methods for crate::objdetect::Dictionary
FaceDetectorYNTrait: Mutable methods for crate::objdetect::FaceDetectorYN
FaceDetectorYNTraitConst: Constant methods for crate::objdetect::FaceDetectorYN
FaceRecognizerSFTrait: Mutable methods for crate::objdetect::FaceRecognizerSF
FaceRecognizerSFTraitConst: Constant methods for crate::objdetect::FaceRecognizerSF
GraphicalCodeDetectorTrait: Mutable methods for crate::objdetect::GraphicalCodeDetector
GraphicalCodeDetectorTraitConst: Constant methods for crate::objdetect::GraphicalCodeDetector
GridBoardTrait: Mutable methods for crate::objdetect::GridBoard
GridBoardTraitConst: Constant methods for crate::objdetect::GridBoard
HOGDescriptorTrait: Mutable methods for crate::objdetect::HOGDescriptor
HOGDescriptorTraitConst: Constant methods for crate::objdetect::HOGDescriptor
QRCodeDetectorArucoTrait: Mutable methods for crate::objdetect::QRCodeDetectorAruco
QRCodeDetectorArucoTraitConst: Constant methods for crate::objdetect::QRCodeDetectorAruco
QRCodeDetectorTrait: Mutable methods for crate::objdetect::QRCodeDetector
QRCodeDetectorTraitConst: Constant methods for crate::objdetect::QRCodeDetector
QRCodeEncoderTrait: Mutable methods for crate::objdetect::QRCodeEncoder
QRCodeEncoderTraitConst: Constant methods for crate::objdetect::QRCodeEncoder
SimilarRectsTrait: Mutable methods for crate::objdetect::SimilarRects
SimilarRectsTraitConst: Constant methods for crate::objdetect::SimilarRects

Functions§

create_face_detection_mask_generator
draw_detected_corners_charuco: Draws a set of Charuco corners
draw_detected_corners_charuco_def: Draws a set of Charuco corners
draw_detected_diamonds: Draw a set of detected ChArUco Diamond markers
draw_detected_diamonds_def: Draw a set of detected ChArUco Diamond markers
draw_detected_markers: Draw detected markers in image
draw_detected_markers_def: Draw detected markers in image
extend_dictionary: Extend base dictionary by new nMarkers
extend_dictionary_def: Extend base dictionary by new nMarkers
generate_image_marker: Generate a canonical marker image
generate_image_marker_def: Generate a canonical marker image
get_predefined_dictionary: Returns one of the predefined dictionaries defined in PredefinedDictionaryType
get_predefined_dictionary_i32: Returns one of the predefined dictionaries referenced by DICT_*.
group_rectangles: Groups the object candidate rectangles.
group_rectangles_def: Groups the object candidate rectangles.
group_rectangles_levels: Groups the object candidate rectangles.
group_rectangles_levels_def: @overload
group_rectangles_levelweights: Groups the object candidate rectangles.
group_rectangles_meanshift: This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
group_rectangles_meanshift_def: @overload
group_rectangles_weights: Groups the object candidate rectangles.
group_rectangles_weights_def: @overload

Type Aliases§

DetectionBasedTracker_Object

Module objdetectCopy item path