eVision.EasyDeepLearning.EClassificationDataset Class Reference

eVision.EasyDeepLearning.EClassificationDataset manages a dataset of images. More...

Inheritance diagram for eVision.EasyDeepLearning.EClassificationDataset:

Collaboration diagram for eVision.EasyDeepLearning.EClassificationDataset:

Public Member Functions
None	__init__ (self)
	Constructs a eVision.EasyDeepLearning.EClassificationDataset object.
None	__init__ (self, eVision.EasyDeepLearning.EClassificationDataset other)
	Copy constructor of a eVision.EasyDeepLearning.EClassificationDataset object.
None	__init__ (self, eVision.EasyDeepLearning.EDataset other)
	Copy constructor of a eVision.EasyDeepLearning.EClassificationDataset object from a eVision.EasyDeepLearning.EDataset object.
None	Clear (self)
	Clears the dataset.
eVision.EBaseROI|None	GetImageCopyWithDataAugmentation (self, int index, eVision.EasyDeepLearning.EDLDataAugmentationType generationType)
	Generates a new image from the i-th image of the dataset.
eVision.EBaseROI|None	GetImageCopyWithDataAugmentation (self, int index)
	Generates a new image from the i-th image of the dataset.
None	GetImageCopyWithDataAugmentation (self, int index, eVision.EBaseROI img, eVision.EasyDeepLearning.EDLDataAugmentationType generationType)
	Generates a new image from the i-th image of the dataset.
None	GetImageCopyWithDataAugmentation (self, int index, eVision.EBaseROI img)
	Generates a new image from the i-th image of the dataset.
None	SplitDataset (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2, float proportion, bool random)
	Splits the dataset in two parts to be used for training and validation respectively.
None	SplitDataset (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2, float proportion)
	Splits the dataset in two parts to be used for training and validation respectively.
None	SplitDataset (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2)
	Splits the dataset in two parts to be used for training and validation respectively.
None	SplitDatasetForSegmentation (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2, float proportion, bool random)
	Splits the dataset in two parts for a supervised segmenter.
None	SplitDatasetForSegmentation (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2, float proportion)
	Splits the dataset in two parts for a supervised segmenter.
None	SplitDatasetForSegmentation (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2)
	Splits the dataset in two parts for a supervised segmenter.
None	SplitDatasetForLocator (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2, float proportion, bool random)
	Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.
None	SplitDatasetForLocator (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2, float proportion)
	Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.
None	SplitDatasetForLocator (self, eVision.EasyDeepLearning.EClassificationDataset d1, eVision.EasyDeepLearning.EClassificationDataset d2)
	Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.
eVision.EasyDeepLearning.EClassificationDataset	ExtractSplit (self, eVision.EasyDeepLearning.EDatasetSplit split, eVision.EasyDeepLearning.EDatasetType type)
	Creates a new eVision.EasyDeepLearning.EClassificationDataset containing only the images of the given split type.
bool	EnableDataAugmentation (self)
	Enable data augmentation.
	EnableDataAugmentation (self, bool value)
	Enable data augmentation.
int	MaxHorizontalShift (self)
	Maximum horizontal shift for data augmentation.
	MaxHorizontalShift (self, int value)
	Maximum horizontal shift for data augmentation.
int	MaxVerticalShift (self)
	Maximum vertical shift for data augmentation.
	MaxVerticalShift (self, int value)
	Maximum vertical shift for data augmentation.
float	MaxRotationAngle (self)
	Maximum rotation angle for data augmentation.
	MaxRotationAngle (self, float value)
	Maximum rotation angle for data augmentation.
bool	EnableVerticalFlip (self)
	Enable vertical flipping in data augmentation.
	EnableVerticalFlip (self, bool value)
	Enable vertical flipping in data augmentation.
bool	EnableHorizontalFlip (self)
	Enable horizontal flipping in data augmentation.
	EnableHorizontalFlip (self, bool value)
	Enable horizontal flipping in data augmentation.
float	MinScale (self)
	Minimum scaling allowed for data augmentation.
	MinScale (self, float value)
	Minimum scaling allowed for data augmentation.
float	MaxScale (self)
	Maximum scaling allowed for data augmentation.
	MaxScale (self, float value)
	Maximum scaling allowed for data augmentation.
float	MaxHorizontalShear (self)
	Maximum absolute horizontal shear.
	MaxHorizontalShear (self, float value)
	Maximum absolute horizontal shear.
float	MaxVerticalShear (self)
	Maximum absolute vertical shear.
	MaxVerticalShear (self, float value)
	Maximum absolute vertical shear.
float	MaxBrightnessOffset (self)
	Maximum absolute brightness offset.
	MaxBrightnessOffset (self, float value)
	Maximum absolute brightness offset.
float	MaxContrastGain (self)
	Maximum contrast gain.
	MaxContrastGain (self, float value)
	Maximum contrast gain.
float	MinContrastGain (self)
	Minimum contrast gain.
	MinContrastGain (self, float value)
	Minimum contrast gain.
float	MinGamma (self)
	Minimum gamma for gamma correction.
	MinGamma (self, float value)
	Minimum gamma for gamma correction.
float	MaxGamma (self)
	Maximum gamma for gamma correction.
	MaxGamma (self, float value)
	Maximum gamma for gamma correction.
float	MaxHueOffset (self)
	Maximum absolute hue offset.
	MaxHueOffset (self, float value)
	Maximum absolute hue offset.
float	MinSaturationGain (self)
	Minimum saturation gain.
	MinSaturationGain (self, float value)
	Minimum saturation gain.
float	MaxSaturationGain (self)
	Maximum saturation gain.
	MaxSaturationGain (self, float value)
	Maximum saturation gain.
float	GaussianNoiseMaximumStandardDeviation (self)
	The Gaussian noise maximum standard deviation.
	GaussianNoiseMaximumStandardDeviation (self, float value)
	The Gaussian noise maximum standard deviation.
float	GaussianNoiseMinimumStandardDeviation (self)
	The Gaussian noise minimum standard deviation.
	GaussianNoiseMinimumStandardDeviation (self, float value)
	The Gaussian noise minimum standard deviation.
float	SpeckleNoiseMaximumStandardDeviation (self)
	The speckle noise maximum standard deviation.
	SpeckleNoiseMaximumStandardDeviation (self, float value)
	The speckle noise maximum standard deviation.
float	SpeckleNoiseMinimumStandardDeviation (self)
