eVision::EasyDeepLearning::EDataset Class Reference

EDataset manages a dataset of images. More...

#include <eVision/EasyDeepLearning/EDataset.h>

Inheritance diagram for eVision::EasyDeepLearning::EDataset:

Collaboration diagram for eVision::EasyDeepLearning::EDataset:

Public Member Functions
	EDataset ()
	Constructs a EDataset object.
	EDataset (const eVision::EasyDeepLearning::EDataset &other)
	Copy constructor of a EDataset object.
virtual	~EDataset () throw ()
eVision::EasyDeepLearning::EDataset &	operator= (const eVision::EasyDeepLearning::EDataset &other)
	Assignment operator.
void	Load (const std::string &path)
	Loads a classification dataset from disk.
void	Load (eVision::ESerializer *serializer)
	Loads a classification dataset from disk.
void	Save (const std::string &path)
	Saves a classification dataset to disk, containing the file paths to the images in the dataset and their associated label.
void	Save (eVision::ESerializer *serializer)
	Saves a classification dataset to disk, containing the file paths to the images in the dataset and their associated label.
void	SaveWithRelativePath (const std::string &path)
	Saves the dataset with image paths relative to the directory in which the dataset file is saved.
void	SetBasePath (const std::string &basePath)
	Sets the base path for the images specified with a relative path.
void	Export (const std::string &directory, eVision::EImageFileType fileType, int quality) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, eVision::EImageFileType fileType) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, bool keepFilename, eVision::EImageFileType fileType, int quality) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, bool keepFilename, eVision::EImageFileType fileType) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, bool keepFilename) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, int width, int height, int channels, eVision::EImageFileType fileType, int quality) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, int width, int height, int channels, eVision::EImageFileType fileType) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, int width, int height, int channels) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, int width, int height, int channels, bool keepFilename, eVision::EImageFileType fileType, int quality) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, int width, int height, int channels, bool keepFilename, eVision::EImageFileType fileType) const
	Exports the dataset and its images to the given directory.
void	Export (const std::string &directory, int width, int height, int channels, bool keepFilename) const
	Exports the dataset and its images to the given directory.
virtual void	Clear ()
	Clears the dataset.
uint32_t	GetWidth () const
	Width of the region of interest of the first image added to the dataset.
uint32_t	GetHeight () const
	Height of the region of interest of the first image added to the dataset.
uint32_t	GetChannels () const
	Number of channels of the first image added to the dataset.
eVision::EasyDeepLearning::EImageAnnotationFormat	GetAvailableImageAnnotationFormat (const std::string &imageFile) const
	Image annotation file formats that are available next to the image file.
eVision::EasyDeepLearning::EImageAnnotationFormat	GetAvailableImageAnnotationFormat (int imgId) const
	Image annotation file formats that are available next to the image file.
void	ImportYOLOTXTAnnotations (int imgId, const std::string &labelFile)
	Imports YOLO TXT annotations (for EasyLocate Bounding Box).
void	ImportYOLOTXTAnnotations (const std::string &labelFile)
	Imports YOLO TXT annotations (for EasyLocate Bounding Box).
void	ImportPascalVOCXMLAnnotations (int imgId)
	Imports Pascal VOC XML annotations (for EasyLocate Bounding Box).
void	ImportPascalVOCXMLAnnotations ()
	Imports Pascal VOC XML annotations (for EasyLocate Bounding Box).
int	AddImage (const std::string &imagePath)
	Adds an image to the dataset.
int	AddImage (const std::string &imagePath, const std::string &label)
	Adds an image to the dataset.
int	AddImage (const std::string &imagePath, int originX, int originY, int width, int height, const eVision::ERegion &mask)
	Adds an image to the dataset.
int	AddImage (const std::string &imagePath, int originX, int originY, int width, int height, const eVision::ERegion &mask, const std::string &label)
	Adds an image to the dataset.
int	AddImage (const eVision::EBaseROI &img)
	Adds an image to the dataset.
int	AddImage (const eVision::EBaseROI &img, const std::string &label)
	Adds an image to the dataset.
int	AddImage (const eVision::EBaseROI &img, int originX, int originY, int width, int height, const eVision::ERegion &mask)
	Adds an image to the dataset.
int	AddImage (const eVision::EBaseROI &img, int originX, int originY, int width, int height, const eVision::ERegion &mask, const std::string &label)
	Adds an image to the dataset.
int	AddImages (const std::string &filter)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImages (const std::string &filter, const std::string &label)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImages (const std::string &filter, int originX, int originY, int width, int height, const eVision::ERegion &mask)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImages (const std::string &filter, int originX, int originY, int width, int height, const eVision::ERegion &mask, const std::string &label)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImagesFromCSV (const std::string &csvFile, int imageColumn, int labelColumn, int skipLines, const std::string &separator)
	Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.
int	AddImagesFromCSV (const std::string &csvFile, int imageColumn, int labelColumn, int skipLines)
	Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.
int	AddImagesFromCSV (const std::string &csvFile, int imageColumn, int labelColumn)
	Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.
int	AddImagesFromCSV (const std::string &csvFile, int imageColumn)
	Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.
int	AddImagesFromCSV (const std::string &csvFile)
	Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.
void	RemoveImage (int imgId)
	Removes the image at the given index. All annotations (label, segmentation, objects) will be lost.
int	GetNumImages () const
	Number of images in the dataset.
int	GetNumImageFiles () const
	Number of different image files contained in the dataset.
int	GetNumImagesForFile (const std::string &filePath) const
	Get the number of images in the dataset that are associated with the given image file.
std::unique_ptr< eVision::EBaseROI >	GetImageCopy (int index) const
	Gets a copy of the i-th image of the dataset.
void	GetImageCopy (int index, eVision::EBaseROI &img) const
	Gets a copy of the i-th image of the dataset.
std::string	GetImagePath (int index) const
	Gets the path of the i-th image of the dataset. If the image was not given using a path, the method will throw an exception.
bool	IsImageFile (int index) const
	Whether the image is stored as a file path towards an image.
bool	IsEmbeddedImage (int index) const
	Whether the image is embedded into the dataset and is not a reference towards an image file.
std::string	GetImageLabel (int index) const
	Gets the label of the i-th image of the dataset. An exception is thrown if the image has no classification label.
void	SetImageLabel (int index, const std::string &label)
	Sets the label of images in the dataset.
void	SetImageLabel (const std::string &filter, const std::string &label)
	Sets the label of images in the dataset.
void	UnsetImageLabel (int index)
	Unset the label of the given image of the dataset. After this operation, the given image will have no classification labels.
bool	HasLabel (int index) const
	Whether an image has a classification label.
int	GetNumLabeledImages () const
	Number of images that have a classification label.
int	GetNumUnlabeledImages () const
	Number of images that doesn't have any classification label.
std::vector< uint32_t >	GetImagesIndexesWithLabel (const std::string &label) const
	Gets a list of index corresponding to the images associated with the given label.
std::vector< uint32_t >	GetImagesIndexesWithLabel (int labelIndex) const
	Gets a list of index corresponding to the images associated with the given label.
std::vector< int >	GetLabeledImagesIndexes () const
	Gets a list of index corresponding to the images that have a classification label.
std::vector< int >	GetTextLabeledImagesIndexes () const
	Gets a list of index corresponding to the images that have a text only label.
std::vector< int >	GetImagesIndexesWithNoLabel () const
	Gets a list of index corresponding to the images with no classification labels.
int	GetNumLabels () const
	Number of labels in the dataset.
void	AddLabel (const std::string &label)
	Adds a label to the dataset.
std::string	GetLabel (int i) const
	Gets the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1).
void	SetLabel (int index, const std::string &label)
	Sets the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1). This operation does not add a new label to the dataset but simply renames an existing label.
float	GetLabelWeight (int index) const
	Gets the weight associated to the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1).
void	SetLabelWeight (int index, const float &weight)
	Sets the weight associated to the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1). This operation does not add a new label.
void	SetLabelColor (int index, const eVision::ERGBColor &color)
	Sets the label color for a classification label.
eVision::ERGBColor	GetLabelColor (int index) const
	Gets the label color for a classification label.
void	SetLabelOpacity (int index, const uint8_t opacity)
	Sets the label opacity for a classification label.
uint8_t	GetLabelOpacity (int index) const
	Gets the label opacity for a classification label.
void	RemoveLabel (const std::string &label)
	Removes a label from the dataset.
void	RemoveLabel (int labelId)
	Removes a label from the dataset.
void	SetSegmentationMap (int imageIndex, const eVision::EROIBW16 &segmentationMap)
	Sets the segmentation map of an image. The segmentation map is a 16-bit EROIBW16 image where the value of each pixel is equal to the corresponding segmentation label index (see EDataset::GetSegmentationLabel).
eVision::EImageBW16	GetSegmentationMap (int imageIndex) const
	Gets the segmentation map of an image. The segmentation map is a 16-bit EROIBW16 image where the value of each pixel is equal to the corresponding segmentation label index (see EDataset::GetSegmentationLabel).
void	UnsetSegmentation (int imageIndex)
	Unsets the segmentation of an image. After this operation, the image will have no segmentation.
void	ResetSegmentation (int imageIndex)
	Resets the segmentation of an image by setting all pixels to background.
void	AddRegionToSegment (int imageIndex, int segmentationLabelIndex, const eVision::ERegion &region)
	Adds a region (see ERegion) to the given segment of an image. If, before this call, the image had no segmentation, the pixels not in the given region will be set to the background segmentation label.
void	AddRegionToSegment (int imageIndex, const std::string &label, const eVision::ERegion &region)
	Adds a region (see ERegion) to the given segment of an image. If, before this call, the image had no segmentation, the pixels not in the given region will be set to the background segmentation label.
eVision::ERegion	GetRegionForSegment (int imageIndex, int segmentationLabelIndex)
	Gets a region corresponding to the pixels of the given segment.
eVision::ERegion	GetRegionForSegment (int imageIndex, const std::string &segmentationLabel)
	Gets a region corresponding to the pixels of the given segment.
int	GetNumSegmentationLabels () const
	Number of segmentation labels. A dataset has always at least one segmentation label: "background".
int	GetNumImagesForSegmentationLabel (int labelId) const
	Number of images containing the given segmentation label.
int	GetNumImagesForSegmentationLabel (const std::string &label) const
	Number of images containing the given segmentation label.
uint64_t	GetNumPixelsForSegmentationLabel (int labelId) const
	Number of pixels assigned to the given segmentation label.
uint64_t	GetNumPixelsForSegmentationLabel (const std::string &label) const
