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added edgeboxes algorithm (#1215)

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samples added

fix edgeboxes_demo

fix edgeboxes_demo

added edgeboxes bib

fix edgeboxes_demo

small fixes

fix edgeboxes_demo

fix warnings

fix warnings

small fixes

detectEdges needs rgb image instead bgr image.

Removed unnecessary protection

small fixes
pull/1408/head
Leonardo lontra 7 years ago committed by Vadim Pisarevsky
parent a44a2ba730
commit 1f8ccc1687
7 changed files with 1313 additions and 0 deletions
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  1. 1
      modules/ximgproc/README.md
  2. 7
      modules/ximgproc/doc/ximgproc.bib
  3. 3
      modules/ximgproc/include/opencv2/ximgproc.hpp
  4. 200
      modules/ximgproc/include/opencv2/ximgproc/edgeboxes.hpp
  5. 94
      modules/ximgproc/samples/edgeboxes_demo.cpp
  6. 38
      modules/ximgproc/samples/edgeboxes_demo.py
  7. 970
      modules/ximgproc/src/edgeboxes.cpp

1
modules/ximgproc/README.md
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@ -2,6 +2,7 @@ Extended Image Processing
=========================
- Structured Forests
- Edge Boxes
- Domain Transform Filter
- Guided Filter
- Adaptive Manifold Filter

7
modules/ximgproc/doc/ximgproc.bib
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@ -1,3 +1,10 @@
@inproceedings{ZitnickECCV14edgeBoxes,
author = {C. Lawrence Zitnick and Piotr Doll{\'a}r},
title = {Edge Boxes: Locating Object Proposals from Edges},
booktitle = {ECCV},
year = {2014},
}
@inproceedings{Dollar2013,
title={Structured forests for fast edge detection},
author={Doll{\'a}r, Piotr and Zitnick, C Lawrence},

3
modules/ximgproc/include/opencv2/ximgproc.hpp
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@ -41,6 +41,7 @@
#include "ximgproc/disparity_filter.hpp"
#include "ximgproc/sparse_match_interpolator.hpp"
#include "ximgproc/structured_edge_detection.hpp"
#include "ximgproc/edgeboxes.hpp"
#include "ximgproc/seeds.hpp"
#include "ximgproc/segmentation.hpp"
#include "ximgproc/fast_hough_transform.hpp"
@ -62,6 +63,8 @@
This module contains implementations of modern structured edge detection algorithms,
i.e. algorithms which somehow takes into account pixel affinities in natural images.
@defgroup ximgproc_edgeboxes EdgeBoxes
@defgroup ximgproc_filters Filters
@defgroup ximgproc_superpixel Superpixels

