File: perf_haar.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
//    Fangfang Bai, fangfang@multicorewareinc.com
//    Jin Ma,       jin@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other materials provided with the distribution.
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//     derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors as is and
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
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//M*/
#include "perf_precomp.hpp"

using namespace perf;
using namespace std;
using namespace cv;
using std::tr1::make_tuple;
using std::tr1::get;

///////////// Haar ////////////////////////

PERF_TEST(HaarFixture, Haar)
{
    vector<Rect> faces;

    Mat img = imread(getDataPath("gpu/haarcascade/basketball1.png"), CV_LOAD_IMAGE_GRAYSCALE);
    ASSERT_TRUE(!img.empty()) << "can't open basketball1.png";
    declare.in(img);

    if (RUN_PLAIN_IMPL)
    {
        CascadeClassifier faceCascade;
        ASSERT_TRUE(faceCascade.load(getDataPath("gpu/haarcascade/haarcascade_frontalface_alt.xml")))
                << "can't load haarcascade_frontalface_alt.xml";

        TEST_CYCLE() faceCascade.detectMultiScale(img, faces,
                                                     1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));

        SANITY_CHECK(faces, 4 + 1e-4);
    }
    else if (RUN_OCL_IMPL)
    {
        ocl::OclCascadeClassifier faceCascade;
        ocl::oclMat oclImg(img);

        ASSERT_TRUE(faceCascade.load(getDataPath("gpu/haarcascade/haarcascade_frontalface_alt.xml")))
                << "can't load haarcascade_frontalface_alt.xml";

        OCL_TEST_CYCLE() faceCascade.detectMultiScale(oclImg, faces,
                                     1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));

        SANITY_CHECK(faces, 4 + 1e-4);
    }
    else
        OCL_PERF_ELSE
}

typedef std::tr1::tuple<std::string, std::string, int> Cascade_Image_MinSize_t;
typedef perf::TestBaseWithParam<Cascade_Image_MinSize_t> Cascade_Image_MinSize;

OCL_PERF_TEST_P(Cascade_Image_MinSize, CascadeClassifier,
                testing::Combine(testing::Values( string("cv/cascadeandhog/cascades/haarcascade_frontalface_alt.xml"),
                                                  string("cv/cascadeandhog/cascades/haarcascade_frontalface_alt2.xml") ),
                                 testing::Values( string("cv/shared/lena.png"),
                                                  string("cv/cascadeandhog/images/bttf301.png"),
                                                  string("cv/cascadeandhog/images/class57.png") ),
                                 testing::Values(30, 64, 90)))
{
    const string cascasePath = get<0>(GetParam());
    const string imagePath   = get<1>(GetParam());
    const int min_size = get<2>(GetParam());
    Size minSize(min_size, min_size);
    vector<Rect> faces;

    Mat img = imread(getDataPath(imagePath), IMREAD_GRAYSCALE);
    ASSERT_FALSE(img.empty()) << "Can't load source image: " << getDataPath(imagePath);
    equalizeHist(img, img);
    declare.in(img);

    if (RUN_PLAIN_IMPL)
    {
        CascadeClassifier cc;
        ASSERT_TRUE(cc.load(getDataPath(cascasePath))) << "Can't load cascade file: " << getDataPath(cascasePath);

        while (next())
        {
            faces.clear();

            startTimer();
            cc.detectMultiScale(img, faces, 1.1, 3, CV_HAAR_SCALE_IMAGE, minSize);
            stopTimer();
        }
    }
    else if (RUN_OCL_IMPL)
    {
        ocl::oclMat uimg(img);
        ocl::OclCascadeClassifier cc;
        ASSERT_TRUE(cc.load(getDataPath(cascasePath))) << "Can't load cascade file: " << getDataPath(cascasePath);

        while (next())
        {
            faces.clear();
            ocl::finish();

            startTimer();
            cc.detectMultiScale(uimg, faces, 1.1, 3, CV_HAAR_SCALE_IMAGE, minSize);
            stopTimer();
        }
    }
    else
        OCL_PERF_ELSE

    //sort(faces.begin(), faces.end(), comparators::RectLess());
    SANITY_CHECK_NOTHING();//(faces, min_size/5);
    // using SANITY_CHECK_NOTHING() since OCL and PLAIN version may find different faces number
}