	Number of pixels assigned to the given segmentation label.
bool	HasSegmentation (int imageId) const
	Whether the image has a segmentation.
bool	HasForegroundSegments (int imageId) const
	Whether the image segmentation contains segments that are not background. If the image has no segmentation, this method throws an exception.
int	GetNumSegmentedBlobs (int imageId) const
	Number of segmented blobs in the image for all non-background labels or for a specific label.
int	GetNumSegmentedBlobs (int imageId, const std::string &label) const
	Number of segmented blobs in the image for all non-background labels or for a specific label.
int	GetNumSegmentedBlobs (int imageId, int labelId) const
	Number of segmented blobs in the image for all non-background labels or for a specific label.
std::string	GetSegmentationLabel (int index) const
	Gets the segmentation label.
void	SetSegmentationLabel (int index, const std::string &label)
	Sets the segmentation labels.
int	AddSegmentationLabel (const std::string &label, float labelWeight)
	Adds a segmentation label. The index of the new segmentation labels is returned by the method.
int	AddSegmentationLabel (const std::string &label)
	Adds a segmentation label. The index of the new segmentation labels is returned by the method.
void	RemoveSegmentationLabel (const std::string &label)
	Remove a segmentation label.
void	RemoveSegmentationLabel (int index)
	Remove a segmentation label.
float	GetSegmentationLabelWeight (int index) const
	Gets the segmentation label weights.
float	GetSegmentationLabelWeight (const std::string &label) const
	Gets the segmentation label weights.
void	SetSegmentationLabelWeight (int index, float weight)
	Sets the segmentation label weight.
void	SetSegmentationLabelWeight (const std::string &label, float weight)
	Sets the segmentation label weight.
void	SetSegmentationLabelColor (int index, const eVision::ERGBColor &color)
	Sets the label color for a segmentation label.
eVision::ERGBColor	GetSegmentationLabelColor (int index) const
	Gets the label color for a segmentation label.
void	SetSegmentationLabelOpacity (int index, const uint8_t opacity)
	Sets the label opacity for a segmentation label.
uint8_t	GetSegmentationLabelOpacity (int index) const
	Gets the label opacity for a segmentation label.
int	GetNumImagesWithSegmentation () const
	Number of images that have a ground truth segmentation.
int	GetNumImagesWithoutSegmentation () const
	Number of images that doesn't have a ground truth segmentation.
int	GetNumImagesWithoutForegroundSegments () const
	Number of images that have a ground truth segmentation that has no foreground segments and is thus entirely composed of background pixels.
int	GetNumImagesWithForegroundSegments () const
	Number of images that have a ground truth segmentation that has foreground segments and is this not entirely composed of background pixels.
void	ResetImageObjectLabeling (int imageIndex)
	Resets the object labeling for the specified image. This sets the image as having been labeled for object detection with no object present in the image.
void	UnsetImageObjectLabeling (int imageIndex)
	Unsets the object labeling for the specified image. This sets the image as not having been labeled for object detection. This image won't be use for training with a ELocator tool.
std::vector< eVision::EasyDeepLearning::ELocatorObject >	GetImageObjects (int imageIndex) const
	Gets all the objects of the specified image.
int	GetImageNumObjects (int imageIndex) const
	Number of objects for the specified image.
eVision::EasyDeepLearning::ELocatorObject	GetImageObject (int imageIndex, int objectIndex) const
	Gets the specified object for the given image.
void	SetImageObject (int imageIndex, int objectIndex, const eVision::EasyDeepLearning::ELocatorObject &obj)
	Sets the specified object for the given image.
void	SetImageObject (int imageIndex, int objectIndex, const std::string &label)
	Sets the specified object for the given image.
void	SetImageObject (int imageIndex, int objectIndex, const eVision::ERectangleRegion &region)
	Sets the specified object for the given image.
void	AddImageObject (int imageIndex, const eVision::EasyDeepLearning::ELocatorObject &obj)
	Adds an object to the image.
void	AddImageObject (int imageIndex, const std::string &label, const eVision::ERectangleRegion &box)
	Adds an object to the image.
void	RemoveImageObject (int imageIndex, int objectIndex)
	Removes an object from an image.
int	GetNumObjectLabels () const
	Number of object labels.
std::string	GetObjectLabel (int labelIndex) const
	Object label.
void	SetObjectLabel (int labelIndex, const std::string &newLabel)
	Sets an object label.
void	SetObjectLabel (const std::string &oldLabel, const std::string &newLabel)
	Sets an object label.
void	AddObjectLabel (const std::string &label, float weight)
	Adds an object label.
void	AddObjectLabel (const std::string &label)
	Adds an object label.
void	RemoveObjectLabel (int labelIndex)
	Removes an object label.
void	RemoveObjectLabel (const std::string &label)
	Removes an object label.
void	SetObjectLabelWeight (int labelIndex, float weight)
	Sets the weight of an object label.
void	SetObjectLabelWeight (const std::string &label, float weight)
	Sets the weight of an object label.
float	GetObjectLabelWeight (int labelIndex) const
	Gets the weight of an object label.
float	GetObjectLabelWeight (const std::string &label) const
	Gets the weight of an object label.
void	SetObjectLabelColor (int index, const eVision::ERGBColor &color)
	Sets the label color for an object label.
eVision::ERGBColor	GetObjectLabelColor (int index) const
	Gets the label color for an object label.
void	SetObjectLabelOpacity (int index, const uint8_t opacity)
	Sets the label opacity for an object label.
uint8_t	GetObjectLabelOpacity (int index) const
	Gets the label opacity for an object label.
bool	HasObjectLabeling (int imageIndex) const
	Whether the image is labeled for object detection and use with ELocator.
int	GetNumImagesWithObjectLabeling () const
	Number of images in the dataset that are labeled for object detection.
int	GetNumImagesWithoutObjectLabeling () const
	Number of images in the dataset that are not labeled for object detection.
int	GetNumImagesWithoutObjects () const
	Number of images in the dataset that are labeled for object detection but have been associated with no object.
int	GetNumImagesWithObjects () const
	Number of images in the dataset that are labeled for object detection and have 1 or more objects.
int	GetNumImagesWithObjectLabel (int labelIdx) const
	Number of images that contain an object with the given label.
int	GetNumImagesWithObjectLabel (const std::string &label) const
	Number of images that contain an object with the given label.
uint64_t	GetNumObjectsWithLabel (int labelIdx) const
	Number of objects with the given label.
uint64_t	GetNumObjectsWithLabel (const std::string &label) const
	Number of objects with the given label.
float	GetSameLabelMaxOverlap () const
	Maximum overlap between objects with the same label in the dataset.
float	GetAbsoluteMaxOverlap () const
	Absolute maximum overlap between objects in the dataset.
int	GetMaxNumObjectPerImage () const
	Maximum number of objects for an image in the dataset. If no images has object labeling (see EDataset::HasObjectLabeling), the method returns -1.
void	SetObjectSize (int objectSize)
	Object size for EasyLocate Interest point.
int	GetObjectSize () const
	Object size for EasyLocate Interest point.
bool	HasOCRAnnotations (int imgId) const
	Whether the specified image has OCR annotations.
void	UnsetOCRAnnotations (int imgId)
	Unsets the OCR labeling for the specified image.
std::vector< eVision::EasyDeepOCR::ETextLine >	GetOCRAnnotations (int imgId) const
	Ground truth text lines for the specified image.
void	SetOCRAnnotations (int imgId, const std::vector< eVision::EasyDeepOCR::ETextLine > &texts)
	Sets the ground truth text lines for the specified image.
int	GetNumImagesWithOCR () const
	Number of images with OCR annotations.
int	GetNumImagesWithoutOCR () const
	Number of images without OCR annotations.
int	GetNumTexts () const
	Number of annotated texts in the dataset.
eVision::EasyDeepLearning::EDataset	GetTextDataset () const
	Transforms the current dataset to have only subwindow containing the actual text to read.
eVision::EasyDeepLearning::EDataset	GetTextOrientationDataset () const
	Transforms the current text dataset to duplicate it with half image flipped, adding corresponding ground truth.
void	SplitDataset (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2, float proportion, bool random) const
	Splits the dataset in two parts to be used for training and validation respectively.
void	SplitDataset (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2, float proportion) const
	Splits the dataset in two parts to be used for training and validation respectively.
void	SplitDataset (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2) const
	Splits the dataset in two parts to be used for training and validation respectively.
void	SplitDatasetForSegmentation (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2, float proportion, bool random) const
	Splits the dataset in two parts for a supervised segmenter. The two parts are to be used for training and validation respectively.
void	SplitDatasetForSegmentation (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2, float proportion) const
	Splits the dataset in two parts for a supervised segmenter. The two parts are to be used for training and validation respectively.
void	SplitDatasetForSegmentation (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2) const
	Splits the dataset in two parts for a supervised segmenter. The two parts are to be used for training and validation respectively.
void	SplitDatasetForLocator (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2, float proportion, bool random) const
	Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.
void	SplitDatasetForLocator (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2, float proportion) const
	Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.
void	SplitDatasetForLocator (eVision::EasyDeepLearning::EDataset &d1, eVision::EasyDeepLearning::EDataset &d2) const
	Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.
eVision::EasyDeepLearning::EDatasetSplit	GetSplit (float trainingProportion, float validationPropotion, eVision::EasyDeepLearning::EDeepLearningToolType toolType, const std::string &goodLabel) const
	Generates a split.
eVision::EasyDeepLearning::EDatasetSplit	GetSplit (float trainingProportion, float validationPropotion, eVision::EasyDeepLearning::EDeepLearningToolType toolType, const std::string &goodLabel, uint32_t seed) const
	Generates a split.
eVision::EasyDeepLearning::EDatasetSplit	GetSplit (int numTrainingImages, int numValidationImages, eVision::EasyDeepLearning::EDeepLearningToolType toolType, const std::string &goodLabel) const
	Generates a split.
eVision::EasyDeepLearning::EDatasetSplit	GetSplit (int numTrainingImages, int numValidationImages, eVision::EasyDeepLearning::EDeepLearningToolType toolType, const std::string &goodLabel, uint32_t seed) const
	Generates a split.
eVision::EasyDeepLearning::EDataset	ExtractSplit (const eVision::EasyDeepLearning::EDatasetSplit &split, eVision::EasyDeepLearning::EDatasetType type) const
	Creates a new EDataset containing only the images of the given split type.
void	SetRegionOfInterest (int imageIndex, int xOrg, int yOrg, int width, int height)
	Sets the region of interest for the specified image.
int	GetRegionOfInterestOriginX (int imageIndex) const
	Region of interest origin abscissa for the specified image.
int	GetRegionOfInterestOriginY (int imageIndex) const
	Region of interest origin ordinate for the specified image.
int	GetRegionOfInterestWidth (int imageIndex) const
	Region of interest width for the specified image.
int	GetRegionOfInterestHeight (int imageIndex) const
	Region of interest height for the specified image.
eVision::ERegion	GetMask (int imageIndex) const
	Gets the mask region/don't care area for the given image. The mask is defined with respect to the region of interest of the image.
void	SetMask (int imageIndex, const eVision::ERegion &mask)
	Sets the mask region/don't care area for the given image. The mask is defined with respect to the region of interest of the image.
	EDataset (CppWrapperInternal::InternalConstructor)

