Algorithm Interfaces¶

class smqtk.algorithms.SmqtkAlgorithm[source]¶

Parent class for all algorithm interfaces.

name¶

Returns:	The name of this class type.
Return type:	str

Here we list and briefly describe the high level algorithm interfaces which SMQTK provides. There is at least one implementation available for each interface. Some implementations will require additional dependencies that cannot be packaged with SMQTK.

Classifier¶

This interface represents algorithms that classify DescriptorElement instances into discrete labels or label confidences.

class smqtk.algorithms.classifier.Classifier[source]¶

Interface for algorithms that classify input descriptors into discrete labels and/or label confidences.

classify(d, factory=<smqtk.representation.classification_element_factory.ClassificationElementFactory object>, overwrite=False)[source]¶

Classify the input descriptor against one or more discrete labels, outputting a ClassificationElement containing the classification result.

We return confidence values for each label the configured model contains. Implementations may act in a discrete manner whereby only one label is marked with a 1 value (others being 0), or in a continuous manner whereby each label is given a confidence-like value in the [0, 1] range.

The returned ClassificationElement will have the same UUID as the input DescriptorElement.

Parameters:	d (smqtk.representation.DescriptorElement) – Input descriptor to classify factory (smqtk.representation.ClassificationElementFactory) – Classification element factory. The default factory yields MemoryClassificationElement instances. overwrite (bool) – Recompute classification of the input descriptor and set the results to the ClassificationElement produced by the factory.
Raises:	ValueError – The given descriptor element did not have a vector to operate on. RuntimeError – Could not perform classification for some reason (see message in raised exception).
Returns:	Classification result element
Return type:	smqtk.representation.ClassificationElement

classify_async(d_iter, factory=<smqtk.representation.classification_element_factory.ClassificationElementFactory object>, overwrite=False, procs=None, use_multiprocessing=False, ri=None)[source]¶

Asynchronously classify the DescriptorElements in the given iterable.

Parameters:	d_iter (collections.Iterable[smqtk.representation.DescriptorElement]) – Iterable of DescriptorElements factory (smqtk.representation.ClassificationElementFactory) – Classifier element factory to use for element generation. The default factory yields MemoryClassificationElement instances. overwrite (bool) – Recompute classification of the input descriptor and set the results to the ClassificationElement produced by the factory. procs (None \| int) – Explicit number of cores/thread/processes to use. use_multiprocessing (bool) – Use multiprocessing instead of threading. ri (float \| None) – Progress reporting interval in seconds. Set to a value > 0 to enable. Disabled by default.
Returns:	Mapping of input DescriptorElement instances to the computed ClassificationElement. ClassificationElement UUID’s are congruent with the UUID of the DescriptorElement
Return type:	dict[smqtk.representation.DescriptorElement, smqtk.representation.ClassificationElement]

get_labels()[source]¶

Get the sequence of class labels that this classifier can classify descriptors into. This includes the negative label.

Returns:	Sequence of possible classifier labels.
Return type:	collections.Sequence[collections.Hashable]
Raises:	RuntimeError – No model loaded.

DescriptorGenerator¶

This interface represents algorithms that generate whole-content descriptor vectors for a single given input DataElement instance. The input DataElement must be of a content type that the DescriptorGenerator supports, referenced against the DescriptorGenerator.valid_content_types method.

The compute_descriptor method also requires a DescriptorElementFactory instance to tell the algorithm how to generate the DescriptorElement it should return. The returned DescriptorElement instance will have a type equal to the name of the DescriptorGenerator class that generated it, and a UUID that is the same as the input DataElement instance.

If a DescriptorElement implementation that supports persistant storage is generated, and there is already a descriptor associated with the given type name and UUID values, the descriptor is returned without re-computation.

If the overwrite parameter is True, the DescriptorGenerator instance will re-compute a descriptor for the input DataElement, setting it to the generated DescriptorElement. This will overwrite descriptor data in persistant storage if the DescriptorElement type used supports it.

This interface supports a high-level, implementation agnostic asynchronous descriptor computation method. This is given an iterable of DataElement instances, a single DescriptorElementFactory that is used to produce all descriptor

class smqtk.algorithms.descriptor_generator.DescriptorGenerator[source]¶

Base abstract Feature Descriptor interface

compute_descriptor(data, descr_factory=<smqtk.representation.descriptor_element_factory.DescriptorElementFactory object>, overwrite=False)[source]¶

Given some data, return a descriptor element containing a descriptor vector.

