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267 lines
11 KiB
TeX
267 lines
11 KiB
TeX
\section{Object Detection}
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\cvclass{gpu::HOGDescriptor}
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Histogram of Oriented Gradients descriptor and detector.
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\begin{lstlisting}
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struct CV_EXPORTS HOGDescriptor
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{
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enum { DEFAULT_WIN_SIGMA = -1 };
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enum { DEFAULT_NLEVELS = 64 };
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enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
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HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
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Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
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int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
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double threshold_L2hys=0.2, bool gamma_correction=true,
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int nlevels=DEFAULT_NLEVELS);
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size_t getDescriptorSize() const;
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size_t getBlockHistogramSize() const;
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void setSVMDetector(const vector<float>& detector);
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static vector<float> getDefaultPeopleDetector();
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static vector<float> getPeopleDetector48x96();
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static vector<float> getPeopleDetector64x128();
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void detect(const GpuMat& img, vector<Point>& found_locations,
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double hit_threshold=0, Size win_stride=Size(),
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Size padding=Size());
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void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
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double hit_threshold=0, Size win_stride=Size(),
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Size padding=Size(), double scale0=1.05,
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int group_threshold=2);
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void getDescriptors(const GpuMat& img, Size win_stride,
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GpuMat& descriptors,
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int descr_format=DESCR_FORMAT_COL_BY_COL);
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Size win_size;
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Size block_size;
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Size block_stride;
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Size cell_size;
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int nbins;
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double win_sigma;
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double threshold_L2hys;
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bool gamma_correction;
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int nlevels;
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private:
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// Hidden
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}
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\end{lstlisting}
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Interfaces of all methods are kept similar to CPU HOG descriptor and detector analogues as much as possible.
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\cvCppFunc{gpu::HOGDescriptor::HOGDescriptor}
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Creates HOG descriptor and detector.
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\cvdefCpp{HOGDescriptor::HOGDescriptor(Size win\_size=Size(64, 128),\par
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Size block\_size=Size(16, 16), Size block\_stride=Size(8, 8),\par
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Size cell\_size=Size(8, 8), int nbins=9,\par
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double win\_sigma=DEFAULT\_WIN\_SIGMA,\par
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double threshold\_L2hys=0.2, bool gamma\_correction=true,\par
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int nlevels=DEFAULT\_NLEVELS);}
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\begin{description}
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\cvarg{win\_size}{Detection window size. Must be aligned to block size and block stride.}
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\cvarg{block\_size}{Block size in pixels. Must be aligned to cell size. Only (16,16) is supported for now.}
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\cvarg{block\_stride}{Block stride. Must be a multiple of cell size.}
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\cvarg{cell\_size}{Cell size. Only (8, 8) is supported for now.}
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\cvarg{nbins}{Number of bins. Only 9 bins per cell is supported for now.}
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\cvarg{win\_sigma}{Gaussian smoothing window parameter.}
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\cvarg{threshold\_L2Hys}{L2-Hys normalization method shrinkage.}
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\cvarg{gamma\_correction}{Do gamma correction preprocessing or not.}
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\cvarg{nlevels}{Maximum number of detection window increases.}
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\end{description}
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\cvCppFunc{gpu::HOGDescriptor::getDescriptorSize}
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Returns number of coefficients required for the classification.
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\cvdefCpp{size\_t HOGDescriptor::getDescriptorSize() const;}
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\cvCppFunc{gpu::HOGDescriptor::getBlockHistogramSize}
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Returns block histogram size.
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\cvdefCpp{size\_t HOGDescriptor::getBlockHistogramSize() const;}
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\cvCppFunc{gpu::HOGDescriptor::setSVMDetector}
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Sets coefficients for the linear SVM classifier.
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\cvdefCpp{void HOGDescriptor::setSVMDetector(const vector<float>\& detector);}
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\cvCppFunc{gpu::HOGDescriptor::getDefaultPeopleDetector}
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Returns coefficients of the classifier trained for people detection (for default window size).
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\cvdefCpp{static vector<float> HOGDescriptor::getDefaultPeopleDetector();}
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\cvCppFunc{gpu::HOGDescriptor::getPeopleDetector48x96}
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Returns coefficients of the classifier trained for people detection (for 48x96 windows).
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\cvdefCpp{static vector<float> HOGDescriptor::getPeopleDetector48x96();}
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\cvCppFunc{gpu::HOGDescriptor::getPeopleDetector64x128}
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Returns coefficients of the classifier trained for people detection (for 64x128 windows).
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\cvdefCpp{static vector<float> HOGDescriptor::getPeopleDetector64x128();}
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\cvCppFunc{gpu::HOGDescriptor::detect}
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Perfroms object detection without multiscale window.
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\cvdefCpp{void HOGDescriptor::detect(const GpuMat\& img,\par
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vector<Point>\& found\_locations, double hit\_threshold=0,\par
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Size win\_stride=Size(), Size padding=Size());}
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\begin{description}
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\cvarg{img}{Source image. \texttt{CV\_8UC1} and \texttt{CV\_8UC4}types are supported for now.}
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\cvarg{found\_locations}{Will contain left-top corner points of detected objects boundaries.}
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\cvarg{hit\_threshold}{Threshold for the distance between features and SVM classifying plane. Usually it's 0 and should be specfied in the detector coefficients (as the last free coefficient), but if the free coefficient is omitted (it's allowed) you can specify it manually here.}
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\cvarg{win\_stride}{Window stride. Must be a multiple of block stride.}
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\cvarg{padding}{Mock parameter to keep CPU interface compatibility. Must be (0,0).}
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\end{description}
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\cvCppFunc{gpu::HOGDescriptor::detectMultiScale}
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Perfroms object detection with multiscale window.
