NVIDIA DeepStream SDK

https://docs.nvidia.com/metropolis/deepstream/dev-guide/
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### Start the Web Server

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2includes_2civetweb_8h_source.html

Example of initializing the CivetWeb server with configuration options.

```c
const char *options[] = {
"document_root", "/var/www",
"listening_ports", "80,443s",
NULL
};
struct mg_context *ctx = mg_start(&my_func, NULL, options);
```

--------------------------------

### GStreamer Pipeline Configuration

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmultiurisrcbin.html

Example pipeline setup for nvmultiurisrcbin with multiple input sources.

```bash
gst-launch-1.0 nvmultiurisrcbin \
port=9000 ip-address=localhost \
batched-push-timeout=33333 max-batch-size=10 \
drop-pipeline-eos=1 live-source=1 \
uri-list=file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4,file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 width=1920 height=1080 \
! nvmultistreamtiler ! nveglglessink
```

--------------------------------

### start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1GuardDataRender.html

Starts the rendering process with the specified content and optional path.

```APIDOC
### ErrCode start(const std::string &content, const std::string &path="")

Initializes and starts the rendering engine using the provided content string and optional path.
```

--------------------------------

### start()

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classdeepstream_1_1NvDsTilerEventHandler.html

Starts the tiler event handler.

```APIDOC
## bool deepstream::NvDsTilerEventHandler::start()

### Description
Initializes and starts the tiler event handler thread.

### Returns
- **bool** - Returns true if the handler started successfully, false otherwise.
```

--------------------------------

### Scale-Normalize Configuration Examples

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinferserver.html

Examples for configuring scale factors, channel offsets, and mean files for normalization.

```text
scale_factor: 0.0078
```

```text
channel_offsets: [77.5, 21.2, 11.8]
```

```text
mean_file: “./model_meanfile.ppm”
```

--------------------------------

### Preprocessing Configuration Examples

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinferserver.html

Examples for configuring preprocessing parameters such as network format, tensor order, scaling, and normalization.

```text
network_format: IMAGE_FORMAT_RGB
```

```text
tensor_order: TENSOR_ORDER_NONE
```

```text
tensor_name: “input_0”
```

```text
frame_scaling_hw: FRAME_SCALING_HW_GPU
```

```text
frame_scaling_filter: 1
```

```text
maintain_aspect_ratio: 0
```

```text
symmetric_padding: 0
```

```text
normalize { scale_factor: 1.0 channel_offsets: [0, 0, 0] }
```

--------------------------------

### start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1GuardDataFilter.html

Starts the data process with the specified content and optional path.

```APIDOC
### ErrCode start(const std::string & content, const std::string & path = "")

Initializes and starts the data process.

#### Parameters
- **content** (const std::string &) - The configuration content.
- **path** (const std::string &) - Optional path associated with the content.
```

--------------------------------

### AMQP Configuration File Example

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Example configuration file entries for an AMQP broker running on a local machine.

```ini
[message-broker]
hostname = localhost
username = guest
password = guest
port = 5672
exchange = amq.topic
topic = topicname
amqp-framesize = 131072
#amqp-heartbeat = 0
#share-connection = 1
```

--------------------------------

### Install RealSense SDK

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_Depth_Camera.html

Installs the librealsense2 utilities and development headers.

```bash
sudo apt-get install librealsense2-utils librealsense2-dev
```

--------------------------------

### start()

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classdeepstream_1_1NvDsModelEngineWatchOTFTrigger.html

Starts the file watch trigger mechanism.

```APIDOC
### bool start()

Starts the file watch thread to monitor for model engine updates.

#### Returns
- **bool** - Returns true if the trigger started successfully, false otherwise.
```

--------------------------------

### start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1GuardDataBridge.html

Initializes and starts the data bridge operation.

```APIDOC
## ErrCode start(const std::string &content, const std::string &path="")

### Description
Starts the data bridge using the provided configuration content and optional path.

### Parameters
- **content** (const std::string&) - Configuration content string.
- **path** (const std::string&) - Optional path parameter.
```

--------------------------------

### Compile and Install Gst-nvreplay

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvreplay.html

Commands to build and install the plugin from the source directory.

```bash
cd deepstream/sources/gst-plugins/gst-nvreplay/
make && sudo -E make install
```

--------------------------------

### Install System Dependencies

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Libraries.html

Execute the provided script to install necessary system-level packages.

```bash
$ sudo sh scripts/install_sys_pkgs.sh
```

--------------------------------

### REDIS adapter configuration example

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Example configuration file entries for connecting the REDIS protocol adapter to a local server.

```ini
[message-broker]
hostname=localhost
port=6379
password=password
payloadkey=metadata
consumergroup=mygroup
consumername=myname
streamsize=10000
```

--------------------------------

### Install Docker Engine

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_on_WSL2.html

Install Docker and its dependencies on the Ubuntu WSL instance.

```bash
$ sudo apt-get update
$ sudo apt-get install -y apt-transport-https ca-certificates curl gnupg-agent software-properties-common
$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
$ sudo apt-key fingerprint 0EBFCD88
$ sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
$ sudo apt-get update
$ sudo apt-get install -y docker-ce docker-ce-cli containerd.io --fix-missing

```

--------------------------------

### Initialize DeepStream Setup

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/Pipetuner-guide.html

Execute the setup script to pull required containers and download necessary NGC models for DeepStream.

```bash
$ bash setup.sh deepstream
```

