def __init__(self,fileName):
self.mask = imread(fileName) > 0
self.notMask = logical_not(self.mask)
self.positives = count_nonzero(self.mask)*1.0
self.negatives = (self.mask.size - self.positives)*1.0
tp = count_nonzero(logical_and(self.mask,binary))
return fp,tp
def getBlackWhiteFromBinary(img):
return dstack((img,img,img))
if __name__ == '__main__':
seterr(all='warn')
skindata, nonskindata = loadmat('data/skin.mat')['sdata'].reshape((-1, 3)).astype(float128), loadmat('data/nonskin.mat')['ndata'].reshape((-1, 3)).astype(float128)
iters = 10
show = False
usekmeans = False
gmmskin, gmmnonskin = gmmEM(skindata, 2, iters,show,usekmeans), gmmEM(nonskindata, 2, iters,show,usekmeans)
img = imread('data/image.png').astype(float128) / 255.0
imshape = img.shape
img = img.reshape((-1, 3))
skinp, nonskinp = gmmskin.getP(img), gmmnonskin.getP(img)
res = skinp > nonskinp
res = res.reshape((imshape[0],imshape[1]))
res = logical_not(res)
gt = GroundTruth('data/mask.png')
fp, tp = gt.checkClassification(res)
print "false positive ratio: ", fp*1.0/gt.negatives
print "true positive ratio: ", tp*1.0/gt.positives
res = getBlackWhiteFromBinary(res)
pylab.figure()
pylab.imshow(res)
pylab.show()
