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DeepStream 6.1 update

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This commit is contained in:
Marcos Luciano
2022-05-26 08:05:06 -03:00
parent 4192a16751
commit ed387abf5d
3 changed files with 259 additions and 79 deletions
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4
nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
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@@ -193,8 +193,8 @@ YoloLayer::configureWithFormat (
assert(inputDims != nullptr); assert(inputDims != nullptr);
} }
int YoloLayer::enqueue ( int32_t YoloLayer::enqueue (
int batchSize, void const* const* inputs, void* const* outputs, void* workspace, int32_t batchSize, void const* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) noexcept cudaStream_t stream) noexcept
{ {
if (m_Type == 2) { // YOLOR incorrect param: scale_x_y = 2.0 if (m_Type == 2) { // YOLOR incorrect param: scale_x_y = 2.0

4
nvdsinfer_custom_impl_Yolo/yoloPlugins.h
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@@ -81,8 +81,8 @@ public:
int initialize () noexcept override { return 0; } int initialize () noexcept override { return 0; }
void terminate () noexcept override {} void terminate () noexcept override {}
size_t getWorkspaceSize (int maxBatchSize) const noexcept override { return 0; } size_t getWorkspaceSize (int maxBatchSize) const noexcept override { return 0; }
int enqueue ( int32_t enqueue (
int batchSize, void const* const* inputs, void* const* outputs, int32_t batchSize, void const* const* inputs, void* const* outputs,
void* workspace, cudaStream_t stream) noexcept override; void* workspace, cudaStream_t stream) noexcept override;
size_t getSerializationSize() const noexcept override; size_t getSerializationSize() const noexcept override;
void serialize (void* buffer) const noexcept override; void serialize (void* buffer) const noexcept override;

330
readme.md
View File

@@ -1,11 +1,12 @@
# DeepStream-Yolo # DeepStream-Yolo
NVIDIA DeepStream SDK 6.0.1 configuration for YOLO models NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models
### Future updates ### Future updates
* New documentation for multiple models * New documentation for multiple models
* DeepStream tutorials * DeepStream tutorials
* Native YOLOX support
* Native PP-YOLO support * Native PP-YOLO support
* Dynamic batch-size * Dynamic batch-size
@@ -44,20 +45,36 @@ NVIDIA DeepStream SDK 6.0.1 configuration for YOLO models
### Requirements ### Requirements
#### x86 platform #### DeepStream 6.1 on x86 platform
* [Ubuntu 18.04](https://releases.ubuntu.com/18.04.6/) * [Ubuntu 20.04](https://releases.ubuntu.com/20.04/)
* [CUDA 11.4](https://developer.nvidia.com/cuda-toolkit) * [CUDA 11.6 Update 1](https://developer.nvidia.com/cuda-11-6-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=runfile_local)
* [TensorRT 8.0 GA (8.0.1)](https://developer.nvidia.com/tensorrt) * [TensorRT 8.2 GA Update 4 (8.2.5.1)](https://developer.nvidia.com/nvidia-tensorrt-8x-download)
* [cuDNN >= 8.2](https://developer.nvidia.com/cudnn) * [NVIDIA Driver 510.47.03](https://www.nvidia.com.br/Download/index.aspx)
* [NVIDIA Driver >= 470.63.01](https://www.nvidia.com.br/Download/index.aspx) * [NVIDIA DeepStream SDK 6.1](https://developer.nvidia.com/deepstream-sdk)
* [NVIDIA DeepStream SDK 6.0.1 (6.0)](https://developer.nvidia.com/deepstream-sdk) * [GStreamer 1.16.2](https://gstreamer.freedesktop.org/)
* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) * [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
#### Jetson platform #### DeepStream 6.0.1 / 6.0 on x86 platform
* [JetPack 4.6.1](https://developer.nvidia.com/embedded/jetpack) * [Ubuntu 18.04](https://releases.ubuntu.com/18.04.6/)