Public Attributes
CppWrapperInternal::WrappedClass	wrappedClass_

Detailed Description

EDataset manages a dataset of images.

A dataset is a collection of images with different types of labeling: labeling for the classification of images, labeling for the segmentation of pixels, and/or labeling for the detection of objects (classification and localization).

The dataset maintains 3 sets of labels for each type of labeling:

• the classification labels that characterize an entire image;
• the segmentation labels that characterize the pixels of an image; and
• the object labels that characterize axis-aligned rectangle region of an image.

The classification and segmentation labels are entirely user defined. The set of segmentation labels will always contain at least the "Background" label representing pixels of the images that have no relevant information for your task (for example, in a defect segmentation application, the "Background" pixels would be the pixels without any defects).

For each type of labeling, an image can either be unlabeled or labeled. When an image in unlabeled for a given type of labeling, the image won't be used for training a deep learning tool that requires this type of labeling.

The image in the dataset can be stored as path to an image file or as an eVision image structure. Supported structures are 8-bits monochrome (EImageBW8), 16-bits monochrome (EImageBW16), and 24-bits color (EROIC24, EImageC24).

The dataset associates with each image a region of interest and a mask/don't care area. By default, the region of interest of an image is its full extent and its mask is empty.

A dataset can contain images with different sizes (with and height of their region of interest). However, the dataset has a default resolution (see EDataset::GetWidth and EDataset::GetHeight) that is used by deep learning tools that require the same input image size such as the EClassifier. When the images have different sizes, the default resolution will be the resolution of the region of interest of the first image added to the dataset.

License(s): BasicTypes

Constructor & Destructor Documentation

EDataset() [1/3]

eVision::EasyDeepLearning::EDataset::EDataset

(

)

Constructs a EDataset object.

EDataset() [2/3]

eVision::EasyDeepLearning::EDataset::EDataset

(

const eVision::EasyDeepLearning::EDataset &

other

)

Copy constructor of a EDataset object.

Parameters

other

Reference to the EDataset object that should be copied

~EDataset()

eVision::EasyDeepLearning::EDataset::~EDataset ( ) throw (  )

virtual

EDataset() [3/3]

eVision::EasyDeepLearning::EDataset::EDataset

(

CppWrapperInternal::InternalConstructor

)

Member Function Documentation

AddImage() [1/8]

int eVision::EasyDeepLearning::EDataset::AddImage

(

const eVision::EBaseROI &

img

)

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

img

The image

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImage() [2/8]

int eVision::EasyDeepLearning::EDataset::AddImage	(	const eVision::EBaseROI &	img,
		const std::string &	label )

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

img	The image
label	The label

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImage() [3/8]

int eVision::EasyDeepLearning::EDataset::AddImage	(	const eVision::EBaseROI &	img,
		int	originX,
		int	originY,
		int	width,
		int	height,
		const eVision::ERegion &	mask )

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

img	The image
originX	Region of interest origin abscissa
originY	Region of interest origin ordinate
width	Region of interest width
height	Region of interest height
mask	The mask for the image (with respect to the region of interest)

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImage() [4/8]

int eVision::EasyDeepLearning::EDataset::AddImage	(	const eVision::EBaseROI &	img,
		int	originX,
		int	originY,
		int	width,
		int	height,
		const eVision::ERegion &	mask,
		const std::string &	label )

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

img	The image
originX	Region of interest origin abscissa
originY	Region of interest origin ordinate
width	Region of interest width
height	Region of interest height
mask	The mask for the image (with respect to the region of interest)
label	The label

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImage() [5/8]

int eVision::EasyDeepLearning::EDataset::AddImage

(

const std::string &

imagePath

)

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

imagePath

The path to an image

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImage() [6/8]

int eVision::EasyDeepLearning::EDataset::AddImage	(	const std::string &	imagePath,
		const std::string &	label )

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

imagePath	The path to an image
label	The label

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImage() [7/8]

int eVision::EasyDeepLearning::EDataset::AddImage	(	const std::string &	imagePath,
		int	originX,
		int	originY,
		int	width,
		int	height,
		const eVision::ERegion &	mask )

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

imagePath	The path to an image
originX	Region of interest origin abscissa
originY	Region of interest origin ordinate
width	Region of interest width
height	Region of interest height
mask	The mask for the image (with respect to the region of interest)

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImage() [8/8]

int eVision::EasyDeepLearning::EDataset::AddImage	(	const std::string &	imagePath,
		int	originX,
		int	originY,
		int	width,
		int	height,
		const eVision::ERegion &	mask,
		const std::string &	label )

Adds an image to the dataset.

The image can be specified by its path on the filesystem (parameter imagePath) or by an eVision image buffer (parameter img).

By default, an image will have no classification label and no segmentation. The label of the image can be directly specified when adding the image to the dataset. If the given label was not in the classification labels of the dataset, it will be automatically added to them.

If no region of interest and/or mask is specified, the region of interest of the image will be its full extent and its mask will be empty.

The method returns the index of the image in the dataset. This index is equal to EDataset::GetNumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Parameters

imagePath	The path to an image
originX	Region of interest origin abscissa
originY	Region of interest origin ordinate
width	Region of interest width
height	Region of interest height
mask	The mask for the image (with respect to the region of interest)
label	The label

When adding eVision images (EBaseROI) to a dataset, the dataset will retain a copy of the image.

When specifying an individual region of interest and/or mask, the dataset will retain a copy of these.

AddImageObject() [1/2]

void eVision::EasyDeepLearning::EDataset::AddImageObject	(	int	imageIndex,
		const eVision::EasyDeepLearning::ELocatorObject &	obj )

Adds an object to the image.

Parameters

imageIndex	Index of the image
obj	Object

The image will be marked as labeled for object detection (EDataset::HasObjectLabeling equals to true) after a call to this method.

The object must be located at least partially within the region of interest of the image. Otherwise, when the object is completely outside of the region of interest, an exception will be thrown.

If the label of the object does not exist in the object labels of the dataset, the label will be added to the object labels of the dataset.

AddImageObject() [2/2]

void eVision::EasyDeepLearning::EDataset::AddImageObject	(	int	imageIndex,
		const std::string &	label,
		const eVision::ERectangleRegion &	box )

Adds an object to the image.

Parameters

imageIndex	Index of the image
label	Label of the object
box	Axis-aligned rectangle

The image will be marked as labeled for object detection (EDataset::HasObjectLabeling equals to true) after a call to this method.

The object must be located at least partially within the region of interest of the image. Otherwise, when the object is completely outside of the region of interest, an exception will be thrown.

If the label of the object does not exist in the object labels of the dataset, the label will be added to the object labels of the dataset.

AddImages() [1/4]

int eVision::EasyDeepLearning::EDataset::AddImages

(

const std::string &

filter

)

Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.

By default, all the images will have no classification label and no classification. However, a label can be directly specified for all of the images.

The method returns the number of images added to the dataset.

Parameters

filter

A glob filter

The filter is a glob pattern. This means the wildcard characters "*" and "?" correspond to "zero or more character" and "a single character" respectively. For example, the filter "*_good_*.png" will match any filename that contains the string "_good_" and has a png extension.

AddImages() [2/4]

int eVision::EasyDeepLearning::EDataset::AddImages	(	const std::string &	filter,
		const std::string &	label )

Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.

By default, all the images will have no classification label and no classification. However, a label can be directly specified for all of the images.

The method returns the number of images added to the dataset.

Parameters

filter	A glob filter
label	A label.

The filter is a glob pattern. This means the wildcard characters "*" and "?" correspond to "zero or more character" and "a single character" respectively. For example, the filter "*_good_*.png" will match any filename that contains the string "_good_" and has a png extension.

AddImages() [3/4]

int eVision::EasyDeepLearning::EDataset::AddImages	(	const std::string &	filter,
		int	originX,
		int	originY,
		int	width,
		int	height,
		const eVision::ERegion &	mask )

Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.

By default, all the images will have no classification label and no classification. However, a label can be directly specified for all of the images.

The method returns the number of images added to the dataset.

