def get_data():
X = []
y = []
basePath = join(dir_path, "..", "common", "data", "GTSRB_subset_2")
imageFileNamesClass1 = absolute_file_paths([join(basePath, "class1")])
for imgPath in imageFileNamesClass1:
X.append(io.imread(imgPath))
y.append(0)
imageFileNamesClass2 = absolute_file_paths([join(basePath, "class2")])
for imgPath in imageFileNamesClass2:
X.append(io.imread(imgPath))
y.append(1)
X = (X - np.min(X)) / np.max(X)
y = to_categorical(y, 2)
return X, y
def save_test_data(test_path, test_targets_path, test_coords_path,
test_shape_path, orig_test_dir, orig_test_targets_dir):
"""Loads, formats, and re-saves test data from original directories."""
print('in save_test_data')
# Gets original data files.
test_files = sorted(absolute_file_paths(orig_test_dir))
test_targets_files = sorted(absolute_file_paths(orig_test_targets_dir))
# Loads and preprocesses data.
test, test_coords, test_shape = load_data(test_files)
test_targets, _, _ = load_data(test_targets_files, norm=False)
# Re-saves data in specified directories.
np.save(test_path, test)
np.save(test_targets_path, test_targets)
with open(test_coords_path, "wb") as a, open(test_shape_path, "wb") as b:
pickle.dump(test_coords, a)
pickle.dump(test_shape, b)
return test, test_targets, test_coords, test_shape
def save_train_data(train_path, valid_path, train_targets_path,
valid_targets_path, orig_train_dir, orig_valid_dir,
orig_train_targets_dir, orig_valid_targets_dir):
"""Loads, formats, and re-saves train data from original directories."""
print('in save_train_data')
# Gets original data files.
train_files = sorted(absolute_file_paths(orig_train_dir))
valid_files = sorted(absolute_file_paths(orig_valid_dir))
train_targets_files = sorted(absolute_file_paths(orig_train_targets_dir))
valid_targets_files = sorted(absolute_file_paths(orig_valid_targets_dir))
# Loads and preprocesses data.
train, _, _ = load_data(train_files)
valid, _, _ = load_data(valid_files)
train_targets, _, _ = load_data(train_targets_files, norm=False)
valid_targets, _, _ = load_data(valid_targets_files, norm=False)
# Re-saves data in specified directories.
np.save(train_path, train)
np.save(valid_path, valid)
np.save(train_targets_path, train_targets)
np.save(valid_targets_path, valid_targets)
return train, valid, train_targets, valid_targets