	The speckle noise minimum standard deviation.
	SpeckleNoiseMinimumStandardDeviation (self, float value)
	The speckle noise minimum standard deviation.
float	SaltAndPepperNoiseMaximumDensity (self)
	The maximum density of the salt and pepper noise.
	SaltAndPepperNoiseMaximumDensity (self, float value)
	The maximum density of the salt and pepper noise.
float	SaltAndPepperNoiseMinimumDensity (self)
	The minimum density of the salt and pepper noise.
	SaltAndPepperNoiseMinimumDensity (self, float value)
	The minimum density of the salt and pepper noise.
Public Member Functions inherited from eVision.EasyDeepLearning.EDataset
None	__init__ (self)
	Constructs a eVision.EasyDeepLearning.EDataset object.
None	__init__ (self, eVision.EasyDeepLearning.EDataset other)
	Copy constructor of a eVision.EasyDeepLearning.EDataset object.
None	Load (self, str path)
	Loads a classification dataset from disk.
None	Load (self, eVision.ESerializer|None serializer)
	Loads a classification dataset from disk.
None	Save (self, str path)
	Saves a classification dataset to disk, containing the file paths to the images in the dataset and their associated label.
None	Save (self, eVision.ESerializer|None serializer)
	Saves a classification dataset to disk, containing the file paths to the images in the dataset and their associated label.
None	SaveWithRelativePath (self, str path)
	Saves the dataset with image paths relative to the directory in which the dataset file is saved.
None	Export (self, str directory, eVision.EImageFileType fileType, int quality)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, eVision.EImageFileType fileType)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, bool keepFilename, eVision.EImageFileType fileType, int quality)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, bool keepFilename, eVision.EImageFileType fileType)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, bool keepFilename)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, int width, int height, int channels, eVision.EImageFileType fileType, int quality)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, int width, int height, int channels, eVision.EImageFileType fileType)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, int width, int height, int channels)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, int width, int height, int channels, bool keepFilename, eVision.EImageFileType fileType, int quality)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, int width, int height, int channels, bool keepFilename, eVision.EImageFileType fileType)
	Exports the dataset and its images to the given directory.
None	Export (self, str directory, int width, int height, int channels, bool keepFilename)
	Exports the dataset and its images to the given directory.
eVision.EasyDeepLearning.EImageAnnotationFormat	GetAvailableImageAnnotationFormat (self, str imageFile)
	Image annotation file formats that are available next to the image file.
eVision.EasyDeepLearning.EImageAnnotationFormat	GetAvailableImageAnnotationFormat (self, int imgId)
	Image annotation file formats that are available next to the image file.
None	ImportYOLOTXTAnnotations (self, int imgId, str labelFile)
	Imports YOLO TXT annotations (for EasyLocate Bounding Box).
None	ImportYOLOTXTAnnotations (self, str labelFile)
	Imports YOLO TXT annotations (for EasyLocate Bounding Box).
None	ImportPascalVOCXMLAnnotations (self, int imgId)
	Imports Pascal VOC XML annotations (for EasyLocate Bounding Box).
None	ImportPascalVOCXMLAnnotations (self)
	Imports Pascal VOC XML annotations (for EasyLocate Bounding Box).
int	AddImage (self, str imagePath)
	Adds an image to the dataset.
int	AddImage (self, str imagePath, str label)
	Adds an image to the dataset.
int	AddImage (self, str imagePath, int originX, int originY, int width, int height, eVision.ERegion mask)
	Adds an image to the dataset.
int	AddImage (self, str imagePath, int originX, int originY, int width, int height, eVision.ERegion mask, str label)
	Adds an image to the dataset.
int	AddImage (self, eVision.EBaseROI img)
	Adds an image to the dataset.
int	AddImage (self, eVision.EBaseROI img, str label)
	Adds an image to the dataset.
int	AddImage (self, eVision.EBaseROI img, int originX, int originY, int width, int height, eVision.ERegion mask)
	Adds an image to the dataset.
int	AddImage (self, eVision.EBaseROI img, int originX, int originY, int width, int height, eVision.ERegion mask, str label)
	Adds an image to the dataset.
int	AddImages (self, str filter)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImages (self, str filter, str label)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImages (self, str filter, int originX, int originY, int width, int height, eVision.ERegion mask)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImages (self, str filter, int originX, int originY, int width, int height, eVision.ERegion mask, str label)
	Adds all the images present in the directory specified by the parameter path and whose filename matches the filter.
int	AddImagesFromCSV (self, str csvFile, int imageColumn, int labelColumn, int skipLines, str 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 (self, str 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 (self, str 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 (self, str 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 (self, str 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.
None	RemoveImage (self, int imgId)
	Removes the image at the given index.
int	GetNumImagesForFile (self, str filePath)
	Get the number of images in the dataset that are associated with the given image file.
eVision.EBaseROI|None	GetImageCopy (self, int index)
	Gets a copy of the i-th image of the dataset.
None	GetImageCopy (self, int index, eVision.EBaseROI img)
	Gets a copy of the i-th image of the dataset.
str	GetImagePath (self, int index)
	Gets the path of the i-th image of the dataset.
bool	IsImageFile (self, int index)
	Whether the image is stored as a file path towards an image.
bool	IsEmbeddedImage (self, int index)
	Whether the image is embedded into the dataset and is not a reference towards an image file.
str	GetImageLabel (self, int index)
	Gets the label of the i-th image of the dataset.
None	SetImageLabel (self, int index, str label)
	Sets the label of images in the dataset.
None	SetImageLabel (self, str filter, str label)
	Sets the label of images in the dataset.
None	UnsetImageLabel (self, int index)
	Unset the label of the given image of the dataset.
