OpenCV for Unity
2.6.3
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.
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The class implements the random forest predictor. More...
Public Member Functions | |
bool | getCalculateVarImportance () |
void | setCalculateVarImportance (bool val) |
int | getActiveVarCount () |
void | setActiveVarCount (int val) |
TermCriteria | getTermCriteria () |
void | setTermCriteria (TermCriteria val) |
Mat | getVarImportance () |
void | getVotes (Mat samples, Mat results, int flags) |
double | getOOBError () |
Public Member Functions inherited from OpenCVForUnity.MlModule.DTrees | |
int | getMaxCategories () |
void | setMaxCategories (int val) |
int | getMaxDepth () |
void | setMaxDepth (int val) |
int | getMinSampleCount () |
void | setMinSampleCount (int val) |
int | getCVFolds () |
void | setCVFolds (int val) |
bool | getUseSurrogates () |
void | setUseSurrogates (bool val) |
bool | getUse1SERule () |
void | setUse1SERule (bool val) |
bool | getTruncatePrunedTree () |
void | setTruncatePrunedTree (bool val) |
float | getRegressionAccuracy () |
void | setRegressionAccuracy (float val) |
Mat | getPriors () |
void | setPriors (Mat val) |
Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel | |
int | getVarCount () |
Returns the number of variables in training samples. More... | |
override bool | empty () |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More... | |
bool | isTrained () |
Returns true if the model is trained. More... | |
bool | isClassifier () |
Returns true if the model is classifier. More... | |
bool | train (TrainData trainData, int flags) |
Trains the statistical model. More... | |
bool | train (TrainData trainData) |
Trains the statistical model. More... | |
bool | train (Mat samples, int layout, Mat responses) |
Trains the statistical model. More... | |
float | calcError (TrainData data, bool test, Mat resp) |
Computes error on the training or test dataset. More... | |
virtual float | predict (Mat samples, Mat results, int flags) |
Predicts response(s) for the provided sample(s) More... | |
virtual float | predict (Mat samples, Mat results) |
Predicts response(s) for the provided sample(s) More... | |
virtual float | predict (Mat samples) |
Predicts response(s) for the provided sample(s) More... | |
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
IntPtr | getNativeObjAddr () |
virtual void | clear () |
Clears the algorithm state. More... | |
void | save (string filename) |
virtual string | getDefaultName () |
Public Member Functions inherited from OpenCVForUnity.DisposableObject | |
void | Dispose () |
void | ThrowIfDisposed () |
Static Public Member Functions | |
static new RTrees | __fromPtr__ (IntPtr addr) |
static new RTrees | create () |
static new RTrees | load (string filepath, string nodeName) |
Loads and creates a serialized RTree from a file. More... | |
static new RTrees | load (string filepath) |
Loads and creates a serialized RTree from a file. More... | |
Static Public Member Functions inherited from OpenCVForUnity.MlModule.DTrees | |
static new DTrees | __fromPtr__ (IntPtr addr) |
static DTrees | create () |
Creates the empty model. More... | |
static DTrees | load (string filepath, string nodeName) |
Loads and creates a serialized DTrees from a file. More... | |
static DTrees | load (string filepath) |
Loads and creates a serialized DTrees from a file. More... | |
Static Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel | |
static new StatModel | __fromPtr__ (IntPtr addr) |
Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
static Algorithm | __fromPtr__ (IntPtr addr) |
Static Public Member Functions inherited from OpenCVForUnity.DisposableObject | |
static IntPtr | ThrowIfNullIntPtr (IntPtr ptr) |
Protected Member Functions | |
override void | Dispose (bool disposing) |
Protected Member Functions inherited from OpenCVForUnity.MlModule.StatModel | |
override void | Dispose (bool disposing) |
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject | |
DisposableOpenCVObject () | |
DisposableOpenCVObject (IntPtr ptr) | |
DisposableOpenCVObject (bool isEnabledDispose) | |
DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose) | |
Protected Member Functions inherited from OpenCVForUnity.DisposableObject | |
DisposableObject () | |
DisposableObject (bool isEnabledDispose) | |
Additional Inherited Members | |
Public Attributes inherited from OpenCVForUnity.MlModule.DTrees | |
const int | PREDICT_AUTO = 0 |
const int | PREDICT_SUM = (1 << 8) |
const int | PREDICT_MAX_VOTE = (2 << 8) |
const int | PREDICT_MASK = (3 << 8) |
Public Attributes inherited from OpenCVForUnity.MlModule.StatModel | |
const int | UPDATE_MODEL = 1 |
const int | RAW_OUTPUT = 1 |
const int | COMPRESSED_INPUT = 2 |
const int | PREPROCESSED_INPUT = 4 |
Properties inherited from OpenCVForUnity.DisposableObject | |
bool | IsDisposed [get, protected set] |
bool | IsEnabledDispose [get, set] |
The class implements the random forest predictor.
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Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.
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protectedvirtual |
Reimplemented from OpenCVForUnity.MlModule.DTrees.
int OpenCVForUnity.MlModule.RTrees.getActiveVarCount | ( | ) |
bool OpenCVForUnity.MlModule.RTrees.getCalculateVarImportance | ( | ) |
double OpenCVForUnity.MlModule.RTrees.getOOBError | ( | ) |
Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.
TermCriteria OpenCVForUnity.MlModule.RTrees.getTermCriteria | ( | ) |
Mat OpenCVForUnity.MlModule.RTrees.getVarImportance | ( | ) |
Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.
Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.
samples | Array containing the samples for which votes will be calculated. |
results | Array where the result of the calculation will be written. |
flags | Flags for defining the type of RTrees. |
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Loads and creates a serialized RTree from a file.
Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
filepath | path to serialized RTree |
nodeName | name of node containing the classifier |
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static |
Loads and creates a serialized RTree from a file.
Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
filepath | path to serialized RTree |
nodeName | name of node containing the classifier |
void OpenCVForUnity.MlModule.RTrees.setActiveVarCount | ( | int | val | ) |
void OpenCVForUnity.MlModule.RTrees.setCalculateVarImportance | ( | bool | val | ) |
void OpenCVForUnity.MlModule.RTrees.setTermCriteria | ( | TermCriteria | val | ) |