Raises:	RuntimeError – Descriptor extraction failure of some kind. ValueError – Given data element content was not of a valid type with respect to this descriptor.
Parameters:	data (smqtk.representation.DataElement) – Some kind of input data for the feature descriptor. descr_factory (smqtk.representation.DescriptorElementFactory) – Factory instance to produce the wrapping descriptor element instance. The default factory produces `DescriptorMemoryElement` instances by default. overwrite (bool) – Whether or not to force re-computation of a descriptor vector for the given data even when there exists a precomputed vector in the generated DescriptorElement as generated from the provided factory. This will overwrite the persistently stored vector if the provided factory produces a DescriptorElement implementation with such storage.
Returns:	Result descriptor element. UUID of this output descriptor is the same as the UUID of the input data element.
Return type:	smqtk.representation.DescriptorElement

compute_descriptor_async(data_iter, descr_factory=<smqtk.representation.descriptor_element_factory.DescriptorElementFactory object>, overwrite=False, procs=None, **kwds)[source]¶

Asynchronously compute feature data for multiple data items.

Base implementation additional keyword arguments:

use_mp [= False]: If multi-processing should be used vs. multi-threading.

Parameters:	data_iter (collections.Iterable[smqtk.representation.DataElement]) – Iterable of data elements to compute features for. These must have UIDs assigned for feature association in return value. descr_factory (smqtk.representation.DescriptorElementFactory) – Factory instance to produce the wrapping descriptor element instance. The default factory produces `DescriptorMemoryElement` instances by default. overwrite (bool) – Whether or not to force re-computation of a descriptor vectors for the given data even when there exists precomputed vectors in the generated DescriptorElements as generated from the provided factory. This will overwrite the persistently stored vectors if the provided factory produces a DescriptorElement implementation such storage. procs (int \| None) – Optional specification of how many processors to use when pooling sub-tasks. If None, we attempt to use all available cores.
Raises:	ValueError – An input DataElement was of a content type that we cannot handle.
Returns:	Mapping of input DataElement UUIDs to the computed descriptor element for that data. DescriptorElement UUID’s are congruent with the UUID of the data element it is the descriptor of.
Return type:	dict[collections.Hashable, smqtk.representation.DescriptorElement]

ImageReader¶

class smqtk.algorithms.image_io.ImageReader[source]¶

Interface for algorithms that load a raster image matrix from a data element.

is_valid_element(data_element)[source]¶

Check if the given DataElement instance reports a content type that matches one of the MIME types reported by valid_content_types.

This override checks if the DataElement has the matrix property as the MatrixDataElement would provide, and that its value of an expected type.

Parameters:	data_element (smqtk.representation.DataElement) – Data element instance to check.
Returns:	True if the given element has a valid content type as reported by `valid_content_types`, and False if not.
Return type:	bool

load_as_matrix(data_element, pixel_crop=None)[source]¶

Load an image matrix from the given data element.

If the given DataElement instance defines a matrix property this method simply returns that. This is intended to interface with instances of smqtk.representation.data_element.matrix.MatrixDataElement.

When not loading from a short-cut matrix, matrix return format is ImageReader implementation dependant. Implementations of this interface should specify and describe their return type.

Aside from the exceptions documented below, other exceptions may be raised when an image fails to load that are implementation dependent.

Parameters:	data_element (smqtk.representation.DataElement) – DataElement to load image data from. pixel_crop (None\|smqtk.representation.AxisAlignedBoundingBox) – Optional bounding box specifying a pixel sub-region to load from the given data. If this is provided it must represent a valid sub-region within the loaded image, otherwise a RuntimeError is raised. Handling of non-integer aligned boxes are implementation dependant.
Raises:	RuntimeError – A crop region was specified but did not specify a valid sub-region of the image. AssertionError – The `data_element` provided defined a `matrix` attribute/property, but its access did not result in an expected value. ValueError – This error is raised when: The given `data_element` was not of a valid content type. A `pixel_crop` bounding box was provided but was zero volume. `pixel_crop` bounding box vertices are not fully represented by integers.
Returns:	Numpy ndarray of the image data. Specific return format is implementation dependant.
Return type:	numpy.ndarray

HashIndex¶

This interface describes specialized NearestNeighborsIndex implementations designed to index hash codes (bit vectors) via the hamming distance function. Implementations of this interface are primarily used with the LSHNearestNeighborIndex implementation.

Unlike the NearestNeighborsIndex interface from which this interface descends, HashIndex instances are build with an iterable of numpy.ndarray and nn returns a numpy.ndarray.

class smqtk.algorithms.nn_index.hash_index.HashIndex[source]¶

Specialized NearestNeighborsIndex for indexing unique hash codes bit-vectors) in memory (numpy arrays) using the hamming distance metric.

