niblack_thresholding.cpp 4.25 KB
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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) 2014, Beat Kueng (beat-kueng@gmx.net), Lukas Vogel, Morten Lysgaard
// Third party copyrights are property of their respective owners.
//
// 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.
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//     this list of conditions and the following disclaimer in the documentation
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//   * The name of the copyright holders may not be used to endorse or promote products
//     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
// 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,
// indirect, incidental, special, exemplary, or consequential damages
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// loss of use, data, or profits; or business interruption) however caused
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//M*/


#include "precomp.hpp"
#include <cmath>

namespace cv {
namespace ximgproc {

void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
        int type, int blockSize, double delta )
{
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    // Input grayscale image
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    Mat src = _src.getMat();
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    CV_Assert(src.channels() == 1);
    CV_Assert(blockSize % 2 == 1 && blockSize > 1);
    type &= THRESH_MASK;
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    // Compute local threshold (T = mean + k * stddev)
    // using mean and standard deviation in the neighborhood of each pixel
    // (intermediate calculations are done with floating-point precision)
    Mat thresh;
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    {
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        // note that: Var[X] = E[X^2] - E[X]^2
        Mat mean, sqmean, stddev;
        boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
                Point(-1,-1), true, BORDER_REPLICATE);
        sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
                Point(-1,-1), true, BORDER_REPLICATE);
        sqrt(sqmean - mean.mul(mean), stddev);
        thresh = mean + stddev * static_cast<float>(delta);
        thresh.convertTo(thresh, src.depth());
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    }

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    // Prepare output image
    _dst.create(src.size(), src.type());
    Mat dst = _dst.getMat();
    CV_Assert(src.data != dst.data);  // no inplace processing
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    // Apply thresholding: ( pixel > threshold ) ? foreground : background
    Mat mask;
    switch (type)
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    {
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    case THRESH_BINARY:      // dst = (src > thresh) ? maxval : 0
    case THRESH_BINARY_INV:  // dst = (src > thresh) ? 0 : maxval
        compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
        dst.setTo(0);
        dst.setTo(maxValue, mask);
        break;
    case THRESH_TRUNC:       // dst = (src > thresh) ? thresh : src
        compare(src, thresh, mask, CMP_GT);
        src.copyTo(dst);
        thresh.copyTo(dst, mask);
        break;
    case THRESH_TOZERO:      // dst = (src > thresh) ? src : 0
    case THRESH_TOZERO_INV:  // dst = (src > thresh) ? 0 : src
        compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
        dst.setTo(0);
        src.copyTo(dst, mask);
        break;
    default:
        CV_Error( CV_StsBadArg, "Unknown threshold type" );
        break;
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    }
}

} // namespace ximgproc
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} // namespace cv