Parameters

filter	A glob filter
originX	Region of interest origin abscissa
originY	Region of interest origin ordinate
width	Region of interest width
height	Region of interest height
mask	The mask for the images

The filter is a glob pattern. This means the wildcard characters "*" and "?" correspond to "zero or more character" and "a single character" respectively. For example, the filter "*_good_*.png" will match any filename that contains the string "_good_" and has a png extension.

AddImages() [4/4]

int eVision::EasyDeepLearning::EDataset::AddImages	(	const std::string &	filter,
		int	originX,
		int	originY,
		int	width,
		int	height,
		const eVision::ERegion &	mask,
		const std::string &	label )

Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.

By default, all the images will have no classification label and no classification. However, a label can be directly specified for all of the images.

The method returns the number of images added to the dataset.

Parameters

filter	A glob filter
originX	Region of interest origin abscissa
originY	Region of interest origin ordinate
width	Region of interest width
height	Region of interest height
mask	The mask for the images
label	A label.

The filter is a glob pattern. This means the wildcard characters "*" and "?" correspond to "zero or more character" and "a single character" respectively. For example, the filter "*_good_*.png" will match any filename that contains the string "_good_" and has a png extension.

AddImagesFromCSV() [1/5]

int eVision::EasyDeepLearning::EDataset::AddImagesFromCSV

(

const std::string &

csvFile

)

Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.

If the image path in the CSV file are relative, we use the directory containing the given CSV file as the root of the image paths.

The method returns the number of images added to the dataset.

AddImagesFromCSV() [2/5]

int eVision::EasyDeepLearning::EDataset::AddImagesFromCSV	(	const std::string &	csvFile,
		int	imageColumn )

Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.

If the image path in the CSV file are relative, we use the directory containing the given CSV file as the root of the image paths.

The method returns the number of images added to the dataset.

AddImagesFromCSV() [3/5]

int eVision::EasyDeepLearning::EDataset::AddImagesFromCSV	(	const std::string &	csvFile,
		int	imageColumn,
		int	labelColumn )

Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.

If the image path in the CSV file are relative, we use the directory containing the given CSV file as the root of the image paths.

The method returns the number of images added to the dataset.

AddImagesFromCSV() [4/5]

int eVision::EasyDeepLearning::EDataset::AddImagesFromCSV	(	const std::string &	csvFile,
		int	imageColumn,
		int	labelColumn,
		int	skipLines )

Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.

If the image path in the CSV file are relative, we use the directory containing the given CSV file as the root of the image paths.

The method returns the number of images added to the dataset.

AddImagesFromCSV() [5/5]

int eVision::EasyDeepLearning::EDataset::AddImagesFromCSV	(	const std::string &	csvFile,
		int	imageColumn,
		int	labelColumn,
		int	skipLines,
		const std::string &	separator )

Adds images using a CSV (Comma Separated Value) file where each row correspond to an image and the file path and label of each image is given in the columns imageColumn and labelColumn respectively.

If the image path in the CSV file are relative, we use the directory containing the given CSV file as the root of the image paths.

The method returns the number of images added to the dataset.

AddLabel()

void eVision::EasyDeepLearning::EDataset::AddLabel

(

const std::string &

label

)

Adds a label to the dataset.

Parameters

label

Name of the new label

AddObjectLabel() [1/2]

void eVision::EasyDeepLearning::EDataset::AddObjectLabel

(

const std::string &

label

)

Adds an object label.

Parameters

label

Label to add

AddObjectLabel() [2/2]

void eVision::EasyDeepLearning::EDataset::AddObjectLabel	(	const std::string &	label,
		float	weight )

Adds an object label.

Parameters

label	Label to add
weight	Weight of the label

AddRegionToSegment() [1/2]

void eVision::EasyDeepLearning::EDataset::AddRegionToSegment	(	int	imageIndex,
		const std::string &	label,
		const eVision::ERegion &	region )

Adds a region (see ERegion) to the given segment of an image. If, before this call, the image had no segmentation, the pixels not in the given region will be set to the background segmentation label.

Parameters

imageIndex	Image index
label	Segmentation label
region	Region to add

AddRegionToSegment() [2/2]

void eVision::EasyDeepLearning::EDataset::AddRegionToSegment	(	int	imageIndex,
		int	segmentationLabelIndex,
		const eVision::ERegion &	region )

Adds a region (see ERegion) to the given segment of an image. If, before this call, the image had no segmentation, the pixels not in the given region will be set to the background segmentation label.

Parameters

imageIndex	Image index
segmentationLabelIndex	Segmentation label index
region	Region to add

AddSegmentationLabel() [1/2]

int eVision::EasyDeepLearning::EDataset::AddSegmentationLabel

(

const std::string &

label

)

Adds a segmentation label. The index of the new segmentation labels is returned by the method.

Parameters

label

Name of the segmentation label

AddSegmentationLabel() [2/2]

int eVision::EasyDeepLearning::EDataset::AddSegmentationLabel	(	const std::string &	label,
		float	labelWeight )

Adds a segmentation label. The index of the new segmentation labels is returned by the method.

Parameters

label	Name of the segmentation label
labelWeight	Weight of the segmentation label

Clear()

void eVision::EasyDeepLearning::EDataset::Clear ( )

virtual

Clears the dataset.

Reimplemented in eVision::EasyDeepLearning::EClassificationDataset.

Export() [1/12]

void eVision::EasyDeepLearning::EDataset::Export

(

const std::string &

directory

)

const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory

A string containing the full path to the directory.

Export() [2/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		bool	keepFilename ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
keepFilename	Keep the original filename of the images

Export() [3/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		bool	keepFilename,
		eVision::EImageFileType	fileType ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
keepFilename	Keep the original filename of the images
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.

Export() [4/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		bool	keepFilename,
		eVision::EImageFileType	fileType,
		int	quality ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
keepFilename	Keep the original filename of the images
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.
quality	Quality or compression parameters for EBaseROI::SavePng, EBaseROI::SaveJpeg, or EBaseROI::SaveJpeg2K. A value of -1 means the default value.

Export() [5/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		eVision::EImageFileType	fileType ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.

Export() [6/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		eVision::EImageFileType	fileType,
		int	quality ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.
quality	Quality or compression parameters for EBaseROI::SavePng, EBaseROI::SaveJpeg, or EBaseROI::SaveJpeg2K. A value of -1 means the default value.

Export() [7/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		int	width,
		int	height,
		int	channels ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
width	Width of the image.
height	Height of the image.
channels	Number of channels of the image (only 1 and 3 are valid, for grayscale and RGB respectively).

Export() [8/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		int	width,
		int	height,
		int	channels,
		bool	keepFilename ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
width	Width of the image.
height	Height of the image.
channels	Number of channels of the image (only 1 and 3 are valid, for grayscale and RGB respectively).
keepFilename	Keep the original filename of the images

Export() [9/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		int	width,
		int	height,
		int	channels,
		bool	keepFilename,
		eVision::EImageFileType	fileType ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
width	Width of the image.
height	Height of the image.
channels	Number of channels of the image (only 1 and 3 are valid, for grayscale and RGB respectively).
keepFilename	Keep the original filename of the images
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.

Export() [10/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		int	width,
		int	height,
		int	channels,
		bool	keepFilename,
		eVision::EImageFileType	fileType,
		int	quality ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
width	Width of the image.
height	Height of the image.
channels	Number of channels of the image (only 1 and 3 are valid, for grayscale and RGB respectively).
keepFilename	Keep the original filename of the images
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.
quality	Quality or compression parameters for EBaseROI::SavePng, EBaseROI::SaveJpeg, or EBaseROI::SaveJpeg2K. A value of -1 means the default value.

Export() [11/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		int	width,
		int	height,
		int	channels,
		eVision::EImageFileType	fileType ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
width	Width of the image.
height	Height of the image.
channels	Number of channels of the image (only 1 and 3 are valid, for grayscale and RGB respectively).
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.

Export() [12/12]

void eVision::EasyDeepLearning::EDataset::Export	(	const std::string &	directory,
		int	width,
		int	height,
		int	channels,
		eVision::EImageFileType	fileType,
		int	quality ) const

Exports the dataset and its images to the given directory.

• A new EDataset object with relative paths to the images is saved into the given directory.
• The exported images will be placed in a sub directory named "Images".

By default, the images will be saved under the filename "Image_[id].[ext]" where [id] is the index of the image in the dataset and [ext] is the image extension. If fileType is set to EImageFileType_Auto, [ext] will be the same as the original image. Otherwise, [ext] will be deduced from the specified file type. If keepFilename is set to true, the images will keep their original filename (except for their extensions). In two images have the same filename, an index will be automatically added to avoid nay conflict. A width, height, and number of channels can be optionally specified to reformat all the images in the dataset. Note that, in this case, only the ROI of the image will be exported.

Parameters

directory	A string containing the full path to the directory.
width	Width of the image.
height	Height of the image.
channels	Number of channels of the image (only 1 and 3 are valid, for grayscale and RGB respectively).
fileType	File type for the exported file. If EImageFileType_Auto, the same file type as the original image is used.
quality	Quality or compression parameters for EBaseROI::SavePng, EBaseROI::SaveJpeg, or EBaseROI::SaveJpeg2K. A value of -1 means the default value.

ExtractSplit()

eVision::EasyDeepLearning::EDataset eVision::EasyDeepLearning::EDataset::ExtractSplit	(	const eVision::EasyDeepLearning::EDatasetSplit &	split,
		eVision::EasyDeepLearning::EDatasetType	type ) const

Creates a new EDataset containing only the images of the given split type.

Parameters

split	Split
type	Type of the split to extract

GetAbsoluteMaxOverlap()

float eVision::EasyDeepLearning::EDataset::GetAbsoluteMaxOverlap

(

)

const

Absolute maximum overlap between objects in the dataset.

GetAvailableImageAnnotationFormat() [1/2]

eVision::EasyDeepLearning::EImageAnnotationFormat eVision::EasyDeepLearning::EDataset::GetAvailableImageAnnotationFormat

(

const std::string &

imageFile

)

const

Image annotation file formats that are available next to the image file.