200
modules/ximgproc/include/opencv2/ximgproc/edgeboxes.hpp
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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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, 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*/
#ifndef __OPENCV_EDGEBOXES_HPP__
#define __OPENCV_EDGEBOXES_HPP__
#include <opencv2/core.hpp>
namespace cv
{
namespace ximgproc
{
//! @addtogroup ximgproc_edgeboxes
//! @{
// bounding box data structures
typedef struct
{
int x, y, w, h;
float score;
} Box;
typedef std::vector<Box> Boxes;
/** @brief Class implementing EdgeBoxes algorithm from @cite ZitnickECCV14edgeBoxes :
*/
class CV_EXPORTS_W EdgeBoxes : public Algorithm
{
public:
/** @brief Returns array containing proposal boxes.
@param edge_map edge image.
@param orientation_map orientation map.
@param boxes proposal boxes.
*/
CV_WRAP virtual void getBoundingBoxes(InputArray edge_map, InputArray orientation_map, CV_OUT std::vector<Rect> &boxes) = 0;
/** @brief Returns the step size of sliding window search.
*/
CV_WRAP virtual float getAlpha() const = 0;
/** @brief Sets the step size of sliding window search.
*/
CV_WRAP virtual void setAlpha(float value) = 0;
/** @brief Returns the nms threshold for object proposals.
*/
CV_WRAP virtual float getBeta() const = 0;
/** @brief Sets the nms threshold for object proposals.
*/
CV_WRAP virtual void setBeta(float value) = 0;
/** @brief Returns adaptation rate for nms threshold.
*/
CV_WRAP virtual float getEta() const = 0;
/** @brief Sets the adaptation rate for nms threshold.
*/
CV_WRAP virtual void setEta(float value) = 0;
/** @brief Returns the min score of boxes to detect.
*/
CV_WRAP virtual float getMinScore() const = 0;
/** @brief Sets the min score of boxes to detect.
*/
CV_WRAP virtual void setMinScore(float value) = 0;
/** @brief Returns the max number of boxes to detect.
*/
CV_WRAP virtual int getMaxBoxes() const = 0;
/** @brief Sets max number of boxes to detect.
*/
CV_WRAP virtual void setMaxBoxes(int value) = 0;
/** @brief Returns the edge min magnitude.
*/
CV_WRAP virtual float getEdgeMinMag() const = 0;
/** @brief Sets the edge min magnitude.
*/
CV_WRAP virtual void setEdgeMinMag(float value) = 0;
/** @brief Returns the edge merge threshold.
*/
CV_WRAP virtual float getEdgeMergeThr() const = 0;
/** @brief Sets the edge merge threshold.
*/
CV_WRAP virtual void setEdgeMergeThr(float value) = 0;
/** @brief Returns the cluster min magnitude.
*/
CV_WRAP virtual float getClusterMinMag() const = 0;
/** @brief Sets the cluster min magnitude.
*/
CV_WRAP virtual void setClusterMinMag(float value) = 0;
/** @brief Returns the max aspect ratio of boxes.
*/
CV_WRAP virtual float getMaxAspectRatio() const = 0;
/** @brief Sets the max aspect ratio of boxes.
*/
CV_WRAP virtual void setMaxAspectRatio(float value) = 0;
/** @brief Returns the minimum area of boxes.
*/
CV_WRAP virtual float getMinBoxArea() const = 0;
/** @brief Sets the minimum area of boxes.
*/
CV_WRAP virtual void setMinBoxArea(float value) = 0;
/** @brief Returns the affinity sensitivity.
*/
CV_WRAP virtual float getGamma() const = 0;
/** @brief Sets the affinity sensitivity
*/
CV_WRAP virtual void setGamma(float value) = 0;
/** @brief Returns the scale sensitivity.
*/
CV_WRAP virtual float getKappa() const = 0;
/** @brief Sets the scale sensitivity.
*/
CV_WRAP virtual void setKappa(float value) = 0;
};
/** @brief Creates a Edgeboxes
@param alpha step size of sliding window search.
@param beta nms threshold for object proposals.
@param eta adaptation rate for nms threshold.
@param minScore min score of boxes to detect.
@param maxBoxes max number of boxes to detect.
@param edgeMinMag edge min magnitude. Increase to trade off accuracy for speed.
@param edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed.
@param clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed.
@param maxAspectRatio max aspect ratio of boxes.
@param minBoxArea minimum area of boxes.
@param gamma affinity sensitivity.
@param kappa scale sensitivity.
*/
CV_EXPORTS_W Ptr<EdgeBoxes>
createEdgeBoxes(float alpha=0.65f,
float beta=0.75f,
float eta=1,
float minScore=0.01f,
int maxBoxes=10000,
float edgeMinMag=0.1f,
float edgeMergeThr=0.5f,
float clusterMinMag=0.5f,
float maxAspectRatio=3,
float minBoxArea=1000,
float gamma=2,
float kappa=1.5f);
//! @}
}
}
#endif /* __OPENCV_EDGEBOXES_HPP__ */

94
modules/ximgproc/samples/edgeboxes_demo.cpp
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/*
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
(3-clause BSD License)
Copyright (C) 2013, OpenCV Foundation, 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:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions 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.
* Neither the names of the copyright holders nor the names of the contributors
may 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 copyright holders 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.
*/
#include "opencv2/ximgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
using namespace cv::ximgproc;
static void help()
{
std::cout << std::endl <<
"This sample demonstrates structured edge detection and edgeboxes." << std::endl <<
"Usage:" << std::endl <<
"./edgeboxes_demo [<model>] [<input_image>]" << std::endl;
}
int main(int argc, char **argv)
{
if (argc < 3)
{
help();
return -1;
}
Ptr<StructuredEdgeDetection> pDollar = createStructuredEdgeDetection(argv[1]);
Mat im;
im = imread(argv[2]);
Mat rgb_im;
cvtColor(im, rgb_im, COLOR_BGR2RGB);
rgb_im.convertTo(rgb_im, CV_32F, 1.0 / 255.0f);
Mat edge_im;
pDollar->detectEdges(rgb_im, edge_im);
// computes orientation from edge map
Mat O;
pDollar->computeOrientation(edge_im, O);
// apply edge nms
Mat edge_nms;
pDollar->edgesNms(edge_im, O, edge_nms, 2, 0, 1, true);
std::vector<Rect> boxes;
Ptr<EdgeBoxes> edgeboxes = createEdgeBoxes();
edgeboxes->setMaxBoxes(30);
edgeboxes->getBoundingBoxes(edge_nms, O, boxes);
for(int i = 0; i < (int)boxes.size(); i++)
{
Point p1(boxes[i].x, boxes[i].y), p2(boxes[i].x + boxes[i].width, boxes[i].y + boxes[i].height);
Scalar color(0, 255, 0);
rectangle(im, p1, p2, color, 1);
}
imshow("im", im);
waitKey(0);
return 0;
}

