apply_dl_classifier — Infer the class affiliations for a set of images using the deep-learning-based classifier.
apply_dl_classifier applies the deep-learning-based classifier given by DLClassifierHandle on the set of input images stored in the input object tuple Images. It returns the results in the result handle DLClassifierResultHandle. For information how to retrieve the corresponding results stored in DLClassifierResultHandle, please refer to the reference of the operator get_dl_classifier_result.
The tuple of images Images can contain an arbitrary number of images. Please notice that this only holds for apply_dl_classifier and not for train_dl_classifier_batch. When the set Images contains more images than the batch size of the neural network, apply_dl_classifier iterates over batches of images of the same size as the batch size of the network. Therefore, it should be noted that the runtime of processing a given batch is independent of whether the batch is filled up or it just consists of a single image. On the other hand, if fewer images than 'batch_size' are classified in one operator call, the network still requires the full amount of memory. Therefore, it is recommended to adapt the batch size accordingly. For further details corresponding the batch size of the network, please refer to set_dl_classifier_param.
Note that the images must be processed before feeding them into the operator apply_dl_classifier in order to have the correct size, gray value range, number of channels and type. We would like to stress the image type: the images must be of type 'real'. For a possibly necessary conversion the operator convert_image_type is available. The procedure preprocess_dl_classifier_images provides great guidance on how to implement such a preprocessing stage.
For an explanation of the concept of deep-learning-based classification see the introduction of chapter Deep Learning / Classification.
To run this operator, cuDNN is required. For further details, please refer to the Installation Guide, paragraph Requirements for Deep Learning.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Tuple of input images.
Handle of the deep-learning-based classifier.
Handle of the deep learning classification results.
If the parameters are valid, the operator apply_dl_classifier returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
read_dl_classifier, train_dl_classifier_batch, set_dl_classifier_param
Deep Learning Inference