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110 lines
4.0 KiB
C++
110 lines
4.0 KiB
C++
//
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// Created by 庾金科 on 26/10/2017.
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//
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#ifndef SWIFTPR_NIBLACKTHRESHOLD_H
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#define SWIFTPR_NIBLACKTHRESHOLD_H
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#include <opencv2/opencv.hpp>
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using namespace cv;
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enum LocalBinarizationMethods{
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BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
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BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
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BINARIZATION_WOLF = 2, //!< Wolf's technique. See @cite Wolf2004 .
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BINARIZATION_NICK = 3 //!< NICK technique. See @cite Khurshid2009 .
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};
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void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
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int type, int blockSize, double k, int binarizationMethod )
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{
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// Input grayscale image
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Mat src = _src.getMat();
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CV_Assert(src.channels() == 1);
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CV_Assert(blockSize % 2 == 1 && blockSize > 1);
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if (binarizationMethod == BINARIZATION_SAUVOLA) {
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CV_Assert(src.depth() == CV_8U);
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}
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type &= THRESH_MASK;
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// Compute local threshold (T = mean + k * stddev)
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// using mean and standard deviation in the neighborhood of each pixel
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// (intermediate calculations are done with floating-point precision)
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Mat test;
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Mat thresh;
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{
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// note that: Var[X] = E[X^2] - E[X]^2
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Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
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double srcMin, stddevMax;
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boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
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Point(-1,-1), true, BORDER_REPLICATE);
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sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
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Point(-1,-1), true, BORDER_REPLICATE);
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variance = sqmean - mean.mul(mean);
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sqrt(variance, stddev);
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switch (binarizationMethod)
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{
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case BINARIZATION_NIBLACK:
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thresh = mean + stddev * static_cast<float>(k);
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break;
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case BINARIZATION_SAUVOLA:
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thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
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break;
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case BINARIZATION_WOLF:
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minMaxIdx(src, &srcMin,NULL);
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minMaxIdx(stddev, NULL, &stddevMax);
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thresh = mean - static_cast<float>(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
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break;
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case BINARIZATION_NICK:
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sqrt(variance + sqmean, sqrtVarianceMeanSum);
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thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
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break;
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default:
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// CV_Error( CV_StsBadArg, "Unknown binarization method" );
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CV_Error(-5, "Unknown binarization method");
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break;
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}
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thresh.convertTo(thresh, src.depth());
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thresh.convertTo(test, src.depth());
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//
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// cv::imshow("imagex",test);
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// cv::waitKey(0);
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}
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// Prepare output image
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_dst.create(src.size(), src.type());
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Mat dst = _dst.getMat();
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CV_Assert(src.data != dst.data); // no inplace processing
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// Apply thresholding: ( pixel > threshold ) ? foreground : background
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Mat mask;
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switch (type)
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{
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case THRESH_BINARY: // dst = (src > thresh) ? maxval : 0
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case THRESH_BINARY_INV: // dst = (src > thresh) ? 0 : maxval
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compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
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dst.setTo(0);
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dst.setTo(maxValue, mask);
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break;
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case THRESH_TRUNC: // dst = (src > thresh) ? thresh : src
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compare(src, thresh, mask, CMP_GT);
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src.copyTo(dst);
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thresh.copyTo(dst, mask);
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break;
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case THRESH_TOZERO: // dst = (src > thresh) ? src : 0
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case THRESH_TOZERO_INV: // dst = (src > thresh) ? 0 : src
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compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
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dst.setTo(0);
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src.copyTo(dst, mask);
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break;
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default:
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// CV_Error( CV_StsBadArg, "Unknown threshold type" );
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CV_Error(-5, "Unknown threshold type");
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break;
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}
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}
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#endif //SWIFTPR_NIBLACKTHRESHOLD_H
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