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OpenCV
3.4.20
Open Source Computer Vision
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The class implements Histogram of Oriented Gradients ([50]) object detector. More...
#include <opencv2/cudaobjdetect.hpp>
Public Types | |
enum | { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL } |
Public Member Functions | |
virtual void | compute (InputArray img, OutputArray descriptors, Stream &stream=Stream::Null())=0 |
Returns block descriptors computed for the whole image. More... | |
virtual void | detect (InputArray img, std::vector< Point > &found_locations, std::vector< double > *confidences=NULL)=0 |
Performs object detection without a multi-scale window. More... | |
virtual void | detectMultiScale (InputArray img, std::vector< Rect > &found_locations, std::vector< double > *confidences=NULL)=0 |
Performs object detection with a multi-scale window. More... | |
virtual size_t | getBlockHistogramSize () const =0 |
Returns the block histogram size. More... | |
virtual Mat | getDefaultPeopleDetector () const =0 |
Returns coefficients of the classifier trained for people detection. More... | |
virtual int | getDescriptorFormat () const =0 |
virtual size_t | getDescriptorSize () const =0 |
Returns the number of coefficients required for the classification. More... | |
virtual bool | getGammaCorrection () const =0 |
virtual int | getGroupThreshold () const =0 |
virtual double | getHitThreshold () const =0 |
virtual double | getL2HysThreshold () const =0 |
virtual int | getNumLevels () const =0 |
virtual double | getScaleFactor () const =0 |
virtual double | getWinSigma () const =0 |
virtual Size | getWinStride () const =0 |
virtual void | setDescriptorFormat (int descr_format)=0 |
virtual void | setGammaCorrection (bool gamma_correction)=0 |
Flag to specify whether the gamma correction preprocessing is required or not. More... | |
virtual void | setGroupThreshold (int group_threshold)=0 |
virtual void | setHitThreshold (double hit_threshold)=0 |
virtual void | setL2HysThreshold (double threshold_L2hys)=0 |
L2-Hys normalization method shrinkage. More... | |
virtual void | setNumLevels (int nlevels)=0 |
Maximum number of detection window increases. More... | |
virtual void | setScaleFactor (double scale0)=0 |
Coefficient of the detection window increase. More... | |
virtual void | setSVMDetector (InputArray detector)=0 |
Sets coefficients for the linear SVM classifier. More... | |
virtual void | setWinSigma (double win_sigma)=0 |
Gaussian smoothing window parameter. More... | |
virtual void | setWinStride (Size win_stride)=0 |
Window stride. It must be a multiple of block stride. More... | |
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Algorithm () | |
virtual | ~Algorithm () |
virtual void | clear () |
Clears the algorithm state. More... | |
virtual bool | empty () const |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More... | |
virtual String | getDefaultName () const |
virtual void | read (const FileNode &fn) |
Reads algorithm parameters from a file storage. More... | |
virtual void | save (const String &filename) const |
virtual void | write (FileStorage &fs) const |
Stores algorithm parameters in a file storage. More... | |
void | write (FileStorage &fs, const String &name) const |
void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
Static Public Member Functions | |
static Ptr< HOG > | create (Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), int nbins=9) |
Creates the HOG descriptor and detector. More... | |
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template<typename _Tp > | |
static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
Loads algorithm from the file. More... | |
template<typename _Tp > | |
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
Loads algorithm from a String. More... | |
template<typename _Tp > | |
static Ptr< _Tp > | read (const FileNode &fn) |
Reads algorithm from the file node. More... | |
Additional Inherited Members | |
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void | writeFormat (FileStorage &fs) const |
The class implements Histogram of Oriented Gradients ([50]) object detector.
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pure virtual |
Returns block descriptors computed for the whole image.
img | Source image. See cuda::HOGDescriptor::detect for type limitations. |
descriptors | 2D array of descriptors. |
stream | CUDA stream. |
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static |
Creates the HOG descriptor and detector.
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. |
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pure virtual |
Performs object detection without a multi-scale window.
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. |
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pure virtual |
Performs object detection with a multi-scale window.
img | Source image. See cuda::HOGDescriptor::detect for type limitations. |
found_locations | Detected objects boundaries. |
confidences | Optional output array for confidences. |
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Returns the block histogram size.
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Returns coefficients of the classifier trained for people detection.
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Returns the number of coefficients required for the classification.
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Descriptor storage format:
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Flag to specify whether the gamma correction preprocessing is required or not.
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pure virtual |
Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles.
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pure virtual |
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.
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pure virtual |
L2-Hys normalization method shrinkage.
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pure virtual |
Maximum number of detection window increases.
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pure virtual |
Coefficient of the detection window increase.
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pure virtual |
Sets coefficients for the linear SVM classifier.
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pure virtual |
Gaussian smoothing window parameter.
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pure virtual |
Window stride. It must be a multiple of block stride.