from __future__ import annotations
import json
from enum import Enum
from typing import TYPE_CHECKING, Literal
import numpy as np
import numpy.typing as npt
from py3dtiles.exceptions import InvalidPntsError
from py3dtiles.tileset.content.feature_table import (
FeatureTable,
FeatureTableBody,
FeatureTableHeader,
)
from py3dtiles.typing import FeatureTableHeaderDataType
if TYPE_CHECKING:
from .tile_content import TileContentHeader
[docs]
class SemanticPoint(Enum):
NONE = 0
POSITION = 1
POSITION_QUANTIZED = 2
RGBA = 3
RGB = 4
RGB565 = 5
NORMAL = 6
NORMAL_OCT16P = 7
BATCH_ID = 8
[docs]
class SemanticCategory(Enum):
NONE = 0
POSITION = 1
COLOR = 2
NORMAL = 3
BATCH = 4
SEMANTIC_CATEGORY_MAP = {
SemanticPoint.NONE: SemanticCategory.NONE,
SemanticPoint.POSITION: SemanticCategory.POSITION,
SemanticPoint.POSITION_QUANTIZED: SemanticCategory.POSITION,
SemanticPoint.RGB: SemanticCategory.COLOR,
SemanticPoint.RGBA: SemanticCategory.COLOR,
SemanticPoint.RGB565: SemanticCategory.COLOR,
SemanticPoint.NORMAL: SemanticCategory.NORMAL,
SemanticPoint.NORMAL_OCT16P: SemanticCategory.NORMAL,
SemanticPoint.BATCH_ID: SemanticCategory.BATCH,
}
SEMANTIC_TYPE_MAP = {
SemanticPoint.POSITION: np.float32,
SemanticPoint.POSITION_QUANTIZED: np.uint16,
SemanticPoint.RGB: np.uint8,
SemanticPoint.RGBA: np.uint8,
SemanticPoint.RGB565: np.uint16,
SemanticPoint.NORMAL: np.float32,
SemanticPoint.NORMAL_OCT16P: np.uint8,
}
SEMANTIC_DIMENSION_MAP = {
SemanticPoint.POSITION: 3,
SemanticPoint.POSITION_QUANTIZED: 3,
SemanticPoint.RGB: 3,
SemanticPoint.RGBA: 4,
SemanticPoint.RGB565: 1,
SemanticPoint.NORMAL: 3,
SemanticPoint.NORMAL_OCT16P: 2,
}
SEMANTIC_ITEM_SIZE_MAP = {
semantic: SEMANTIC_DIMENSION_MAP[semantic]
* np.dtype(SEMANTIC_TYPE_MAP[semantic]).itemsize
for semantic in SEMANTIC_TYPE_MAP
}
[docs]
def check_semantic_type(semantic: SemanticPoint, category: SemanticCategory) -> None:
"""
This function checks if the category of the semantic is the same as the parameter category.
If not, it raises an InvalidPntsError exception.
"""
if SEMANTIC_CATEGORY_MAP[semantic] != category:
raise InvalidPntsError(
f"The category of {semantic} is {SEMANTIC_CATEGORY_MAP[semantic]}, it cannot be used to set {category}"
)
[docs]
def check_array_size(
array: npt.NDArray[np.float32 | np.uint16 | np.uint8],
semantic: SemanticPoint,
nb_points: int,
) -> None:
"""
This function checks if the size of the given array is correct according semantic and nb_points.
If not, it raises an InvalidPntsError exception.
