Added concurrent DL Streamer and DeepStream sample

docs/source/dev_guide/concurrent.md

@@ -0,0 +1,37 @@
+# Concurrent usage of DL Streamer and DeepStream.
+
+This tutorial explains how to simultaneously run DL Streamer and DeepStream on a single machine for optimal performance.
+
+### Overview
+Systems equipped with both NVIDIA GPUs and Intel hardware (GPU/NPU/CPU) can achieve enhanced performance by distributing workloads across available accelerators. Rather than relying solely on DeepStream for pipeline execution, you can offload additional processing tasks to Intel accelerators, maximizing system resource utilization.
+
+A Python script (concurrent_dls_and_ds.py) is provided to facilitate this concurrent setup. It assumes that Docker and Python are properly installed and configured. The Ubuntu 24.04 is currently the only supported operating system.
+
+## How it works
+
+1. Using intel/dlstreamer:2025.2.0-ubuntu24 image, the sample downloads yolov8_license_plate_detector and ch_PP-OCRv4_rec_infer models to \./public directory if they were not downloaded yet.
+2. Using nvcr.io/nvidia/deepstream:8.0-samples-multiarch image it downloads deepstream_tao_apps repository to \./deepstream_tao_apps directory. Then downloads models for License Plate Recognition (LPR), makes a custom library and copies dict.txt to the current directory, in case deepstream_tao_apps does not exist.
+3. Hardware detection depending on setup
+- Run pipeline simultaneously on both devices for:
+  - both Nvidia and Intel GPUs
+  - Nvidia GPU and Intel NPU
+  - Nvidia GPU with Intel CPU
+- Run pipeline directly per device for:
+  - Intel GPU
+  - Nvidia GPU
+  - Intel NPU
+  - Intel CPU
+
+## How to use
+
+```sh
+python3 ./concurrent_dls_and_ds.py <input> LPR <output>
+```
+
+- Input can be rtsp, https or file.
+- License Plate Recognition (LPR) is currently the only pipeline supported.
+- Output is the filename. For example parameter: Output.mp4 or Output will create files Output_dls.mp4 (DL Streamer output) and/or Output_ds.mp4 (DeepStream output). 
+
+## Notes
+
+First-time download of the Docker images and models could take a longer time.

docs/source/dev_guide/dev_guide_index.md

@@ -68,6 +68,7 @@
   - [Pre-processing description (`input_preproc`)](./model_proc_file.md#pre-processing-description)
   - [Post-processing description (`output_postproc`)](./model_proc_file.md#post-processing-description-output_postproc)
 - [Pipeline Optimizer](./optimizer.md)
+- [Concurrent usage of DL Streamer and DeepStream.](./concurrent.md)
 
 <!--hide_directive
 :::{toctree}

samples/gstreamer/README.md

@@ -36,6 +36,8 @@ Samples separated into several categories
     * [ONVIF Camera Discovery Sample](./python/onvif_cameras_discovery/README.md) - demonstrates automatic discovery of ONVIF-compatible cameras on the network and launches corresponding DL Streamer pipelines for video analysis.
 4. Benchmark
     * [Benchmark Sample](./benchmark/README.md) - measures overall performance of single-channel or multi-channel video analytics pipelines
+5. Concurrent use of DL Streamer and DeepStream
+	* [Concurrent use Sample](./concurrent/README.md) - runs pipelines on DL Streamer and/or DeepStream
 
 ## How To Build And Run
 

samples/gstreamer/concurrent/README.md

@@ -0,0 +1,32 @@
+# Concurrent use of DL Streamer and DeepStream
+
+This sample detects hardware and runs pipelines using DL Streamer and/or DeepStream.
+
+## How it works
+
+1. Using intel/dlstreamer:2025.2.0-ubuntu24 image, the sample downloads yolov8_license_plate_detector and ch_PP-OCRv4_rec_infer models to \./public directory if they were not downloaded yet.
+2. Using nvcr.io/nvidia/deepstream:8.0-samples-multiarch image it downloads deepstream_tao_apps repository to \./deepstream_tao_apps directory. Then downloads models for License Plate Recognition (LPR), makes a custom library and copies dict.txt to the current directory, in case deepstream_tao_apps does not exist.
+3. Hardware detection depending on setup
+- Run pipeline simultaneously on both devices for:
+  - both Nvidia and Intel GPUs
+  - Nvidia GPU and Intel NPU
+  - Nvidia GPU with Intel CPU
+- Run pipeline directly per device for:
+  - Intel GPU
+  - Nvidia GPU
+  - Intel NPU
+  - Intel CPU
+
+## How to use
+
+```sh
+./concurrent_dls_and_ds.sh <input> LPR <output>
+```
+
+- Input can be rtsp, https or file.
+- License Plate Recognition (LPR) is currently the only pipeline supported.
+- Output is the filename. For example parameter: Output.mp4 or Output will create files Output_dls.mp4 (DL Streamer output) and/or Output_ds.mp4 (DeepStream output). 
+
+## Notes
+
+First-time download of the Docker images and models could take a longer time.

