# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):
# 
# Path: ldif/util/path_util.py
# def get_path_to_ldif_root():
# def get_path_to_ldif_parent():
# def package_to_abs(path):
# def gaps_path():
# def test_data_path():
# def util_test_data_path():
# def create_test_output_dir():


import os
import time
import tensorflow as tf
from ldif.util import file_util
from ldif.util import path_util
  synset, mesh_hash = rgb_path.split('/')[-4:-2]
  ensure_synset_valid(synset)
  ensure_hash_valid(mesh_hash)
  return synset, mesh_hash


def rgb_path_to_npz_path(rgb_path, split, dataset='shapenet-occnet'):
  synset, mesh_hash = rgb_path_to_synset_and_hash(rgb_path)
  npz_path = 'ROOT_DIR/%s/%s/%s/%s.npz' % (
      dataset, split, synset, mesh_hash)
  return npz_path


def parse_npz_path(path):
  """Extracts the split, synset, and mesh hash from an npz path."""
  split, synset, npz = path.split('/')[-3:]
  if '.npz' not in npz:
    raise ValueError('Error parsing hash from %s' % path)
  if synset not in synset_to_cat:
    raise ValueError('Error parsing synset from %s' % path)
  try:
    ensure_split_valid(split)
  except ValueError:
    raise ValueError('Error parsing split from %s' % path)
  mesh_hash = npz.replace('.npz', '')
  return split, synset, mesh_hash


def read_png_to_float_npy_with_reraising(path):
  try:

# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):
# 
# Path: ldif/util/path_util.py
# def get_path_to_ldif_root():
# def get_path_to_ldif_parent():
# def package_to_abs(path):
# def gaps_path():
# def test_data_path():
# def util_test_data_path():
# def create_test_output_dir():


# Path: ldif/inference/util.py
import os
import time
import tensorflow as tf
from ldif.util import file_util
from ldif.util import path_util

  synset, mesh_hash = rgb_path.split('/')[-4:-2]
  ensure_synset_valid(synset)
  ensure_hash_valid(mesh_hash)
  return synset, mesh_hash


def rgb_path_to_npz_path(rgb_path, split, dataset='shapenet-occnet'):
  synset, mesh_hash = rgb_path_to_synset_and_hash(rgb_path)
  npz_path = 'ROOT_DIR/%s/%s/%s/%s.npz' % (
      dataset, split, synset, mesh_hash)
  return npz_path


def parse_npz_path(path):
  """Extracts the split, synset, and mesh hash from an npz path."""
  split, synset, npz = path.split('/')[-3:]
  if '.npz' not in npz:
    raise ValueError('Error parsing hash from %s' % path)
  if synset not in synset_to_cat:
    raise ValueError('Error parsing synset from %s' % path)
  try:
    ensure_split_valid(split)
  except ValueError:
    raise ValueError('Error parsing split from %s' % path)
  mesh_hash = npz.replace('.npz', '')
  return split, synset, mesh_hash


def read_png_to_float_npy_with_reraising(path):
  try:

    im = file_util.read_image(path) / 255.0