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16MPixel Bilder + Arbeiten an Statistik

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chk
2026-05-31 17:39:25 +02:00
parent ca98774657
commit 761919bb93
212 changed files with 159773 additions and 81014 deletions
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1462
pipeline/3_multiview_bundle_adjustment_v5.py Normal file
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pipeline/4_robotState_estimation_v7.py Normal file
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#!/usr/bin/env python3
"""
4_robotState_estimation_v7.py
Estimate the Arm1 joint angle from ArUco marker positions.
What this script does
---------------------
1. Loads a robot configuration JSON and extracts the local marker positions of Arm1.
2. Loads an observation JSON with marker positions (e.g. aruco_positions_optimized.json).
3. Matches observed markers to Arm1 markers by marker_id.
4. Estimates the Arm1 angle in two ways:
Option 1 (single marker, optional):
----------------------------------
If a world-space joint origin is known, estimate angle from one marker by comparing
the vector from joint origin -> marker in the model and in the observation.
Option 2 (marker pairs, default and robust):
------------------------------------------
For every pair of Arm1 markers, estimate the rotation angle from the relative vector
between the two markers. This is translation-invariant and usually the preferred method.
5. Aggregates all valid estimates using a weighted circular mean and reports:
mean angle, circular variance, circular std, linear std on wrapped samples, and per-estimate details.
Coordinate conventions
----------------------
The provided robot config says:
- right-handed coordinate system
- x right, y backward, z up
Arm1 rotates around the x-axis (joint axis [-1, 0, 0] in the supplied config).
The script therefore works in 3D and automatically respects the joint axis sign.
Usage
-----
python 4_robotState_estimation_v7.py \
--robot-json robot_running_2026_05_30.json \
--aruco-json aruco_positions_optimized.json
Optional:
--joint-origin-world X Y Z # enables single-marker estimates
--output-json result.json
--arm-link Arm1
The script prints a concise report and can also write a JSON summary.
Notes
-----
- This script is intentionally defensive: it accepts positions in meters or millimeters.
- It does not require any additional robot state beyond the Arm1 marker geometry.
- If only a subset of Arm1 markers is observed, the pairwise estimator still works.
"""
from __future__ import annotations
import argparse
import json
import math
from dataclasses import dataclass, asdict
from itertools import combinations
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Sequence, Tuple
import numpy as np
# ----------------------------
# Data structures
# ----------------------------
@dataclass
class MarkerSpec:
marker_id: int
local_position_mm: np.ndarray
@dataclass
class MarkerObservation:
marker_id: int
position_mm: np.ndarray
link: Optional[str] = None
@dataclass
class AngleEstimate:
source: str
angle_rad: float
angle_deg: float
weight: float
marker_ids: Tuple[int, ...]
# ----------------------------
# Helpers
# ----------------------------
def _as_np3(value: Sequence[float]) -> np.ndarray:
arr = np.asarray(value, dtype=float).reshape(3)
return arr
def load_json(path: Path) -> dict:
with path.open("r", encoding="utf-8") as f:
return json.load(f)
def unit_scale_to_mm(units: Optional[str]) -> float:
if not units:
return 1.0
u = str(units).strip().lower()
if u in {"mm", "millimeter", "millimeters"}:
return 1.0
if u in {"m", "meter", "meters"}:
return 1000.0
if u in {"cm", "centimeter", "centimeters"}:
return 10.0
# conservative default
return 1.0
def normalize_angle_rad(angle: float) -> float:
"""Wrap to (-pi, pi]."""
return (angle + math.pi) % (2 * math.pi) - math.pi
def angle_between_vectors(v_from: np.ndarray, v_to: np.ndarray, axis: np.ndarray) -> float:
"""
Signed angle rotating v_from onto v_to around axis.
Uses atan2( axis_hat · (v_from × v_to), v_from · v_to ).