samples/gstreamer/concurrent/concurrent_dls_and_ds.sh

@@ -0,0 +1,198 @@
+#!/bin/bash
+# ==============================================================================
+# Copyright (C) 2026 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+# ==============================================================================
+
+declare -A DLSTREAMER_PIPELINES
+declare -A DEEPSTREAM_PIPELINES
+
+# Get arguments
+INPUT="$1"
+PIPELINE="$2"
+OUTPUT="$3"
+
+if [[ ${OUTPUT} =~ \.mp4 ]]; then
+    OUTPUT="${OUTPUT%.*}"
+fi
+
+# Check if input is rtsp or uri or file
+if [[ ${INPUT} =~ 'rtsp://' ]]; then
+    SOURCE="rtspsrc location=${INPUT}"
+elif [[ ${INPUT} =~ 'https://' ]]; then
+    SOURCE="urisourcebin buffer-size=4096 uri=${INPUT}"
+else
+    SOURCE="filesrc location=/working_dir/${INPUT}"
+fi
+
+# Definition of pipelines
+DLSTREAMER_PIPELINES[LPR]="gst-launch-1.0 ${SOURCE} ! decodebin3 ! vapostproc ! video/x-raw\(memory:VAMemory\) ! queue \
+! gvadetect model=/working_dir/public/yolov8_license_plate_detector/FP32/yolov8_license_plate_detector.xml \
+device=GPU pre-process-backend=va ! queue ! videoconvert ! \
+gvaclassify model=/working_dir/public/ch_PP-OCRv4_rec_infer/FP32/ch_PP-OCRv4_rec_infer.xml device=GPU pre-process-backend=va \
+! queue ! vapostproc ! gvawatermark ! gvafpscounter ! vah264enc bitrate=2000 ! h264parse ! mp4mux ! filesink location=/working_dir/${OUTPUT}_dls.mp4"
+
+
+DEEPSTREAM_PIPELINES[LPR]="gst-launch-1.0 ${SOURCE} ! qtdemux ! h264parse ! nvv4l2decoder ! m.sink_0 nvstreammux \
+name=m batch-size=1 width=1920 height=1080 batched-push-timeout=40000 ! queue ! nvvideoconvert \
+! video/x-raw\(memory:NVMM\),format=RGBA ! nvinfer \
+config-file-path=/working_dir/deepstream_tao_apps/configs/nvinfer/trafficcamnet_tao/pgie_trafficcamnet_config.txt \
+unique-id=1 ! queue ! nvinfer \
+config-file-path=/working_dir/deepstream_tao_apps/configs/nvinfer/LPD_us_tao/sgie_lpd_DetectNet2_us.txt unique-id=2 \
+! queue ! nvinfer config-file-path=/working_dir/deepstream_tao_apps/configs/nvinfer/lpr_us_tao/sgie_lpr_us_config.txt \
+unique-id=3 ! queue ! nvdsosd display-text=1 display-bbox=1 display-mask=0 process-mode=1 ! nvvideoconvert \
+! video/x-raw\(memory:NVMM\),format=NV12 ! nvv4l2h264enc bitrate=2000000 ! h264parse ! qtmux \
+! filesink location=/working_dir/${OUTPUT}_ds.mp4 sync=false"
+
+# Check if pipeline is valid
+if [[ ! ${DLSTREAMER_PIPELINES[${PIPELINE}]} || ! ${DEEPSTREAM_PIPELINES[${PIPELINE}]} ]]; then
+    printf 'Pipeline %s not found.\n' "${PIPELINE}"
+    printf 'Available pipelines: '
+    for key in "${!DLSTREAMER_PIPELINES[@]}"; do
+        printf '%s ' "$key"
+    done
+    printf '\n'
+    exit 1
+fi
+
+# Check if there is /dev/dri folder to run on GPU
+if [[ -e "/dev/dri" ]]; then
+  DEVICE_DRI="--device /dev/dri --group-add $(stat -c "%g" /dev/dri/render* | head -1)"
+fi
+
+# Check if there is /dev/accel folder to run on NPU
+if [[ -e "/dev/accel" ]]; then
