eVision.EasyDeepLearning.EDataset Class Reference

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

Inheritance diagram for eVision.EasyDeepLearning.EDataset:

Public Member Functions
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.
None	Clear (self)
	Clears the dataset.
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.
None	SplitDataset (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset d2, float proportion, bool random)
	Splits the dataset in two parts to be used for training and validation respectively.
None	SplitDataset (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset d2, float proportion)
	Splits the dataset in two parts to be used for training and validation respectively.
None	SplitDataset (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset d2)
	Splits the dataset in two parts to be used for training and validation respectively.
None	SplitDatasetForSegmentation (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset d2, float proportion, bool random)
	Splits the dataset in two parts for a supervised segmenter.
None	SplitDatasetForSegmentation (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset d2, float proportion)
	Splits the dataset in two parts for a supervised segmenter.
None	SplitDatasetForSegmentation (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset d2)
	Splits the dataset in two parts for a supervised segmenter.
None	SplitDatasetForLocator (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset 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.EDataset d1, eVision.EasyDeepLearning.EDataset d2, float proportion)
	Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.
None	SplitDatasetForLocator (self, eVision.EasyDeepLearning.EDataset d1, eVision.EasyDeepLearning.EDataset d2)
	Splits the dataset in two parts for training and validation of a eVision.EasyDeepLearning.ELocator tool.
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.
eVision.EasyDeepLearning.EDataset	ExtractSplit (self, eVision.EasyDeepLearning.EDatasetSplit split, eVision.EasyDeepLearning.EDatasetType type)
	Creates a new eVision.EasyDeepLearning.EDataset containing only the images of the given split type.
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.

Protected Member Functions
	_BasePath__setter (self, str value)
	Sets the base path for the images specified with a relative path.

Properties
	BasePath

Detailed Description

eVision.EasyDeepLearning.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 (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 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

Constructor & Destructor Documentation

__init__() [1/2]

None eVision.EasyDeepLearning.EDataset.__init__

(

self

)

Constructs a eVision.EasyDeepLearning.EDataset object.

__init__() [2/2]

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

Copy constructor of a eVision.EasyDeepLearning.EDataset object.

Parameters

other

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

Member Function Documentation

_BasePath__setter()

eVision.EasyDeepLearning.EDataset._BasePath__setter ( self, str value )

protected

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

Remarks:

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

AbsoluteMaxOverlap()

float eVision.EasyDeepLearning.EDataset.AbsoluteMaxOverlap

(

self

)

Absolute maximum overlap between objects in the dataset.

AddImage() [1/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

Parameters

img

The image

AddImage() [2/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		eVision.EBaseROI	img,
		int	originX,
		int	originY,
		int	width,
		int	height,
		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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

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)

AddImage() [3/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		eVision.EBaseROI	img,
		int	originX,
		int	originY,
		int	width,
		int	height,
		eVision.ERegion	mask,
		str	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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

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

AddImage() [4/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		eVision.EBaseROI	img,
		str	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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

Parameters

img	The image
label	The label

AddImage() [5/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		str	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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

Parameters

imagePath

The path to an image

AddImage() [6/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		str	imagePath,
		int	originX,
		int	originY,
		int	width,
		int	height,
		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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

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)

AddImage() [7/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		str	imagePath,
		int	originX,
		int	originY,
		int	width,
		int	height,
		eVision.ERegion	mask,
		str	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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

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

AddImage() [8/8]

int eVision.EasyDeepLearning.EDataset.AddImage	(		self,
		str	imagePath,
		str	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 eVision.EasyDeepLearning.EDataset.NumImages - 1 and can be used to add, remove, or modify the annotations for the image.

Remarks:

When adding eVision images (eVision.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.

Parameters

imagePath	The path to an image
label	The label

AddImageObject() [1/2]

None eVision.EasyDeepLearning.EDataset.AddImageObject	(		self,
		int	imageIndex,
		eVision.EasyDeepLearning.ELocatorObject	obj )

Adds an object to the image.

Remarks:

The image will be marked as labeled for object detection (eVision.EasyDeepLearning.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.

Parameters

imageIndex	Index of the image
obj	Object

AddImageObject() [2/2]

None eVision.EasyDeepLearning.EDataset.AddImageObject	(		self,
		int	imageIndex,
		str	label,
		eVision.ERectangleRegion	box )

Adds an object to the image.

Remarks:

The image will be marked as labeled for object detection (eVision.EasyDeepLearning.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.

Parameters

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

AddImages() [1/4]

int eVision.EasyDeepLearning.EDataset.AddImages	(		self,
		str	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.

Remarks:

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.