--------------------------------

### Setup Triton Server on Jetson

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html

Commands to navigate to the samples directory and execute the setup script for Triton Server and backends on Jetson devices.

```bash
$ cd /opt/nvidia/deepstream/deepstream/samples/
$ sudo ./triton_backend_setup.sh
```

--------------------------------

### start_i

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1abiDataMixer.html

Starts the mixer with the provided configuration.

```APIDOC
## virtual ErrCode start_i(const char *configStr, uint32_t strLen, const char *path) = 0

### Description
Initializes and starts the mixer using the provided configuration string and path.

### Parameters
- **configStr** (const char *) - Configuration string.
- **strLen** (uint32_t) - Length of the configuration string.
- **path** (const char *) - Path associated with the configuration.
```

--------------------------------

### Initialize Metropolis MTMC Setup

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/Pipetuner-guide.html

Execute the setup script for Metropolis MTMC workflows.

```bash
$ bash setup.sh metropolis
```

--------------------------------

### Parse DS Example YAML

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2apps_2apps-common_2includes_2deepstream__config__yaml_8h_source.html

Parses the DS example configuration from a YAML file.

```c
gboolean parse_dsexample_yaml(NvDsDsExampleConfig *config, gchar *cfg_file_path)
```

--------------------------------

### GuardCB Usage Examples

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2includes_2ds3d_2common_2hpp_2obj_8hpp_source.html

Examples demonstrating how to initialize GuardCB from existing abiCallBackT objects or std::function instances.

```cpp
 * examples to get GuardCB from abiCallBackT:
 * using abiErrorCB = abiCallBackT<ErrCode, const char*>;
 * const abiErrorCB& cb = ...;
 * GuardCB<abiErrorCB> guardCB(cb);
 * guardCB(ErrCode::kGood, "sample");
 * examples to get GuardCB from std::function
 * auto errFn = [](ErrCode, const char*) {...};
 * GuardCB<abiErrorCB> guardCB(errFn);
 * guardCB(ErrCode::kGood, "sample");
```

--------------------------------

### Install GLib 2.0

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Command to install the GLib 2.0 library and development headers.

```bash
sudo apt-get install libglib2.0 libglib2.0-dev
```

--------------------------------

### Postprocessing Configuration Examples

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinferserver.html

Examples for configuring postprocessing parameters including labels, detection, classification, segmentation, and other network types.

```text
labelfile_path: “=/home/ubuntu/model_labels.txt”
```

```text
detection { num_detected_classes: 4 simple_cluster { threshold: 0.2 } }
```

```text
classification { threshold: 0.6 }
```

```text
segmentation { threshold: 0.2 num_segmentation_classes: 2 }
```

```text
other {}
```

```text
Triton_classification { topk: 1 }
```

--------------------------------

### start(const std::string &content, const std::string &path)

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1GuardDataLoader.html

Starts the data processing task with the provided content and optional path.

```APIDOC
## ErrCode start(const std::string &content, const std::string &path = "")

### Description
Starts the data processing operation using the specified content string and an optional file path.

### Parameters
- **content** (const std::string &) - Required - The content data to be processed.
- **path** (const std::string &) - Optional - The file path associated with the data, defaults to an empty string.
```

--------------------------------

### Install MQTT Adapter Dependencies

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Commands to install required libraries for the MQTT protocol adapter.

```bash
sudo apt-get install libglib2.0 libglib2.0-dev
```

```bash
sudo apt-get install libssl-dev
```

```bash
sudo apt-get install libcjson-dev
```

```bash
sudo apt-add-repository ppa:mosquitto-dev/mosquitto-ppa
sudo apt-get update
sudo apt-get install libmosquitto-dev mosquitto
```

--------------------------------

### CUDA Installation Error Reference

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html

Example of GPG verification errors that may occur during CUDA repository setup.

```text
W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64 InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC
E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64 InRelease' is no longer signed.
```

--------------------------------

### Compile Sample Application

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_MultiModal_Lidar_Camera_V2XFusion.html

Build and install the sample application from source.

```bash
$ make
$ sudo make install
```

--------------------------------

### Install additional multimedia packages

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_docker_containers.html

Run this script inside the container to install libraries for audio parsing and CPU-based encoding/decoding.

```bash
/opt/nvidia/deepstream/deepstream/user_additional_install.sh
```

--------------------------------

### Navigate to Sample Directory

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_MultiModal_Lidar_Camera_V2XFusion.html

Change to the sample application directory and optionally grant permissions.

```bash
$ cd /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-3d-lidar-sensor-fusion
# sudo chmod -R a+rw . # Grant read/write permission if running on Jetson host
```

--------------------------------

### Setup Kafka Server and Topic

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_MultiModal_Lidar_Camera_BEVFusion.html

Initializes a Kafka server and creates a topic for messaging tests.

```bash
$ docker run -p 9092:9092 apache/kafka:3.7.0 # start kafka server with port 9092
$ bin/kafka-topics.sh --create --topic quick-test --bootstrap-server localhost:9092 # creat topic 'quick-test'
```

--------------------------------

### Inference Builder Dependency Build Error

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_troubleshooting.html

Example of the build failure output when installing Inference Builder dependencies.

```text
ERROR: Failed building wheel for typed-ast
  error: command '/usr/bin/x86_64-linux-gnu-gcc' failed with exit code 1
Failed to build typed-ast
ERROR: Failed to build installable wheels for some pyproject.toml based projects (typed-ast)
```

