deployment/Detection task/README.md

Exportable code

Exportable code is a .zip archive that contains simple demo to get and visualize result of model inference.

Structure of generated zip

• README.md
• model
  • model.xml
  • model.bin
  • config.json
• python
  • model_wrappers (Optional)
    • __init__.py
    • model_wrappers required to run demo
  • LICENSE
  • demo.py
  • requirements.txt

NOTE: Zip archive contains model_wrappers when ModelAPI has no appropriate standard model wrapper for the model.

Prerequisites

• Python 3.8
• Git

Install requirements to run demo

1. Install prerequisites. You may also need to install pip. For example, on Ubuntu execute the following command to get pip installed:

   sudo apt install python3-pip
   

2. Create clean virtual environment:

   One of the possible ways for creating a virtual environment is to use virtualenv:

   python -m pip install virtualenv
   python -m virtualenv <directory_for_environment>
   

   Before starting to work inside virtual environment, it should be activated:

   On Linux and macOS:

   source <directory_for_environment>/bin/activate
   

   On Windows:

   .\<directory_for_environment>\Scripts\activate
   

   Please make sure that the environment contains wheel by calling the following command:

   python -m pip install wheel
   

   NOTE: On Linux and macOS, you may need to type python3 instead of python.

3. Install requirements in the environment:

   python -m pip install -r requirements.txt
   

Usecase

1. Running the demo.py application with the -h option yields the following usage message:

   usage: demo.py [-h] -i INPUT -m MODELS [MODELS ...] [-it {sync,async}] [-l] [--no_show] [-d {CPU,GPU}] [--output OUTPUT]
   
   Options:
   -h, --help            Show this help message and exit.
   -i INPUT, --input INPUT
                           Required. An input to process. The input must be a single image, a folder of images, video file or camera id.
   -m MODELS [MODELS ...], --models MODELS [MODELS ...]
                           Optional. Path to directory with trained model and configuration file. If you provide several models you will start the task chain pipeline with the provided models in the order in which they were specified. Default value points to deployed model folder '../model'.
   -it {sync,async}, --inference_type {sync,async}
                           Optional. Type of inference for single model.
   -l, --loop            Optional. Enable reading the input in a loop.
   --no_show             Optional. Disables showing inference results on UI.
   -d {CPU,GPU}, --device {CPU,GPU}
                           Optional. Device to infer the model.
   --output OUTPUT       Optional. Output path to save input data with predictions.
   

2. As a model parameter the default value ../model will be used. Or you can specify the other path to the model directory from generated zip. You can pass as input a single image, a folder of images, a video file, or a web camera id. So you can use the following command to do inference with a pre-trained model:

   python3 demo.py -i <path_to_video>/inputVideo.mp4
   

   You can press Q to stop inference during demo running.

   NOTE: If you provide a single image as input, the demo processes and renders it quickly, then exits. To continuously visualize inference results on the screen, apply the --loop option, which enforces processing a single image in a loop. In this case, you can stop the demo by pressing Q button or killing the process in the terminal (Ctrl+C for Linux).

   NOTE: Default configuration contains info about pre- and post processing for inference and is guaranteed to be correct. Also you can change config.json that specifies the confidence threshold and color for each class visualization, but any changes should be made with caution.

3. To save inferenced results with predictions on it, you can specify the folder path, using --output. It works for images, videos, image folders and web cameras. To prevent issues, do not specify it together with a --loop parameter.

   python3 demo.py \
      --input <path_to_image>/inputImage.jpg \
      --models ../model \
      --output resulted_images
   

4. To run a demo on a web camera, you need to know its ID. You can check a list of camera devices by running this command line on Linux system:

   sudo apt-get install v4l-utils
   v4l2-ctl --list-devices
   

   The output will look like this:

   Integrated Camera (usb-0000:00:1a.0-1.6):
      /dev/video0
   

   After that, you can use this /dev/video0 as a camera ID for --input.

Troubleshooting

1. If you have access to the Internet through the proxy server only, please use pip with proxy call as demonstrated by command below:

   python -m pip install --proxy http://<usr_name>:<password>@<proxyserver_name>:<port#> <pkg_name>
   

2. If you use Anaconda environment, you should consider that OpenVINO has limited Conda support for Python 3.6 and 3.7 versions only. But the demo package requires python 3.8. So please use other tools to create the environment (like venv or virtualenv) and use pip as a package manager.

3. If you have problems when you try to use pip install command, please update pip version by following command:

   python -m pip install --upgrade pip