"""
a = np.asarray(axis, dtype=float).reshape(3)
an = np.linalg.norm(a)
if an == 0:
raise ValueError("Rotation axis must be non-zero.")
a = a / an
v1 = np.asarray(v_from, dtype=float).reshape(3)
v2 = np.asarray(v_to, dtype=float).reshape(3)
n1 = np.linalg.norm(v1)
n2 = np.linalg.norm(v2)
if n1 == 0 or n2 == 0:
raise ValueError("Cannot compute angle for zero-length vector.")
cross_term = float(np.dot(a, np.cross(v1, v2)))
dot_term = float(np.dot(v1, v2))
return math.atan2(cross_term, dot_term)
def weighted_circular_mean(angles_rad: Sequence[float], weights: Sequence[float]) -> Tuple[float, float, float]:
"""
Returns:
mean_angle_rad, circular_variance, circular_std_rad
circular_variance = 1 - R, where R is the normalized resultant length.
circular_std_rad = sqrt(-2 ln R) if R>0 else pi.
"""
if len(angles_rad) == 0:
raise ValueError("No angles provided.")
ang = np.asarray(angles_rad, dtype=float)
w = np.asarray(weights, dtype=float)
w = np.clip(w, 0.0, None)
if np.sum(w) <= 0:
w = np.ones_like(w)
s = np.sum(w * np.sin(ang))
c = np.sum(w * np.cos(ang))
mean = math.atan2(s, c)
R = math.sqrt(s * s + c * c) / float(np.sum(w))
R = min(max(R, 0.0), 1.0)
circular_variance = 1.0 - R
circular_std = math.sqrt(max(0.0, -2.0 * math.log(max(R, 1e-15)))) if R > 0 else math.pi
return mean, circular_variance, circular_std
def weighted_linear_std_rad(angles_rad: Sequence[float], mean_rad: float, weights: Sequence[float]) -> float:
if len(angles_rad) == 0:
return float("nan")
ang = np.asarray([normalize_angle_rad(a - mean_rad) for a in angles_rad], dtype=float)
w = np.asarray(weights, dtype=float)
w = np.clip(w, 0.0, None)
if np.sum(w) <= 0:
w = np.ones_like(w)
mu = np.sum(w * ang) / np.sum(w)
var = np.sum(w * (ang - mu) ** 2) / np.sum(w)
return float(math.sqrt(max(0.0, var)))
def extract_arm_markers(robot: dict, arm_link_name: str) -> Tuple[np.ndarray, List[MarkerSpec], np.ndarray]:
"""
Returns:
joint_origin_mm, markers, joint_axis
"""
links = robot.get("links", {})
if arm_link_name not in links:
# case-insensitive fallback
lookup = {k.lower(): k for k in links.keys()}
if arm_link_name.lower() not in lookup:
raise KeyError(f"Link '{arm_link_name}' not found in robot.json.")
arm_link_name = lookup[arm_link_name.lower()]
arm = links[arm_link_name]
jt = arm.get("jointToParent", {})
joint_origin_mm = _as_np3(jt.get("origin", [0, 0, 0]))
joint_axis = _as_np3(jt.get("axis", [1, 0, 0]))
markers = []
for m in arm.get("markers", []):
if "id" not in m or "position" not in m:
continue
markers.append(MarkerSpec(marker_id=int(m["id"]), local_position_mm=_as_np3(m["position"])))
if not markers:
raise ValueError(f"No markers found on link '{arm_link_name}'.")
