hyperparams = dict((k, v) for k, v in hyperparams.items() if v is not None)
# Show the image and the ground truth
display_dataset(img, gt, RGB_BANDS, LABEL_VALUES, palette, viz)
# display_dataset(ori_img, ori_gt, RGB_BANDS, LABEL_VALUES, palette, viz)
color_gt = convert_to_color(gt)
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)
# Show the image and the ground truth
display_dataset(img, gt, RGB_BANDS, LABEL_VALUES, palette, viz)
color_gt = convert_to_color(gt)
# KD
if (T_BAND_GROUP != 0):
display_dataset(t_img, gt, RGB_BANDS, LABEL_VALUES, palette, viz)
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)
