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()