589 lines
18 KiB
Markdown
589 lines
18 KiB
Markdown
# DeepStream-Yolo
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NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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### Future updates (comming soon, stay tuned)
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* New documentation for multiple models
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* DeepStream tutorials
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* Native PP-YOLO support
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* Models benchmark
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* GPU NMS
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* Dynamic batch-size
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### Improvements on this repository
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* Darknet CFG params parser (it doesn't need to edit nvdsparsebbox_Yolo.cpp or another file for native models)
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* Support for new_coords, beta_nms and scale_x_y params
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* Support for new models
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* Support for new layers
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* Support for new activations
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* Support for convolutional groups
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* Support for INT8 calibration
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* Support for non square models
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* **Support for implicit and channel layers (YOLOR)**
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* **YOLOv5 6.0 native support**
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* **Initial YOLOR native support**
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##
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### Getting started
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* [Requirements](#requirements)
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* [Tested models](#tested-models)
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* [dGPU installation](#dgpu-installation)
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* [Basic usage](#basic-usage)
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* [YOLOv5 usage](#yolov5-usage)
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* [YOLOR usage](#yolor-usage)
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* [INT8 calibration](#int8-calibration)
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* [Using your custom model](docs/customModels.md)
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##
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### Requirements
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* [Ubuntu 18.04](https://releases.ubuntu.com/18.04.6/)
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* [CUDA 11.4.3](https://developer.nvidia.com/cuda-toolkit)
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* [TensorRT 8.0 GA (8.0.1)](https://developer.nvidia.com/tensorrt)
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* [cuDNN >= 8.2](https://developer.nvidia.com/cudnn)
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* [NVIDIA Driver >= 470.63.01](https://www.nvidia.com.br/Download/index.aspx)
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* [NVIDIA DeepStream SDK 6.0](https://developer.nvidia.com/deepstream-sdk)
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* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
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**For YOLOv5 and YOLOR**:
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* [PyTorch >= 1.7.0](https://pytorch.org/get-started/locally/)
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##
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### Tested models
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* [YOLOR-CSP](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp.cfg) [[pt]](https://drive.google.com/file/d/1ZEqGy4kmZyD-Cj3tEFJcLSZenZBDGiyg/view?usp=sharing)
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* [YOLOR-CSP*](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp.cfg) [[pt]](https://drive.google.com/file/d/1OJKgIasELZYxkIjFoiqyn555bcmixUP2/view?usp=sharing)
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* [YOLOR-CSP-X](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp_x.cfg) [[pt]](https://drive.google.com/file/d/1L29rfIPNH1n910qQClGftknWpTBgAv6c/view?usp=sharing)
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* [YOLOR-CSO-X*](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp_x.cfg) [[pt]](https://drive.google.com/file/d/1NbMG3ivuBQ4S8kEhFJ0FIqOQXevGje_w/view?usp=sharing)
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* [YOLOv5 6.0](https://github.com/ultralytics/yolov5) [[pt]](https://github.com/ultralytics/yolov5/releases/tag/v6.0)
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* [YOLOv4x-Mish](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights)]
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* [YOLOv4-CSP](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights)]
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* [YOLOv4](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights)]
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* [YOLOv4-Tiny](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights)]
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* [YOLOv3-SPP](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-spp.cfg)] [[weights](https://pjreddie.com/media/files/yolov3-spp.weights)]
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* [YOLOv3](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg)] [[weights](https://pjreddie.com/media/files/yolov3.weights)]
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* [YOLOv3-Tiny-PRN](https://github.com/WongKinYiu/PartialResidualNetworks) [[cfg](https://raw.githubusercontent.com/WongKinYiu/PartialResidualNetworks/master/cfg/yolov3-tiny-prn.cfg)] [[weights](https://github.com/WongKinYiu/PartialResidualNetworks/raw/master/model/yolov3-tiny-prn.weights)]
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* [YOLOv3-Tiny](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-tiny.cfg)] [[weights](https://pjreddie.com/media/files/yolov3-tiny.weights)]
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* [YOLOv3-Lite](https://github.com/dog-qiuqiu/MobileNet-Yolo) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/MobileNet-Yolo/master/MobileNetV2-YOLOv3-Lite/COCO/MobileNetV2-YOLOv3-Lite-coco.cfg)] [[weights](https://github.com/dog-qiuqiu/MobileNet-Yolo/raw/master/MobileNetV2-YOLOv3-Lite/COCO/MobileNetV2-YOLOv3-Lite-coco.weights)]
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* [YOLOv3-Nano](https://github.com/dog-qiuqiu/MobileNet-Yolo) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/MobileNet-Yolo/master/MobileNetV2-YOLOv3-Nano/COCO/MobileNetV2-YOLOv3-Nano-coco.cfg)] [[weights](https://github.com/dog-qiuqiu/MobileNet-Yolo/raw/master/MobileNetV2-YOLOv3-Nano/COCO/MobileNetV2-YOLOv3-Nano-coco.weights)]
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* [YOLO-Fastest 1.1](https://github.com/dog-qiuqiu/Yolo-Fastest) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/Yolo-Fastest/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1-xl.cfg)] [[weights](https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1-xl.weights)]
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* [YOLO-Fastest-XL 1.1](https://github.com/dog-qiuqiu/Yolo-Fastest) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/Yolo-Fastest/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1.cfg)] [[weights](https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1.weights)]
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* [YOLOv2](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2.cfg)] [[weights](https://pjreddie.com/media/files/yolov2.weights)]
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* [YOLOv2-Tiny](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg)] [[weights](https://pjreddie.com/media/files/yolov2-tiny.weights)]
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##
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### dGPU installation
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To install the DeepStream on dGPU (x86 platform), without docker, we need to do some steps to prepare the computer.
