A class to find the positions of the ColorCharts in the image.
More...
#include <opencv2/mcc/checker_detector.hpp>
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virtual Ptr< mcc::CChecker > | getBestColorChecker ()=0 |
| Get the best color checker. By the best it means the one detected with the highest confidence. More...
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virtual std::vector< Ptr< CChecker > > | getListColorChecker ()=0 |
| Get the list of all detected colorcheckers. More...
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virtual bool | process (InputArray image, const TYPECHART chartType, const std::vector< Rect > ®ionsOfInterest, const int nc=1, bool useNet=false, const Ptr< DetectorParameters > ¶ms=DetectorParameters::create())=0 |
| Find the ColorCharts in the given image. More...
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virtual bool | process (InputArray image, const TYPECHART chartType, const int nc=1, bool useNet=false, const Ptr< DetectorParameters > ¶ms=DetectorParameters::create())=0 |
| Find the ColorCharts in the given image. More...
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virtual bool | setNet (dnn::Net net)=0 |
| Set the net which will be used to find the approximate bounding boxes for the color charts. More...
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| Algorithm () |
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virtual | ~Algorithm () |
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virtual void | clear () |
| Clears the algorithm state. More...
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virtual bool | empty () const |
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
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virtual String | getDefaultName () const |
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virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage. More...
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virtual void | save (const String &filename) const |
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virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage. More...
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void | write (FileStorage &fs, const String &name) const |
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
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A class to find the positions of the ColorCharts in the image.
◆ create()
Python: |
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| cv.mcc.CCheckerDetector.create( | | ) -> | retval |
| cv.mcc.CCheckerDetector_create( | | ) -> | retval |
◆ getBestColorChecker()
Python: |
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| 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.
◆ getListColorChecker()
virtual std::vector<Ptr<CChecker> > cv::mcc::CCheckerDetector::getListColorChecker |
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pure virtual |
Python: |
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| cv.mcc.CCheckerDetector.getListColorChecker( | | ) -> | retval |
Get the list of all detected colorcheckers.
- Returns
- checkers vector of colorcheckers
◆ process() [1/2]
Python: |
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| cv.mcc.CCheckerDetector.process( | image, chartType[, nc[, useNet[, params]]] | ) -> | retval |
| cv.mcc.CCheckerDetector.processWithROI( | image, chartType, regionsOfInterest[, nc[, useNet[, params]]] | ) -> | 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
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image | image in color space BGR |
chartType | type of the chart to detect |
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. |
useNet | if it is true the network provided using the setNet() is used for preliminary search for regions where chart could be present, inside the regionsOfInterest provied. |
params | parameters of the detection system. More information about them can be found in the struct DetectorParameters. |
- Returns
- true if atleast one chart is detected otherwise false
◆ process() [2/2]
Python: |
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| cv.mcc.CCheckerDetector.process( | image, chartType[, nc[, useNet[, params]]] | ) -> | retval |
| cv.mcc.CCheckerDetector.processWithROI( | image, chartType, regionsOfInterest[, nc[, useNet[, params]]] | ) -> | 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
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image | image in color space BGR |
chartType | type of the chart to detect |
nc | number of charts in the image, if you don't know the exact then keeping this number high helps. |
useNet | if it is true the network provided using the setNet() is used for preliminary search for regions where chart could be present, inside the regionsOfInterest provied. |
params | parameters of the detection system. More information about them can be found in the struct DetectorParameters. |
- Returns
- true if atleast one chart is detected otherwise false
◆ setNet()
virtual bool cv::mcc::CCheckerDetector::setNet |
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dnn::Net |
net | ) |
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pure virtual |
Python: |
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| cv.mcc.CCheckerDetector.setNet( | net | ) -> | retval |
Set the net which will be used to find the approximate bounding boxes for the color charts.
It is not necessary to use this, but this usually results in better detection rate.
- Parameters
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net | the neural network, if the network in empty, then the function will return false. |
- Returns
- true if it was able to set the detector's network, false otherwise.
The documentation for this class was generated from the following file: