Face detection using the Cambridge face tracker and Node.js.

Building the project

1. Install zeromq (used for communicating between Node.js and the CLM framework).
   On OSX (assuming homebrew installed): brew update && brew install zeromq
   On Linux: sudo apt-get install libzmq3-dev

2. Install OpenCV 2.X (can be omitted once node-opencv upgrades to OpenCV 3+).
   On OSX: brew install opencv
   On Linux: follow the instructions here.

3. Make sure you have node (v0.10.29 recommended) and npm (usually comes with node) properly installed. If not, follow the instructions here.
   Note: Although not required, it's recommended to use nvm to manage different versions of node on the same machine. Here's a good tutorial for setting up on linux.

4. Clone the repository (or download zip): git clone https://github.com/erasaur/face-detect.git

5. Install required node packages:

   cd <path to face detect>
   npm install
   

6. The CLM framework depends on cmake, OpenCV 3.0.0 (or newer), tbb, and boost; you will need to install these to build the project. On OSX:

   brew update
   brew tap homebrew/science
   brew install opencv3
   brew install tbb
   brew install boost
   

   For Windows and Linux, you can follow the instructions provided with the CLM framework to download required dependencies.

7. For OSX, download zeromq C++ bindings and include it in the header search path. You can do that by moving the file into /usr/local/include.

8. Install protobuf (version 2.X).
   On OXS: brew install protobuf
   On Linux: follow the instructions here.

9. Assuming all necessary components have been installed, build the project by running:

   cd <path to face detect>
   cmake -D BUILD_TYPE=RELEASE .
   make -j2
   

Running the project

cd <path to face detect>
./run.sh

Once the server starts listening for connections, visit https://localhost:3000 to begin face detection.