# Path: ldif/datasets/shapenet.py
# def ensure_shape_and_resize_if_needed(orig_renders, batch_size, frame_count,
#                                       channel_count, orig_height, orig_width,
#                                       target_height, target_width):
# def select_image(images, indices):
# def apply_noise_to_depth(depth, stddev):
# def add_noise_to_xyz(xyz, stddev):
# def add_noise_in_normal_direction(xyz, normals, stddev):
#   def __init__(self, model_config):
#   def full_near_surface_samples(self):
#   def full_uniform_samples(self):
#   def _subsample(self, samples, sample_count):
#   def proto_name(self):
#   def depth_render(self):
#   def apply_transformation(self, tx):
#   def crop_input(self, crop_count=1024):
#   def crop_supervision(self, crop_count=1024):
#   def xyz_render(self):
#   def depth_renders(self):
#   def random_depth_indices(self):
#   def random_depth_images(self):
#   def random_depth_to_worlds(self):
#   def xyz_renders(self):
#   def depth_to_world(self):
#   def renders(self):
#   def lum_renders(self):
#   def chosen_renders(self):
#   def random_lum_render(self):
#   def random_xyz_render(self):
#   def grid(self):
#   def world2grid(self):
#   def _finite_wrapper(self, t):
#   def sample_bounding_box(self):
#   def sample_sdf_near_surface(self, sample_count):
#   def sample_sdf_uniform(self, sample_count):
#   def all_uniform_samples(self):
#   def _set_points_and_normals(self):
#   def all_surface_points(self):
#   def all_normals(self):
#   def sample_zero_set_points_and_normals(self, sample_count):
#   def __init__(self, lower, upper):
#   def from_samples(cls, samples):
# def build_placeholder_interface(model_config,
#                                 proto='ShapeNetNSSDodecaSparseLRGMediumSlimPC'):
# class ShapeNetExample(object):
# class BoundingBox(object):
# 
# Path: ldif/util/random_util.py
# def random_shuffle_along_dim(tensor, dim):
# def random_pan_rotations(batch_size):
# def random_pan_rotation_np():
# def random_rotations(batch_size):
# def random_rotation_np():
# def random_scales(batch_size, minval, maxval):
# def random_transformation(origin):
# def random_zoom_transformation(origin):
# def translation_to_tx(t):
# def rotation_to_tx(rot):


import tensorflow as tf
from ldif.datasets import shapenet
from ldif.util import random_util
# Lint as: python3
"""Code for preprocessing training examples.

This code can be aware of the existence of individual datasets, but it can't be
aware of their internals.
"""


# LDIF is an internal package, should be imported last.
# pylint: disable=g-bad-import-order
# pylint: enable=g-bad-import-order


# Main entry point for preprocessing. This code uses the model config to
# generate an appropriate training example. It uses duck typing, in that
# it should dispatch generation to a dataset to handle internals, and verify
# that the training example contains the properties associated with the config.
# But it doesn't care about anything else.
def preprocess(model_config):
  """Generates a training example object from the model config."""
  # TODO(kgenova) Check if dataset is shapenet. If so, return a ShapeNet
  # training example.
  training_example = shapenet.ShapeNetExample(model_config)

  # TODO(kgenova) Look at the model config and verify that nothing is missing.
  if model_config.hparams.da == 'f':
    return training_example
  elif model_config.hparams.da == 'p':  # Pan:
    tx = random_util.random_pan_rotations(model_config.hparams.bs)
  elif model_config.hparams.da == 'r':  # SO(3):

# Path: ldif/datasets/shapenet.py
# def ensure_shape_and_resize_if_needed(orig_renders, batch_size, frame_count,
#                                       channel_count, orig_height, orig_width,
#                                       target_height, target_width):
# def select_image(images, indices):
# def apply_noise_to_depth(depth, stddev):
# def add_noise_to_xyz(xyz, stddev):
# def add_noise_in_normal_direction(xyz, normals, stddev):
#   def __init__(self, model_config):
#   def full_near_surface_samples(self):
#   def full_uniform_samples(self):
#   def _subsample(self, samples, sample_count):
#   def proto_name(self):
#   def depth_render(self):
#   def apply_transformation(self, tx):
#   def crop_input(self, crop_count=1024):
#   def crop_supervision(self, crop_count=1024):
#   def xyz_render(self):
#   def depth_renders(self):
#   def random_depth_indices(self):
#   def random_depth_images(self):
#   def random_depth_to_worlds(self):
#   def xyz_renders(self):
#   def depth_to_world(self):
#   def renders(self):
#   def lum_renders(self):
#   def chosen_renders(self):
#   def random_lum_render(self):
#   def random_xyz_render(self):
#   def grid(self):
#   def world2grid(self):
#   def _finite_wrapper(self, t):
#   def sample_bounding_box(self):
#   def sample_sdf_near_surface(self, sample_count):
#   def sample_sdf_uniform(self, sample_count):
#   def all_uniform_samples(self):
#   def _set_points_and_normals(self):
#   def all_surface_points(self):
#   def all_normals(self):
#   def sample_zero_set_points_and_normals(self, sample_count):
#   def __init__(self, lower, upper):
#   def from_samples(cls, samples):
# def build_placeholder_interface(model_config,
#                                 proto='ShapeNetNSSDodecaSparseLRGMediumSlimPC'):
# class ShapeNetExample(object):
# class BoundingBox(object):
# 
# Path: ldif/util/random_util.py
# def random_shuffle_along_dim(tensor, dim):
# def random_pan_rotations(batch_size):
# def random_pan_rotation_np():
# def random_rotations(batch_size):
# def random_rotation_np():
# def random_scales(batch_size, minval, maxval):
# def random_transformation(origin):
# def random_zoom_transformation(origin):
# def translation_to_tx(t):
# def rotation_to_tx(rot):


# Path: ldif/datasets/preprocess.py
import tensorflow as tf
from ldif.datasets import shapenet
from ldif.util import random_util

# Lint as: python3
"""Code for preprocessing training examples.

This code can be aware of the existence of individual datasets, but it can't be
aware of their internals.
"""


# LDIF is an internal package, should be imported last.
# pylint: disable=g-bad-import-order
# pylint: enable=g-bad-import-order


# Main entry point for preprocessing. This code uses the model config to
# generate an appropriate training example. It uses duck typing, in that
# it should dispatch generation to a dataset to handle internals, and verify
# that the training example contains the properties associated with the config.
# But it doesn't care about anything else.
def preprocess(model_config):
  """Generates a training example object from the model config."""
  # TODO(kgenova) Check if dataset is shapenet. If so, return a ShapeNet
  # training example.
  training_example = shapenet.ShapeNetExample(model_config)

  # TODO(kgenova) Look at the model config and verify that nothing is missing.
  if model_config.hparams.da == 'f':
    return training_example
  elif model_config.hparams.da == 'p':  # Pan:
    tx = random_util.random_pan_rotations(model_config.hparams.bs)
  elif model_config.hparams.da == 'r':  # SO(3):

    tx = random_util.random_rotations(model_config.hparams.bs)