Detecting colorcheckers using basic algorithms

In this tutorial you will learn how to use the 'mcc' module to detect colorcharts in a image. Here we will only use the basic detection algorithm. In the next tutorial you will see how you can improve detection accuracy using a neural network.

Building

When building OpenCV, run the following command to build all the contrib module:

cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules/

Or only build the mcc module:

cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules/mcc

Or make sure you check the mcc module in the GUI version of CMake: cmake-gui.

Source Code of the sample

run
<path_of_your_opencv_build_directory>/bin/example_mcc_chart_detection -t=<type_of_chart> -v=<optional_path_to_video_if_not_provided_webcam_will_be_used.mp4> --ci=<optional_camera_id_needed_only_if_video_not_provided> --nc=<optional_maximum_number_of_charts_to_look_for>

• -t=# is the chart type where 0 (Standard), 1 (DigitalSG), 2 (Vinyl)
• –ci=# is the camera ID where 0 (default is the main camera), 1 (secondary camera) etc
• –nc=# By default its values is 1 which means only the best chart will be detected

Examples:

Run a movie on a standard macbeth chart:
/home/opencv/build/bin/example_mcc_chart_detection -t=0 -v=mcc24.mp4

Or run on a vinyl macbeth chart from camera 0:
/home/opencv/build/bin/example_mcc_chart_detection -t=2 --ci=0

Or run on a vinyl macbeth chart, detecting the best 5 charts(Detections can be less than 5 but never more):
/home/opencv/build/bin/example_mcc_chart_detection -t=2 --ci=0 --nc=5

#include <opencv2/core.hpp>

#include <opencv2/highgui.hpp>
#include <opencv2/mcc.hpp>
#include <iostream>

using namespace std;
using namespace cv;
using namespace mcc;

const char *about = "Basic chart detection";
const char *keys = {
 "{ help h usage ? | | show this message }"
 "{t | | chartType: 0-Standard, 1-DigitalSG, 2-Vinyl }"
 "{v | | Input from video file, if ommited, input comes from camera }"
 "{ci | 0 | Camera id if input doesnt come from video (-v) }"
 "{nc | 1 | Maximum number of charts in the image }"};

int main(int argc, char *argv[])
{

 // ----------------------------------------------------------
 // Scroll down a bit (~40 lines) to find actual relevant code
 // ----------------------------------------------------------

 CommandLineParser parser(argc, argv, keys);
 parser.about(about);

 int t = parser.get<int>("t");

 CV_Assert(0 <= t && t <= 2);
 TYPECHART chartType = TYPECHART(t);

 int camId = parser.get<int>("ci");
 int nc = parser.get<int>("nc");
 String video;
 if (parser.has("v"))
 video = parser.get<String>("v");

 if (!parser.check())
 {
 parser.printErrors();
 return 0;
 }

 VideoCapture inputVideo;
 int waitTime;
 if (!video.empty())
 {
 inputVideo.open(video);
 waitTime = 10;
 }
 else
 {
 inputVideo.open(camId);
 waitTime = 10;
 }

 //--------------------------------------------------------------------------
 //-------------------------Actual Relevant Code-----------------------------
 //--------------------------------------------------------------------------

 while (inputVideo.grab())
 {

 Mat image, imageCopy;
 inputVideo.retrieve(image);
 imageCopy = image.clone();
 Ptr<CCheckerDetector> detector = CCheckerDetector::create();
 // Marker type to detect
 if (!detector->process(image, chartType, nc))
 {
 printf("ChartColor not detected \n");
 }
 else
 {

 // get checker
 std::vector<Ptr<mcc::CChecker>> checkers = detector->getListColorChecker();

 for (Ptr<mcc::CChecker> checker : checkers)
 {
 // current checker
 Ptr<CCheckerDraw> cdraw = CCheckerDraw::create(checker);
 cdraw->draw(image);
 }
 }
 imshow("image result | q or esc to quit", image);
 imshow("original", imageCopy);
 char key = (char)waitKey(waitTime);
 if (key == 27)
 break;
 }

 return 0;
}

Explanation

1. Set header and namespaces

   #include <opencv2/mcc.hpp>
   using namespace std;
   using namespace cv;
   using namespace mcc;

   If you want you can set the namespace like the code above.

2. Create the detector object

   Ptr<CCheckerDetector> detector = CCheckerDetector::create();

   This is just to create the object.

3. Run the detector

   detector->process(image, chartType);

   If the detector successfully detects atleast one chart, it return true otherwise it returns false. In the above given code we print a failure message if no chart were detected. Otherwise if it were successful, the list of colorcharts is stored inside the detector itself, we will see in the next step on how to extract it. By default it will detect atmost one chart, but you can tune the third parameter, nc(maximum number of charts), for detecting more charts.

4. Get List of ColorCheckers

   std::vector<cv::Ptr<mcc::CChecker>> checkers;
   detector->getListColorChecker(checkers);

   All the colorcheckers that were detected are now stored in the 'checkers' vector.

5. Draw the colorcheckers back to the image

   for(Ptr<mcc::CChecker> checker : checkers)
   {
    // current checker
    Ptr<CCheckerDraw> cdraw = CCheckerDraw::create(checker);
    cdraw->draw(image);
   }

   Loop through all the checkers one by one and then draw them.