NAME

PDL::OpenCV::Objdetect - PDL bindings for OpenCV BaseCascadeClassifier, CascadeClassifier, HOGDescriptor, QRCodeDetector

SYNOPSIS

 use PDL::OpenCV::Objdetect;

FUNCTIONS

groupRectangles

  Signature: (indx [io,phys] rectList(n1=4,n1d0); int [o,phys] weights(n2d0); int [phys] groupThreshold(); double [phys] eps())

 NO BROADCASTING.

 $weights = groupRectangles($rectList,$groupThreshold); # with defaults
 $weights = groupRectangles($rectList,$groupThreshold,$eps);

@overload

groupRectangles ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

METHODS for PDL::OpenCV::BaseCascadeClassifier

Subclass of PDL::OpenCV::Algorithm

METHODS for PDL::OpenCV::CascadeClassifier

Cascade classifier class for object detection.

new

 $obj = PDL::OpenCV::CascadeClassifier->new;

new2

Loads a classifier from a file.

 $obj = PDL::OpenCV::CascadeClassifier->new2($filename);

Parameters:

filename: Name of the file from which the classifier is loaded.

empty

Checks whether the classifier has been loaded.

 $res = $obj->empty;

load

Loads a classifier from a file.

 $res = $obj->load($filename);

Parameters:

filename: Name of the file from which the classifier is loaded. The file may contain an old HAAR classifier trained by the haartraining application or a new cascade classifier trained by the traincascade application.

read

Reads a classifier from a FileStorage node.

 $res = $obj->read($node);

@note The file may contain a new cascade classifier (trained traincascade application) only.

CascadeClassifier_detectMultiScale

  Signature: ([phys] image(l2,c2,r2); indx [o,phys] objects(n3=4,n3d0); double [phys] scaleFactor(); int [phys] minNeighbors(); int [phys] flags(); indx [phys] minSize(n7); indx [phys] maxSize(n8); CascadeClassifierWrapper * self)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. NO BROADCASTING.

 $objects = $obj->detectMultiScale($image); # with defaults
 $objects = $obj->detectMultiScale($image,$scaleFactor,$minNeighbors,$flags,$minSize,$maxSize);

The function is parallelized with the TBB library. @note - (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py

Parameters:

image: Matrix of the type CV_8U containing an image where objects are detected.
objects: Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
scaleFactor: Parameter specifying how much the image size is reduced at each image scale.
minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it.
flags: Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
minSize: Minimum possible object size. Objects smaller than that are ignored.
maxSize: Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.

CascadeClassifier_detectMultiScale ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

CascadeClassifier_detectMultiScale2

  Signature: ([phys] image(l2,c2,r2); indx [o,phys] objects(n3=4,n3d0); int [o,phys] numDetections(n4d0); double [phys] scaleFactor(); int [phys] minNeighbors(); int [phys] flags(); indx [phys] minSize(n8); indx [phys] maxSize(n9); CascadeClassifierWrapper * self)

 NO BROADCASTING.

 ($objects,$numDetections) = $obj->detectMultiScale2($image); # with defaults
 ($objects,$numDetections) = $obj->detectMultiScale2($image,$scaleFactor,$minNeighbors,$flags,$minSize,$maxSize);

@overload

Parameters:

image: Matrix of the type CV_8U containing an image where objects are detected.
objects: Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
numDetections: Vector of detection numbers for the corresponding objects. An object's number of detections is the number of neighboring positively classified rectangles that were joined together to form the object.
scaleFactor: Parameter specifying how much the image size is reduced at each image scale.
minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it.
flags: Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
minSize: Minimum possible object size. Objects smaller than that are ignored.
maxSize: Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.

CascadeClassifier_detectMultiScale2 ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

CascadeClassifier_detectMultiScale3

  Signature: ([phys] image(l2,c2,r2); indx [o,phys] objects(n3=4,n3d0); int [o,phys] rejectLevels(n4d0); double [o,phys] levelWeights(n5d0); double [phys] scaleFactor(); int [phys] minNeighbors(); int [phys] flags(); indx [phys] minSize(n9); indx [phys] maxSize(n10); byte [phys] outputRejectLevels(); CascadeClassifierWrapper * self)

 NO BROADCASTING.

