def routine(images, filename):
tot_samples = len(images)
haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
mat = numpy.zeros((haar.featuresNum, tot_samples), dtype=numpy.float32)
for i in xrange(tot_samples):
featureVec = haar.calFeatureForImg(images[i])
for j in xrange(haar.featuresNum):
mat[j][i] = featureVec[j]
numpy.save(filename, mat)
def routine(images, filename):
tot_samples = len(images)
haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
mat = numpy.zeros((haar.featuresNum, tot_samples), dtype = numpy.float32)
for i in xrange(tot_samples):
featureVec = haar.calFeatureForImg(images[i])
for j in xrange(haar.featuresNum):
mat[j][i] = featureVec[j]
numpy.save(filename, mat)
nonFace = ImageSet(TRAINING_NONFACE, sampleNum = NEGATIVE_SAMPLE)
tot_samples = Face.sampleNum + nonFace.sampleNum
haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
if os.path.isfile(FEATURE_FILE_TRAINING + ".npy"):
_mat = numpy.load(FEATURE_FILE_TRAINING + ".npy")
else:
if DEBUG_MODEL is True:
_mat = numpy.zeros((haar.featuresNum, tot_samples))
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()
class Cascade:
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)]
def train(self):
raise ("Unfinished")
detection_rate = 0
from config import EXPECTED_FPR_PRE_LAYYER
from config import EXPECTED_FPR
from config import LABEL_NEGATIVE
cur_fpr = 1.0
mat = self._mat
label = self._label
for i in xrange(self.limit):
if cur_fpr < EXPECTED_FPR:
break
else:
cache_filename = ADABOOST_CACHE_FILE + str(i)
if os.path.isfile(cache_filename):
self.strong_classifier[i] = getCachedAdaBoost(mat = self._mat,
label = self._label,
filename= cache_filename,
limit = ADABOOST_LIMIT)
else:
self.strong_classifier[i] = AdaBoost(mat, label, limit = ADABOOST_LIMIT)
output, fpr = self.strong_classifier[i].train()
cur_fpr *= fpr
fp_num = fpr * numpy.count_nonzero(label == LABEL_NEGATIVE)
self.strong_classifier[i].saveModel(cache_filename)
mat, label = self.updateTrainingDate(mat, output, fp_num)
self.classifierNum += 1
def updateTrainingDate(self, mat, output, fp_num):
fp_num = int(fp_num)
assert len(output) == self._label.size
_mat = numpy.zeros((FEATURE_NUM, POSITIVE_SAMPLE + fp_num), dtype=numpy.float16)
_mat[:, :POSITIVE_SAMPLE] = mat[:, :POSITIVE_SAMPLE]
"""
for i in xrange(POSITIVE_SAMPLE):
for j in xrange(FEATURE_NUM):
mat[j][i] = self._mat[j][i]
"""
counter = 0
# only reserve negative samples which are classified wrong
for i in xrange(POSITIVE_SAMPLE, self._label.size):
if output[i] != self._label[i]:
for j in xrange(FEATURE_NUM):
_mat[j][POSITIVE_SAMPLE + counter] = mat[j][i]
counter += 1
assert counter == fp_num
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(fp_num)]
_label = numpy.array(Label_Face + Label_NonFace)
return _mat, _label
def predict(self):
output = numpy.zeros(POSITIVE_SAMPLE + NEGATIVE_SAMPLE, dtype= numpy.float16)
for i in xrange(self.classifierNum):
self.strong_classifier[i].prediction(mat, th = 0)
"""unfinished"""
def save(self):
pass
def is_goodenough(self):
pass
class Cascade:
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 range(self.Face.sampleNum):
featureVec = self.haar.calFeatureForImg(
self.Face.images[i])
for j in range(self.haar.featuresNum):
self._mat[j][i] = featureVec[j]
for i in range(self.nonFace.sampleNum):
featureVec = self.haar.calFeatureForImg(
self.nonFace.images[i])
for j in range(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 range(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in range(NEGATIVE_SAMPLE)]
self._label = numpy.array(Label_Face + Label_NonFace)
self.limit = limit
self.classifierNum = 0
self.strong_classifier = [None for i in range(limit)]
def train(self):
raise ("Unfinished")
detection_rate = 0
from config import EXPECTED_FPR_PRE_LAYYER
from config import EXPECTED_FPR
from config import LABEL_NEGATIVE
cur_fpr = 1.0
mat = self._mat
label = self._label
for i in xrange(self.limit):
if cur_fpr < EXPECTED_FPR:
break
else:
cache_filename = ADABOOST_CACHE_FILE + str(i)
if os.path.isfile(cache_filename):
self.strong_classifier[i] = getCachedAdaBoost(
mat=self._mat,
label=self._label,
filename=cache_filename,
limit=ADABOOST_LIMIT)
else:
self.strong_classifier[i] = AdaBoost(mat,
label,
limit=ADABOOST_LIMIT)
output, fpr = self.strong_classifier[i].train()
cur_fpr *= fpr
fp_num = fpr * numpy.count_nonzero(label == LABEL_NEGATIVE)
self.strong_classifier[i].saveModel(cache_filename)
mat, label = self.updateTrainingDate(mat, output, fp_num)
self.classifierNum += 1
def updateTrainingDate(self, mat, output, fp_num):
fp_num = int(fp_num)
assert len(output) == self._label.size
_mat = numpy.zeros((FEATURE_NUM, POSITIVE_SAMPLE + fp_num),
dtype=numpy.float16)
_mat[:, :POSITIVE_SAMPLE] = mat[:, :POSITIVE_SAMPLE]
"""
for i in xrange(POSITIVE_SAMPLE):
for j in xrange(FEATURE_NUM):
mat[j][i] = self._mat[j][i]
"""
counter = 0
# only reserve negative samples which are classified wrong
for i in range(POSITIVE_SAMPLE, self._label.size):
if output[i] != self._label[i]:
for j in range(FEATURE_NUM):
_mat[j][POSITIVE_SAMPLE + counter] = mat[j][i]
counter += 1
assert counter == fp_num
Label_Face = [+1 for i in range(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in range(fp_num)]
_label = numpy.array(Label_Face + Label_NonFace)
return _mat, _label
def predict(self):
output = numpy.zeros(POSITIVE_SAMPLE + NEGATIVE_SAMPLE,
dtype=numpy.float16)
for i in range(self.classifierNum):
self.strong_classifier[i].prediction(mat, th=0)
"""unfinished"""
def save(self):
pass
def is_goodenough(self):
pass