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Adding macbeth chart detector to objdetect module from opencv_contrib (#26906)
* Added mcc to opencv modules * Removed color correction module * Updated parameters return type * Added python sample for macbeth_chart_detection * Added models.yml support to samples * Removed unnecessary headers and classes * fixed datatype conversion * fixed datatype conversion * Cleaned headers and added reference/actual colors to samples * Added mcc tutorial * fixed datatype and header * replaced unsigned with int * Aligned actual and reference color function, added imread * Fixed shadow variable * Updated samples * Added last frame colors prints * updated detector class * Added getter functions and useNet function * Refactoring * Fixes in test * fixed infinite divison issue
This commit is contained in:
Gursimar Singh
GitHub
@@ -47,6 +47,7 @@
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#include "opencv2/core.hpp"
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#include "opencv2/objdetect/aruco_detector.hpp"
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#include "opencv2/objdetect/graphical_code_detector.hpp"
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#include "opencv2/objdetect/mcc_checker_detector.hpp"
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/**
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@defgroup objdetect Object Detection
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@@ -0,0 +1,300 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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/*
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* MIT License
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*
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* Copyright (c) 2018 Pedro Diamel Marrero Fernández
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef OPENCV_OBJDETECT_MCC_CHECKER_DETECTOR_HPP
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#define OPENCV_OBJDETECT_MCC_CHECKER_DETECTOR_HPP
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#include <opencv2/core.hpp>
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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//---------------------------------------------------------------
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// #define MCC_DEBUG
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//---------------------------------------------------------------
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namespace cv
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{
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namespace mcc
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{
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//! @addtogroup mcc
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//! @{
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/** ColorChart
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*
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* @brief enum to hold the type of the checker
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*/
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enum ColorChart
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{
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MCC24 = 0, ///< Standard Macbeth Chart with 24 squares
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SG140, ///< DigitalSG with 140 squares
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VINYL18, ///< DKK color chart with 12 squares and 6 rectangle
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};
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/** CChecker
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*
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* @brief checker object
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*
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* This class contains the information about the detected checkers,i.e, their
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* type, the corners of the chart, the color profile, the cost, centers chart,
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* etc.
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*/
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class CV_EXPORTS_W CChecker: public Algorithm
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{
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public:
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CChecker() {}
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virtual ~CChecker() {}
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/** @brief Create a new CChecker object.
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*
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* @return A pointer to the implementation of the CChecker
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*/
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CV_WRAP static Ptr<CChecker> create();
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public:
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CV_WRAP virtual void setTarget(ColorChart _target) = 0;
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CV_WRAP virtual void setBox(std::vector<Point2f> _box) = 0;
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CV_WRAP virtual void setChartsRGB(Mat _chartsRGB) = 0;
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CV_WRAP virtual void setChartsYCbCr(Mat _chartsYCbCr) = 0;
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CV_WRAP virtual void setCost(float _cost) = 0;
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CV_WRAP virtual void setCenter(Point2f _center) = 0;
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CV_WRAP virtual ColorChart getTarget() = 0;
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CV_WRAP virtual std::vector<Point2f> getBox() = 0;
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/** @brief Computes and returns the coordinates of the central parts of the charts modules.
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*
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* This method computes transformation matrix from the checkers's coordinates (`CChecker::getBox()`)
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* and find by this the coordinates of the central parts of the charts modules.
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* It is used in `CCheckerDetector::draw()` and in `ChartsRGB` calculation.
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*/
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CV_WRAP virtual std::vector<Point2f> getColorCharts() = 0;
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CV_WRAP virtual Mat getChartsRGB(bool getStats = true) = 0;
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CV_WRAP virtual Mat getChartsYCbCr() = 0;
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CV_WRAP virtual float getCost() = 0;
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CV_WRAP virtual Point2f getCenter() = 0;
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};
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/** @brief struct DetectorParametersMCC is used by CCheckerDetector
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*/
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struct CV_EXPORTS_W_SIMPLE DetectorParametersMCC
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{
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CV_WRAP DetectorParametersMCC(){
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adaptiveThreshWinSizeMin=23;
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adaptiveThreshWinSizeMax=153;
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adaptiveThreshWinSizeStep=16;
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adaptiveThreshConstant=7;
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minContoursAreaRate=0.003;
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minContoursArea=100;
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confidenceThreshold=0.5;
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minContourSolidity=0.9;
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findCandidatesApproxPolyDPEpsMultiplier=0.05;
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borderWidth=0;
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B0factor=1.25f;
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maxError=0.1f;
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minContourPointsAllowed=4;
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minContourLengthAllowed=100;
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minInterContourDistance=100;
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minInterCheckerDistance=10000;
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minImageSize=1000;
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minGroupSize=4;
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}
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/// minimum window size for adaptive thresholding before finding contours (default 23).
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CV_PROP_RW int adaptiveThreshWinSizeMin;
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/// maximum window size for adaptive thresholding before finding contours (default 153).
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CV_PROP_RW int adaptiveThreshWinSizeMax;
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/// increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax during the thresholding (default 16).
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CV_PROP_RW int adaptiveThreshWinSizeStep;
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/// constant for adaptive thresholding before finding contours (default 7)
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CV_PROP_RW double adaptiveThreshConstant;
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/** @brief determine minimum area for marker contour to be detected
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*
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* This is defined as a rate respect to the area of the input image. Used only if neural network is used (default 0.03).
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*/
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CV_PROP_RW double minContoursAreaRate;
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/** @brief determine minimum area for marker contour to be detected
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*
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* This is defined as the actual area. Used only if neural network is used (default 100).
