Pipeline mit KAmera-Position

pipeline/1_detect_aruco_observations.py

@@ -0,0 +1,587 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import os
+import hashlib
+import time
+import uuid
+from typing import Dict, Any
+
+import cv2
+import numpy as np
+
+
+# ------------------------------------------------------------
+# Utilities
+# ------------------------------------------------------------
+
+def load_intrinsics_npz(npz_path: str):
+    data = np.load(npz_path)
+
+    for k in ('camera_matrix', 'mtx', 'K'):
+        if k in data:
+            K = data[k].astype(np.float32)
+            break
+    else:
+        raise KeyError('Camera matrix not found in npz')
+
+    for k in ('dist_coeffs', 'dist', 'D'):
+        if k in data:
+            D = data[k].astype(np.float32).reshape(-1, 1)
+            break
+    else:
+        D = np.zeros((5, 1), dtype=np.float32)
+
+    return K, D
+
+
+# ------------------------------------------------------------
+
+def load_robot_vision_config(robot_json_path: str):
+
+    with open(robot_json_path, 'r', encoding='utf-8') as f:
+        robot = json.load(f)
+
+    vision_config = robot.get('vision_config', {})
+
+    marker_type = vision_config.get('MarkerType', 'DICT_4X4_250')
+    marker_size = float(vision_config.get('MarkerSize', 0.025))
+
+    return {
+        'MarkerType': marker_type,
+        'MarkerSize': marker_size
+    }
+
+
+# ------------------------------------------------------------
+
+def get_aruco_detector(dict_name: str):
+
+    mapping = {
+        'DICT_4X4_250': cv2.aruco.DICT_4X4_250,
+        'DICT_5X5_100': cv2.aruco.DICT_5X5_100,
+        'DICT_6X6_250': cv2.aruco.DICT_6X6_250,
+        'DICT_ARUCO_ORIGINAL': cv2.aruco.DICT_ARUCO_ORIGINAL,
+    }
+
+    dict_id = mapping.get(dict_name, cv2.aruco.DICT_4X4_250)
+
+    dictionary = cv2.aruco.getPredefinedDictionary(dict_id)
+
+    try:
+        params = cv2.aruco.DetectorParameters()
+    except Exception:
+        params = cv2.aruco.DetectorParameters_create()
+
+    try:
+        detector = cv2.aruco.ArucoDetector(dictionary, params)
+        return detector, None
+
+    except Exception:
+        return None, (dictionary, params)
+
+
+# ------------------------------------------------------------
+
+def detect_markers(image, detector_tuple):
+
+    detector, fallback = detector_tuple
+
+    if detector is not None:
+
+        corners, ids, rejected = detector.detectMarkers(image)
+
+    else:
+
+        dictionary, params = fallback
+
+        corners, ids, rejected = cv2.aruco.detectMarkers(
+            image,
+            dictionary,
+            parameters=params
+        )
+
+    return corners, ids, rejected
+
+
+# ------------------------------------------------------------
+
+def hash_file(path):
+
+    sha = hashlib.sha256()
+
+    with open(path, 'rb') as f:
+
+        while True:
+
+            chunk = f.read(1024 * 1024)
+
+            if not chunk:
+                break
+
+            sha.update(chunk)
+
+    return sha.hexdigest()
+
+
+# ------------------------------------------------------------
+
+def polygon_mask(shape, polygon):
+
+    mask = np.zeros(shape, dtype=np.uint8)
+
+    cv2.fillConvexPoly(
+        mask,
+        polygon.astype(np.int32),
+        255
+    )
+
+    return mask
+
+
+# ------------------------------------------------------------
+
+def shrink_polygon(points, scale=0.80):
+
+    center = np.mean(points, axis=0)
+
+    shrunk = center + (points - center) * scale
+
+    return shrunk.astype(np.float32)