+  DEVICE_ACCEL="--device /dev/accel --group-add $(stat -c "%g" /dev/accel/accel* | head -1)"
+fi
+
+# Variable for running commands from DL Streamer Docker
+DLSTREAMER_DOCKER="docker run -i --rm -v ${PWD}:/working_dir ${DEVICE_DRI} ${DEVICE_ACCEL} \
+-v ~/.Xauthority:/root/.Xauthority  -v /tmp/.X11-unix/:/tmp/.X11-unix/  -e DISPLAY=$DISPLAY  -v /dev/bus/usb:/dev/bus/usb \
+--env ZE_ENABLE_ALT_DRIVERS=libze_intel_npu.so \
+--env MODELS_PATH=/working_dir \
+intel/dlstreamer:2025.2.0-ubuntu24 /bin/bash -c"
+
+DEEPSTREAM_SETUP_LPR=$(cat <<EOF
+if [[ -e "/working_dir/deepstream_tao_apps" ]]; then
+  exit 0
+fi
+
+git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git
+
+set -e
+
+cd /working_dir/deepstream_tao_apps
+mkdir -p ./models/trafficcamnet
+cd ./models/trafficcamnet
+wget --no-check-certificate --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/trafficcamnet/pruned_onnx_v1.0.4/files?redirect=true&path=resnet18_trafficcamnet_pruned.onnx' -O resnet18_trafficcamnet_pruned.onnx
+wget --no-check-certificate --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/trafficcamnet/pruned_onnx_v1.0.4/files?redirect=true&path=resnet18_trafficcamnet_pruned_int8.txt' -O resnet18_trafficcamnet_pruned_int8.txt
+
+cd /working_dir/deepstream_tao_apps
+mkdir -p ./models/LPD_us
+cd ./models/LPD_us
+wget --no-check-certificate --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/lpdnet/pruned_v2.3.1/files?redirect=true&path=LPDNet_usa_pruned_tao5.onnx' -O LPDNet_usa_pruned_tao5.onnx
+wget --no-check-certificate --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/lpdnet/pruned_v2.3.1/files?redirect=true&path=usa_cal_10.1.0.bin' -O usa_cal_10.1.0.bin
+wget --no-check-certificate https://api.ngc.nvidia.com/v2/models/nvidia/tao/lpdnet/versions/pruned_v1.0/files/usa_lpd_label.txt
+
+cd /working_dir/deepstream_tao_apps
+mkdir -p ./models/LPR_us
+cd ./models/LPR_us
+wget --no-check-certificate --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/lprnet/deployable_onnx_v1.1/files?redirect=true&path=us_lprnet_baseline18_deployable.onnx' -O us_lprnet_baseline18_deployable.onnx
+touch labels_us.txt
+
+
+cd /working_dir/deepstream_tao_apps/apps/tao_others/deepstream_lpr_app/nvinfer_custom_lpr_parser/
+
+make
+
+cp /working_dir/deepstream_tao_apps/apps/tao_others/deepstream_lpr_app/dict_us.txt /working_dir/dict.txt
+
+EOF
+)
+
+DEEPSTREAM_DOCKER="docker run -i --rm --network=host --gpus all -e DISPLAY=$DISPLAY --device /dev/snd -v /tmp/.X11-unix/:/tmp/.X11-unix -v ${PWD}:/working_dir -w /working_dir nvcr.io/nvidia/deepstream:8.0-samples-multiarch /bin/bash -c"
+
+# Check if there are models in current directory and download if necessary
+if [[ ! -e "${PWD}/public/yolov8_license_plate_detector" ]]; then
+    printf 'Downloading models....\n'
+    eval "${DLSTREAMER_DOCKER}" + '"/opt/intel/dlstreamer/samples/download_public_models.sh yolov8_license_plate_detector,ch_PP-OCRv4_rec_infer"'
+fi
+
