Implementation of the Vanilla CNN described in the paper Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project. http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/
Written by Ishay Tubi : ishay2b [at] gmail [dot] com https://www.linkedin.com/in/ishay2b
This software is provided as is, without any warranty, with no legal constraints. The data is provided to allow easy deployment, is it not mine, a link to the original owners of the data is provided.
=============================================== Prerequisites:
Caffe, Python, Numpy, dlib
=============================================== Environment variables
To run the code on OSX (i.e. anaconda python), where ROOT means repository main folder export PYTHONPATH=$(ROOT):$PYTHONPATH export PYTHONHOME=/Applications/anaconda # To resolve issues running python layers from command line.
=============================================== Paths needed in PYTHONPATH
CAFFE_ROOT the path to the caffe distribute folder. CAFFE_ROOT+"/python" will be added to PYTHONPATH DLIB_ROOT - Dlib’s python module - if not already in PYTHONPATH. ROOT is the git main path.
=============================================== How to run this script? use mainLoop.py
To run all steps assign STEPS with FULL_STEPS: STEPS = FULL_STEPS
Or run a partial script like this: STEPS = ['testset']
The steps needed to run:
- Calculate train data mean matrix or load already calculated trainMean.png.
- Calculate train data std matrix or load already calculated trainSTD.png.
- Create train set hdf.
- Create test set hdf.
- Train from random initialization by running this command from ROOT path, dump both stdout and error to log.txt: caffe.bin train -caffeData/solver solver_adam_vanilla.prototxt >>log.txt 2>&1
- Plot the error by parsing the log (from ROOT directory): python python/parseLog.py log.txt
- Create benchmarks once using STEPS=['createAFLW_TestSet', 'createAFW_TestSet']
- Run benchmarks test by: STEPS=['testAFW_TestSet', 'testAFLW_TestSet']
=============================================== Main functions used
BBox - generic box class with helpers. ErrorAcum - accumulates the error DataRow is a class with landmarks, image and parsed from CSV. Can accept bounding box and crop/scale to desired size. createDataRowsFromCSV - translates CSV file into a list of DataRow. Passing the CSV parser as a parameter for each format. Predictor - a wrapper for caffe network. Call predictor.preprocess() to get image subtracted by mean and divided by std image. Also returns the landmarks scaled -0.5..+0.5.