EClassifier Class
EClassifier allows to train a classifier using an EClassificationDataset object and classify new images.
As required by Deep Learning techniques, the input image of EClassifier must be of the same format (width, height, number of channels). By default, this format will be the one of the first image added to the dataset used for training unless its width and height is smaller than the minimum width and height supported by the classifier (See EClassifier::MinimumWidth and EClassifier::MinimumHeight). In this case, the input resolution will be the minimum resolution supported by the classifier. The format can also be specified by the EClassifier::Width, EClassifier::Height and EClassifier::Channels. methods.
By default, images that don't satisfy the image format of the classifier are automatically reformatted. This behavior can be controlled through the EClassifier::EnableAutomaticImageReformat method. When the automatic image reformatting is disabled, training or classifying an image that doesn't satisfy the input image format will result in an exception.
Once trained, the input image format cannot be changed.
Base Class: EDeepLearningTool
Namespace: Euresys::Open_eVision_2_11::EasyDeepLearning
Methods
Classifies images and returns the complete results as an
EClassificationResult object.
The method throws an exception if the input image does not fulfill the input specification.
Number of channels for input images of the classifier. The number of channels can be either 1 (monochrome image) or 3 (RGB image). By default, this value will be set from the format of the first image added to the training dataset.
Enable automatic image reformat (true by default).
Enable histogram equalization of all images passing through the classifier. (false by default)
Gets a heatmap associated with the given label. A heatmap is an image that indicates which pixels in the original image best explain the given label.
Height for input images of the classifier. By default, this value will be set from the format of the first image of the dataset used for training.
Gets the label from its index. If the classifier is not trained, the method will throw an exception.
Minimum height for input images of the classifier. This value is equal to 128.
Minimum width for input images of the classifier. This value is equal to 128.
Number of labels of this classifier. If the classifier is not trained, the method will throw an exception.
Gets the metrics obtained with the training dataset at the given iteration.
The iterations are indexed between
0 and
EClassifier - 1.
Gets the metrics obtained with the validation dataset at the given iteration.
The iterations are indexed between
0 and
EClassifier - 1.
Width for input images of the classifier. By default, this value will be set from the format of the first image of the dataset used for training.
Loads a classifier. The given
ESerializer must have been created for reading.
Saves a classifier. The given
ESerializer must have been created for writing.
Serializes the classifier.
Number of channels for input images of the classifier. The number of channels can be either 1 (monochrome image) or 3 (RGB image). By default, this value will be set from the format of the first image added to the training dataset.
Enable automatic image reformat (true by default).
Enable histogram equalization of all images passing through the classifier. (false by default)
Height for input images of the classifier. By default, this value will be set from the format of the first image of the dataset used for training.
Number of labels of this classifier. If the classifier is not trained, the method will throw an exception.
Width for input images of the classifier. By default, this value will be set from the format of the first image of the dataset used for training.
EClassifier Class
EClassifier allows to train a classifier using an EClassificationDataset object and classify new images.
As required by Deep Learning techniques, the input image of EClassifier must be of the same format (width, height, number of channels). By default, this format will be the one of the first image added to the dataset used for training unless its width and height is smaller than the minimum width and height supported by the classifier (See EClassifier::MinimumWidth and EClassifier::MinimumHeight). In this case, the input resolution will be the minimum resolution supported by the classifier. The format can also be specified by the EClassifier::Width, EClassifier::Height and EClassifier::Channels. methods.
By default, images that don't satisfy the image format of the classifier are automatically reformatted. This behavior can be controlled through the EClassifier::EnableAutomaticImageReformat method. When the automatic image reformatting is disabled, training or classifying an image that doesn't satisfy the input image format will result in an exception.
Once trained, the input image format cannot be changed.
Base Class: EDeepLearningTool
Namespace: Euresys.Open_eVision_2_11.EasyDeepLearning
Properties
Number of channels for input images of the classifier. The number of channels can be either 1 (monochrome image) or 3 (RGB image). By default, this value will be set from the format of the first image added to the training dataset.
Enable automatic image reformat (true by default).
Enable histogram equalization of all images passing through the classifier. (false by default)
Height for input images of the classifier. By default, this value will be set from the format of the first image of the dataset used for training.
Minimum height for input images of the classifier. This value is equal to 128.
Minimum width for input images of the classifier. This value is equal to 128.
Number of labels of this classifier. If the classifier is not trained, the method will throw an exception.
Width for input images of the classifier. By default, this value will be set from the format of the first image of the dataset used for training.
Methods
Classifies images and returns the complete results as an
EClassificationResult object.
The method throws an exception if the input image does not fulfill the input specification.
Gets a heatmap associated with the given label. A heatmap is an image that indicates which pixels in the original image best explain the given label.
Gets the label from its index. If the classifier is not trained, the method will throw an exception.
Gets the metrics obtained with the training dataset at the given iteration.
The iterations are indexed between
0 and
EClassifier - 1.
Gets the metrics obtained with the validation dataset at the given iteration.
The iterations are indexed between
0 and
EClassifier - 1.
Loads a classifier. The given
ESerializer must have been created for reading.
Saves a classifier. The given
ESerializer must have been created for writing.
Serializes the classifier.