mirror of
https://github.com/opencv/opencv.git
synced 2026-07-31 08:13:04 +04:00
cleaned RST formatting a bit
This commit is contained in:
Vadim Pisarevsky
@@ -3,366 +3,204 @@ Object Detection
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.. highlight:: cpp
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.. index:: gpu::HOGDescriptor
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.. _gpu::HOGDescriptor:
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gpu::HOGDescriptor
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------------------
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`id=0.263285034412 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/gpu/gpu%3A%3AHOGDescriptor>`__
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.. ctype:: gpu::HOGDescriptor
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Histogram of Oriented Gradients
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Histogram of Oriented Gradients
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dalal_hog
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descriptor and detector.
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descriptor and detector. ::
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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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::
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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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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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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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..
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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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Interfaces of all methods are kept similar to CPU HOG descriptor and detector analogues as much as possible.
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.. index:: gpu::HOGDescriptor::HOGDescriptor
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cv::gpu::HOGDescriptor::HOGDescriptor
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-------------------------------------
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`id=0.377426649644 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/gpu/gpu%3A%3AHOGDescriptor%3A%3AHOGDescriptor>`__
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.. cfunction:: HOGDescriptor::HOGDescriptor(Size win_size=Size(64, 128),
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Size block_size=Size(16, 16), Size block_stride=Size(8, 8),
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Size cell_size=Size(8, 8), int nbins=9,
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double win_sigma=DEFAULT_WIN_SIGMA,
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Size cell_size=Size(8, 8), int nbins=9,
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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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Creates HOG descriptor and detector.
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:param win_size: Detection window size. Must be aligned to block size and block stride.
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:param win_size: Detection window size. Must be aligned to block size and block stride.
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:param block_size: Block size in pixels. Must be aligned to cell size. Only (16,16) is supported for now.
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:param block_stride: Block stride. Must be a multiple of cell size.
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:param cell_size: Cell size. Only (8, 8) is supported for now.
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:param nbins: Number of bins. Only 9 bins per cell is supported for now.
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:param win_sigma: Gaussian smoothing window parameter.
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:param threshold_L2Hys: L2-Hys normalization method shrinkage.
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:param gamma_correction: Do gamma correction preprocessing or not.
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:param block_size: Block size in pixels. Must be aligned to cell size. Only (16,16) is supported for now.
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:param block_stride: Block stride. Must be a multiple of cell size.
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:param cell_size: Cell size. Only (8, 8) is supported for now.
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:param nbins: Number of bins. Only 9 bins per cell is supported for now.
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:param win_sigma: Gaussian smoothing window parameter.
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:param threshold_L2Hys: L2-Hys normalization method shrinkage.
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:param gamma_correction: Do gamma correction preprocessing or not.
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:param nlevels: Maximum number of detection window increases.
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.. index:: gpu::HOGDescriptor::getDescriptorSize
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cv::gpu::HOGDescriptor::getDescriptorSize
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-----------------------------------------
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.. index:: gpu::HOGDescriptor::getDescriptorSize
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.. cfunction:: size_t HOGDescriptor::getDescriptorSize() const
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cv::gpu::HOGDescriptor::getDescriptorSize
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-----------------------------------------
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.. cfunction:: size_t HOGDescriptor::getDescriptorSize() const
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Returns number of coefficients required for the classification.
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.. index:: gpu::HOGDescriptor::getBlockHistogramSize
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cv::gpu::HOGDescriptor::getBlockHistogramSize
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---------------------------------------------
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.. index:: gpu::HOGDescriptor::getBlockHistogramSize
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.. cfunction:: size_t HOGDescriptor::getBlockHistogramSize() const
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cv::gpu::HOGDescriptor::getBlockHistogramSize
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---------------------------------------------
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.. cfunction:: size_t HOGDescriptor::getBlockHistogramSize() const
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Returns block histogram size.
