Examples of human detection:
----------------------------
(samples are from the ETHZ dataset)
- single file:
detectorPLS.exe -d -c Config.hd.INRIA.64x128.2s.txt -i input/humans/image_00000696_0.png -o ./  -b -s 
(detect humans in input/humans/image_00000696_0.png and write the result to the current dir)


- multiple files in a directory:
detectorPLS.exe -d -c Config.hd.INRIA.64x128.2s.txt -i input/humans -o output/ -b -s
(detect humans from images in input/humans, write result to output directory)





Note: if you don't want to run software to learn the PLS models for faces, you 
can copy models previously learned from directory 'training/faces/models' to the 
current directory. Doing that, you can skip steps 1 and 2 below.


Example to learn a new model for faces without retraining (about 1 minute):
---------------------------------------------------------------------------
(positive samples from Caltech dataset, negative samples from INRIA dataset):

-> 1st: learn the PLS model for the data:
detectorPLS.exe -t -c ConfigLearnFaces.txt -n training/faces/negative/ -p training/faces/positive/ -m Learned.Faces -I 0 -M 5 

-> 2nd: create a configuration file named ConfigFace.txt (used to execute face detection):
As result of the previous step, you'll have a file called Learned.Faces.ret00.yml in the current directory.
Create a file called Config.fd.Caltech.32x42.1s.txt with the following lines (don't forget the line with the last #):

-- cut here ---
#
model <Face, 0, 0.999, Learned.Faces.ret00.yml>
scale <Face, 42, 200, 0.12, 0.1851, 1.15>

#
-- cut here ---

-> 3nd: execute face detection:
detectorPLS.exe -d -c Config.fd.Caltech.32x42.1s  -i input/faces -o output/ -b -s





Example to learn a new model for faces with retraining (about 20 minutes):
--------------------------------------------------------------------------
-> 1st: learn the PLS model for the data:
detectorPLS.exe -t -c ConfigLearnFaces.txt -n training/faces/negative/ -p training/faces/positive/ -m Learned.Faces -T 0.01 -I 1 -M 5 -R 3000

-> 2nd: create a configuration file named ConfigFace.txt:
As result of the previous step, you'll have two files called Learned.Faces.ret??.yml in the current directory.
Create a file called ConfigFace.Ret.txt with the following lines (don't forget the line with the last #):

-- cut here ---
#
model <Face, 0, 0.999, Learned.Faces.ret01.yml>
scale <Face, 42, 200, 0.12, 0.1851, 1.15>

#
-- cut here ---

-> 3nd: execute face detection:
detectorPLS.exe -d -c ConfigFace.Ret.txt  -i input/faces -o output/ -b -s