* [NVIDIA DeepStream SDK 6.0.1 (6.0)](https://developer.nvidia.com/deepstream-sdk) * [CUDA 11.4 Update 1](https://developer.nvidia.com/cuda-11-4-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=18.04&target_type=runfile_local)
* [TensorRT 8.0 GA (8.0.1)](https://developer.nvidia.com/nvidia-tensorrt-8x-download)
* [NVIDIA Driver >= 470.63.01](https://www.nvidia.com.br/Download/index.aspx)
* [NVIDIA DeepStream SDK 6.0.1 / 6.0](https://developer.nvidia.com/deepstream-sdk-download-tesla-archived)
* [GStreamer 1.14.5](https://gstreamer.freedesktop.org/)
* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
#### DeepStream 6.1 on Jetson platform
* [JetPack 5.0.1 DP](https://developer.nvidia.com/embedded/jetpack)
* [NVIDIA DeepStream SDK 6.1](https://developer.nvidia.com/deepstream-sdk)
* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
#### DeepStream 6.0.1 / 6.0 on Jetson platform
* [JetPack 4.6.1](https://developer.nvidia.com/embedded/jetpack-sdk-461)
* [NVIDIA DeepStream SDK 6.0.1 / 6.0](https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads-archived)
* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) * [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
### For YOLOv5 and YOLOR ### For YOLOv5 and YOLOR
@@ -68,7 +85,7 @@ NVIDIA DeepStream SDK 6.0.1 configuration for YOLO models
#### Jetson platform #### Jetson platform
* [PyTorch >= 1.7.0](https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048) * [PyTorch >= 1.7.0](https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-11-now-available/72048)
## ##
@@ -152,11 +169,129 @@ NOTE: Used maintain-aspect-ratio=1 in config_infer file for YOLOv4 (with letter_
To install the DeepStream on dGPU (x86 platform), without docker, we need to do some steps to prepare the computer. To install the DeepStream on dGPU (x86 platform), without docker, we need to do some steps to prepare the computer.
<details><summary>Open</summary> <details><summary>DeepStream 6.1</summary>
#### 1. Disable Secure Boot in BIOS #### 1. Disable Secure Boot in BIOS
<details><summary>If you are using a laptop with newer Intel/AMD processors, please update the kernel to newer version.</summary> #### 2. Install dependencies
```
sudo apt-get update
sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake
sudo apt-get install python3 python3-dev python3-pip
sudo apt-get install dkms
sudo apt-get install libssl1.1 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4 libyaml-cpp-dev
sudo apt-get install linux-headers-$(uname -r)
```
**NOTE**: Purge all NVIDIA driver, CUDA, etc (replace $CUDA_PATH to your CUDA path).
```
sudo nvidia-uninstall
sudo $CUDA_PATH/bin/cuda-uninstaller
sudo apt-get remove --purge '*nvidia*'
sudo apt-get remove --purge '*cuda*'
sudo apt-get remove --purge '*cudnn*'
sudo apt-get remove --purge '*tensorrt*'
sudo apt autoremove --purge && sudo apt autoclean && sudo apt clean
```
#### 3. Install CUDA Keyring
```
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update
```
#### 4. Download and install NVIDIA Driver
* TITAN, GeForce RTX / GTX series and RTX / Quadro series
```
wget https://us.download.nvidia.com/XFree86/Linux-x86_64/510.47.03/NVIDIA-Linux-x86_64-510.47.03.run
```
* Data center / Tesla series
```
wget https://us.download.nvidia.com/tesla/510.47.03/NVIDIA-Linux-x86_64-510.47.03.run
```
* Install
```
sudo sh NVIDIA-Linux-x86_64-510.47.03.run --silent --disable-nouveau --dkms --install-libglvnd
```
**NOTE**: If you are using a laptop with NVIDIA Optimius, run
```
sudo apt-get install nvidia-prime
sudo prime-select nvidia
```
#### 5. Download and install CUDA
```
wget https://developer.download.nvidia.com/compute/cuda/11.6.1/local_installers/cuda_11.6.1_510.47.03_linux.run
sudo sh cuda_11.6.1_510.47.03_linux.run --silent --toolkit