--------------------------------

### Build and Run the Application

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_service_maker_cpp.html

Commands to compile the project and execute the binary with a video source.

```bash
$ mkdir build && cd build && cmake .. && make
```

```bash
$ ./my-deepstream-app file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4
```

--------------------------------

### Install Python Dependencies

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Libraries.html

Install required Python packages using the provided installation script.

```bash
$ sh scripts/install_python_pkgs.sh
```

--------------------------------

### Install Inference Builder Prerequisites

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Inference_Builder.html

Install necessary system packages including protobuf compiler and Python development tools.

```bash
sudo apt update
sudo apt install -y protobuf-compiler python3.12-dev python3.12-venv
```

--------------------------------

### Install NVIDIA Container Toolkit

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_on_WSL2.html

Configure the NVIDIA repository and install the toolkit to enable GPU support in Docker containers.

```bash
$ curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
$ curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
$ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
$ sudo systemctl restart docker

```

--------------------------------

### start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/sources_2gst-plugins_2gst-nvinfer_2gstnvinfer__impl_8h_source.html

Starts the inference context.

```APIDOC
## NvDsInferStatus start()

### Description
Initializes and starts the inference context for the GstNvInfer element.
```

--------------------------------

### Install GStreamer migration prerequisites

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_FAQ.html

Install necessary build tools and dependencies before compiling a newer GStreamer version.

```bash
apt-get install python3-pip
pip3 install meson
pip3 install ninja
apt-get install libmount-dev
apt-get install flex
apt-get install flex bison
apt-get install libglib2.0-dev
```

--------------------------------

### start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1GuardDataMixer.html

Starts the mixer operation.

```APIDOC
## ErrCode start(const std::string &content, const std::string &path="")

### Description
Initializes and starts the mixer using the provided content and optional path.

### Parameters
- **content** (const std::string &) - Configuration content.
- **path** (const std::string &) - Optional path for initialization.
```

--------------------------------

### Build and Install 3D Action Recognition Application

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_Action.html

Execute these commands within the application source directory to compile and install the project.

```bash
$ make
$ make install
```

--------------------------------

### mg_start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2includes_2civetweb_8h.html

Starts a new CivetWeb web server instance.

```APIDOC
## mg_start

### Description
Starts a web server instance with the provided callbacks, user data, and configuration options.

### Signature
`struct mg_context *mg_start(const struct mg_callbacks *callbacks, void *user_data, const char **configuration_options)`
```

--------------------------------

### start()

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1GuardDataLoader.html

Starts the data processing operation.

```APIDOC
## ErrCode start(const std::string& _content_, const std::string& _path_ = "")

### Description
Initiates the data processing task using the provided content string and an optional path.
```

--------------------------------

### start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classgstnvinferserver_1_1GstNvInferServerImpl.html

Reads the configuration file and sets up the processing context for inference.

```APIDOC
## NvDsInferStatus start()

### Description
Reads the configuration file and sets up the processing context. This function validates the configuration, creates the inference context (either gRPC or C-API mode), and starts the output thread.
```

--------------------------------

### start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classdeepstream_1_1NvDsTilerEventHandler.html

Starts the event handler operations.

```APIDOC
## bool start()

### Description
Initializes and starts the event handling threads and processes.

### Returns
- **bool** - Returns true if successful, false otherwise.
```

--------------------------------

### Initialize Virtual Environment and Dependencies

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Inference_Builder.html

Prepare the Python environment and install required dependencies for the project.

```bash
cd inference_builder
git submodule update --init --recursive
python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt
```

--------------------------------

### Initialize Sample Data

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/Pipetuner-guide.html

Unzip the sample dataset and execute the setup script for either DeepStream or Metropolis Microservices.

```bash
$ unzip pipe-tuner-sample.zip
$ cd pipe-tuner-sample/scripts

$ # DeepStream or Metropolis Microservices users should run only one of the following two commands based on their usage
$ bash setup.sh deepstream            # DeepStream users

$ bash setup.sh metropolis            # Metropolis Microservices users
```

--------------------------------

### DsExampleCtxInit

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2gst-plugins_2gst-dsexample-cuda_2dsexample__lib_2dsexample__lib_8h.html

Initializes a new DsExample context with the provided initialization parameters.

```APIDOC
## DsExampleCtx *DsExampleCtxInit(DsExampleInitParams *init_params)

### Description
Initializes the DsExample context using the specified initialization parameters.

### Parameters
- **init_params** (DsExampleInitParams*) - Required - Pointer to the initialization parameters structure.
```

--------------------------------

### mg_start2

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2includes_2civetweb_8h.html

Starts a CivetWeb web server instance using initialization and error data structures.

```APIDOC
## struct mg_context* mg_start2(struct mg_init_data *init, struct mg_error_data *error)

### Description
Starts a new CivetWeb server instance. It accepts initialization parameters and an error structure to capture startup issues.
```

--------------------------------

### NvDsInferStatus start()

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2gst-plugins_2gst-nvinfer_2gstnvinfer__impl_8h_source.html

Initializes and starts the inference context.

```APIDOC
## start

### Description
Starts the inference context process.

### Returns
- **NvDsInferStatus** - Returns the status of the start operation.
```