38
modules/ximgproc/samples/edgeboxes_demo.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
This sample demonstrates structured edge detection and edgeboxes.
Usage:
edgeboxes_demo.py [<model>] [<input_image>]
'''
import cv2
import numpy as np
import sys
if __name__ == '__main__':
print(__doc__)
model = sys.argv[1]
im = cv2.imread(sys.argv[2])
edge_detection = cv2.ximgproc.createStructuredEdgeDetection(model)
rgb_im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
edges = edge_detection.detectEdges(np.float32(rgb_im) / 255.0)
orimap = edge_detection.computeOrientation(edges)
edges = edge_detection.edgesNms(edges, orimap)
edge_boxes = cv2.ximgproc.createEdgeBoxes()
edge_boxes.setMaxBoxes(30)
boxes = edge_boxes.getBoundingBoxes(edges, orimap)
for b in boxes:
x, y, w, h = b
cv2.rectangle(im, (x, y), (x+w, y+h), (0, 255, 0), 1, cv2.LINE_AA)
cv2.imshow("edges", edges)
cv2.imshow("edgeboxes", im)
cv2.waitKey(0)
cv2.destroyAllWindows()

970
modules/ximgproc/src/edgeboxes.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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// * 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 Intel Corporation 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*/
/*
Generate Edge Boxes object proposals in given image(s).
Compute Edge Boxes object proposals as described in:
C. Lawrence Zitnick and Piotr Dollár
"Edge Boxes: Locating Object Proposals from Edges", ECCV 2014.
The proposal boxes are fast to compute and give state-of-the-art recall.
OpenCV port by: Leonardo Lontra <lhe dot lontra at gmail dot com>
*/
#include "precomp.hpp"
using namespace cv;
using namespace std;
inline int clamp(int v, int min, int max)
{
return v < min ? min : v > max ? max : v;
}
namespace cv
{
namespace ximgproc
{
class EdgeBoxesImpl : public EdgeBoxes
{
public:
EdgeBoxesImpl(float alpha,
float beta,
float eta,
float minScore,
int maxBoxes,
float edgeMinMag,
float edgeMergeThr,
float clusterMinMag,
float maxAspectRatio,
float minBoxArea,
float gamma,
float kappa);
virtual void getBoundingBoxes(InputArray edge_map, InputArray orientation_map, std::vector<Rect> &boxes);
float getAlpha() const { return _alpha; }
void setAlpha(float value)
{
_alpha = value;
_sxStep = sqrt(1 / _alpha);
_ayStep = (1 + _alpha) / (2 * _alpha);
_xyStepRatio = (1 - _alpha) / (1 + _alpha);
}
float getBeta() const { return _beta; }
void setBeta(float value) { _beta = value; }
float getEta() const { return _eta; }
void setEta(float value) { _eta = value; }
float getMinScore() const { return _minScore; }
void setMinScore(float value) { _minScore = value; }
int getMaxBoxes() const { return _maxBoxes; }
void setMaxBoxes(int value) { _maxBoxes = value; }
float getEdgeMinMag() const { return _edgeMinMag; }
void setEdgeMinMag(float value) { _edgeMinMag = value; }
float getEdgeMergeThr() const { return _edgeMergeThr; }
void setEdgeMergeThr(float value) { _edgeMergeThr = value; }
float getClusterMinMag() const { return _clusterMinMag; }
void setClusterMinMag(float value) { _clusterMinMag = value; }
float getMaxAspectRatio() const { return _maxAspectRatio; }
void setMaxAspectRatio(float value) { _maxAspectRatio = value; }
float getMinBoxArea() const { return _minBoxArea; }
void setMinBoxArea(float value) { _minBoxArea = value; }
