def evaluateFile(args):
hdf5_gt_file,hdf5_pred_file,threshes,funcs,save_segs,out = args
hdf5_gt = h5py.File(hdf5_gt_file, 'r')
hdf5_aff = h5py.File(hdf5_pred_file, 'r')
gt = np.asarray(hdf5_gt[hdf5_gt.keys()[0]],dtype='uint32')
aff = np.asarray(hdf5_aff[hdf5_aff.keys()[0]],dtype='float32')
aff = np.transpose(aff,(1,2,3,0))
dims = np.array(aff.shape,dtype='uint32')
print 'dims:',aff.shape
# trim gt data - only works for perfect cubes
gt_data_dimension = gt.shape[0]
data_dimension = aff.shape[1]
if gt_data_dimension != data_dimension:
print("Data dimension do not match. Clip the GT borders.")
padding = (gt_data_dimension - data_dimension) / 2
gt = gt[padding:(-1*padding),padding:(-1*padding),padding:(-1*padding)]
print"New GT data shape :",gt.shape
# change both to fortran order
gt = np.array(gt,order='F')
aff = np.array(aff,order='F')
# evaluate call
print "gt shape:",gt.shape
print "aff shape:",aff.shape
if not os.path.exists(out):
os.makedirs(out)
f = open(out+'info.txt', 'w')
f.write('gt: '+hdf5_gt_file+'\n')
f.write('pred: '+hdf5_pred_file+'\n')
f.write('pred_dims: '+np.array_str(dims))
f.close()
eval(gt,aff,threshes,funcs,save_segs,out)
threshes = [100+i*100 for i in range(0,10)]+[i*1000 for i in range(2,11)]+[i*10000 for i in range(2,11)] # 100...1,000...100,000
# threshes = [100+i*300 for i in range(0,4)]+[i*3000 for i in range(1,4)]+[i*30000 for i in range(2,4)] # 100...1,000...100,000 small
# threshes = [i*10 for i in range(0,21)]+ [i*25000 for i in range(1,11)]
print threshes
save_segs = False
# output folder
out = 'out/test_paper/'
# funcs
#funcs = ['square','linear','fel','threshold','watershed','lowhigh']
funcs = ['linear','square','threshold']
# eval call
gt = np.frombuffer(gt,dtype='uint32').reshape(dims[0:3])
affs = np.frombuffer(aff,dtype='float32').reshape(dims)
start = time.clock()
map = eval(gt,affs,threshes,funcs,save_segs,out)
print "time",(time.clock()-start)
# saving
'''
for i in range(funcs):
str = funcs[i]
newFileByteArray = bytearray(map[str])
#newFile.write(newFileByteArray)
'''