Parameters

filter

A glob filter

AddImages() [2/4]

int eVision.EasyDeepLearning.EDataset.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.

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.

Remarks:

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.

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

AddImages() [3/4]

int eVision.EasyDeepLearning.EDataset.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.

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.

Remarks:

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.

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.

AddImages() [4/4]

int eVision.EasyDeepLearning.EDataset.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.

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.

Remarks:

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.

Parameters

filter	A glob filter
label	A label.

AddImagesFromCSV() [1/5]

int eVision.EasyDeepLearning.EDataset.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.

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	(		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.

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	(		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.

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	(		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.

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	(		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.

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()

None eVision.EasyDeepLearning.EDataset.AddLabel	(		self,
		str	label )

Adds a label to the dataset.

Parameters

label

Name of the new label

AddObjectLabel() [1/2]

None eVision.EasyDeepLearning.EDataset.AddObjectLabel	(		self,
		str	label )

Adds an object label.

Parameters

label

Label to add

AddObjectLabel() [2/2]

None eVision.EasyDeepLearning.EDataset.AddObjectLabel	(		self,
		str	label,
		float	weight )

Adds an object label.

Parameters

label	Label to add
weight	Weight of the label

AddRegionToSegment() [1/2]

None eVision.EasyDeepLearning.EDataset.AddRegionToSegment	(		self,
		int	imageIndex,
		int	segmentationLabelIndex,
		eVision.ERegion	region )

Adds a region (see eVision.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

AddRegionToSegment() [2/2]

None eVision.EasyDeepLearning.EDataset.AddRegionToSegment	(		self,
		int	imageIndex,
		str	label,
		eVision.ERegion	region )

Adds a region (see eVision.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

AddSegmentationLabel() [1/2]

int eVision.EasyDeepLearning.EDataset.AddSegmentationLabel	(		self,
		str	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	(		self,
		str	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

Channels()

int eVision.EasyDeepLearning.EDataset.Channels

(

self

)

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

Clear()

None eVision.EasyDeepLearning.EDataset.Clear

(

self

)

Clears the dataset.

Reimplemented in eVision.EasyDeepLearning.EClassificationDataset.

Export() [1/12]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		bool	keepFilename )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		bool	keepFilename,
		eVision.EImageFileType	fileType )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		bool	keepFilename,
		eVision.EImageFileType	fileType,
		int	quality )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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 eVision.EBaseROI.SavePng, eVision.EBaseROI.SaveJpeg, or eVision.EBaseROI.SaveJpeg2K. A value of -1 means the default value.

Export() [5/12]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		eVision.EImageFileType	fileType )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		eVision.EImageFileType	fileType,
		int	quality )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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 eVision.EBaseROI.SavePng, eVision.EBaseROI.SaveJpeg, or eVision.EBaseROI.SaveJpeg2K. A value of -1 means the default value.

Export() [7/12]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		int	width,
		int	height,
		int	channels )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		int	width,
		int	height,
		int	channels,
		bool	keepFilename )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.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.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.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.

• A new eVision.EasyDeepLearning.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 eVision.EBaseROI.SavePng, eVision.EBaseROI.SaveJpeg, or eVision.EBaseROI.SaveJpeg2K. A value of -1 means the default value.

Export() [11/12]

None eVision.EasyDeepLearning.EDataset.Export	(		self,
		str	directory,
		int	width,
		int	height,
		int	channels,
		eVision.EImageFileType	fileType )

Exports the dataset and its images to the given directory.

• A new eVision.EasyDeepLearning.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]

None eVision.EasyDeepLearning.EDataset.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.

• A new eVision.EasyDeepLearning.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 eVision.EBaseROI.SavePng, eVision.EBaseROI.SaveJpeg, or eVision.EBaseROI.SaveJpeg2K. A value of -1 means the default value.

ExtractSplit()

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

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

Parameters

split	Split
type	Type of the split to extract

Reimplemented in eVision.EasyDeepLearning.EClassificationDataset.

GetAvailableImageAnnotationFormat() [1/2]

eVision.EasyDeepLearning.EImageAnnotationFormat eVision.EasyDeepLearning.EDataset.GetAvailableImageAnnotationFormat	(		self,
		int	imgId )

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

Parameters

imgId

Index of an image

GetAvailableImageAnnotationFormat() [2/2]

eVision.EasyDeepLearning.EImageAnnotationFormat eVision.EasyDeepLearning.EDataset.GetAvailableImageAnnotationFormat	(		self,
		str	imageFile )

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

Parameters

imageFile

Path to an image file

GetImageCopy() [1/2]

eVision.EBaseROI | None eVision.EasyDeepLearning.EDataset.GetImageCopy	(		self,
		int	index )

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

Parameters

index

The index of the image.

