Datasets:
File size: 20,414 Bytes
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"""Generate one offline HTML page comparing tracking and RTK trajectories."""
from __future__ import annotations
import argparse
import csv
import html
import math
from dataclasses import dataclass
from pathlib import Path
import plotly.graph_objects as go
import plotly.io as pio
from plotly.subplots import make_subplots
EXPECTED_FLIGHTS = tuple(f"flight_{index:02d}" for index in range(1, 17))
RTK_COLOR = "#16803c"
TRACKING_COLOR = "#d44a3a"
VEHICLE_COLOR = "#24527a"
GRID_COLOR = "#dfe5e8"
TEXT_COLOR = "#243238"
MINIMUM_AXIS_DISPLAY_RATIO = 0.30
@dataclass
class Trajectory:
flight_id: str
timestamp_us: list[int]
tracking_x_m: list[float]
tracking_y_m: list[float]
tracking_z_m: list[float]
rtk_x_m: list[float]
rtk_y_m: list[float]
rtk_z_m: list[float]
error_3d_m: list[float]
@property
def relative_time_s(self) -> list[float]:
first = self.timestamp_us[0]
return [(value - first) / 1e6 for value in self.timestamp_us]
@property
def rmse_3d_m(self) -> float:
return math.sqrt(
math.fsum(value * value for value in self.error_3d_m)
/ len(self.error_3d_m)
)
def parse_args() -> argparse.Namespace:
script_dir = Path(__file__).resolve().parent
default_root = script_dir.parent
parser = argparse.ArgumentParser(
description="生成全部航次识别轨迹与 RTK 轨迹的离线 HTML"
)
parser.add_argument(
"dataset_root",
nargs="?",
type=Path,
default=default_root,
help="数据集根目录;默认根据脚本位置确定",
)
parser.add_argument(
"--input",
type=Path,
help="轨迹 CSV;默认为 DATASET_ROOT/script/output/paired_errors.csv",
)
parser.add_argument(
"--output",
type=Path,
help="HTML 路径;默认为 DATASET_ROOT/script/output/trajectories.html",
)
return parser.parse_args()
def finite_float(row: dict[str, str], key: str, line_number: int) -> float:
try:
value = float(row[key])
except (KeyError, TypeError, ValueError) as error:
raise ValueError(f"第 {line_number} 行的 {key} 无法解析") from error
if not math.isfinite(value):
raise ValueError(f"第 {line_number} 行的 {key} 不是有限数")
return value
def empty_trajectory(flight_id: str) -> Trajectory:
return Trajectory(
flight_id=flight_id,
timestamp_us=[],
tracking_x_m=[],
tracking_y_m=[],
tracking_z_m=[],
rtk_x_m=[],
rtk_y_m=[],
rtk_z_m=[],
error_3d_m=[],
)
def read_trajectories(path: Path) -> list[Trajectory]:
required_columns = {
"flight_id",
"timestamp_us",
"tracking_x_v_m",
"tracking_y_v_m",
"tracking_z_v_m",
"reference_x_v_m",
"reference_y_v_m",
"reference_z_v_m",
"error_3d_m",
}
grouped: dict[str, Trajectory] = {}
with path.open("r", encoding="utf-8", newline="") as stream:
reader = csv.DictReader(stream)
fieldnames = set(reader.fieldnames or ())
missing = sorted(required_columns - fieldnames)
if missing:
raise ValueError(f"{path} 缺少字段:{', '.join(missing)}")
for line_number, row in enumerate(reader, start=2):
flight_id = row["flight_id"]
if flight_id not in EXPECTED_FLIGHTS:
raise ValueError(f"第 {line_number} 行包含未知航次:{flight_id}")
trajectory = grouped.setdefault(
flight_id, empty_trajectory(flight_id)
)
try:
timestamp_us = int(row["timestamp_us"])
except (TypeError, ValueError) as error:
raise ValueError(
f"第 {line_number} 行的 timestamp_us 无法解析"
) from error
trajectory.timestamp_us.append(timestamp_us)
trajectory.tracking_x_m.append(
finite_float(row, "tracking_x_v_m", line_number)
)
trajectory.tracking_y_m.append(
finite_float(row, "tracking_y_v_m", line_number)
)
trajectory.tracking_z_m.append(
finite_float(row, "tracking_z_v_m", line_number)
)
trajectory.rtk_x_m.append(
