image-match/python/opencv/modules/features2d/src/blobdetector.cpp

/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// 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.
//
//   * 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.
//
//   * The name of the copyright holders may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// 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
// 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,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/

#include "precomp.hpp"
#include <iterator>
#include <limits>

#include <opencv2/core/utils/logger.hpp>

// Requires CMake flag: DEBUG_opencv_features2d=ON
//#define DEBUG_BLOB_DETECTOR

#ifdef DEBUG_BLOB_DETECTOR
#include "opencv2/highgui.hpp"
#endif

namespace cv
{

// TODO: To be removed in 5.x branch
const std::vector<std::vector<cv::Point> >& SimpleBlobDetector::getBlobContours() const
{
    CV_Error(Error::StsNotImplemented, "Method SimpleBlobDetector::getBlobContours() is not implemented");
}

class CV_EXPORTS_W SimpleBlobDetectorImpl : public SimpleBlobDetector
{
public:

  explicit SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());

  virtual void read( const FileNode& fn ) CV_OVERRIDE;
  virtual void write( FileStorage& fs ) const CV_OVERRIDE;

  void setParams(const SimpleBlobDetector::Params& _params ) CV_OVERRIDE {
    SimpleBlobDetectorImpl::validateParameters(_params);
    params = _params;
  }

  SimpleBlobDetector::Params getParams() const CV_OVERRIDE { return params; }

  static void validateParameters(const SimpleBlobDetector::Params& p)
  {
      if (p.thresholdStep <= 0)
          CV_Error(Error::StsBadArg, "thresholdStep>0");

      if (p.minThreshold > p.maxThreshold || p.minThreshold < 0)
          CV_Error(Error::StsBadArg, "0<=minThreshold<=maxThreshold");

      if (p.minDistBetweenBlobs <=0 )
          CV_Error(Error::StsBadArg, "minDistBetweenBlobs>0");

      if (p.minArea > p.maxArea || p.minArea <=0)
          CV_Error(Error::StsBadArg, "0<minArea<=maxArea");

      if (p.minCircularity > p.maxCircularity || p.minCircularity <= 0)
          CV_Error(Error::StsBadArg, "0<minCircularity<=maxCircularity");

      if (p.minInertiaRatio > p.maxInertiaRatio || p.minInertiaRatio <= 0)
          CV_Error(Error::StsBadArg, "0<minInertiaRatio<=maxInertiaRatio");

      if (p.minConvexity > p.maxConvexity || p.minConvexity <= 0)
          CV_Error(Error::StsBadArg, "0<minConvexity<=maxConvexity");
  }

protected:
  struct CV_EXPORTS Center
  {
      Point2d location;
      double radius;
      double confidence;
  };

  virtual void detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) CV_OVERRIDE;
  virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> &centers,
                         std::vector<std::vector<Point> > &contours, std::vector<Moments> &moments) const;
  virtual const std::vector<std::vector<Point> >& getBlobContours() const CV_OVERRIDE;

  Params params;
  std::vector<std::vector<Point> > blobContours;
};

/*
*  SimpleBlobDetector
*/
SimpleBlobDetector::Params::Params()
{
    thresholdStep = 10;
    minThreshold = 50;
    maxThreshold = 220;
    minRepeatability = 2;
    minDistBetweenBlobs = 10;

    filterByColor = true;
    blobColor = 0;

    filterByArea = true;
    minArea = 25;
    maxArea = 5000;

    filterByCircularity = false;
    minCircularity = 0.8f;
    maxCircularity = std::numeric_limits<float>::max();

    filterByInertia = true;
    //minInertiaRatio = 0.6;
    minInertiaRatio = 0.1f;
    maxInertiaRatio = std::numeric_limits<float>::max();

    filterByConvexity = true;
    //minConvexity = 0.8;
    minConvexity = 0.95f;
    maxConvexity = std::numeric_limits<float>::max();

    collectContours = false;
}

void SimpleBlobDetector::Params::read(const cv::FileNode& fn )
{
    thresholdStep = fn["thresholdStep"];
    minThreshold = fn["minThreshold"];
    maxThreshold = fn["maxThreshold"];

    minRepeatability = (size_t)(int)fn["minRepeatability"];
    minDistBetweenBlobs = fn["minDistBetweenBlobs"];

    filterByColor = (int)fn["filterByColor"] != 0 ? true : false;
    blobColor = (uchar)(int)fn["blobColor"];

    filterByArea = (int)fn["filterByArea"] != 0 ? true : false;
    minArea = fn["minArea"];
    maxArea = fn["maxArea"];

    filterByCircularity = (int)fn["filterByCircularity"] != 0 ? true : false;
    minCircularity = fn["minCircularity"];
    maxCircularity = fn["maxCircularity"];

    filterByInertia = (int)fn["filterByInertia"] != 0 ? true : false;
    minInertiaRatio = fn["minInertiaRatio"];
    maxInertiaRatio = fn["maxInertiaRatio"];