 ($objects,$rejectLevels,$levelWeights) = $obj->detectMultiScale3($image); # with defaults
 ($objects,$rejectLevels,$levelWeights) = $obj->detectMultiScale3($image,$scaleFactor,$minNeighbors,$flags,$minSize,$maxSize,$outputRejectLevels);

@overload This function allows you to retrieve the final stage decision certainty of classification. For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter. For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage. This value can then be used to separate strong from weaker classifications. A code sample on how to use it efficiently can be found below:

     Mat img;
     vector<double> weights;
     vector<int> levels;
     vector<Rect> detections;
     CascadeClassifier model("/path/to/your/model.xml");
     model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
     cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;

CascadeClassifier_detectMultiScale3 ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

isOldFormatCascade

 $res = $obj->isOldFormatCascade;

CascadeClassifier_getOriginalWindowSize

  Signature: (indx [o,phys] res(n2=2); CascadeClassifierWrapper * self)

 $res = $obj->getOriginalWindowSize;

CascadeClassifier_getOriginalWindowSize ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

getFeatureType

 $res = $obj->getFeatureType;

convert

 $res = PDL::OpenCV::CascadeClassifier::convert($oldcascade,$newcascade);

METHODS for PDL::OpenCV::HOGDescriptor

Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.

the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 . 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

new

Creates the HOG descriptor and detector with default params.

 $obj = PDL::OpenCV::HOGDescriptor->new;

aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )

HOGDescriptor_new2

  Signature: (indx [phys] _winSize(n2=2); indx [phys] _blockSize(n3=2); indx [phys] _blockStride(n4=2); indx [phys] _cellSize(n5=2); int [phys] _nbins(); int [phys] _derivAperture(); double [phys] _winSigma(); int [phys] _histogramNormType(); double [phys] _L2HysThreshold(); byte [phys] _gammaCorrection(); int [phys] _nlevels(); byte [phys] _signedGradient(); char * klass; [o] HOGDescriptorWrapper * res)

 $obj = PDL::OpenCV::HOGDescriptor->new2($_winSize,$_blockSize,$_blockStride,$_cellSize,$_nbins); # with defaults
 $obj = PDL::OpenCV::HOGDescriptor->new2($_winSize,$_blockSize,$_blockStride,$_cellSize,$_nbins,$_derivAperture,$_winSigma,$_histogramNormType,$_L2HysThreshold,$_gammaCorrection,$_nlevels,$_signedGradient);

@overload

Parameters:

_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.

HOGDescriptor_new2 ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

new3

 $obj = PDL::OpenCV::HOGDescriptor->new3($filename);

@overload

Parameters:

filename: The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.

getDescriptorSize

Returns the number of coefficients required for the classification.

 $res = $obj->getDescriptorSize;

checkDetectorSize

Checks if detector size equal to descriptor size.

 $res = $obj->checkDetectorSize;

getWinSigma

Returns winSigma value

 $res = $obj->getWinSigma;

HOGDescriptor_setSVMDetector

  Signature: ([phys] svmdetector(l2,c2,r2); HOGDescriptorWrapper * self)

Sets coefficients for the linear SVM classifier.

 $obj->setSVMDetector($svmdetector);

Parameters:

svmdetector: coefficients for the linear SVM classifier.

HOGDescriptor_setSVMDetector ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

load

loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.

 $res = $obj->load($filename); # with defaults
 $res = $obj->load($filename,$objname);

Parameters:

filename: Path of the file to read.
objname: The optional name of the node to read (if empty, the first top-level node will be used).

save

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

 $obj->save($filename); # with defaults
 $obj->save($filename,$objname);

Parameters:

filename: File name
objname: Object name

HOGDescriptor_compute

  Signature: ([phys] img(l2,c2,r2); float [o,phys] descriptors(n3d0); indx [phys] winStride(n4); indx [phys] padding(n5); indx [phys] locations(n6,n6d0); HOGDescriptorWrapper * self)

Computes HOG descriptors of given image. NO BROADCASTING.