GetImageCopy() [2/2]

None eVision.EasyDeepLearning.EDataset.GetImageCopy	(		self,
		int	index,
		eVision.EBaseROI	img )

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()

str eVision.EasyDeepLearning.EDataset.GetImageLabel	(		self,
		int	index )

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	(		self,
		int	imageIndex )

Number of objects for the specified image.

Parameters

imageIndex

Index of the image

GetImageObject()

eVision.EasyDeepLearning.ELocatorObject eVision.EasyDeepLearning.EDataset.GetImageObject	(		self,
		int	imageIndex,
		int	objectIndex )

Gets the specified object for the given image.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and eVision.EasyDeepLearning.EDataset.GetImageNumObjects

GetImageObjects()

list[eVision._eVision.EasyDeepLearning.ELocatorObject] eVision.EasyDeepLearning.EDataset.GetImageObjects	(		self,
		int	imageIndex )

Gets all the objects of the specified image.

Parameters

imageIndex

Index of the image

GetImagePath()

str eVision.EasyDeepLearning.EDataset.GetImagePath	(		self,
		int	index )

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]

list[int] eVision.EasyDeepLearning.EDataset.GetImagesIndexesWithLabel	(		self,
		int	labelIndex )

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 eVision.EasyDeepLearning.EDataset.NumLabels - 1)

GetImagesIndexesWithLabel() [2/2]

list[int] eVision.EasyDeepLearning.EDataset.GetImagesIndexesWithLabel	(		self,
		str	label )

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

Parameters

label

The label

GetLabel()

str eVision.EasyDeepLearning.EDataset.GetLabel	(		self,
		int	i )

Gets the i-th label of the dataset (starting from 0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).

Parameters

i	Label index

GetLabelColor()

eVision.ERGBColor eVision.EasyDeepLearning.EDataset.GetLabelColor	(		self,
		int	index )

Gets the label color for a classification label.

Parameters

index

Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).

GetLabelOpacity()

int eVision.EasyDeepLearning.EDataset.GetLabelOpacity	(		self,
		int	index )

Gets the label opacity for a classification label.

Parameters

index

Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).

GetLabelWeight()

float eVision.EasyDeepLearning.EDataset.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).

Parameters

index

Label index

GetMask()

eVision.ERegion eVision.EasyDeepLearning.EDataset.GetMask	(		self,
		int	imageIndex )

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

GetNumImagesForFile()

int eVision.EasyDeepLearning.EDataset.GetNumImagesForFile	(		self,
		str	filePath )

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

Remarks:

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).

Parameters

filePath

the path to the image.

GetNumImagesForSegmentationLabel() [1/2]

int eVision.EasyDeepLearning.EDataset.GetNumImagesForSegmentationLabel	(		self,
		int	labelId )

Number of images containing the given segmentation label.

Parameters

labelId

Index of the segmentation label

GetNumImagesForSegmentationLabel() [2/2]

int eVision.EasyDeepLearning.EDataset.GetNumImagesForSegmentationLabel	(		self,
		str	label )

Number of images containing the given segmentation label.

Parameters

label

Segmentation label

GetNumImagesWithObjectLabel() [1/2]

int eVision.EasyDeepLearning.EDataset.GetNumImagesWithObjectLabel	(		self,
		int	labelIdx )

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

Parameters

labelIdx

Index of the object label.

GetNumImagesWithObjectLabel() [2/2]

int eVision.EasyDeepLearning.EDataset.GetNumImagesWithObjectLabel	(		self,
		str	label )

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

Parameters

label

Label.

GetNumObjectsWithLabel() [1/2]

int eVision.EasyDeepLearning.EDataset.GetNumObjectsWithLabel	(		self,
		int	labelIdx )

Number of objects with the given label.

Parameters

labelIdx

Index of the object label.

GetNumObjectsWithLabel() [2/2]

int eVision.EasyDeepLearning.EDataset.GetNumObjectsWithLabel	(		self,
		str	label )

Number of objects with the given label.

Parameters

label

Label.

GetNumPixelsForSegmentationLabel() [1/2]

int eVision.EasyDeepLearning.EDataset.GetNumPixelsForSegmentationLabel	(		self,
		int	labelId )

Number of pixels assigned to the given segmentation label.

Parameters

labelId

Index of the segmentation label

GetNumPixelsForSegmentationLabel() [2/2]

int eVision.EasyDeepLearning.EDataset.GetNumPixelsForSegmentationLabel	(		self,
		str	label )

Number of pixels assigned to the given segmentation label.