--------------------------------

### Domain Startup

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/sources_2includes_2civetweb_8h_source.html

Starts a new domain with the specified configuration options.

```c
CIVETWEB_API int mg_start_domain2(struct mg_context *ctx, const char **configuration_options, struct mg_error_data *error)
```

--------------------------------

### Start RabbitMQ Service

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Command to start the RabbitMQ service if it is not running.

```bash
sudo service rabbitmq-server start
```

--------------------------------

### Verify Dataset Preparation

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_MultiModal_Lidar_Camera_V2XFusion.html

Navigate to the scripts directory and verify the presence of the example dataset.

```bash
$ cd /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-3d-lidar-sensor-fusion/v2xfusion/scripts
$ ls V2X-Seq-SPD-Example.zip # verify the dataset is ready
```

--------------------------------

### Run Sample MV3DT Workflows

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_MV3DT.html

Commands to execute the 4-camera and 12-camera synthetic warehouse examples.

```bash
sudo xhost + # Grant container access to display
./scripts/test_4cam_ds.sh
```

```bash
sudo xhost + # Grant container access to display
./scripts/test_12cam_ds.sh
```

--------------------------------

### Install Additional System Dependencies

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Install glib and jansson development libraries.

```bash
sudo apt-get install libglib2.0 libglib2.0-dev
sudo apt-get install libjansson4 libjansson-dev
```

--------------------------------

### DsExampleCtxInit

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2gst-plugins_2gst-dsexample-cuda_2dsexample__lib_2dsexample__lib_8h.html

Initializes the DsExample context with the provided parameters.

```APIDOC
## DsExampleCtx* DsExampleCtxInit(DsExampleInitParams *init_params)

### Description
Initializes a new DsExample context based on the provided initialization parameters.

### Parameters
- **init_params** (DsExampleInitParams*) - Required - Pointer to the initialization parameters structure.
```

--------------------------------

### Run dsexample in full frame mode

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_sample_custom_gstream.html

Construct a gst-launch pipeline to process full frames using the sample plugin.

```bash
$ gst-launch-1.0 filesrc location= <mp4-file> ! qtdemux ! h264parse ! nvv4l2decoder ! m.sink_0 nvstreammux name=m batch-size=1 width=1280 height=720 ! nvvideoconvert ! dsexample full-frame=1 <other-properties> ! nvdsosd ! nv3dsink
```

```bash
$ gst-launch-1.0 filesrc location= <mp4-file> ! qtdemux ! h264parse ! nvv4l2decoder ! m.sink_0 nvstreammux name=m batch-size=1 width=1280 height=720 ! nvvideoconvert ! dsexample full-frame=1 <other-properties> ! nvdsosd ! nveglglessink
```

--------------------------------

### Verify CUDA Installation

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_FAQ.html

Check the currently installed version of the CUDA toolkit.

```bash
$ nvcc --version
```

--------------------------------

### Verify installed WSL distributions

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_on_WSL2.html

Confirm that the Ubuntu distribution is correctly installed.

```bash
$ wsl --list

```

--------------------------------

### Install V2XFusion Dependencies

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_MultiModal_Lidar_Sensor_Fusion.html

Installs required Python packages for the V2XFusion pipeline.

```bash
$ pip install gdown python-lzf # with sudo if running on Jetson host
```

--------------------------------

### Start RTSP Server

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/service-maker_2includes_2pipeline_8hpp_source.html

Initializes an RTSP server for the pipeline.

```cpp
uint32_t startRTSP(uint16_t rtsp_port, uint16_t udp_port, uint32_t buffer_size=0)
```

--------------------------------

### Configure Sub-batching for Multiple Tracker Algorithms

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example configuration for a 4-stream batch split into three sub-batches using NvDCF, NvSORT, and IOU trackers.

```ini
[tracker]
enable=1
tracker-width=960
tracker-height=544
ll-lib-file=/opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so

ll-config-file=config_tracker_NvDCF_accuracy.yml;config_tracker_NvSORT.yml;config_tracker_IOU.yml
sub-batches=0,1;2;3
```

--------------------------------

### Configure deepstream-app for People Tracking

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example configuration for the [primary-gie] and [tracker] groups within a deepstream-app configuration file.

```ini
## Other groups

[primary-gie]
## Use PeopleNet as PGIE
config-file=config_infer_primary_PeopleNet.txt
## Other [primary-gie] configs

[tracker]
## Specify the low level tracker (for example NvSORT)
# ll-config-file=config_tracker_IOU.yml
ll-config-file=config_tracker_NvSORT.yml
# ll-config-file=config_tracker_NvDCF_perf.yml
# ll-config-file=config_tracker_NvDCF_accuracy.yml
# ll-config-file=config_tracker_NvDeepSORT.yml
## Other [tracker] configs
```

--------------------------------

### Start Docker service

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_on_WSL2.html

Manually start the Docker daemon if the service is not running.

```bash
$ sudo dockerd &

```

--------------------------------

### Configure Camera Info for SV3DT

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example of a YAML configuration file defining the 3x4 projection matrix and 3D model dimensions for Single-View 3D Tracking.

```yaml
# camInfo-01.yml

# The 3x4 camera projection matrix (in row-major):
#    996.229 -202.405  -9.121 -1.185
#    105.309  478.174 890.944 1.743
#    -0.170   -0.859   0.481  -1085.484
projectionMatrix_3x4:
- 996.229
- -202.405
- -9.121
- -1.185
- 105.309
- 478.174
- 890.944
- 1.743
- -0.170
- -0.859
- 0.481
- -1085.484

# The human model.
modelInfo:
  height: 250.0
  radius: 30.0 # "radius" represents the actual radius of the 3D cylinder, or half of a cuboid's bottom edge
```

