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399 lines
17 KiB
399 lines
17 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#ifndef __OPENCV_CUDA_HPP__ |
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#define __OPENCV_CUDA_HPP__ |
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#ifndef __cplusplus |
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# error cuda.hpp header must be compiled as C++ |
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#endif |
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#include "opencv2/core/cuda.hpp" |
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/** |
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@defgroup cuda CUDA-accelerated Computer Vision |
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@ref cuda_intro "Introduction page" |
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@{ |
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@defgroup cuda_objdetect Object Detection |
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@} |
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*/ |
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namespace cv { namespace cuda { |
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// |
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//! @addtogroup cuda_objdetect |
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//! @{ |
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struct CV_EXPORTS HOGConfidence |
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{ |
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double scale; |
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std::vector<Point> locations; |
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std::vector<double> confidences; |
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std::vector<double> part_scores[4]; |
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}; |
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/** @brief The class implements Histogram of Oriented Gradients (@cite Dalal2005) object detector. |
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Interfaces of all methods are kept similar to the CPU HOG descriptor and detector analogues as much |
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as possible. |
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@note |
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- An example applying the HOG descriptor for people detection can be found at |
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opencv\_source\_code/samples/cpp/peopledetect.cpp |
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- A CUDA example applying the HOG descriptor for people detection can be found at |
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opencv\_source\_code/samples/gpu/hog.cpp |
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- (Python) An example applying the HOG descriptor for people detection can be found at |
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opencv\_source\_code/samples/python2/peopledetect.py |
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*/ |
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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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/** @brief Creates the HOG descriptor and detector. |
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@param win\_size Detection window size. Align to block size and block stride. |
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@param block\_size Block size in pixels. Align to cell size. Only (16,16) is supported for now. |
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@param block\_stride Block stride. It 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 are 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 Flag to specify whether the gamma correction preprocessing is required or |
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not. |
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@param nlevels Maximum number of detection window increases. |
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*/ |
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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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/** @brief Returns the number of coefficients required for the classification. |
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*/ |
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size_t getDescriptorSize() const; |
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/** @brief Returns the block histogram size. |
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*/ |
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size_t getBlockHistogramSize() const; |
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/** @brief Sets coefficients for the linear SVM classifier. |
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*/ |
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void setSVMDetector(const std::vector<float>& detector); |
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/** @brief Returns coefficients of the classifier trained for people detection (for default window size). |
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*/ |
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static std::vector<float> getDefaultPeopleDetector(); |
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/** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows). |
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*/ |
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static std::vector<float> getPeopleDetector48x96(); |
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/** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows). |
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*/ |
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static std::vector<float> getPeopleDetector64x128(); |
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/** @brief Performs object detection without a multi-scale 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 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. |
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Usually it is 0 and should be specfied in the detector coefficients (as the last free |
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coefficient). But if the free coefficient is omitted (which is allowed), you can specify it |
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manually here. |
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@param win\_stride Window stride. It must be a multiple of block stride. |
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@param padding Mock parameter to keep the CPU interface compatibility. It must be (0,0). |
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*/ |
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void detect(const GpuMat& img, std::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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/** @brief Performs object detection with a multi-scale window. |
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@param img Source image. See cuda::HOGDescriptor::detect for type limitations. |
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@param found\_locations Detected objects boundaries. |
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@param hit\_threshold Threshold for the distance between features and SVM classifying plane. See |
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cuda::HOGDescriptor::detect for details. |
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@param win\_stride Window stride. It must be a multiple of block stride. |
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@param padding Mock parameter to keep the CPU interface compatibility. It must be (0,0). |
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@param scale0 Coefficient of the detection window increase. |
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@param group\_threshold Coefficient to regulate the similarity threshold. When detected, some |
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objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles . |
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*/ |
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void detectMultiScale(const GpuMat& img, std::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 computeConfidence(const GpuMat& img, std::vector<Point>& hits, double hit_threshold, |
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Size win_stride, Size padding, std::vector<Point>& locations, std::vector<double>& confidences); |
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void computeConfidenceMultiScale(const GpuMat& img, std::vector<Rect>& found_locations, |
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double hit_threshold, Size win_stride, Size padding, |
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std::vector<HOGConfidence> &conf_out, int group_threshold); |
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/** @brief Returns block descriptors computed for the whole image. |
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@param img Source image. See cuda::HOGDescriptor::detect for type limitations. |
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@param win\_stride Window stride. It must be a multiple of block stride. |
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@param descriptors 2D array of descriptors. |
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@param descr\_format Descriptor storage format: |
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- **DESCR\_FORMAT\_ROW\_BY\_ROW** - Row-major order. |
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- **DESCR\_FORMAT\_COL\_BY\_COL** - Column-major order. |
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The function is mainly used to learn the classifier. |
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*/ |
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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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protected: |
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void computeBlockHistograms(const GpuMat& img); |
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void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle); |
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double getWinSigma() const; |
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bool checkDetectorSize() const; |
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static int numPartsWithin(int size, int part_size, int stride); |
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static Size numPartsWithin(Size size, Size part_size, Size stride); |
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// Coefficients of the separating plane |
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float free_coef; |
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GpuMat detector; |
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// Results of the last classification step |
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GpuMat labels, labels_buf; |
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Mat labels_host; |
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// Results of the last histogram evaluation step |
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GpuMat block_hists, block_hists_buf; |
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// Gradients conputation results |
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GpuMat grad, qangle, grad_buf, qangle_buf; |
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// returns subbuffer with required size, reallocates buffer if nessesary. |
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static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf); |
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static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf); |
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std::vector<GpuMat> image_scales; |
