Class cv::mcc::CCheckerDetector#

A class to find the positions of the ColorCharts in the image.

#include <opencv2/objdetect/mcc_checker_detector.hpp>

Collaboration diagram for cv::mcc::CCheckerDetector:

Public Member Functions#

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

A class to find the positions of the ColorCharts in the image.

Member Function Documentation#

draw()#

void cv::mcc::CCheckerDetector::draw(
std::vector< Ptr< CChecker > > & checkers,
InputOutputArray img,
const Scalar color = CV_RGB(0, 250, 0),
const int thickness = 2 )

Python:

cv.mcc.CCheckerDetector.draw(checkers, img[, color[, thickness]]) -> img

Draws the checker to the given image.

Parameters

img — image in color space BGR
checkers — The checkers which will be drawn by this object.
color — The color by with which the squares of the checker will be drawn
thickness — The thickness with which the sqaures will be drawn

getBestColorChecker()#

Ptr< mcc::CChecker > cv::mcc::CCheckerDetector::getBestColorChecker()

Python:

cv.mcc.CCheckerDetector.getBestColorChecker() -> retval

Get the best color checker. By the best it means the one detected with the highest confidence.

Returns

checker A single colorchecker, if atleast one colorchecker was detected, ‘nullptr’ otherwise.

getColorChartType()#

ColorChart cv::mcc::CCheckerDetector::getColorChartType()

Python:

cv.mcc.CCheckerDetector.getColorChartType() -> retval

getDetectionParams()#

const DetectorParametersMCC & cv::mcc::CCheckerDetector::getDetectionParams()

Python:

cv.mcc.CCheckerDetector.getDetectionParams() -> retval

getListColorChecker()#

std::vector< Ptr< CChecker > > cv::mcc::CCheckerDetector::getListColorChecker()

Python:

cv.mcc.CCheckerDetector.getListColorChecker() -> retval

Get the list of all detected colorcheckers.

Returns

checkers vector of colorcheckers

getRefColors()#

Mat cv::mcc::CCheckerDetector::getRefColors()

Python:

cv.mcc.CCheckerDetector.getRefColors() -> retval

Gets the reference color for chart.

process()#

bool cv::mcc::CCheckerDetector::process(
InputArray image,
const int nc = 1 )

Python:

cv.mcc.CCheckerDetector.process(image[, nc]) -> retval

cv.mcc.CCheckerDetector.processWithROI(image, regionsOfInterest[, nc]) -> retval

Find the ColorCharts in the given image.

Differs from the above one only in the arguments.

This version searches for the chart in the full image.

The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker()

Parameters

image — image in color space BGR
nc — number of charts in the image, if you don’t know the exact then keeping this number high helps.

Returns

true if atleast one chart is detected otherwise false

process()#

bool cv::mcc::CCheckerDetector::process(
InputArray image,
const std::vector< Rect > & regionsOfInterest,
const int nc = 1 )

Python:

cv.mcc.CCheckerDetector.process(image[, nc]) -> retval

cv.mcc.CCheckerDetector.processWithROI(image, regionsOfInterest[, nc]) -> retval

Find the ColorCharts in the given image.

The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker()

Parameters

image — image in color space BGR
regionsOfInterest — regions of image to look for the chart, if it is empty, charts are looked for in the entire image
nc — number of charts in the image, if you don’t know the exact then keeping this number high helps.

Returns

true if atleast one chart is detected otherwise false

setColorChartType()#

void cv::mcc::CCheckerDetector::setColorChartType(ColorChart chartType)

Python:

cv.mcc.CCheckerDetector.setColorChartType(chartType)

Sets the color chart type for MCC detection.

Parameters

chartType — ColorChart enum specifying the type of color chart to detect.

setDetectionParams()#

void cv::mcc::CCheckerDetector::setDetectionParams(const DetectorParametersMCC & params)

Python:

cv.mcc.CCheckerDetector.setDetectionParams(params)

Sets the detection paramaters for mcc.

Parameters

params — DetectorParametersMCC structure containing detection configuration parameters.

create()#

static Ptr< CCheckerDetector > cv::mcc::CCheckerDetector::create()

Python:

cv.mcc.CCheckerDetector.create() -> retval

cv.mcc.CCheckerDetector.create(net) -> retval

cv.mcc.CCheckerDetector_create() -> retval

cv.mcc.CCheckerDetector_create(net) -> retval

Returns the implementation of the CCheckerDetector.

Source file#

The documentation for this class was generated from the following file:

opencv2/objdetect/mcc_checker_detector.hpp