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.. index:: gpu::HOGDescriptor::setSVMDetector
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cv::gpu::HOGDescriptor::setSVMDetector
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--------------------------------------
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.. index:: gpu::HOGDescriptor::setSVMDetector
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.. cfunction:: void HOGDescriptor::setSVMDetector(const vector<float>\& detector)
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cv::gpu::HOGDescriptor::setSVMDetector
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--------------------------------------
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.. cfunction:: void HOGDescriptor::setSVMDetector(const vector<float>\& detector)
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Sets coefficients for the linear SVM classifier.
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.. index:: gpu::HOGDescriptor::getDefaultPeopleDetector
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cv::gpu::HOGDescriptor::getDefaultPeopleDetector
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------------------------------------------------
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.. index:: gpu::HOGDescriptor::getDefaultPeopleDetector
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.. cfunction:: static vector<float> HOGDescriptor::getDefaultPeopleDetector()
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cv::gpu::HOGDescriptor::getDefaultPeopleDetector
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------------------------------------------------
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.. cfunction:: static vector<float> HOGDescriptor::getDefaultPeopleDetector()
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Returns coefficients of the classifier trained for people detection (for default window size).
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.. index:: gpu::HOGDescriptor::getPeopleDetector48x96
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cv::gpu::HOGDescriptor::getPeopleDetector48x96
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----------------------------------------------
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.. index:: gpu::HOGDescriptor::getPeopleDetector48x96
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.. cfunction:: static vector<float> HOGDescriptor::getPeopleDetector48x96()
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cv::gpu::HOGDescriptor::getPeopleDetector48x96
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----------------------------------------------
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.. cfunction:: static vector<float> HOGDescriptor::getPeopleDetector48x96()
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Returns coefficients of the classifier trained for people detection (for 48x96 windows).
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.. index:: gpu::HOGDescriptor::getPeopleDetector64x128
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cv::gpu::HOGDescriptor::getPeopleDetector64x128
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-----------------------------------------------
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.. index:: gpu::HOGDescriptor::getPeopleDetector64x128
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.. cfunction:: static vector<float> HOGDescriptor::getPeopleDetector64x128()
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cv::gpu::HOGDescriptor::getPeopleDetector64x128
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-----------------------------------------------
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.. cfunction:: static vector<float> HOGDescriptor::getPeopleDetector64x128()
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Returns coefficients of the classifier trained for people detection (for 64x128 windows).
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.. index:: gpu::HOGDescriptor::detect
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cv::gpu::HOGDescriptor::detect
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------------------------------
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.. index:: gpu::HOGDescriptor::detect
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cv::gpu::HOGDescriptor::detect
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------------------------------
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.. cfunction:: void HOGDescriptor::detect(const GpuMat\& img,
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vector<Point>\& found_locations, double hit_threshold=0,
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Size win_stride=Size(), Size padding=Size())
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Perfroms object detection without multiscale window.
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Perfroms object detection without multiscale window.
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:param img: Source image. ``CV_8UC1`` and ``CV_8UC4`` types are supported for now.
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:param found_locations: Will contain left-top corner points of detected objects boundaries.
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:param 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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:param img: Source image. ``CV_8UC1`` and ``CV_8UC4`` types are supported for now.
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:param found_locations: Will contain left-top corner points of detected objects boundaries.
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:param 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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:param padding: Mock parameter to keep CPU interface compatibility. Must be (0,0).
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:param win_stride: Window stride. Must be a multiple of block stride.
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.. index:: gpu::HOGDescriptor::detectMultiScale
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cv::gpu::HOGDescriptor::detectMultiScale
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:param padding: Mock parameter to keep CPU interface compatibility. Must be (0,0).