bool	HasLabel (self, int index)
	Whether an image has a classification label.
list[int]	GetImagesIndexesWithLabel (self, str label)
	Gets a list of index corresponding to the images associated with the given label.
list[int]	GetImagesIndexesWithLabel (self, int labelIndex)
	Gets a list of index corresponding to the images associated with the given label.
None	AddLabel (self, str label)
	Adds a label to the dataset.
str	GetLabel (self, int i)
	Gets the i-th label of the dataset (starting from 0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
None	SetLabel (self, int index, str label)
	Sets the i-th label of the dataset (starting from 0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
float	GetLabelWeight (self, int index)
	Gets the weight associated to the i-th label of the dataset (starting from 0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
None	SetLabelWeight (self, int index, float weight)
	Sets the weight associated to the i-th label of the dataset (starting from 0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
None	SetLabelColor (self, int index, eVision.ERGBColor color)
	Sets the label color for a classification label.
eVision.ERGBColor	GetLabelColor (self, int index)
	Gets the label color for a classification label.
None	SetLabelOpacity (self, int index, int opacity)
	Sets the label opacity for a classification label.
int	GetLabelOpacity (self, int index)
	Gets the label opacity for a classification label.
None	RemoveLabel (self, str label)
	Removes a label from the dataset.
None	RemoveLabel (self, int labelId)
	Removes a label from the dataset.
None	SetSegmentationMap (self, int imageIndex, eVision.EROIBW16 segmentationMap)
	Sets the segmentation map of an image.
eVision.EImageBW16	GetSegmentationMap (self, int imageIndex)
	Gets the segmentation map of an image.
None	UnsetSegmentation (self, int imageIndex)
	Unsets the segmentation of an image.
None	ResetSegmentation (self, int imageIndex)
	Resets the segmentation of an image by setting all pixels to background.
None	AddRegionToSegment (self, int imageIndex, int segmentationLabelIndex, eVision.ERegion region)
	Adds a region (see eVision.ERegion) to the given segment of an image.
None	AddRegionToSegment (self, int imageIndex, str label, eVision.ERegion region)
	Adds a region (see eVision.ERegion) to the given segment of an image.
eVision.ERegion	GetRegionForSegment (self, int imageIndex, int segmentationLabelIndex)
	Gets a region corresponding to the pixels of the given segment.
eVision.ERegion	GetRegionForSegment (self, int imageIndex, str segmentationLabel)
	Gets a region corresponding to the pixels of the given segment.
int	GetNumImagesForSegmentationLabel (self, int labelId)
	Number of images containing the given segmentation label.
int	GetNumImagesForSegmentationLabel (self, str label)
	Number of images containing the given segmentation label.
int	GetNumPixelsForSegmentationLabel (self, int labelId)
	Number of pixels assigned to the given segmentation label.
int	GetNumPixelsForSegmentationLabel (self, str label)
	Number of pixels assigned to the given segmentation label.
bool	HasSegmentation (self, int imageId)
	Whether the image has a segmentation.
bool	HasForegroundSegments (self, int imageId)
	Whether the image segmentation contains segments that are not background.
int	GetNumSegmentedBlobs (self, int imageId)
	Number of segmented blobs in the image for all non-background labels or for a specific label.
int	GetNumSegmentedBlobs (self, int imageId, str label)
	Number of segmented blobs in the image for all non-background labels or for a specific label.
int	GetNumSegmentedBlobs (self, int imageId, int labelId)
	Number of segmented blobs in the image for all non-background labels or for a specific label.
str	GetSegmentationLabel (self, int index)
	Gets the segmentation label.
None	SetSegmentationLabel (self, int index, str label)
	Sets the segmentation labels.
int	AddSegmentationLabel (self, str label, float labelWeight)
	Adds a segmentation label.
int	AddSegmentationLabel (self, str label)
	Adds a segmentation label.
None	RemoveSegmentationLabel (self, str label)
	Remove a segmentation label.
None	RemoveSegmentationLabel (self, int index)
	Remove a segmentation label.
float	GetSegmentationLabelWeight (self, int index)
	Gets the segmentation label weights.
float	GetSegmentationLabelWeight (self, str label)
	Gets the segmentation label weights.
None	SetSegmentationLabelWeight (self, int index, float weight)
	Sets the segmentation label weight.
None	SetSegmentationLabelWeight (self, str label, float weight)
	Sets the segmentation label weight.
None	SetSegmentationLabelColor (self, int index, eVision.ERGBColor color)
	Sets the label color for a segmentation label.
eVision.ERGBColor	GetSegmentationLabelColor (self, int index)
	Gets the label color for a segmentation label.
None	SetSegmentationLabelOpacity (self, int index, int opacity)
	Sets the label opacity for a segmentation label.
int	GetSegmentationLabelOpacity (self, int index)
	Gets the label opacity for a segmentation label.
None	ResetImageObjectLabeling (self, int imageIndex)
	Resets the object labeling for the specified image.
None	UnsetImageObjectLabeling (self, int imageIndex)
	Unsets the object labeling for the specified image.
list[eVision._eVision.EasyDeepLearning.ELocatorObject]	GetImageObjects (self, int imageIndex)
	Gets all the objects of the specified image.
int	GetImageNumObjects (self, int imageIndex)
	Number of objects for the specified image.
eVision.EasyDeepLearning.ELocatorObject	GetImageObject (self, int imageIndex, int objectIndex)
	Gets the specified object for the given image.
None	SetImageObject (self, int imageIndex, int objectIndex, eVision.EasyDeepLearning.ELocatorObject obj)
	Sets the specified object for the given image.
None	SetImageObject (self, int imageIndex, int objectIndex, str label)
	Sets the specified object for the given image.
None	SetImageObject (self, int imageIndex, int objectIndex, eVision.ERectangleRegion region)
	Sets the specified object for the given image.
None	AddImageObject (self, int imageIndex, eVision.EasyDeepLearning.ELocatorObject obj)
	Adds an object to the image.
None	AddImageObject (self, int imageIndex, str label, eVision.ERectangleRegion box)
	Adds an object to the image.
None	RemoveImageObject (self, int imageIndex, int objectIndex)
	Removes an object from an image.
str	GetObjectLabel (self, int labelIndex)
	Object label.
None	SetObjectLabel (self, int labelIndex, str newLabel)
