新增 N300/H32 rscap 到 V1 中间格式的导出工具与单元测试
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
lichun.qu
Cursor
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"""V2 .rscap readers and sensor decoders for export to V1 intermediate format."""
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@@ -0,0 +1,254 @@
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from __future__ import annotations
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import binascii
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import io
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import struct
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from dataclasses import dataclass, asdict
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from pathlib import Path
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from typing import BinaryIO, Iterator
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FILE_MAGIC = "RAW_SERIAL_CAPTURE_FILE_V2"
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RECORD_MAGIC = "RAW_SERIAL_RECORD_V2"
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FOOTER_MAGIC = "RAW_SERIAL_CAPTURE_FOOTER_V2"
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def read_7bit_int(stream: BinaryIO) -> int:
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value = 0
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shift = 0
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while True:
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raw = stream.read(1)
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if not raw:
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raise EOFError("truncated .NET string length")
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value |= (raw[0] & 0x7F) << shift
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if not raw[0] & 0x80:
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return value
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shift += 7
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if shift > 35:
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raise ValueError("invalid .NET string length")
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def read_dotnet_string(stream: BinaryIO) -> str:
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length = read_7bit_int(stream)
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raw = stream.read(length)
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if len(raw) != length:
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raise EOFError("truncated .NET string")
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return raw.decode("utf-8")
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def read_i32(stream: BinaryIO) -> int:
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raw = stream.read(4)
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if len(raw) != 4:
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raise EOFError("truncated int32")
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return struct.unpack("<i", raw)[0]
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def read_i64(stream: BinaryIO) -> int:
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raw = stream.read(8)
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if len(raw) != 8:
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raise EOFError("truncated int64")
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return struct.unpack("<q", raw)[0]
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def read_u32(stream: BinaryIO) -> int:
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raw = stream.read(4)
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if len(raw) != 4:
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raise EOFError("truncated uint32")
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return struct.unpack("<I", raw)[0]
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@dataclass(frozen=True)
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class CaptureHeader:
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sensor_kind: str
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session_id: str
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session_start_utc_ticks: int
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session_start_monotonic_ticks: int
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monotonic_frequency: int
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port: str
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baud: int
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file_start_utc_ticks: int
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@dataclass(frozen=True)
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class RawChunk:
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sequence: int
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receive_utc_ticks: int
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receive_monotonic_ticks: int
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raw: bytes
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record_file_offset: int
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raw_file_offset: int
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record_crc32: int
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crc_valid: bool
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@dataclass(frozen=True)
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class CaptureFooter:
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clean_close: bool
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records: int
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bytes: int
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first_sequence: int
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last_sequence: int
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dropped_chunks: int
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dropped_bytes: int
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crc_valid: bool
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@dataclass
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class CaptureFile:
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path: str
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header: CaptureHeader
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chunks: list[RawChunk]
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footer: CaptureFooter | None
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truncated_tail: bool = False
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def read_header(stream: BinaryIO) -> CaptureHeader:
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if read_dotnet_string(stream) != FILE_MAGIC:
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raise ValueError("not a V2 raw capture file")
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version = read_i32(stream)
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if version != 2:
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raise ValueError(f"unsupported capture version: {version}")
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return CaptureHeader(
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sensor_kind=read_dotnet_string(stream),
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session_id=read_dotnet_string(stream),
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session_start_utc_ticks=read_i64(stream),
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session_start_monotonic_ticks=read_i64(stream),
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monotonic_frequency=read_i64(stream),
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port=read_dotnet_string(stream),
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baud=read_i32(stream),
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file_start_utc_ticks=read_i64(stream),
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)
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def parse_record_body(body: bytes, record_file_offset: int, record_crc: int) -> RawChunk:
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stream = io.BytesIO(body)
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if read_dotnet_string(stream) != RECORD_MAGIC:
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raise ValueError("invalid record magic")
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sequence = read_i64(stream)
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receive_utc_ticks = read_i64(stream)
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receive_monotonic_ticks = read_i64(stream)
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raw_length = read_i32(stream)
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if raw_length < 0 or raw_length > 64 * 1024 * 1024:
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raise ValueError(f"invalid raw length: {raw_length}")
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raw_offset = record_file_offset + 4 + stream.tell()
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raw = stream.read(raw_length)
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if len(raw) != raw_length:
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raise EOFError("truncated raw bytes")