return joint_origin_mm, markers, joint_axis
def load_observations(observations_json: dict) -> List[MarkerObservation]:
markers = observations_json.get("markers", [])
units = observations_json.get("units", {})
scale = 1.0
if isinstance(units, dict):
scale = unit_scale_to_mm(units.get("length"))
else:
scale = unit_scale_to_mm(units)
obs: List[MarkerObservation] = []
for m in markers:
if "marker_id" not in m and "id" not in m:
continue
marker_id = int(m.get("marker_id", m.get("id")))
if "position_mm" in m:
pos = _as_np3(m["position_mm"])
elif "position_m" in m:
pos = _as_np3(m["position_m"]) * 1000.0
elif "position" in m:
pos = _as_np3(m["position"]) * scale
else:
continue
obs.append(MarkerObservation(marker_id=marker_id, position_mm=pos, link=m.get("link")))
return obs
def match_markers(specs: Sequence[MarkerSpec], observations: Sequence[MarkerObservation]) -> Dict[int, Tuple[np.ndarray, np.ndarray]]:
"""
Returns marker_id -> (local_position_mm, observed_position_mm)
"""
spec_map = {m.marker_id: m.local_position_mm for m in specs}
out = {}
for o in observations:
if o.marker_id in spec_map:
out[o.marker_id] = (spec_map[o.marker_id], o.position_mm)
return out
def estimate_pairwise_angles(
matched: Dict[int, Tuple[np.ndarray, np.ndarray]],
axis: np.ndarray,
min_baseline_mm: float = 1.0,
) -> List[AngleEstimate]:
estimates: List[AngleEstimate] = []
ids = sorted(matched.keys())
for id1, id2 in combinations(ids, 2):
p1_local, p1_obs = matched[id1]
p2_local, p2_obs = matched[id2]
v_local = p2_local - p1_local
v_obs = p2_obs - p1_obs
baseline_local = float(np.linalg.norm(v_local))
baseline_obs = float(np.linalg.norm(v_obs))
if baseline_local < min_baseline_mm or baseline_obs < min_baseline_mm:
continue
try:
angle = angle_between_vectors(v_local, v_obs, axis)
except ValueError:
continue
# Weight long baselines a little more strongly; if a pair is very short, it becomes noisy.
weight = max(1e-6, baseline_local * baseline_obs)
estimates.append(
AngleEstimate(
source="pair",
angle_rad=normalize_angle_rad(angle),
angle_deg=math.degrees(normalize_angle_rad(angle)),
weight=weight,
marker_ids=(id1, id2),
)
)
return estimates
def estimate_single_marker_angles(
matched: Dict[int, Tuple[np.ndarray, np.ndarray]],
joint_origin_world_mm: np.ndarray,
axis: np.ndarray,
) -> List[AngleEstimate]:
"""
Single-marker estimates require a known world-space joint origin.
For each marker, compare:
v_local = marker_local - joint_origin_local
v_obs = marker_obs - joint_origin_world
In the provided Arm1 config the joint origin is the local origin of Arm1, so
joint_origin_local is taken as [0,0,0].
"""
estimates: List[AngleEstimate] = []
joint_origin_world_mm = _as_np3(joint_origin_world_mm)
for marker_id, (p_local, p_obs) in matched.items():
v_local = p_local # Arm1 local origin is at the joint
v_obs = p_obs - joint_origin_world_mm
if np.linalg.norm(v_local) < 1e-9 or np.linalg.norm(v_obs) < 1e-9:
continue
try:
angle = angle_between_vectors(v_local, v_obs, axis)
except ValueError:
continue
weight = float(np.linalg.norm(v_local))
estimates.append(
AngleEstimate(
source="single",
angle_rad=normalize_angle_rad(angle),
angle_deg=math.degrees(normalize_angle_rad(angle)),
weight=weight,
marker_ids=(marker_id,),
)
)
return estimates
def build_report(
arm_link_name: str,
joint_origin_mm: np.ndarray,
joint_axis: np.ndarray,
matched: Dict[int, Tuple[np.ndarray, np.ndarray]],
estimates: List[AngleEstimate],
) -> dict:
if not estimates:
raise ValueError("No valid angle estimates could be computed.")