	Sets an object label.
None	SetObjectLabel (self, str oldLabel, str newLabel)
	Sets an object label.
None	AddObjectLabel (self, str label, float weight)
	Adds an object label.
None	AddObjectLabel (self, str label)
	Adds an object label.
None	RemoveObjectLabel (self, int labelIndex)
	Removes an object label.
None	RemoveObjectLabel (self, str label)
	Removes an object label.
None	SetObjectLabelWeight (self, int labelIndex, float weight)
	Sets the weight of an object label.
None	SetObjectLabelWeight (self, str label, float weight)
	Sets the weight of an object label.
float	GetObjectLabelWeight (self, int labelIndex)
	Gets the weight of an object label.
float	GetObjectLabelWeight (self, str label)
	Gets the weight of an object label.
None	SetObjectLabelColor (self, int index, eVision.ERGBColor color)
	Sets the label color for an object label.
eVision.ERGBColor	GetObjectLabelColor (self, int index)
	Gets the label color for an object label.
None	SetObjectLabelOpacity (self, int index, int opacity)
	Sets the label opacity for an object label.
int	GetObjectLabelOpacity (self, int index)
	Gets the label opacity for an object label.
bool	HasObjectLabeling (self, int imageIndex)
	Whether the image is labeled for object detection and use with eVision.EasyDeepLearning.ELocator.
int	GetNumImagesWithObjectLabel (self, int labelIdx)
	Number of images that contain an object with the given label.
int	GetNumImagesWithObjectLabel (self, str label)
	Number of images that contain an object with the given label.
int	GetNumObjectsWithLabel (self, int labelIdx)
	Number of objects with the given label.
int	GetNumObjectsWithLabel (self, str label)
	Number of objects with the given label.
bool	HasOCRAnnotations (self, int imgId)
	Whether the specified image has OCR annotations.
None	UnsetOCRAnnotations (self, int imgId)
	Unsets the OCR labeling for the specified image.
list[eVision._eVision.EasyDeepOCR.ETextLine]	GetOCRAnnotations (self, int imgId)
	Ground truth text lines for the specified image.
None	SetOCRAnnotations (self, int imgId, list[eVision._eVision.EasyDeepOCR.ETextLine] texts)
	Sets the ground truth text lines for the specified image.
eVision.EasyDeepLearning.EDatasetSplit	GetSplit (self, float trainingProportion, float validationPropotion, eVision.EasyDeepLearning.EDeepLearningToolType toolType, str goodLabel)
	Generates a split.
eVision.EasyDeepLearning.EDatasetSplit	GetSplit (self, float trainingProportion, float validationPropotion, eVision.EasyDeepLearning.EDeepLearningToolType toolType, str goodLabel, int seed)
	Generates a split.
None	SetRegionOfInterest (self, int imageIndex, int xOrg, int yOrg, int width, int height)
	Sets the region of interest for the specified image.
int	GetRegionOfInterestOriginX (self, int imageIndex)
	Region of interest origin abscissa for the specified image.
int	GetRegionOfInterestOriginY (self, int imageIndex)
	Region of interest origin ordinate for the specified image.
int	GetRegionOfInterestWidth (self, int imageIndex)
	Region of interest width for the specified image.
int	GetRegionOfInterestHeight (self, int imageIndex)
	Region of interest height for the specified image.
eVision.ERegion	GetMask (self, int imageIndex)
	Gets the mask region/don't care area for the given image.
None	SetMask (self, int imageIndex, eVision.ERegion mask)
	Sets the mask region/don't care area for the given image.
int	Width (self)
	Width of the region of interest of the first image added to the dataset.
int	Height (self)
	Height of the region of interest of the first image added to the dataset.
int	Channels (self)
	Number of channels of the first image added to the dataset.
int	NumImages (self)
	Number of images in the dataset.
int	NumImageFiles (self)
	Number of different image files contained in the dataset.
int	NumLabeledImages (self)
	Number of images that have a classification label.
int	NumUnlabeledImages (self)
	Number of images that doesn't have any classification label.
list[int]	LabeledImagesIndexes (self)
	Gets a list of index corresponding to the images that have a classification label.
list[int]	TextLabeledImagesIndexes (self)
	Gets a list of index corresponding to the images that have a text only label.
list[int]	ImagesIndexesWithNoLabel (self)
	Gets a list of index corresponding to the images with no classification labels.
int	NumLabels (self)
	Number of labels in the dataset.
int	NumSegmentationLabels (self)
	Number of segmentation labels.
int	NumImagesWithSegmentation (self)
	Number of images that have a ground truth segmentation.
int	NumImagesWithoutSegmentation (self)
	Number of images that doesn't have a ground truth segmentation.
int	NumImagesWithoutForegroundSegments (self)
	Number of images that have a ground truth segmentation that has no foreground segments and is thus entirely composed of background pixels.
int	NumImagesWithForegroundSegments (self)
	Number of images that have a ground truth segmentation that has foreground segments and is this not entirely composed of background pixels.
int	NumObjectLabels (self)
	Number of object labels.
int	NumImagesWithObjectLabeling (self)
	Number of images in the dataset that are labeled for object detection.
int	NumImagesWithoutObjectLabeling (self)
	Number of images in the dataset that are not labeled for object detection.
int	NumImagesWithoutObjects (self)
	Number of images in the dataset that are labeled for object detection but have been associated with no object.
int	NumImagesWithObjects (self)
	Number of images in the dataset that are labeled for object detection and have 1 or more objects.
float	SameLabelMaxOverlap (self)
	Maximum overlap between objects with the same label in the dataset.
float	AbsoluteMaxOverlap (self)
	Absolute maximum overlap between objects in the dataset.
int	MaxNumObjectPerImage (self)
	Maximum number of objects for an image in the dataset.
int	ObjectSize (self)
	Object size for EasyLocate Interest point.
	ObjectSize (self, int value)
	Object size for EasyLocate Interest point.
int	NumImagesWithOCR (self)
	Number of images with OCR annotations.
int	NumImagesWithoutOCR (self)
	Number of images without OCR annotations.
int	NumTexts (self)
	Number of annotated texts in the dataset.
eVision.EasyDeepLearning.EDataset	TextDataset (self)
	Transforms the current dataset to have only subwindow containing the actual text to read.
eVision.EasyDeepLearning.EDataset	TextOrientationDataset (self)
	Transforms the current text dataset to duplicate it with half image flipped, adding corresponding ground truth.