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crc_valid = (binascii.crc32(body) & 0xFFFFFFFF) == record_crc
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return RawChunk(
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sequence=sequence,
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receive_utc_ticks=receive_utc_ticks,
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receive_monotonic_ticks=receive_monotonic_ticks,
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raw=raw,
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record_file_offset=record_file_offset,
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raw_file_offset=raw_offset,
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record_crc32=record_crc,
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crc_valid=crc_valid,
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)
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def parse_footer(body: bytes, expected_crc: int) -> CaptureFooter:
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stream = io.BytesIO(body)
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if read_dotnet_string(stream) != FOOTER_MAGIC:
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raise ValueError("invalid footer magic")
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clean_close = stream.read(1) == b"\x01"
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records = read_i64(stream)
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raw_bytes = read_i64(stream)
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first_sequence = read_i64(stream)
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last_sequence = read_i64(stream)
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dropped_chunks = read_i64(stream)
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dropped_bytes = read_i64(stream)
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return CaptureFooter(
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clean_close=clean_close,
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records=records,
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bytes=raw_bytes,
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first_sequence=first_sequence,
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last_sequence=last_sequence,
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dropped_chunks=dropped_chunks,
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dropped_bytes=dropped_bytes,
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crc_valid=(binascii.crc32(body) & 0xFFFFFFFF) == expected_crc,
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)
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def read_capture(path: Path) -> CaptureFile:
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chunks: list[RawChunk] = []
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footer = None
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truncated = False
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with path.open("rb") as stream:
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header = read_header(stream)
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while True:
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record_offset = stream.tell()
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length_raw = stream.read(4)
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if not length_raw:
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break
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if len(length_raw) != 4:
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truncated = True
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break
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length = struct.unpack("<i", length_raw)[0]
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try:
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if length == -1:
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footer_length = read_i32(stream)
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if footer_length < 0 or footer_length > 1024 * 1024:
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raise ValueError("invalid footer length")
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footer_body = stream.read(footer_length)
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if len(footer_body) != footer_length:
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raise EOFError("truncated footer")
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footer = parse_footer(footer_body, read_u32(stream))
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break
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if length <= 0 or length > 64 * 1024 * 1024:
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raise ValueError("invalid record length")
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body = stream.read(length)
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if len(body) != length:
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raise EOFError("truncated record body")
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record_crc = read_u32(stream)
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chunks.append(parse_record_body(body, record_offset, record_crc))
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except (EOFError, ValueError):
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truncated = True
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break
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return CaptureFile(str(path), header, chunks, footer, truncated)
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def sequence_gaps(chunks: list[RawChunk]) -> list[tuple[int, int, int]]:
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result = []
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for previous, current in zip(chunks, chunks[1:]):
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if current.sequence > previous.sequence + 1:
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result.append((previous.sequence, current.sequence, current.sequence - previous.sequence - 1))
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return result
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def file_summary(capture: CaptureFile) -> dict:
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gaps = sequence_gaps(capture.chunks)
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sequences = [chunk.sequence for chunk in capture.chunks]
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return {
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"path": capture.path,
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"sensor": capture.header.sensor_kind,
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"session_id": capture.header.session_id,
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"port": capture.header.port,
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"baud": capture.header.baud,
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"chunks_read": len(capture.chunks),
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"bytes_read": sum(len(chunk.raw) for chunk in capture.chunks),
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"first_sequence": sequences[0] if sequences else None,
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"last_sequence": sequences[-1] if sequences else None,
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"missing_chunks": sum(gap[2] for gap in gaps),
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"gap_count": len(gaps),
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"bad_record_crc": sum(not chunk.crc_valid for chunk in capture.chunks),
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"truncated_tail": capture.truncated_tail,
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"footer": None if capture.footer is None else asdict(capture.footer),
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"gaps": gaps[:100],
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}
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def iter_contiguous_segments(chunks: list[RawChunk]) -> Iterator[tuple[int, list[RawChunk]]]:
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if not chunks:
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return
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segment_id = 0
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current = [chunks[0]]
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for previous, chunk in zip(chunks, chunks[1:]):
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if chunk.sequence != previous.sequence + 1:
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yield segment_id, current
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segment_id += 1
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current = [chunk]
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else:
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current.append(chunk)
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yield segment_id, current
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@@ -0,0 +1,224 @@
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"""Decode RoboSense H32 MSOP V2 .rscap into Cartesian frames (metres).