float getGamma() const { return _gamma; }
void setGamma(float value) { _gamma = value; }
float getKappa() const { return _kappa; }
void setKappa(float value)
{
_kappa = value;
_scaleNorm.resize(10000);
for (int i = 0; i < 10000; i++) _scaleNorm[i] = pow(1.f / i, _kappa);
}
//! the destructor
virtual ~EdgeBoxesImpl() {}
private:
float _alpha;
float _beta;
float _eta;
float _minScore;
int _maxBoxes;
float _edgeMinMag;
float _edgeMergeThr;
float _clusterMinMag;
float _maxAspectRatio;
float _minBoxArea;
float _gamma;
float _kappa;
// edge segment information (see clusterEdges)
int h, w; // image dimensions
int _segCnt; // total segment count
Mat _segIds; // segment ids (-1/0 means no segment)
vector<float> _segMag; // segment edge magnitude sums
vector<Point2i> _segP; // segment lower-right pixel
vector<vector<float> > _segAff; // segment affinities
vector<vector<int> > _segAffIdx; // segment neighbors
// data structures for efficiency (see prepDataStructs)
Mat _segIImg, _magIImg;
Mat _hIdxImg, _vIdxImg;
vector<vector<int> > _hIdxs, _vIdxs;
vector<float> _scaleNorm;
float _sxStep, _ayStep, _xyStepRatio;
// data structures for efficiency (see scoreBox)
Mat _sWts;
Mat _sDone, _sMap, _sIds;
int _sId;
// helper routines
static bool boxesCompare(const Box &a, const Box &b) { return a.score < b.score; }
void clusterEdges(Mat &edgeMap, Mat &orientationMap);
void prepDataStructs(Mat &edgeMap);
void scoreAllBoxes(Boxes &boxes);
void scoreBox(Box &box);
void refineBox(Box &box);
float boxesOverlap(Box &a, Box &b);
void boxesNms(Boxes &boxes, float thr, float eta, int maxBoxes);
};
EdgeBoxesImpl::EdgeBoxesImpl(float alpha,
float beta,
float eta,
float minScore,
int maxBoxes,
float edgeMinMag,
float edgeMergeThr,
float clusterMinMag,
float maxAspectRatio,
float minBoxArea,
float gamma,
float kappa)
: _alpha(alpha),
_beta(beta),
_eta(eta),
_minScore(minScore),
_maxBoxes(maxBoxes),
_edgeMinMag(edgeMinMag),
_edgeMergeThr(edgeMergeThr),
_clusterMinMag(clusterMinMag),
_maxAspectRatio(maxAspectRatio),
_minBoxArea(minBoxArea),
_gamma(gamma),
_kappa(kappa)
{
// initialize step sizes
_sxStep = sqrt(1 / _alpha);
_ayStep = (1 + _alpha) / (2 * _alpha);
_xyStepRatio = (1 - _alpha) / (1 + _alpha);
// create _scaleNorm
_scaleNorm.resize(10000);
for (int i = 0; i < 10000; i++) _scaleNorm[i] = pow(1.f / i, _kappa);
}
void EdgeBoxesImpl::clusterEdges(Mat &edgeMap, Mat &orientationMap)
{
int x, y, xd, yd, i, j;
// greedily merge connected edge pixels into clusters (create _segIds)
_segIds = Mat::zeros(w, h, DataType<int>::type);
_segCnt = 1;
for (x = 0; x < w; x++)
{
const float *e_ptr = edgeMap.ptr<float>(x);
int *s_ptr = _segIds.ptr<int>(x);
for (y = 0; y < h; y++)
{
if (x == 0 || y == 0 || x == w - 1 || y == h - 1 || e_ptr[y] <= _edgeMinMag)
{
s_ptr[y] = -1;
}
}
}
for (x = 1; x < w - 1; x++)
{
int *s_ptr = _segIds.ptr<int>(x);
for (y = 1; y < h - 1; y++)
{
if (s_ptr[y] != 0) continue;
float sumv = 0;
int x0 = x;
int y0 = y;
vector<float> vs;
vector<int> xs, ys;
while (sumv < _edgeMergeThr)
{
_segIds.at<int>(x0, y0) = _segCnt;
float o0 = orientationMap.at<float>(x0, y0);
float o1, v;
bool found;
for (xd = -1; xd <= 1; xd++)
{
const int *s0_ptr = _segIds.ptr<int>(x0 + xd);
const float *o_ptr = orientationMap.ptr<float>(x0 + xd);
for (yd = -1; yd <= 1; yd++)
