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Added Custom Calibration Pattern Module.

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Daniel Angelov 10 years ago
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commit e6f8c43995
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  1. 2
      modules/ccalib/CMakeLists.txt
  2. 202
      modules/ccalib/doc/customPattern.rst
  3. 149
      modules/ccalib/include/opencv2/ccalib.hpp
  4. 489
      modules/ccalib/src/ccalib.cpp

2
modules/ccalib/CMakeLists.txt
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set(the_description "Custom Calibration Pattern")
ocv_define_module(ccalib opencv_core opencv_imgproc opencv_calib3d opencv_features2d)

202
modules/ccalib/doc/customPattern.rst
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Custom Calibration Pattern
==========================
.. highlight:: cpp
CustomPattern
-------------
A custom pattern class that can be used to calibrate a camera and to further track the translation and rotation of the pattern.
.. ocv:class:: CustomPattern : public Algorithm
CustomPattern::CustomPattern
----------------------------
CustomPattern constructor.
.. ocv:function:: CustomPattern()
CustomPattern::create
---------------------
A method that initializes the class and generates the necessary detectors, extractors and matchers.
.. ocv:function:: bool create(InputArray pattern, const Size2f boardSize, OutputArray output = noArray())
:param pattern: The image, which will be used as a pattern. If the desired pattern is part of a bigger image, you can crop it out using image(roi).
:param boardSize: The size of the pattern in physical dimensions. These will be used to scale the points when the calibration occurs.
:param output: A matrix that is the same as the input pattern image, but has all the feature points drawn on it.
:return Returns whether the initialization was successful or not. Possible reason for failure may be that no feature points were detected.
.. seealso::
:ocv:func:`getFeatureDetector`,
:ocv:func:`getDescriptorExtractor`,
:ocv:func:`getDescriptorMatcher`
.. note::
* Determine the number of detected feature points can be done through :ocv:func:`getPatternPoints` method.
* The feature detector, extractor and matcher cannot be changed after initialization.
CustomPattern::findPattern
--------------------------
Finds the pattern in the input image
.. ocv:function:: bool findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points, const double ratio = 0.7, const double proj_error = 8.0, const bool refine_position = false, OutputArray out = noArray(), OutputArray H = noArray(), OutputArray pattern_corners = noArray());
:param image: The input image where the pattern is searched for.
:param matched_features: A ``vector<Point2f>`` of the projections of calibration pattern points, matched in the image. The points correspond to the ``pattern_points``.``matched_features`` and ``pattern_points`` have the same size.
:param pattern_points: A ``vector<Point3f>`` of calibration pattern points in the calibration pattern coordinate space.
:param ratio: A ratio used to threshold matches based on D. Lowe's point ratio test.
:param proj_error: The maximum projection error that is allowed when the found points are back projected. A lower projection error will be beneficial for eliminating mismatches. Higher values are recommended when the camera lens has greater distortions.
:param refine_position: Whether to refine the position of the feature points with :ocv:func:`cornerSubPix`.
:param out: An image showing the matched feature points and a contour around the estimated pattern.
:param H: The homography transformation matrix between the pattern and the current image.
:param pattern_corners: A ``vector<Point2f>`` containing the 4 corners of the found pattern.
:return The method return whether the pattern was found or not.
CustomPattern::isInitialized
----------------------------
.. ocv:function:: bool isInitialized()
:return If the class is initialized or not.
CustomPattern::getPatternPoints
-------------------------------
.. ocv:function:: void getPatternPoints(OutputArray original_points)
:param original_points: Fills the vector with the points found in the pattern.
CustomPattern::getPixelSize
---------------------------
.. ocv:function:: double getPixelSize()
:return Get the physical pixel size as initialized by the pattern.
CustomPattern::setFeatureDetector
---------------------------------
.. ocv:function:: bool setFeatureDetector(Ptr<FeatureDetector> featureDetector)
:param featureDetector: Set a new FeatureDetector.
:return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`.
