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289 lines
11 KiB
C++
289 lines
11 KiB
C++
/*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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//
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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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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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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_CUDAOBJDETECT_HPP__
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#define __OPENCV_CUDAOBJDETECT_HPP__
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#ifndef __cplusplus
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# error cudaobjdetect.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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@addtogroup cuda
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@{
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@defgroup cudaobjdetect Object Detection
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@}
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*/
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namespace cv { namespace cuda {
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//! @addtogroup cudaobjdetect
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//! @{
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//
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// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector
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//
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/** @brief The class implements Histogram of Oriented Gradients (@cite Dalal2005) object detector.
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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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class CV_EXPORTS HOG : public Algorithm
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{
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public:
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enum
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{
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DESCR_FORMAT_ROW_BY_ROW,
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DESCR_FORMAT_COL_BY_COL
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};
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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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*/
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static Ptr<HOG> create(Size win_size = Size(64, 128),
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Size block_size = Size(16, 16),
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Size block_stride = Size(8, 8),
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Size cell_size = Size(8, 8),
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int nbins = 9);
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//! Gaussian smoothing window parameter.
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virtual void setWinSigma(double win_sigma) = 0;
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virtual double getWinSigma() const = 0;
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//! L2-Hys normalization method shrinkage.
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virtual void setL2HysThreshold(double threshold_L2hys) = 0;
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virtual double getL2HysThreshold() const = 0;
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//! Flag to specify whether the gamma correction preprocessing is required or not.
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virtual void setGammaCorrection(bool gamma_correction) = 0;
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virtual bool getGammaCorrection() const = 0;
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//! Maximum number of detection window increases.
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virtual void setNumLevels(int nlevels) = 0;
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virtual int getNumLevels() const = 0;
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//! 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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virtual void setHitThreshold(double hit_threshold) = 0;
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virtual double getHitThreshold() const = 0;
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//! Window stride. It must be a multiple of block stride.
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virtual void setWinStride(Size win_stride) = 0;
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virtual Size getWinStride() const = 0;
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//! Coefficient of the detection window increase.
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virtual void setScaleFactor(double scale0) = 0;
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virtual double getScaleFactor() const = 0;
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//! 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.
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//! See groupRectangles.
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virtual void setGroupThreshold(int group_threshold) = 0;
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virtual int getGroupThreshold() const = 0;
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//! 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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virtual void setDescriptorFormat(int descr_format) = 0;
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virtual int getDescriptorFormat() const = 0;
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/** @brief Returns the number of coefficients required for the classification.
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*/
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virtual size_t getDescriptorSize() const = 0;
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/** @brief Returns the block histogram size.
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*/
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virtual size_t getBlockHistogramSize() const = 0;
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/** @brief Sets coefficients for the linear SVM classifier.
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*/
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virtual void setSVMDetector(InputArray detector) = 0;
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/** @brief Returns coefficients of the classifier trained for people detection.
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*/
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virtual Mat getDefaultPeopleDetector() const = 0;
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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 confidences Optional output array for confidences.
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*/
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virtual void detect(InputArray img,
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std::vector<Point>& found_locations,
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std::vector<double>* confidences = NULL) = 0;
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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 confidences Optional output array for confidences.
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*/
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virtual void detectMultiScale(InputArray img,
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std::vector<Rect>& found_locations,
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std::vector<double>* confidences = NULL) = 0;
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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 descriptors 2D array of descriptors.
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@param stream CUDA stream.
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*/
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virtual void compute(InputArray img,
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OutputArray descriptors,
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Stream& stream = Stream::Null()) = 0;
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};
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//
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// CascadeClassifier
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//
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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 : public Algorithm
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{
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public:
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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. The working haar models can be found at opencv_folder/data/haarcascades_cuda/
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*/
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static Ptr<CascadeClassifier> create(const String& filename);
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/** @overload
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*/
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static Ptr<CascadeClassifier> create(const FileStorage& file);
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//! 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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virtual void setMaxObjectSize(Size maxObjectSize) = 0;
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virtual Size getMaxObjectSize() const = 0;
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//! Minimum possible object size. Objects smaller than that are ignored.
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virtual void setMinObjectSize(Size minSize) = 0;
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virtual Size getMinObjectSize() const = 0;
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//! Parameter specifying how much the image size is reduced at each image scale.
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virtual void setScaleFactor(double scaleFactor) = 0;
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virtual double getScaleFactor() const = 0;
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//! Parameter specifying how many neighbors each candidate rectangle should have
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//! to retain it.
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virtual void setMinNeighbors(int minNeighbors) = 0;
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virtual int getMinNeighbors() const = 0;
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virtual void setFindLargestObject(bool findLargestObject) = 0;
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virtual bool getFindLargestObject() = 0;
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virtual void setMaxNumObjects(int maxNumObjects) = 0;
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virtual int getMaxNumObjects() const = 0;
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virtual Size getClassifierSize() const = 0;
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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 objects Buffer to store detected objects (rectangles).
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@param stream CUDA stream.
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To get final array of detected objects use CascadeClassifier::convert method.
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@code
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Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(...);
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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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cascade_gpu->detectMultiScale(image_gpu, objbuf);
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std::vector<Rect> faces;
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cascade_gpu->convert(objbuf, faces);
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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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virtual void detectMultiScale(InputArray image,
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OutputArray objects,
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Stream& stream = Stream::Null()) = 0;
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/** @brief Converts objects array from internal representation to standard vector.
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@param gpu_objects Objects array in internal representation.
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@param objects Resulting array.
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*/
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virtual void convert(OutputArray gpu_objects,
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std::vector<Rect>& objects) = 0;
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};
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//! @}
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}} // namespace cv { namespace cuda {
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#endif /* __OPENCV_CUDAOBJDETECT_HPP__ */
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