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`id=0.125190830083 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/gpu/gpu%3A%3AHOGDescriptor%3A%3AdetectMultiScale>`__
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.. index:: gpu::HOGDescriptor::detectMultiScale
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cv::gpu::HOGDescriptor::detectMultiScale
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----------------------------------------
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.. cfunction:: void HOGDescriptor::detectMultiScale(const GpuMat\& img,
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vector<Rect>\& found_locations, double hit_threshold=0,
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Size win_stride=Size(), Size padding=Size(),
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vector<Rect>\& found_locations, double hit_threshold=0,
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Size win_stride=Size(), Size padding=Size(),
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double scale0=1.05, int group_threshold=2)
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Perfroms object detection with multiscale window.
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:param img: Source image. See :func:`gpu::HOGDescriptor::detect` for type limitations.
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:param found_locations: Will contain detected objects boundaries.
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:param hit_threshold: The threshold for the distance between features and SVM classifying plane. See :func:`gpu::HOGDescriptor::detect` for details.
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:param win_stride: Window stride. Must be a multiple of block stride.
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|
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:param padding: Mock parameter to keep CPU interface compatibility. Must be (0,0).
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double scale0=1.05, int group_threshold=2)
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|
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Perfroms object detection with multiscale window.
|
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|
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:param img: Source image. See :func:`gpu::HOGDescriptor::detect` for type limitations.
|
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:param found_locations: Will contain detected objects boundaries.
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:param hit_threshold: The threshold for the distance between features and SVM classifying plane. See :func:`gpu::HOGDescriptor::detect` for details.
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:param win_stride: Window stride. Must be a multiple of block stride.
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:param padding: Mock parameter to keep CPU interface compatibility. Must be (0,0).
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See :func:`groupRectangles` .
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.. index:: gpu::HOGDescriptor::getDescriptors
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:param scale0: Coefficient of the detection window increase.
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cv::gpu::HOGDescriptor::getDescriptors
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--------------------------------------
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:param group_threshold: After detection some objects could be covered by many rectangles. This coefficient regulates similarity threshold. 0 means don't perform grouping.
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See :func:`groupRectangles` .
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.. index:: gpu::HOGDescriptor::getDescriptors
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cv::gpu::HOGDescriptor::getDescriptors
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--------------------------------------
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.. cfunction:: void HOGDescriptor::getDescriptors(const GpuMat\& img,
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Size win_stride, GpuMat\& descriptors,
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int descr_format=DESCR_FORMAT_COL_BY_COL)
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Returns block descriptors computed for the whole image. It's mainly used for classifier learning purposes.
|
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|
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|
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|
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|
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|
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|
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:param img: Source image. See :func:`gpu::HOGDescriptor::detect` for type limitations.
|
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.. cfunction:: void HOGDescriptor::getDescriptors(const GpuMat\& img,
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Size win_stride, GpuMat\& descriptors,
|
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int descr_format=DESCR_FORMAT_COL_BY_COL)
|
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|
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Returns block descriptors computed for the whole image. It's mainly used for classifier learning purposes.
|
||||
|
||||
|
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:param win_stride: Window stride. Must be a multiple of block stride.
|
||||
|
||||
|
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:param descriptors: 2D array of descriptors.
|
||||
|
||||
|
||||
:param img: Source image. See :func:`gpu::HOGDescriptor::detect` for type limitations.
|
||||
|
||||
:param win_stride: Window stride. Must be a multiple of block stride.
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@@ -370,73 +208,45 @@ cv::gpu::HOGDescriptor::getDescriptors
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:param descriptors: 2D array of descriptors.