.. note::
* It is left to user discretion to select matching feature detector, extractor and matchers. Please consult the documentation for each to confirm coherence.
CustomPattern::setDescriptorExtractor
-------------------------------------
.. ocv:function:: bool setDescriptorExtractor(Ptr<DescriptorExtractor> extractor)
:param extractor: Set a new DescriptorExtractor.
:return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`.
CustomPattern::setDescriptorMatcher
-----------------------------------
.. ocv:function:: bool setDescriptorMatcher(Ptr<DescriptorMatcher> matcher)
:param matcher: Set a new DescriptorMatcher.
:return Is it successfully set? Will fail if the object is already initialized by :ocv:func:`create`.
CustomPattern::getFeatureDetector
---------------------------------
.. ocv:function:: Ptr<FeatureDetector> getFeatureDetector()
:return The used FeatureDetector.
CustomPattern::getDescriptorExtractor
-------------------------------------
.. ocv:function:: Ptr<DescriptorExtractor> getDescriptorExtractor()
:return The used DescriptorExtractor.
CustomPattern::getDescriptorMatcher
-----------------------------------
.. ocv:function:: Ptr<DescriptorMatcher> getDescriptorMatcher()
:return The used DescriptorMatcher.
CustomPattern::calibrate
------------------------
Calibrates the camera.
.. ocv:function:: double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON))
See :ocv:func:`calibrateCamera` for parameter information.
CustomPattern::findRt
---------------------
Finds the rotation and translation vectors of the pattern.
.. ocv:function:: bool findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE)
.. ocv:function:: bool findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE)
:param image: The image, in which the rotation and translation of the pattern will be found.
See :ocv:func:`solvePnP` for parameter information.
CustomPattern::findRtRANSAC
---------------------------
Finds the rotation and translation vectors of the pattern using RANSAC.
.. ocv:function:: bool findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE)
.. ocv:function:: bool findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE)
:param image: The image, in which the rotation and translation of the pattern will be found.
See :ocv:func:`solvePnPRANSAC` for parameter information.
CustomPattern::drawOrientation
------------------------------
Draws the ``(x,y,z)`` axis on the image, in the center of the pattern, showing the orientation of the pattern.
.. ocv:function:: void drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec, InputArray cameraMatrix, InputArray distCoeffs, double axis_length = 3, double axis_width = 2)
:param image: The image, based on which the rotation and translation was calculated. The axis will be drawn in color - ``x`` - in red, ``y`` - in green, ``z`` - in blue.
:param tvec: Translation vector.
:param rvec: Rotation vector.
:param cameraMatrix: The camera matrix.
:param distCoeffs: The distortion coefficients.
:param axis_length: The length of the axis symbol.
:param axis_width: The width of the axis symbol.

149
modules/ccalib/include/opencv2/ccalib.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) 2014, 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:
//
// * 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_CCALIB_HPP__
#define __OPENCV_CCALIB_HPP__
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <vector>
namespace cv{
class CV_EXPORTS CustomPattern : public Algorithm
{
public:
CustomPattern();
virtual ~CustomPattern();
bool create(InputArray pattern, const Size2f boardSize, OutputArray output = noArray());
bool findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points, const double ratio = 0.7,
const double proj_error = 8.0, const bool refine_position = false, OutputArray out = noArray(),
OutputArray H = noArray(), OutputArray pattern_corners = noArray());
bool isInitialized();
void getPatternPoints(OutputArray original_points);
/*
Returns a vector<Point> of the original points.
*/
double getPixelSize();
/*
Get the pixel size of the pattern
*/
bool setFeatureDetector(Ptr<FeatureDetector> featureDetector);
bool setDescriptorExtractor(Ptr<DescriptorExtractor> extractor);
bool setDescriptorMatcher(Ptr<DescriptorMatcher> matcher);
Ptr<FeatureDetector> getFeatureDetector();
Ptr<DescriptorExtractor> getDescriptorExtractor();
Ptr<DescriptorMatcher> getDescriptorMatcher();
double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
/*
Calls the calirateCamera function with the same inputs.