 $descriptors = $obj->compute($img); # with defaults
 $descriptors = $obj->compute($img,$winStride,$padding,$locations);

Parameters:

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

HOGDescriptor_compute ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

HOGDescriptor_detect

  Signature: ([phys] img(l2,c2,r2); indx [o,phys] foundLocations(n3=2,n3d0); double [o,phys] weights(n4d0); double [phys] hitThreshold(); indx [phys] winStride(n6); indx [phys] padding(n7); indx [phys] searchLocations(n8,n8d0); HOGDescriptorWrapper * self)

Performs object detection without a multi-scale window. NO BROADCASTING.

 ($foundLocations,$weights) = $obj->detect($img); # with defaults
 ($foundLocations,$weights) = $obj->detect($img,$hitThreshold,$winStride,$padding,$searchLocations);

Parameters:

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.

HOGDescriptor_detect ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

HOGDescriptor_detectMultiScale

  Signature: ([phys] img(l2,c2,r2); indx [o,phys] foundLocations(n3=4,n3d0); double [o,phys] foundWeights(n4d0); double [phys] hitThreshold(); indx [phys] winStride(n6); indx [phys] padding(n7); double [phys] scale(); double [phys] finalThreshold(); byte [phys] useMeanshiftGrouping(); HOGDescriptorWrapper * self)

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. NO BROADCASTING.

 ($foundLocations,$foundWeights) = $obj->detectMultiScale($img); # with defaults
 ($foundLocations,$foundWeights) = $obj->detectMultiScale($img,$hitThreshold,$winStride,$padding,$scale,$finalThreshold,$useMeanshiftGrouping);

Parameters:

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.
finalThreshold: Final threshold
useMeanshiftGrouping: indicates grouping algorithm

HOGDescriptor_detectMultiScale ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

HOGDescriptor_computeGradient

  Signature: ([phys] img(l2,c2,r2); [io,phys] grad(l3,c3,r3); [io,phys] angleOfs(l4,c4,r4); indx [phys] paddingTL(n5); indx [phys] paddingBR(n6); HOGDescriptorWrapper * self)

Computes gradients and quantized gradient orientations.

 $obj->computeGradient($img,$grad,$angleOfs); # with defaults
 $obj->computeGradient($img,$grad,$angleOfs,$paddingTL,$paddingBR);

Parameters:

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

HOGDescriptor_computeGradient ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

HOGDescriptor_getDefaultPeopleDetector

  Signature: (float [o,phys] res(n1d0))

Returns coefficients of the classifier trained for people detection (for 64x128 windows). NO BROADCASTING.

 $res = PDL::OpenCV::HOGDescriptor::getDefaultPeopleDetector;

HOGDescriptor_getDefaultPeopleDetector ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

HOGDescriptor_getDaimlerPeopleDetector

  Signature: (float [o,phys] res(n1d0))

Returns coefficients of the classifier trained for people detection (for 48x96 windows). NO BROADCASTING.

 $res = PDL::OpenCV::HOGDescriptor::getDaimlerPeopleDetector;

HOGDescriptor_getDaimlerPeopleDetector ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

METHODS for PDL::OpenCV::QRCodeDetector

Groups the object candidate rectangles.

Parameters:

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.

new

 $obj = PDL::OpenCV::QRCodeDetector->new;

setEpsX

sets the epsilon used during the horizontal scan of QR code stop marker detection.

 $obj->setEpsX($epsX);

Parameters:

epsX: Epsilon neighborhood, which allows you to determine the horizontal pattern of the scheme 1:1:3:1:1 according to QR code standard.

setEpsY

sets the epsilon used during the vertical scan of QR code stop marker detection.

