.. role:: raw-html-m2r(raw) :format: html Azure Video Analyzer Edge client library for Python =================================================== Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules (such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data) to it and use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to: * Simplify interactions with the `Microsoft Azure IoT SDKs `_ * Programmatically construct pipeline topologies and live pipelines `Package (PyPI) `_ | `Product documentation `_ | `Direct methods `_ | `Pipelines `_ | `Source code `_ | `Samples `_ Getting started --------------- Install the package ^^^^^^^^^^^^^^^^^^^ Install the Video Analyzer Edge client library for Python with pip: .. code-block:: bash pip install azure-media-videoanalyzer-edge Prerequisites ^^^^^^^^^^^^^ * Python 2.7, or 3.6 or later is required to use this package. * You need an active `Azure subscription `_\ , and a IoT device connection string to use this package. * To interact with Azure IoT Hub you will need to run ``pip install azure-iot-hub`` * You will need to use the version of the SDK that corresponds to the version of the Video Analyzer Edge module you are using. | SDK | Video Analyzer edge module | |---|---| | 1.0.0b1 | 1.0 | Creating a pipeline topology and making requests ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Please visit the `Examples <#examples>`_ for starter code. Key concepts ------------ Pipeline topology vs live pipeline ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A *pipeline topology* is a blueprint or template for creating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A *live pipeline* references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit `pipeline topologies and live pipelines `_. CloudToDeviceMethod ^^^^^^^^^^^^^^^^^^^ The ``CloudToDeviceMethod`` is part of the `azure-iot-hub SDk `_. This method allows you to communicate one way notifications to a device in your IoT hub. In our case, we want to communicate various direct methods such as ``PipelineTopologySetRequest`` and ``PipelineTopologyGetRequest``. To use ``CloudToDeviceMethod`` you need to pass in two parameters: ``method_name`` and ``payload``. The first parameter, ``method_name``\ , is the name of the direct method request you are sending. Make sure to use each method's predefined ``method_name`` property. For example, ``PipelineTopologySetRequest.method_name``. The second parameter, ``payload``\ , sends the entire serialization of the pipeline topology request. For example, ``PipelineTopologySetRequest.serialize()`` Examples -------- Creating a pipeline topology ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To create a pipeline topology you need to define sources and sinks. .. code-block:: python #Parameters user_name_param = ParameterDeclaration(name="rtspUserName",type="String",default="testusername") password_param = ParameterDeclaration(name="rtspPassword",type="SecretString",default="testpassword") url_param = ParameterDeclaration(name="rtspUrl",type="String",default="rtsp://www.sample.com") hub_param = ParameterDeclaration(name="hubSinkOutputName",type="String") #Source and Sink source = RtspSource(name="rtspSource", endpoint=UnsecuredEndpoint(url="${rtspUrl}",credentials=UsernamePasswordCredentials(username="${rtspUserName}",password="${rtspPassword}"))) node = NodeInput(node_name="rtspSource") sink = IotHubMessageSink("msgSink", nodeInput, "${hubSinkOutputName}") pipeline_topology_properties = PipelineTopologyProperties() pipeline_topology_properties.parameters = [user_name_param, password_param, url_param, hub_param] pipeline_topology_properties.sources = [source] pipeline_topology_properties.sinks = [sink] pipeline_topology = PipelineTopology(name=pipeline_topology_name,properties=pipeline_topology_properties) Creating a live pipeline ^^^^^^^^^^^^^^^^^^^^^^^^ To create a live pipeline, you need to have an existing pipeline topology. .. code-block:: python url_param = ParameterDefinition(name="rtspUrl", value=pipeline_url) pass_param = ParameterDefinition(name="rtspPassword", value='testpass') live_pipeline_properties = LivePipelineProperties(description="Sample pipeline description", topology_name=pipeline_topology_name, parameters=[url_param]) live_pipeline = LivePipeline(name=live_pipeline_name, properties=live_pipeline_properties) Invoking a direct method ^^^^^^^^^^^^^^^^^^^^^^^^ To invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's ``CloudToDeviceMethod``. .. code-block:: python set_method_request = PipelineTopologySetRequest(pipeline_topology=pipeline_topology) direct_method = CloudToDeviceMethod(method_name=set_method_request.method_name, payload=set_method_request.serialize()) registry_manager = IoTHubRegistryManager(connection_string) registry_manager.invoke_device_module_method(device_id, module_d, direct_method) To try different pipeline topologies with the SDK, please see the official `Samples `_. Troubleshooting --------------- * When sending a method request using the IoT Hub's ``CloudToDeviceMethod`` remember to not type in the method request name directly. Instead use ``[MethodRequestName.method_name]`` * Make sure to serialize the entire method request before passing it to ``CloudToDeviceMethod`` Next steps ---------- * `Samples `_ * `Azure IoT Device SDK `_ * `Azure IoTHub Service SDK `_ Contributing ------------ This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com. If you encounter any issues, please open an issue on our `Github `_. When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the `Microsoft Open Source Code of Conduct `_. For more information, see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments. :raw-html-m2r:`` Indices and tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. toctree:: :maxdepth: 5 :glob: :caption: Developer Documentation azure.media.videoanalyzeredge.rst