jobDir = (os.path.split(dirname(filePath))[-1])
expDir = (os.path.split((filePath))[-1])
cf.project = cf.project or jobDir
cf.experment = cf.experment or expDir
cf.savename = '%s-%s-%s'%(cf.netdir,cf.experment,cf.project)
cf.toValGtPath = cf.toValGtPath or cf.toGtPath
#cf.valArgs = cf.valArgs or cf.trainArgs
c.update(cf)
c.cf = cf
c.weightsPrefix = fileJoinPath(__file__,pathjoin(c.tmpdir,'weights/%s-%s'%(c.netdir,c.experment)))
#show- map(readimg,c.names[:10])
if __name__ == '__main__':
setMod('train')
img = readimg(c.names[0])
gt = readgt(c.names[0])
show(img,gt)
loga(gt)
pass
# valNames=c.names,
## loadcsv=1,
# logFormat='dice:{dice:.3f}, loss:{loss:.3f}',
# sortkey='loss',
## loged=False,
## saveResoult=False,
# )
c.names.sort(key=lambda x:readgt(x).shape[0])
# c.names[0] = '01'
# f = open(pathjoin(args.out,'Pr-all1.txt'),'w')
# f.read()
# f.write('Pre\tRec\tAcc\tM\n')
for name in c.names[:]:
img,gt = readimg(name),readgt(name)>0
prob = predict(toimg(name))
re = prob.argmax(2)
# res= re*1.0
# e.evalu(re,gt,name)
show(img,gt,re)
# imsave(pathjoin(args.out,name+'.png'),uint8(re))
# for x in range(0,prob.shape[1]):
# for y in range(0,prob.shape[0]):
# res[y][x] = prob[y][x][1]
# imsave(pathjoin(args.out,name+'res.png'),res)
# f = open(pathjoin(args.out,'Pr-'+name+'.txt'),'w')
c.predictInterface = predictInterface
predict = predictInterface.predict
# c.predict = predict
# e = Evalu(diceEvalu,
## evaluName='restore-%s'%restore,
# valNames=c.names,
## loadcsv=1,
# logFormat='dice:{dice:.3f}, loss:{loss:.3f}',
# sortkey='loss',
## loged=False,
## saveResoult=False,
# )
c.names.sort(key=lambda x: readgt(x).shape[0])
for name in c.names[:]:
# img,gt = readimg(name),readgt(name)>0
img = readimg(name) > 0
prob = predict(toimg(name))
re = prob.argmax(2)
# res= re*1.0
# e.evalu(re,gt,name)
# show(img,gt,re)
imsave(
pathjoin(u'/home/victoria/0-images/眼底照数据集和标签/new_res',
name + '.png'), uint8(re))
# imsave(pathjoin(args.out,name+'.png'),uint8(re))
# for x in range(0,prob.shape[1]):
# for y in range(0,prob.shape[0]):
## M = 0.02
# if (prob[y][x][1] >= prob[y][x][0]):
# res[y][x] = prob[y][x][1]