```
* Export environment variables
```
nano ~/.bashrc
```
* Add
```
export PATH=/usr/local/cuda-11.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64\${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
```
* Run
```
source ~/.bashrc
```
#### 6. Download from [NVIDIA website](https://developer.nvidia.com/nvidia-tensorrt-8x-download) and install the TensorRT
TensorRT 8.2 GA Update 4 for Ubuntu 20.04 and CUDA 11.0, 11.1, 11.2, 11.3, 11.4 and 11.5 DEB local repo Package
```
sudo dpkg -i nv-tensorrt-repo-ubuntu2004-cuda11.4-trt8.2.5.1-ga-20220505_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-ubuntu2004-cuda11.4-trt8.2.5.1-ga-20220505/82307095.pub
sudo apt-get update
sudo apt install tensorrt
```
#### 7. Download from [NVIDIA website](https://developer.nvidia.com/deepstream-sdk) and install the DeepStream SDK
DeepStream 6.1 for Servers and Workstations (.deb)
```
sudo apt-get install ./deepstream-6.1_6.1.0-1_amd64.deb
rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin
sudo ln -snf /usr/local/cuda-11.6 /usr/local/cuda
```
#### 8. Reboot the computer
```
sudo reboot
```
</details>
<details><summary>DeepStream 6.0.1 / 6.0</summary>
#### 1. Disable Secure Boot in BIOS
<details><summary>If you are using a laptop with newer Intel/AMD processors and your Graphics in Settings->Details->About tab is llvmpipe, please update the kernel.</summary>
``` ```
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100_5.11.0-051100.202102142330_all.deb wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100_5.11.0-051100.202102142330_all.deb
@@ -172,10 +307,10 @@ sudo reboot
#### 2. Install dependencies #### 2. Install dependencies
``` ```
sudo apt-get update
sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake
sudo apt-get install python3 python3-dev python3-pip sudo apt-get install python3 python3-dev python3-pip
sudo apt install libssl1.0.0 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4 sudo apt install libssl1.0.0 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4
sudo apt-get install libglvnd-dev
sudo apt-get install linux-headers-$(uname -r) sudo apt-get install linux-headers-$(uname -r)
``` ```
@@ -185,9 +320,11 @@ sudo apt-get install linux-headers-$(uname -r)
sudo apt-get install dkms sudo apt-get install dkms
``` ```
**NOTE**: Purge all NVIDIA driver, CUDA, etc. **NOTE**: Purge all NVIDIA driver, CUDA, etc (replace $CUDA_PATH to your CUDA path).
``` ```
sudo nvidia-uninstall
sudo $CUDA_PATH/bin/cuda-uninstaller
sudo apt-get remove --purge '*nvidia*' sudo apt-get remove --purge '*nvidia*'
sudo apt-get remove --purge '*cuda*' sudo apt-get remove --purge '*cuda*'
sudo apt-get remove --purge '*cudnn*' sudo apt-get remove --purge '*cudnn*'
@@ -195,54 +332,46 @@ sudo apt-get remove --purge '*tensorrt*'
sudo apt autoremove --purge && sudo apt autoclean && sudo apt clean sudo apt autoremove --purge && sudo apt autoclean && sudo apt clean
``` ```
#### 3. Disable Nouveau #### 3. Install CUDA Keyring
``` ```
sudo nano /etc/modprobe.d/blacklist-nouveau.conf wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update
``` ```
* Add #### 4. Download and install NVIDIA Driver
```
blacklist nouveau
options nouveau modeset=0
```
* Run
```
sudo update-initramfs -u
```
#### 4. Reboot the computer
```
sudo reboot
```
#### 5. Download and install NVIDIA Driver without xconfig
* TITAN, GeForce RTX / GTX series and RTX / Quadro series * TITAN, GeForce RTX / GTX series and RTX / Quadro series
``` ```
wget https://us.download.nvidia.com/XFree86/Linux-x86_64/470.103.01/NVIDIA-Linux-x86_64-470.103.01.run wget https://us.download.nvidia.com/XFree86/Linux-x86_64/470.129.06/NVIDIA-Linux-x86_64-470.129.06.run
sudo sh NVIDIA-Linux-x86_64-470.103.01.run
``` ```
