We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic and infrastructural problems.

Our goal is to democratize computer vision; one should not have to be a computer vision, deep learning, and distributed computing expert to have access to state-of-the-art distributed computer vision algorithms. We provide access to state-of-art distributed computer vision algorithms as a cloud service through Web Interface & APIs. Researchers, Students and Developers will be able to access these distributed computer vision algorithms and the computation power through small number of click and minimal lines of code.

To setup project cloudcv on your local machine, you need install docker first. After installing docker on your machine, just follow the instructions given in the next section.

1. Run the following git clone (specify a directory of your choosing if you like):

    git clone https://github.com/batra-mlp-lab/CloudCV.git cloudcv
   

2. Run virtualenv on the git cloned directory to setup the Python virtual environment:

    virtualenv cloudcv
   

3. cd into the name of the directory into which you cloned the git repository

    cd cloudcv
   

4. Activate the virtual environment(it is recommended to use virtual environment):

    source bin/activate
   

5. Change directory to Docker and run the bash script to create Docker containers:

    cd Docker && ./build.sh
   

6. Change directory to Docker and run the bash script to create Docker containers:

    ./run_server.sh run 80 443
   

When the image building completes then you can visit 127.0.0.1 and check if CloudCV server is running or not.

7. Now, for setting up workers, just run the command:

    ./worker-cpu
   

8. Now, visit http://127.0.0.1 in your browser and you should be all set.

• Whenever you want to stop the docker containers, then run the command ./stopContainer

• To remove all the images, run the command docker rm $(docker ps -a -q)

• To make yourself familiar with the codebase, check the file DirectoryDocumentation.txt

• For any other queries, open issues or you can chat with developers at our gitter channel.

• Official Documentation available at this link.