aqual to HOGDescriptor, Size(16,16), Size(8,8), Size(8,8), 9 )
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
sets winSize with given value.
sets blockSize with given value.
sets blockStride with given value.
sets cellSize with given value.
sets nbins with given value.
sets derivAperture with given value.
sets winSigma with given value.
sets histogramNormType with given value.
sets L2HysThreshold with given value.
sets gammaCorrection with given value.
sets nlevels with given value.
sets signedGradient with given value.
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
the HOGDescriptor which cloned to create a new one.
Matrix of the type CV_8U containing an image where HOG features will be calculated.
Matrix of the type CV_32F
Window stride. It must be a multiple of block stride.
Padding
Vector of Point
Matrix contains the image to be computed
Matrix of type CV_32FC2 contains computed gradients
Matrix of type CV_8UC2 contains quantized gradient orientations
Padding from top-left
Padding from bottom-right
cloned HOGDescriptor
Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
Vector of point where each point contains left-top corner point of detected object boundaries.
Vector that will contain confidence values for each detected object.
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.
Window stride. It must be a multiple of block stride.
Padding
Vector of Point includes set of requested locations to be evaluated.
Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
Vector of point where each point contains left-top corner point of detected object boundaries.
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.
Window stride. It must be a multiple of block stride.
Padding
Vector of Point includes locations to search.
Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
Vector of rectangles where each rectangle contains the detected object.
Vector that will contain confidence values for each detected object.
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.
Window stride. It must be a multiple of block stride.
Padding
Coefficient of the detection window increase.
Final threshold
indicates grouping algorithm
Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
Vector of rectangles where each rectangle contains the detected object.
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.
Window stride. It must be a multiple of block stride.
Padding
Coefficient of the detection window increase.
Final threshold
indicates grouping algorithm
Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
Vector of rectangles where each rectangle contains the detected object.
Vector of DetectionROI
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.
Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
Vector of Point
Vector of Point where each Point is detected object's top-left point.
confidences
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
padding
Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.)
Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
Relative difference between sides of the rectangles to merge them into a group.
Path of the file to read.
The optional name of the node to read (if empty, the first top-level node will be used).
File name
Object name
coefficients for the linear SVM classifier.
File storage
Object name
Generated using TypeDoc
the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs Dalal2005 .
useful links:
Source: opencv2/objdetect.hpp.