--------------------------------

### Start CAN Context

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/sources_2apps_2sample__apps_2deepstream-can-orientation-app_2deepstream__can__context_8hpp.html

Initializes and starts the CAN context processing with a task quit callback.

```cpp
DS3D_EXPORT_API ErrCode NvDs_CanContextStart  | ( | DsCanContext *  | _ctx_ ,   
---|---|---|---  
|  | NvDsCanContextTaskQuitCb | _cb_ ,   
|  | void *  | _usrData_  
| ) | |
```

--------------------------------

### Run Dsexample Plugin Pipeline

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_FAQ.html

Pipelines demonstrating the use of the dsexample plugin on dGPU and Jetson platforms.

```bash
$ gst-launch-1.0 filesrc location = ./streams/sample_1080p_h264.mp4 ! qtdemux ! h264parse ! nvv4l2decoder \
! m.sink_0 nvstreammux name=m width=1280 height=720 batch-size=1  ! nvinfer config-file-path= ./configs/deepstream-app/config_infer_primary.txt \
! dsexample full-frame=1 ! nvvideoconvert ! nvdsosd ! nveglglessink sync=0
```

```bash
$ gst-launch-1.0 filesrc location = ./streams/sample_1080p_h264.mp4 ! qtdemux ! h264parse ! nvv4l2decoder \
! m.sink_0 nvstreammux name=m width=1280 height=720 batch-size=1  ! nvinfer config-file-path= ./configs/deepstream-app/config_infer_primary.txt \
! dsexample full-frame=1 ! nvvideoconvert ! nvdsosd ! nv3dsink sync=0
```

--------------------------------

### Tracker Output Formats

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Examples of tracker output lines demonstrating the impact of enabling outputVisibility and SV3DT foot output features.

```text
person 0 0.0 0 0.0 1302.667236 135.852036 1340.975830 241.724579 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.917301
person 1 0.0 0 0.0 878.249023 195.080475 913.410950 320.695618 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.899029
```

```text
person 0 0.0 0 0.0 1302.667236 135.852036 1340.975830 241.724579 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.917301 0.966531
person 1 0.0 0 0.0 878.249023 195.080475 913.410950 320.695618 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.899029 0.930824
```

```text
person 0 0.0 0 0.0 1302.667236 135.852036 1340.975830 241.724579 0.0 0.0 0.0 12.345678 34.567890 0.0 0.0 0.917301 0.966531 1314.492554 239.495193
person 1 0.0 0 0.0 878.249023 195.080475 913.410950 320.695618 0.0 0.0 0.0 12.345678 34.567890 0.0 0.0 0.899029 0.930824 899.187500 316.670013
```

--------------------------------

### Shadow Tracking Data Sample

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example of the text file format for shadow tracking data, where each line represents an object's state in a specific frame.

```text
1 11 2 0 0.0  296.346130 262.343445  333.428864  376.817291 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.407002 2 1.000000
1 22 2 0 0.0 1663.921875 857.167725 1752.483521 1049.053223 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.912138 2 1.000000
...
```

--------------------------------

### Gst-nvdsanalytics Configuration Examples

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdsanalytics.html

Examples of configuration property settings for the Gst-nvdsanalytics plugin.

```text
enable=1
```

```text
config-width=1280
```

```text
config-height=720
```

```text
osd-mode=2
```

```text
display-font-size=12
```

```text
roi-RF= 295;643;579;634;642;913;56;828 roi-OC= 295;643;579;634;642;913;56;828
```

```text
inverse-roi=0
```

```text
class-id=0;1;2 or class-id=-1
```

```text
object-threshold=10
```

```text
direction-South=284;840;360;662
```

```text
line-crossing-Exit=789;672;1084;900;851;773;1203;732
```

```text
extended=0
```

```text
mode=loose  mode=strict mode=balanced
```

--------------------------------

### ISO8601 Log Format Example

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2libs_2nvdsinferserver_2tritonserver__include_2tritonserver_8h.html

Example of the ISO8601 log format output.

```text
2024-05-20T20:03:26Z I 3415 model_lifecycle.cc:441] "AsyncLoad() 'simple'"
```

--------------------------------

### Run Can Orientation Examples

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Can_Orientation.html

Commands to execute the application with various input formats and display configurations.

```bash
$ deepstream-can-orientation-app -c ds_can_orientation_jpg.yaml
```

```bash
$  deepstream-can-orientation-app -c ds_can_orientation_raw.yaml -r data/test_samples_raw.grey
```

```bash
$ deepstream-can-orientation-app -c ds_can_orientation_raw.yaml -r data/test_samples_raw.grey -l
```

```bash
$ deepstream-can-orientation-app -c ds_can_orientation_basler_cam.yaml
```

