def process_dataset(dataset, n_clusters, pca_enabled=False):
X = np.load("./data/" + dataset + '.npy')
if pca_enabled:
pca = utils.load_model(dataset + '_pca')
X = pca.transform(utils.flat(X)).reshape(X.shape[0], X.shape[1], -1)
segments = segmentation(X, n_clusters)
np.save("./results/" + dataset + "_segments.npy", segments)
color_segments = color_true_map(segments, back_color=[1, 1, 1])
save_image(color_segments, dataset + "_segments")
print("Segments:", len(np.bincount(segments.reshape(-1))) - 1)
def process_dataset(dataset, colors):
y = np.load("./data/" + dataset + '_labels.npy')
pred = np.load("./data/" + dataset + '_clasification.npy')
segments = np.load("./results/" + dataset + '_segments.npy')
test_mask = np.load("./data/" + dataset + '_test_mask.npy').reshape(
y.shape)
sc_pred = classify_segments(pred, segments)
sc_score = utils.balanced_score(y[test_mask], sc_pred[test_mask])
sc_cm = utils.confusion_matrix(y[test_mask], sc_pred[test_mask])
utils.save_json({"sc": sc_score}, dataset + "_sc_score")
utils.save_csv(sc_cm, dataset + "_sc_cm")
color_map = color_true_map(sc_pred, labels_colors=colors)
save_image(color_map, dataset + "_sc_clasification")
def process_dataset(dataset):
X_img = np.load("./data/" + dataset + '.npy')
segments = np.load("./results/" + dataset + '_segments.npy')
seg_labels = range(len(np.bincount(segments.reshape(-1))))
n_endmembers = error_curve(dataset, X_img, segments, seg_labels)
_, endmembers, abundances = unmix_segments(X_img, segments, seg_labels,
n_endmembers)
plot_endmembers(dataset + "_seg_endmember", endmembers)
np.save("./results/" + dataset + "_seg_endmembers.npy", endmembers)
np.save("./results/" + dataset + "_seg_abundances.npy", abundances)
abun_map = np.empty((X_img.shape[0], X_img.shape[1], n_endmembers))
for seg in seg_labels:
abun_map[segments == seg, :] = abundances[seg, :]
for i in range(n_endmembers):
abun_color = color_abundance_map(abun_map[:, :, i], [255, 255, 255])
save_image(abun_color, dataset + "_seg_abundances_" + str(i))
def process_dataset(dataset, colors):
y = np.load("./data/" + dataset + '_labels.npy')
pred = np.load("./data/" + dataset + '_clasification.npy')
test_mask = np.load("./data/" + dataset + '_test_mask.npy').reshape(
y.shape)
mv_sizes = [3, 5, 9]
mv_pred = [majority_vote(pred, size) for size in mv_sizes]
mv_scores = [
utils.balanced_score(y[test_mask], p[test_mask]) for p in mv_pred
]
mv_cm = [
utils.confusion_matrix(y[test_mask], p[test_mask]) for p in mv_pred
]
keys = ["_mv_{}".format(size) for size in mv_sizes]
utils.save_json(dict(zip(keys, mv_scores)), dataset + "_mv_scores")
for i in range(len(mv_sizes)):
utils.save_csv(mv_cm[i], dataset + keys[i] + "_cm")
color_map = color_true_map(mv_pred[i], labels_colors=colors)
save_image(color_map, dataset + keys[i] + "_clasification")
def generate_work_data(dataset, labels, colors, parameters, pca_enabled=False):
X_img = np.load('./data/' + dataset + '.npy')
y_img = np.load('./data/' + dataset + '_labels.npy')
save_image(color_true_map(y_img, labels_colors=colors),
dataset + "_labels")
X = utils.flat(X_img)
y = utils.flat(y_img)
train_ratio, val_ratio = 0.1, 0.1
test_ratio = 1 - (train_ratio + val_ratio)
tv_mask, test_mask = utils.balanced_train_test_mask(
y, np.isin(y, labels), test_ratio)
train_mask, val_mask = utils.balanced_train_test_mask(
y, tv_mask, val_ratio / (val_ratio + train_ratio))
np.save("./data/" + dataset + "_train_mask.npy", train_mask)
np.save("./data/" + dataset + "_val_mask.npy", val_mask)
np.save("./data/" + dataset + "_test_mask.npy", test_mask)
if pca_enabled:
pca = utils.pca(X[tv_mask, :], 0.99)
utils.save_model(pca, dataset + '_pca')
train = pca.transform(X[train_mask, :])
test = pca.transform(X[test_mask])
flat = pca.transform(X)
else:
train = X[train_mask, :]
test = X[test_mask, :]
flat = X
svc = utils.svc(train, y[train_mask], parameters["C"], parameters["gamma"])
utils.save_model(svc, dataset + '_svc')
test_pred = svc.predict(test)
np.save("./data/" + dataset + "_test_pred.npy", test_pred)
classification = svc.predict(flat).reshape(y_img.shape).astype(np.uint8)
np.save("./data/" + dataset + "_clasification.npy", classification)
save_image(color_true_map(classification, labels_colors=colors),
dataset + "_clasification")
score = utils.balanced_score(y[test_mask], test_pred)
utils.save_json({"original": score}, dataset + "_original_score")
print("Test Score:", score)