Additional Inherited Members
Protected Member Functions inherited from eVision.EasyDeepLearning.EDataset
	_BasePath__setter (self, str value)
	Sets the base path for the images specified with a relative path.
Properties inherited from eVision.EasyDeepLearning.EDataset
	BasePath

Detailed Description

eVision.EasyDeepLearning.EClassificationDataset 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 (eVision.EImageBW8), 16-bits monochrome (eVision.EImageBW16), and 24-bits color (eVision.EROIC24, eVision.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 eVision.EasyDeepLearning.EClassificationDataset object is also responsible for providing tools to do data augmentation. Data augmentation is the process of generating new images on-the-fly by applying affine transformations to those already in the dataset. Data augmentation allows a deep neural network to be invariant to the applied transformation without having to capture and label real world images containing those transformations.

A dataset can contain images with different sizes (with and height of their region of interest). However, the dataset has a default resolution (see eVision.EasyDeepLearning.EDataset.Width and eVision.EasyDeepLearning.EDataset.Height) that is used by deep learning tools that require the same input image size such as the eVision.EasyDeepLearning.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.

Remarks:

License(s): BasicTypes

Deprecated

Deprecated in version 26.10. Scheduled to be removed in version 27.10.

Constructor & Destructor Documentation

__init__() [1/3]

None eVision.EasyDeepLearning.EClassificationDataset.__init__

(

self

)

Constructs a eVision.EasyDeepLearning.EClassificationDataset object.

__init__() [2/3]

None eVision.EasyDeepLearning.EClassificationDataset.__init__	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	other )

Copy constructor of a eVision.EasyDeepLearning.EClassificationDataset object.

Parameters

other

Reference to the eVision.EasyDeepLearning.EClassificationDataset object that should be copied

__init__() [3/3]

None eVision.EasyDeepLearning.EClassificationDataset.__init__	(		self,
		eVision.EasyDeepLearning.EDataset	other )

Copy constructor of a eVision.EasyDeepLearning.EClassificationDataset object from a eVision.EasyDeepLearning.EDataset object.

Parameters

other

Reference to the eVision.EasyDeepLearning.EDataset object that should be copied

Member Function Documentation

Clear()

None eVision.EasyDeepLearning.EClassificationDataset.Clear

(

self

)

Clears the dataset.

Reimplemented from eVision.EasyDeepLearning.EDataset.

EnableDataAugmentation() [1/2]

bool eVision.EasyDeepLearning.EClassificationDataset.EnableDataAugmentation

(

self

)

Enable data augmentation.

EnableDataAugmentation() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.EnableDataAugmentation	(		self,
		bool	value )

Enable data augmentation.

EnableHorizontalFlip() [1/2]

bool eVision.EasyDeepLearning.EClassificationDataset.EnableHorizontalFlip

(

self

)

Enable horizontal flipping in data augmentation.

EnableHorizontalFlip() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.EnableHorizontalFlip	(		self,
		bool	value )

Enable horizontal flipping in data augmentation.

EnableVerticalFlip() [1/2]

bool eVision.EasyDeepLearning.EClassificationDataset.EnableVerticalFlip

(

self

)

Enable vertical flipping in data augmentation.

EnableVerticalFlip() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.EnableVerticalFlip	(		self,
		bool	value )

Enable vertical flipping in data augmentation.