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\cvdefCpp{void HOGDescriptor::detectMultiScale(const GpuMat\& img,\par
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vector<Rect>\& found\_locations, double hit\_threshold=0,\par
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Size win\_stride=Size(), Size padding=Size(),\par
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double scale0=1.05, int group\_threshold=2);}
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\begin{description}
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\cvarg{img}{Source image. See \cvCppCross{gpu::HOGDescriptor::detect} for type limitations.}
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\cvarg{found\_locations}{Will contain detected objects boundaries.}
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\cvarg{hit\_threshold}{The threshold for the distance between features and SVM classifying plane. See \cvCppCross{gpu::HOGDescriptor::detect} for details.}
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\cvarg{win\_stride}{Window stride. Must be a multiple of block stride.}
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\cvarg{padding}{Mock parameter to keep CPU interface compatibility. Must be (0,0).}
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\cvarg{scale0}{Coefficient of the detection window increase.}
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\cvarg{group\_threshold}{After detection some objects could be covered by many rectangles. This coefficient regulates similarity threshold. 0 means don't perform grouping.\newline
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See \cvCppCross{groupRectangles}.}
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\end{description}
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\cvCppFunc{gpu::HOGDescriptor::getDescriptors}
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Returns block descriptors computed for the whole image. It's mainly used for classifier learning purposes.
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\cvdefCpp{void HOGDescriptor::getDescriptors(const GpuMat\& img,\par
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Size win\_stride, GpuMat\& descriptors,\par
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int descr\_format=DESCR\_FORMAT\_COL\_BY\_COL);}
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\begin{description}
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\cvarg{img}{Source image. See \cvCppCross{gpu::HOGDescriptor::detect} for type limitations.}
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\cvarg{win\_stride}{Window stride. Must be a multiple of block stride.}
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\cvarg{descriptors}{2D array of descriptors.}
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\cvarg{descr\_format}{Descriptor storage format:
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\begin{description}
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\cvarg{DESCR\_FORMAT\_ROW\_BY\_ROW}{Row-major order.}
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\cvarg{DESCR\_FORMAT\_COL\_BY\_COL}{Column-major order.}
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\end{description}}
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\end{description}
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\cvclass{gpu::CascadeClassifier\_GPU}
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The cascade classifier class for object detection.
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\begin{lstlisting}
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class CV_EXPORTS CascadeClassifier_GPU
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{
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public:
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CascadeClassifier_GPU();
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CascadeClassifier_GPU(const string& filename);
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~CascadeClassifier_GPU();
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bool empty() const;
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bool load(const string& filename);
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void release();
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/* returns number of detected objects */
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int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
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/* Finds only the largest object. Special mode for need to training*/
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bool findLargestObject;
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/* Draws rectangles in input image */
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bool visualizeInPlace;
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Size getClassifierSize() const;
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};
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\end{lstlisting}
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\cvfunc{cv::gpu::CascadeClassifier\_GPU::CascadeClassifier\_GPU}\par
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Loads the classifier from file.
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\cvdefCpp{cv::CascadeClassifier\_GPU(const string\& filename);}
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\begin{description}
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\cvarg{filename}{Name of file from which classifier will be load. Only old haar classifier (trained by haartraining application) and NVidia's nvbin are supported.}
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\end{description}
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\cvfunc{cv::gpu::CascadeClassifier\_GPU::empty}
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Checks if the classifier has been loaded or not.
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\cvdefCpp{bool CascadeClassifier\_GPU::empty() const;}
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\cvfunc{cv::gpu::CascadeClassifier\_GPU::load}
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Loads the classifier from file. The previous content is destroyed.
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\cvdefCpp{bool CascadeClassifier\_GPU::load(const string\& filename);}
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\begin{description}
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\cvarg{filename}{Name of file from which classifier will be load. Only old haar classifier (trained by haartraining application) and NVidia's nvbin are supported.}
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\end{description}
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\cvfunc{cv::gpu::CascadeClassifier\_GPU::release}
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Destroys loaded classifier.
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\cvdefCpp{void CascadeClassifier\_GPU::release()}
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\cvfunc{cv::gpu::CascadeClassifier\_GPU::detectMultiScale}
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Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
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\cvdefCpp{int CascadeClassifier\_GPU::detectMultiScale(const GpuMat\& image, GpuMat\& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());}
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\begin{description}
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\cvarg{image}{Matrix of type \texttt{CV\_8U} containing the image in which to detect objects.}
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\cvarg{objects}{Buffer to store detected objects (rectangles). If it is empty, it will be allocated with default size. If not empty, function will search not more than N objects, where N = sizeof(objectsBufer's data)/sizeof(cv::Rect).}
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\cvarg{scaleFactor}{Specifies how much the image size is reduced at each image scale.}
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\cvarg{minNeighbors}{Specifies how many neighbors should each candidate rectangle have to retain it.}
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\cvarg{minSize}{The minimum possible object size. Objects smaller than that are ignored.}
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\end{description}
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The function returns number of detected objects, so you can retrieve them as in following example:
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\begin{lstlisting}
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cv::gpu::CascadeClassifier_GPU cascade_gpu(...);
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Mat image_cpu = imread(...)
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GpuMat image_gpu(image_cpu);
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GpuMat objbuf;
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int detections_number = cascade_gpu.detectMultiScale( image_gpu,
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objbuf, 1.2, minNeighbors);
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Mat obj_host;
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// download only detected number of rectangles
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objbuf.colRange(0, detections_number).download(obj_host);
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Rect* faces = obj_host.ptr<Rect>();
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for(int i = 0; i < detections_num; ++i)
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cv::rectangle(image_cpu, faces[i], Scalar(255));
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imshow("Faces", image_cpu);
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\end{lstlisting}
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See also: \cvCppCross{CascadeClassifier::detectMultiScale}.
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