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demo.py
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demo.py
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"""
Main file to run SUnAA on a basic example (DC1)
"""
import argparse
import os
import scipy.io as sio
from src.noise import AdditiveWhiteGaussianNoise as AWGN
from src.model import SUnAA
from src.metrics import RMSE, SRE
# NOTE Change to use your own data here
DATA_DIR = "./data/"
DATASET = "DC1"
DICTIONARY = "EE.mat"
ABUNDANCES = "XT.mat"
HSI = "Y_clean.mat"
def main(args):
# Load data
Y = sio.loadmat(os.path.join(DATA_DIR, DATASET, HSI))["Y_clean"]
print(f"Y shape => {Y.shape}")
D = sio.loadmat(os.path.join(DATA_DIR, DATASET, DICTIONARY))["EE"]
print(f"D shape => {D.shape}")
A_gt = sio.loadmat(os.path.join(DATA_DIR, DATASET, ABUNDANCES))["XT"]
print(f"A shape => {A_gt.shape}")
p = args.num_endmembers
print(f"Number of endmembers to be found: {p}")
# Apply noise
noise = AWGN(args.SNR)
# Reshape Y
H, W, L = Y.shape
N = H * W
Y = Y.transpose(2, 0, 1).reshape(L, N)
Y = noise.apply(Y, seed=args.seed)
# Reshape ground truth abundances
M, h, w = A_gt.shape
assert h == H
assert w == W
A_gt = A_gt.reshape(M, N)
# Call model
model = SUnAA(T=args.iters)
A, B = model.solve(Y, D, p)
# NOTE Current A is low rank
# NOTE Full rank can be obtained as follow
A_full = B @ A
# Compute metrics
sre = SRE()
rmse = RMSE()
print(f"SRE => {round(sre(A_full, A_gt), 2)}")
print(f"RMSE = > {round(rmse(A_full, A_gt), 2)}")
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"-N",
"--SNR",
help="Signal to Noise Ratio value (SNR)",
type=float,
default=None,
)
parser.add_argument(
"-T",
"--iters",
help="Outer iterations in SUnAA (T)",
type=int,
default=500,
)
parser.add_argument(
"-p",
"--num_endmembers",
help="Number of endmembers (p)",
type=int,
default=5,
)
parser.add_argument(
"-s",
"--seed",
help="Seeding random number generator (needed for reproducibility)",
type=int,
default=0,
)
args = parser.parse_args()
main(args)