Skip to content

Latest commit

 

History

History

nvidia_car_classification

README.md

Car Detection and Classification (Nvidia detectors and classifiers, Nvidia tracker)

The app reproduces deepstream-test2 app in the Savant framework. The pipeline detects and tracks cars, and applies car type, color and make classification models to detected cars. The results are displayed on the frames with bounding boxes, track ids and classification labels.

Preview:

Tested on platforms:

  • Nvidia Turing, Ampere
  • Nvidia Jetson Orin family

Demonstrated adapters:

  • RTSP source adapter;
  • Always-ON RTSP sink adapter.

A step-by-step tutorial.

Prerequisites

git clone https://github.com/insight-platform/Savant.git
cd Savant
git lfs pull
./utils/check-environment-compatible

Note: Ubuntu 22.04 runtime configuration guide helps to configure the runtime to run Savant pipelines.

Build Engines

The demo uses models that are compiled into TensorRT engines the first time the demo is run. This takes time. Optionally, you can prepare the engines before running the demo by using the command:

# you are expected to be in Savant/ directory

./scripts/run_module.py --build-engines samples/nvidia_car_classification/module.yml

Run Demo

# you are expected to be in Savant/ directory

# if x86
docker compose -f samples/nvidia_car_classification/docker-compose.x86.yml up

# if Jetson
docker compose -f samples/nvidia_car_classification/docker-compose.l4t.yml up

# open 'rtsp://127.0.0.1:554/stream/nvidia-sample-processed' in your player
# or visit 'http://127.0.0.1:888/stream/nvidia-sample-processed/' (LL-HLS)

# Ctrl+C to stop running the compose bundle

Performance Measurement

Download the video file to the data folder. For example:

# you are expected to be in Savant/ directory

mkdir -p data && curl -o data/deepstream_sample_720p.mp4 \
   https://eu-central-1.linodeobjects.com/savant-data/demo/deepstream_sample_720p.mp4

Now you are ready to run the performance benchmark with the following command:

./samples/nvidia_car_classification/run_perf.sh

Note: Change the value of the DATA_LOCATION variable in the run_perf.sh script if you changed the video.