API Reference

Ikomia API is divided into 4 main modules, each one may contain sub-modules. A large part of this API is based on C++ bindings, those classes are easily identifiable since we apply a naming convention where all C++ classes begin with the C capital letter.

Utils module

ikomia.utils.pyutils	Module offering various helper tools.
ikomia.utils.pyqtutils	Module providing helper functions to design your own task widget.
ikomia.utils.qtconversion	Module dedicated to wrap widget instance from Qt-based Python frameworks to C++ Qt.
ikomia.utils.displayIO	Module dedicated to the visualization of workflow components.

Core module

ikomia.core.pycore	Module offering core features to handle tasks, I/O, parameters and widgets.
ikomia.core.task	Module dedicated to high-level features around task management.

DataProcess module

ikomia.dataprocess.pydataprocess	Module offering implementation of specialization classes to handle inputs/outputs and tasks involved in Ikomia workflows for concrete use cases.
ikomia.dataprocess.registry	Module dedicated to algorithms management from the Ikomia platform.
ikomia.dataprocess.workflow	Module dedicated to workflow management.
ikomia.dataprocess.datadictIO	Module providing Ikomia workflow I/O implementation for data stored as Python dict.

DNN module

ikomia.dnn.dnntrain	Module dedicated to Deep Learning training.
ikomia.dnn.datasetio	Module dedicated to provide default implementation of Ikomia Deep Learning dataset structure.
ikomia.dnn.dataset	Module providing dataset loaders from various source formats.
ikomia.dnn.torch.models	Model providing helper funtions to create TorchVision Deep Learning models.
ikomia.dnn.torch.datasetmapper	Module providing implementation of a PyTorch dataset mapper for Ikomia dataset structure.