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}; |
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//////////////////////////// CascadeClassifier //////////////////////////// |
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/** @brief Cascade classifier class used for object detection. Supports HAAR and LBP cascades. : |
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@note |
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- A cascade classifier example can be found at |
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opencv\_source\_code/samples/gpu/cascadeclassifier.cpp |
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- A Nvidea API specific cascade classifier example can be found at |
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opencv\_source\_code/samples/gpu/cascadeclassifier\_nvidia\_api.cpp |
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*/ |
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class CV_EXPORTS CascadeClassifier_CUDA |
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{ |
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public: |
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CascadeClassifier_CUDA(); |
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/** @brief Loads the classifier from a file. Cascade type is detected automatically by constructor parameter. |
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@param filename Name of the file from which the classifier is loaded. Only the old haar classifier |
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(trained by the haar training application) and NVIDIA's nvbin are supported for HAAR and only new |
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type of OpenCV XML cascade supported for LBP. |
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*/ |
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CascadeClassifier_CUDA(const String& filename); |
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~CascadeClassifier_CUDA(); |
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/** @brief Checks whether the classifier is loaded or not. |
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*/ |
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bool empty() const; |
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/** @brief Loads the classifier from a file. The previous content is destroyed. |
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@param filename Name of the file from which the classifier is loaded. Only the old haar classifier |
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(trained by the haar training application) and NVIDIA's nvbin are supported for HAAR and only new |
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type of OpenCV XML cascade supported for LBP. |
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*/ |
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bool load(const String& filename); |
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/** @brief Destroys the loaded classifier. |
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*/ |
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void release(); |
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/** @overload */ |
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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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/** @brief Detects objects of different sizes in the input image. |
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@param image Matrix of type CV\_8U containing an image where objects should be detected. |
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@param objectsBuf Buffer to store detected objects (rectangles). If it is empty, it is allocated |
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with the default size. If not empty, the function searches not more than N objects, where |
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N = sizeof(objectsBufer's data)/sizeof(cv::Rect). |
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@param maxObjectSize Maximum possible object size. Objects larger than that are ignored. Used for |
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second signature and supported only for LBP cascades. |
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@param scaleFactor Parameter specifying how much the image size is reduced at each image scale. |
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@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have |
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to retain it. |
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@param minSize Minimum possible object size. Objects smaller than that are ignored. |
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The detected objects are returned as a list of rectangles. |
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The function returns the number of detected objects, so you can retrieve them as in the following |
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example: |
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@code |
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cuda::CascadeClassifier_CUDA 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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@endcode |
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@sa CascadeClassifier::detectMultiScale |
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*/ |
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int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4); |
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bool findLargestObject; |
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bool visualizeInPlace; |
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Size getClassifierSize() const; |
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private: |
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struct CascadeClassifierImpl; |
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CascadeClassifierImpl* impl; |
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struct HaarCascade; |
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struct LbpCascade; |
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friend class CascadeClassifier_CUDA_LBP; |
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}; |
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//! @} cuda_objdetect |
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//////////////////////////// Labeling //////////////////////////// |
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//! @addtogroup cuda |
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//! @{ |
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//!performs labeling via graph cuts of a 2D regular 4-connected graph. |
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CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels, |
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GpuMat& buf, Stream& stream = Stream::Null()); |
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//!performs labeling via graph cuts of a 2D regular 8-connected graph. |
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CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight, |
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GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight, |
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GpuMat& labels, |
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GpuMat& buf, Stream& stream = Stream::Null()); |
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//! compute mask for Generalized Flood fill componetns labeling. |
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CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null()); |
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//! performs connected componnents labeling. |
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CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null()); |
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//! @} |
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//////////////////////////// Calib3d //////////////////////////// |
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//! @addtogroup cuda_calib3d |
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//! @{ |
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CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, |
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GpuMat& dst, Stream& stream = Stream::Null()); |
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CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec, |
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const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst, |
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Stream& stream = Stream::Null()); |
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/** @brief Finds the object pose from 3D-2D point correspondences. |
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@param object Single-row matrix of object points. |
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@param image Single-row matrix of image points. |
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@param camera\_mat 3x3 matrix of intrinsic camera parameters. |
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@param dist\_coef Distortion coefficients. See undistortPoints for details. |
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@param rvec Output 3D rotation vector. |
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@param tvec Output 3D translation vector. |
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@param use\_extrinsic\_guess Flag to indicate that the function must use rvec and tvec as an |
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initial transformation guess. It is not supported for now. |
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@param num\_iters Maximum number of RANSAC iterations. |
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@param max\_dist Euclidean distance threshold to detect whether point is inlier or not. |
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@param min\_inlier\_count Flag to indicate that the function must stop if greater or equal number |
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of inliers is achieved. It is not supported for now. |
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@param inliers Output vector of inlier indices. |
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*/ |
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CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat, |
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const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false, |
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int num_iters=100, float max_dist=8.0, int min_inlier_count=100, |
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std::vector<int>* inliers=NULL); |
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//! @} |
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//////////////////////////// VStab //////////////////////////// |
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//! @addtogroup cuda |
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//! @{ |
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//! removes points (CV_32FC2, single row matrix) with zero mask value |
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CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask); |
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CV_EXPORTS void calcWobbleSuppressionMaps( |
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int left, int idx, int right, Size size, const Mat &ml, const Mat &mr, |
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GpuMat &mapx, GpuMat &mapy); |
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//! @} |
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}} // namespace cv { namespace cuda { |
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#endif /* __OPENCV_CUDA_HPP__ */
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