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*/
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CV_PROP_RW double minContoursArea;
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/// minimum confidence for a bounding box detected by neural network to classify as detection.(default 0.5) (0<=confidenceThreshold<=1)
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CV_PROP_RW double confidenceThreshold;
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/// minimum solidity of a contour for it be detected as a square in the chart. (default 0.9).
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CV_PROP_RW double minContourSolidity;
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/// multipler to be used in ApproxPolyDP function (default 0.05)
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CV_PROP_RW double findCandidatesApproxPolyDPEpsMultiplier;
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/// width of the padding used to pass the inital neural network detection in the succeeding system.(default 0)
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CV_PROP_RW int borderWidth;
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/// distance between two neighboring squares of the same chart as a ratio of the large dimension of a square (default 1.25).
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CV_PROP_RW float B0factor;
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/// maximum allowed error in the detection of a chart (default 0.1).
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CV_PROP_RW float maxError;
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/// minimum points in a detected contour (default 4).
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CV_PROP_RW int minContourPointsAllowed;
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/// minimum length of a contour (default 100).
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CV_PROP_RW int minContourLengthAllowed;
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/// minimum distance between two contours (default 100).
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CV_PROP_RW int minInterContourDistance;
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/// minimum distance between two checkers (default 10000).
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CV_PROP_RW int minInterCheckerDistance;
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/// minimum size of the smaller dimension of the image (default 1000).
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CV_PROP_RW int minImageSize;
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/// minimum number of squares in a chart that must be detected (default 4).
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CV_PROP_RW int minGroupSize;
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};
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/** @brief A class to find the positions of the ColorCharts in the image.
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*/
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class CV_EXPORTS_W CCheckerDetector : public Algorithm
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{
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public:
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/** @brief Find the ColorCharts in the given image.
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*
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* The found charts are not returned but instead stored in the
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* detector, these can be accessed later on using getBestColorChecker()
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* and getListColorChecker()
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* @param image image in color space BGR
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* @param regionsOfInterest regions of image to look for the chart, if
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* it is empty, charts are looked for in the
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* entire image
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* @param nc number of charts in the image, if you don't know the exact
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* then keeping this number high helps.
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* @return true if atleast one chart is detected otherwise false
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*/
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CV_WRAP_AS(processWithROI) virtual bool
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process(InputArray image, const std::vector<Rect> ®ionsOfInterest,
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const int nc = 1) = 0;
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/** @brief Find the ColorCharts in the given image.
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*
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* Differs from the above one only in the arguments.
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*
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* This version searches for the chart in the full image.
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*
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* The found charts are not returned but instead stored in the
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* detector, these can be accessed later on using getBestColorChecker()
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* and getListColorChecker()
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* @param image image in color space BGR
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* @param nc number of charts in the image, if you don't know the exact
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* then keeping this number high helps.
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* @return true if atleast one chart is detected otherwise false
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*/
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CV_WRAP virtual bool
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process(InputArray image, const int nc = 1) = 0;
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/** @brief Get the best color checker. By the best it means the one
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* detected with the highest confidence.
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* @return checker A single colorchecker, if atleast one colorchecker
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* was detected, 'nullptr' otherwise.
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*/
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CV_WRAP virtual Ptr<mcc::CChecker> getBestColorChecker() = 0;
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/** @brief Get the list of all detected colorcheckers
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* @return checkers vector of colorcheckers
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*/
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CV_WRAP virtual std::vector<Ptr<CChecker>> getListColorChecker() = 0;
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/** @brief Returns the implementation of the CCheckerDetector.
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*
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*/
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CV_WRAP static Ptr<CCheckerDetector> create();
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/** @brief Set the net which will be used to find the approximate
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* bounding boxes for the color charts. And returns the implementation of the CCheckerDetector.
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*
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* It is not necessary to use this, but this usually results in
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* better detection rate.
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*
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* @param net the neural network, if the network in empty, then
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* the function will return false.
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*/
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CV_WRAP static Ptr<CCheckerDetector> create(const dnn::Net &net);
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/** @brief Draws the checker to the given image.
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* @param img image in color space BGR
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* @param checkers The checkers which will be drawn by this object.
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* @param color The color by with which the squares of the checker
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* will be drawn
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* @param thickness The thickness with which the sqaures will be
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* drawn
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*/
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CV_WRAP virtual void draw(std::vector<Ptr<CChecker>>& checkers, InputOutputArray img, const Scalar color = CV_RGB(0,250,0), const int thickness = 2) = 0;
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/** @brief Gets the reference color for chart.
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*/
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CV_WRAP virtual Mat getRefColors() = 0;
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/** @brief Sets the detection paramaters for mcc.
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* @param params DetectorParametersMCC structure containing detection configuration parameters.
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*/
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CV_WRAP virtual void setDetectionParams(const DetectorParametersMCC ¶ms) = 0;
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/** @brief Sets the color chart type for MCC detection.
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* @param chartType ColorChart enum specifying the type of color chart to detect.
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*/
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CV_WRAP virtual void setColorChartType(ColorChart chartType) = 0;
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/** @brief Enables or disables the use of the neural network for detection.
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* @param useDnn Boolean flag to indicate whether to use neural network (true) or not (false).
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*/
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CV_WRAP virtual void setUseDnnModel(bool useDnn) = 0;
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CV_WRAP virtual bool getUseDnnModel() const = 0;
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CV_WRAP virtual const DetectorParametersMCC& getDetectionParams() const = 0;
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CV_WRAP virtual ColorChart getColorChartType() const = 0;
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};
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//! @} mcc
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} // namespace mcc
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} // namespace cv
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#endif
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