    filterByConvexity = (int)fn["filterByConvexity"] != 0 ? true : false;
    minConvexity = fn["minConvexity"];
    maxConvexity = fn["maxConvexity"];

    collectContours = (int)fn["collectContours"] != 0 ? true : false;
}

void SimpleBlobDetector::Params::write(cv::FileStorage& fs) const
{
    fs << "thresholdStep" << thresholdStep;
    fs << "minThreshold" << minThreshold;
    fs << "maxThreshold" << maxThreshold;

    fs << "minRepeatability" << (int)minRepeatability;
    fs << "minDistBetweenBlobs" << minDistBetweenBlobs;

    fs << "filterByColor" << (int)filterByColor;
    fs << "blobColor" << (int)blobColor;

    fs << "filterByArea" << (int)filterByArea;
    fs << "minArea" << minArea;
    fs << "maxArea" << maxArea;

    fs << "filterByCircularity" << (int)filterByCircularity;
    fs << "minCircularity" << minCircularity;
    fs << "maxCircularity" << maxCircularity;

    fs << "filterByInertia" << (int)filterByInertia;
    fs << "minInertiaRatio" << minInertiaRatio;
    fs << "maxInertiaRatio" << maxInertiaRatio;

    fs << "filterByConvexity" << (int)filterByConvexity;
    fs << "minConvexity" << minConvexity;
    fs << "maxConvexity" << maxConvexity;

    fs << "collectContours" << (int)collectContours;
}

SimpleBlobDetectorImpl::SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters) :
params(parameters)
{
}

void SimpleBlobDetectorImpl::read( const cv::FileNode& fn )
{
    SimpleBlobDetector::Params rp;
    rp.read(fn);
    SimpleBlobDetectorImpl::validateParameters(rp);
    params = rp;
}

void SimpleBlobDetectorImpl::write( cv::FileStorage& fs ) const
{
    writeFormat(fs);
    params.write(fs);
}

void SimpleBlobDetectorImpl::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> &centers,
                                       std::vector<std::vector<Point> > &contoursOut, std::vector<Moments> &momentss) const
{
    CV_INSTRUMENT_REGION();

    Mat image = _image.getMat(), binaryImage = _binaryImage.getMat();
    CV_UNUSED(image);
    centers.clear();
    contoursOut.clear();
    momentss.clear();

    std::vector < std::vector<Point> > contours;
    findContours(binaryImage, contours, RETR_LIST, CHAIN_APPROX_NONE);

#ifdef DEBUG_BLOB_DETECTOR
    Mat keypointsImage;
    cvtColor(binaryImage, keypointsImage, COLOR_GRAY2RGB);

    Mat contoursImage;
    cvtColor(binaryImage, contoursImage, COLOR_GRAY2RGB);
    drawContours( contoursImage, contours, -1, Scalar(0,255,0) );
    imshow("contours", contoursImage );
#endif

    for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
    {
        Center center;
        center.confidence = 1;
        Moments moms = moments(contours[contourIdx]);
        if (params.filterByArea)
        {
            double area = moms.m00;
            if (area < params.minArea || area >= params.maxArea)
                continue;
        }

        if (params.filterByCircularity)
        {
            double area = moms.m00;
            double perimeter = arcLength(contours[contourIdx], true);
            double ratio = 4 * CV_PI * area / (perimeter * perimeter);
            if (ratio < params.minCircularity || ratio >= params.maxCircularity)
                continue;
        }

        if (params.filterByInertia)
        {
            double denominator = std::sqrt(std::pow(2 * moms.mu11, 2) + std::pow(moms.mu20 - moms.mu02, 2));
            const double eps = 1e-2;
            double ratio;
            if (denominator > eps)
            {
                double cosmin = (moms.mu20 - moms.mu02) / denominator;
                double sinmin = 2 * moms.mu11 / denominator;
                double cosmax = -cosmin;
                double sinmax = -sinmin;

                double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
                double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
                ratio = imin / imax;
            }
            else
            {
                ratio = 1;
            }

            if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
                continue;

            center.confidence = ratio * ratio;
        }

        if (params.filterByConvexity)
        {
            std::vector < Point > hull;
            convexHull(contours[contourIdx], hull);
            double area = moms.m00;
            double hullArea = contourArea(hull);
            if (fabs(hullArea) < DBL_EPSILON)
                continue;
            double ratio = area / hullArea;
            if (ratio < params.minConvexity || ratio >= params.maxConvexity)
                continue;
        }

        if(moms.m00 == 0.0)
            continue;
        center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);

        if (params.filterByColor)
        {
            if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
                continue;
        }

        //compute blob radius
        {
            std::vector<double> dists;
            for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)
            {
                Point2d pt = contours[contourIdx][pointIdx];
                dists.push_back(norm(center.location - pt));
            }
            std::sort(dists.begin(), dists.end());
            center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
        }

        centers.push_back(center);
        if (params.collectContours)
        {
            contoursOut.push_back(contours[contourIdx]);
            momentss.push_back(moms);
        }