Parameters

imageFile

Path to an image file

GetAvailableImageAnnotationFormat() [2/2]

eVision::EasyDeepLearning::EImageAnnotationFormat eVision::EasyDeepLearning::EDataset::GetAvailableImageAnnotationFormat

(

int

imgId

)

const

Image annotation file formats that are available next to the image file.

Parameters

imgId

Index of an image

GetChannels()

uint32_t eVision::EasyDeepLearning::EDataset::GetChannels

(

)

const

Number of channels of the first image added to the dataset.

GetHeight()

uint32_t eVision::EasyDeepLearning::EDataset::GetHeight

(

)

const

Height of the region of interest of the first image added to the dataset.

GetImageCopy() [1/2]

std::unique_ptr< eVision::EBaseROI > eVision::EasyDeepLearning::EDataset::GetImageCopy

(

int

index

)

const

Gets a copy of the i-th image of the dataset.

Parameters

index

The index of the image.

GetImageCopy() [2/2]

void eVision::EasyDeepLearning::EDataset::GetImageCopy	(	int	index,
		eVision::EBaseROI &	img ) const

Gets a copy of the i-th image of the dataset.

Parameters

index	The index of the image.
img	Image object to copy the image into.

GetImageLabel()

std::string eVision::EasyDeepLearning::EDataset::GetImageLabel

(

int

index

)

const

Gets the label of the i-th image of the dataset. An exception is thrown if the image has no classification label.

Parameters

index

The index of the image for which to get the label.

GetImageNumObjects()

int eVision::EasyDeepLearning::EDataset::GetImageNumObjects

(

int

imageIndex

)

const

Number of objects for the specified image.

Parameters

imageIndex

Index of the image

GetImageObject()

eVision::EasyDeepLearning::ELocatorObject eVision::EasyDeepLearning::EDataset::GetImageObject	(	int	imageIndex,
		int	objectIndex ) const

Gets the specified object for the given image.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and EDataset::GetImageNumObjects

GetImageObjects()

std::vector< eVision::EasyDeepLearning::ELocatorObject > eVision::EasyDeepLearning::EDataset::GetImageObjects

(

int

imageIndex

)

const

Gets all the objects of the specified image.

Parameters

imageIndex

Index of the image

GetImagePath()

std::string eVision::EasyDeepLearning::EDataset::GetImagePath

(

int

index

)

const

Gets the path of the i-th image of the dataset. If the image was not given using a path, the method will throw an exception.

Parameters

index

The index of the image for which to get the path.

GetImagesIndexesWithLabel() [1/2]

std::vector< uint32_t > eVision::EasyDeepLearning::EDataset::GetImagesIndexesWithLabel

(

const std::string &

label

)

const

Gets a list of index corresponding to the images associated with the given label.

Parameters

label

The label

GetImagesIndexesWithLabel() [2/2]

std::vector< uint32_t > eVision::EasyDeepLearning::EDataset::GetImagesIndexesWithLabel

(

int

labelIndex

)

const

Gets a list of index corresponding to the images associated with the given label.

Parameters

labelIndex

The index of the label (starting from 0 to EDataset::GetNumLabels - 1)

GetImagesIndexesWithNoLabel()

std::vector< int > eVision::EasyDeepLearning::EDataset::GetImagesIndexesWithNoLabel

(

)

const

Gets a list of index corresponding to the images with no classification labels.

GetLabel()

std::string eVision::EasyDeepLearning::EDataset::GetLabel

(

int

i

)

const

Gets the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1).

Parameters

i	Label index

GetLabelColor()

eVision::ERGBColor eVision::EasyDeepLearning::EDataset::GetLabelColor

(

int

index

)

const

Gets the label color for a classification label.

Parameters

index

Label index (0 to EDataset::GetNumLabels - 1).

GetLabeledImagesIndexes()

std::vector< int > eVision::EasyDeepLearning::EDataset::GetLabeledImagesIndexes

(

)

const

Gets a list of index corresponding to the images that have a classification label.

GetLabelOpacity()

uint8_t eVision::EasyDeepLearning::EDataset::GetLabelOpacity

(

int

index

)

const

Gets the label opacity for a classification label.

Parameters

index

Label index (0 to EDataset::GetNumLabels - 1).

GetLabelWeight()

float eVision::EasyDeepLearning::EDataset::GetLabelWeight

(

int

index

)

const

Gets the weight associated to the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1).

Parameters

index

Label index

GetMask()

eVision::ERegion eVision::EasyDeepLearning::EDataset::GetMask

(

int

imageIndex

)

const

Gets the mask region/don't care area for the given image. The mask is defined with respect to the region of interest of the image.

Parameters

imageIndex

Index of the image for which to get the mask region

GetMaxNumObjectPerImage()

int eVision::EasyDeepLearning::EDataset::GetMaxNumObjectPerImage

(

)

const

Maximum number of objects for an image in the dataset. If no images has object labeling (see EDataset::HasObjectLabeling), the method returns -1.

GetNumImageFiles()

int eVision::EasyDeepLearning::EDataset::GetNumImageFiles

(

)

const

Number of different image files contained in the dataset.

The number of image files can be different than the number of images of the dataset. An image file can correspond to several dataset images (with different ROI).

GetNumImages()

int eVision::EasyDeepLearning::EDataset::GetNumImages

(

)

const

Number of images in the dataset.

GetNumImagesForFile()

int eVision::EasyDeepLearning::EDataset::GetNumImagesForFile

(

const std::string &

filePath

)

const

Get the number of images in the dataset that are associated with the given image file.

Parameters

filePath

the path to the image.

The number of image files can be different than the number of images of the dataset. An image file can correspond to several dataset images (with different ROI).

GetNumImagesForSegmentationLabel() [1/2]

int eVision::EasyDeepLearning::EDataset::GetNumImagesForSegmentationLabel

(

const std::string &

label

)

const

Number of images containing the given segmentation label.

Parameters

label

Segmentation label

GetNumImagesForSegmentationLabel() [2/2]

int eVision::EasyDeepLearning::EDataset::GetNumImagesForSegmentationLabel

(

int

labelId

)

const

Number of images containing the given segmentation label.

Parameters

labelId

Index of the segmentation label

GetNumImagesWithForegroundSegments()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithForegroundSegments

(

)

const

Number of images that have a ground truth segmentation that has foreground segments and is this not entirely composed of background pixels.

GetNumImagesWithObjectLabel() [1/2]

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithObjectLabel

(

const std::string &

label

)

const

Number of images that contain an object with the given label.

Parameters

label

Label.

GetNumImagesWithObjectLabel() [2/2]

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithObjectLabel

(

int

labelIdx

)

const

Number of images that contain an object with the given label.

Parameters

labelIdx

Index of the object label.

GetNumImagesWithObjectLabeling()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithObjectLabeling

(

)

const

Number of images in the dataset that are labeled for object detection.

GetNumImagesWithObjects()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithObjects

(

)

const

Number of images in the dataset that are labeled for object detection and have 1 or more objects.

GetNumImagesWithOCR()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithOCR

(

)

const

Number of images with OCR annotations.

GetNumImagesWithoutForegroundSegments()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithoutForegroundSegments

(

)

const

Number of images that have a ground truth segmentation that has no foreground segments and is thus entirely composed of background pixels.

GetNumImagesWithoutObjectLabeling()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithoutObjectLabeling

(

)

const

Number of images in the dataset that are not labeled for object detection.

GetNumImagesWithoutObjects()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithoutObjects

(

)

const

Number of images in the dataset that are labeled for object detection but have been associated with no object.

GetNumImagesWithoutOCR()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithoutOCR

(

)

const

Number of images without OCR annotations.

GetNumImagesWithoutSegmentation()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithoutSegmentation

(

)

const

Number of images that doesn't have a ground truth segmentation.

GetNumImagesWithSegmentation()

int eVision::EasyDeepLearning::EDataset::GetNumImagesWithSegmentation

(

)

const

Number of images that have a ground truth segmentation.

GetNumLabeledImages()

int eVision::EasyDeepLearning::EDataset::GetNumLabeledImages

(

)

const

Number of images that have a classification label.

GetNumLabels()

int eVision::EasyDeepLearning::EDataset::GetNumLabels

(

)

const

Number of labels in the dataset.

GetNumObjectLabels()

int eVision::EasyDeepLearning::EDataset::GetNumObjectLabels

(

)

const

Number of object labels.

GetNumObjectsWithLabel() [1/2]

uint64_t eVision::EasyDeepLearning::EDataset::GetNumObjectsWithLabel

(

const std::string &

label

)

const

Number of objects with the given label.

Parameters

label

Label.

GetNumObjectsWithLabel() [2/2]

uint64_t eVision::EasyDeepLearning::EDataset::GetNumObjectsWithLabel

(

int

labelIdx

)

const

Number of objects with the given label.

Parameters

labelIdx

Index of the object label.

GetNumPixelsForSegmentationLabel() [1/2]

uint64_t eVision::EasyDeepLearning::EDataset::GetNumPixelsForSegmentationLabel

(

const std::string &

label

)

const

Number of pixels assigned to the given segmentation label.

Parameters

label

Segmentation label

GetNumPixelsForSegmentationLabel() [2/2]

uint64_t eVision::EasyDeepLearning::EDataset::GetNumPixelsForSegmentationLabel

(

int

labelId

)

const

Number of pixels assigned to the given segmentation label.

Parameters

labelId

Index of the segmentation label

GetNumSegmentationLabels()

int eVision::EasyDeepLearning::EDataset::GetNumSegmentationLabels

(

)

const

Number of segmentation labels. A dataset has always at least one segmentation label: "background".

GetNumSegmentedBlobs() [1/3]

int eVision::EasyDeepLearning::EDataset::GetNumSegmentedBlobs

(

int

imageId

)

const

Number of segmented blobs in the image for all non-background labels or for a specific label.