*/
bool findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE);
bool findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = ITERATIVE);
/*
Uses solvePnP to find the rotation and translation of the pattern
with respect to the camera frame.
*/
bool findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100,
float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE);
bool findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100,
float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = ITERATIVE);
/*
Uses solvePnPRansac()
*/
void drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec, InputArray cameraMatrix,
InputArray distCoeffs, double axis_length = 3, double axis_width = 2);
/*
pattern_corners -> projected over the image position of the edges of the pattern.
*/
private:
Mat img_roi;
std::vector<Point2f> obj_corners;
double pxSize;
bool initialized;
Ptr<FeatureDetector> detector;
Ptr<DescriptorExtractor> descriptorExtractor;
Ptr<DescriptorMatcher> descriptorMatcher;
std::vector<KeyPoint> keypoints;
std::vector<Point3f> points3d;
Mat descriptor;
bool init(Mat& image, const float pixel_size, OutputArray output = noArray());
bool findPatternPass(const Mat& image, std::vector<Point2f>& matched_features, std::vector<Point3f>& pattern_points,
Mat& H, std::vector<Point2f>& scene_corners, const double pratio, const double proj_error,
const bool refine_position = false, const Mat& mask = Mat(), OutputArray output = noArray());
void scaleFoundPoints(const double squareSize, const std::vector<KeyPoint>& corners, std::vector<Point3f>& points3d);
void check_matches(std::vector<Point2f>& matched, const std::vector<Point2f>& pattern, std::vector<DMatch>& good, std::vector<Point3f>& pattern_3d, const Mat& H);
void keypoints2points(const std::vector<KeyPoint>& in, std::vector<Point2f>& out);
void updateKeypointsPos(std::vector<KeyPoint>& in, const std::vector<Point2f>& new_pos);
void refinePointsPos(const Mat& img, std::vector<Point2f>& p);
void refineKeypointsPos(const Mat& img, std::vector<KeyPoint>& kp);
};
} // cv
#endif

489
modules/ccalib/src/ccalib.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) 2014, 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:
//
// * 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_CCALIB_CPP__
#define __OPENCV_CCALIB_CPP__
#include "ccalib.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/core/types_c.h> // CV_TERM
#include <opencv2/core/core_c.h> // CV_RGB
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <vector>
#include <cstring>
using namespace std;
#define MIN_CONTOUR_AREA_PX 100
#define MIN_CONTOUR_AREA_RATIO 0.2
#define MAX_CONTOUR_AREA_RATIO 5
#define MIN_POINTS_FOR_H 10
#define MAX_PROJ_ERROR_PX 5.0
namespace cv{
CustomPattern::CustomPattern()
{
initialized = false;
}
bool CustomPattern::create(InputArray pattern, const Size2f boardSize, OutputArray output)
{
CV_Assert(!pattern.empty() && (boardSize.area() > 0));
Mat img = pattern.getMat();
float pixel_size = (boardSize.width > boardSize.height)? // Choose the longer side for more accurate calculation
float(img.cols) / boardSize.width: // width is longer
float(img.rows) / boardSize.height; // height is longer
