if DATAVIZ:
# Data exploration : compute and show the mean spectrums
mean_spectrums, std_spectrums = explore_spectrums(
img, gt, LABEL_VALUES, viz, ignored_labels=IGNORED_LABELS)
with open("mean_spectrum_Salinas.txt", 'w') as f:
for ln, lv in mean_spectrums.items():
f.write(str(lv))
f.write('\n')
plot_spectrums(mean_spectrums, viz, title='Mean spectrum/class')
plot_spectrums_(std_spectrums, viz, title='Std spectrum/class')
results = []
# run the experiment several times
for run in range(N_RUNS):
if TRAIN_GT is not None and TEST_GT is not None:
train_gt = open_file(TRAIN_GT)
test_gt = open_file(TEST_GT)
elif TRAIN_GT is not None:
train_gt = open_file(TRAIN_GT)
test_gt = np.copy(gt)
w, h = test_gt.shape
test_gt[(train_gt > 0)[:w, :h]] = 0
elif TEST_GT is not None:
test_gt = open_file(TEST_GT)
else:
# Sample random training spectral
gt_ = gt[(PATCH_SIZE // 2):-(PATCH_SIZE // 2),
(PATCH_SIZE // 2):-(PATCH_SIZE // 2)]
train_gt, test_gt = sample_gt(gt_,
SAMPLE_PERCENTAGE,
mode=SAMPLING_MODE)
if DATAVIZ:
# Data exploration : compute and show the mean spectrums
mean_spectrums = explore_spectrums(img,
gt,
LABEL_VALUES,
viz,
ignored_labels=IGNORED_LABELS)
plot_spectrums(mean_spectrums, viz, title="Mean spectrum/class")
results = []
# run the experiment several times
for run in range(N_RUNS):
if TRAIN_GT is not None and TEST_GT is not None:
print("Using existing train/test split...")
train_gt = open_file(TRAIN_GT)['train_gt']
test_gt = open_file(TEST_GT)['test_gt']
elif TRAIN_GT is not None:
train_gt = open_file(TRAIN_GT)
test_gt = np.copy(gt)
w, h = test_gt.shape
test_gt[(train_gt > 0)[:w, :h]] = 0
elif TEST_GT is not None:
test_gt = open_file(TEST_GT)
else:
# Sample random training spectra
train_gt, test_gt = sample_gt(gt,
SAMPLE_PERCENTAGE,
mode=SAMPLING_MODE)
scipy.io.savemat("test.mat", {'test_gt': test_gt})
args = parser.parse_args()
CUDA_DEVICE = get_device(args.cuda)
MODEL = args.model
# Testing file
MAT = args.mat
N_CLASSES = args.n_classes
INFERENCE = args.image
TEST_STRIDE = args.test_stride
CHECKPOINT = args.checkpoint
img_filename = os.path.basename(INFERENCE)
basename = MODEL + img_filename
dirname = os.path.dirname(INFERENCE)
img = open_file(INFERENCE)
if MAT is not None:
img = img[MAT]
# Normalization
img = np.asarray(img, dtype='float32')
img = (img - np.min(img)) / (np.max(img) - np.min(img))
N_BANDS = img.shape[-1]
hyperparams = vars(args)
hyperparams.update({
'n_classes': N_CLASSES,
'n_bands': N_BANDS,
'device': CUDA_DEVICE,
'ignored_labels': [0]
})
hyperparams = dict((k, v) for k, v in hyperparams.items() if v is not None)
N_BANDS = (img1.shape[-1], img2.shape[-1])
# Instantiate the experiment based on predefined networks
hyperparams.update({
"n_classes": N_CLASSES,
"n_bands": N_BANDS,
"ignored_labels": IGNORED_LABELS,
"device": CUDA_DEVICE,
})
hyperparams = dict((k, v) for k, v in hyperparams.items() if v is not None)
results = []
# run the experiment several times
for run in range(N_RUNS):
if TRAIN_GT is not None and TEST_GT is not None:
train_gt = open_file(TRAIN_GT)['TRLabel']
test_gt = open_file(TEST_GT)['TSLabel']
elif TRAIN_GT is not None:
train_gt = open_file(TRAIN_GT)
test_gt = np.copy(gt)
w, h = test_gt.shape
test_gt[(train_gt > 0)[:w, :h]] = 0
elif TEST_GT is not None:
test_gt = open_file(TEST_GT)
else:
# Sample random training spectra
train_gt, test_gt = sample_gt(gt,
SAMPLE_PERCENTAGE,
mode=SAMPLING_MODE)
print("{} samples selected (over {})".format(np.count_nonzero(train_gt),
np.count_nonzero(gt)))
args = parser.parse_args()
CUDA_DEVICE = get_device(args.cuda)
MODEL = args.model
# Testing file
MAT = args.mat
MAT_LIDAR = args.mat_lidar
N_CLASSES = args.n_classes
INFERENCE = args.image_hsi
TEST_STRIDE = args.test_stride
CHECKPOINT = args.checkpoint
img_filename = os.path.basename(INFERENCE)
basename = MODEL + img_filename
dirname = os.path.dirname(INFERENCE)
img = open_file(INFERENCE)
if MAT is not None:
img = img[MAT]
# Normalization
img = np.asarray(img, dtype="float32")
img = (img - np.min(img)) / (np.max(img) - np.min(img))
img_lidar = open_file(args.image_lidar)
if MAT_LIDAR is not None:
img_lidar = img_lidar[MAT_LIDAR]
# Normalization
img_lidar = np.asarray(img_lidar, dtype="float32")
img_lidar = np.expand_dims(img_lidar, axis=2)
img_lidar = (img_lidar - np.min(img_lidar)) / (np.max(img_lidar) -
np.min(img_lidar))