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Angle / distance conventions follow ``RSLidarH32_3D_RawCaptureNet48``:
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azimuth = normalize(-(block_az + horizontal[ch])), altitude = vertical[ch],
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distance_mm = raw * distance_unit_mm, then:
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x = d_m * cos(alt) * cos(az)
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y = d_m * cos(alt) * sin(az)
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z = d_m * sin(alt)
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MSOP-only captures do not include DIFOP; vertical angles default to a uniform
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-16°…+16° fan, horizontal channel offsets default to 0.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import numpy as np
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from .capture_format_v2 import CaptureFile
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PACKET_LENGTH = 1248
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DATA_START = 42
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BLOCKS = 12
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BLOCK_LENGTH = 100
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CHANNELS = 32
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MIN_FRAME_POINTS_DEFAULT = 100
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def default_vertical_deg() -> np.ndarray:
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return -16.0 + np.arange(CHANNELS, dtype=np.float64) * (32.0 / (CHANNELS - 1))
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def default_horizontal_deg() -> np.ndarray:
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return np.zeros(CHANNELS, dtype=np.float64)
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def read_u16_be(packet: bytes, index: int) -> int:
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return (packet[index] << 8) | packet[index + 1]
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def device_timestamp_ms(packet: bytes) -> int:
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seconds = int.from_bytes(packet[20:26], "big")
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microseconds = int.from_bytes(packet[26:30], "big")
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return seconds * 1000 + microseconds // 1000
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def distance_unit_mm(packet: bytes, *, auto: bool = True, fallback: float = 2.5) -> float:
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if not auto:
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return float(fallback)
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return 2.5 if packet[17] == 1 else 0.5
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def normalize_azimuth_deg(angle: float) -> float:
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while angle > 180.0:
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angle -= 360.0
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while angle < -180.0:
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angle += 360.0
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return angle
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@dataclass
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class LidarFrameExport:
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t_start_s: float
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t_end_s: float
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points_xyz: np.ndarray # (N, 3) metres
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def decode_packet_points(
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packet: bytes,
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vertical_deg: np.ndarray,
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horizontal_deg: np.ndarray,
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*,
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min_range_m: float = 0.3,
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max_range_m: float = 120.0,
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) -> tuple[list[float], np.ndarray]:
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"""Decode one MSOP packet into block azimuths and concatenated XYZ points."""
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if len(packet) != PACKET_LENGTH:
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return [], np.zeros((0, 3), dtype=np.float64)
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unit = distance_unit_mm(packet)
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az_list: list[float] = []
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chunks: list[np.ndarray] = []
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idx = DATA_START
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for _block in range(BLOCKS):
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if idx + BLOCK_LENGTH > PACKET_LENGTH or packet[idx] != 255 or packet[idx + 1] != 238:
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break
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az = read_u16_be(packet, idx + 2) * 0.01
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az_list.append(az)
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pts = _block_points(
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packet,
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idx,
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az,
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unit,
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vertical_deg,
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horizontal_deg,
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min_range_m=min_range_m,
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max_range_m=max_range_m,
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)
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if pts.shape[0]:
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chunks.append(pts)
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idx += BLOCK_LENGTH
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if not chunks:
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return az_list, np.zeros((0, 3), dtype=np.float64)
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return az_list, np.vstack(chunks)
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def _block_points(
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packet: bytes,
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block_offset: int,
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az_deg: float,
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unit_mm: float,
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vertical_deg: np.ndarray,
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horizontal_deg: np.ndarray,
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*,
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min_range_m: float,
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max_range_m: float,
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) -> np.ndarray:
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xs: list[float] = []
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ys: list[float] = []
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zs: list[float] = []
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idx = block_offset + 4 # after FF EE + azimuth
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for ch in range(CHANNELS):
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raw = read_u16_be(packet, idx)
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idx += 3
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if raw == 0:
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continue
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d_m = (raw * unit_mm) * 0.001
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if d_m < min_range_m or d_m > max_range_m:
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continue
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az_ch = np.deg2rad(normalize_azimuth_deg(-(az_deg + float(horizontal_deg[ch]))))
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alt = np.deg2rad(float(vertical_deg[ch]))
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cos_alt = np.cos(alt)
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xs.append(d_m * cos_alt * np.cos(az_ch))
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ys.append(d_m * cos_alt * np.sin(az_ch))
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zs.append(d_m * np.sin(alt))
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if not xs:
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return np.zeros((0, 3), dtype=np.float64)
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return np.column_stack([xs, ys, zs]).astype(np.float64, copy=False)
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def iter_h32_frames(
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capture: CaptureFile,
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*,
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min_frame_points: int = MIN_FRAME_POINTS_DEFAULT,
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frame_stride: int = 1,
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min_range_m: float = 0.3,
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max_range_m: float = 120.0,
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max_points_per_frame: int | None = None,
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vertical_deg: np.ndarray | None = None,
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horizontal_deg: np.ndarray | None = None,
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) -> list[LidarFrameExport]:
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"""Assemble MSOP packets into frames using the 270°→90° azimuth wrap."""
