OpenCV
4.10.0
Open Source Computer Vision
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Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector. More...
#include <opencv2/objdetect.hpp>
Public Types | |
enum | { DEFAULT_NLEVELS = 64 } |
enum | DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL , DESCR_FORMAT_ROW_BY_ROW } |
enum | HistogramNormType { L2Hys = 0 } |
Public Member Functions | |
HOGDescriptor () | |
Creates the HOG descriptor and detector with default parameters. | |
HOGDescriptor (const HOGDescriptor &d) | |
HOGDescriptor (const String &filename) | |
HOGDescriptor (Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1, HOGDescriptor::HistogramNormType _histogramNormType=HOGDescriptor::L2Hys, double _L2HysThreshold=0.2, bool _gammaCorrection=false, int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false) | |
virtual | ~HOGDescriptor () |
Default destructor. | |
bool | checkDetectorSize () const |
Checks if detector size equal to descriptor size. | |
virtual void | compute (InputArray img, std::vector< float > &descriptors, Size winStride=Size(), Size padding=Size(), const std::vector< Point > &locations=std::vector< Point >()) const |
Computes HOG descriptors of given image. | |
virtual void | computeGradient (InputArray img, InputOutputArray grad, InputOutputArray angleOfs, Size paddingTL=Size(), Size paddingBR=Size()) const |
Computes gradients and quantized gradient orientations. | |
virtual void | copyTo (HOGDescriptor &c) const |
clones the HOGDescriptor | |
virtual void | detect (InputArray img, std::vector< Point > &foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const std::vector< Point > &searchLocations=std::vector< Point >()) const |
Performs object detection without a multi-scale window. | |
virtual void | detect (InputArray img, std::vector< Point > &foundLocations, std::vector< double > &weights, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const std::vector< Point > &searchLocations=std::vector< Point >()) const |
Performs object detection without a multi-scale window. | |
virtual void | detectMultiScale (InputArray img, std::vector< Rect > &foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), double scale=1.05, double groupThreshold=2.0, bool useMeanshiftGrouping=false) const |
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. | |
virtual void | detectMultiScale (InputArray img, std::vector< Rect > &foundLocations, std::vector< double > &foundWeights, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), double scale=1.05, double groupThreshold=2.0, bool useMeanshiftGrouping=false) const |
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. | |
virtual void | detectMultiScaleROI (InputArray img, std::vector< cv::Rect > &foundLocations, std::vector< DetectionROI > &locations, double hitThreshold=0, int groupThreshold=0) const |
evaluate specified ROI and return confidence value for each location in multiple scales | |
virtual void | detectROI (InputArray img, const std::vector< cv::Point > &locations, std::vector< cv::Point > &foundLocations, std::vector< double > &confidences, double hitThreshold=0, cv::Size winStride=Size(), cv::Size padding=Size()) const |
evaluate specified ROI and return confidence value for each location | |
size_t | getDescriptorSize () const |
Returns the number of coefficients required for the classification. | |
double | getWinSigma () const |
Returns winSigma value. | |
void | groupRectangles (std::vector< cv::Rect > &rectList, std::vector< double > &weights, int groupThreshold, double eps) const |
Groups the object candidate rectangles. | |
virtual bool | load (const String &filename, const String &objname=String()) |
loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file | |
virtual bool | read (FileNode &fn) |
Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node. | |
virtual void | save (const String &filename, const String &objname=String()) const |
saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file | |
virtual void | setSVMDetector (InputArray svmdetector) |
Sets coefficients for the linear SVM classifier. | |
virtual void | write (FileStorage &fs, const String &objname) const |
Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage. | |
Static Public Member Functions | |
static std::vector< float > | getDaimlerPeopleDetector () |
Returns coefficients of the classifier trained for people detection (for 48x96 windows). | |
static std::vector< float > | getDefaultPeopleDetector () |
Returns coefficients of the classifier trained for people detection (for 64x128 windows). | |
Public Attributes | |
Size | blockSize |
Block size in pixels. Align to cell size. Default value is Size(16,16). | |
Size | blockStride |
Block stride. It must be a multiple of cell size. Default value is Size(8,8). | |
Size | cellSize |
Cell size. Default value is Size(8,8). | |
int | derivAperture |
not documented | |
float | free_coef |
not documented | |
bool | gammaCorrection |
Flag to specify whether the gamma correction preprocessing is required or not. | |
HOGDescriptor::HistogramNormType | histogramNormType |
histogramNormType | |
double | L2HysThreshold |
L2-Hys normalization method shrinkage. | |
int | nbins |
Number of bins used in the calculation of histogram of gradients. Default value is 9. | |
int | nlevels |
Maximum number of detection window increases. Default value is 64. | |
UMat | oclSvmDetector |
coefficients for the linear SVM classifier used when OpenCL is enabled | |
bool | signedGradient |
Indicates signed gradient will be used or not. | |
std::vector< float > | svmDetector |
coefficients for the linear SVM classifier. | |
double | winSigma |
Gaussian smoothing window parameter. | |
Size | winSize |
Detection window size. Align to block size and block stride. Default value is Size(64,128). | |
Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs [63] .
useful links:
https://hal.inria.fr/inria-00548512/document/
https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor
http://www.learnopencv.com/histogram-of-oriented-gradients
http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial
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inline |
Creates the HOG descriptor and detector with default parameters.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
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inline |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
_winSize | sets winSize with given value. |
_blockSize | sets blockSize with given value. |
_blockStride | sets blockStride with given value. |
_cellSize | sets cellSize with given value. |
_nbins | sets nbins with given value. |
_derivAperture | sets derivAperture with given value. |
_winSigma | sets winSigma with given value. |
_histogramNormType | sets histogramNormType with given value. |
_L2HysThreshold | sets L2HysThreshold with given value. |
_gammaCorrection | sets gammaCorrection with given value. |
_nlevels | sets nlevels with given value. |
_signedGradient | sets signedGradient with given value. |
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inline |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
filename | The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier. |
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inline |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
d | the HOGDescriptor which cloned to create a new one. |
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inlinevirtual |
Default destructor.
bool cv::HOGDescriptor::checkDetectorSize | ( | ) | const |
Checks if detector size equal to descriptor size.
