# Path: ldif/util/camera_util.py
# def look_at(eye, center, world_up):
# def roll_pitch_yaw_to_rotation_matrices(roll_pitch_yaw):
# 
# Path: ldif/util/tf_util.py
# def assert_shape(tensor, shape, name):
# def log(msg, t):
# def tile_new_axis(t, axis, length):
# def zero_by_mask(mask, vals, replace_with=0.0):
# def remove_element(t, elt, axis):


from ldif.util import camera_util
from ldif.util import tf_util
import tensorflow as tf
    # the batching dimensions broadcast up the coordinate grid, and the
    # coordinate center broadcasts up along the height and width.
    # Per-pixel, the algebraic distance is v^T * M * v, where M is the matrix
    # of the conic section, and v is the homogeneous column vector [x y z 1]^T.
    half_distance = tf.matmul(
        quadric, homogeneous_sample_coords, transpose_b=True)
    rank = batching_rank + 2
    half_distance = tf.transpose(
        half_distance, perm=list(range(rank - 2)) + [rank - 1, rank - 2])
    algebraic_distance = tf.reduce_sum(
        tf.multiply(homogeneous_sample_coords, half_distance), axis=-1)
    return tf.reshape(algebraic_distance,
                      batching_dimensions + [sample_count, 1])


def decode_covariance_roll_pitch_yaw(radius, invert=False):
  """Converts 6-D radus vectors to the corresponding covariance matrices.

  Args:
    radius: Tensor with shape [..., 6]. First three numbers are covariances of
      the three Gaussian axes. Second three numbers are the roll-pitch-yaw
      rotation angles of the Gaussian frame.
    invert: Whether to return the inverse covariance.

  Returns:
     Tensor with shape [..., 3, 3]. The 3x3 (optionally inverted) covariance
     matrices corresponding to the input radius vectors.
  """
  d = 1.0 / (radius[..., 0:3] + DIV_EPSILON) if invert else radius[..., 0:3]
  diag = tf.matrix_diag(d)

# Path: ldif/util/camera_util.py
# def look_at(eye, center, world_up):
# def roll_pitch_yaw_to_rotation_matrices(roll_pitch_yaw):
# 
# Path: ldif/util/tf_util.py
# def assert_shape(tensor, shape, name):
# def log(msg, t):
# def tile_new_axis(t, axis, length):
# def zero_by_mask(mask, vals, replace_with=0.0):
# def remove_element(t, elt, axis):


# Path: ldif/representation/quadrics.py
from ldif.util import camera_util
from ldif.util import tf_util
import tensorflow as tf

    # the batching dimensions broadcast up the coordinate grid, and the
    # coordinate center broadcasts up along the height and width.
    # Per-pixel, the algebraic distance is v^T * M * v, where M is the matrix
    # of the conic section, and v is the homogeneous column vector [x y z 1]^T.
    half_distance = tf.matmul(
        quadric, homogeneous_sample_coords, transpose_b=True)
    rank = batching_rank + 2
    half_distance = tf.transpose(
        half_distance, perm=list(range(rank - 2)) + [rank - 1, rank - 2])
    algebraic_distance = tf.reduce_sum(
        tf.multiply(homogeneous_sample_coords, half_distance), axis=-1)
    return tf.reshape(algebraic_distance,
                      batching_dimensions + [sample_count, 1])


def decode_covariance_roll_pitch_yaw(radius, invert=False):
  """Converts 6-D radus vectors to the corresponding covariance matrices.

  Args:
    radius: Tensor with shape [..., 6]. First three numbers are covariances of
      the three Gaussian axes. Second three numbers are the roll-pitch-yaw
      rotation angles of the Gaussian frame.
    invert: Whether to return the inverse covariance.

  Returns:
     Tensor with shape [..., 3, 3]. The 3x3 (optionally inverted) covariance
     matrices corresponding to the input radius vectors.
  """
  d = 1.0 / (radius[..., 0:3] + DIV_EPSILON) if invert else radius[..., 0:3]
  diag = tf.matrix_diag(d)

  rotation = camera_util.roll_pitch_yaw_to_rotation_matrices(radius[..., 3:6])