finite_float(row, "reference_x_v_m", line_number)
)
trajectory.rtk_y_m.append(
finite_float(row, "reference_y_v_m", line_number)
)
trajectory.rtk_z_m.append(
finite_float(row, "reference_z_v_m", line_number)
)
trajectory.error_3d_m.append(
finite_float(row, "error_3d_m", line_number)
)
missing_flights = sorted(set(EXPECTED_FLIGHTS) - set(grouped))
if missing_flights:
raise ValueError(f"{path} 缺少航次:{', '.join(missing_flights)}")
trajectories = [grouped[flight_id] for flight_id in EXPECTED_FLIGHTS]
for trajectory in trajectories:
if len(trajectory.timestamp_us) < 2:
raise ValueError(f"{trajectory.flight_id} 的有效轨迹点不足 2 个")
order = sorted(
range(len(trajectory.timestamp_us)),
key=trajectory.timestamp_us.__getitem__,
)
for field_name in (
"timestamp_us",
"tracking_x_m",
"tracking_y_m",
"tracking_z_m",
"rtk_x_m",
"rtk_y_m",
"rtk_z_m",
"error_3d_m",
):
values = getattr(trajectory, field_name)
setattr(trajectory, field_name, [values[index] for index in order])
return trajectories
def scene_aspect_ratio(trajectory: Trajectory) -> dict[str, float]:
coordinates = (
trajectory.tracking_x_m + trajectory.rtk_x_m + [0.0],
trajectory.tracking_y_m + trajectory.rtk_y_m + [0.0],
trajectory.tracking_z_m + trajectory.rtk_z_m + [0.0],
)
spans = [max(values) - min(values) for values in coordinates]
maximum_span = max(spans)
if maximum_span <= 0.0:
return {"x": 1.0, "y": 1.0, "z": 1.0}
ratios = [
max(span / maximum_span, MINIMUM_AXIS_DISPLAY_RATIO)
for span in spans
]
return {"x": ratios[0], "y": ratios[1], "z": ratios[2]}
def build_overview(trajectories: list[Trajectory]) -> go.Figure:
rows = 4
columns = 4
titles = [
(
f"<b>{trajectory.flight_id}</b>"
f" · RMSE {trajectory.rmse_3d_m:.3f} m"
)
for trajectory in trajectories
]
figure = make_subplots(
rows=rows,
cols=columns,
specs=[
[{"type": "scene"} for _ in range(columns)]
for _ in range(rows)
],
subplot_titles=titles,
horizontal_spacing=0.035,
vertical_spacing=0.065,
)
for index, trajectory in enumerate(trajectories):
row = index // columns + 1
column = index % columns + 1
figure.add_trace(
go.Scatter3d(
x=trajectory.rtk_x_m,
y=trajectory.rtk_y_m,
z=trajectory.rtk_z_m,
mode="lines",
line={"color": RTK_COLOR, "width": 6},
name="RTK 轨迹",
legendgroup="rtk",
showlegend=index == 0,
customdata=[
[time_s]
for time_s in trajectory.relative_time_s
],
hovertemplate=(
"<b>RTK</b><br>"
"X=%{x:.2f} m<br>Y=%{y:.2f} m<br>Z=%{z:.2f} m<br>"
"t=%{customdata[0]:.1f} s<extra></extra>"
),
),
row=row,
col=column,
)
figure.add_trace(
go.Scatter3d(
x=[0.0],
y=[0.0],
z=[0.0],
mode="markers+text",
marker={
"color": VEHICLE_COLOR,
"size": 7,
"symbol": "diamond",
"line": {"color": "#ffffff", "width": 1},
},
text=["车辆原点"],
textposition="top center",
textfont={"color": VEHICLE_COLOR, "size": 11},
name="车辆原点",
legendgroup="vehicle",
showlegend=index == 0,
hovertemplate=(
"<b>车辆原点</b><br>"
"X=0.00 m<br>Y=0.00 m<br>Z=0.00 m<extra></extra>"
),
),
row=row,
col=column,
)
figure.add_trace(
go.Scatter3d(
x=trajectory.tracking_x_m,
y=trajectory.tracking_y_m,
z=trajectory.tracking_z_m,
mode="lines",
line={"color": TRACKING_COLOR, "width": 5},
name="识别轨迹",
legendgroup="tracking",
showlegend=index == 0,
customdata=[
[time_s, error_m]
for time_s, error_m in zip(
trajectory.relative_time_s,
trajectory.error_3d_m,
)
],
hovertemplate=(
"<b>识别</b><br>"
"X=%{x:.2f} m<br>Y=%{y:.2f} m<br>Z=%{z:.2f} m<br>"
"t=%{customdata[0]:.1f} s<br>"
"3D 误差=%{customdata[1]:.3f} m<extra></extra>"
),
),
row=row,
col=column,
)