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virtual |
Computes HOG descriptors of given image.
img | Matrix of the type CV_8U containing an image where HOG features will be calculated. |
descriptors | Matrix of the type CV_32F |
winStride | Window stride. It must be a multiple of block stride. |
padding | Padding |
locations | Vector of Point |
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virtual |
Computes gradients and quantized gradient orientations.
img | Matrix contains the image to be computed |
grad | Matrix of type CV_32FC2 contains computed gradients |
angleOfs | Matrix of type CV_8UC2 contains quantized gradient orientations |
paddingTL | Padding from top-left |
paddingBR | Padding from bottom-right |
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virtual |
clones the HOGDescriptor
c | cloned HOGDescriptor |
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virtual |
Performs object detection without a multi-scale window.
img | Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
foundLocations | Vector of point where each point contains left-top corner point of detected object boundaries. |
hitThreshold | 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. |
winStride | Window stride. It must be a multiple of block stride. |
padding | Padding |
searchLocations | Vector of Point includes locations to search. |
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virtual |
Performs object detection without a multi-scale window.
img | Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
foundLocations | Vector of point where each point contains left-top corner point of detected object boundaries. |
weights | Vector that will contain confidence values for each detected object. |
hitThreshold | 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. |
winStride | Window stride. It must be a multiple of block stride. |
padding | Padding |
searchLocations | Vector of Point includes set of requested locations to be evaluated. |
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virtual |
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
img | Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
foundLocations | Vector of rectangles where each rectangle contains the detected object. |
hitThreshold | 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. |
winStride | Window stride. It must be a multiple of block stride. |
padding | Padding |
scale | Coefficient of the detection window increase. |
groupThreshold | Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. |
useMeanshiftGrouping | indicates grouping algorithm |
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virtual |
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
img | Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
foundLocations | Vector of rectangles where each rectangle contains the detected object. |
foundWeights | Vector that will contain confidence values for each detected object. |
hitThreshold | 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. |
winStride | Window stride. It must be a multiple of block stride. |
padding | Padding |
scale | Coefficient of the detection window increase. |
groupThreshold | Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. |
useMeanshiftGrouping | indicates grouping algorithm |
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virtual |
evaluate specified ROI and return confidence value for each location in multiple scales
img | Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
foundLocations | Vector of rectangles where each rectangle contains the detected object. |
locations | Vector of DetectionROI |
hitThreshold | 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. |
groupThreshold | Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. |
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virtual |
evaluate specified ROI and return confidence value for each location
img | Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. |
locations | Vector of Point |
foundLocations | Vector of Point where each Point is detected object's top-left point. |
confidences | confidences |
hitThreshold | 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 |
winStride | winStride |
padding | padding |
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static |
Returns coefficients of the classifier trained for people detection (for 48x96 windows).
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static |
Returns coefficients of the classifier trained for people detection (for 64x128 windows).
size_t cv::HOGDescriptor::getDescriptorSize | ( | ) | const |
Returns the number of coefficients required for the classification.
double cv::HOGDescriptor::getWinSigma | ( | ) | const |
Returns winSigma value.
void cv::HOGDescriptor::groupRectangles | ( | std::vector< cv::Rect > & | rectList, |
std::vector< double > & | weights, | ||
int | groupThreshold, | ||
double | eps | ||
) | const |
Groups the object candidate rectangles.
rectList | Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.) |
weights | Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.) |
groupThreshold | Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. |
eps | Relative difference between sides of the rectangles to merge them into a group. |
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virtual |
loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
filename | Name of the file to read. |
objname | The optional name of the node to read (if empty, the first top-level node will be used). |
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virtual |
Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node.
fn | File node |
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virtual |
saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
filename | File name |
objname | Object name |
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virtual |
Sets coefficients for the linear SVM classifier.
svmdetector | coefficients for the linear SVM classifier. |
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virtual |
Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.
fs | File storage |
objname | Object name |
Size cv::HOGDescriptor::blockSize |
Block size in pixels. Align to cell size. Default value is Size(16,16).
Size cv::HOGDescriptor::blockStride |
Block stride. It must be a multiple of cell size. Default value is Size(8,8).
int cv::HOGDescriptor::derivAperture |
not documented
float cv::HOGDescriptor::free_coef |
not documented
bool cv::HOGDescriptor::gammaCorrection |
Flag to specify whether the gamma correction preprocessing is required or not.
HOGDescriptor::HistogramNormType cv::HOGDescriptor::histogramNormType |
histogramNormType
double cv::HOGDescriptor::L2HysThreshold |
L2-Hys normalization method shrinkage.
int cv::HOGDescriptor::nbins |
Number of bins used in the calculation of histogram of gradients. Default value is 9.
int cv::HOGDescriptor::nlevels |
Maximum number of detection window increases. Default value is 64.
UMat cv::HOGDescriptor::oclSvmDetector |
coefficients for the linear SVM classifier used when OpenCL is enabled
bool cv::HOGDescriptor::signedGradient |
Indicates signed gradient will be used or not.
std::vector<float> cv::HOGDescriptor::svmDetector |
coefficients for the linear SVM classifier.
double cv::HOGDescriptor::winSigma |
Gaussian smoothing window parameter.
Size cv::HOGDescriptor::winSize |
Detection window size. Align to block size and block stride. Default value is Size(64,128).