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vertical = default_vertical_deg() if vertical_deg is None else np.asarray(vertical_deg, dtype=np.float64)
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horizontal = default_horizontal_deg() if horizontal_deg is None else np.asarray(horizontal_deg, dtype=np.float64)
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if vertical.shape != (CHANNELS,) or horizontal.shape != (CHANNELS,):
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raise ValueError(f"vertical/horizontal must have shape ({CHANNELS},)")
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frames: list[LidarFrameExport] = []
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point_chunks: list[np.ndarray] = []
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t_start: float | None = None
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t_end: float | None = None
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prev_az: float | None = None
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kept = 0
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stride = max(1, int(frame_stride))
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def emit() -> None:
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nonlocal point_chunks, t_start, t_end, kept
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if not point_chunks or t_start is None or t_end is None:
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point_chunks = []
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t_start = t_end = None
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return
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points = np.vstack(point_chunks)
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point_chunks = []
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start_s, end_s = t_start, t_end
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t_start = t_end = None
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if points.shape[0] < min_frame_points:
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return
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if kept % stride != 0:
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kept += 1
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return
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kept += 1
|
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if max_points_per_frame is not None and points.shape[0] > max_points_per_frame:
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select = np.linspace(0, points.shape[0] - 1, max_points_per_frame, dtype=int)
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points = points[select]
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if end_s <= start_s:
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end_s = start_s + 0.1
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frames.append(LidarFrameExport(t_start_s=start_s, t_end_s=end_s, points_xyz=points))
|
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for chunk in capture.chunks:
|
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packet = chunk.raw
|
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if len(packet) != PACKET_LENGTH:
|
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continue
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packet_t = device_timestamp_ms(packet) * 1e-3
|
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unit = distance_unit_mm(packet)
|
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idx = DATA_START
|
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for _block in range(BLOCKS):
|
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if idx + BLOCK_LENGTH > PACKET_LENGTH or packet[idx] != 255 or packet[idx + 1] != 238:
|
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break
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az = read_u16_be(packet, idx + 2) * 0.01
|
||||
if prev_az is not None and prev_az > 270.0 and az < 90.0:
|
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emit()
|
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prev_az = az
|
||||
pts = _block_points(
|
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packet,
|
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idx,