{
if (s0_ptr[y0 + yd] != 0) continue;
found = false;
for (i = 0; i < (int)xs.size(); i++)
{
if (xs[i] == x0 + xd && ys[i] == y0 + yd)
{
found = true;
break;
}
}
if (found) continue;
o1 = o_ptr[y0 + yd];
v = fabs(o1 - o0) / (float)CV_PI;
if (v > .5f) v = 1 - v;
vs.push_back(v);
xs.push_back(x0 + xd);
ys.push_back(y0 + yd);
}
}
float minv = 1000;
j = 0;
for (i = 0; i < (int)vs.size(); i++)
{
if (vs[i] < minv)
{
minv = vs[i];
x0 = xs[i];
y0 = ys[i];
j = i;
}
}
sumv += minv;
if (minv < 1000) vs[j] = 1000;
}
_segCnt++;
}
}
// merge or remove small segments
_segMag.resize(_segCnt, 0);
for (x = 1; x < w - 1; x++)
{
const float *e_ptr = edgeMap.ptr<float>(x);
const int *s_ptr = _segIds.ptr<int>(x);
for (y = 1; y < h - 1; y++)
{
j = s_ptr[y];
if (j > 0) _segMag[j] += e_ptr[y];
}
}
for (x = 1; x < w - 1; x++)
{
int *s_ptr = _segIds.ptr<int>(x);
for (y = 1; y < h - 1; y++)
{
j = s_ptr[y];
if (j > 0 && _segMag[j] <= _clusterMinMag)
s_ptr[y] = 0;
}
}
i = 1;
while (i > 0)
{
i = 0;
for (x = 1; x < w - 1; x++)
{
int *s0_ptr = _segIds.ptr<int>(x);
const float *o0_ptr = orientationMap.ptr<float>(x);
for (y = 1; y < h - 1; y++)
{
if (s0_ptr[y] != 0) continue;
float o0 = o0_ptr[y];
float o1, v, minv = 1000;
j = 0;
for (xd = -1; xd <= 1; xd++)
{
const int *s1_ptr = _segIds.ptr<int>(x+xd);
const float *o1_ptr = orientationMap.ptr<float>(x+xd);
for (yd = -1; yd <= 1; yd++)
{
if (s1_ptr[y + yd] <= 0) continue;
o1 = o1_ptr[y + yd];
v = fabs(o1 - o0) / (float)CV_PI;
if (v > .5f) v = 1 - v;
if (v < minv)
{
minv = v;
j = s1_ptr[y + yd];
}
}
}
s0_ptr[y] = j;
if (j > 0) i++;
}
}
}
// compactify representation
_segMag.assign(_segCnt, 0);
vector<int> map(_segCnt, 0);
_segCnt = 1;
for (x = 1; x < w - 1; x++)
{
const float *e_ptr = edgeMap.ptr<float>(x);
const int *s_ptr = _segIds.ptr<int>(x);
for (y = 1; y < h - 1; y++)
{
j = s_ptr[y];
if (j > 0) _segMag[j] += e_ptr[y];
}
}
for (i = 0; i < (int)_segMag.size(); i++)
{
if (_segMag[i] > 0) map[i] = _segCnt++;
}
for (x = 1; x < w - 1; x++)
{
int *s_ptr = _segIds.ptr<int>(x);
for (y = 1; y < h - 1; y++)
{
j = s_ptr[y];
if (j > 0) s_ptr[y] = map[j];
}
}
// compute positional means and recompute _segMag
_segMag.assign(_segCnt, 0);
vector<float> meanX(_segCnt, 0), meanY(_segCnt, 0);
vector<float> meanOx(_segCnt, 0), meanOy(_segCnt, 0), meanO(_segCnt, 0);
for (x = 1; x < w - 1; x++)
{
int *s_ptr = _segIds.ptr<int>(x);
const float *e_ptr = edgeMap.ptr<float>(x);
const float *o_ptr = orientationMap.ptr<float>(x);
for (y = 1; y < h - 1; y++)
{
j = s_ptr[y];
if (j <= 0) continue;
float m = e_ptr[y];
float o = o_ptr[y];
_segMag[j] += m;
meanOx[j] += m * cos(2 * o);
meanOy[j] += m * sin(2 * o);
meanX[j] += m * x;
meanY[j] += m * y;
}
}
for (i = 0; i < _segCnt; i++)
{
if (_segMag[i] > 0)
{
float m = _segMag[i];
meanX[i] /= m;
meanY[i] /= m;
meanO[i] = atan2(meanOy[i] / m, meanOx[i] / m) / 2;
}
}
// compute segment affinities
_segAff.resize(_segCnt);
_segAffIdx.resize(_segCnt);
for (i = 0; i < _segCnt; i++)
{
_segAff[i].resize(0);
_segAffIdx[i].resize(0);
}
const int rad = 2;
for (x = rad; x < w - rad; x++)
{
const int *s0_ptr = _segIds.ptr<int>(x);
for (y = rad; y < h - rad; y++)
{
int s0 = s0_ptr[y];
if (s0 <= 0) continue;
for (xd = -rad; xd <= rad; xd++)
{
const int *s1_ptr = _segIds.ptr<int>(x+xd);