return init(img, pixel_size, output);
}
bool CustomPattern::init(Mat& image, const float pixel_size, OutputArray output)
{
image.copyTo(img_roi);
//Setup object corners
obj_corners = std::vector<Point2f>(4);
obj_corners[0] = Point2f(0, 0); obj_corners[1] = Point2f(img_roi.cols, 0);
obj_corners[2] = Point2f(img_roi.cols, img_roi.rows); obj_corners[3] = Point2f(0, img_roi.rows);
if (!detector) // if no detector chosen, use default
{
detector = FeatureDetector::create("ORB");
detector->set("nFeatures", 2000);
detector->set("scaleFactor", 1.15);
detector->set("nLevels", 30);
}
detector->detect(img_roi, keypoints);
if (keypoints.empty())
{
initialized = false;
return initialized;
}
refineKeypointsPos(img_roi, keypoints);
if (!descriptorExtractor) // if no extractor chosen, use default
descriptorExtractor = DescriptorExtractor::create("ORB");
descriptorExtractor->compute(img_roi, keypoints, descriptor);
if (!descriptorMatcher)
descriptorMatcher = DescriptorMatcher::create("BruteForce-Hamming(2)");
// Scale found points by pixelSize
pxSize = pixel_size;
scaleFoundPoints(pxSize, keypoints, points3d);
if (output.needed())
{
Mat out;
drawKeypoints(img_roi, keypoints, out, CV_RGB(255, 0, 0));
out.copyTo(output);
}
initialized = !keypoints.empty();
return initialized; // initialized if any keypoints are found
}
CustomPattern::~CustomPattern() {}
bool CustomPattern::isInitialized()
{
return initialized;
}
bool CustomPattern::setFeatureDetector(Ptr<FeatureDetector> featureDetector)
{
if (!initialized)
{
this->detector = featureDetector;
return true;
}
else
return false;
}
bool CustomPattern::setDescriptorExtractor(Ptr<DescriptorExtractor> extractor)
{
if (!initialized)
{
this->descriptorExtractor = extractor;
return true;
}
else
return false;
}
bool CustomPattern::setDescriptorMatcher(Ptr<DescriptorMatcher> matcher)
{
if (!initialized)
{
this->descriptorMatcher = matcher;
return true;
}
else
return false;
}
Ptr<FeatureDetector> CustomPattern::getFeatureDetector()
{
return detector;
}
Ptr<DescriptorExtractor> CustomPattern::getDescriptorExtractor()
{
return descriptorExtractor;
}
Ptr<DescriptorMatcher> CustomPattern::getDescriptorMatcher()
{
return descriptorMatcher;
}
void CustomPattern::scaleFoundPoints(const double pixelSize,
const vector<KeyPoint>& corners, vector<Point3f>& points3d)
{
for (unsigned int i = 0; i < corners.size(); ++i)
{
points3d.push_back(Point3f(
corners[i].pt.x * pixelSize,
corners[i].pt.y * pixelSize,
0));
}
}
//Takes a descriptor and turns it into an (x,y) point
void CustomPattern::keypoints2points(const vector<KeyPoint>& in, vector<Point2f>& out)
{
out.clear();
out.reserve(in.size());
for (size_t i = 0; i < in.size(); ++i)
{
out.push_back(in[i].pt);
}
}
void CustomPattern::updateKeypointsPos(vector<KeyPoint>& in, const vector<Point2f>& new_pos)
{
for (size_t i = 0; i < in.size(); ++i)
{
in[i].pt= new_pos[i];
}
}
void CustomPattern::refinePointsPos(const Mat& img, vector<Point2f>& p)
{
Mat gray;
cvtColor(img, gray, COLOR_RGB2GRAY);
cornerSubPix(gray, p, Size(10, 10), Size(-1, -1),
TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 30, 0.1));
}
void CustomPattern::refineKeypointsPos(const Mat& img, vector<KeyPoint>& kp)
{
vector<Point2f> points;