"""
if len(array) != nb_points * SEMANTIC_DIMENSION_MAP[semantic]:
raise InvalidPntsError(
f"The array {SEMANTIC_CATEGORY_MAP[semantic]} has a wrong size. Expecting a size of "
f"{nb_points * SEMANTIC_DIMENSION_MAP[semantic]}, got {len(array)}"
)
[docs]
class PntsFeatureTableBody(FeatureTableBody):
def __init__(
self,
positions: None | (npt.NDArray[np.float32 | np.uint16]) = None,
color: npt.NDArray[np.uint8 | np.uint16] | None = None,
normal: npt.NDArray[np.float32 | np.uint8] | None = None,
) -> None:
self.position = (
positions if positions is not None else np.array([], dtype=np.float32)
)
self.color = color
self.normal = normal
[docs]
@classmethod
def from_array(
cls, feature_table_header: PntsFeatureTableHeader, array: npt.NDArray[np.uint8]
) -> PntsFeatureTableBody:
feature_table_body = cls()
nb_points = feature_table_header.points_length
# extract positions
feature_table_body.position = cls._fetch_semantic_from_array( # type: ignore [assignment] # there is an error on dtype, but _fetch_semantic_from_array runs check_semantic_type.
array,
feature_table_header.positions,
feature_table_header.positions_offset,
nb_points,
SemanticCategory.POSITION,
)
# extract colors
if feature_table_header.colors != SemanticPoint.NONE:
feature_table_body.color = cls._fetch_semantic_from_array( # type: ignore [assignment] # there is an error on dtype, but _fetch_semantic_from_array runs check_semantic_type.
array,
feature_table_header.colors,
feature_table_header.colors_offset,
nb_points,
SemanticCategory.COLOR,
)
# extract normals
if feature_table_header.normal != SemanticPoint.NONE:
feature_table_body.normal = cls._fetch_semantic_from_array( # type: ignore [assignment] # there is an error on dtype, but _fetch_semantic_from_array runs check_semantic_type.
array,
feature_table_header.normal,
feature_table_header.colors_offset,
nb_points,
SemanticCategory.NORMAL,
)
return feature_table_body
[docs]
def to_array(self) -> npt.NDArray[np.uint8]:
position_array = self.position.view(np.uint8)
length_array = len(position_array)
if self.color is not None:
color_array = self.color.view(np.uint8)
length_array += len(color_array)
else:
color_array = np.array([], dtype=np.uint8)
if self.normal is not None:
normal_array = self.normal.view(np.uint8)
length_array += len(normal_array)
else:
normal_array = np.array([], dtype=np.uint8)
padding_str = " " * ((8 - length_array) % 8 % 8)
padding = np.frombuffer(padding_str.encode("utf-8"), dtype=np.uint8)
return np.concatenate((position_array, color_array, normal_array, padding))
@staticmethod
def _fetch_semantic_from_array(
array: npt.NDArray[np.uint8],
semantic: SemanticPoint,
offset: int,
nb_points: int,
category: SemanticCategory,
) -> npt.NDArray[np.float32 | np.uint16 | np.uint8]:
check_semantic_type(semantic, category)
semantic_array = array[
offset : offset + SEMANTIC_ITEM_SIZE_MAP[semantic] * nb_points
].view(SEMANTIC_TYPE_MAP[semantic])
check_array_size(semantic_array, semantic, nb_points)
return semantic_array
[docs]
class PntsFeatureTable(FeatureTable[PntsFeatureTableHeader, PntsFeatureTableBody]):
def __init__(self) -> None:
self.header = PntsFeatureTableHeader()
self.body = PntsFeatureTableBody()
[docs]
def nb_points(self) -> int:
return self.header.points_length
[docs]
def to_array(self) -> npt.NDArray[np.uint8]:
fth_arr = self.header.to_array()
ftb_arr = self.body.to_array()
return np.concatenate((fth_arr, ftb_arr))
[docs]
@staticmethod
def from_array(
tile_header: TileContentHeader, array: npt.NDArray[np.uint8]
) -> PntsFeatureTable:
# build feature table header
feature_table_header = PntsFeatureTableHeader.from_array(
array[: tile_header.ft_json_byte_length]
)
feature_table_body = PntsFeatureTableBody.from_array(
feature_table_header,
array[
tile_header.ft_json_byte_length : tile_header.ft_json_byte_length
