Python Sample Apps and Bindings Source Details

Sample Application Source Details

The following table shows the location of the Python sample applications under

Python sample application source details

Reference test application

Path inside the GitHub repo


Simple test application 1


Simple example of how to use DeepStream elements for a single H.264 stream: filesrc → decode → nvstreammux → nvinfer (primary detector) → nvdsosd → renderer.

Simple test application 2


Simple example of how to use DeepStream elements for a single H.264 stream: filesrc → decode → nvstreammux → nvinfer (primary detector) → nvtracker → nvinfer (secondary classifier) → nvdsosd → renderer.

Simple test application 3


Builds on deepstream-test1 (simple test application 1) to demonstrate how to:

  • Use multiple sources in the pipeline

  • Use a uridecodebin to accept any type of input (e.g. RTSP/File), any GStreamer supported container format, and any codec

  • Configure Gst-nvstreammux to generate a batch of frames and infer on it for better resource utilization

  • Extract the stream metadata, which contains useful information about the frames in the batched buffer

Simple test application 4


Builds on deepstream-test1 for a single H.264 stream: filesrc, decode, nvstreammux, nvinfer, nvdsosd, renderer to demonstrate how to:

  • Use the Gst-nvmsgconv and Gst-nvmsgbroker plugins in the pipeline

  • Create NVDS_META_EVENT_MSG type metadata and attach it to the buffer

  • Use NVDS_META_EVENT_MSG for different types of objects, e.g. vehicle and person

  • Implement “copy” and “free” functions for use if metadata is extended through the extMsg field

USB camera source application


Simple test application 1 modified to process a single stream from a USB camera.

RTSP output application


Simple test application 1 modified to output visualization stream over RTSP.

Image data access application


Builds on simple test application 3 to demonstrate how to:

  • Access decoded frames as NumPy arrays in the pipeline

  • Check detection confidence of detected objects (DBSCAN or NMS clustering required)

  • Modify frames and see the changes reflected downstream in the pipeline

  • Use OpenCV to annotate the frames and save them to file

SSD detector output parser application


Demonstrates how to perform custom post-processing for inference output from Triton Inference Server:

  • Use SSD model on Triton Inference Server for object detection

  • Enable custom post-processing and raw tensor export for Triton Inference Server via configuration file settings

  • Access inference output tensors in the pipeline for post-processing in Python

  • Add detected objects to the metadata

  • Output the OSD visualization to MP4 file

Optical flow application


Demonstrates how to obtain opticalflow meta data and also demonstrates how to:

  • Access optical flow vectors as numpy array

  • Visualize optical flow using obtained flow vectors and OpenCV

Segmentation application


Demonstrates how to obtain segmentation meta data and also demonstrates how to:

  • Acess segmentation masks as numpy array

  • Visualize segmentation using obtained masks and OpenCV

Analytics application


Demonstrates how to use the nvdsanalytics plugin and obtain analytics metadata

Runtime source add/delete application


Demonstrates how to add and delete input sources at runtime

Face redaction application


Demonstrates how to access image data and perform face redaction

RTSP input/output application


Multi-stream pipeline with RTSP input and output

Preprocess application


Demonstrates how to use nvdspreprocess plugin and perform custom preprocessing on provided ROIs

Demuxer application


Builds on deepstream-test3 to demonstrate how to use nvstreamdemux plugin to split batches and output separate buffer/streams.

CuPy application


Demonstrates how to access GPU buffer in a multistream source as a CuPy array and modify images in place.

Segmask application


Demonstrates how to extract NvOSD_MaskParams from stream metadata and resize and binarize mask array for interpretable segmentation mask.

Custom binding application


Demonstrates how to use NvDsUserMeta to attach and extract custom data structure written in Python bindings to/from the buffer.

Python Bindings and Application Development

This section provides details about DeepStream application development in Python. Python bindings are available here: . Read more about Pyds API here.


  • Ubuntu 22.04

  • DeepStream SDK 7.0 or later

  • Python 3.10

  • Gst Python v1.20.3

If Gst python installation is missing on Jetson, follow the instructions in bindings readme.

Running Sample Applications

  1. Clone the deepstream_python_apps repo under <DeepStream 6.4 ROOT>/sources:

    git clone
  2. This will create the following directory:

    <DeepStream 6.4 ROOT>/sources/deepstream_python_apps
  3. The Python apps are under the apps directory. Go into each app directory and follow instructions in the README.


    The app configuration files contain relative paths for models.

Pipeline Construction

DeepStream pipelines can be constructed using Gst Python, the GStreamer framework’s Python bindings. See sample applications main functions for pipeline construction examples.