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<details><summary>Open</summary>
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#### 1. Disable Secure Boot in BIOS
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<details><summary>If you are using a laptop with newer Intel/AMD processors, please update the kernel to newer version.</summary>
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```
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wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100_5.11.0-051100.202102142330_all.deb
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wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
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wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-image-unsigned-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
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wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-modules-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
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sudo dpkg -i *.deb
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sudo reboot
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```
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</details>
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#### 2. Install dependencies
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```
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sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake
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sudo apt-get install python3 python3-dev python3-pip
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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
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sudo apt-get install linux-headers-$(uname -r)
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```
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**NOTE**: Install DKMS if you are using the default Ubuntu kernel
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```
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sudo apt-get install dkms
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```
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**NOTE**: Purge all NVIDIA driver, CUDA, etc.
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#### 3. Disable Nouveau
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```
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sudo nano /etc/modprobe.d/blacklist-nouveau.conf
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```
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* Add
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```
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blacklist nouveau
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options nouveau modeset=0
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```
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* Run
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```
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sudo update-initramfs -u
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```
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#### 4. Reboot the computer
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```
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sudo reboot
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```
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#### 5. Download and install NVIDIA Driver without xconfig
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```
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wget https://us.download.nvidia.com/tesla/470.82.01/NVIDIA-Linux-x86_64-470.82.01.run
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sudo sh NVIDIA-Linux-x86_64-470.82.01.run
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```
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**NOTE**: If you are using default Ubuntu kernel, enable the DKMS during the installation. Else, you can skip this driver installation and install the NVIDIA driver from CUDA runfile (next step).
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#### 6. Download and install CUDA 11.4.3 without NVIDIA Driver
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```
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wget https://developer.download.nvidia.com/compute/cuda/11.4.3/local_installers/cuda_11.4.3_470.82.01_linux.run
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sudo sh cuda_11.4.3_470.82.01_linux.run
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```
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* Export environment variables
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```
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nano ~/.bashrc
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```
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* Add
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```
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export PATH=/usr/local/cuda-11.4/bin${PATH:+:${PATH}}
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export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64\${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
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```
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* Run
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```
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source ~/.bashrc
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sudo ldconfig
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```
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**NOTE**: If you are using a laptop with NVIDIA Optimius, run
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```
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sudo apt-get install nvidia-prime
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sudo prime-select nvidia
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```
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#### 7. Download from [NVIDIA website](https://developer.nvidia.com/nvidia-tensorrt-8x-download) and install the TensorRT 8.0 GA (8.0.1)
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```
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echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda-repo.list
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wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
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sudo apt-key add 7fa2af80.pub
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sudo apt-get update
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sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb
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sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub
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sudo apt-get update
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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
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```
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#### 8. Download from [NVIDIA website](https://developer.nvidia.com/deepstream-sdk) and install the DeepStream SDK 6.0
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```
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sudo apt-get install ./deepstream-6.0_6.0.0-1_amd64.deb
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rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin
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```
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#### 9. Reboot the computer
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```
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sudo reboot
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```
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</details>
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##
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### Basic usage
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#### 1. Download the repo
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```
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git clone https://github.com/marcoslucianops/DeepStream-Yolo.git
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cd DeepStream-Yolo
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```
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#### 2. Download cfg and weights files from your model and move to DeepStream-Yolo folder
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#### 3. Compile lib
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* x86 platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* Jetson platform
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```
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CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
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```
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#### 4. Edit config_infer_primary.txt for your model (example for YOLOv4)
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```
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[property]
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...