keypoints2points(kp, points);
refinePointsPos(img, points);
updateKeypointsPos(kp, points);
}
template<typename Tstve>
void deleteStdVecElem(vector<Tstve>& v, int idx)
{
v[idx] = v.back();
v.pop_back();
}
void CustomPattern::check_matches(vector<Point2f>& matched, const vector<Point2f>& pattern, vector<DMatch>& good,
vector<Point3f>& pattern_3d, const Mat& H)
{
vector<Point2f> proj;
perspectiveTransform(pattern, proj, H);
int deleted = 0;
double error_sum = 0;
double error_sum_filtered = 0;
for (uint i = 0; i < proj.size(); ++i)
{
double error = norm(matched[i] - proj[i]);
error_sum += error;
if (error >= MAX_PROJ_ERROR_PX)
{
deleteStdVecElem(good, i);
deleteStdVecElem(matched, i);
deleteStdVecElem(pattern_3d, i);
++deleted;
}
else
{
error_sum_filtered += error;
}
}
}
bool CustomPattern::findPatternPass(const Mat& image, vector<Point2f>& matched_features, vector<Point3f>& pattern_points,
Mat& H, vector<Point2f>& scene_corners, const double pratio, const double proj_error,
const bool refine_position, const Mat& mask, OutputArray output)
{
if (!initialized) {return false; }
matched_features.clear();
pattern_points.clear();
vector<vector<DMatch> > matches;
vector<KeyPoint> f_keypoints;
Mat f_descriptor;
detector->detect(image, f_keypoints, mask);
if (refine_position) refineKeypointsPos(image, f_keypoints);
descriptorExtractor->compute(image, f_keypoints, f_descriptor);
descriptorMatcher->knnMatch(f_descriptor, descriptor, matches, 2); // k = 2;
vector<DMatch> good_matches;
vector<Point2f> obj_points;
for(int i = 0; i < f_descriptor.rows; ++i)
{
if(matches[i][0].distance < pratio * matches[i][1].distance)
{
const DMatch& dm = matches[i][0];
good_matches.push_back(dm);
// "keypoints1[matches[i].queryIdx] has a corresponding point in keypoints2[matches[i].trainIdx]"
matched_features.push_back(f_keypoints[dm.queryIdx].pt);
pattern_points.push_back(points3d[dm.trainIdx]);
obj_points.push_back(keypoints[dm.trainIdx].pt);
}
}
if (good_matches.size() < MIN_POINTS_FOR_H) return false;
Mat h_mask;
H = findHomography(obj_points, matched_features, RANSAC, proj_error, h_mask);
if (H.empty())
{
// cout << "findHomography() returned empty Mat." << endl;
return false;
}
for(unsigned int i = 0; i < good_matches.size(); ++i)
{
if(!h_mask.data[i])
{
deleteStdVecElem(good_matches, i);
deleteStdVecElem(matched_features, i);
deleteStdVecElem(pattern_points, i);
}
}
if (good_matches.empty()) return false;
uint numb_elem = good_matches.size();
check_matches(matched_features, obj_points, good_matches, pattern_points, H);
if (good_matches.empty() || numb_elem < good_matches.size()) return false;
// Get the corners from the image
scene_corners = vector<Point2f>(4);
perspectiveTransform(obj_corners, scene_corners, H);
// Check correctnes of H
// Is it a convex hull?
bool cConvex = isContourConvex(scene_corners);
if (!cConvex) return false;
// Is the hull too large or small?
double scene_area = contourArea(scene_corners);
if (scene_area < MIN_CONTOUR_AREA_PX) return false;
double ratio = scene_area/img_roi.size().area();
if ((ratio < MIN_CONTOUR_AREA_RATIO) ||
(ratio > MAX_CONTOUR_AREA_RATIO)) return false;
// Is any of the projected points outside the hull?