--------------------------------

### Past-frame Tracker Output

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example of past-frame tracker output lines using a fixed KITTI-style layout.

```text
person 0 0.0 0 0.0 1302.667236 135.852036 1340.975830 241.724579 0.0 0.0 0.0 -1.000000 -1.000000 0.0 0.0 0.917301
person 1 0.0 0 0.0 878.249023 195.080475 913.410950 320.695618 0.0 0.0 0.0 12.345678 34.567890 0.0 0.0 0.899029
```

--------------------------------

### Query Low-Level Library Capabilities

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Used by the plugin to determine library requirements like color formats and memory types before starting processing sessions.

```c
NvMOTStatus NvMOT_Query (
     uint16_t customConfigFilePathSize,
     char* pCustomConfigFilePath,
     NvMOTQuery *pQuery
);
```

--------------------------------

### Base Configuration for NvDsTracker

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example configuration block for the BaseConfig section, which must be placed at the top of low-level tracker configuration files.

```yaml
BaseConfig:
  minDetectorConfidence: 0.5
  checkClassMatch: 1
  useBatchNumForFrameId: 0
```

--------------------------------

### Sample Terminated Track List Output (deepstream-app)

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example of the text file format generated when saving terminated track history via the deepstream-app configuration.

```text
0 7 2 0 0.0 1535.194092 94.266541 1603.132812 301.653625 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.000000 2 1.000000
1 7 2 0 0.0 1535.938232 94.234810 1603.121338 301.769501 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.982758 2 1.000000
...
```

--------------------------------

### Sample Terminated Track List Output (Low-level Tracker)

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example of the text file format generated when saving terminated track information directly from the low-level tracker library, showing both SV3DT disabled and enabled states.

```text
# SV3DT output disabled
31,48,558,104,31,74,0.949,-1.000,-1.000,-1,0,0.994,-1,-1
32,48,558,104,31,74,0.951,-1.000,-1.000,-1,0,0.995,-1,-1
...

# SV3DT output enabled
31,48,558,104,31,74,0.949,1254.535,2962.867,-1,0,0.994,581,176,-15|-34|-14|-35|-13|-35|-10|-36|-6|-36|-3|-36|0|-36|1|-36|2|-35|16|35|15|35|13|36|9|37|6|37|3|37|0|37|0|36
32,48,558,104,31,74,0.951,1255.602,2968.294,-1,0,0.995,581,176,-14|-34|-14|-35|-13|-35|-10|-35|-6|-36|-3|-36|0|-36|1|-36|2|-35|15|35|15|35|13|36|9|37|6|37|3|37|0|37|0|36
...
```

--------------------------------

### Setup Python Virtual Environment

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Libraries.html

Create and activate a Python virtual environment to manage dependencies.

```bash
# Create virtual environment
$ python3 -m venv deepstream_libraries_env

# Activate virtual environment
$ source deepstream_libraries_env/bin/activate

# Verify activation
$ which python3  # Should point to virtual environment
```

--------------------------------

### Default Log Format Example

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2libs_2nvdsinferserver_2tritonserver__include_2tritonserver_8h.html

Example of the default log format output.

```text
I0520 20:03:25.829575 3355 model_lifecycle.cc:441] "AsyncLoad() 'simple'"
```

--------------------------------

### Configure NvDCF Tracker for Re-Association

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvtracker.html

Example configuration for enabling target re-association in the NvDCF tracker using the config_tracker_NvDCF_accuracy.yml file.

```yaml
TrajectoryManagement:
  useUniqueID: 0      # Use 64-bit long Unique ID when assignining tracker ID. Default is [true]
  enableReAssoc: 1    # Enable Re-Assoc
  minMatchingScore4Overall: 0    # min matching score for overall
  minTrackletMatchingScore: 0.5644    # min tracklet similarity score for re-assoc
  matchingScoreWeight4TrackletSimilarity: 1.0    # weight for tracklet similarity score
  minTrajectoryLength4Projection: 36    # min trajectory length required to make projected trajectory
  prepLength4TrajectoryProjection: 50    # the length of the trajectory during which the state estimator is updated to make projections
  trajectoryProjectionLength: 94    # the length of the projected trajectory
  maxAngle4TrackletMatching: 106    # max angle difference for tracklet matching [degree]
  minSpeedSimilarity4TrackletMatching: 0.0967    # min speed similarity for tracklet matching
  minBboxSizeSimilarity4TrackletMatching: 0.5577    # min bbox size similarity for tracklet matching
  maxTrackletMatchingTimeSearchRange: 20    # the search space in time for max tracklet similarity
  trajectoryProjectionProcessNoiseScale: 0.0100    # trajectory projector's process noise scale w.r.t. state estimator
  trajectoryProjectionMeasurementNoiseScale: 100    # trajectory projector's measurement noise scale w.r.t. state estimator
  trackletSpacialSearchRegionScale: 0.2598    # the search region scale for peer tracklet

ReID:
  reidType: 0    # The type of reid among { DUMMY=0, NvDEEPSORT=1, Reid based reassoc=2, both NvDEEPSORT and reid based reassoc=3}
```

--------------------------------

### Initialize BEVFusion Directory Permissions

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_3D_MultiModal_Lidar_Camera_BEVFusion.html

Prepare the environment by setting display variables and granting read/write permissions to the sample application directory.

```bash
# run the following command line outside of the container.
$ export DISPLAY=0.0 # set the correct display number if DISPLAY is not exported
$ xhost +
$ cd /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-3d-lidar-sensor-fusion
$ sudo chmod -R a+rw . # Grant read/write permission for all of the files in this folder
```