Parameters

imageId

Image index

A segmented blob is a contiguous area assigned to a label different than "Background".

GetNumSegmentedBlobs() [2/3]

int eVision::EasyDeepLearning::EDataset::GetNumSegmentedBlobs	(	int	imageId,
		const std::string &	label ) const

Number of segmented blobs in the image for all non-background labels or for a specific label.

Parameters

imageId	Image index
label	Label

A segmented blob is a contiguous area assigned to a label different than "Background".

GetNumSegmentedBlobs() [3/3]

int eVision::EasyDeepLearning::EDataset::GetNumSegmentedBlobs	(	int	imageId,
		int	labelId ) const

Number of segmented blobs in the image for all non-background labels or for a specific label.

Parameters

imageId	Image index
labelId	Label index

A segmented blob is a contiguous area assigned to a label different than "Background".

GetNumTexts()

int eVision::EasyDeepLearning::EDataset::GetNumTexts

(

)

const

Number of annotated texts in the dataset.

GetNumUnlabeledImages()

int eVision::EasyDeepLearning::EDataset::GetNumUnlabeledImages

(

)

const

Number of images that doesn't have any classification label.

GetObjectLabel()

std::string eVision::EasyDeepLearning::EDataset::GetObjectLabel

(

int

labelIndex

)

const

Object label.

Parameters

labelIndex

Index of the object label between 0 and EDataset::GetNumObjectLabels

GetObjectLabelColor()

eVision::ERGBColor eVision::EasyDeepLearning::EDataset::GetObjectLabelColor

(

int

index

)

const

Gets the label color for an object label.

Parameters

index

Label index (0 to EDataset::GetNumLabels - 1).

GetObjectLabelOpacity()

uint8_t eVision::EasyDeepLearning::EDataset::GetObjectLabelOpacity

(

int

index

)

const

Gets the label opacity for an object label.

Parameters

index

Label index (0 to EDataset::GetNumLabels - 1).

GetObjectLabelWeight() [1/2]

float eVision::EasyDeepLearning::EDataset::GetObjectLabelWeight

(

const std::string &

label

)

const

Gets the weight of an object label.

Parameters

label

Label

GetObjectLabelWeight() [2/2]

float eVision::EasyDeepLearning::EDataset::GetObjectLabelWeight

(

int

labelIndex

)

const

Gets the weight of an object label.

Parameters

labelIndex

Index of the object label

GetObjectSize()

int eVision::EasyDeepLearning::EDataset::GetObjectSize

(

)

const

Object size for EasyLocate Interest point.

GetOCRAnnotations()

std::vector< eVision::EasyDeepOCR::ETextLine > eVision::EasyDeepLearning::EDataset::GetOCRAnnotations

(

int

imgId

)

const

Ground truth text lines for the specified image.

Parameters

imgId

Index of the image.

GetRegionForSegment() [1/2]

eVision::ERegion eVision::EasyDeepLearning::EDataset::GetRegionForSegment	(	int	imageIndex,
		const std::string &	segmentationLabel )

Gets a region corresponding to the pixels of the given segment.

Parameters

imageIndex	Image index
segmentationLabel	Segmentation label

GetRegionForSegment() [2/2]

eVision::ERegion eVision::EasyDeepLearning::EDataset::GetRegionForSegment	(	int	imageIndex,
		int	segmentationLabelIndex )

Gets a region corresponding to the pixels of the given segment.

Parameters

imageIndex	Image index
segmentationLabelIndex	Segmentation label index

GetRegionOfInterestHeight()

int eVision::EasyDeepLearning::EDataset::GetRegionOfInterestHeight

(

int

imageIndex

)

const

Region of interest height for the specified image.

Parameters

imageIndex

Index of the image.

GetRegionOfInterestOriginX()

int eVision::EasyDeepLearning::EDataset::GetRegionOfInterestOriginX

(

int

imageIndex

)

const

Region of interest origin abscissa for the specified image.

Parameters

imageIndex

Index of the image.

GetRegionOfInterestOriginY()

int eVision::EasyDeepLearning::EDataset::GetRegionOfInterestOriginY

(

int

imageIndex

)

const

Region of interest origin ordinate for the specified image.

Parameters

imageIndex

Index of the image.

GetRegionOfInterestWidth()

int eVision::EasyDeepLearning::EDataset::GetRegionOfInterestWidth

(

int

imageIndex

)

const

Region of interest width for the specified image.

Parameters

imageIndex

Index of the image.

GetSameLabelMaxOverlap()

float eVision::EasyDeepLearning::EDataset::GetSameLabelMaxOverlap

(

)

const

Maximum overlap between objects with the same label in the dataset.

GetSegmentationLabel()

std::string eVision::EasyDeepLearning::EDataset::GetSegmentationLabel

(

int

index

)

const

Gets the segmentation label.

Parameters

index

Index of the segmentation label

The segmentation label index 0 is reserved and corresponds to the "background" label. This segmentation label can't be changed.

GetSegmentationLabelColor()

eVision::ERGBColor eVision::EasyDeepLearning::EDataset::GetSegmentationLabelColor

(

int

index

)

const

Gets the label color for a segmentation label.

Parameters

index

Label index (0 to EDataset::GetNumLabels - 1).

GetSegmentationLabelOpacity()

uint8_t eVision::EasyDeepLearning::EDataset::GetSegmentationLabelOpacity

(

int

index

)

const

Gets the label opacity for a segmentation label.

Parameters

index

Label index (0 to EDataset::GetNumLabels - 1).

GetSegmentationLabelWeight() [1/2]

float eVision::EasyDeepLearning::EDataset::GetSegmentationLabelWeight

(

const std::string &

label

)

const

Gets the segmentation label weights.

Parameters

label

Segmentation label

GetSegmentationLabelWeight() [2/2]

float eVision::EasyDeepLearning::EDataset::GetSegmentationLabelWeight

(

int

index

)

const

Gets the segmentation label weights.

Parameters

index

Index of the segmentation label

GetSegmentationMap()

eVision::EImageBW16 eVision::EasyDeepLearning::EDataset::GetSegmentationMap

(

int

imageIndex

)

const

Gets the segmentation map of an image. The segmentation map is a 16-bit EROIBW16 image where the value of each pixel is equal to the corresponding segmentation label index (see EDataset::GetSegmentationLabel).

If an image has no segmentation (EDataset::HasSegmentation), the getter will throw an exception.

Parameters

imageIndex

Image index

GetSplit() [1/4]

eVision::EasyDeepLearning::EDatasetSplit eVision::EasyDeepLearning::EDataset::GetSplit	(	float	trainingProportion,
		float	validationPropotion,
		eVision::EasyDeepLearning::EDeepLearningToolType	toolType,
		const std::string &	goodLabel ) const

Generates a split.

Parameters

trainingProportion	Approximate proportion of training images
validationPropotion	Approximate proportion of validation images
toolType	Tool type for which to generate the split
goodLabel	The good label for EasySegment Unsupervised split

GetSplit() [2/4]

eVision::EasyDeepLearning::EDatasetSplit eVision::EasyDeepLearning::EDataset::GetSplit	(	float	trainingProportion,
		float	validationPropotion,
		eVision::EasyDeepLearning::EDeepLearningToolType	toolType,
		const std::string &	goodLabel,
		uint32_t	seed ) const

Generates a split.

Parameters

trainingProportion	Approximate proportion of training images
validationPropotion	Approximate proportion of validation images
toolType	Tool type for which to generate the split
goodLabel	The good label for EasySegment Unsupervised split
seed	Seed for randomization

GetSplit() [3/4]

eVision::EasyDeepLearning::EDatasetSplit eVision::EasyDeepLearning::EDataset::GetSplit	(	int	numTrainingImages,
		int	numValidationImages,
		eVision::EasyDeepLearning::EDeepLearningToolType	toolType,
		const std::string &	goodLabel ) const

Generates a split.

Parameters

numTrainingImages	Number of training images
numValidationImages	Number of validation images
toolType	Tool type for which to generate the split
goodLabel	The good label for EasySegment Unsupervised split

GetSplit() [4/4]

eVision::EasyDeepLearning::EDatasetSplit eVision::EasyDeepLearning::EDataset::GetSplit	(	int	numTrainingImages,
		int	numValidationImages,
		eVision::EasyDeepLearning::EDeepLearningToolType	toolType,
		const std::string &	goodLabel,
		uint32_t	seed ) const

Generates a split.

Parameters

numTrainingImages	Number of training images
numValidationImages	Number of validation images
toolType	Tool type for which to generate the split
goodLabel	The good label for EasySegment Unsupervised split
seed	Seed for randomization

GetTextDataset()

eVision::EasyDeepLearning::EDataset eVision::EasyDeepLearning::EDataset::GetTextDataset

(

)

const

Transforms the current dataset to have only subwindow containing the actual text to read.

GetTextLabeledImagesIndexes()

std::vector< int > eVision::EasyDeepLearning::EDataset::GetTextLabeledImagesIndexes

(

)

const

Gets a list of index corresponding to the images that have a text only label.

GetTextOrientationDataset()

eVision::EasyDeepLearning::EDataset eVision::EasyDeepLearning::EDataset::GetTextOrientationDataset

(

)

const

Transforms the current text dataset to duplicate it with half image flipped, adding corresponding ground truth.

GetWidth()

uint32_t eVision::EasyDeepLearning::EDataset::GetWidth

(

)

const

Width of the region of interest of the first image added to the dataset.

HasForegroundSegments()

bool eVision::EasyDeepLearning::EDataset::HasForegroundSegments

(

int

imageId

)

const

Whether the image segmentation contains segments that are not background. If the image has no segmentation, this method throws an exception.

Parameters

imageId

Image index

HasLabel()

bool eVision::EasyDeepLearning::EDataset::HasLabel

(

int

index

)

const

Whether an image has a classification label.

Parameters

index

Index of an image.

HasObjectLabeling()

bool eVision::EasyDeepLearning::EDataset::HasObjectLabeling

(

int

imageIndex

)

const

Whether the image is labeled for object detection and use with ELocator.

