This project uses Python and OpenVINO toolkit for counting the number of people in a given frame.
OpenVINO toolkit is used for faster inference.
It uses a MobileNetV2-like backbone model.
Article related to this project can be found here -
https://towardsdatascience.com/introduction-to-openvino-897e705a1f0a
- OpenVINO Toolkit Setup
- Directory Structure
- Executing the Program
- Docker
- References
- Usage
https://software.intel.com/en-us/openvino-toolkit/choose-download
https://software.intel.com/en-us/openvino-toolkit/documentation/get-started
.
├── inference.py
├── output_snapshots
├── people_count.py
└── resources
└── config.json
2 directories, 3 files
people_count.py
- It is the python program which counts the number of people in a given frame.
inference.py
- It is a python program which contains the OpenVINO specific code.
config.json
- It is a json file which determines the source of the video. You can specify the url to a video file or your webcam id.
Run the following command to activate the OpenVINO environment.
source /opt/intel/openvino/bin/setupvars.sh -pyver 3.6
.
Run the python file by passing in the required command line arguments.
LINUX
python3 people_count.py -m path_to_model/person-detection-retail-0013/FP32/person-detection-retail-0013.xml -l /opt/intel/openvino/inference_engine/lib/intel64/libcpu_extension_sse4.so -d CPU -pt 0.7
WINDOWS
python3 people_count.py -m path_to_model/person-detection-retail-0013/FP32/person-detection-retail-0013.xml -l path_to_libCPU.dll -d CPU -pt 0.7
Pass the path of the model to the -m parameter and pass the path of the library based on the device using the -l parameter.
If you are using Linux, you will find a .so file and If you are using Windows then you will find a .dll file.
There are separate library files for separate devices.
For example, If you are using Linux then you will encounter these library files, among others
libcpu_extension_sse4.so
libHeteroPlugin.so
libmyriadPlugin.so
NOTE - Make sure that you have properly installed and configured the OpenVINO toolkit before running the python program.
To run this program in a docker container. Use the following command.
docker pull alpha10/open-vino-docker
This project borrows heavily from store-aisle-monitor-python project from the Intel IOT Devkit.
Store Aisle Monitor - https://github.com/intel-iot-devkit/store-aisle-monitor-python
Intel IOT Devkit - https://github.com/intel-iot-devkit
Intel allows you to use or modify their code if you meet the below mentioned conditions.
If you want to use any code from this project or from any project of the Intel iot-devkit then you should crefully read the below menioned conditions.
Copyright (c) 2018 Intel Corporation.
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