--------------------------------

### Install glib 2.0 dependency

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Command to install glib 2.0 development libraries.

```bash
apt-get install libglib2.0 libglib2.0-dev
```

--------------------------------

### Install RabbitMQ Server

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvmsgbroker.html

Command to install the RabbitMQ server package for the AMQP broker.

```bash
sudo apt-get install rabbitmq-server
```

--------------------------------

### Basic Pipeline Setup

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdsdynamicsrcbin.html

Initializes the nvdsdynamicsrcbin element and adds it to a GStreamer pipeline.

```C
// Create pipeline elements
GstElement *pipeline = gst_pipeline_new("dynamicsrcbin-test");
GstElement *src = gst_element_factory_make("nvdsdynamicsrcbin", "src");
GstElement *sink = gst_element_factory_make("fakesink", "sink");

// Add elements to pipeline and link them
gst_bin_add_many(GST_BIN(pipeline), src, sink, NULL);
gst_element_link_many(src, sink, NULL);

// Set pipeline to PLAYING state
gst_element_set_state(pipeline, GST_STATE_PLAYING);
```

--------------------------------

### Install TensorRT 10.16.0.72

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html

Installs specific TensorRT development packages via apt-get.

```bash
version="10.16.0.72-1+cuda13.2"
sudo apt-get install libnvinfer-dev=${version} libnvinfer-dispatch-dev=${version} \
libnvinfer-dispatch10=${version} libnvinfer-headers-dev=${version} libnvinfer-headers-plugin-dev=${version} \
libnvinfer-safe-headers-dev=${version} libnvinfer-lean-dev=${version} libnvinfer-lean10=${version} \
libnvinfer-plugin-dev=${version} libnvinfer-plugin10=${version} libnvinfer-vc-plugin-dev=${version} \
libnvinfer-vc-plugin10=${version} libnvinfer10=${version} libnvonnxparsers-dev=${version} \
libnvonnxparsers10=${version} tensorrt-dev=${version} \
libnvinfer-headers-python-plugin-dev=${version} libnvinfer-win-builder-resource10=${version}
```

--------------------------------

### Start Easy-NMOS Containers

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_ref_app_nmos.html

Execute this command to launch the NMOS services defined in the docker-compose.yml file.

```bash
docker-compose up
```

--------------------------------

### Install DeepStream Libraries Wheel

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Libraries.html

Install the DeepStream Libraries package using pip.

```bash
$ pip3 install deepstream_libraries-1.4-cp312-cp312-linux_x86_64.whl
```

--------------------------------

### initialize

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classnvdsinfer_1_1BackendContext.html

Initializes the backend context.

```APIDOC
## NvDsInferStatus initialize()

### Description
Performs initialization for the backend context and returns the status.
```

--------------------------------

### Verify Docker installation

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_on_WSL2.html

Run the hello-world container to confirm Docker is functioning correctly.

```bash
$ sudo docker run hello-world

```

--------------------------------

### prepare

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classdeepstream_1_1Pipeline.html

Initializes the pipeline, optionally with a message listener callback.

```APIDOC
## int deepstream::Pipeline::prepare()
## int deepstream::Pipeline::prepare(std::function<void(Pipeline &, const Message &)> listener)

### Description
Initializes the pipeline. An optional listener callback can be provided to capture messages generated by the pipeline.

### Parameters
- **listener** (std::function<void(Pipeline &, const Message &)>) - Optional - A callback function to handle messages.
```

--------------------------------

### Property Installation Macros

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/sources_2includes_2gst-nvcommon_8h.html

Macros for installing standard DeepStream properties on GObject classes.

```c
#define PROP_NVDS_GPU_ID_INSTALL(gobject_class)
```

```c
#define PROP_NVBUF_MEMORY_TYPE_INSTALL(gobject_class)
```

```c
#define PROP_COMPUTE_HW_INSTALL(gobject_class)
```

```c
#define PROP_INTERPOLATION_METHOD_INSTALL(gobject_class)
```

--------------------------------

### Response Header Start

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/sources_2includes_2civetweb_8h_source.html

Starts sending the response header with the given status code.

```c
CIVETWEB_API int mg_response_header_start(struct mg_connection *conn, int status)
```

--------------------------------

### Configure Environment Variables

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_AutoMagicCalib.html

Example configuration for the compose/.env file.

```text
AUTO_MAGIC_CALIB_MS_PORT=8000
AUTO_MAGIC_CALIB_UI_PORT=5000
PROJECT_DIR=../../projects
MODEL_DIR=../../models
HOST_IP="<your_host_ip>"
# VIOS_BASE_URL=http://<VIOS_HOST_IP>:30888  # Uncomment and update this to support RTSP streams
```

--------------------------------

### Video Data Transfer Pipeline Example

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvdsucx.html

Demonstrates splitting a video pipeline across two nodes using serversink and clientsrc elements.

```bash
gst-launch-1.0 uridecodebin uri="file:///sample_1080p.mp4" async-handling=1 name=src1 src1. ! \
queue ! nvvideoconvert ! 'video/x-raw(memory:NVMM),format=NV12,width=1920,height=1080' ! \
nvdsucxserversink addr=192.168.100.1 port=4000 buf-type=nvdsucx-buf-video
```

```bash
gst-launch-1.0 nvdsucxclientsrc addr=192.168.100.1 port=4000 nvbuf-memory-type=2 num-nvbuf=4 buf-type=nvdsucx-buf-video ! \
'video/x-raw(memory:NVMM),format=NV12,width=1920,height=1080,framerate=30/1' ! \
queue ! nvvideoconvert ! nvv4l2h264enc ! h264parse ! qtmux name=mux_0 ! \
filesink sync=1 async=0 qos=0 location=~/out_1080p.mp4
```