for index, trajectory in enumerate(trajectories):
scene_name = "scene" if index == 0 else f"scene{index + 1}"
figure.layout[scene_name].update(
{
"xaxis": {
"title": "X (m)",
"gridcolor": GRID_COLOR,
"backgroundcolor": "#f8fafb",
},
"yaxis": {
"title": "Y (m)",
"gridcolor": GRID_COLOR,
"backgroundcolor": "#f8fafb",
},
"zaxis": {
"title": "Z (m)",
"gridcolor": GRID_COLOR,
"backgroundcolor": "#f8fafb",
},
"aspectmode": "manual",
"aspectratio": scene_aspect_ratio(trajectory),
}
)
figure.update_layout(
height=1500,
paper_bgcolor="#ffffff",
plot_bgcolor="#f8fafb",
font={"family": "Arial, sans-serif", "color": TEXT_COLOR},
margin={"l": 20, "r": 20, "t": 72, "b": 25},
legend={
"orientation": "h",
"x": 0.5,
"xanchor": "center",
"y": 1.045,
"yanchor": "bottom",
},
hovermode="closest",
)
return figure
def build_detail(trajectories: list[Trajectory]) -> go.Figure:
figure = go.Figure()
for index, trajectory in enumerate(trajectories):
visible = index == 0
figure.add_trace(
go.Scatter3d(
x=trajectory.rtk_x_m,
y=trajectory.rtk_y_m,
z=trajectory.rtk_z_m,
mode="lines",
line={"color": RTK_COLOR, "width": 7},
name="RTK 轨迹",
legendgroup="rtk",
visible=visible,
customdata=[
[time_s]
for time_s in trajectory.relative_time_s
],
hovertemplate=(
"<b>RTK</b><br>"
"X=%{x:.2f} m<br>Y=%{y:.2f} m<br>Z=%{z:.2f} m<br>"
"t=%{customdata[0]:.1f} s<extra></extra>"
),
)
)
figure.add_trace(
go.Scatter3d(
x=[0.0],
y=[0.0],
z=[0.0],
mode="markers+text",
marker={
"color": VEHICLE_COLOR,
"size": 8,
"symbol": "diamond",
"line": {"color": "#ffffff", "width": 1},
},
text=["车辆原点"],
textposition="top center",
textfont={"color": VEHICLE_COLOR, "size": 12},
name="车辆原点",
legendgroup="vehicle",
visible=visible,
hovertemplate=(
"<b>车辆原点</b><br>"
"X=0.00 m<br>Y=0.00 m<br>Z=0.00 m<extra></extra>"
),
)
)
figure.add_trace(
go.Scatter3d(
x=trajectory.tracking_x_m,
y=trajectory.tracking_y_m,
z=trajectory.tracking_z_m,
mode="lines",
line={"color": TRACKING_COLOR, "width": 6},
name="识别轨迹",
legendgroup="tracking",
visible=visible,
customdata=[
[time_s, error_m]
for time_s, error_m in zip(
trajectory.relative_time_s,
trajectory.error_3d_m,
)
],
hovertemplate=(
"<b>识别</b><br>"
"X=%{x:.2f} m<br>Y=%{y:.2f} m<br>Z=%{z:.2f} m<br>"
"t=%{customdata[0]:.1f} s<br>"
"3D 误差=%{customdata[1]:.3f} m<extra></extra>"
),
)
)
buttons = []
for index, trajectory in enumerate(trajectories):
visibility = [
trace_index // 3 == index
for trace_index in range(3 * len(trajectories))
]
buttons.append(
{
"label": trajectory.flight_id,
"method": "update",
"args": [
{"visible": visibility},
{
"title": {
"text": (
f"{trajectory.flight_id}"
f" · 3D RMSE {trajectory.rmse_3d_m:.3f} m"
),
"x": 0.5,
},
"scene.aspectmode": "manual",
"scene.aspectratio": scene_aspect_ratio(trajectory),
},
],
}
)
first = trajectories[0]
figure.update_layout(
height=720,
title={
"text": f"{first.flight_id} · 3D RMSE {first.rmse_3d_m:.3f} m",
"x": 0.5,
},
paper_bgcolor="#ffffff",
font={"family": "Arial, sans-serif", "color": TEXT_COLOR},
margin={"l": 15, "r": 15, "t": 95, "b": 10},
scene={
"xaxis": {
"title": "X 前向 (m)",
"gridcolor": GRID_COLOR,
"backgroundcolor": "#f8fafb",
},
"yaxis": {
"title": "Y 左向 (m)",
"gridcolor": GRID_COLOR,
"backgroundcolor": "#f8fafb",
},
"zaxis": {
"title": "Z 上向 (m)",
"gridcolor": GRID_COLOR,
"backgroundcolor": "#f8fafb",
},
"aspectmode": "manual",
"aspectratio": scene_aspect_ratio(first),
},
legend={
"orientation": "h",
"x": 0.5,
"xanchor": "center",
"y": 1.02,
"yanchor": "bottom",
},
updatemenus=[
