You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
47 lines
1.2 KiB
47 lines
1.2 KiB
2 years ago
|
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 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]
|
||
|
|
|
||
|
|
|
||
|
|
model = getCachedAdaBoost(filename = ADABOOST_CACHE_FILE + str(0), limit = 10)
|
||
|
|
|
||
|
|
output = model.prediction(mat, th=0)
|
||
|
|
|
||
|
|
detectionRate = numpy.count_nonzero(output[0:100] == LABEL_POSITIVE) * 1./ 100
|
||
|
|
|
||
|
|
print output
|
||
|
|
|
||
|
|
|
||
|
|
|