for (yd = -rad; yd <= rad; yd++)
{
int s1 = s1_ptr[y + yd];
if (s1 <= s0) continue;
bool found = false;
for (i = 0; i < (int)_segAffIdx[s0].size(); i++)
{
if (_segAffIdx[s0][i] == s1)
{
found = true;
break;
}
}
if (found) continue;
float o = atan2(meanY[s0] - meanY[s1], meanX[s0] - meanX[s1]) + (float)CV_PI / 2.0f;
float a = fabs(cos(meanO[s0] - o) * cos(meanO[s1] - o));
a = pow(a, _gamma);
_segAff[s0].push_back(a);
_segAffIdx[s0].push_back(s1);
_segAff[s1].push_back(a);
_segAffIdx[s1].push_back(s0);
}
}
}
}
// compute _segC and _segR
_segP.resize(_segCnt);
for (x = 1; x < w - 1; x++)
{
const int *s_ptr = _segIds.ptr<int>(x);
for (y = 1; y < h - 1; y++)
{
j = s_ptr[y];
if (j > 0)
{
_segP[j] = Point2i(x, y);
}
}
}
}
void EdgeBoxesImpl::prepDataStructs(Mat &edgeMap)
{
int y, x, i;
// create _segIImg
Mat E1 = Mat::zeros(w, h, DataType<float>::type);
for (i=0; i < _segCnt; i++)
{
if (_segMag[i] > 0) E1.at<float>(_segP[i].x, _segP[i].y) = _segMag[i];
}
_segIImg = Mat::zeros(w+1, h+1, DataType<float>::type);
_magIImg = Mat::zeros(w+1, h+1, DataType<float>::type);
for (x=1; x < w; x++)
{
const float *e_ptr = edgeMap.ptr<float>(x);
const float *e1_ptr = E1.ptr<float>(x);
const float *si0_ptr = _segIImg.ptr<float>(x);
float *si1_ptr = _segIImg.ptr<float>(x+1);
const float *mi0_ptr = _magIImg.ptr<float>(x);
float *mi1_ptr =_magIImg.ptr<float>(x+1);
for (y=1; y < h; y++)
{
// create _segIImg
si1_ptr[y+1] = e1_ptr[y] + si0_ptr[y+1] + si1_ptr[y] - si0_ptr[y];
float e = e_ptr[y] > _edgeMinMag ? e_ptr[y] : 0;
// create _magIImg
mi1_ptr[y+1] = e +mi0_ptr[y+1] + mi1_ptr[y] - mi0_ptr[y];
}
}
// create remaining data structures
int s = 0;
int s1;
_hIdxs.resize(h);
_hIdxImg = Mat::zeros(w, h, DataType<int>::type);
for (y = 0; y < h; y++)
{
s = 0;
_hIdxs[y].push_back(s);
for (x = 0; x < w; x++)
{
s1 = _segIds.at<int>(x, y);
if (s1 != s)
{
s = s1;
_hIdxs[y].push_back(s);
}
_hIdxImg.at<int>(x, y) = (int)_hIdxs[y].size() - 1;
}
}
_vIdxs.resize(w);
_vIdxImg = Mat::zeros(w, h, DataType<int>::type);
for (x = 0; x < w; x++)
{
s = 0;
_vIdxs[x].push_back(s);
for (y = 0; y < h; y++)
{
s1 = _segIds.at<int>(x, y);
if (s1 != s)
{
s = s1;
_vIdxs[x].push_back(s);
}
_vIdxImg.at<int>(x, y) = (int)_vIdxs[x].size() - 1;
}
}
// initialize scoreBox() data structures
int n = _segCnt + 1;
_sWts = Mat::zeros(n, 1, DataType<float>::type);
_sDone = Mat::zeros(n, 1, DataType<int>::type);
_sMap = Mat::zeros(n, 1, DataType<int>::type);
_sIds = Mat::zeros(n, 1, DataType<int>::type);
for (i = 0; i < n; i++) _sDone.at<int>(0, i) = -1;
_sId = 0;
}
void EdgeBoxesImpl::scoreBox(Box &box)
{
int i, j, k, q, bh, bw, y0, x0, y1, x1, y0m, y1m, x0m, x1m;
float *sWts = (float *)_sWts.data;
int *sDone = (int *)_sDone.data;
int *sMap = (int *)_sMap.data;
int *sIds = (int *)_sIds.data;
int sId = _sId++;
// add edge count inside box
y1 = clamp(box.y + box.h, 0, h - 1);
y0 = box.y = clamp(box.y, 0, h - 1);
x1 = clamp(box.x + box.w, 0, w - 1);
x0 = box.x = clamp(box.x, 0, w - 1);
bh = box.h = y1 - box.y;
bh /= 2;
bw = box.w = x1 - box.x;
bw /= 2;
float v = _segIImg.at<float>(x0, y0) + _segIImg.at<float>(x1 + 1, y1 + 1)
- _segIImg.at<float>(x1 + 1, y0) - _segIImg.at<float>(x0, y1 + 1);
// subtract middle quarter of edges
y0m = y0 + bh / 2;
y1m = y0m + bh;
x0m = x0 + bw / 2;
x1m = x0m + bw;