 $obj->setEpsY($epsY);

Parameters:

epsY: Epsilon neighborhood, which allows you to determine the vertical pattern of the scheme 1:1:3:1:1 according to QR code standard.

QRCodeDetector_detect

  Signature: ([phys] img(l2,c2,r2); [o,phys] points(l3,c3,r3); byte [o,phys] res(); QRCodeDetectorWrapper * self)

Detects QR code in image and returns the quadrangle containing the code. NO BROADCASTING.

 ($points,$res) = $obj->detect($img);

Parameters:

img: grayscale or color (BGR) image containing (or not) QR code.
points: Output vector of vertices of the minimum-area quadrangle containing the code.

QRCodeDetector_detect ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

QRCodeDetector_decode

  Signature: ([phys] img(l2,c2,r2); [phys] points(l3,c3,r3); [o,phys] straight_qrcode(l4,c4,r4); QRCodeDetectorWrapper * self; [o] StringWrapper* res)

Decodes QR code in image once it's found by the detect() method. NO BROADCASTING.

 ($straight_qrcode,$res) = $obj->decode($img,$points);

Returns UTF8-encoded output string or empty string if the code cannot be decoded.

Parameters:

img: grayscale or color (BGR) image containing QR code.
points: Quadrangle vertices found by detect() method (or some other algorithm).
straight_qrcode: The optional output image containing rectified and binarized QR code

QRCodeDetector_decode ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

QRCodeDetector_decodeCurved

  Signature: ([phys] img(l2,c2,r2); [phys] points(l3,c3,r3); [o,phys] straight_qrcode(l4,c4,r4); QRCodeDetectorWrapper * self; [o] StringWrapper* res)

Decodes QR code on a curved surface in image once it's found by the detect() method. NO BROADCASTING.

 ($straight_qrcode,$res) = $obj->decodeCurved($img,$points);

Returns UTF8-encoded output string or empty string if the code cannot be decoded.

Parameters:

img: grayscale or color (BGR) image containing QR code.
points: Quadrangle vertices found by detect() method (or some other algorithm).
straight_qrcode: The optional output image containing rectified and binarized QR code

QRCodeDetector_decodeCurved ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

QRCodeDetector_detectAndDecode

  Signature: ([phys] img(l2,c2,r2); [o,phys] points(l3,c3,r3); [o,phys] straight_qrcode(l4,c4,r4); QRCodeDetectorWrapper * self; [o] StringWrapper* res)

Both detects and decodes QR code NO BROADCASTING.

 ($points,$straight_qrcode,$res) = $obj->detectAndDecode($img);

Parameters:

img: grayscale or color (BGR) image containing QR code.
points: optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
straight_qrcode: The optional output image containing rectified and binarized QR code

QRCodeDetector_detectAndDecode ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

QRCodeDetector_detectAndDecodeCurved

  Signature: ([phys] img(l2,c2,r2); [o,phys] points(l3,c3,r3); [o,phys] straight_qrcode(l4,c4,r4); QRCodeDetectorWrapper * self; [o] StringWrapper* res)

Both detects and decodes QR code on a curved surface NO BROADCASTING.

 ($points,$straight_qrcode,$res) = $obj->detectAndDecodeCurved($img);

Parameters:

img: grayscale or color (BGR) image containing QR code.
points: optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
straight_qrcode: The optional output image containing rectified and binarized QR code

QRCodeDetector_detectAndDecodeCurved ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

QRCodeDetector_detectMulti

  Signature: ([phys] img(l2,c2,r2); [o,phys] points(l3,c3,r3); byte [o,phys] res(); QRCodeDetectorWrapper * self)

Detects QR codes in image and returns the vector of the quadrangles containing the codes. NO BROADCASTING.

 ($points,$res) = $obj->detectMulti($img);

Parameters:

img: grayscale or color (BGR) image containing (or not) QR codes.
points: Output vector of vector of vertices of the minimum-area quadrangle containing the codes.