Parameters

label

Segmentation label

GetNumSegmentedBlobs() [1/3]

int eVision.EasyDeepLearning.EDataset.GetNumSegmentedBlobs	(		self,
		int	imageId )

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

Remarks:

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

Parameters

imageId

Image index

GetNumSegmentedBlobs() [2/3]

int eVision.EasyDeepLearning.EDataset.GetNumSegmentedBlobs	(		self,
		int	imageId,
		int	labelId )

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

Remarks:

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

Parameters

imageId	Image index
labelId	Label index

GetNumSegmentedBlobs() [3/3]

int eVision.EasyDeepLearning.EDataset.GetNumSegmentedBlobs	(		self,
		int	imageId,
		str	label )

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

Remarks:

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

Parameters

imageId	Image index
label	Label

GetObjectLabel()

str eVision.EasyDeepLearning.EDataset.GetObjectLabel	(		self,
		int	labelIndex )

Object label.

Parameters

labelIndex

Index of the object label between 0 and eVision.EasyDeepLearning.EDataset.NumObjectLabels

GetObjectLabelColor()

eVision.ERGBColor eVision.EasyDeepLearning.EDataset.GetObjectLabelColor	(		self,
		int	index )

Gets the label color for an object label.

Parameters

index

Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).

GetObjectLabelOpacity()

int eVision.EasyDeepLearning.EDataset.GetObjectLabelOpacity	(		self,
		int	index )

Gets the label opacity for an object label.

Parameters

index

Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).

GetObjectLabelWeight() [1/2]

float eVision.EasyDeepLearning.EDataset.GetObjectLabelWeight	(		self,
		int	labelIndex )

Gets the weight of an object label.

Parameters

labelIndex

Index of the object label

GetObjectLabelWeight() [2/2]

float eVision.EasyDeepLearning.EDataset.GetObjectLabelWeight	(		self,
		str	label )

Gets the weight of an object label.

Parameters

label

Label

GetOCRAnnotations()

list[eVision._eVision.EasyDeepOCR.ETextLine] eVision.EasyDeepLearning.EDataset.GetOCRAnnotations	(		self,
		int	imgId )

Ground truth text lines for the specified image.

Parameters

imgId

Index of the image.

GetRegionForSegment() [1/2]

eVision.ERegion eVision.EasyDeepLearning.EDataset.GetRegionForSegment	(		self,
		int	imageIndex,
		int	segmentationLabelIndex )

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

Parameters

imageIndex	Image index
segmentationLabelIndex	Segmentation label index

GetRegionForSegment() [2/2]

eVision.ERegion eVision.EasyDeepLearning.EDataset.GetRegionForSegment	(		self,
		int	imageIndex,
		str	segmentationLabel )

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

Parameters

imageIndex	Image index
segmentationLabel	Segmentation label

GetRegionOfInterestHeight()

int eVision.EasyDeepLearning.EDataset.GetRegionOfInterestHeight	(		self,
		int	imageIndex )

Region of interest height for the specified image.

Parameters

imageIndex

Index of the image.

GetRegionOfInterestOriginX()

int eVision.EasyDeepLearning.EDataset.GetRegionOfInterestOriginX	(		self,
		int	imageIndex )

Region of interest origin abscissa for the specified image.

Parameters

imageIndex

Index of the image.

GetRegionOfInterestOriginY()

int eVision.EasyDeepLearning.EDataset.GetRegionOfInterestOriginY	(		self,
		int	imageIndex )

Region of interest origin ordinate for the specified image.

Parameters

imageIndex

Index of the image.

GetRegionOfInterestWidth()

int eVision.EasyDeepLearning.EDataset.GetRegionOfInterestWidth	(		self,
		int	imageIndex )

Region of interest width for the specified image.

Parameters

imageIndex

Index of the image.

GetSegmentationLabel()

str eVision.EasyDeepLearning.EDataset.GetSegmentationLabel	(		self,
		int	index )

Gets the segmentation label.

Remarks:

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

Parameters

index

Index of the segmentation label

GetSegmentationLabelColor()

eVision.ERGBColor eVision.EasyDeepLearning.EDataset.GetSegmentationLabelColor	(		self,
		int	index )

Gets the label color for a segmentation label.

Parameters

index

Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).

GetSegmentationLabelOpacity()

int eVision.EasyDeepLearning.EDataset.GetSegmentationLabelOpacity	(		self,
		int	index )

Gets the label opacity for a segmentation label.

Parameters

index

Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).

GetSegmentationLabelWeight() [1/2]

float eVision.EasyDeepLearning.EDataset.GetSegmentationLabelWeight	(		self,
		int	index )

Gets the segmentation label weights.

