def set_dset_list(self, data_dir, down_sampling=True):
""" Fill scene_information with the static environment features that will be used as part of the input of Static
Scene Feature Extractor module in SafeGAN"""
_dir = os.path.dirname(os.path.realpath(__file__))
_dir = _dir.split("/")[:-2]
_dir = "/".join(_dir)
directory = _dir + '/datasets/safegan_dataset/'
self.list_data_files = sorted([
get_dset_name(os.path.join(data_dir, _path).split("/")[-1])
for _path in os.listdir(data_dir)
])
for name in self.list_data_files:
path_group = os.path.join(directory, get_dset_group_name(name))
""" The inputs are the boundary points between the traversable and non-traversable areas. It is
possible to take all points or just a sample"""
path = os.path.join(path_group, name)
map = np.load(path + "/world_points_boundary.npy")
if self.down_samples != -1 and down_sampling and map.shape[
0] > self.down_samples:
down_sampling = (map.shape[0] // self.down_samples)
sampled = map[::down_sampling]
map = sampled[:self.down_samples]
self.scene_information[name] = torch.from_numpy(map).type(
torch.float).to(device)
def get_homography_and_map(dset, annotated_points_name = '/world_points_boundary.npy'):
directory = get_root_dir() + '/data/'
path_group = os.path.join(directory, get_dset_group_name(dset))
path = os.path.join(path_group, dset)
h_matrix = pd.read_csv(path + '/{}_homography.txt'.format(dset), delim_whitespace=True, header=None).values
if 'txt' in annotated_points_name:
map = np.loadtxt(path + annotated_points_name, delimiter=' ')
elif 'jpg' in annotated_points_name:
map = load_bin_map(path + annotated_points_name)
else:
map = np.load(path + annotated_points_name)
return map, h_matrix
def set_dset_list(self, data_dir, down_sampling=True, down_samples=200):
directory = get_root_dir() + '/datasets/safegan_dataset/'
self.list_data_files = sorted([get_dset_name(os.path.join(data_dir, _path).split("/")[-1]) for _path in os.listdir(data_dir)])
for name in self.list_data_files:
path_group = os.path.join(directory, get_dset_group_name(name))
""" The inputs are the boundary points between the traversable and non-traversable areas. It is
possible to take all points or just a sample"""
path = os.path.join(path_group, name)
map = np.load(path + "/world_points_boundary.npy")
if down_samples != -1 and down_sampling and map.shape[0] > down_samples:
down_sampling = (map.shape[0] // down_samples)
sampled = map[::down_sampling]
map = sampled[:down_samples]
self.scene_information[name] = torch.from_numpy(map).type(torch.float).to(device)
def set_dset_list(self, data_dir):
""" Fill scene_information with the static environment features that will be used as part of the input of Static
Scene Feature Extractor module in SafeGAN"""
directory = get_root_dir() + '/datasets/safegan_dataset/'
self.list_data_files = sorted([
get_dset_name(os.path.join(data_dir, _path).split("/")[-1])
for _path in os.listdir(data_dir)
])
for name in self.list_data_files:
path_group = os.path.join(directory, get_dset_group_name(name))
if self.pool_static_type == "physical_attention_no_encoder":
""" In this case the features are the one extracted by one of Segmentation Networks I trained on the new dataset
I created. The features are taken before the last upsample layers."""
path = os.path.join(path_group + "/segmented_features", name)
features = np.load(path + "_segmentation_features.npy")
features = torch.from_numpy(features).type(
torch.float).to(device)
elif self.pool_static_type == "physical_attention_with_encoder":
""" In this case the input is the raw image or the segmented one (by one of the Segmentation Networks I trained
on the new dataset I created). This image is then encoded by a Deep Network like ResNet"""
path = os.path.join(path_group + "/segmented_scenes", name)
image = plt.imread(path + ".jpg")
image = torch.from_numpy(image).type(torch.float).to(device)
# Images fed to the model must be a Float tensor of dimension N, 3, 256, 256, where N is the batch size.
# PyTorch follows the NCHW convention, which means the channels dimension (C) must precede the size dimensions
image = image.permute(2, 0, 1)
# Normalize the image
image = self.transform(image)
features = self.attention_encoder(image.unsqueeze(0))
else:
print(
"ERROR in recognizing physical attention pool static type")
exit()
self.scene_information[name] = features
def get_homography(dset):
directory = get_root_dir() + '/data/'
path_group = os.path.join(directory, get_dset_group_name(dset))
path = os.path.join(path_group, dset)
h_matrix = pd.read_csv(path + '/{}_homography.txt'.format(dset), delim_whitespace=True, header=None).values
return h_matrix
def get_path(dset):
directory = get_root_dir() + '/data/'
path_group = os.path.join(directory, get_dset_group_name(dset))
path = os.path.join(path_group, dset)
return path