* Data center / Tesla series * Data center / Tesla series
``` ```
wget https://us.download.nvidia.com/tesla/470.103.01/NVIDIA-Linux-x86_64-470.103.01.run wget https://us.download.nvidia.com/tesla/470.129.06/NVIDIA-Linux-x86_64-470.129.06.run
sudo sh NVIDIA-Linux-x86_64-470.103.01.run
``` ```
**NOTE**: Only if you are using default Ubuntu kernel, enable the DKMS during the installation. * Install
#### 6. Download and install CUDA 11.4.3 without NVIDIA Driver
``` ```
wget https://developer.download.nvidia.com/compute/cuda/11.4.3/local_installers/cuda_11.4.3_470.82.01_linux.run sudo sh NVIDIA-Linux-x86_64-470.129.06.run --silent --disable-nouveau --dkms --install-libglvnd
sudo sh cuda_11.4.3_470.82.01_linux.run ```
**NOTE**: If you are using a laptop with NVIDIA Optimius, run
```
sudo apt-get install nvidia-prime
sudo prime-select nvidia
```
#### 5. Download and install CUDA
```
wget https://developer.download.nvidia.com/compute/cuda/11.4.1/local_installers/cuda_11.4.1_470.57.02_linux.run
sudo sh cuda_11.4.1_470.57.02_linux.run --silent --toolkit
``` ```
* Export environment variables * Export environment variables
@@ -262,38 +391,42 @@ export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64\${LD_LIBRARY_PATH:+:${LD_LIBRA
``` ```
source ~/.bashrc source ~/.bashrc
sudo ldconfig
``` ```
**NOTE**: If you are using a laptop with NVIDIA Optimius, run
#### 6. Download from [NVIDIA website](https://developer.nvidia.com/nvidia-tensorrt-8x-download) and install the TensorRT
TensorRT 8.0.1 GA for Ubuntu 18.04 and CUDA 11.3 DEB local repo package
``` ```
sudo apt-get install nvidia-prime
sudo prime-select nvidia
```
#### 7. Download from [NVIDIA website](https://developer.nvidia.com/nvidia-tensorrt-8x-download) and install the TensorRT 8.0 GA (8.0.1)
```
echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda-repo.list
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo apt-key add 7fa2af80.pub
sudo apt-get update
sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub
sudo apt-get update sudo apt-get update
sudo apt-get install libnvinfer8=8.0.1-1+cuda11.3 libnvinfer-plugin8=8.0.1-1+cuda11.3 libnvparsers8=8.0.1-1+cuda11.3 libnvonnxparsers8=8.0.1-1+cuda11.3 libnvinfer-bin=8.0.1-1+cuda11.3 libnvinfer-dev=8.0.1-1+cuda11.3 libnvinfer-plugin-dev=8.0.1-1+cuda11.3 libnvparsers-dev=8.0.1-1+cuda11.3 libnvonnxparsers-dev=8.0.1-1+cuda11.3 libnvinfer-samples=8.0.1-1+cuda11.3 libnvinfer-doc=8.0.1-1+cuda11.3 sudo apt-get install libnvinfer8=8.0.1-1+cuda11.3 libnvinfer-plugin8=8.0.1-1+cuda11.3 libnvparsers8=8.0.1-1+cuda11.3 libnvonnxparsers8=8.0.1-1+cuda11.3 libnvinfer-bin=8.0.1-1+cuda11.3 libnvinfer-dev=8.0.1-1+cuda11.3 libnvinfer-plugin-dev=8.0.1-1+cuda11.3 libnvparsers-dev=8.0.1-1+cuda11.3 libnvonnxparsers-dev=8.0.1-1+cuda11.3 libnvinfer-samples=8.0.1-1+cuda11.3 libnvinfer-doc=8.0.1-1+cuda11.3
``` ```
#### 8. Download from [NVIDIA website](https://developer.nvidia.com/deepstream-sdk) and install the DeepStream SDK 6.0.1 (6.0) #### 7. Download from [NVIDIA website](https://developer.nvidia.com/deepstream-sdk) and install the DeepStream SDK
* DeepStream 6.0.1 for Servers and Workstations (.deb)
``` ```
sudo apt-get install ./deepstream-6.0_6.0.1-1_amd64.deb sudo apt-get install ./deepstream-6.0_6.0.1-1_amd64.deb
```
* DeepStream 6.0 for Servers and Workstations (.deb)
```
sudo apt-get install ./deepstream-6.0_6.0.0-1_amd64.deb
```
* Run
```
rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin
sudo ln -snf /usr/local/cuda-11.4 /usr/local/cuda sudo ln -snf /usr/local/cuda-11.4 /usr/local/cuda
``` ```
#### 9. Reboot the computer #### 8. Reboot the computer
``` ```
sudo reboot sudo reboot
@@ -316,13 +449,25 @@ cd DeepStream-Yolo
#### 3. Compile lib #### 3. Compile lib
* x86 platform * DeepStream 6.1 on x86 platform
