def __init__(self, face_dir="", nonface_dir="", train=True, limit=30):
#tot_samples = 0
self.Face = ImageSet(face_dir, sampleNum=POSITIVE_SAMPLE)
self.nonFace = ImageSet(nonface_dir, sampleNum=NEGATIVE_SAMPLE)
tot_samples = self.Face.sampleNum + self.nonFace.sampleNum
self.classifier = AdaBoost
self.haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
if os.path.isfile(FEATURE_FILE_TRAINING + ".npy"):
self._mat = numpy.load(FEATURE_FILE_TRAINING + ".npy")
else:
if DEBUG_MODEL is True:
self._mat = numpy.zeros((self.haar.featuresNum, tot_samples))
for i in xrange(self.Face.sampleNum):
print(i)
featureVec = self.haar.calFeatureForImg(
self.Face.images[i])
for j in xrange(self.haar.featuresNum):
self._mat[j][i] = featureVec[j]
for i in xrange(self.nonFace.sampleNum):
featureVec = self.haar.calFeatureForImg(
self.nonFace.images[i])
print(i)
for j in xrange(self.haar.featuresNum):
self._mat[j][i + self.Face.sampleNum] = featureVec[j]
numpy.save(FEATURE_FILE_TRAINING, self._mat)
else:
from mapReduce import map
from mapReduce import reduce
map(self.Face, self.nonFace)
self._mat = reduce()
featureNum, sampleNum = self._mat.shape
assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE)
assert featureNum == FEATURE_NUM
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(NEGATIVE_SAMPLE)]
self._label = numpy.array(Label_Face + Label_NonFace)
self.limit = limit
self.classifierNum = 0
self.strong_classifier = [None for i in xrange(limit)]
def __init__(self, face_dir = "", nonface_dir = "", train = True, limit = 30):
#tot_samples = 0
self.Face = ImageSet(face_dir, sampleNum = POSITIVE_SAMPLE)
self.nonFace = ImageSet(nonface_dir, sampleNum = NEGATIVE_SAMPLE)
tot_samples = self.Face.sampleNum + self.nonFace.sampleNum
self.classifier = AdaBoost
self.haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
if os.path.isfile(FEATURE_FILE_TRAINING + ".npy"):
self._mat = numpy.load(FEATURE_FILE_TRAINING + ".npy")
else:
if DEBUG_MODEL is True:
self._mat = numpy.zeros((self.haar.featuresNum, tot_samples))
for i in xrange(self.Face.sampleNum):
featureVec = self.haar.calFeatureForImg(self.Face.images[i])
for j in xrange(self.haar.featuresNum):
self._mat[j][i ] = featureVec[j]
for i in xrange(self.nonFace.sampleNum):
featureVec = self.haar.calFeatureForImg(self.nonFace.images[i])
for j in xrange(self.haar.featuresNum):
self._mat[j][i + self.Face.sampleNum] = featureVec[j]
numpy.save(FEATURE_FILE_TRAINING, self._mat)
else:
from mapReduce import map
from mapReduce import reduce
map(self.Face, self.nonFace)
self._mat = reduce()
featureNum, sampleNum = self._mat.shape
assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE)
assert featureNum == FEATURE_NUM
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(NEGATIVE_SAMPLE)]
self._label = numpy.array(Label_Face + Label_NonFace)
self.limit = limit
self.classifierNum = 0
self.strong_classifier = [None for i in xrange(limit)]
for i in xrange(Face.sampleNum):
featureVec = haar.calFeatureForImg(Face.images[i])
for j in xrange(haar.featuresNum):
_mat[j][i ] = featureVec[j]
for i in xrange(nonFace.sampleNum):
featureVec = haar.calFeatureForImg(nonFace.images[i])
for j in xrange(haar.featuresNum):
_mat[j][i + Face.sampleNum] = featureVec[j]
numpy.save(FEATURE_FILE_TRAINING, _mat)
else:
from mapReduce import map
from mapReduce import reduce
map(Face, nonFace)
_mat = reduce()
mat = _mat
featureNum, sampleNum = _mat.shape
assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE)
assert featureNum == FEATURE_NUM
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(NEGATIVE_SAMPLE)]
label = numpy.array(Label_Face + Label_NonFace)
cache_filename = ADABOOST_CACHE_FILE + str(0)