Parameters

imageIndex

Index of the image

HasOCRAnnotations()

bool eVision::EasyDeepLearning::EDataset::HasOCRAnnotations

(

int

imgId

)

const

Whether the specified image has OCR annotations.

Parameters

imgId

Index of the image.

HasSegmentation()

bool eVision::EasyDeepLearning::EDataset::HasSegmentation

(

int

imageId

)

const

Whether the image has a segmentation.

Parameters

imageId

Image index

ImportPascalVOCXMLAnnotations() [1/2]

void eVision::EasyDeepLearning::EDataset::ImportPascalVOCXMLAnnotations

(

)

Imports Pascal VOC XML annotations (for EasyLocate Bounding Box).

This method may add new labels to the object labels.

The version that do not take an image index attempts to import the annotation from all images currently in the dataset.

ImportPascalVOCXMLAnnotations() [2/2]

void eVision::EasyDeepLearning::EDataset::ImportPascalVOCXMLAnnotations

(

int

imgId

)

Imports Pascal VOC XML annotations (for EasyLocate Bounding Box).

This method may add new labels to the object labels.

The version that do not take an image index attempts to import the annotation from all images currently in the dataset.

Parameters

imgId

The image index for which to import the annotation

ImportYOLOTXTAnnotations() [1/2]

void eVision::EasyDeepLearning::EDataset::ImportYOLOTXTAnnotations

(

const std::string &

labelFile

)

Imports YOLO TXT annotations (for EasyLocate Bounding Box).

You need to specify the list of labels associated with the YOLO TXT annotations through an array of string or through a file containing this list. This method may add new labels to the object labels.

The version that do not take an image index attempts to import the annotation from all images currently in the dataset.

Parameters

labelFile

Path to a file containing the label list

ImportYOLOTXTAnnotations() [2/2]

void eVision::EasyDeepLearning::EDataset::ImportYOLOTXTAnnotations	(	int	imgId,
		const std::string &	labelFile )

Imports YOLO TXT annotations (for EasyLocate Bounding Box).

You need to specify the list of labels associated with the YOLO TXT annotations through an array of string or through a file containing this list. This method may add new labels to the object labels.

The version that do not take an image index attempts to import the annotation from all images currently in the dataset.

Parameters

imgId	The image index for which to import the annotation
labelFile	Path to a file containing the label list

IsEmbeddedImage()

bool eVision::EasyDeepLearning::EDataset::IsEmbeddedImage

(

int

index

)

const

Whether the image is embedded into the dataset and is not a reference towards an image file.

Parameters

index

Index of the image.

IsImageFile()

bool eVision::EasyDeepLearning::EDataset::IsImageFile

(

int

index

)

const

Whether the image is stored as a file path towards an image.

Parameters

index

Index of the image.

Load() [1/2]

void eVision::EasyDeepLearning::EDataset::Load

(

const std::string &

path

)

Loads a classification dataset from disk.

Parameters

path	A string containing the full path to the dataset file.

Load() [2/2]

void eVision::EasyDeepLearning::EDataset::Load

(

eVision::ESerializer *

serializer

)

Loads a classification dataset from disk.

Parameters

serializer

The serializer.

operator=()

eVision::EasyDeepLearning::EDataset & eVision::EasyDeepLearning::EDataset::operator=

(

const eVision::EasyDeepLearning::EDataset &

other

)

Assignment operator.

Parameters

other

Reference to the EDataset object used for the assignment

RemoveImage()

void eVision::EasyDeepLearning::EDataset::RemoveImage

(

int

imgId

)

Removes the image at the given index. All annotations (label, segmentation, objects) will be lost.

Parameters

imgId

Index of the image to remove

RemoveImageObject()

void eVision::EasyDeepLearning::EDataset::RemoveImageObject	(	int	imageIndex,
		int	objectIndex )

Removes an object from an image.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and EDataset::GetImageNumObjects

RemoveLabel() [1/2]

void eVision::EasyDeepLearning::EDataset::RemoveLabel

(

const std::string &

label

)

Removes a label from the dataset.

All the images associated with this label will be set to unlabeled (see EDataset::HasLabel).

Parameters

label

Name of the label to remove

RemoveLabel() [2/2]

void eVision::EasyDeepLearning::EDataset::RemoveLabel

(

int

labelId

)

Removes a label from the dataset.

All the images associated with this label will be set to unlabeled (see EDataset::HasLabel).

Parameters

labelId

Index of the label to remove

RemoveObjectLabel() [1/2]

void eVision::EasyDeepLearning::EDataset::RemoveObjectLabel

(

const std::string &

label

)

Removes an object label.

Parameters

label

Label to remove

RemoveObjectLabel() [2/2]

void eVision::EasyDeepLearning::EDataset::RemoveObjectLabel

(

int

labelIndex

)

Removes an object label.

Parameters

labelIndex

Index of the object label to remove

RemoveSegmentationLabel() [1/2]

void eVision::EasyDeepLearning::EDataset::RemoveSegmentationLabel

(

const std::string &

label

)

Remove a segmentation label.

This method sets all the pixels in the dataset assigned to this label to the background label.

Parameters

label

Name of the segmentation label to remove

RemoveSegmentationLabel() [2/2]

void eVision::EasyDeepLearning::EDataset::RemoveSegmentationLabel

(

int

index

)

Remove a segmentation label.

This method sets all the pixels in the dataset assigned to this label to the background label.

Parameters

index

Index of the segmentation label to remove

ResetImageObjectLabeling()

void eVision::EasyDeepLearning::EDataset::ResetImageObjectLabeling

(

int

imageIndex

)

Resets the object labeling for the specified image. This sets the image as having been labeled for object detection with no object present in the image.

Parameters

imageIndex

Index of the image

ResetSegmentation()

void eVision::EasyDeepLearning::EDataset::ResetSegmentation

(

int

imageIndex

)

Resets the segmentation of an image by setting all pixels to background.

Parameters

imageIndex

Image index

Save() [1/2]

void eVision::EasyDeepLearning::EDataset::Save

(

const std::string &

path

)

Saves a classification dataset to disk, containing the file paths to the images in the dataset and their associated label.

Parameters

path	A string containing the full path to the dataset file.

This method only save the image that were given to this EDataset instance as EBaseROI pointers.

To obtain a portable EDataset file, please use EDataset::Export.

Save() [2/2]

void eVision::EasyDeepLearning::EDataset::Save

(

eVision::ESerializer *

serializer

)

Saves a classification dataset to disk, containing the file paths to the images in the dataset and their associated label.

Parameters

serializer

The serializer.

This method only save the image that were given to this EDataset instance as EBaseROI pointers.

To obtain a portable EDataset file, please use EDataset::Export.

SaveWithRelativePath()

void eVision::EasyDeepLearning::EDataset::SaveWithRelativePath

(

const std::string &

path

)

Saves the dataset with image paths relative to the directory in which the dataset file is saved.

SetBasePath()

void eVision::EasyDeepLearning::EDataset::SetBasePath

(

const std::string &

basePath

)

Sets the base path for the images specified with a relative path.

Parameters

basePath

Base path

The base path is not serialized. It must be set after loading a dataset file.

SetImageLabel() [1/2]

void eVision::EasyDeepLearning::EDataset::SetImageLabel	(	const std::string &	filter,
		const std::string &	label )

Sets the label of images in the dataset.

Parameters

filter	A glob filter
label	The label

The filter is a glob pattern. This means the wildcard characters "*" and "?" correspond to "zero or more character" and "a single character" respectively. For example, the filter "*_good_*.png" will match any filename that contains the string "_good_" and has a png extension.

SetImageLabel() [2/2]

void eVision::EasyDeepLearning::EDataset::SetImageLabel	(	int	index,
		const std::string &	label )

Sets the label of images in the dataset.

Parameters

index	The index of the image for which to set the label.
label	The label

The filter is a glob pattern. This means the wildcard characters "*" and "?" correspond to "zero or more character" and "a single character" respectively. For example, the filter "*_good_*.png" will match any filename that contains the string "_good_" and has a png extension.

SetImageObject() [1/3]

void eVision::EasyDeepLearning::EDataset::SetImageObject	(	int	imageIndex,
		int	objectIndex,
		const eVision::EasyDeepLearning::ELocatorObject &	obj )

Sets the specified object for the given image.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and EDataset::GetImageNumObjects
obj	Object

If the label of the object does not exist in the object labels of the dataset, the label will be added to the object labels of the dataset.

SetImageObject() [2/3]

void eVision::EasyDeepLearning::EDataset::SetImageObject	(	int	imageIndex,
		int	objectIndex,
		const eVision::ERectangleRegion &	region )

Sets the specified object for the given image.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and EDataset::GetImageNumObjects
region	New region for the object

If the label of the object does not exist in the object labels of the dataset, the label will be added to the object labels of the dataset.

SetImageObject() [3/3]

void eVision::EasyDeepLearning::EDataset::SetImageObject	(	int	imageIndex,
		int	objectIndex,
		const std::string &	label )

Sets the specified object for the given image.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and EDataset::GetImageNumObjects
label	New label for the object

If the label of the object does not exist in the object labels of the dataset, the label will be added to the object labels of the dataset.

SetLabel()

void eVision::EasyDeepLearning::EDataset::SetLabel	(	int	index,
		const std::string &	label )

Sets the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1). This operation does not add a new label to the dataset but simply renames an existing label.

Parameters

index	Label index
label	Replacement label

SetLabelColor()

void eVision::EasyDeepLearning::EDataset::SetLabelColor	(	int	index,
		const eVision::ERGBColor &	color )

Sets the label color for a classification label.

Parameters

index	Label index (0 to EDataset::GetNumLabels - 1).
color	New color for the label

SetLabelOpacity()

void eVision::EasyDeepLearning::EDataset::SetLabelOpacity	(	int	index,
		const uint8_t	opacity )

Sets the label opacity for a classification label.

