export cli only from pointcloud supported formats in CLI

Author: tgoelles

CHANGELOG.rst

@@ -5,6 +5,17 @@ All notable changes to this project will be documented in this file.
 The format is based on `Keep a Changelog <https://keepachangelog.com/en/1.0.0/>`_,
 and this project adheres to `Semantic Versioning <https://semver.org/spec/v2.0.0.html>`_.
 
+Unreleased
+-------------
+
+Added
+~~~~~~
+- exporting of las file using laspy
+
+Fixed
+~~~~~~
+- wrong helpt for pointcloudset convert CLI: the bag or mcap dir needs to come right after the command
+
 0.10.1- (2025-04-28)
 -------------
 Changed

src/pointcloudset/io/dataset/commandline.py

@@ -5,18 +5,19 @@
 
 import click  # needed for documentation
 import typer
-from pyntcloud.io import TO_FILE
 from rich.console import Console
 from rosbags.highlevel import AnyReader
 
 import pointcloudset
 from pointcloudset import Dataset
+from pointcloudset.io import POINTCLOUD_TO_FILE
 
 app = typer.Typer()
 console = Console()
 
-TO_FILE_PYNTCLOUD = list(TO_FILE.keys())
-TO_FILE_CLI = TO_FILE_PYNTCLOUD.append("POINTCLOUDSET")
+
+TO_FILE_PYNTCLOUD = [k.lower() for k in POINTCLOUD_TO_FILE.keys()]
+TO_FILE_CLI = TO_FILE_PYNTCLOUD + ["pointcloudset"]
 
 
 @app.command()
@@ -35,16 +36,16 @@ def convert(
     Examples:
 
     convert all ROS1 bag files in a directory
-    $ pointcloudset convert -d converted .
+    $ pointcloudset convert . -d converted
 
     convert all frames of bagfile xyz.bag into csv files
-    $ pointcloudset convert -o csv -d converted_csv xyz.bag
+    $ pointcloudset convert xyz.bag -o csv -d converted_csv xyz.bag
 
     convert a ROS2 directoy to a pointcloudset file
-    $ pointcloudset convert -d converted something_ros2
+    $ pointcloudset convert something_ros2 -d converted
 
     convert the first 10 frames of a bag file int0las files
-    $ pointcloudset convert -o las -d converted_las --start 1 --end 10 xyz.bag
+    $ pointcloudset convert xyz.bag -o las -d converted_las --start 1 --end 10
     """
     console.line()
     console.rule(f"pointcloudset {pointcloudset.__version__}")
@@ -66,7 +67,7 @@ def convert(
                     folder_to_write=folder_to_write_path,
                 )
                 console.print(f"{Path(bagfile_path).name} converted to {folder_to_write_path}")
-            elif output_format.upper() in TO_FILE_PYNTCLOUD:
+            elif output_format.lower() in TO_FILE_CLI:
                 _convert_bag2files(
                     topic,
                     start_frame_number,
@@ -145,7 +146,7 @@ def _gen_file_paths(file_name):
 
 def _gen_folder(folder_to_write: str, ros_file_path: str, output_format: str) -> Path:
     """Generate the folder to write the converted files to."""
-    suffix = "_pointcloudset" if output_format == "POINTCLOUDSET" else ""
+    suffix = "_pointcloudset" if output_format == "POINTCLOUDSET" else f"_{output_format.lower()}"
     folder_to_write_path = Path(folder_to_write).joinpath(Path(ros_file_path).stem + suffix)
     folder_to_write_path.mkdir(exist_ok=False, parents=True)
     return folder_to_write_path
@@ -179,11 +180,11 @@ def _convert_bag2files(
     if end_frame_number is None:
         end_frame_number = len(dataset)
     for frame in range(start_frame_number, end_frame_number):
-        pyntcloud = dataset[frame].to_instance("PYNTCLOUD")
+        frame_pc = dataset[frame]
         orig_file = Path(ros_file_path).stem
         filename = folder_to_write_path.joinpath(f"{orig_file}_{frame}.{output_format.lower()}")
         console.print(f"frame {frame} of {Path(ros_file_path).name} converted to {filename}")
-        pyntcloud.to_file(filename.as_posix())
+        frame_pc.to_file(filename)
 
 
 typer_click_object = typer.main.get_command(app)

src/pointcloudset/io/pointcloud/las.py

@@ -12,19 +12,6 @@
 LAS_VERSION = "1.4"
 LAS_PRECISION = 0.0001  # m
 
-_NUMPY2LAS = {
-    "uint8": "u1",
-    "int8": "i1",
-    "uint16": "u2",
-    "int16": "i2",
-    "uint32": "u4",
-    "int32": "i4",
-    "uint64": "u8",
-    "int64": "i8",
-    "float32": "f4",
-    "float64": "f8",
-}
-
 
 def _choose_scale_offset(axis: np.ndarray) -> tuple[float, float]:
     """
@@ -46,6 +33,55 @@ def _choose_scale_offset(axis: np.ndarray) -> tuple[float, float]:
     return scale, offset
 
 
+def _best_las_type(arr: np.ndarray) -> str:
+    """
+    Return the smallest LAS data-type code ('u1', 'i1', …, 'f8') that can
+    represent *arr* loss-lessly.
+
+    LAS → NumPy mapping codes:
+    u1/i1  : unsigned/signed   8-bit integer
+    u2/i2  : unsigned/signed  16-bit integer
+    u4/i4  : unsigned/signed  32-bit integer
+    u8/i8  : unsigned/signed  64-bit integer
+    f4/f8  : 32- / 64-bit float
+    """
+    dt = arr.dtype
+
+    # Integers
+    if dt.kind in {"u", "i"}:
+        lo, hi = int(arr.min()), int(arr.max())
+        if dt.kind == "u":  # unsigned
+            if hi <= np.iinfo(np.uint8).max:
+                return "u1"
+            if hi <= np.iinfo(np.uint16).max:
+                return "u2"
+            if hi <= np.iinfo(np.uint32).max:
+                return "u4"
+            return "u8"
+        else:  # signed
+            if lo >= np.iinfo(np.int8).min and hi <= np.iinfo(np.int8).max:
+                return "i1"
+            if lo >= np.iinfo(np.int16).min and hi <= np.iinfo(np.int16).max:
+                return "i2"
+            if lo >= np.iinfo(np.int32).min and hi <= np.iinfo(np.int32).max:
+                return "i4"
+            return "i8"
+
+    # Floats
+    if dt.kind == "f":
+        # If nothing is lost when casting to f4, prefer it.
+        if np.allclose(arr.astype(np.float32), arr, rtol=0, atol=0):
+            return "f4"
+        return "f8"
+
+    #  Booleans / bit-fields
+    if dt.kind == "b":
+        return "u1"  # 0 / 1
+
+    # default to 64-bit float
+    return "f8"
+
+
 def write_las(pointcloud: "PointCloud", file_path: Path) -> None:
     """
     Export to LAS/LAZ (point format 7).  Coordinates are stored at *precision_hint* or the
@@ -80,8 +116,7 @@ def write_las(pointcloud: "PointCloud", file_path: Path) -> None:
     #  LAS ExtraBytes for everything else
     extra = {c for c in df.columns if c.lower() not in builtin}
     for col in sorted(extra):
-        las_type = _NUMPY2LAS.get(df[col].dtype.name, "f8")
+        las_type = _best_las_type(df[col].to_numpy())
         las.add_extra_dim(laspy.ExtraBytesParams(name=col, type=las_type))
         las[col] = df[col].to_numpy()
-
     las.write(Path(file_path))