#ifdef DEBUG_BLOB_DETECTOR
        circle( keypointsImage, center.location, 1, Scalar(0,0,255), 1 );
#endif
    }
#ifdef DEBUG_BLOB_DETECTOR
    imshow("bk", keypointsImage );
    waitKey();
#endif
}

void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray mask)
{
    CV_INSTRUMENT_REGION();

    keypoints.clear();
    blobContours.clear();

    CV_Assert(params.minRepeatability != 0);
    Mat grayscaleImage;
    if (image.channels() == 3 || image.channels() == 4)
        cvtColor(image, grayscaleImage, COLOR_BGR2GRAY);
    else
        grayscaleImage = image.getMat();

    if (grayscaleImage.type() != CV_8UC1) {
        CV_Error(Error::StsUnsupportedFormat, "Blob detector only supports 8-bit images!");
    }

    CV_CheckGT(params.thresholdStep, 0.0f, "");
    if (params.minThreshold + params.thresholdStep >= params.maxThreshold)
    {
        // https://github.com/opencv/opencv/issues/6667
        CV_LOG_ONCE_INFO(NULL, "SimpleBlobDetector: params.minDistBetweenBlobs is ignored for case with single threshold");
#if 0  // OpenCV 5.0
        CV_CheckEQ(params.minRepeatability, 1u, "Incompatible parameters for case with single threshold");
#else
        if (params.minRepeatability != 1)
            CV_LOG_WARNING(NULL, "SimpleBlobDetector: params.minRepeatability=" << params.minRepeatability << " is incompatible for case with single threshold. Empty result is expected.");
#endif
    }

    std::vector < std::vector<Center> > centers;
    std::vector<Moments> momentss;
    for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
    {
        Mat binarizedImage;
        threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);

        std::vector < Center > curCenters;
        std::vector<std::vector<Point> > curContours;
        std::vector<Moments> curMomentss;
        findBlobs(grayscaleImage, binarizedImage, curCenters, curContours, curMomentss);
        std::vector < std::vector<Center> > newCenters;
        std::vector<std::vector<Point> > newContours;
        std::vector<Moments> newMomentss;
        for (size_t i = 0; i < curCenters.size(); i++)
        {
            bool isNew = true;
            for (size_t j = 0; j < centers.size(); j++)
            {
                double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
                isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
                if (!isNew)
                {
                    centers[j].push_back(curCenters[i]);

                    size_t k = centers[j].size() - 1;
                    while( k > 0 && curCenters[i].radius < centers[j][k-1].radius )
                    {
                        centers[j][k] = centers[j][k-1];
                        k--;
                    }

                    if (params.collectContours)
                    {
                        if (curCenters[i].confidence > centers[j][k].confidence
                            || (curCenters[i].confidence == centers[j][k].confidence && curMomentss[i].m00 > momentss[j].m00))
                        {
                            blobContours[j] = curContours[i];
                            momentss[j] = curMomentss[i];
                        }
                    }
                    centers[j][k] = curCenters[i];

                    break;
                }
            }
            if (isNew)
            {
                newCenters.push_back(std::vector<Center> (1, curCenters[i]));
                if (params.collectContours)
                {
                    newContours.push_back(curContours[i]);
                    newMomentss.push_back(curMomentss[i]);
                }
            }
        }
        std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
        if (params.collectContours)
        {
            std::copy(newContours.begin(), newContours.end(), std::back_inserter(blobContours));
            std::copy(newMomentss.begin(), newMomentss.end(), std::back_inserter(momentss));
        }
    }

    for (size_t i = 0; i < centers.size(); i++)
    {
        if (centers[i].size() < params.minRepeatability)
            continue;
        Point2d sumPoint(0, 0);
        double normalizer = 0;
        for (size_t j = 0; j < centers[i].size(); j++)
        {
            sumPoint += centers[i][j].confidence * centers[i][j].location;
            normalizer += centers[i][j].confidence;
        }
        sumPoint *= (1. / normalizer);
        KeyPoint kpt(sumPoint, (float)(centers[i][centers[i].size() / 2].radius) * 2.0f);
        keypoints.push_back(kpt);
    }

    if (!mask.empty())
    {
        if (params.collectContours)
        {
            KeyPointsFilter::runByPixelsMask2VectorPoint(keypoints, blobContours, mask.getMat());
        }
        else
        {
            KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
        }
    }
}

const std::vector<std::vector<Point> >& SimpleBlobDetectorImpl::getBlobContours() const {
    return blobContours;
}

Ptr<SimpleBlobDetector> SimpleBlobDetector::create(const SimpleBlobDetector::Params& params)
{
    SimpleBlobDetectorImpl::validateParameters(params);
    return makePtr<SimpleBlobDetectorImpl>(params);
}

String SimpleBlobDetector::getDefaultName() const
{
    return (Feature2D::getDefaultName() + ".SimpleBlobDetector");
}

}