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Removed mcc dependency
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@gursimarsingh
gursimarsingh committed Mar 4, 2025
1 parent 8fdf3e3 commit 52da0aa
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3 changes: 0 additions & 3 deletions modules/ccm/CMakeLists.txt
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Expand Up @@ -4,9 +4,6 @@ ocv_define_module(ccm
opencv_imgproc
opencv_3d
opencv_dnn
opencv_mcc
WRAP
python
)
add_dependencies(opencv_ccm opencv_mcc)
include_directories(${OPENCV_EXTRA_MODULES_PATH}/modules/mcc/include)
202 changes: 103 additions & 99 deletions modules/ccm/samples/color_correction_model.cpp
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Expand Up @@ -3,19 +3,16 @@

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

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

const char *about = "Basic chart detection";
const char *keys =
"{ help h | | 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) }"
"{f | 1 | Path of the file to process (-v) }"
Expand All @@ -37,14 +34,9 @@ int main(int argc, char *argv[])
return 0;
}

int t = parser.get<int>("t");
int nc = parser.get<int>("nc");
string filepath = parser.get<string>("f");

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


if (!parser.check())
{
parser.printErrors();
Expand All @@ -59,99 +51,111 @@ int main(int argc, char *argv[])
}
//! [get_messages_of_image]

Mat imageCopy = image.clone();
Ptr<CCheckerDetector> detector = CCheckerDetector::create();
// Marker type to detect
if (!detector->process(image, chartType, nc))
{
printf("ChartColor not detected \n");
return 2;
Mat src(24, 1, CV_64FC3);

// Hardcoded values. Image used: opencv_extra/testdata/cv/mcc/mcc_ccm_test.jpg
double values[24][3] = {
{0.380463, 0.31696, 0.210053},
{0.649781, 0.520561, 0.452553},
{0.323114, 0.37593, 0.50123},
{0.314785, 0.396522, 0.258116},
{0.452971, 0.418602, 0.578767},
{0.34908, 0.608649, 0.652283},
{0.691127, 0.517818, 0.144984},
{0.208668, 0.224391, 0.485851},
{0.657849, 0.378126, 0.304115},
{0.285762, 0.229671, 0.31913},
{0.513422, 0.685031, 0.337381},
{0.786459, 0.676133, 0.246303},
{0.11751, 0.135079, 0.383441},
{0.190745, 0.470513, 0.296844},
{0.587832, 0.299132, 0.196117},
{0.783908, 0.746261, 0.294357},
{0.615481, 0.359983, 0.471403},
{0.107095, 0.370516, 0.573142},
{0.708598, 0.718936, 0.740915},
{0.593812, 0.612474, 0.63222},
{0.489774, 0.510077, 0.521757},
{0.380591, 0.398499, 0.393662},
{0.27461, 0.293267, 0.275244},
{0.180753, 0.194968, 0.145006}
};

// Assign values to src
for (int i = 0; i < 24; i++) {
src.at<cv::Vec3d>(i, 0) = cv::Vec3d(values[i][0], values[i][1], values[i][2]);
}
//! [get_color_checker]
vector<Ptr<mcc::CChecker>> checkers = detector->getListColorChecker();
//! [get_color_checker]
for (Ptr<mcc::CChecker> checker : checkers)
{
//! [create]
Ptr<CCheckerDraw> cdraw = CCheckerDraw::create(checker);
cdraw->draw(image);
Mat chartsRGB = checker->getChartsRGB();
Mat src = chartsRGB.col(1).clone().reshape(3, chartsRGB.rows/3);
src /= 255.0;
//! [create]

//compte color correction matrix
//! [get_ccm_Matrix]
ColorCorrectionModel model1(src, COLORCHECKER_Macbeth);
model1.run();
Mat ccm = model1.getCCM();
std::cout<<"ccm "<<ccm<<std::endl;
double loss = model1.getLoss();
std::cout<<"loss "<<loss<<std::endl;
//! [get_ccm_Matrix]
/* brief More models with different parameters, try it & check the document for details.
*/
// model1.setColorSpace(COLOR_SPACE_sRGB);
// model1.setCCM_TYPE(CCM_3x3);
// model1.setDistance(DISTANCE_CIE2000);
// model1.setLinear(LINEARIZATION_GAMMA);
// model1.setLinearGamma(2.2);
// model1.setLinearDegree(3);
// model1.setSaturatedThreshold(0, 0.98);