{
"buttons": buttons,
"direction": "down",
"showactive": True,
"active": 0,
"x": 0.01,
"xanchor": "left",
"y": 1.13,
"yanchor": "top",
}
],
)
return figure
def write_html(
output_path: Path,
input_path: Path,
trajectories: list[Trajectory],
) -> None:
config = {
"displaylogo": False,
"responsive": True,
"scrollZoom": True,
"toImageButtonOptions": {
"format": "png",
"filename": "tracking_rtk_trajectory",
"scale": 2,
},
}
overview_html = pio.to_html(
build_overview(trajectories),
include_plotlyjs=True,
full_html=False,
config=config,
)
detail_html = pio.to_html(
build_detail(trajectories),
include_plotlyjs=False,
full_html=False,
config=config,
)
sample_count = sum(len(item.timestamp_us) for item in trajectories)
source_name = html.escape(input_path.name)
document = f"""<!doctype html>
<html lang="zh-CN">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>识别轨迹与 RTK 轨迹</title>
<style>
:root {{
color-scheme: light;
--ink: {TEXT_COLOR};
--muted: #647278;
--line: #dfe5e8;
--panel: #ffffff;
--page: #f3f6f7;
}}
* {{ box-sizing: border-box; }}
body {{
margin: 0;
color: var(--ink);
background: var(--page);
font-family: Arial, "Microsoft YaHei", sans-serif;
}}
main {{ width: min(1580px, 100%); margin: 0 auto; padding: 28px; }}
header {{ margin: 0 0 18px; }}
h1 {{ margin: 0 0 8px; font-size: 28px; letter-spacing: .01em; }}
p {{ margin: 0; color: var(--muted); line-height: 1.65; }}
.facts {{ display: flex; flex-wrap: wrap; gap: 8px; margin-top: 14px; }}
.fact {{
padding: 6px 10px;
border: 1px solid var(--line);
border-radius: 999px;
background: var(--panel);
color: #45555c;
font-size: 13px;
}}
section {{
margin-top: 18px;
padding: 18px;
border: 1px solid var(--line);
border-radius: 14px;
background: var(--panel);
box-shadow: 0 3px 16px rgba(23, 43, 51, .05);
}}
h2 {{ margin: 0 0 6px; font-size: 18px; }}
.plot {{ width: 100%; overflow: hidden; }}
footer {{ padding: 16px 2px 0; color: var(--muted); font-size: 12px; }}
@media (max-width: 720px) {{
main {{ padding: 14px; }}
section {{ padding: 8px; border-radius: 10px; }}
h1 {{ font-size: 22px; }}
}}
</style>
</head>
<body>
<main>
<header>
<h1>识别轨迹与 RTK 轨迹</h1>
<p>绿色为 RTK 轨迹,橙色为识别轨迹,蓝色菱形为车辆原点;小跨度方向采用最低显示厚度,坐标值保持不变。</p>
<div class="facts">
<span class="fact">{len(trajectories)} 个航次</span>
<span class="fact">{sample_count:,} 个配对点</span>
<span class="fact">X 前向 · Y 左向 · Z 上向</span>
</div>
</header>
<section>
<h2>全部航次三维轨迹</h2>
<div class="plot">{overview_html}</div>
</section>
<section>
<h2>单航次三维查看</h2>
<p>通过下拉菜单选择航次,可旋转、缩放并悬停查看坐标。</p>
<div class="plot">{detail_html}</div>
</section>
<footer>数据:{source_name}</footer>
</main>
</body>
</html>
"""
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(document, encoding="utf-8")
def main() -> None:
args = parse_args()
dataset_root = args.dataset_root.resolve()
input_path = (
args.input.resolve()
if args.input
else dataset_root / "script" / "output" / "paired_errors.csv"
)
output_path = (
args.output.resolve()
if args.output
else dataset_root / "script" / "output" / "trajectories.html"
)
if input_path == output_path:
raise ValueError("输入 CSV 与输出 HTML 不能是同一文件")
trajectories = read_trajectories(input_path)
write_html(output_path, input_path, trajectories)
print(f"HTML: {output_path}")
print(
f"航次: {len(trajectories)}, "
f"轨迹点: {sum(len(item.timestamp_us) for item in trajectories)}, "
f"文件大小: {output_path.stat().st_size / 1024 / 1024:.2f} MiB"
)
if __name__ == "__main__":
main()
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