Parameters

index

Index of the segmentation label

GetSegmentationLabelWeight() [2/2]

float eVision.EasyDeepLearning.EDataset.GetSegmentationLabelWeight	(		self,
		str	label )

Gets the segmentation label weights.

Parameters

label

Segmentation label

GetSegmentationMap()

eVision.EImageBW16 eVision.EasyDeepLearning.EDataset.GetSegmentationMap	(		self,
		int	imageIndex )

Gets the segmentation map of an image.

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

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

Parameters

imageIndex

Image index

GetSplit() [1/2]

eVision.EasyDeepLearning.EDatasetSplit eVision.EasyDeepLearning.EDataset.GetSplit	(		self,
		float	trainingProportion,
		float	validationPropotion,
		eVision.EasyDeepLearning.EDeepLearningToolType	toolType,
		str	goodLabel )

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() [2/2]

eVision.EasyDeepLearning.EDatasetSplit eVision.EasyDeepLearning.EDataset.GetSplit	(		self,
		float	trainingProportion,
		float	validationPropotion,
		eVision.EasyDeepLearning.EDeepLearningToolType	toolType,
		str	goodLabel,
		int	seed )

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

HasForegroundSegments()

bool eVision.EasyDeepLearning.EDataset.HasForegroundSegments	(		self,
		int	imageId )

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	(		self,
		int	index )

Whether an image has a classification label.

Parameters

index

Index of an image.

HasObjectLabeling()

bool eVision.EasyDeepLearning.EDataset.HasObjectLabeling	(		self,
		int	imageIndex )

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

Parameters

imageIndex

Index of the image

HasOCRAnnotations()

bool eVision.EasyDeepLearning.EDataset.HasOCRAnnotations	(		self,
		int	imgId )

Whether the specified image has OCR annotations.

Parameters

imgId

Index of the image.

HasSegmentation()

bool eVision.EasyDeepLearning.EDataset.HasSegmentation	(		self,
		int	imageId )

Whether the image has a segmentation.

Parameters

imageId

Image index

Height()

int eVision.EasyDeepLearning.EDataset.Height

(

self

)

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

ImagesIndexesWithNoLabel()

list[int] eVision.EasyDeepLearning.EDataset.ImagesIndexesWithNoLabel

(

self

)

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

ImportPascalVOCXMLAnnotations() [1/2]

None eVision.EasyDeepLearning.EDataset.ImportPascalVOCXMLAnnotations

(

self

)

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]

None eVision.EasyDeepLearning.EDataset.ImportPascalVOCXMLAnnotations	(		self,
		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]

None eVision.EasyDeepLearning.EDataset.ImportYOLOTXTAnnotations	(		self,
		int	imgId,
		str	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

ImportYOLOTXTAnnotations() [2/2]

None eVision.EasyDeepLearning.EDataset.ImportYOLOTXTAnnotations	(		self,
		str	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

IsEmbeddedImage()

bool eVision.EasyDeepLearning.EDataset.IsEmbeddedImage	(		self,
		int	index )

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	(		self,
		int	index )

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

Parameters

index

Index of the image.

LabeledImagesIndexes()

list[int] eVision.EasyDeepLearning.EDataset.LabeledImagesIndexes

(

self

)

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

Load() [1/2]

None eVision.EasyDeepLearning.EDataset.Load	(		self,
		eVision.ESerializer | None	serializer )

Loads a classification dataset from disk.

Parameters

serializer

The serializer.

Load() [2/2]

None eVision.EasyDeepLearning.EDataset.Load	(		self,
		str	path )

Loads a classification dataset from disk.

Parameters

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

MaxNumObjectPerImage()

int eVision.EasyDeepLearning.EDataset.MaxNumObjectPerImage

(

self

)

Maximum number of objects for an image in the dataset.

If no images has object labeling (see eVision.EasyDeepLearning.EDataset.HasObjectLabeling), the method returns -1.

NumImageFiles()

int eVision.EasyDeepLearning.EDataset.NumImageFiles

(

self

)

Number of different image files contained in the dataset.

Remarks:

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).

NumImages()

int eVision.EasyDeepLearning.EDataset.NumImages

(

self

)

Number of images in the dataset.

NumImagesWithForegroundSegments()

int eVision.EasyDeepLearning.EDataset.NumImagesWithForegroundSegments

(

self

)

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

NumImagesWithObjectLabeling()

int eVision.EasyDeepLearning.EDataset.NumImagesWithObjectLabeling

(

self

)

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

NumImagesWithObjects()

int eVision.EasyDeepLearning.EDataset.NumImagesWithObjects

(

self

)

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

NumImagesWithOCR()

int eVision.EasyDeepLearning.EDataset.NumImagesWithOCR

(

self

)

Number of images with OCR annotations.