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# 0=RGB, 1=BGR, 2=GRAYSCALE
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model-color-format=0
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# CFG
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custom-network-config=yolov4.cfg
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# Weights
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model-file=yolov4.weights
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# Generated TensorRT model (will be created if it doesn't exist)
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model-engine-file=model_b1_gpu0_fp32.engine
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# Model labels file
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labelfile-path=labels.txt
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# Batch size
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batch-size=1
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# 0=FP32, 1=INT8, 2=FP16 mode
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network-mode=0
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# Number of classes in label file
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num-detected-classes=80
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...
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[class-attrs-all]
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# CONF_THRESH
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pre-cluster-threshold=0.25
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```
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#### 5. Run
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```
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deepstream-app -c deepstream_app_config.txt
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```
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**NOTE**: If you want to use YOLOv2 or YOLOv2-Tiny models, change the deepstream_app_config.txt file before run it
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```
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...
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[primary-gie]
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enable=1
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gpu-id=0
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gie-unique-id=1
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nvbuf-memory-type=0
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config-file=config_infer_primary_yoloV2.txt
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```
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**NOTE**: The config_infer_primary.txt file uses cluster-mode=4 and NMS = 0.45 (via code) when beta_nms isn't available (when beta_nms is available, NMS = beta_nms), while the config_infer_primary_yoloV2.txt file uses cluster-mode=2 and nms-iou-threshold=0.45 to set NMS.
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##
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### YOLOv5 usage
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#### 1. Copy gen_wts_yoloV5.py from DeepStream-Yolo/utils to [ultralytics/yolov5](https://github.com/ultralytics/yolov5) folder
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#### 2. Open the ultralytics/yolov5 folder
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#### 3. Download pt file from [ultralytics/yolov5](https://github.com/ultralytics/yolov5/releases/tag/v6.0) website (example for YOLOv5n)
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```
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wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5n.pt
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```
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#### 4. Generate cfg and wts files (example for YOLOv5n)
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```
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python3 gen_wts_yoloV5.py -w yolov5n.pt
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```
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#### 5. Copy generated cfg and wts files to DeepStream-Yolo folder
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#### 6. Open DeepStream-Yolo folder
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#### 7. Compile lib
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* x86 platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* Jetson platform
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```
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CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
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```
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#### 8. Edit config_infer_primary_yoloV5.txt for your model (example for YOLOv5n)
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```
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[property]
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...
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# 0=RGB, 1=BGR, 2=GRAYSCALE
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model-color-format=0
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# CFG
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custom-network-config=yolov5n.cfg
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# WTS
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model-file=yolov5n.wts
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# Generated TensorRT model (will be created if it doesn't exist)
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model-engine-file=model_b1_gpu0_fp32.engine
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# Model labels file
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labelfile-path=labels.txt
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# Batch size
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batch-size=1
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# 0=FP32, 1=INT8, 2=FP16 mode
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network-mode=0
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# Number of classes in label file
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num-detected-classes=80
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...
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[class-attrs-all]
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# CONF_THRESH
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pre-cluster-threshold=0.25
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```
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#### 8. Change the deepstream_app_config.txt file
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```
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...
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[primary-gie]
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enable=1
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gpu-id=0
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gie-unique-id=1
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nvbuf-memory-type=0
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config-file=config_infer_primary_yoloV5.txt
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```
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#### 9. Run
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```
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deepstream-app -c deepstream_app_config.txt
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```
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**NOTE**: For YOLOv5 P6 or custom models, check the gen_wts_yoloV5.py args and use them according to your model
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* Input weights (.pt) file path **(required)**
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```
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-w or --weights
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```
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* Input cfg (.yaml) file path
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```
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-c or --yaml
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```
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* Model width **(default = 640 / 1280 [P6])**
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```
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-mw or --width
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```
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* Model height **(default = 640 / 1280 [P6])**
