def param_list2str(p_list, prefix=None, keep_empty=False):
if is_scalar(p_list):
p_list = param_str2list(p_list, keep_empty)
u_p_list = [unicode(p) for p in p_list]
ret = "|".join(
[prefixed(t, prefix) for t in u_p_list if (t or keep_empty)])
return unicode(ret)
def param_list2str(p_list, prefix=None, keep_empty=False):
if is_scalar(p_list):
p_list = param_str2list(p_list, keep_empty)
u_p_list = [unicode(p) for p in p_list]
ret = "|".join([prefixed(t, prefix)
for t in u_p_list if (t or keep_empty)])
return unicode(ret)
def split_train_test(ds_path):
prefix = utils.prefixed(ds_path, BTS_SUBDIR)
meta_path = prefix('meta.txt')
with open(meta_path, 'r') as f:
lines = f.readlines()
random.shuffle(lines)
train, test = lines[:-100], lines[-100:]
train_path, test_path = prefix('train.txt'), prefix('test.txt')
print(
f'Splitting total {len(lines)} samples to {len(train)} train / {len(test)} test samples'
)
with open(train_path, 'w') as f:
f.writelines(train)
with open(test_path, 'w') as f:
f.writelines(test)
def iterate_files(ds_path):
if not ds_path:
return
prefix = utils.prefixed(ds_path)
meta_path = prefix('meta')
for meta in iterate_metas(meta_path):
for cam in meta['cams'].values():
image_path = prefix(cam['img'])
image_name = os.path.split(image_path)[-1]
image = Image.open(image_path)
depth_path = cam.get('depth_map_name')
depth_name, depth = None, None
if depth_path:
depth_path = prefix(depth_path)
depth_name = os.path.split(depth_path)[-1]
depth = np.fromfile(depth_path, np.float32) / 100
w, h = image.size
depth = np.reshape(depth, (h, w))
yield cam, image_path, depth_path, image_name, depth_name, image, depth
def get_command(self):
"Returns the current command (i.e. all, entropy, etc etc.)"
scheme, netloc, path, params, query, fragment = urlparse.urlparse(self.path)
if utils.prefixed(path, self.path_prefix()):
return path[len(self.path_prefix()) :]
raise BadPrefix()