NumImagesWithoutForegroundSegments()

int eVision.EasyDeepLearning.EDataset.NumImagesWithoutForegroundSegments

(

self

)

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

NumImagesWithoutObjectLabeling()

int eVision.EasyDeepLearning.EDataset.NumImagesWithoutObjectLabeling

(

self

)

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

NumImagesWithoutObjects()

int eVision.EasyDeepLearning.EDataset.NumImagesWithoutObjects

(

self

)

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

NumImagesWithoutOCR()

int eVision.EasyDeepLearning.EDataset.NumImagesWithoutOCR

(

self

)

Number of images without OCR annotations.

NumImagesWithoutSegmentation()

int eVision.EasyDeepLearning.EDataset.NumImagesWithoutSegmentation

(

self

)

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

NumImagesWithSegmentation()

int eVision.EasyDeepLearning.EDataset.NumImagesWithSegmentation

(

self

)

Number of images that have a ground truth segmentation.

NumLabeledImages()

int eVision.EasyDeepLearning.EDataset.NumLabeledImages

(

self

)

Number of images that have a classification label.

NumLabels()

int eVision.EasyDeepLearning.EDataset.NumLabels

(

self

)

Number of labels in the dataset.

NumObjectLabels()

int eVision.EasyDeepLearning.EDataset.NumObjectLabels

(

self

)

Number of object labels.

NumSegmentationLabels()

int eVision.EasyDeepLearning.EDataset.NumSegmentationLabels

(

self

)

Number of segmentation labels.

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

NumTexts()

int eVision.EasyDeepLearning.EDataset.NumTexts

(

self

)

Number of annotated texts in the dataset.

NumUnlabeledImages()

int eVision.EasyDeepLearning.EDataset.NumUnlabeledImages

(

self

)

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

ObjectSize() [1/2]

int eVision.EasyDeepLearning.EDataset.ObjectSize

(

self

)

Object size for EasyLocate Interest point.

ObjectSize() [2/2]

eVision.EasyDeepLearning.EDataset.ObjectSize	(		self,
		int	value )

Object size for EasyLocate Interest point.

RemoveImage()

None eVision.EasyDeepLearning.EDataset.RemoveImage	(		self,
		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()

None eVision.EasyDeepLearning.EDataset.RemoveImageObject	(		self,
		int	imageIndex,
		int	objectIndex )

Removes an object from an image.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and eVision.EasyDeepLearning.EDataset.GetImageNumObjects

RemoveLabel() [1/2]

None eVision.EasyDeepLearning.EDataset.RemoveLabel	(		self,
		int	labelId )

Removes a label from the dataset.

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

Parameters

labelId

Index of the label to remove

RemoveLabel() [2/2]

None eVision.EasyDeepLearning.EDataset.RemoveLabel	(		self,
		str	label )

Removes a label from the dataset.

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

Parameters

label

Name of the label to remove

RemoveObjectLabel() [1/2]

None eVision.EasyDeepLearning.EDataset.RemoveObjectLabel	(		self,
		int	labelIndex )

Removes an object label.

Parameters

labelIndex

Index of the object label to remove

RemoveObjectLabel() [2/2]

None eVision.EasyDeepLearning.EDataset.RemoveObjectLabel	(		self,
		str	label )

Removes an object label.

Parameters

label

Label to remove

RemoveSegmentationLabel() [1/2]

None eVision.EasyDeepLearning.EDataset.RemoveSegmentationLabel	(		self,
		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

RemoveSegmentationLabel() [2/2]

None eVision.EasyDeepLearning.EDataset.RemoveSegmentationLabel	(		self,
		str	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

ResetImageObjectLabeling()

None eVision.EasyDeepLearning.EDataset.ResetImageObjectLabeling	(		self,
		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()

None eVision.EasyDeepLearning.EDataset.ResetSegmentation	(		self,
		int	imageIndex )

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

Parameters

imageIndex

Image index

SameLabelMaxOverlap()

float eVision.EasyDeepLearning.EDataset.SameLabelMaxOverlap

(

self

)

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

Save() [1/2]

None eVision.EasyDeepLearning.EDataset.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.

Remarks:

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

To obtain a portable eVision.EasyDeepLearning.EDataset file, please use eVision.EasyDeepLearning.EDataset.Export.

Parameters

serializer

The serializer.

Save() [2/2]

None eVision.EasyDeepLearning.EDataset.Save	(		self,
		str	path )

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

Remarks:

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

To obtain a portable eVision.EasyDeepLearning.EDataset file, please use eVision.EasyDeepLearning.EDataset.Export.

