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90 lines
2.5 KiB
90 lines
2.5 KiB
"""
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Programmer : EOF
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File : trainingAdaBoost.py
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Date : 2015.12.29
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E-mail : jasonleaster@163.com
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"""
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from config import POSITIVE_SAMPLE
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from config import NEGATIVE_SAMPLE
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from config import TRAINING_IMG_HEIGHT
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from config import TRAINING_IMG_WIDTH
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from config import FEATURE_FILE_TRAINING
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from config import FEATURE_NUM
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from config import ADABOOST_LIMIT
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from config import ADABOOST_CACHE_FILE
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from config import DEBUG_MODEL
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from config import TRAINING_FACE
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from config import TRAINING_NONFACE
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from haarFeature import Feature
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from image import ImageSet
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from adaboost import AdaBoost
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from adaboost import getCachedAdaBoost
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import os
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import numpy
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Face = ImageSet(TRAINING_FACE, sampleNum=POSITIVE_SAMPLE)
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nonFace = ImageSet(TRAINING_NONFACE, sampleNum=NEGATIVE_SAMPLE)
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tot_samples = Face.sampleNum + nonFace.sampleNum
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haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
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if os.path.isfile(FEATURE_FILE_TRAINING + ".npy"):
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_mat = numpy.load(FEATURE_FILE_TRAINING + ".npy")
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else:
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if DEBUG_MODEL is True:
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_mat = numpy.zeros((haar.featuresNum, tot_samples))
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for i in range(Face.sampleNum):
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print(str(round(i/Face.sampleNum*100, 2))+'%')
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featureVec = haar.calFeatureForImg(Face.images[i])
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for j in range(haar.featuresNum):
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_mat[j][i] = featureVec[j]
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for i in range(nonFace.sampleNum):
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print(str(round(i/nonFace.sampleNum * 100, 2)) + '%')
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featureVec = haar.calFeatureForImg(nonFace.images[i])
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for j in range(haar.featuresNum):
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_mat[j][i + Face.sampleNum] = featureVec[j]
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numpy.save(FEATURE_FILE_TRAINING, _mat)
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else:
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from mapReduce import map
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from mapReduce import reduce
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map(Face, nonFace)
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_mat = reduce()
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mat = _mat
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featureNum, sampleNum = _mat.shape
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assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE)
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assert featureNum == FEATURE_NUM
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Label_Face = [+1 for i in range(POSITIVE_SAMPLE)]
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Label_NonFace = [-1 for i in range(NEGATIVE_SAMPLE)]
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label = numpy.array(Label_Face + Label_NonFace)
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cache_filename = ADABOOST_CACHE_FILE + str(0)
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if os.path.isfile(cache_filename):
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model = getCachedAdaBoost(mat=_mat,
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label=label,
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filename=cache_filename,
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limit=ADABOOST_LIMIT)
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else:
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model = AdaBoost(mat, label, limit=ADABOOST_LIMIT)
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model.train()
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model.saveModel(cache_filename)
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print(model)
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