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```
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-mh or --height
|
|
```
|
|
|
|
* Model channels **(default = 3)**
|
|
|
|
```
|
|
-mc or --channels
|
|
```
|
|
|
|
* P6 model
|
|
|
|
```
|
|
--p6
|
|
```
|
|
|
|
##
|
|
|
|
### YOLOR usage
|
|
|
|
**NOTE**: For now, available only for YOLOR-CSP, YOLOR-CSP*, YOLOR-CSP-X and YOLOR-CSP-X*.
|
|
|
|
#### 1. Copy gen_wts_yolor.py from DeepStream-Yolo/utils to [yolor](https://github.com/WongKinYiu/yolor) folder
|
|
|
|
#### 2. Open the yolor folder
|
|
|
|
#### 3. Download pt file from [yolor](https://github.com/WongKinYiu/yolor) website
|
|
|
|
#### 4. Generate wts file (example for YOLOR-CSP)
|
|
|
|
```
|
|
python3 gen_wts_yolor.py -w yolor_csp.pt -c cfg/yolor_csp.cfg
|
|
```
|
|
|
|
#### 5. Copy cfg and generated wts files to DeepStream-Yolo folder
|
|
|
|
#### 6. Open DeepStream-Yolo folder
|
|
|
|
#### 7. Compile lib
|
|
|
|
* x86 platform
|
|
|
|
```
|
|
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
* Jetson platform
|
|
|
|
```
|
|
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
#### 8. Edit config_infer_primary_yolor.txt for your model (example for YOLOR-CSP)
|
|
|
|
```
|
|
[property]
|
|
...
|
|
# 0=RGB, 1=BGR, 2=GRAYSCALE
|
|
model-color-format=0
|
|
# CFG
|
|
custom-network-config=yolor_csp.cfg
|
|
# WTS
|
|
model-file=yolor_csp.wts
|
|
# Generated TensorRT model (will be created if it doesn't exist)
|
|
model-engine-file=model_b1_gpu0_fp32.engine
|
|
# Model labels file
|
|
labelfile-path=labels.txt
|
|
# Batch size
|
|
batch-size=1
|
|
# 0=FP32, 1=INT8, 2=FP16 mode
|
|
network-mode=0
|
|
# Number of classes in label file
|
|
num-detected-classes=80
|
|
...
|
|
[class-attrs-all]
|
|
# CONF_THRESH
|
|
pre-cluster-threshold=0.25
|
|
```
|
|
|
|
#### 8. Change the deepstream_app_config.txt file
|
|
|
|
```
|
|
...
|
|
[primary-gie]
|
|
enable=1
|
|
gpu-id=0
|
|
gie-unique-id=1
|
|
nvbuf-memory-type=0
|
|
config-file=config_infer_primary_yolor.txt
|
|
```
|
|
|
|
#### 9. Run
|
|
|
|
```
|
|
deepstream-app -c deepstream_app_config.txt
|
|
```
|
|
|
|
##
|
|
|
|
### INT8 calibration
|
|
|
|
#### 1. Install OpenCV
|
|
|
|
```
|
|
sudo apt-get install libopencv-dev
|
|
```
|
|
|
|
#### 2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support
|
|
|
|
* x86 platform
|
|
|
|
```
|
|
cd DeepStream-Yolo
|
|
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
* Jetson platform
|
|
|
|
```
|
|
cd DeepStream-Yolo
|
|
CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
#### 3. For COCO dataset, download the [val2017](https://drive.google.com/file/d/1gbvfn7mcsGDRZ_luJwtITL-ru2kK99aK/view?usp=sharing), extract, and move to DeepStream-Yolo folder
|
|
|
|
##### Select 1000 random images from COCO dataset to run calibration
|
|
|
|
```
|
|
mkdir calibration
|
|
```
|
|
|
|
```
|
|
for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do \
|
|
cp ${jpg} calibration/; \
|
|
done
|
|
```
|
|
|
|
##### Create the calibration.txt file with all selected images
|
|
|
|
```
|
|
realpath calibration/*jpg > calibration.txt
|
|
```
|
|
|
|
##### Set environment variables
|
|
|
|
```
|
|
export INT8_CALIB_IMG_PATH=calibration.txt
|
|
export INT8_CALIB_BATCH_SIZE=1
|
|
```
|
|
|
|
##### Change config_infer_primary.txt file
|
|
|
|
```
|
|
...
|
|
model-engine-file=model_b1_gpu0_fp32.engine
|
|
#int8-calib-file=calib.table
|
|
...
|
|
network-mode=0
|
|
...
|
|
```
|
|
|
|
* To
|
|
|
|
```
|
|
...
|
|
model-engine-file=model_b1_gpu0_int8.engine
|
|
int8-calib-file=calib.table
|
|
...
|
|
network-mode=1
|
|
...
|
|
```
|
|
|
|
##### Run
|
|
|
|
```
|
|
deepstream-app -c deepstream_app_config.txt
|
|
```
|
|
|
|
**NOTE**: NVIDIA recommends at least 500 images to get a good accuracy. In this example I used 1000 images to get better accuracy (more images = more accuracy). Higher INT8_CALIB_BATCH_SIZE values will increase the accuracy and calibration speed. Set it according to you GPU memory. This process can take a long time.
|
|
|
|
##
|
|
|
|
### 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 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.
|
|
|
|
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.
|
|
|
|
##
|
|
|
|
My projects: https://www.youtube.com/MarcosLucianoTV (new videos and tutorials comming soon)
|