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)]
from config import FEATURE_NUM
from config import ADABOOST_LIMIT
from config import ADABOOST_CACHE_FILE
from config import DEBUG_MODEL
from config import TRAINING_FACE
from config import TRAINING_NONFACE
from haarFeature import Feature
from image import ImageSet
from adaboost import AdaBoost
from adaboost import getCachedAdaBoost
import os
import numpy
Face = ImageSet(TRAINING_FACE, sampleNum = POSITIVE_SAMPLE)
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):
from config import *
from adaboost import AdaBoost
#FEATURE_FILE_TESTING = FEATURE_FILE_TRAINING
#
#TESTING_SAMPLE_NUM = SAMPLE_NUM
#TESTING_NEGATIVE_SAMPLE = NEGATIVE_SAMPLE
#TESTING_POSITIVE_SAMPLE = POSITIVE_SAMPLE
fileObj = open(FEATURE_FILE_TESTING, "a+")
# if that is a empty file
if os.stat(FEATURE_FILE_TESTING).st_size == 0:
print "First time to load the testing set ..."
TestSetFace = ImageSet(TEST_FACE, sampleNum=TESTING_POSITIVE_SAMPLE)
TestSetNonFace = ImageSet(TEST_NONFACE, sampleNum=TESTING_NEGATIVE_SAMPLE)
Original_Data_Face = [
TestSetFace.images[i].haarA + TestSetFace.images[i].haarB +
TestSetFace.images[i].haarC + TestSetFace.images[i].haarD
for i in range(TestSetFace.sampleNum)
]
Original_Data_NonFace = [
TestSetNonFace.images[i].haarA + TestSetNonFace.images[i].haarB +
TestSetNonFace.images[i].haarC + TestSetNonFace.images[i].haarD
for i in range(TestSetNonFace.sampleNum)
]
Original_Data = numpy.array(Original_Data_Face + \
from config import TEST_FACE
from config import TEST_NONFACE
from config import TRAINING_IMG_HEIGHT
from config import TRAINING_IMG_WIDTH
from config import ADABOOST_CACHE_FILE
from config import POSITIVE_SAMPLE
from config import LABEL_POSITIVE
from adaboost import getCachedAdaBoost
from image import ImageSet
from haarFeature import Feature
import numpy
face = ImageSet(TEST_FACE, sampleNum=100)
nonFace = ImageSet(TEST_NONFACE, sampleNum=100)
tot_samples = face.sampleNum + nonFace.sampleNum
haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
mat = numpy.zeros((haar.featuresNum, tot_samples))
for i in range(face.sampleNum):
featureVec = haar.calFeatureForImg(face.images[i])
for j in range(haar.featuresNum):
mat[j][i] = featureVec[j]
for i in range(nonFace.sampleNum):
fileObj.write(str(model.G[m].opt_threshold) + "\n")
fileObj.flush()
fileObj.close()
##################################################################
fileObj = open(FEATURE_FILE_TRAINING, "a+")
# if that is a empty file
if os.stat(FEATURE_FILE_TRAINING).st_size == 0:
print "First time to load the training set ..."
TrainingSetFace = ImageSet(TRAINING_FACE, sampleNum=POSITIVE_SAMPLE)
TrainingSetNonFace = ImageSet(TRAINING_NONFACE, sampleNum=NEGATIVE_SAMPLE)
Original_Data_Face = [
TrainingSetFace.images[i].haarA + TrainingSetFace.images[i].haarB +
TrainingSetFace.images[i].haarC + TrainingSetFace.images[i].haarD +
TrainingSetFace.images[i].haarE
for i in range(TrainingSetFace.sampleNum)
]
Original_Data_NonFace = [
TrainingSetNonFace.images[i].haarA +
TrainingSetNonFace.images[i].haarB +
TrainingSetNonFace.images[i].haarC +
TrainingSetNonFace.images[i].haarD + TrainingSetNonFace.images[i].haarE