Implementations of this interface cannot be used in place of something requiring a NearestNeighborsIndex implementation due to the speciality of this interface.

Only unique bit vectors should be indexed. The nn method should not return the same bit vector more than once for any query.

build_index(hashes)[source]¶

Build the index with the given hash codes (bit-vectors).

Subsequent calls to this method should rebuild the current index. This method shall not add to the existing index nor raise an exception to as to protect the current index.

Raises:	ValueError – No data available in the given iterable.
Parameters:	hashes (collections.Iterable[numpy.ndarray[bool]]) – Iterable of descriptor elements to build index over.

count()[source]¶

Returns:	Number of elements in this index.
Return type:	int

nn(h, n=1)[source]¶

Return the nearest N neighbor hash codes as bit-vectors to the given hash code bit-vector.

Distances are in the range [0,1] and are the percent different each neighbor hash is from the query, based on the number of bits contained in the query (normalized hamming distance).

Raises:	ValueError – Current index is empty.
Parameters:	h (numpy.ndarray[bool]) – Hash code to compute the neighbors of. Should be the same bit length as indexed hash codes. n (int) – Number of nearest neighbors to find.
Returns:	Tuple of nearest N hash codes and a tuple of the distance values to those neighbors.
Return type:	(tuple[numpy.ndarray[bool]], tuple[float])

remove_from_index(hashes)[source]¶

Partially remove hashes from this index.

Parameters:	hashes (collections.Iterable[numpy.ndarray[bool]]) – Iterable of numpy boolean hash vectors to remove from this index.
Raises:	ValueError – No data available in the given iterable. KeyError – One or more UIDs provided do not match any stored descriptors.

update_index(hashes)[source]¶

Additively update the current index with the one or more hash vectors given.

If no index exists yet, a new one should be created using the given hash vectors.

Raises:	ValueError – No data available in the given iterable.
Parameters:	hashes (collections.Iterable[numpy.ndarray[bool]]) – Iterable of numpy boolean hash vectors to add to this index.

LshFunctor¶

Implementations of this interface define the generation of a locality-sensitive hash code for a given DescriptorElement. These are used in LSHNearestNeighborIndex instances.

class smqtk.algorithms.nn_index.lsh.functors.LshFunctor[source]¶

Locality-sensitive hashing functor interface.

The aim of such a function is to be able to generate hash codes (bit-vectors) such that similar items map to the same or similar hashes with a high probability. In other words, it aims to maximize hash collision for similar items.

Building Models

Some hash functions want to build a model based on some training set of descriptors. Due to the non-standard nature of algorithm training and model building, please refer to the specific implementation for further information on whether model training is needed and how it is accomplished.

get_hash(descriptor)[source]¶

Get the locality-sensitive hash code for the input descriptor.

Parameters:	descriptor (numpy.ndarray[float]) – Descriptor vector we should generate the hash of.
Returns:	Generated bit-vector as a numpy array of booleans.
Return type:	numpy.ndarray[bool]

NearestNeighborsIndex¶

This interface defines a method to build an index from a set of DescriptorElement instances (NearestNeighborsIndex.build_index) and a nearest-neighbors query function for getting a number of near neighbors to e query DescriptorElement (NearestNeighborsIndex.nn).

Building an index requires that some non-zero number of DescriptorElement instances be passed into the build_index method. Subsequent calls to this method should rebuild the index model, not add to it. If an implementation supports persistant storage of the index, it should overwrite the configured index.

The nn method uses a single DescriptorElement to query the current index for a specified number of nearest neighbors. Thus, the NearestNeighborsIndex instance must have a non-empty index loaded for this method to function. If the provided query DescriptorElement does not have a set vector, this method will also fail with an exception.

This interface additionally requires that implementations define a count method, which returns the number of distinct DescriptorElement instances are in the index.

class smqtk.algorithms.nn_index.NearestNeighborsIndex[source]¶

Common interface for descriptor-based nearest-neighbor computation over a built index of descriptors.

Implementations, if they allow persistent storage of their index, should take the necessary parameters at construction time. Persistent storage content should be (over)written build_index is called.

Implementations should be thread safe and appropriately protect internal model components from concurrent access and modification.

build_index(descriptors)[source]¶

Build the index with the given descriptor data elements.

Subsequent calls to this method should rebuild the current index. This method shall not add to the existing index nor raise an exception to as to protect the current index.

Raises:	ValueError – No data available in the given iterable.
Parameters:	descriptors (collections.Iterable[smqtk.representation.DescriptorElement]) – Iterable of descriptor elements to build index over.

count()[source]¶

Returns:	Number of elements in this index.
Return type:	int

nn(d, n=1)[source]¶

Return the nearest N neighbors to the given descriptor element.