```
CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on x86 platform
``` ```
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
``` ```
* Jetson platform * DeepStream 6.1 on Jetson platform
```
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on Jetson platform
``` ```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
@@ -403,13 +548,25 @@ python3 gen_wts_yoloV5.py -w yolov5n.pt
#### 7. Compile lib #### 7. Compile lib
* x86 platform * DeepStream 6.1 on x86 platform
```
CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on x86 platform
``` ```
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
``` ```
* Jetson platform * DeepStream 6.1 on Jetson platform
```
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on Jetson platform
``` ```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
@@ -522,13 +679,25 @@ python3 gen_wts_yolor.py -w yolor_csp.pt -c cfg/yolor_csp.cfg
#### 7. Compile lib #### 7. Compile lib
* x86 platform * DeepStream 6.1 on x86 platform
```
CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on x86 platform
``` ```
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
``` ```
* Jetson platform * DeepStream 6.1 on Jetson platform
```
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on Jetson platform
``` ```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
@@ -593,14 +762,28 @@ sudo apt-get install libopencv-dev
#### 2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support #### 2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support
* x86 platform * DeepStream 6.1 on x86 platform
```
cd DeepStream-Yolo
CUDA_VER=11.6 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on x86 platform
``` ```
cd DeepStream-Yolo cd DeepStream-Yolo
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
``` ```
* Jetson platform * DeepStream 6.1 on Jetson platform
```
cd DeepStream-Yolo
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on Jetson platform
``` ```
cd DeepStream-Yolo cd DeepStream-Yolo
@@ -668,12 +851,9 @@ deepstream-app -c deepstream_app_config.txt
### Extract metadata ### Extract metadata
You can get metadata from deepstream in Python and C++. For C++, you need edit deepstream-app or deepstream-test code. For Python your need install and edit [deepstream_python_apps](https://github.com/NVIDIA-AI-IOT/deepstream_python_apps). You can get metadata from deepstream in Python and C/C++. For C/C++, you need edit deepstream-app or deepstream-test code. For Python your need install and edit [deepstream_python_apps](https://github.com/NVIDIA-AI-IOT/deepstream_python_apps).
You need manipulate NvDsObjectMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsObjectMeta.html)/[C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsObjectMeta.html)), NvDsFrameMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsFrameMeta.html)/[C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsFrameMeta.html)) and NvOSD_RectParams ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvOSD/NvOSD_RectParams.html)/[C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvOSD__RectParams.html)) to get label, position, etc. of bboxes. Basically, you need manipulate NvDsObjectMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsObjectMeta.html)/[C/C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsObjectMeta.html)) and NvDsFrameMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsFrameMeta.html)/[C/C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/struct__NvDsFrameMeta.html)) to get label, position, etc. of bboxes.
In C++ deepstream-app application, your code need be in analytics_done_buf_prob function.
In C++/Python deepstream-test application, your code need be in osd_sink_pad_buffer_probe/tiler_src_pad_buffer_probe function.
## ##