mean_rad, circ_var, circ_std_rad = weighted_circular_mean(
[e.angle_rad for e in estimates],
[e.weight for e in estimates],
)
lin_std_rad = weighted_linear_std_rad(
[e.angle_rad for e in estimates],
mean_rad,
[e.weight for e in estimates],
)
# unwrap around mean for a stable arithmetic mean/std reporting
unwrapped_deg = []
for e in estimates:
d = math.degrees(normalize_angle_rad(e.angle_rad - mean_rad))
unwrapped_deg.append(d)
report = {
"link": arm_link_name,
"joint_origin_mm": joint_origin_mm.tolist(),
"joint_axis": joint_axis.tolist(),
"num_matched_markers": len(matched),
"num_estimates": len(estimates),
"mean_angle_rad": mean_rad,
"mean_angle_deg": math.degrees(mean_rad),
"circular_variance": circ_var,
"circular_std_rad": circ_std_rad,
"circular_std_deg": math.degrees(circ_std_rad),
"linear_std_rad": lin_std_rad,
"linear_std_deg": math.degrees(lin_std_rad),
"estimate_spread_deg_unwrapped": {
"min": float(np.min(unwrapped_deg)) if unwrapped_deg else None,
"max": float(np.max(unwrapped_deg)) if unwrapped_deg else None,
"mean_abs": float(np.mean(np.abs(unwrapped_deg))) if unwrapped_deg else None,
},
"estimates": [asdict(e) for e in estimates],
}
return report
def pretty_print_report(report: dict) -> None:
print("\nArm angle estimation report")
print("=" * 32)
print(f"Link : {report['link']}")
print(f"Matched markers : {report['num_matched_markers']}")
print(f"Valid estimates : {report['num_estimates']}")
print(f"Mean angle : {report['mean_angle_deg']:.3f} deg")
print(f"Circular variance : {report['circular_variance']:.6f}")
print(f"Circular std : {report['circular_std_deg']:.3f} deg")
print(f"Linear std (wrapped): {report['linear_std_deg']:.3f} deg")
print("\nIndividual estimates")
print("-" * 32)
for e in report["estimates"]:
mids = ",".join(str(x) for x in e["marker_ids"])
print(f"{e['source']:>6s} markers [{mids:<8s}] angle={e['angle_deg']:>8.3f} deg weight={e['weight']:.3f}")
def estimate_arm1_state(
robot_json: dict,
observations_json: dict,
arm_link_name: str = "Arm1",
joint_origin_world_mm: Optional[Sequence[float]] = None,
) -> dict:
joint_origin_mm, arm_markers, joint_axis = extract_arm_markers(robot_json, arm_link_name)
observations = load_observations(observations_json)
matched = match_markers(arm_markers, observations)
if not matched:
available = sorted(m.marker_id for m in arm_markers)
observed = sorted(o.marker_id for o in observations)
raise ValueError(
f"No Arm1 marker matches found. Arm markers: {available}. Observed IDs present in file: {observed[:25]}"
+ ("..." if len(observed) > 25 else "")
)
estimates = estimate_pairwise_angles(matched, axis=joint_axis)
# Optional single-marker estimates only if a world-space joint origin is supplied.
if joint_origin_world_mm is not None:
estimates.extend(estimate_single_marker_angles(matched, _as_np3(joint_origin_world_mm), axis=joint_axis))
report = build_report(
arm_link_name=arm_link_name,
joint_origin_mm=joint_origin_mm,
joint_axis=joint_axis,
matched=matched,
estimates=estimates,
)
return report
def main() -> int:
parser = argparse.ArgumentParser(description="Estimate Arm1 angle from ArUco marker positions.")
parser.add_argument("--robot-json", type=Path, default=Path("robot.json"), help="Path to robot config JSON.")
parser.add_argument("--aruco-json", type=Path, default=Path("aruco_positions_optimized.json"), help="Path to marker observation JSON.")
parser.add_argument("--arm-link", type=str, default="Arm1", help="Name of the link to estimate.")
parser.add_argument(
"--joint-origin-world",
type=float,
nargs=3,
default=None,
metavar=("X", "Y", "Z"),
help="Optional world-space joint origin for single-marker estimates.",
)
parser.add_argument("--output-json", type=Path, default=None, help="Optional path to write the result JSON.")
