Update to 1.0.2 release

HOWTO.md

@@ -16,7 +16,7 @@ This guide provides resources for DeepStream application development in Python.
 ## Prerequisites
 
 * Ubuntu 18.04
-* [DeepStream SDK 5.0](https://developer.nvidia.com/deepstream-download) or later
+* [DeepStream SDK 5.1](https://developer.nvidia.com/deepstream-download) or later
 * Python 3.6
 * [Gst Python](https://gstreamer.freedesktop.org/modules/gst-python.html) v1.14.5
 
@@ -38,11 +38,11 @@ If missing, install with the following steps:
 <a name="run_samples"></a>
 ## Running Sample Applications
 
-Clone the deepstream_python_apps repo under <DeepStream 5.0 ROOT>/sources:
+Clone the deepstream_python_apps repo under <DeepStream 5.1 ROOT>/sources:
 git clone https://github.com/NVIDIA-AI-IOT/deepstream_python_apps
 
 This will create the following directory:  
-```<DeepStream 5.0 ROOT>/sources/deepstream_python_apps```  
+```<DeepStream 5.1 ROOT>/sources/deepstream_python_apps```  
 
 The Python apps are under the "apps" directory.  
 Go into each app directory and follow instructions in the README.  
@@ -204,5 +204,5 @@ This function populates the input buffer with a timestamp generated according to
 <a name="imagedata_access"></a>
 ## 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](https://docs.nvidia.com/metropolis/deepstream/5.0/python-api/index.html).
+Decoded images are accessible as NumPy arrays via the `get_nvds_buf_surface` function. This function is documented in the [API Guide](https://docs.nvidia.com/metropolis/deepstream/5.1/python-api/index.html).
 Please see the [deepstream-imagedata-multistream](apps/deepstream-imagedata-multistream) sample application for an example of image data usage.

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
+Copyright (c) 2019 - 2021 NVIDIA CORPORATION. All rights reserved.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,7 +2,7 @@
 
 This repository contains Python bindings and sample applications for the [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).  
 
-SDK version supported: 5.0  
+SDK version supported: 5.1  
 
 Download the latest release package complete with bindings and sample applications from the [release section](../../releases).  
 
@@ -45,6 +45,17 @@ We currently provide the following sample applications:
 * [deepstream-ssd-parser](apps/deepstream-ssd-parser) -- SSD model inference via Triton server with output parsing in Python
 * [deepstream-test1-usbcam](apps/deepstream-test1-usbcam) -- deepstream-test1 pipeline with USB camera input
 * [deepstream-test1-rtsp-out](apps/deepstream-test1-rtsp-out) -- deepstream-test1 pipeline with RTSP output
+* [deepstream-opticalflow](apps/deepstream-opticalflow) -- optical flow and visualization pipeline with flow vectors returned in NumPy array
+* [deepstream-segmentation](apps/deepstream-segmentation) -- segmentation and visualization pipeline with segmentation mask returned in NumPy array  
+* [deepstream-nvdsanalytics](apps/deepstream-nvdsanalytics) -- multistream pipeline with analytics plugin  
+
+Of these applications, the following have been updated or added in this release:
+* deepstream-test2: added option to enable output of past frame tracking data
+* deepstream-test4: callback functions are registered only once to avoid race condition
+* deepstream-imagedata-multistream: the probe function now modifies images in-place in addition to saving copies of them  
+* deepstream-opticalflow: new sample application to demonstrate optical flow functionality
+* deepstream-segmentation: new sample application to demonstrate segmentation functionality
+* deepstream-nvdsnalaytics: new sample application to demonstrate analytics functionality
 
 Detailed application information is provided in each application's subdirectory under [apps](apps).  
 

apps/README

@@ -1,5 +1,5 @@
 ################################################################################
-# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
 #
 # Permission is hereby granted, free of charge, to any person obtaining a
 # copy of this software and associated documentation files (the "Software"),
@@ -25,7 +25,7 @@ DeepStream SDK Python Bindings
 ================================================================================
 Setup pre-requisites:
 - Ubuntu 18.04
-- NVIDIA DeepStream SDK 5.0 Developer Preview
+- NVIDIA DeepStream SDK 5.1
 - Python 3.6
 - Gst-python
 
@@ -55,12 +55,15 @@ The DeepStream Python package includes:
    deepstream-ssd-parser
    deepstream-test1-rtsp-out
    deepstream-test1-usbcam
+   deepstream-opticalflow
+   deepstream-segmentation
+   deepstream-nvdsanalytics
 
 --------------------------------------------------------------------------------
 Installing Pre-requisites:
 --------------------------------------------------------------------------------
 
-DeepStream SDK 5.0 Developer Preview
+DeepStream SDK 5.1 Developer Preview
 --------------------
 Download and install from https://developer.nvidia.com/deepstream-download
 
@@ -78,12 +81,12 @@ $ sudo apt install python3-gi python3-dev python3-gst-1.0 -y
 --------------------------------------------------------------------------------
 Running the samples
 --------------------------------------------------------------------------------
-The apps are configured to work from inside the DeepStream SDK 5.0 installation.
+The apps are configured to work from inside the DeepStream SDK 5.1 installation.
 
-Clone the deepstream_python_apps repo under <DeepStream 5.0 ROOT>/sources:
+Clone the deepstream_python_apps repo under <DeepStream 5.1 ROOT>/sources:
 $ git clone https://github.com/NVIDIA-AI-IOT/deepstream_python_apps
 This will create the following directory:
-<DeepStream 5.0 ROOT>/sources/deepstream_python_apps
+<DeepStream 5.1 ROOT>/sources/deepstream_python_apps
 
 Follow README in each app's directory to run the app.
 
@@ -99,7 +102,7 @@ The general steps are:
    Release Notes at https://developer.nvidia.com/deepstream-sdk for more info.
    Note that the deepstream-ssd-parser app requires the Triton docker on x86_64.
 2. Run the docker with Python Bindings mapped using the following option:
-   -v <path to this python bindings directory>:/opt/nvidia/deepstream/deepstream-5.0/sources/python
+   -v <path to this python bindings directory>:/opt/nvidia/deepstream/deepstream/sources/python
 3. Inside the container, install packages required by all samples:
    $ sudo apt update
    $ sudo apt install python3-gi python3-dev python3-gst-1.0 -y

apps/deepstream-imagedata-multistream/README

@@ -1,5 +1,5 @@
 ################################################################################
-# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020-2021, NVIDIA CORPORATION. All rights reserved.
 #
 # Permission is hereby granted, free of charge, to any person obtaining a
 # copy of this software and associated documentation files (the "Software"),
@@ -21,7 +21,7 @@
 ################################################################################
 
 Prerequisites:
-- DeepStreamSDK 5.0
+- DeepStreamSDK 5.1
 - Python 3.6
 - Gst-python
 - NumPy package
@@ -42,6 +42,10 @@ This document describes the sample deepstream-imagedata-multistream application.
 This sample builds on top of the deepstream-test3 sample to demonstrate how to:
 
 * Access imagedata in a multistream source
+* Modify the images in-place. Changes made to the buffer will reflect in the downstream but  
+  color format, resolution and numpy transpose operations are not permitted.  
+* Make a copy of the image, modify it and save to a file. These changes are made on the copy  
+  of the image and will not be seen downstream.
 * Extract the stream metadata, imagedata, which contains useful information about the
   frames in the batched buffer.
 * Annotating detected objects within certain confidence interval