def backward(self, prefix = "/tmp/", N=5, T=2, K=2):
_2d_transform = MDWT()
_2d_level = "LL"*K
for k in range(K):
tmp = decomposition.readL(prefix, "000")
t_transform = MCDWT(tmp.shape) # t_transform = SMCTF(tmp.shape)
t_transform.backward(prefix + _2d_level, N, T)
_2d_transform.backward(prefix, N)
_2d_level = "LL"*k
def LHtoBlack(path, N):
for i in range(N):
LH, HL, HH = decomposition.readH(path, "{:03d}".format(i))
LL = decomposition.readL(path, "{:03d}".format(i))
y = math.ceil(LH.shape[0] / 2)
x = math.ceil(LH.shape[1] / 2)
LH[x][y][0] = 255
LH[x][y][1] = 255
LH[x][y][2] = 255
return LL, LH, HL, HH
import weighted_average as predictor
if args.predictor == 3:
import left_prediction as predictor
if args.predictor == 4:
import right_prediction as predictor
if args.predictor == 5:
import left_MC_prediction as predictor
if args.predictor == 6:
import right_MC_prediction as predictor
if args.predictor == 7:
import offset_prediction as predictor
if args.backward:
if __debug__:
print("Backward transform")
# The first image is read only for knowing the dimenssions of
# the images.
p = decomposition.readL(args.prefix, "000")
d = MCDWT(p.shape)
d.backward(args.prefix, args.decompositions, args.iterations)
else:
if __debug__:
print("Forward transform")
p = decomposition.readL(args.prefix, "000")
d = MCDWT(p.shape)
p = d.forward(args.prefix, args.decompositions, args.iterations)
parser.add_argument("-N",
help="Number of decompositions",
default=5,
type=int)
parser.add_argument("-T",
help="Number of temporal levels",
default=2,
type=int)
args = parser.parse_args()
if args.backward:
if __debug__:
print("Backward transform")
p = decomposition.readL("{}000".format(args.mc_decompositions))
d = MCDWT(p.shape)
d.backward(args.mc_decompositions, args.decompositions, args.N, args.T)
else:
if __debug__:
print("Forward transform")
p = decomposition.readL("{}000".format(args.decompositions))
d = MCDWT(p.shape)
p = d.forward(args.decompositions, args.mc_decompositions, args.N,
args.T)
from MDWT import MDWT
sys.path.insert(0, "..")
from src.IO import decomposition
'''CALCULAR ENERGIA DE CADA BANDA'''
print("JLL --> {}\n".format('Energia LL/Energia HH'))
print("JHL --> {}\n".format('Energia HL/Energia HH'))
print("JLH --> {}\n\n".format('Energia LH/Energia HH'))
'''MODIFICA LA SUBBANDA HH PONIENDOLA EN NEGRO CON UN PUNTO BLANCO EN EL CENTRO'''
for i in range(args.N):
LH, HL, HH = decomposition.readH("{}{:03d}".format(args.decompositions, i))
LL = decomposition.readL("{}{:03d}".format(args.decompositions, i))
y = math.ceil(HH.shape[0]/2)
x = math.ceil(HH.shape[1]/2)
HH = HH * 0
HH[x][y][0] = 255
HH[x][y][1] = 255
HH[x][y][2] = 255
decomposition.writeH([LH,HL,HH],"{}{:03d}".format(args.decompositions, i))
'''SE RECONSTRUYE LA IMAGEN Y SE VUELVE A DESCOMPONERLA'''
d.backward(args.decompositions, '/tmp/recons_MDWT_', args.N)
d.forward('/tmp/recons_MDWT_', args.decompositions, args.N)