+# Check for Intel and Nvidia hardware
+INTEL_GPU=$(lspci -nn | grep -E 'VGA|3D|Display' | grep -i "Intel")
+NVIDIA_GPU=$(lspci -nn | grep -E 'VGA|3D|Display' | grep -i "NVIDIA")
+INTEL_CPU=$(lscpu | grep -i "Intel")
+
+print_intel_detected() {
+    local HARDWARE="$1"
+    printf -- "---------------------------------------\n Intel %s detected. \
+Using DL Streamer\n---------------------------------------\n\n" "${HARDWARE}"
+}
+
+print_nvidia_detected() {
+    printf -- "----------------------------------------\n NVIDIA GPU detected. \
+Using DeepStream\n----------------------------------------\n\n"
+}
+
+eval_dlstreamer_pipeline() {
+    printf 'PIPELINE:\n%s\n\n' "${DLSTREAMER_PIPELINES[${PIPELINE}]}"
+    eval "${DLSTREAMER_DOCKER}" + "\"${DLSTREAMER_PIPELINES[${PIPELINE}]}\"" &
+}
+
+eval_deepstream_pipeline() {
+    printf 'PIPELINE:\n%s\n\n' "${DEEPSTREAM_PIPELINES[${PIPELINE}]}"
+    eval "${DEEPSTREAM_DOCKER}" + "\"${DEEPSTREAM_SETUP_LPR}\""
+    eval "${DEEPSTREAM_DOCKER}" + "\"${DEEPSTREAM_PIPELINES[${PIPELINE}]}\"" &
+}
+
+replace_in_dlstreamer_pipeline() {
+    local FROM="$1"
+    local TO="$2"
+    DLSTREAMER_PIPELINES[${PIPELINE}]=${DLSTREAMER_PIPELINES[${PIPELINE}]//"${FROM}"/"${TO}"}
+}
+
+# Run pipeline
+if [[ -n "${NVIDIA_GPU}" && -n "${INTEL_GPU}" ]]; then
+    print_nvidia_detected
+    print_intel_detected "GPU"
+    eval_dlstreamer_pipeline
+    eval_deepstream_pipeline
+elif [[ -n "${NVIDIA_GPU}" && -e "/dev/accel" ]]; then
+    print_nvidia_detected
+    print_intel_detected "NPU"
+    replace_in_dlstreamer_pipeline "GPU" "NPU"
+    eval_dlstreamer_pipeline
+    eval_deepstream_pipeline
+elif [[ -n "${NVIDIA_GPU}" && -n "${INTEL_CPU}" ]]; then
+    print_nvidia_detected
+    print_intel_detected "CPU"
+    replace_in_dlstreamer_pipeline "GPU" "CPU"
+    replace_in_dlstreamer_pipeline "vapostproc !" ""
+    replace_in_dlstreamer_pipeline "pre-process-backend=va" ""
+    replace_in_dlstreamer_pipeline "video/x-raw\\(memory:VAMemory\) !" ""
+    replace_in_dlstreamer_pipeline "vah264enc bitrate=2000" "openh264enc bitrate=2000000"
+    eval_dlstreamer_pipeline
+    eval_deepstream_pipeline
+elif [[ -n "${INTEL_GPU}" ]]; then
+    print_intel_detected "GPU"
+    eval_dlstreamer_pipeline
+elif [[ -n "${NVIDIA_GPU}" ]]; then
+    print_nvidia_detected
+    eval_deepstream_pipeline
+elif [[ -e "/dev/accel" ]]; then
+    print_intel_detected "NPU"
+    replace_in_dlstreamer_pipeline "GPU" "NPU"
+    eval_dlstreamer_pipeline
+elif [[ -n "${INTEL_CPU}" ]]; then
+    print_intel_detected "CPU"
+    replace_in_dlstreamer_pipeline "GPU" "CPU"
+    replace_in_dlstreamer_pipeline "vapostproc !" ""
+    replace_in_dlstreamer_pipeline "pre-process-backend=va" ""
+    replace_in_dlstreamer_pipeline "video/x-raw\\(memory:VAMemory\) !" ""
+    replace_in_dlstreamer_pipeline "vah264enc bitrate=2000" "openh264enc bitrate=2000000"
+    eval_dlstreamer_pipeline
+fi
+
+# wait because of evals with &
+wait