view release on metacpan or  search on metacpan

Objdetect/funclist.pl  view on Meta::CPAN

(
['','groupRectangles','@overload',0,'void',['vector_Rect','rectList','',['/IO','/Ref']],['vector_int','weights','',['/O','/Ref']],['int','groupThreshold','',[]],['double','eps','0.2',[]]],
['CascadeClassifier','empty','@brief Checks whether the classifier has been loaded.',1,'bool'],
['CascadeClassifier','load','@brief Loads a classifier from a file.

    @param 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.',1,'bool',['String','filename','',['/C','/Ref']]],
['CascadeClassifier','read','@brief Reads a classifier from a FileStorage node.

    @note The file may contain a new cascade classifier (trained traincascade application) only.',1,'bool',['FileNode','node','',['/C','/Ref']]],
['CascadeClassifier','detectMultiScale','@brief Detects objects of different sizes in the input image. The detected objects are returned as a list
    of rectangles.

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

    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',1,'void',['Mat','image','',[]],['vector_Rect','objects','',['/O','/Ref']],['double','scaleFactor','1.1',[]],['int','minNeighbors','3',[]],['int','flags','0',[]],['Size','minSize','Size()',[...
['CascadeClassifier','detectMultiScale','@overload
    @param image Matrix of the type CV_8U containing an image where objects are detected.
    @param objects Vector of rectangles where each rectangle contains the detected object, the
    rectangles may be partially outside the original image.
    @param 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.
    @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
    @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
    to retain it.
    @param flags Parameter with the same meaning for an old cascade as in the function
    cvHaarDetectObjects. It is not used for a new cascade.
    @param minSize Minimum possible object size. Objects smaller than that are ignored.
    @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.',1,'void',['Mat','image','',[]],['vector_Rect','objects','',['/O','/Ref']],['vector_int','numDetections...
['CascadeClassifier','detectMultiScale','@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:
    @code
    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;
    @endcode',1,'void',['Mat','image','',[]],['vector_Rect','objects','',['/O','/Ref']],['vector_int','rejectLevels','',['/O','/Ref']],['vector_double','levelWeights','',['/O','/Ref']],['double','scaleFactor','1.1',[]],['int','minNeighbors','3',[]],[...
['CascadeClassifier','isOldFormatCascade','',1,'bool'],
['CascadeClassifier','getOriginalWindowSize','',1,'Size'],
['CascadeClassifier','getFeatureType','',1,'int'],
['CascadeClassifier','convert','',0,'bool',['String','oldcascade','',['/C','/Ref']],['String','newcascade','',['/C','/Ref']]],
['HOGDescriptor','getDescriptorSize','@brief Returns the number of coefficients required for the classification.',1,'size_t'],
['HOGDescriptor','checkDetectorSize','@brief Checks if detector size equal to descriptor size.',1,'bool'],
['HOGDescriptor','getWinSigma','@brief Returns winSigma value',1,'double'],
['HOGDescriptor','setSVMDetector','@brief Sets coefficients for the linear SVM classifier.
    @param svmdetector coefficients for the linear SVM classifier.',1,'void',['Mat','svmdetector','',[]]],
['HOGDescriptor','load','@brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
    @param filename Path of the file to read.
    @param objname The optional name of the node to read (if empty, the first top-level node will be used).',1,'bool',['String','filename','',['/C','/Ref']],['String','objname','String()',['/C','/Ref']]],
['HOGDescriptor','save','@brief saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
    @param filename File name
    @param objname Object name',1,'void',['String','filename','',['/C','/Ref']],['String','objname','String()',['/C','/Ref']]],
['HOGDescriptor','compute','@brief Computes HOG descriptors of given image.
    @param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
    @param descriptors Matrix of the type CV_32F
    @param winStride Window stride. It must be a multiple of block stride.
    @param padding Padding
    @param locations Vector of Point',1,'void',['Mat','img','',[]],['vector_float','descriptors','',['/O','/Ref']],['Size','winStride','Size()',[]],['Size','padding','Size()',[]],['vector_Point','locations','std::vector<Point>()',['/C','/Ref']]],
['HOGDescriptor','detect','@brief Performs object detection without a multi-scale window.
    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
    @param weights Vector that will contain confidence values for each detected object.
    @param 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.
    @param winStride Window stride. It must be a multiple of block stride.
    @param padding Padding
    @param searchLocations Vector of Point includes set of requested locations to be evaluated.',1,'void',['Mat','img','',[]],['vector_Point','foundLocations','',['/O','/Ref']],['vector_double','weights','',['/O','/Ref']],['double','hitThreshold','0'...
['HOGDescriptor','detectMultiScale','@brief Detects objects of different sizes in the input image. The detected objects are returned as a list
    of rectangles.
    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
    @param foundWeights Vector that will contain confidence values for each detected object.
    @param 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.
    @param winStride Window stride. It must be a multiple of block stride.
    @param padding Padding
    @param scale Coefficient of the detection window increase.
    @param finalThreshold Final threshold
    @param useMeanshiftGrouping indicates grouping algorithm',1,'void',['Mat','img','',[]],['vector_Rect','foundLocations','',['/O','/Ref']],['vector_double','foundWeights','',['/O','/Ref']],['double','hitThreshold','0',[]],['Size','winStride','Size(...
['HOGDescriptor','computeGradient','@brief  Computes gradients and quantized gradient orientations.
    @param img Matrix contains the image to be computed
    @param grad Matrix of type CV_32FC2 contains computed gradients
    @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations
    @param paddingTL Padding from top-left
    @param paddingBR Padding from bottom-right',1,'void',['Mat','img','',[]],['Mat','grad','',['/IO']],['Mat','angleOfs','',['/IO']],['Size','paddingTL','Size()',[]],['Size','paddingBR','Size()',[]]],
['HOGDescriptor','getDefaultPeopleDetector','@brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).',0,'vector_float'],
['HOGDescriptor','getDaimlerPeopleDetector','@brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).',0,'vector_float'],
['QRCodeDetector','setEpsX','@brief sets the epsilon used during the horizontal scan of QR code stop marker detection.
     @param epsX Epsilon neighborhood, which allows you to determine the horizontal pattern
     of the scheme 1:1:3:1:1 according to QR code standard.',1,'void',['double','epsX','',[]]],
['QRCodeDetector','setEpsY','@brief sets the epsilon used during the vertical scan of QR code stop marker detection.
     @param epsY Epsilon neighborhood, which allows you to determine the vertical pattern
     of the scheme 1:1:3:1:1 according to QR code standard.',1,'void',['double','epsY','',[]]],
['QRCodeDetector','detect','@brief Detects QR code in image and returns the quadrangle containing the code.
     @param img grayscale or color (BGR) image containing (or not) QR code.
     @param points Output vector of vertices of the minimum-area quadrangle containing the code.',1,'bool',['Mat','img','',[]],['Mat','points','',['/O']]],
['QRCodeDetector','decode','@brief Decodes QR code in image once it\'s found by the detect() method.

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