--------------------------------

### create_dsexample_bin

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2apps_2apps-common_2includes_2deepstream__dsexample_8h_source.html

Creates a GStreamer bin containing the DSExample element and its associated components based on the provided configuration.

```APIDOC
## create_dsexample_bin

### Description
Creates a GStreamer bin and configures the DSExample element using the provided NvDsDsExampleConfig structure. This function initializes the internal elements including the queue, converter, and filter.

### Signature
`gboolean create_dsexample_bin (NvDsDsExampleConfig *config, NvDsDsExampleBin *bin);`

### Parameters
- **config** (NvDsDsExampleConfig*) - Required - Pointer to the configuration structure containing properties like processing dimensions, batch size, and GPU ID.
- **bin** (NvDsDsExampleBin*) - Required - Pointer to the structure that will hold references to the created GStreamer bin and its internal elements.

### Returns
- **gboolean** - Returns TRUE if the bin was created successfully, FALSE otherwise.
```

--------------------------------

### Verify pyservicemaker installation

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_service_maker_python_quick_start.html

Check if the pyservicemaker package is correctly installed and accessible in the Python environment.

```python
python3 -c "import pyservicemaker; print('pyservicemaker ready')"
```

--------------------------------

### Install Ubuntu 24.04 on WSL

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_on_WSL2.html

Install the specific Ubuntu version required for DeepStream 9.1.

```bash
$ wsl --install Ubuntu-24.04

```

--------------------------------

### install(std::function<void(Pipeline &, int key)> keyboard_listener)

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classdeepstream_1_1Pipeline.html

Registers a callback function to capture and process keyboard events.

```APIDOC
## Pipeline& install(std::function<void(Pipeline &, int key)> keyboard_listener)

### Description
Installs a listener callback that is triggered when keyboard events occur in the pipeline.

### Parameters
- **keyboard_listener** (std::function<void(Pipeline &, int key)>) - A callback function that receives the pipeline instance and the key code.

### Returns
- **Pipeline&** - Returns a reference to the current Pipeline instance for method chaining.
```

--------------------------------

### Install NVIDIA Driver 595.58.03

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html

Commands to set permissions and execute the NVIDIA driver installer.

```bash
$ chmod 755 NVIDIA-Linux-x86_64-595.58.03.run
$ sudo ./NVIDIA-Linux-x86_64-595.58.03.run --no-cc-version-check
```

--------------------------------

### Install DeepStream via Tar Package

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html

Extracts and installs the DeepStream SDK from a tarball archive.

```bash
$  sudo tar -xvf deepstream_sdk_v9.1.0_jetson.tbz2 -C /
$ cd /opt/nvidia/deepstream/deepstream-9.1
$ sudo ./install.sh
$ sudo ldconfig
```

--------------------------------

### ds3d::abiProcess::start_i

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/classds3d_1_1abiDataFilter.html

Initializes the data processor with a configuration string and path.

```APIDOC
## Method: ds3d::abiProcess::start_i

### Description
Initializes the data processor using a provided configuration string and file path.

### Signature
`virtual ErrCode start_i(const char * configStr, uint32_t strLen, const char * path)`

### Parameters
- **configStr** (const char *) - The configuration string for the processor.
- **strLen** (uint32_t) - The length of the configuration string.
- **path** (const char *) - The path associated with the processor configuration.

### Returns
- **ErrCode** - Returns an error code indicating the success or failure of the initialization.
```

--------------------------------

### Install OpenTelemetry C++

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html

Compiles and installs the OpenTelemetry C++ SDK from source with OTLP support.

```bash
$ cd ~
$ mkdir otel_src && cd otel_src && git clone --branch v1.23.0 --recurse-submodules https://github.com/open-telemetry/opentelemetry-cpp
$ cd opentelemetry-cpp
$ mkdir build && cd build
$ sudo apt install cmake
$ cmake .. -DBUILD_SHARED_LIBS=ON -DWITH_OTLP_GRPC=ON -DWITH_OTLP_HTTP=ON -DBUILD_TESTING=OFF
$ cmake --build . --parallel $(nproc)
$ ctest -V (Optional: run tests for verification)
$ sudo cmake --install .
```

--------------------------------

### Trace compute output start constant

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/sources_2libs_2nvdsinferserver_2tritonserver__include_2tritonserver_8h_source.html

Constant indicating the start of compute output tracing.

```c
TRITONSERVER_TRACE_COMPUTE_OUTPUT_START
```

--------------------------------

### parse_dsexample_yaml

Source: https://docs.nvidia.com/metropolis/deepstream/dev-guide/sdk-api/9_81_2sources_2apps_2apps-common_2includes_2deepstream__config__yaml_8h.html

Parses example plugin configuration from a YAML file.

```APIDOC
## gboolean parse_dsexample_yaml(NvDsDsExampleConfig *config, gchar *cfg_file_path)

### Description
Parses the example plugin configuration settings from a specified YAML file.
```
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