Parameters

index	Label index (0 to EDataset::GetNumLabels - 1).
opacity	New opacity for the label

SetLabelWeight()

void eVision::EasyDeepLearning::EDataset::SetLabelWeight	(	int	index,
		const float &	weight )

Sets the weight associated to the i-th label of the dataset (starting from 0 to EDataset::GetNumLabels - 1). This operation does not add a new label.

Parameters

index	Label index
weight	Weight to be set

SetMask()

void eVision::EasyDeepLearning::EDataset::SetMask	(	int	imageIndex,
		const eVision::ERegion &	mask )

Sets the mask region/don't care area for the given image. The mask is defined with respect to the region of interest of the image.

Parameters

imageIndex	Index of the image for which to get the mask region
mask	Mask to set on the image

SetObjectLabel() [1/2]

void eVision::EasyDeepLearning::EDataset::SetObjectLabel	(	const std::string &	oldLabel,
		const std::string &	newLabel )

Sets an object label.

Parameters

oldLabel	Old label to change
newLabel	New label to be set

SetObjectLabel() [2/2]

void eVision::EasyDeepLearning::EDataset::SetObjectLabel	(	int	labelIndex,
		const std::string &	newLabel )

Sets an object label.

Parameters

labelIndex	Index of the object label
newLabel	New label to be set

SetObjectLabelColor()

void eVision::EasyDeepLearning::EDataset::SetObjectLabelColor	(	int	index,
		const eVision::ERGBColor &	color )

Sets the label color for an object label.

Parameters

index	Label index (0 to EDataset::GetNumLabels - 1).
color	New color for the label

SetObjectLabelOpacity()

void eVision::EasyDeepLearning::EDataset::SetObjectLabelOpacity	(	int	index,
		const uint8_t	opacity )

Sets the label opacity for an object label.

Parameters

index	Label index (0 to EDataset::GetNumLabels - 1).
opacity	New opacity for the label

SetObjectLabelWeight() [1/2]

void eVision::EasyDeepLearning::EDataset::SetObjectLabelWeight	(	const std::string &	label,
		float	weight )

Sets the weight of an object label.

Parameters

label	Label
weight	New weight

SetObjectLabelWeight() [2/2]

void eVision::EasyDeepLearning::EDataset::SetObjectLabelWeight	(	int	labelIndex,
		float	weight )

Sets the weight of an object label.

Parameters

labelIndex	Index of the object label
weight	New weight

SetObjectSize()

void eVision::EasyDeepLearning::EDataset::SetObjectSize

(

int

objectSize

)

Object size for EasyLocate Interest point.

Parameters

objectSize

New object size

SetOCRAnnotations()

void eVision::EasyDeepLearning::EDataset::SetOCRAnnotations	(	int	imgId,
		const std::vector< eVision::EasyDeepOCR::ETextLine > &	texts )

Sets the ground truth text lines for the specified image.

Parameters

imgId	Index of the image.
texts	Text lines of the image.

SetRegionOfInterest()

void eVision::EasyDeepLearning::EDataset::SetRegionOfInterest	(	int	imageIndex,
		int	xOrg,
		int	yOrg,
		int	width,
		int	height )

Sets the region of interest for the specified image.

Parameters

imageIndex	Index of the image.
xOrg	ROI origin abscissa
yOrg	ROI origin ordinate
width	ROI width
height	ROI height

Any objects or segmentation located outside of the region of interest will be lost.

SetSegmentationLabel()

void eVision::EasyDeepLearning::EDataset::SetSegmentationLabel	(	int	index,
		const std::string &	label )

Sets the segmentation labels.

Parameters

index	Index of the segmentation label
label	String representing the segmentation label

The segmentation label index 0 is reserved and corresponds to the "background" label. This segmentation label can't be changed.

SetSegmentationLabelColor()

void eVision::EasyDeepLearning::EDataset::SetSegmentationLabelColor	(	int	index,
		const eVision::ERGBColor &	color )

Sets the label color for a segmentation label.

Parameters

index	Label index (0 to EDataset::GetNumLabels - 1).
color	New color for the label

SetSegmentationLabelOpacity()

void eVision::EasyDeepLearning::EDataset::SetSegmentationLabelOpacity	(	int	index,
		const uint8_t	opacity )

Sets the label opacity for a segmentation label.

Parameters

index	Label index (0 to EDataset::GetNumLabels - 1).
opacity	New opacity for the label

SetSegmentationLabelWeight() [1/2]

void eVision::EasyDeepLearning::EDataset::SetSegmentationLabelWeight	(	const std::string &	label,
		float	weight )

Sets the segmentation label weight.

Parameters

label	Segmentation label
weight	Weight of the segmentation label

SetSegmentationLabelWeight() [2/2]

void eVision::EasyDeepLearning::EDataset::SetSegmentationLabelWeight	(	int	index,
		float	weight )

Sets the segmentation label weight.

Parameters

index	Index of the segmentation label
weight	Weight of the segmentation label

SetSegmentationMap()

void eVision::EasyDeepLearning::EDataset::SetSegmentationMap	(	int	imageIndex,
		const eVision::EROIBW16 &	segmentationMap )

Sets the segmentation map of an image. The segmentation map is a 16-bit EROIBW16 image where the value of each pixel is equal to the corresponding segmentation label index (see EDataset::GetSegmentationLabel).

If an image has no segmentation (EDataset::HasSegmentation), the getter will throw an exception.

Parameters

imageIndex	Image index
segmentationMap	Segmentation map

SplitDataset() [1/3]

void eVision::EasyDeepLearning::EDataset::SplitDataset	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2 ) const

Splits the dataset in two parts to be used for training and validation respectively.

Parameters

d1	First part of the dataset
d2	Second part of the dataset

SplitDataset() [2/3]

void eVision::EasyDeepLearning::EDataset::SplitDataset	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2,
		float	proportion ) const

Splits the dataset in two parts to be used for training and validation respectively.

Parameters

d1	First part of the dataset
d2	Second part of the dataset
proportion	Proportion of image of each class to put into the first part. The remaining images are put in d2

SplitDataset() [3/3]

void eVision::EasyDeepLearning::EDataset::SplitDataset	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2,
		float	proportion,
		bool	random ) const

Splits the dataset in two parts to be used for training and validation respectively.

Parameters

d1	First part of the dataset
d2	Second part of the dataset
proportion	Proportion of image of each class to put into the first part. The remaining images are put in d2
random	Randomly sample the images.

SplitDatasetForLocator() [1/3]

void eVision::EasyDeepLearning::EDataset::SplitDatasetForLocator	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2 ) const

Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.

Parameters

d1	First part of the dataset
d2	Second part of the dataset

The method ensures that all the object labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.

SplitDatasetForLocator() [2/3]

void eVision::EasyDeepLearning::EDataset::SplitDatasetForLocator	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2,
		float	proportion ) const

Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.

Parameters

d1	First part of the dataset
d2	Second part of the dataset
proportion	Proportion of image of each class to put into the first part. The remaining images are put in d2

The method ensures that all the object labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.

SplitDatasetForLocator() [3/3]

void eVision::EasyDeepLearning::EDataset::SplitDatasetForLocator	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2,
		float	proportion,
		bool	random ) const

Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.

Parameters

d1	First part of the dataset
d2	Second part of the dataset
proportion	Proportion of image of each class to put into the first part. The remaining images are put in d2
random	Randomly sample the images.

The method ensures that all the object labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.

SplitDatasetForSegmentation() [1/3]

void eVision::EasyDeepLearning::EDataset::SplitDatasetForSegmentation	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2 ) const

Splits the dataset in two parts for a supervised segmenter. The two parts are to be used for training and validation respectively.

Parameters

d1	First part of the dataset
d2	Second part of the dataset

The method ensures that all the segmentation labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.

SplitDatasetForSegmentation() [2/3]

void eVision::EasyDeepLearning::EDataset::SplitDatasetForSegmentation	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2,
		float	proportion ) const

Splits the dataset in two parts for a supervised segmenter. The two parts are to be used for training and validation respectively.

Parameters

d1	First part of the dataset
d2	Second part of the dataset
proportion	Proportion of image of each class to put into the first part. The remaining images are put in d2

The method ensures that all the segmentation labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.

SplitDatasetForSegmentation() [3/3]

void eVision::EasyDeepLearning::EDataset::SplitDatasetForSegmentation	(	eVision::EasyDeepLearning::EDataset &	d1,
		eVision::EasyDeepLearning::EDataset &	d2,
		float	proportion,
		bool	random ) const

Splits the dataset in two parts for a supervised segmenter. The two parts are to be used for training and validation respectively.

Parameters

d1	First part of the dataset
d2	Second part of the dataset
proportion	Proportion of image of each class to put into the first part. The remaining images are put in d2
random	Randomly sample the images.

The method ensures that all the segmentation labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.

UnsetImageLabel()

void eVision::EasyDeepLearning::EDataset::UnsetImageLabel

(

int

index

)

Unset the label of the given image of the dataset. After this operation, the given image will have no classification labels.

Parameters

index

Index of the image for which to unset the label

UnsetImageObjectLabeling()

void eVision::EasyDeepLearning::EDataset::UnsetImageObjectLabeling

(

int

imageIndex

)

Unsets the object labeling for the specified image. This sets the image as not having been labeled for object detection. This image won't be use for training with a ELocator tool.

Parameters

imageIndex

Index of the image

UnsetOCRAnnotations()

void eVision::EasyDeepLearning::EDataset::UnsetOCRAnnotations

(

int

imgId

)

Unsets the OCR labeling for the specified image.

Parameters

imgId

Index of the image.

UnsetSegmentation()

void eVision::EasyDeepLearning::EDataset::UnsetSegmentation

(

int

imageIndex

)

Unsets the segmentation of an image. After this operation, the image will have no segmentation.

Parameters

imageIndex

Image index

Member Data Documentation

wrappedClass_

CppWrapperInternal::WrappedClass eVision::EasyDeepLearning::EDataset::wrappedClass_