args = parser.parse_args()
robot_json = load_json(args.robot_json)
observations_json = load_json(args.aruco_json)
report = estimate_arm1_state(
robot_json=robot_json,
observations_json=observations_json,
arm_link_name=args.arm_link,
joint_origin_world_mm=args.joint_origin_world,
)
pretty_print_report(report)
if args.output_json:
args.output_json.parent.mkdir(parents=True, exist_ok=True)
with args.output_json.open("w", encoding="utf-8") as f:
json.dump(report, f, indent=2, ensure_ascii=False)
print(f"\nWrote JSON summary to: {args.output_json}")
return 0
if __name__ == "__main__":
raise SystemExit(main())

0
pipeline/9_poseEvaluation.py → pipeline/9_evaluateMarker.py
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124
pipeline/aruco_positions_initial.json
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@@ -1,124 +0,0 @@
{
"schema_version": "1.2",
"stage": "initial_triangulation",
"created_utc": "2026-05-29T17:30:29Z",
"summary": {
"num_cameras": 3,
"num_markers": 8,
"num_constraints": 25
},
"markers": [
{
"marker_id": 122,
"position_m": [
0.14895405123790784,
-0.2643844341363741,
0.16564713440852427
],
"position_mm": [
148.95405123790783,
-264.38443413637407,
165.64713440852427
],
"link": "Arm2"
},
{
"marker_id": 198,
"position_m": [
0.11667067734641628,
-0.05481782829770797,
0.13564627196635054
],
"position_mm": [
116.67067734641628,
-54.81782829770797,
135.64627196635055
],
"link": "Arm1"
},
{
"marker_id": 210,
"position_m": [
0.020141782836696105,
-0.019633567005699154,
7.847443788934222e-06
],
"position_mm": [
20.141782836696105,
-19.633567005699152,
0.007847443788934223
],
"link": "Board"
},
{
"marker_id": 211,
"position_m": [
0.2496094175197415,
-0.009326316290609264,
-0.0005379225707190097
],
"position_mm": [
249.6094175197415,
-9.326316290609263,
-0.5379225707190097
],
"link": "Board"
},
{
"marker_id": 214,
"position_m": [
0.3498840022857963,
-0.009714324911855806,
-2.6845703214407957e-05
],
"position_mm": [
349.8840022857963,
-9.714324911855806,
-0.026845703214407955
],
"link": "Board"
},
{
"marker_id": 215,
"position_m": [
0.24976330898445626,
-0.08967780487999989,
-0.00011862459122980862
],
"position_mm": [
249.76330898445624,
-89.6778048799999,
-0.11862459122980862
],
"link": "Board"
},
{
"marker_id": 229,
"position_m": [
0.11592480843458029,
-0.1392249195451953,
0.14252312456392438
],
"position_mm": [
115.9248084345803,
-139.22491954519532,
142.52312456392437
],
"link": "Arm1"
},
{
"marker_id": 243,
"position_m": [
0.11871032400722982,
-0.17057910506578056,
0.0996476225849942
],
"position_mm": [
118.71032400722981,
-170.57910506578057,
99.6476225849942
],
"link": "Arm1"
}
]
}

123
pipeline/aruco_positions_optimized.json
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@@ -1,123 +0,0 @@
{
"schema_version": "1.2",
"created_utc": "2026-05-29T17:30:30Z",
"summary": {
"num_cameras": 3,
"num_markers": 8,
"num_constraints": 25
},
"markers": [
{
"marker_id": 122,
"position_m": [
0.16408414603046484,
-0.2598816877789887,
0.17784668754227578
],
"position_mm": [
164.08414603046484,
-259.8816877789887,
177.8466875422758
],
"link": "Arm2"
},
{
"marker_id": 198,
"position_m": [
0.11704671868929717,
-0.05006352997427808,
0.13545744846179997
],
"position_mm": [
117.04671868929717,
-50.063529974278076,
135.45744846179997
],
"link": "Arm1"
},
{
"marker_id": 210,
"position_m": [
0.019883267288547467,
-0.019568887683375974,
-6.02159742251129e-05
],
"position_mm": [
19.883267288547465,
-19.568887683375973,
-0.0602159742251129
],
"link": "Board"
},
{
"marker_id": 211,
"position_m": [
0.24989200282636703,
-0.009772675866705374,
1.0334501290569615e-05
],
"position_mm": [
249.89200282636702,
-9.772675866705374,
0.010334501290569615
],
"link": "Board"
},
{
"marker_id": 214,
"position_m": [
0.34989199483349664,
-0.009735514620349328,
1.4271508683918328e-05
],
"position_mm": [
349.89199483349665,
-9.735514620349328,
0.014271508683918327
],
