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Manual-Dependencies-Installation.md

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Manual installation and building of dependencies

These instructions are aimed to manually recreate a native environment similar to the one produced by the Dockerfile.

They are tested on Ubuntu 18.04 (Bionic Beaver) with Jetpack 4.4.1.

  1. Make sure these packages are installed at system level (other required packages are not listed here since they're included with Jetpack, check the Dockerfile for a complete list):
sudo apt install git, python3-pip, python3-opencv python3-libnvinfer python-gi-dev cuda-toolkit-10-2
  1. Clone this repo:
git clone <copy https or ssh url>.git
  1. Copy any .egg-info file under docker/ to the python's dist-packages dir, so that system-level installed packages are visible by Pypi:
sudo cp docker/*.egg-info /usr/lib/python3/dist-packages/
  1. Install the requirements listed on requirements.txt:
pip3 install -r requirements.txt

If any version above fails or you want to ignore the pinned versions for some reason, try:

# Only run this if you don't want to use the pinned versions
pip3 install -r requirements.in -c docker/constraints.docker

  1. Install Nvidia DeepStream: Aside from the system requirements of th previous step, you also need to install DeepStream 5.0 (no need to install Kafka protocol adaptor) and also make sure to install the corresponding python bindings for GStreamer gst-python, and for DeepStream pyds.

  2. Compile YOLOv4 plugin for DeepStream: After installing DeepStream, compile the YOLOv4 plugin for DeepStream:

cd <this repo path>/deepstream_plugin_yolov4
export CUDA_VER=10.2
make

If all went well, you should see a library libnvdsinfer_custom_impl_Yolo.so in that directory.

  1. Download TensorRT engine file from here and save it as yolo/facemask_y4tiny_1024_608_fp16.trt.

  2. Now you should be ready to run. By default, the device /dev/video0 will be used, but other devices can be set as first argument:

# Use default input camera /dev/video0
python3 maskcam_run.py

# Equivalent as above:
python3 maskcam_run.py v4l2:///dev/video0

# Process an mp4 file instead (no network functions, MQTT and static file server disabled)
python3 maskcam_run.py file:///path/to/video.mp4

# Read from Raspi2 camera using device-id
python3 maskcam_run.py argus:///0

Check the main README.md for more parameters that can be configured before running, using environment variables.