ExtractSplit()

eVision.EasyDeepLearning.EClassificationDataset eVision.EasyDeepLearning.EClassificationDataset.ExtractSplit	(		self,
		eVision.EasyDeepLearning.EDatasetSplit	split,
		eVision.EasyDeepLearning.EDatasetType	type )

Creates a new eVision.EasyDeepLearning.EClassificationDataset containing only the images of the given split type.

Parameters

split	Split
type	Type of the split to extract

Reimplemented from eVision.EasyDeepLearning.EDataset.

GaussianNoiseMaximumStandardDeviation() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation

(

self

)

The Gaussian noise maximum standard deviation.

The Gaussian noise is an additive noise sampled from a Gaussian distribution of deviation between eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation (also called the normal distribution).

Its value must be superior or equal to eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

GaussianNoiseMaximumStandardDeviation() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation	(		self,
		float	value )

The Gaussian noise maximum standard deviation.

The Gaussian noise is an additive noise sampled from a Gaussian distribution of deviation between eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation (also called the normal distribution).

Its value must be superior or equal to eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

GaussianNoiseMinimumStandardDeviation() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation

(

self

)

The Gaussian noise minimum standard deviation.

The Gaussian noise is an additive noise sampled from a Gaussian distribution of deviation between eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation (also called the normal distribution).

Its value must be between 0 and eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

GaussianNoiseMinimumStandardDeviation() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation	(		self,
		float	value )

The Gaussian noise minimum standard deviation.

The Gaussian noise is an additive noise sampled from a Gaussian distribution of deviation between eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation (also called the normal distribution).

Its value must be between 0 and eVision.EasyDeepLearning.EClassificationDataset.GaussianNoiseMaximumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

GetImageCopyWithDataAugmentation() [1/4]

eVision.EBaseROI | None eVision.EasyDeepLearning.EClassificationDataset.GetImageCopyWithDataAugmentation	(		self,
		int	index )

Generates a new image from the i-th image of the dataset.

For the variant where a pointer is returned, the user is responsible for clearing the memory of the returned pointer.

Remarks:

If the data augmentation fails, the method will throw an exception.

Parameters

index

The index of the image.

GetImageCopyWithDataAugmentation() [2/4]

eVision.EBaseROI | None eVision.EasyDeepLearning.EClassificationDataset.GetImageCopyWithDataAugmentation	(		self,
		int	index,
		eVision.EasyDeepLearning.EDLDataAugmentationType	generationType )

Generates a new image from the i-th image of the dataset.

For the variant where a pointer is returned, the user is responsible for clearing the memory of the returned pointer.

Remarks:

If the data augmentation fails, the method will throw an exception.

Parameters

index	The index of the image.
generationType	The type of transformation to generate (default: random).

GetImageCopyWithDataAugmentation() [3/4]

None eVision.EasyDeepLearning.EClassificationDataset.GetImageCopyWithDataAugmentation	(		self,
		int	index,
		eVision.EBaseROI	img )

Generates a new image from the i-th image of the dataset.

For the variant where a pointer is returned, the user is responsible for clearing the memory of the returned pointer.

Remarks:

If the data augmentation fails, the method will throw an exception.

Parameters

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

GetImageCopyWithDataAugmentation() [4/4]

None eVision.EasyDeepLearning.EClassificationDataset.GetImageCopyWithDataAugmentation	(		self,
		int	index,
		eVision.EBaseROI	img,
		eVision.EasyDeepLearning.EDLDataAugmentationType	generationType )

Generates a new image from the i-th image of the dataset.

For the variant where a pointer is returned, the user is responsible for clearing the memory of the returned pointer.

Remarks:

If the data augmentation fails, the method will throw an exception.

Parameters

index	The index of the image.
img	Image object to copy the image into.
generationType	The type of transformation to generate (default: random).

MaxBrightnessOffset() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxBrightnessOffset

(

self

)

Maximum absolute brightness offset.

Its value must be between 0 and 1.

Remarks:

Brightness transformation is performed by adding a value taken between -eVision.EasyDeepLearning.EClassificationDataset.MaxBrightnessOffset and +eVision.EasyDeepLearning.EClassificationDataset.MaxBrightnessOffset to each pixel of the normalized image.

MaxBrightnessOffset() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxBrightnessOffset	(		self,
		float	value )

Maximum absolute brightness offset.

Its value must be between 0 and 1.

Remarks:

Brightness transformation is performed by adding a value taken between -eVision.EasyDeepLearning.EClassificationDataset.MaxBrightnessOffset and +eVision.EasyDeepLearning.EClassificationDataset.MaxBrightnessOffset to each pixel of the normalized image.

MaxContrastGain() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain

(

self

)

Maximum contrast gain.

Its value must be strictly positive and over eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain.

Remarks:

Contrast transformation is performed by multiplying each mean-centered pixel value by a gain value taken between eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain and eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain. A contrast transformation does not change the overall brightness of an image.

MaxContrastGain() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain	(		self,
		float	value )

Maximum contrast gain.

Its value must be strictly positive and over eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain.

Remarks:

Contrast transformation is performed by multiplying each mean-centered pixel value by a gain value taken between eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain and eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain. A contrast transformation does not change the overall brightness of an image.

MaxGamma() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxGamma

(

self

)

Maximum gamma for gamma correction.

Its value must be higher than eVision.EasyDeepLearning.EClassificationDataset.MinGamma.

Remarks:

Gamma correction transformation is performed by raising each normalized pixel value to the power of gamma with gamma taken between eVision.EasyDeepLearning.EClassificationDataset.MinGamma and eVision.EasyDeepLearning.EClassificationDataset.MaxGamma.

MaxGamma() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxGamma	(		self,
		float	value )

Maximum gamma for gamma correction.

