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)