"link": "Board"
},
{
"marker_id": 215,
"position_m": [
0.24981646807454147,
-0.08977263518943693,
-1.508171313798581e-05
],
"position_mm": [
249.81646807454146,
-89.77263518943693,
-0.01508171313798581
],
"link": "Board"
},
{
"marker_id": 229,
"position_m": [
0.11773154751353263,
-0.13967941405892637,
0.1437326114716167
],
"position_mm": [
117.73154751353262,
-139.67941405892637,
143.7326114716167
],
"link": "Arm1"
},
{
"marker_id": 243,
"position_m": [
0.11835862963107012,
-0.16963843269272738,
0.10433506872776273
],
"position_mm": [
118.35862963107012,
-169.63843269272738,
104.33506872776273
],
"link": "Arm1"
}
]
}

95
pipeline/aruco_positions_optimized_Set1_v1.json
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@@ -1,95 +0,0 @@
{
"schema_version": "1.0",
"created_utc": "2026-05-29T17:28:02Z",
"summary": {
"num_cameras": 2,
"num_markers": 6,
"num_constraints": 124
},
"markers": [
{
"marker_id": 101,
"position_m": [
0.2763032509383358,
-0.2695119989339591,
0.20699150350169354
],
"position_mm": [
276.3032509383358,
-269.5119989339591,
206.99150350169353
],
"link": "Arm2"
},
{
"marker_id": 124,
"position_m": [
0.2793537536854657,
-0.19507589060009986,
0.20575080844610052
],
"position_mm": [
279.35375368546573,
-195.07589060009985,
205.75080844610054
],
"link": "Arm2"
},
{
"marker_id": 211,
"position_m": [
0.2504668030892929,
-0.009744842404164922,
0.00036153034259814553
],
"position_mm": [
250.4668030892929,
-9.744842404164922,
0.36153034259814554
],
"link": "Board"
},
{
"marker_id": 215,
"position_m": [
0.2505650600131585,
-0.08974350678317572,
-7.314160355311047e-05
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123
pipeline/aruco_positions_optimized_Set3_v3_ohneConstraints.json
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pipeline/aruco_positions_optimized_Set3_v4.json
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pipeline/arucos_state.json
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799
pipeline/arucos_state_set3.json
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652
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]

53
pipeline/run_pipeline_multiview.bat
View File

@@ -1,53 +0,0 @@
@echo off
REM 3_pipeline_multiview.bat
REM Multi-camera ArUco detection and pose estimation pipeline
REM Example: 3 camera views
REM Usage: run_pipeline_multiview.bat
cd /d "%~dp0"
set ROBOT_JSON=..\robot.json
set OUT_DIR=.
REM Camera 1
set IMG_1=render_1a.png
set NPZ_1=render.npz
set CAM_ID_1=cam1
REM Camera 2 (example - you need actual second camera image)
set IMG_2=render_1b.png
set NPZ_2=render.npz
set CAM_ID_2=cam2
REM Camera 3 (example)
set IMG_3=render_1c.png
set NPZ_3=render.npz
set CAM_ID_3=cam3
echo [STEP 1] Detect ArUco markers from all cameras
python 1_detect_aruco_observations.py -i %IMG_1% -npz %NPZ_1% -robot %ROBOT_JSON% -cameraId %CAM_ID_1% -outDir %OUT_DIR%
python 1_detect_aruco_observations.py -i %IMG_2% -npz %NPZ_2% -robot %ROBOT_JSON% -cameraId %CAM_ID_2% -outDir %OUT_DIR%
python 1_detect_aruco_observations.py -i %IMG_3% -npz %NPZ_3% -robot %ROBOT_JSON% -cameraId %CAM_ID_3% -outDir %OUT_DIR%
echo.
echo [STEP 2] Estimate camera poses from detections
python 2_estimate_camera_from_observations.py -i render_3a_aruco_detection.json -robot %ROBOT_JSON% -outDir %OUT_DIR%
python 2_estimate_camera_from_observations.py -i render_3b_aruco_detection.json -robot %ROBOT_JSON% -outDir %OUT_DIR%
python 2_estimate_camera_from_observations.py -i render_3c_aruco_detection.json -robot %ROBOT_JSON% -outDir %OUT_DIR%
echo.
echo [STEP 3] Triangulate marker positions from multi-view observations
python 3_multiview_bundle_adjustment_v6.py ^
-det render_3a_aruco_detection.json ^
-det render_3b_aruco_detection.json ^
-det render_3c_aruco_detection.json ^
-pose render_3a_camera_pose.json ^
-pose render_3b_camera_pose.json ^
-pose render_3c_camera_pose.json ^
-robot %ROBOT_JSON% -lambdaWeight 100.0
echo.
echo [DONE] Pipeline complete