Its value must be higher than eVision.EasyDeepLearning.EClassificationDataset.MinGamma.

Remarks:

Gamma correction transformation is performed by raising each normalized pixel value to the power of gamma with gamma taken between eVision.EasyDeepLearning.EClassificationDataset.MinGamma and eVision.EasyDeepLearning.EClassificationDataset.MaxGamma.

MaxHorizontalShear() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShear

(

self

)

Maximum absolute horizontal shear.

It is represented as an angle from the vertical direction. Its value must be between 0 and 90 degrees.

MaxHorizontalShear() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShear	(		self,
		float	value )

Maximum absolute horizontal shear.

It is represented as an angle from the vertical direction. Its value must be between 0 and 90 degrees.

MaxHorizontalShift() [1/2]

int eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift

(

self

)

Maximum horizontal shift for data augmentation.

The horizontal shift will be between -eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift and +eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift.

MaxHorizontalShift() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift	(		self,
		int	value )

Maximum horizontal shift for data augmentation.

The horizontal shift will be between -eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift and +eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift.

MaxHueOffset() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxHueOffset

(

self

)

Maximum absolute hue offset.

Its value must be between 0 and 180 degrees.

Remarks:

The hue is represented as an angle between 0 and 360 degrees. The hue transformation is performed by rotating the hue of each pixel by a value between -eVision.EasyDeepLearning.EClassificationDataset.MaxHueOffset and +eVision.EasyDeepLearning.EClassificationDataset.MaxHueOffset. This transformation only works for color images.

MaxHueOffset() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxHueOffset	(		self,
		float	value )

Maximum absolute hue offset.

Its value must be between 0 and 180 degrees.

Remarks:

The hue is represented as an angle between 0 and 360 degrees. The hue transformation is performed by rotating the hue of each pixel by a value between -eVision.EasyDeepLearning.EClassificationDataset.MaxHueOffset and +eVision.EasyDeepLearning.EClassificationDataset.MaxHueOffset. This transformation only works for color images.

MaxRotationAngle() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxRotationAngle

(

self

)

Maximum rotation angle for data augmentation.

The rotation angle will be between -eVision.EasyDeepLearning.EClassificationDataset.MaxRotationAngle and +eVision.EasyDeepLearning.EClassificationDataset.MaxRotationAngle.

MaxRotationAngle() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxRotationAngle	(		self,
		float	value )

Maximum rotation angle for data augmentation.

The rotation angle will be between -eVision.EasyDeepLearning.EClassificationDataset.MaxRotationAngle and +eVision.EasyDeepLearning.EClassificationDataset.MaxRotationAngle.

MaxSaturationGain() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxSaturationGain

(

self

)

Maximum saturation gain.

Its value must be over or equal to eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain.

Remarks:

The saturation transformation is performed by multiplying the saturation of each pixel by a value between eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain and eVision.EasyDeepLearning.EClassificationDataset.MaxSaturationGain.

MaxSaturationGain() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxSaturationGain	(		self,
		float	value )

Maximum saturation gain.

Its value must be over or equal to eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain.

Remarks:

The saturation transformation is performed by multiplying the saturation of each pixel by a value between eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain and eVision.EasyDeepLearning.EClassificationDataset.MaxSaturationGain.

MaxScale() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxScale

(

self

)

Maximum scaling allowed for data augmentation.

MaxScale() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxScale	(		self,
		float	value )

Maximum scaling allowed for data augmentation.

MaxVerticalShear() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MaxVerticalShear

(

self

)

Maximum absolute vertical shear.

It is represented as an angle from the horizontal direction. Its value must be between 0 and 90 degrees.

MaxVerticalShear() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxVerticalShear	(		self,
		float	value )

Maximum absolute vertical shear.

It is represented as an angle from the horizontal direction. Its value must be between 0 and 90 degrees.

MaxVerticalShift() [1/2]

int eVision.EasyDeepLearning.EClassificationDataset.MaxVerticalShift

(

self

)

Maximum vertical shift for data augmentation.

The vertical shift will be between -eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift and +eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift.

MaxVerticalShift() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MaxVerticalShift	(		self,
		int	value )

Maximum vertical shift for data augmentation.

The vertical shift will be between -eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift and +eVision.EasyDeepLearning.EClassificationDataset.MaxHorizontalShift.

MinContrastGain() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain

(

self

)

Minimum contrast gain.

Its value must be strictly positive and below eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain.

Remarks:

Contrast transformation is performed by multiplying each mean-centered pixel value by a gain value taken between eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain and eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain. A contrast transformation does not change the overall brightness of an image.

MinContrastGain() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain	(		self,
		float	value )

Minimum contrast gain.

Its value must be strictly positive and below eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain.

Remarks:

Contrast transformation is performed by multiplying each mean-centered pixel value by a gain value taken between eVision.EasyDeepLearning.EClassificationDataset.MinContrastGain and eVision.EasyDeepLearning.EClassificationDataset.MaxContrastGain. A contrast transformation does not change the overall brightness of an image.

MinGamma() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MinGamma

(

self

)

Minimum gamma for gamma correction.

Its value must be strictly positive and below eVision.EasyDeepLearning.EClassificationDataset.MaxGamma.

Remarks:

Gamma correction transformation is performed by raising each normalized pixel value to the power of gamma with gamma taken between eVision.EasyDeepLearning.EClassificationDataset.MinGamma and @Class.MaxGamma`.

MinGamma() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MinGamma	(		self,
		float	value )

Minimum gamma for gamma correction.

Its value must be strictly positive and below eVision.EasyDeepLearning.EClassificationDataset.MaxGamma.