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|
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:param descr_format: Descriptor storage format:
|
||||
* **DESCR_FORMAT_COL_BY_COL** Column-major order.
|
||||
|
||||
|
||||
|
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* **DESCR_FORMAT_ROW_BY_ROW** Row-major order.
|
||||
|
||||
* **DESCR_FORMAT_COL_BY_COL** Column-major order.
|
||||
|
||||
|
||||
.. index:: gpu::CascadeClassifier_GPU
|
||||
|
||||
.. _gpu::CascadeClassifier_GPU:
|
||||
|
||||
gpu::CascadeClassifier_GPU
|
||||
|
||||
.. index:: gpu::CascadeClassifier_GPU
|
||||
--------------------------
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||||
.. ctype:: gpu::CascadeClassifier_GPU
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||||
|
||||
The cascade classifier class for object detection. ::
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||||
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||||
class CV_EXPORTS CascadeClassifier_GPU
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||||
{
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||||
public:
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||||
CascadeClassifier_GPU();
|
||||
CascadeClassifier_GPU(const string& filename);
|
||||
gpu::CascadeClassifier_GPU
|
||||
--------------------------
|
||||
|
||||
`id=0.362290729184 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/gpu/gpu%3A%3ACascadeClassifier_GPU>`__
|
||||
|
||||
.. ctype:: gpu::CascadeClassifier_GPU
|
||||
|
||||
|
||||
|
||||
The cascade classifier class for object detection.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class CV_EXPORTS CascadeClassifier_GPU
|
||||
{
|
||||
public:
|
||||
CascadeClassifier_GPU();
|
||||
CascadeClassifier_GPU(const string& filename);
|
||||
~CascadeClassifier_GPU();
|
||||
|
||||
bool empty() const;
|
||||
bool load(const string& filename);
|
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~CascadeClassifier_GPU();
|
||||
|
||||
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 */
|
||||
int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
|
||||
int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
|
||||
|
||||
/* Finds only the largest object. Special mode for need to training*/
|
||||
bool findLargestObject;
|
||||
|
||||
/* Draws rectangles in input image */
|
||||
|
||||
Size getClassifierSize() const;
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
bool visualizeInPlace;
|
||||
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
|
||||
Size getClassifierSize() const;
|
||||
};
|
||||
.. _cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU:
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
|
||||
-----------------------------------------------------
|
||||
|
||||
`id=0.502164537388 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/gpu/cv%3A%3Agpu%3A%3ACascadeClassifier_GPU%3A%3ACascadeClassifier_GPU>`__
|
||||
|
||||
|
||||
..
|
||||
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
|
||||
|
||||
@@ -444,17 +254,9 @@ cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
|
||||
-----------------------------------------------------
|
||||
|
||||
|
||||
|
||||
:param filename: Name of file from which classifier will be load. Only old haar classifier (trained by haartraining application) and NVidia's nvbin are supported.
|
||||
|
||||
|
||||
.. cfunction:: cv::CascadeClassifier_GPU(const string\& filename)
|
||||
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::empty
|
||||
|
||||
.. _cv::gpu::CascadeClassifier_GPU::empty:
|
||||
Loads the classifier from file.
|
||||
|
||||
:param filename: Name of file from which classifier will be load. Only old haar classifier (trained by haartraining application) and NVidia's nvbin are supported.
|
||||
|
||||
@@ -462,24 +264,11 @@ cv::gpu::CascadeClassifier_GPU::empty
|
||||
|
||||
.. _cv::gpu::CascadeClassifier_GPU::empty:
|
||||
|
||||
|
||||
|
||||
.. cfunction:: bool CascadeClassifier_GPU::empty() const
|
||||
|
||||
Checks if the classifier has been loaded or not.
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU::empty
|
||||
-------------------------------------
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::load
|
||||
.. cfunction:: bool CascadeClassifier_GPU::empty() const
|
||||
|
||||
.. _cv::gpu::CascadeClassifier_GPU::load:
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU::load
|
||||
------------------------------------
|
||||
|
||||
`id=0.831994730738 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/gpu/cv%3A%3Agpu%3A%3ACascadeClassifier_GPU%3A%3Aload>`__
|
||||
|
||||
|
||||
Checks if the classifier has been loaded or not.
