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virtual void | compute (InputArray img, OutputArray descriptors, Stream &stream=Stream::Null())=0 |
| Returns block descriptors computed for the whole image.
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void | detect (InputArray img, std::vector< Point > &found_locations, std::vector< double > &confidences) |
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virtual void | detect (InputArray img, std::vector< Point > &found_locations, std::vector< double > *confidences=NULL)=0 |
| Performs object detection without a multi-scale window.
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void | detectMultiScale (InputArray img, std::vector< Rect > &found_locations, std::vector< double > &confidences) |
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virtual void | detectMultiScale (InputArray img, std::vector< Rect > &found_locations, std::vector< double > *confidences=NULL)=0 |
| Performs object detection with a multi-scale window.
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void | detectMultiScaleWithoutConf (InputArray img, std::vector< Rect > &found_locations) |
| Performs object detection with a multi-scale window.
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void | detectWithoutConf (InputArray img, std::vector< Point > &found_locations) |
| Performs object detection without a multi-scale window.
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virtual size_t | getBlockHistogramSize () const =0 |
| Returns the block histogram size.
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virtual Mat | getDefaultPeopleDetector () const =0 |
| Returns coefficients of the classifier trained for people detection.
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virtual HOGDescriptor::DescriptorStorageFormat | getDescriptorFormat () const =0 |
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virtual size_t | getDescriptorSize () const =0 |
| Returns the number of coefficients required for the classification.
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virtual bool | getGammaCorrection () const =0 |
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virtual int | getGroupThreshold () const =0 |
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virtual double | getHitThreshold () const =0 |
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virtual double | getL2HysThreshold () const =0 |
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virtual int | getNumLevels () const =0 |
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virtual double | getScaleFactor () const =0 |
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virtual double | getWinSigma () const =0 |
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virtual Size | getWinStride () const =0 |
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virtual void | setDescriptorFormat (HOGDescriptor::DescriptorStorageFormat descr_format)=0 |
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virtual void | setGammaCorrection (bool gamma_correction)=0 |
| Flag to specify whether the gamma correction preprocessing is required or not.
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virtual void | setGroupThreshold (int group_threshold)=0 |
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virtual void | setHitThreshold (double hit_threshold)=0 |
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virtual void | setL2HysThreshold (double threshold_L2hys)=0 |
| L2-Hys normalization method shrinkage.
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virtual void | setNumLevels (int nlevels)=0 |
| Maximum number of detection window increases.
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virtual void | setScaleFactor (double scale0)=0 |
| Coefficient of the detection window increase.
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virtual void | setSVMDetector (InputArray detector)=0 |
| Sets coefficients for the linear SVM classifier.
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virtual void | setWinSigma (double win_sigma)=0 |
| Gaussian smoothing window parameter.
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virtual void | setWinStride (Size win_stride)=0 |
| Window stride. It must be a multiple of block stride.
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| Algorithm () |
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virtual | ~Algorithm () |
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virtual void | clear () |
| Clears the algorithm state.
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virtual bool | empty () const |
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
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virtual String | getDefaultName () const |
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virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage.
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virtual void | save (const String &filename) const |
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
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virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage.
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void | write (FileStorage &fs, const String &name) const |
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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.
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template<typename _Tp > |
static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file.
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template<typename _Tp > |
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String.
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template<typename _Tp > |
static Ptr< _Tp > | read (const FileNode &fn) |
| Reads algorithm from the file node.
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The class implements Histogram of Oriented Gradients ([63]) 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