Remarks:

Gamma correction transformation is performed by raising each normalized pixel value to the power of gamma with gamma taken between eVision.EasyDeepLearning.EClassificationDataset.MinGamma and @Class.MaxGamma`.

MinSaturationGain() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain

(

self

)

Minimum saturation gain.

Its value must be strictly positive.

Remarks:

The saturation transformation is performed by multiplying the saturation of each pixel by a value between eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain and eVision.EasyDeepLearning.EClassificationDataset.MaxSaturationGain.

MinSaturationGain() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain	(		self,
		float	value )

Minimum saturation gain.

Its value must be strictly positive.

Remarks:

The saturation transformation is performed by multiplying the saturation of each pixel by a value between eVision.EasyDeepLearning.EClassificationDataset.MinSaturationGain and eVision.EasyDeepLearning.EClassificationDataset.MaxSaturationGain.

MinScale() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.MinScale

(

self

)

Minimum scaling allowed for data augmentation.

MinScale() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.MinScale	(		self,
		float	value )

Minimum scaling allowed for data augmentation.

SaltAndPepperNoiseMaximumDensity() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity

(

self

)

The maximum density of the salt and pepper noise.

The salt and pepper noise sets the value of a number of randomly selected (between eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity and eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity) pixels to its minimum or maximum value.

Its value must be between eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity and 1.

Remarks:

This noise is computed after all the other noises.

SaltAndPepperNoiseMaximumDensity() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity	(		self,
		float	value )

The maximum density of the salt and pepper noise.

The salt and pepper noise sets the value of a number of randomly selected (between eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity and eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity) pixels to its minimum or maximum value.

Its value must be between eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity and 1.

Remarks:

This noise is computed after all the other noises.

SaltAndPepperNoiseMinimumDensity() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity

(

self

)

The minimum density of the salt and pepper noise.

The salt and pepper noise sets the value of a number of randomly selected (between eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity and eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity) pixels to its minimum or maximum value.

Its value must be between 0 and eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity.

Remarks:

This noise is computed after all the other noises.

SaltAndPepperNoiseMinimumDensity() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity	(		self,
		float	value )

The minimum density of the salt and pepper noise.

The salt and pepper noise sets the value of a number of randomly selected (between eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMinimumDensity and eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity) pixels to its minimum or maximum value.

Its value must be between 0 and eVision.EasyDeepLearning.EClassificationDataset.SaltAndPepperNoiseMaximumDensity.

Remarks:

This noise is computed after all the other noises.

SpeckleNoiseMaximumStandardDeviation() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation

(

self

)

The speckle noise maximum standard deviation.

The speckle noise is a multiplicative noise sampled from a Gamma distribution with a mean of 1 and with its standard deviation between eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation.

Its value must be strictly higher than eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

SpeckleNoiseMaximumStandardDeviation() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation	(		self,
		float	value )

The speckle noise maximum standard deviation.

The speckle noise is a multiplicative noise sampled from a Gamma distribution with a mean of 1 and with its standard deviation between eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation.

Its value must be strictly higher than eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

SpeckleNoiseMinimumStandardDeviation() [1/2]

float eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation

(

self

)

The speckle noise minimum standard deviation.

The speckle noise is a multiplicative noise sampled from a Gamma distribution with a mean of 1 and with its standard deviation between eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation.

Its value must be strictly positive and lower than eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

SpeckleNoiseMinimumStandardDeviation() [2/2]

eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation	(		self,
		float	value )

The speckle noise minimum standard deviation.

The speckle noise is a multiplicative noise sampled from a Gamma distribution with a mean of 1 and with its standard deviation between eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMinimumStandardDeviation and eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation.

Its value must be strictly positive and lower than eVision.EasyDeepLearning.EClassificationDataset.SpeckleNoiseMaximumStandardDeviation.

Remarks:

This noise is computed before the salt and paper noise.

SplitDataset() [1/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDataset	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2 )

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

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDataset() [2/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDataset	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2,
		float	proportion )

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

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDataset() [3/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDataset	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2,
		float	proportion,
		bool	random )

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.

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDatasetForLocator() [1/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDatasetForLocator	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2 )

Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.

Images without object labeling are excluded from the split.

Remarks:

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.

Parameters

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

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDatasetForLocator() [2/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDatasetForLocator	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2,
		float	proportion )

Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.

Images without object labeling are excluded from the split.

Remarks:

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.

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

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDatasetForLocator() [3/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDatasetForLocator	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2,
		float	proportion,
		bool	random )

Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.

Images without object labeling are excluded from the split.

Remarks:

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.

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.

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDatasetForSegmentation() [1/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDatasetForSegmentation	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2 )

Splits the dataset in two parts for a supervised segmenter.

The two parts are to be used for training and validation respectively.

Remarks:

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.

Parameters

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

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDatasetForSegmentation() [2/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDatasetForSegmentation	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2,
		float	proportion )

Splits the dataset in two parts for a supervised segmenter.

The two parts are to be used for training and validation respectively.

Remarks:

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.

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

Reimplemented from eVision.EasyDeepLearning.EDataset.

SplitDatasetForSegmentation() [3/3]

None eVision.EasyDeepLearning.EClassificationDataset.SplitDatasetForSegmentation	(		self,
		eVision.EasyDeepLearning.EClassificationDataset	d1,
		eVision.EasyDeepLearning.EClassificationDataset	d2,
		float	proportion,
		bool	random )

Splits the dataset in two parts for a supervised segmenter.

The two parts are to be used for training and validation respectively.

Remarks:

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.

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.

Reimplemented from eVision.EasyDeepLearning.EDataset.