QRCodeDetector_detectMulti ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

QRCodeDetector_decodeMulti

  Signature: ([phys] img(l2,c2,r2); [phys] points(l3,c3,r3); byte [o,phys] res(); QRCodeDetectorWrapper * self; [o] vector_StringWrapper * decoded_info; [o] vector_MatWrapper * straight_qrcode)

Decodes QR codes in image once it's found by the detect() method.

 ($decoded_info,$straight_qrcode,$res) = $obj->decodeMulti($img,$points);

Parameters:

img: grayscale or color (BGR) image containing QR codes.
decoded_info: UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded.
points: vector of Quadrangle vertices found by detect() method (or some other algorithm).
straight_qrcode: The optional output vector of images containing rectified and binarized QR codes

QRCodeDetector_decodeMulti ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

QRCodeDetector_detectAndDecodeMulti

  Signature: ([phys] img(l2,c2,r2); [o,phys] points(l4,c4,r4); byte [o,phys] res(); QRCodeDetectorWrapper * self; [o] vector_StringWrapper * decoded_info; [o] vector_MatWrapper * straight_qrcode)

Both detects and decodes QR codes NO BROADCASTING.

 ($decoded_info,$points,$straight_qrcode,$res) = $obj->detectAndDecodeMulti($img);

Parameters:

img: grayscale or color (BGR) image containing QR codes.
decoded_info: UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded.
points: optional output vector of vertices of the found QR code quadrangles. Will be empty if not found.
straight_qrcode: The optional output vector of images containing rectified and binarized QR codes

QRCodeDetector_detectAndDecodeMulti ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

CONSTANTS

PDL::OpenCV::Objdetect::CASCADE_DO_CANNY_PRUNING()
PDL::OpenCV::Objdetect::CASCADE_SCALE_IMAGE()
PDL::OpenCV::Objdetect::CASCADE_FIND_BIGGEST_OBJECT()
PDL::OpenCV::Objdetect::CASCADE_DO_ROUGH_SEARCH()
PDL::OpenCV::Objdetect::HOGDescriptor::L2Hys()
PDL::OpenCV::Objdetect::HOGDescriptor::DEFAULT_NLEVELS()
PDL::OpenCV::Objdetect::HOGDescriptor::DESCR_FORMAT_COL_BY_COL()
PDL::OpenCV::Objdetect::HOGDescriptor::DESCR_FORMAT_ROW_BY_ROW()

To install PDL::OpenCV, copy and paste the appropriate command in to your terminal.

cpanm

cpanm PDL::OpenCV

CPAN shell

perl -MCPAN -e shell
install PDL::OpenCV

For more information on module installation, please visit the detailed CPAN module installation guide.

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NAME

SYNOPSIS

FUNCTIONS

groupRectangles

METHODS for PDL::OpenCV::BaseCascadeClassifier

METHODS for PDL::OpenCV::CascadeClassifier

new

new2

empty

load

read

CascadeClassifier_detectMultiScale

CascadeClassifier_detectMultiScale2

CascadeClassifier_detectMultiScale3

isOldFormatCascade

CascadeClassifier_getOriginalWindowSize

getFeatureType

convert

METHODS for PDL::OpenCV::HOGDescriptor

new

HOGDescriptor_new2

new3

getDescriptorSize

checkDetectorSize

getWinSigma

HOGDescriptor_setSVMDetector

load

save

HOGDescriptor_compute

HOGDescriptor_detect

HOGDescriptor_detectMultiScale

HOGDescriptor_computeGradient

HOGDescriptor_getDefaultPeopleDetector

HOGDescriptor_getDaimlerPeopleDetector

METHODS for PDL::OpenCV::QRCodeDetector

new

setEpsX

setEpsY

QRCodeDetector_detect

QRCodeDetector_decode

QRCodeDetector_decodeCurved

QRCodeDetector_detectAndDecode

QRCodeDetector_detectAndDecodeCurved

QRCodeDetector_detectMulti

QRCodeDetector_decodeMulti

QRCodeDetector_detectAndDecodeMulti

CONSTANTS

Module Install Instructions