|
||||
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::load
|
||||
|
||||
@@ -487,17 +276,9 @@ cv::gpu::CascadeClassifier_GPU::load
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU::load
|
||||
------------------------------------
|
||||
|
||||
|
||||
|
||||
:param filename: Name of file from which classifier will be load. Only old haar classifier (trained by haartraining application) and NVidia's nvbin are supported.
|
||||
|
||||
|
||||
.. cfunction:: bool CascadeClassifier_GPU::load(const string\& filename)
|
||||
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::release
|
||||
|
||||
.. _cv::gpu::CascadeClassifier_GPU::release:
|
||||
Loads the classifier from file. The previous content is destroyed.
|
||||
|
||||
:param filename: Name of file from which classifier will be load. Only old haar classifier (trained by haartraining application) and NVidia's nvbin are supported.
|
||||
|
||||
@@ -505,71 +286,42 @@ cv::gpu::CascadeClassifier_GPU::release
|
||||
|
||||
.. _cv::gpu::CascadeClassifier_GPU::release:
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void CascadeClassifier_GPU::release()
|
||||
|
||||
Destroys loaded classifier.
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU::release
|
||||
---------------------------------------
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::detectMultiScale
|
||||
|
||||
.. cfunction:: void CascadeClassifier_GPU::release()
|
||||
|
||||
Destroys loaded classifier.
|
||||
|
||||
.. index:: cv::gpu::CascadeClassifier_GPU::detectMultiScale
|
||||
|
||||
.. _cv::gpu::CascadeClassifier_GPU::detectMultiScale:
|
||||
|
||||
|
||||
`id=0.0605957110589 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/gpu/cv%3A%3Agpu%3A%3ACascadeClassifier_GPU%3A%3AdetectMultiScale>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: int CascadeClassifier_GPU::detectMultiScale(const GpuMat\& image, GpuMat\& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size())
|
||||
|
||||
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param image: Matrix of type ``CV_8U`` containing the image in which to detect objects.
|
||||
|
||||
|
||||
:param 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).
|
||||
cv::gpu::CascadeClassifier_GPU::detectMultiScale
|
||||
------------------------------------------------
|
||||
.. cfunction:: int CascadeClassifier_GPU::detectMultiScale(const GpuMat\& image, GpuMat\& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size())
|
||||
|
||||
Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
|
||||
|
||||
|
||||
:param image: Matrix of type ``CV_8U`` containing the image in which to detect objects.
|
||||
|
||||
:param 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).
|
||||
|
||||
:param scaleFactor: Specifies how much the image size is reduced at each image scale.
|
||||
|
||||
|
||||
:param minSize: The minimum possible object size. Objects smaller than that are ignored.
|
||||
|
||||
|
||||
|
||||
The function returns number of detected objects, so you can retrieve them as in following example:
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU cascade_gpu(...);
|
||||
|
||||
Mat image_cpu = imread(...)
|
||||
GpuMat image_gpu(image_cpu);
|
||||
|
||||
GpuMat objbuf;
|
||||
|
||||
:param minNeighbors: Specifies how many neighbors should each candidate rectangle have to retain it.
|
||||
|
||||
:param minSize: The minimum possible object size. Objects smaller than that are ignored.
|
||||
|
||||
The function returns number of detected objects, so you can retrieve them as in following example: ::
|
||||
|
||||
cv::gpu::CascadeClassifier_GPU cascade_gpu(...);
|
||||
|
||||
Mat image_cpu = imread(...)
|
||||
GpuMat image_gpu(image_cpu);
|
||||
|
||||
GpuMat objbuf;
|
||||
int detections_number = cascade_gpu.detectMultiScale( image_gpu,
|
||||
objbuf, 1.2, minNeighbors);
|
||||
Mat obj_host;
|
||||
// download only detected number of rectangles
|
||||
objbuf.colRange(0, detections_number).download(obj_host);
|
||||
|
||||
|
||||
Mat obj_host;
|
||||
// download only detected number of rectangles
|
||||
|
||||
Reference in New Issue
Block a user