/* If you use a customized ColorChecker, you can use your own reference color values and corresponding color space in a way like:
*/
//! [reference_color_values]
// cv::Mat ref = (Mat_<Vec3d>(18, 1) <<
// Vec3d(100, 0.00520000001, -0.0104),
// Vec3d(73.0833969, -0.819999993, -2.02099991),
// Vec3d(62.493, 0.425999999, -2.23099995),
// Vec3d(50.4640007, 0.446999997, -2.32399988),
// Vec3d(37.7970009, 0.0359999985, -1.29700005),
// Vec3d(0, 0, 0),
// Vec3d(51.5880013, 73.5179977, 51.5690002),
// Vec3d(93.6989975, -15.7340002, 91.9420013),
// Vec3d(69.4079971, -46.5940018, 50.4869995),
// Vec3d(66.61000060000001, -13.6789999, -43.1720009),
// Vec3d(11.7110004, 16.9799995, -37.1759987),
// Vec3d(51.973999, 81.9440002, -8.40699959),
// Vec3d(40.5489998, 50.4399986, 24.8490009),
// Vec3d(60.8160019, 26.0690002, 49.4420013),
// Vec3d(52.2529984, -19.9500008, -23.9960003),
// Vec3d(51.2859993, 48.4700012, -15.0579996),
// Vec3d(68.70700069999999, 12.2959995, 16.2129993),
// Vec3d(63.6839981, 10.2930002, 16.7639999));

// ColorCorrectionModel model8(src,ref,COLOR_SPACE_Lab_D50_2);
// model8.run();
//! [reference_color_values]

//! [make_color_correction]
Mat img_;
cvtColor(image, img_, COLOR_BGR2RGB);
img_.convertTo(img_, CV_64F);
const int inp_size = 255;
const int out_size = 255;
img_ = img_ / inp_size;
Mat calibratedImage= model1.infer(img_);
Mat out_ = calibratedImage * out_size;
//! [make_color_correction]

//! [Save_calibrated_image]
// Save the calibrated image to {FILE_NAME}.calibrated.{FILE_EXT}
out_.convertTo(out_, CV_8UC3);
Mat img_out = min(max(out_, 0), out_size);
Mat out_img;
cvtColor(img_out, out_img, COLOR_RGB2BGR);
string filename = filepath.substr(filepath.find_last_of('/')+1);
size_t dotIndex = filename.find_last_of('.');
string baseName = filename.substr(0, dotIndex);
string ext = filename.substr(dotIndex+1, filename.length()-dotIndex);
string calibratedFilePath = baseName + ".calibrated." + ext;
imwrite(calibratedFilePath, out_img);
//! [Save_calibrated_image]

}
//compte color correction matrix
//! [get_ccm_Matrix]
ColorCorrectionModel model1(src, COLORCHECKER_Macbeth);
model1.run();
Mat ccm = model1.getCCM();
std::cout<<"ccm "<<ccm<<std::endl;
double loss = model1.getLoss();
std::cout<<"loss "<<loss<<std::endl;
//! [get_ccm_Matrix]
/* brief More models with different parameters, try it & check the document for details.
*/
// model1.setColorSpace(COLOR_SPACE_sRGB);
// model1.setCCM_TYPE(CCM_3x3);
// model1.setDistance(DISTANCE_CIE2000);
// model1.setLinear(LINEARIZATION_GAMMA);
// model1.setLinearGamma(2.2);
// model1.setLinearDegree(3);
// model1.setSaturatedThreshold(0, 0.98);

