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from config import TEST_FACE
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from config import TEST_NONFACE
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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 ADABOOST_CACHE_FILE
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from config import POSITIVE_SAMPLE
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from config import LABEL_POSITIVE
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from adaboost import getCachedAdaBoost
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from image import ImageSet
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from haarFeature import Feature
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import numpy
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face = ImageSet(TEST_FACE, sampleNum = 100)
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nonFace = ImageSet(TEST_NONFACE, sampleNum = 100)
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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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mat = numpy.zeros((haar.featuresNum, tot_samples))
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for i in range(face.sampleNum):
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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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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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model = getCachedAdaBoost(filename = ADABOOST_CACHE_FILE + str(0), limit = 10)
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output = model.prediction(mat, th=0)
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detectionRate = numpy.count_nonzero(output[0:100] == LABEL_POSITIVE) * 1./ 100
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print output
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