Parameters

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

SaveWithRelativePath()

None eVision.EasyDeepLearning.EDataset.SaveWithRelativePath	(		self,
		str	path )

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

SetImageLabel() [1/2]

None eVision.EasyDeepLearning.EDataset.SetImageLabel	(		self,
		int	index,
		str	label )

Sets the label of images in the dataset.

Remarks:

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.

Parameters

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

SetImageLabel() [2/2]

None eVision.EasyDeepLearning.EDataset.SetImageLabel	(		self,
		str	filter,
		str	label )

Sets the label of images in the dataset.

Remarks:

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.

Parameters

filter	A glob filter
label	The label

SetImageObject() [1/3]

None eVision.EasyDeepLearning.EDataset.SetImageObject	(		self,
		int	imageIndex,
		int	objectIndex,
		eVision.EasyDeepLearning.ELocatorObject	obj )

Sets the specified object for the given image.

Remarks:

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.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and eVision.EasyDeepLearning.EDataset.GetImageNumObjects
obj	Object

SetImageObject() [2/3]

None eVision.EasyDeepLearning.EDataset.SetImageObject	(		self,
		int	imageIndex,
		int	objectIndex,
		eVision.ERectangleRegion	region )

Sets the specified object for the given image.

Remarks:

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.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and eVision.EasyDeepLearning.EDataset.GetImageNumObjects
region	New region for the object

SetImageObject() [3/3]

None eVision.EasyDeepLearning.EDataset.SetImageObject	(		self,
		int	imageIndex,
		int	objectIndex,
		str	label )

Sets the specified object for the given image.

Remarks:

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.

Parameters

imageIndex	Index of the image
objectIndex	Index of the object between 0 and eVision.EasyDeepLearning.EDataset.GetImageNumObjects
label	New label for the object

SetLabel()

None eVision.EasyDeepLearning.EDataset.SetLabel	(		self,
		int	index,
		str	label )

Sets the i-th label of the dataset (starting from 0 to eVision.EasyDeepLearning.EDataset.NumLabels - 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()

None eVision.EasyDeepLearning.EDataset.SetLabelColor	(		self,
		int	index,
		eVision.ERGBColor	color )

Sets the label color for a classification label.

Parameters

index	Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
color	New color for the label

SetLabelOpacity()

None eVision.EasyDeepLearning.EDataset.SetLabelOpacity	(		self,
		int	index,
		int	opacity )

Sets the label opacity for a classification label.

Parameters

index	Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
opacity	New opacity for the label

SetLabelWeight()

None eVision.EasyDeepLearning.EDataset.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).

This operation does not add a new label.

Parameters

index	Label index
weight	Weight to be set

SetMask()

None eVision.EasyDeepLearning.EDataset.SetMask	(		self,
		int	imageIndex,
		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]

None eVision.EasyDeepLearning.EDataset.SetObjectLabel	(		self,
		int	labelIndex,
		str	newLabel )

Sets an object label.

Parameters

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

SetObjectLabel() [2/2]

None eVision.EasyDeepLearning.EDataset.SetObjectLabel	(		self,
		str	oldLabel,
		str	newLabel )

Sets an object label.

Parameters

oldLabel	Old label to change
newLabel	New label to be set

SetObjectLabelColor()

None eVision.EasyDeepLearning.EDataset.SetObjectLabelColor	(		self,
		int	index,
		eVision.ERGBColor	color )

Sets the label color for an object label.

Parameters

index	Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
color	New color for the label

SetObjectLabelOpacity()

None eVision.EasyDeepLearning.EDataset.SetObjectLabelOpacity	(		self,
		int	index,
		int	opacity )

Sets the label opacity for an object label.

Parameters

index	Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
opacity	New opacity for the label

SetObjectLabelWeight() [1/2]

None eVision.EasyDeepLearning.EDataset.SetObjectLabelWeight	(		self,
		int	labelIndex,
		float	weight )

Sets the weight of an object label.

Parameters

labelIndex	Index of the object label
weight	New weight

SetObjectLabelWeight() [2/2]

None eVision.EasyDeepLearning.EDataset.SetObjectLabelWeight	(		self,
		str	label,
		float	weight )

Sets the weight of an object label.

Parameters

label	Label
weight	New weight

SetOCRAnnotations()

None eVision.EasyDeepLearning.EDataset.SetOCRAnnotations	(		self,
		int	imgId,
		list[eVision._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()

None eVision.EasyDeepLearning.EDataset.SetRegionOfInterest	(		self,
		int	imageIndex,
		int	xOrg,
		int	yOrg,
		int	width,
		int	height )

Sets the region of interest for the specified image.