for(unsigned int i = 0; i < good_matches.size(); ++i)
{
if(pointPolygonTest(scene_corners, f_keypoints[good_matches[i].queryIdx].pt, false) < 0)
{
deleteStdVecElem(good_matches, i);
deleteStdVecElem(matched_features, i);
deleteStdVecElem(pattern_points, i);
}
}
if (output.needed())
{
Mat out;
drawMatches(image, f_keypoints, img_roi, keypoints, good_matches, out);
// Draw lines between the corners (the mapped object in the scene - image_2 )
line(out, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2);
line(out, scene_corners[1], scene_corners[2], Scalar(0, 255, 0), 2);
line(out, scene_corners[2], scene_corners[3], Scalar(0, 255, 0), 2);
line(out, scene_corners[3], scene_corners[0], Scalar(0, 255, 0), 2);
out.copyTo(output);
}
return (!good_matches.empty()); // return true if there are enough good matches
}
bool CustomPattern::findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points,
const double ratio, const double proj_error, const bool refine_position, OutputArray out,
OutputArray H, OutputArray pattern_corners)
{
CV_Assert(!image.empty() && proj_error > 0);
Mat img = image.getMat();
vector<Point2f> m_ftrs;
vector<Point3f> pattern_pts;
Mat _H;
vector<Point2f> scene_corners;
if (!findPatternPass(img, m_ftrs, pattern_pts, _H, scene_corners, 0.6, proj_error, refine_position))
return false; // pattern not found
Mat mask = Mat::zeros(img.size(), CV_8UC1);
vector<vector<Point> > obj(1);
vector<Point> scorners_int(scene_corners.size());
for (uint i = 0; i < scene_corners.size(); ++i)
scorners_int[i] = (Point)scene_corners[i]; // for drawContours
obj[0] = scorners_int;
drawContours(mask, obj, 0, Scalar(255), FILLED);
// Second pass
Mat output;
if (!findPatternPass(img, m_ftrs, pattern_pts, _H, scene_corners,
ratio, proj_error, refine_position, mask, output))
return false; // pattern not found
Mat(m_ftrs).copyTo(matched_features);
Mat(pattern_pts).copyTo(pattern_points);
if (out.needed()) output.copyTo(out);
if (H.needed()) _H.copyTo(H);
if (pattern_corners.needed()) Mat(scene_corners).copyTo(pattern_corners);
return (!m_ftrs.empty());
}
void CustomPattern::getPatternPoints(OutputArray original_points)
{
return Mat(keypoints).copyTo(original_points);
}
double CustomPattern::getPixelSize()
{
return pxSize;
}
double CustomPattern::calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags,
TermCriteria criteria)
{
return calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs,
rvecs, tvecs, flags, criteria);
}
bool CustomPattern::findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix,
InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess, int flags)
{
return solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess, flags);
}
bool CustomPattern::findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess, int flags)
{
vector<Point2f> imagePoints;
vector<Point3f> objectPoints;
if (!findPattern(image, imagePoints, objectPoints))
return false;
return solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess, flags);
}
bool CustomPattern::findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess, int iterationsCount,
float reprojectionError, int minInliersCount, OutputArray inliers, int flags)
{
solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess,
iterationsCount, reprojectionError, minInliersCount, inliers, flags);
return true; // for consistency with the other methods
}
bool CustomPattern::findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess, int iterationsCount,
float reprojectionError, int minInliersCount, OutputArray inliers, int flags)
{
vector<Point2f> imagePoints;
vector<Point3f> objectPoints;
if (!findPattern(image, imagePoints, objectPoints))
return false;
solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess,
iterationsCount, reprojectionError, minInliersCount, inliers, flags);
return true;
}
void CustomPattern::drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec,
InputArray cameraMatrix, InputArray distCoeffs,
double axis_length, double axis_width)
{
Point3f ptrCtr3d = Point3f((img_roi.cols * pxSize)/2, (img_roi.rows * pxSize)/2, 0);
vector<Point3f> axis(4);
axis[0] = ptrCtr3d;
axis[1] = Point3f(axis_length * pxSize, 0, 0) + ptrCtr3d;
axis[2] = Point3f(0, axis_length * pxSize, 0) + ptrCtr3d;
axis[3] = Point3f(0, 0, -axis_length * pxSize) + ptrCtr3d;
vector<Point2f> proj_axis;
projectPoints(axis, rvec, tvec, cameraMatrix, distCoeffs, proj_axis);
Mat img = image.getMat();
line(img, proj_axis[0], proj_axis[1], CV_RGB(255, 0, 0), axis_width);
line(img, proj_axis[0], proj_axis[2], CV_RGB(0, 255, 0), axis_width);
line(img, proj_axis[0], proj_axis[3], CV_RGB(0, 0, 255), axis_width);
img.copyTo(image);
}
} // namespace cv
#endif
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