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Prepares dict for choice of training file. The user shall decide what he wants to detect

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julien Lengrand-Lambert
2012-04-24 08:08:25 +02:00
parent e3ffde9dc1
commit a26670a0ac
4 changed files with 39 additions and 31 deletions
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37
FaceParams.py
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@@ -10,13 +10,44 @@ class FaceParams(object):
'''
Simple class used to store parameters used for Face detection
'''
def __init__(self, xml_folder, i_scale=2, h_scale=1.2, h_flags=0, mn=2):
def __init__(self, xml_folder, training_type, i_scale=2, h_scale=1.2, h_flags=0, mn=2):
'''
Constructor
'''
# Creates dictionary for all types of training files
# some of them shall never be used. Perhaps would it be good to lower the dict size, or hide some of them
training_types = {#eyes
'eyes':"haarcascade_eye",
'glasses':"haarcascade_eye_tree_eyeglasses",
'left eye splits':"haarcascade_lefteye_2splits",
'eye pair big':"haarcascade_mcs_eyepair_big",
'eye pair small':"haarcascade_mcs_eyepair_small",
'left eye':"haarcascade_mcs_lefteye",
'right eye':"haarcascade_mcs_righteye",
'right eye splits':"haarcascade_righteye_2splits",
# frontal faces
'frontal face alt':"haarcascade_frontalface_alt",
'frontal face alt2':"haarcascade_frontalface_alt2",
'frontal face':"haarcascade_frontalface_default",
#profile face
'profile face':"haarcascade_profileface",
#body
'full body':"haarcascade_fullbody",
'lower body':"haarcascade_lowerbody",
'upper body mcs':"haarcascade_mcs_upperbody",
'upper body':"haarcascade_upperbody",
#ear
'left ear':"haarcascade_mcs_leftear",
'right ear':"haarcascade_mcs_rightear",
#mouth
'mouth':"haarcascade_mcs_mouth",
#nose
'nose':"haarcascade_mcs_nose"
}
# postpend .xml
cascade_name = training_types[training_type] + ".xml"
# Setting up some default parameters for Face Detection
self.face_cascade = cv.Load(os.path.join(xml_folder, "haarcascade_frontalface_alt.xml"))
self.eye_cascade = cv.Load(os.path.join(xml_folder, "haarcascade_eye.xml"))
self.face_cascade = cv.Load(os.path.join(xml_folder, cascade_name))
# To be defined more precisely
self.min_size = (20,20)

3
facemovie.py
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@@ -25,7 +25,8 @@ class FaceMovie(object):
self.guys = [] # List of pictures in source folder
# Setting up some default parameters for Face Detection
self.face_params = FaceParams(self.params_source)
training_type = "frontal face alt"
self.face_params = FaceParams(self.params_source, training_type)
# Position of the center in output images
self.x_center = 0

2
main_file.py
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@@ -15,7 +15,7 @@ if __name__ == '__main__':
in_fo = raw_input("indicate input folder:")
out_fo = raw_input("indicate output folder:")
par_fo = raw_input("indicate parameters folder:")
elif len(sys.argv) == 2:
elif len(sys.argv) == 4:
print "I trust your inputs!"
[in_fo, out_fo, par_fo] = sys.argv[1, :]
else :

28
test_script.py
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@@ -3,29 +3,5 @@ Created on 16 avr. 2012
@author: jll
'''
import os
import cv
from facemovie import *
# quick and dirty tests
root_fo = "C:\Users\jll\perso\workspace\FaceMovie"
in_fo = os.path.join(root_fo, "input\Axel")
out_fo = os.path.join(root_fo, "output")
par_fo = os.path.join(root_fo, "haarcascades")
my_movie = FaceMovie(in_fo, out_fo, par_fo)
my_movie.list_guys()
my_movie.search_faces()
# I want to change images so that all faces have the same size
my_movie.normalize_faces()
# I want to know the size of the output frame, knowing initial conditions
my_movie.find_out_dims()
# choose your final step
#my_movie.show_faces(1000)
#my_movie.save_faces("output")
my_movie.save_movie("output")
print "Facemovie finished !"
my_dict = {'a b c':1, 'b':2, 'c':3}
print my_dict['a b c']