v -= _magIImg.at<float>(x0m, y0m) + _magIImg.at<float>(x1m + 1, y1m + 1)
- _magIImg.at<float>(x1m + 1, y0m) - _magIImg.at<float>(x0m, y1m + 1);
// short circuit computation if impossible to score highly
float norm = _scaleNorm[bw + bh];
box.score = v * norm;
if (box.score < _minScore)
{
box.score = 0;
return;
}
// find interesecting segments along four boundaries
int cs, ce, rs, re, n = 0;
cs = _hIdxImg.at<int>(x0, y0);
ce = _hIdxImg.at<int>(x1, y0); // top
for (i = cs; i <= ce; i++)
{
j = _hIdxs[y0][i];
if (j > 0 && sDone[j] != sId)
{
sIds[n] = j;
sWts[n] = 1;
sDone[j] = sId;
sMap[j] = n++;
}
}
cs = _hIdxImg.at<int>(x0, y1);
ce = _hIdxImg.at<int>(x1, y1); // bottom
for (i = cs; i <= ce; i++)
{
j = _hIdxs[y1][i];
if (j > 0 && sDone[j] != sId)
{
sIds[n] = j;
sWts[n] = 1;
sDone[j] = sId;
sMap[j] = n++;
}
}
rs = _vIdxImg.at<int>(x0, y0);
re = _vIdxImg.at<int>(x0, y1); // left
for (i = rs; i <= re; i++)
{
j = _vIdxs[x0][i];
if (j > 0 && sDone[j] != sId)
{
sIds[n] = j;
sWts[n] = 1;
sDone[j] = sId;
sMap[j] = n++;
}
}
rs = _vIdxImg.at<int>(x1, y0);
re = _vIdxImg.at<int>(x1, y1); // right
for (i = rs; i <= re; i++)
{
j = _vIdxs[x1][i];
if (j > 0 && sDone[j] != sId)
{
sIds[n] = j;
sWts[n] = 1;
sDone[j] = sId;
sMap[j] = n++;
}
}
// follow connected paths and set weights accordingly (ws=1 means remove)
for (i = 0; i < n; i++)
{
float ws = sWts[i];
j = sIds[i];
for (k = 0; k < (int)_segAffIdx[j].size(); k++)
{
q = _segAffIdx[j][k];
float wq = ws * _segAff[j][k];
if (wq < .05f) continue; // short circuit for efficiency
if (sDone[q] == sId)
{
if (wq > sWts[sMap[q]])
{
sWts[sMap[q]] = wq;
i = min(i, sMap[q] - 1);
}
}
else if (_segP[q].x >= x0 && _segP[q].x <= x1 && _segP[q].y >= y0 && _segP[q].y <= y1)
{
sIds[n] = q;
sWts[n] = wq;
sDone[q] = sId;
sMap[q] = n++;
}
}
}
// finally remove segments connected to boundaries
for (i = 0; i < n; i++)
{
k = sIds[i];
if (_segP[k].x >= x0 && _segP[k].x <= x1 && _segP[k].y >= y0 && _segP[k].y <= y1) v -= sWts[i] * _segMag[k];
}
v *= norm;
if (v < _minScore) v = 0;
box.score = v;
}
void EdgeBoxesImpl::refineBox(Box &box)
{
int yStep = (int)(box.h * _xyStepRatio);
int xStep = (int)(box.w * _xyStepRatio);
while (1)
{
// prepare for iteration
yStep /= 2;
xStep /= 2;
if (yStep <= 2 && xStep <= 2) break;
yStep = max(1, yStep);
xStep = max(1, xStep);
Box B;
// search over y start
B = box;
B.y = box.y - yStep;
B.h = B.h + yStep;
scoreBox(B);
if (B.score <= box.score)
{
B = box;
B.y = box.y + yStep;
B.h = B.h - yStep;
scoreBox(B);
}
if (B.score > box.score) box = B;
// search over y end
B = box;
B.h = B.h + yStep;
scoreBox(B);
if (B.score <= box.score)
{
B = box;
B.h = B.h - yStep;
scoreBox(B);
}
if (B.score > box.score) box = B;
// search over x start
B = box;
B.x = box.x - xStep;
B.w = B.w + xStep;
scoreBox(B);
if (B.score <= box.score)
{
B = box;
B.x = box.x + xStep;
B.w = B.w - xStep;
scoreBox(B);
}
if (B.score > box.score) box = B;
// search over x end
B = box;
B.w = B.w + xStep;
scoreBox(B);
if (B.score <= box.score)
{
B = box;
B.w = B.w - xStep;
scoreBox(B);
}
if (B.score > box.score) box = B;
}
}
void EdgeBoxesImpl::scoreAllBoxes(Boxes &boxes)
{
// get list of all boxes roughly distributed in grid
boxes.resize(0);
int ayRad, sxNum;
float minSize = sqrt(_minBoxArea);
ayRad = (int)(log(_maxAspectRatio) / log(_ayStep * _ayStep));