Raises:	ValueError – Input query descriptor `d` has no vector set. ValueError – Current index is empty.
Parameters:	d (smqtk.representation.DescriptorElement) – Descriptor element to compute the neighbors of. n (int) – Number of nearest neighbors to find.
Returns:	Tuple of nearest N DescriptorElement instances, and a tuple of the distance values to those neighbors.
Return type:	(tuple[smqtk.representation.DescriptorElement], tuple[float])

remove_from_index(uids)[source]¶

Partially remove descriptors from this index associated with the given UIDs.

Parameters:	uids (collections.Iterable[collections.Hashable]) – Iterable of UIDs of descriptors to remove from this index.
Raises:	ValueError – No data available in the given iterable. KeyError – One or more UIDs provided do not match any stored descriptors. The index should not be modified.

update_index(descriptors)[source]¶

Additively update the current index with the one or more descriptor elements given.

If no index exists yet, a new one should be created using the given descriptors.

Raises:	ValueError – No data available in the given iterable.
Parameters:	descriptors (collections.Iterable[smqtk.representation .DescriptorElement]) – Iterable of descriptor elements to add to this index.

ObjectDetector¶

This interface defines a method to generate object detections (DetectionElement) over a given DataElement.

class smqtk.algorithms.object_detection.ObjectDetector[source]¶

Abstract interface to an object detection algorithm.

An object detection algorithm is one that can take in data and output zero or more detection elements, where each detection represents a spatial region in the data.

This high level interface only requires detection element returns (spatial bounding-boxes with associated classification elements).

detect_objects(data_element, de_factory=<smqtk.representation.detection_element_factory.DetectionElementFactory object>, ce_factory=<smqtk.representation.classification_element_factory.ClassificationElementFactory object>)[source]¶

Detect objects in the given data.

UUIDs of detections are based on the hash produced from the combination of:

Detection bounding-box bounding coordinates

Classification label set predicted for a bounding box.

Parameters:	data_element (smqtk.representation.DataElement) – Source data from which to detect objects within. de_factory (smqtk.representation.DetectionElementFactory) – Factory for generating DetectionElement instances. The default factory yields MemoryClassificationElement instances. ce_factory (smqtk.representation.ClassificationElementFactory) – Factory for generating ClassificationElement instances for detections. The default factory yields MemoryClassificationElement instances.
Raises:	ValueError – Given data element content was not of a valid content type that this class reports as valid for object detection.
Returns:	Iterator over result DetectionElement instances as generated by the given DetectionElementFactory, containing classification elements as generated by the given ClassificationElementFactory.
Return type:	collections.Iterable[smqtk.representation.DetectionElement]

RelevancyIndex¶

This interface defines two methods: build_index and rank. The build_index method is, like a NearestNeighborsIndex, used to build an index of DescriptorElement instances. The rank method takes examples of relevant and not-relevant DescriptorElement examples with which the algorithm uses to rank (think sort) the indexed DescriptorElement instances by relevancy (on a [0, 1] scale).

class smqtk.algorithms.relevancy_index.RelevancyIndex[source]¶

Abstract class for IQR index implementations.

Similar to a traditional nearest-neighbors algorithm, An IQR index provides a specialized nearest-neighbors interface that can take multiple examples of positively and negatively relevant exemplars in order to produce a [0, 1] ranking of the indexed elements by determined relevancy.

build_index(descriptors)[source]¶

Build the index based on the given iterable of descriptor elements.

Subsequent calls to this method should rebuild the index, not add to it.

Raises:	ValueError – No data available in the given iterable.
Parameters:	descriptors (collections.Iterable[smqtk.representation.DescriptorElement]) – Iterable of descriptor elements to build index over.

count()[source]¶

Returns:	Number of elements in this index.
Return type:	int

rank(pos, neg)[source]¶

Rank the currently indexed elements given pos positive and neg negative exemplar descriptor elements.

Parameters:	pos (collections.Iterable[smqtk.representation.DescriptorElement]) – Iterable of positive exemplar DescriptorElement instances. This may be optional for some implementations. neg (collections.Iterable[smqtk.representation.DescriptorElement]) – Iterable of negative exemplar DescriptorElement instances. This may be optional for some implementations.
Returns:	Map of indexed descriptor elements to a rank value between [0, 1] (inclusive) range, where a 1.0 means most relevant and 0.0 meaning least relevant.
Return type:	dict[smqtk.representation.DescriptorElement, float]