+ tile_header.ft_bin_byte_length
],
)
# build feature table
feature_table = PntsFeatureTable()
feature_table.header = feature_table_header
feature_table.body = feature_table_body
return feature_table
[docs]
@staticmethod
def from_features(
feature_table_header: PntsFeatureTableHeader,
position_array: npt.NDArray[np.float32 | np.uint16],
color_array: npt.NDArray[np.uint8 | np.uint16] | None = None,
normal_position: npt.NDArray[np.float32 | np.uint8] | None = None,
) -> PntsFeatureTable:
feature_table = PntsFeatureTable()
feature_table.header = feature_table_header
nb_points = feature_table.header.points_length
# set the position array
check_semantic_type(feature_table.header.positions, SemanticCategory.POSITION)
check_array_size(position_array, feature_table.header.positions, nb_points)
feature_table.body.position = position_array
# set the color array
if feature_table.header.colors != SemanticPoint.NONE:
if color_array is None:
raise InvalidPntsError(
f"The argument color_array cannot be None "
f"if the color has a semantic of {feature_table.header.colors} in the feature_table_header"
)
check_semantic_type(feature_table.header.colors, SemanticCategory.COLOR)
check_array_size(color_array, feature_table.header.colors, nb_points)
feature_table.body.color = color_array
# set the normal array
if feature_table.header.normal != SemanticPoint.NONE:
if normal_position is None:
raise InvalidPntsError(
f"The argument normal_array cannot be None "
f"if the color has a semantic of {feature_table.header.normal} in the feature_table_header"
)
check_semantic_type(feature_table.header.positions, SemanticCategory.NORMAL)
check_array_size(normal_position, feature_table.header.normal, nb_points)
feature_table.body.normal = normal_position
return feature_table
[docs]
def get_feature_at(
self, index: int
) -> tuple[
npt.NDArray[np.float32 | np.uint16],
npt.NDArray[np.uint8 | np.uint16] | None,
npt.NDArray[np.float32 | np.uint8] | None,
]:
position = self.get_feature_position_at(index)
color = self.get_feature_color_at(index)
normal = self.get_feature_normal_at(index)
return position, color, normal
[docs]
def get_feature_position_at(
self, index: int
) -> npt.NDArray[np.float32 | np.uint16]:
if index >= self.nb_points():
raise IndexError(
f"The index {index} is out of range. The number of point is {self.nb_points()}"
)
check_semantic_type(self.header.positions, SemanticCategory.POSITION)
dimension = SEMANTIC_DIMENSION_MAP[self.header.positions]
return self.body.position[dimension * index : dimension * (index + 1)]
[docs]
def get_feature_color_at(
self, index: int
) -> npt.NDArray[np.uint8 | np.uint16] | None:
if index >= self.nb_points():
raise IndexError(
f"The index {index} is out of range. The number of point is {self.nb_points()}"
)
if self.header.colors == SemanticPoint.NONE:
return self.header.constant_rgba
if self.body.color is None:
raise InvalidPntsError(
"The feature table body color shouldn't be None "
f"if self.header.colors is {self.header.colors}."
)
check_semantic_type(self.header.colors, SemanticCategory.COLOR)
dimension = SEMANTIC_DIMENSION_MAP[self.header.colors]
return self.body.color[dimension * index : dimension * (index + 1)]
[docs]
def get_feature_normal_at(
self, index: int
) -> npt.NDArray[np.float32 | np.uint8] | None:
if index >= self.nb_points():
raise IndexError(
f"The index {index} is out of range. The number of point is {self.nb_points()}"
)
if self.header.normal == SemanticPoint.NONE:
return None
if self.body.normal is None:
raise InvalidPntsError(
"The feature table body normal shouldn't be None "
f"if self.header.colors is {self.header.normal}."
)
check_semantic_type(self.header.normal, SemanticCategory.NORMAL)
dimension = SEMANTIC_DIMENSION_MAP[self.header.normal]
return self.body.normal[dimension * index : dimension * (index + 1)]