MetaData Access

DeepStream MetaData contains inference results and other information used in analytics. The MetaData is attached to the Gst Buffer received by each pipeline component. The metadata format is described in detail in the SDK MetaData documentation and API Guide. The SDK MetaData library is developed in C/C++. Python bindings provide access to the MetaData from Python applications. Please find Python bindings source and packages at

Memory Management

Memory for MetaData is shared by the Python and C/C++ code paths. For example, a MetaData item may be added by a probe function written in Python and needs to be accessed by a downstream plugin written in C/C++. The deepstream-test4 app contains such usage. The Python garbage collector does not have visibility into memory references in C/C++, and therefore cannot safely manage the lifetime of such shared memory. Because of this complication, Python access to MetaData memory is typically achieved via references without claiming ownership.


When MetaData objects are allocated in Python, an allocation function is provided by the bindings to ensure proper memory ownership of the object. If the constructor is used, the the object will be claimed by the garbage collector when its Python references terminate. However, the object will still need to be accessed by C/C++ code downstream, and therefore must persist beyond those Python references. Example: To allocate an NvDsEventMsgMeta instance, use this:

msg_meta = pyds.alloc_nvds_event_msg_meta() *# get reference to allocated instance without claiming memory ownership*

NOT this:

msg_meta = NvDsEventMsgMeta() *# memory will be freed by the garbage collector when msg_meta goes out of scope in Python*

Allocators are available for the following structs:

  • NvDsVehicleObject: alloc_nvds_vehicle_object()

  • NvDsPersonObject: alloc_nvds_person_object()

  • NvDsFaceObject: alloc_nvds_face_object()

  • NvDsEventMsgMeta: alloc_nvds_event_msg_meta()

  • NvDsEvent: alloc_nvds_event()

  • NvDsPayload: alloc_nvds_payload()

  • Generic buffer: alloc_buffer(size)

String Access

Some MetaData structures contain string fields. Sections below provide details on accessing them.

Setting String Fields

Setting a string field results in the allocation of a string buffer in the underlying C++ code.

obj.type = "Type"

This will cause a memory buffer to be allocated, and the string “TYPE” will be copied into it. This memory is owned by the C code and will be freed later. To free the buffer in Python code, use:



NvOSD_TextParams.display_text string now gets freed automatically when a new string is assigned.

Reading String Fields

Directly reading a string field returns C address of the field in the form of an int, for example:

obj = pyds.NvDsVehicleObject.cast(data);

This will print an int representing the address of obj.type in C (which is a char*). To retrieve the string value of this field, use pyds.get_string(), for example:



Some MetaData instances are stored in GList form. To access the data in a GList node, the data field needs to be cast to the appropriate structure. This casting is done via cast() member function for the target type:


In version v0.5, standalone cast functions were provided. Those are now deprecated and superseded by the cast() functions above:



l_frame = batch_meta.frame_meta_list
frame_meta = pyds.NvDsFrameMeta.cast(

Callback Function Registration

Custom MetaData added to NvDsUserMeta require custom copy and release functions. The MetaData library relies on these custom functions to perform deep-copy of the custom structure, and free allocated resources. These functions are registered as callback function pointers in the NvDsUserMeta structure. Callback functions are registered using these functions:

pyds.set_user_copyfunc(NvDsUserMeta_instance, copy_function)
pyds.set_user_releasefunc(NvDsUserMeta_instance, free_func)


Callbacks need to be unregistered with the bindings library before the application exits. The bindings library currently keeps global references to the registered functions, and these cannot last beyond bindings library unload which happens at application exit. Use the following function to unregister all callbacks:


See the deepstream-test4 sample application for an example of callback registration and deregistration.

Limitation: The bindings library currently only supports a single set of callback functions for each application. The last registered function will be used.

Optimizations and Utilities

Python interpretation is generally slower than running compiled C/C++ code. To provide better performance, some operations are implemented in C and exposed via the bindings interface. This is currently experimental and will expand over time. The following optimized functions are available:

  • pyds.NvOSD_ColorParams.set(double red, double green, double blue, double alpha)

    This is a simple function that performs the same operations as the following: = red = green = blue
    txt_params.text_bg_clr.alpha = alpha

    These are performed on each object in, causing the aggregate processing time to slow down the pipeline. Pushing this function into the C layer helps to increase performance.

  • generate_ts_rfc3339 (buffer, buffer_size)

    This function populates the input buffer with a timestamp generated according to RFC3339: %Y-%m-%dT%H:%M:%S.nnnZ\0

Image Data Access

Decoded images are accessible as NumPy arrays via the get_nvds_buf_surface function. This function is documented in the API Guide. See the deepstream-imagedata-multistream sample application for an example of image data usage.