Remarks:

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

Parameters

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

SetSegmentationLabel()

None eVision.EasyDeepLearning.EDataset.SetSegmentationLabel	(		self,
		int	index,
		str	label )

Sets the segmentation labels.

Remarks:

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

Parameters

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

SetSegmentationLabelColor()

None eVision.EasyDeepLearning.EDataset.SetSegmentationLabelColor	(		self,
		int	index,
		eVision.ERGBColor	color )

Sets the label color for a segmentation label.

Parameters

index	Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
color	New color for the label

SetSegmentationLabelOpacity()

None eVision.EasyDeepLearning.EDataset.SetSegmentationLabelOpacity	(		self,
		int	index,
		int	opacity )

Sets the label opacity for a segmentation label.

Parameters

index	Label index (0 to eVision.EasyDeepLearning.EDataset.NumLabels - 1).
opacity	New opacity for the label

SetSegmentationLabelWeight() [1/2]

None eVision.EasyDeepLearning.EDataset.SetSegmentationLabelWeight	(		self,
		int	index,
		float	weight )

Sets the segmentation label weight.

Parameters

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

SetSegmentationLabelWeight() [2/2]

None eVision.EasyDeepLearning.EDataset.SetSegmentationLabelWeight	(		self,
		str	label,
		float	weight )

Sets the segmentation label weight.

Parameters

label	Segmentation label
weight	Weight of the segmentation label

SetSegmentationMap()

None eVision.EasyDeepLearning.EDataset.SetSegmentationMap	(		self,
		int	imageIndex,
		eVision.EROIBW16	segmentationMap )

Sets the segmentation map of an image.

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

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

Parameters

imageIndex	Image index
segmentationMap	Segmentation map

SplitDataset() [1/3]

None eVision.EasyDeepLearning.EDataset.SplitDataset	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDataset() [2/3]

None eVision.EasyDeepLearning.EDataset.SplitDataset	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDataset() [3/3]

None eVision.EasyDeepLearning.EDataset.SplitDataset	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDatasetForLocator() [1/3]

None eVision.EasyDeepLearning.EDataset.SplitDatasetForLocator	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDatasetForLocator() [2/3]

None eVision.EasyDeepLearning.EDataset.SplitDatasetForLocator	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDatasetForLocator() [3/3]

None eVision.EasyDeepLearning.EDataset.SplitDatasetForLocator	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDatasetForSegmentation() [1/3]

None eVision.EasyDeepLearning.EDataset.SplitDatasetForSegmentation	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDatasetForSegmentation() [2/3]

None eVision.EasyDeepLearning.EDataset.SplitDatasetForSegmentation	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

SplitDatasetForSegmentation() [3/3]

None eVision.EasyDeepLearning.EDataset.SplitDatasetForSegmentation	(		self,
		eVision.EasyDeepLearning.EDataset	d1,
		eVision.EasyDeepLearning.EDataset	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 in eVision.EasyDeepLearning.EClassificationDataset, eVision.EasyDeepLearning.EClassificationDataset, and eVision.EasyDeepLearning.EClassificationDataset.

TextDataset()

eVision.EasyDeepLearning.EDataset eVision.EasyDeepLearning.EDataset.TextDataset

(

self

)

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

TextLabeledImagesIndexes()

list[int] eVision.EasyDeepLearning.EDataset.TextLabeledImagesIndexes

(

self

)

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

TextOrientationDataset()

eVision.EasyDeepLearning.EDataset eVision.EasyDeepLearning.EDataset.TextOrientationDataset

(

self

)

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

UnsetImageLabel()

None eVision.EasyDeepLearning.EDataset.UnsetImageLabel	(		self,
		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()

None eVision.EasyDeepLearning.EDataset.UnsetImageObjectLabeling	(		self,
		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 eVision.EasyDeepLearning.ELocator tool.

Parameters

imageIndex

Index of the image

UnsetOCRAnnotations()

None eVision.EasyDeepLearning.EDataset.UnsetOCRAnnotations	(		self,
		int	imgId )

Unsets the OCR labeling for the specified image.

Parameters

imgId

Index of the image.

UnsetSegmentation()

None eVision.EasyDeepLearning.EDataset.UnsetSegmentation	(		self,
		int	imageIndex )

Unsets the segmentation of an image.

After this operation, the image will have no segmentation.

Parameters

imageIndex

Image index

Width()

int eVision.EasyDeepLearning.EDataset.Width

(

self

)

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

Property Documentation

BasePath

eVision.EasyDeepLearning.EDataset.BasePath

static

Initial value:

=  property(None, _BasePath__setter, None,
        
    )