Source code for py3dtiles.reader.xyz_reader

"""
Reads points from a .xyz or .csv file

Consider XYZIRGB format following FME documentation(*). We do the
following hypothesis and enhancements:

- A header line defining columns in CSV style may be present, but will be ignored (please open an issue if you have a use case where the header is important)
- The separator separating the columns is automagically guessed by the
  reader. This is generally fail safe. It will not harm to use commonly
  accepted separators like space, tab, colon, semi-colon.
- The order of columns is fixed. The reader does the following assumptions:
  - 3 columns mean XYZ
  - 4 columns mean XYZI
  - 6 columns mean XYZRGB
  - 7 columns mean XYZIRGB
  - 8 columns mean XYZIRGB followed by classification data. Classification data must be integers only.
  - all columns after the 8th column will be ignored.

NOTE: we assume RGB are 8 bits components.

(*) See: https://docs.safe.com/fme/html/FME_Desktop_Documentation/FME_ReadersWriters/pointcloudxyz/pointcloudxyz.htm
"""
import csv
import math
from collections.abc import Iterator
from pathlib import Path
from typing import Optional

import numpy as np
import numpy.typing as npt
from pyproj import Transformer

from py3dtiles.typing import MetadataReaderType, OffsetScaleType, PortionItemType




def get_metadata(path: Path) -> MetadataReaderType:
    aabb = None
    point_count = 0
    seek_values = []

    with path.open() as f:
        file_sample = f.read(
            2048
        )  # For performance reasons we just snif the first part
        dialect = csv.Sniffer().sniff(file_sample)

        f.seek(0)
        if csv.Sniffer().has_header(file_sample):
            f.readline()

        while True:
            batch = 10_000
            points = np.zeros((batch, 3))

            offset = f.tell()
            for i in range(batch):
                line = f.readline()
                if not line:
                    points = np.resize(points, (i, 3))
                    break
                points[i] = [float(s) for s in line.split(dialect.delimiter)][:3]

            if points.shape[0] == 0:
                break

            if not point_count % 1_000_000:
                seek_values += [offset]

            point_count += points.shape[0]
            batch_aabb = np.array([np.min(points, axis=0), np.max(points, axis=0)])

            # Update aabb
            if aabb is None:
                aabb = batch_aabb
            else:
                aabb[0] = np.minimum(aabb[0], batch_aabb[0])
                aabb[1] = np.maximum(aabb[1], batch_aabb[1])

        _1M = min(point_count, 1_000_000)
        steps = math.ceil(point_count / _1M)
        if steps != len(seek_values):
            raise ValueError(
                "the size of seek_values should be equal to steps,"
                f"currently steps = {steps} and len(seek_values) = {len(seek_values)}"
            )
        portions: list[PortionItemType] = [
            (i * _1M, min(point_count, (i + 1) * _1M), seek_values[i])
            for i in range(steps)
        ]

        pointcloud_file_portions = [(path, p) for p in portions]

    if aabb is None:
        raise ValueError(f"There is no point in the file {path}")

    return {
        "portions": pointcloud_file_portions,
        "aabb": aabb,
        "crs_in": None,
        "point_count": point_count,
        "avg_min": aabb[0],
    }





def run(
    filename: Path,
    offset_scale: OffsetScaleType,
    portion: PortionItemType,
    transformer: Optional[Transformer],
    color_scale: Optional[float],
    write_intensity: bool,
) -> Iterator[
    tuple[
        npt.NDArray[np.float32],
        npt.NDArray[np.uint8],
        npt.NDArray[np.uint8],
        npt.NDArray[np.uint8],
    ],
]:
    with open(filename) as f:

        dialect = csv.Sniffer().sniff(f.read(2048))
        f.seek(0)
        f.readline()  # skip first line in case there is a header we promised to ignore
        feature_nb = len(f.readline().split(dialect.delimiter))
        if feature_nb < 8:
            feature_nb = 7  # we pad to 7 columns with 0
        if feature_nb > 8:
            feature_nb = 8  # We ignore other data as downstream only 1 value for classification data is supported.
            # Once downstream supports multiple classification values this reader will as well
            # when this line is removed.

        point_count = portion[1] - portion[0]

        step = min(point_count, max((point_count) // 10, 100000))

        f.seek(portion[2])

        for _ in range(0, point_count, step):
            points = np.zeros((step, feature_nb), dtype=np.float32)

            for j in range(step):
                line = f.readline()
                if not line:
                    points = np.resize(points, (j, feature_nb))
                    break
                line_features: list[Optional[float]] = [
                    float(s) for s in line.split(dialect.delimiter)[:feature_nb]
                ]
                if len(line_features) == 3:
                    line_features += [None] * 4  # Insert intensity and RGB
                elif len(line_features) == 4:
                    line_features += [None] * 3  # Insert RGB
                elif len(line_features) == 6:
                    line_features.insert(3, None)  # Insert intensity
                points[j] = line_features

            x, y, z = (points[:, c] for c in [0, 1, 2])

            if transformer:
                x, y, z = transformer.transform(x, y, z)

            x = (x + offset_scale[0][0]) * offset_scale[1][0]
            y = (y + offset_scale[0][1]) * offset_scale[1][1]
            z = (z + offset_scale[0][2]) * offset_scale[1][2]

            coords = np.vstack((x, y, z)).transpose()

            if offset_scale[2] is not None:
                # Apply transformation matrix (because the tile's transform will contain
                # the inverse of this matrix)
                coords = np.dot(coords, offset_scale[2])

            coords = np.ascontiguousarray(coords.astype(np.float32))

            # Read colors: 3 last columns when excluding classification data
            if color_scale is None:
                colors = points[:, 4:7].astype(np.uint8)
            else:
                colors = np.clip(points[:, 4:7] * color_scale, 0, 255).astype(np.uint8)

            if feature_nb > 7:  # we have classification data
                classification = np.array(points[:, 7:], dtype=np.uint8).reshape(-1, 1)
            else:
                classification = np.zeros((points.shape[0], 1), dtype=np.uint8)

            if feature_nb in (4, 7, 8) and write_intensity:
                intensity = np.array(points[:, 3], dtype=np.uint8).reshape(-1, 1)
            else:
                intensity = np.zeros((points.shape[0], 1), dtype=np.uint8)

            yield coords, colors, classification, intensity