sxNum = (int)(ceil(log(max(w, h) / minSize) / log(_sxStep)));
for (int s = 0; s < sxNum; s++)
{
int a, y, x, bh, bw, ky, kx = -1;
float ay, sx;
for (a = 0; a < 2 * ayRad + 1; a++)
{
ay = pow(_ayStep, float(a - ayRad));
sx = minSize * pow(_sxStep, float(s));
bh = (int)(sx / ay);
ky = max(2, (int)(bh * _xyStepRatio));
bw = (int)(sx * ay);
kx = max(2, (int)(bw * _xyStepRatio));
for (x = 0; x < w - bw + kx; x += kx)
{
for (y = 0; y < h - bh + ky; y += ky)
{
Box b;
b.y = y;
b.x = x;
b.h = bh;
b.w = bw;
boxes.push_back(b);
}
}
}
}
// score all boxes, refine top candidates
int i, k = 0, m = (int)boxes.size();
for (i = 0; i < m; i++)
{
scoreBox(boxes[i]);
if (!boxes[i].score) continue;
k++;
refineBox(boxes[i]);
}
sort(boxes.rbegin(), boxes.rend(), boxesCompare);
boxes.resize(k);
}
float EdgeBoxesImpl::boxesOverlap(Box &a, Box &b)
{
float areai, areaj, areaij;
int y0, y1, x0, x1, y1i, x1i, y1j, x1j;
y1i = a.y + a.h;
x1i = a.x + a.w;
if (a.y >= y1i || a.x >= x1i) return 0;
y1j = b.y + b.h;
x1j = b.x + b.w;
if (a.y >= y1j || a.x >= x1j) return 0;
areai = (float) a.w * a.h;
y0 = max(a.y, b.y);
y1 = min(y1i, y1j);
areaj = (float) b.w * b.h;
x0 = max(a.x, b.x);
x1 = min(x1i, x1j);
areaij = (float) max(0, y1 - y0) * max(0, x1 - x0);
return areaij / (areai + areaj - areaij);
}
void EdgeBoxesImpl::boxesNms(Boxes &boxes, float thr, float eta, int maxBoxes)
{
sort(boxes.rbegin(), boxes.rend(), boxesCompare);
if (thr > .99f) return;
const int nBin = 10000;
const float step = 1 / thr;
const float lstep = log(step);
vector<Boxes> kept;
kept.resize(nBin + 1);
int n = (int) boxes.size();
int i = 0;
int j, k, b;
int m = 0;
int d = 1;
while (i < n && m < maxBoxes)
{
b = boxes[i].w * boxes[i].h;
bool keep = 1;
b = clamp((int)(ceil(log(float(b)) / lstep)), d, nBin - d);
for (j = b - d; j <= b + d; j++)
{
for (k = 0; k < (int)kept[j].size(); k++)
{
if (keep)
keep = boxesOverlap(boxes[i], kept[j][k]) <= thr;
}
}
if (keep)
{
kept[b].push_back(boxes[i]);
m++;
}
i++;
if (keep && eta < 1.0f && thr > .5f)
{
thr *= eta;
d = (int)ceil(log(1.0f / thr) / lstep);
}
}
boxes.resize(m);
i = 0;
for (j = 0; j < nBin; j++)
{
for (k = 0; k < (int)kept[j].size(); k++)
{
boxes[i++] = kept[j][k];
}
}
sort(boxes.rbegin(), boxes.rend(), boxesCompare);
}
void EdgeBoxesImpl::getBoundingBoxes(InputArray edge_map, InputArray orientation_map, std::vector<Rect> &boxes)
{
CV_Assert(edge_map.depth() == CV_32F);
CV_Assert(orientation_map.depth() == CV_32F);
Mat E = edge_map.getMat().t();
Mat O = orientation_map.getMat().t();
h = E.cols;
w = E.rows;
clusterEdges(E, O);
prepDataStructs(E);
Boxes b;
scoreAllBoxes(b);
boxesNms(b, _beta, _eta, _maxBoxes);
// create output boxes
int n = (int) b.size();
boxes.resize(n);
for(int i=0; i < n; i++)
{
boxes[i] = Rect((int)b[i].x + 1, (int)b[i].y + 1, (int)b[i].w, (int)b[i].h);
}
}
Ptr<EdgeBoxes> createEdgeBoxes(float alpha,
float beta,
float eta,
float minScore,
int maxBoxes,
float edgeMinMag,
float edgeMergeThr,
float clusterMinMag,
float maxAspectRatio,
float minBoxArea,
float gamma,
float kappa)
{
return makePtr<EdgeBoxesImpl>(alpha,
beta,
eta,
minScore,
maxBoxes,
edgeMinMag,
edgeMergeThr,
clusterMinMag,
maxAspectRatio,
minBoxArea,
gamma,
kappa);
}
}
}
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