/* If you use a customized ColorChecker, you can use your own reference color values and corresponding color space in a way like:
*/
//! [reference_color_values]
// cv::Mat ref = (Mat_<Vec3d>(18, 1) <<
// Vec3d(100, 0.00520000001, -0.0104),
// Vec3d(73.0833969, -0.819999993, -2.02099991),
// Vec3d(62.493, 0.425999999, -2.23099995),
// Vec3d(50.4640007, 0.446999997, -2.32399988),
// Vec3d(37.7970009, 0.0359999985, -1.29700005),
// Vec3d(0, 0, 0),
// Vec3d(51.5880013, 73.5179977, 51.5690002),
// Vec3d(93.6989975, -15.7340002, 91.9420013),
// Vec3d(69.4079971, -46.5940018, 50.4869995),
// Vec3d(66.61000060000001, -13.6789999, -43.1720009),
// Vec3d(11.7110004, 16.9799995, -37.1759987),
// Vec3d(51.973999, 81.9440002, -8.40699959),
// Vec3d(40.5489998, 50.4399986, 24.8490009),
// Vec3d(60.8160019, 26.0690002, 49.4420013),
// Vec3d(52.2529984, -19.9500008, -23.9960003),
// Vec3d(51.2859993, 48.4700012, -15.0579996),
// Vec3d(68.70700069999999, 12.2959995, 16.2129993),
// Vec3d(63.6839981, 10.2930002, 16.7639999));

// ColorCorrectionModel model8(src,ref,COLOR_SPACE_Lab_D50_2);
// model8.run();
//! [reference_color_values]

//! [make_color_correction]
Mat img_;
cvtColor(image, img_, COLOR_BGR2RGB);
img_.convertTo(img_, CV_64F);
const int inp_size = 255;
const int out_size = 255;
img_ = img_ / inp_size;
Mat calibratedImage= model1.infer(img_);
Mat out_ = calibratedImage * out_size;
//! [make_color_correction]

//! [Save_calibrated_image]
// Save the calibrated image to {FILE_NAME}.calibrated.{FILE_EXT}
out_.convertTo(out_, CV_8UC3);
Mat img_out = min(max(out_, 0), out_size);
Mat out_img;
cvtColor(img_out, out_img, COLOR_RGB2BGR);
string filename = filepath.substr(filepath.find_last_of('/')+1);
size_t dotIndex = filename.find_last_of('.');
string baseName = filename.substr(0, dotIndex);
string ext = filename.substr(dotIndex+1, filename.length()-dotIndex);
string calibratedFilePath = baseName + ".calibrated." + ext;
imwrite(calibratedFilePath, out_img);
//! [Save_calibrated_image]

return 0;
}
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2 changes: 1 addition & 1 deletion modules/ccm/src/precomp.hpp
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Expand Up @@ -41,4 +41,4 @@

#include "opencv2/ccm.hpp"

#endif //_MCC_PRECOMP_HPP
#endif //_CCM_PRECOMP_HPP
10 changes: 2 additions & 8 deletions modules/ccm/tutorials/basic_ccm/color_correction_model.markdown
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Expand Up @@ -28,7 +28,7 @@ Or make sure you check the ccm module in the GUI version of CMake: cmake-gui.
Source Code of the sample
-----------

The sample has two parts of code, the first is the color checker detector model, the second part is to make color calibration.
The sample has two parts of code, the first is the color checker detector model, see details at @ref tutorial_mcc_basic_chart_detection, the second part is to make collor calibration.

```
Here are the parameters for ColorCorrectionModel
Expand Down Expand Up @@ -101,13 +101,7 @@ Here are the parameters for ColorCorrectionModel

## Explanation

The first part is to detect the ColorChecker position.
@snippet samples/color_correction_model.cpp get_color_checker
@snippet samples/color_correction_model.cpp get_messages_of_image
Preparation for ColorChecker detection to get messages for the image.

@snippet samples/color_correction_model.cpp create
The CCheckerDetectorobject is created and uses getListColorChecker function to get ColorChecker message.
The first part is to detect the ColorChecker position which can be done with the help of MCC module.

@snippet samples/color_correction_model.cpp get_ccm_Matrix
For every ColorChecker, we can compute a ccm matrix for color correction. Model1 is an object of ColorCorrectionModel class. The parameters should be changed to get the best effect of color correction. See other parameters' detail at the Parameters.
Expand Down

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