|
||||
az,
|
||||
unit,
|
||||
vertical,
|
||||
horizontal,
|
||||
min_range_m=min_range_m,
|
||||
max_range_m=max_range_m,
|
||||
)
|
||||
if pts.shape[0]:
|
||||
if t_start is None:
|
||||
t_start = packet_t
|
||||
t_end = packet_t
|
||||
point_chunks.append(pts)
|
||||
idx += BLOCK_LENGTH
|
||||
|
||||
emit()
|
||||
return frames
|
||||
@@ -0,0 +1,113 @@
|
||||
"""Decode Wheeltec N300 FDILink IMU frames from a V2 .rscap capture."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import struct
|
||||
from dataclasses import dataclass
|
||||
|
||||
import numpy as np
|
||||
|
||||
from .capture_format_v2 import CaptureFile, RawChunk, iter_contiguous_segments
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ImuSample:
|
||||
t_s: float
|
||||
gyro_rad_s: tuple[float, float, float]
|
||||
accel_m_s2: tuple[float, float, float]
|
||||
host_receive_utc_ticks: int
|
||||
device_timestamp_us: int
|
||||
|
||||
|
||||
def crc8_fdilink(data: bytes) -> int:
|
||||
crc = 0
|
||||
for value in data:
|
||||
crc ^= value
|
||||
for _ in range(8):
|
||||
crc = ((crc << 1) ^ 0x07) & 0xFF if crc & 0x80 else (crc << 1) & 0xFF
|
||||
return crc
|
||||
|
||||
|
||||
def crc16_fdilink(data: bytes) -> int:
|
||||
crc = 0
|
||||
for value in data:
|
||||
crc ^= value << 8
|
||||
for _ in range(8):
|
||||
crc = ((crc << 1) ^ 0x1021) & 0xFFFF if crc & 0x8000 else (crc << 1) & 0xFFFF
|
||||
return crc
|
||||
|
||||
|
||||
def _host_ticks_for_span(chunks: list[RawChunk], start: int, end: int) -> int:
|
||||
stream_offset = 0
|
||||
last = chunks[0]
|
||||
for chunk in chunks:
|
||||
next_offset = stream_offset + len(chunk.raw)
|
||||
if start < next_offset and end > stream_offset:
|
||||
last = chunk
|
||||
stream_offset = next_offset
|
||||
return last.receive_utc_ticks
|
||||
|
||||
|
||||
def iter_n300_imu_samples(capture: CaptureFile) -> list[ImuSample]:
|
||||
"""Return CRC-valid MSG_IMU (0x40) samples sorted by device timestamp."""
|
||||
|
||||
samples: list[ImuSample] = []
|
||||
expected_lengths = {0x40: 56, 0x41: 48}
|
||||
for _segment_id, chunks in iter_contiguous_segments(capture.chunks):
|
||||
stream = b"".join(chunk.raw for chunk in chunks)
|
||||
cursor = 0
|
||||
while cursor < len(stream):
|
||||
start = stream.find(b"\xFC", cursor)
|
||||
if start < 0:
|
||||
break
|
||||
if start + 8 > len(stream):
|
||||
break
|
||||
payload_length = stream[start + 2]
|
||||
end = start + payload_length + 8
|
||||
if end > len(stream):
|
||||
if stream.find(b"\xFC", start + 1) < 0:
|
||||
break
|
||||
cursor = start + 1
|
||||
continue
|
||||
frame = stream[start:end]
|
||||
if frame[-1] != 0xFD:
|
||||
cursor = start + 1
|
||||
continue
|
||||
packet_id = frame[1]
|
||||
payload = frame[7:-1]
|
||||
header_ok = crc8_fdilink(frame[:4]) == frame[4]
|
||||
payload_ok = crc16_fdilink(payload) == int.from_bytes(frame[5:7], "big")
|
||||
expected = expected_lengths.get(packet_id)
|
||||
length_ok = expected is None or len(payload) == expected
|
||||
if not (header_ok and payload_ok and length_ok):
|
||||
cursor = start + 1
|
||||
continue
|
||||
if packet_id == 0x40:
|
||||
gyro = struct.unpack_from("<3f", payload, 0)
|
||||
accel = struct.unpack_from("<3f", payload, 12)
|
||||
device_us = struct.unpack_from("<q", payload, 48)[0]
|
||||
samples.append(
|
||||
ImuSample(
|
||||
t_s=float(device_us) * 1e-6,
|
||||
gyro_rad_s=gyro,
|
||||
accel_m_s2=accel,
|
||||
host_receive_utc_ticks=_host_ticks_for_span(chunks, start, end),
|
||||
device_timestamp_us=int(device_us),
|
||||
)
|
||||
)
|
||||
cursor = end
|
||||
samples.sort(key=lambda sample: (sample.t_s, sample.device_timestamp_us))
|
||||
return samples
|
||||
|
||||
|
||||
def samples_to_arrays(samples: list[ImuSample]) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
if not samples:
|
||||
return (
|
||||
np.zeros(0, dtype=np.float64),
|
||||
np.zeros((0, 3), dtype=np.float64),
|
||||
np.zeros((0, 3), dtype=np.float64),
|
||||
)
|
||||
t = np.asarray([sample.t_s for sample in samples], dtype=np.float64)
|
||||
gyro = np.asarray([sample.gyro_rad_s for sample in samples], dtype=np.float64)
|
||||
accel = np.asarray([sample.accel_m_s2 for sample in samples], dtype=np.float64)
|
||||
return t, gyro, accel
|
||||
Reference in New Issue
Block a user