A Demo Application for NVIDIA TensorRT Pose Estimation
This application just displays pose estimation results for camera-captured video or H.264/H.265 video file with NVIDIA TensorRT pose estimation. This application also has a capability to save the results in a CSV file.
- NVIDIA Jetson Nano/TX2/AGX Xavier Developer Kit with JetPack installed
- USB web camera or MIPI CSI camera like Raspberry Pi v2 camera
- Install PyTorch and torchvision. Please refer to this page.
- Install NVIDIA-AI-IOT/torch2trt.
- Install NVIDIA-AI-IOT/trt_pose.
- Install this application.
$ git clone https://github.com/MACNICA-CLAVIS-NV/trt_pose_demo $ cd trt_pose_demo - Copy the resnet18_baseline_att_224x224_A model file to the application directory.
- Copy the human_pose.json task description file to the application directory.
First, clock up your Jetson.
$ sudo nvpmodel -m 0
$ sudo jetson_clocks
The following command starts this application.
$ python3 trt_pose_app.py [-h] [--camera CAMERA_NUM] [--width WIDTH]
[--height HEIGHT] [--fps FPS] [--qsize QSIZE] [--qinfo]
[--mjpg] [--title TITLE] [--nodrop] [--repeat] [--h265]
[--model MODEL] [--task TASK_DESC] [--csv MAX_CSV_REC]
[--csvpath CSV_PATH] [--verbose]
[SRC_FILE]
TRT Pose Demo
positional arguments:
SRC_FILE Source video file
optional arguments:
-h, --help show this help message and exit
--camera CAMERA_NUM, -c CAMERA_NUM
Camera number, use any negative integer for MIPI-CSI
--width WIDTH Capture width
--height HEIGHT Capture height
--fps FPS Capture frame rate
--qsize QSIZE Capture queue size
--qinfo If set, print queue status information
--mjpg If set, capture video in motion jpeg format
--title TITLE Window title
--nodrop If set, disable frame drop feature
--repeat If set, repeat video decoding
--h265 If set, the specified video file will be assumed as
H.265. Otherwise, assumed as H.264
--model MODEL Model weight file
--task TASK_DESC Task description file
--csv MAX_CSV_REC Maximum CSV records
--csvpath CSV_PATH Directory path to save CSV files
--verbose If set, print debug message
For MIPI-CSI camera, use any negative number as the camera number.
$ python3 trt_pose_app.py --camera -1
For USB Web camera, if you camera is detected as /dev/video1, use 1 as the camera number.
$ python3 trt_pose_app.py --camera 1
To get a CSV output, please use the --csv option with the maximum capture frames. You can specify the directory to hold the CSV file with the --csvpath option.
$ python3 trt_pose_app.py --camera 0 --csv 1000 --csvpath ./logs
To use the densenet121_baseline_att_256x256_B_epoch_160.pth pre-trained model which is also released at the resnet18_baseline_att_224x224_A model file, use the --model option.
$ python3 trt_pose_app.py --camera 0 --model densenet121_baseline_att_256x256_B_epoch_160.pth
This application can accept not only camera capture but also H.264 movie file as input. For high resolution movie file input, the --nodrop option might be needed.
$ python3 trt_pose_app.py --nodrop test.mov
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