Vehicle detection using Haar Cascades on Clouds.

This project deploys the Vehicle Detection Application on multiple Chameleon clouds using Ansible Playbook. Vehicle Detection Application uses haar-cascade cars.xml, a pre-trained classifier from the openCV library, trained with 526 rear-end images of cars. The application is deployed on Chameleon clouds with the help of Cloudmesh client, which is a cloud VM management tool for dynamic management of clouds on VMs. The Software Stack required for the deployement of the application on VMs is done by using Ansible, which is an open source platform for configuration management, task automation, and application deployement.

Directory Structure:

• Benchmarking : The directory conatains codes and images directories

  • codes contains the shell scripts used for the benchmarking on:
    • deployement
    • latency
    • workload
  • images contains the images of all the benchmarking results obtained.
    • latency
    • worklaod

• Code directory contains the roles, inventory.txt and playbook.yml

  • roles contains the softwares required for software stack to be downloaded for the project including:

    • git
    • upgrade
    • python
    • openCV
    • vehicle detection

  • inventory.txt includes the ip address of the uploaded VM on chameleon and the username, which should be modified according to ip address provided.

  • playbook.yaml is the ansible script which downlods the software stack on the machine desired.

How to run:

• Update the inventory.txt file with the floating IP assigned to you.
• Run the myScript.sh script present at 'benchmarking/code/deployment'.

The Report.pdf file contains the various observations made and benchmarking carried out during the course of the project.

References:

1. Ansible: https://www.ansible.com/
2. Cloudmesh: https://github.com/cloudmesh/cloudmesh
3. Basic local Vechicle detection application: https://github.com/andrewssobral/vehicle_detection_haarcascades
4. Open-CV Ansible: https://github.com/EDITD/ansible-opencv