+
+
+# ------------------------------------------------------------
+
+def compute_sharpness(gray_image, polygon):
+
+    shrunk = shrink_polygon(polygon, scale=0.80)
+
+    mask = polygon_mask(gray_image.shape, shrunk)
+
+    pixels = gray_image[mask == 255]
+
+    if pixels.size == 0:
+        return 0.0
+
+    temp = np.zeros_like(gray_image)
+    temp[mask == 255] = gray_image[mask == 255]
+
+    lap = cv2.Laplacian(temp, cv2.CV_64F)
+
+    values = lap[mask == 255]
+
+    if values.size == 0:
+        return 0.0
+
+    return float(values.var())
+
+
+# ------------------------------------------------------------
+
+def compute_contrast(gray_image, polygon):
+
+    shrunk = shrink_polygon(polygon, scale=0.80)
+
+    mask = polygon_mask(gray_image.shape, shrunk)
+
+    pixels = gray_image[mask == 255]
+
+    if pixels.size == 0:
+
+        return {
+            'p05': 0.0,
+            'p95': 0.0,
+            'dynamic_range': 0.0,
+            'mean_gray': 0.0,
+            'std_gray': 0.0
+        }
+
+    p05 = float(np.percentile(pixels, 5))
+    p95 = float(np.percentile(pixels, 95))
+
+    return {
+        'p05': p05,
+        'p95': p95,
+        'dynamic_range': float(p95 - p05),
+        'mean_gray': float(np.mean(pixels)),
+        'std_gray': float(np.std(pixels))
+    }
+
+
+# ------------------------------------------------------------
+
+def compute_edge_ratio(corners):
+
+    edge_lengths = []
+
+    for k in range(4):
+
+        p1 = corners[k]
+        p2 = corners[(k + 1) % 4]
+
+        edge_lengths.append(
+            float(np.linalg.norm(p1 - p2))
+        )
+
+    edge_ratio = (
+        max(edge_lengths) /
+        max(1e-6, min(edge_lengths))
+    )
+
+    return edge_ratio, edge_lengths
+
+
+# ------------------------------------------------------------
+
+def compute_geometry_metrics(center, corners, width, height):
+
+    image_center = np.array(
+        [width / 2.0, height / 2.0],
+        dtype=np.float32
+    )
+
+    dist_center = np.linalg.norm(center - image_center)
+
+    max_dist = np.linalg.norm(image_center)
+
+    distance_center_norm = float(
+        dist_center / max(1e-6, max_dist)
+    )
+
+    min_x = np.min(corners[:, 0])
+    max_x = np.max(corners[:, 0])
+
+    min_y = np.min(corners[:, 1])
+    max_y = np.max(corners[:, 1])
+
+    border_distance_px = float(min(
+        min_x,
+        min_y,
+        width - max_x,
+        height - max_y
+    ))
+
+    return {
+        'distance_to_center_norm': distance_center_norm,
+        'distance_to_border_px': border_distance_px
+    }
+
+
+# ------------------------------------------------------------
+
+def compute_confidence(
+    area_px,
+    sharpness,
+    edge_ratio,
+    dynamic_range,
+    border_distance_px
+):
+
+    score = 1.0
+
+    # area
+    score *= min(1.0, area_px / 1500.0)
+
+    # sharpness
+    score *= min(1.0, sharpness / 120.0)
+
+    # edge distortion
+    score *= 1.0 / max(1.0, edge_ratio)
+
+    # contrast
+    score *= min(1.0, dynamic_range / 80.0)
+
+    # border distance
+    score *= min(1.0, max(0.0, border_distance_px) / 50.0)
+
+    score = max(0.0, min(1.0, score))
+
+    return float(score)
+
+
+# ------------------------------------------------------------
+
+def main():
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        '-i',
+        '--image',
+        required=True
+    )
+
+    parser.add_argument(
+        '-npz',
+        '--intrinsics',
+        required=True
+    )
+
+    parser.add_argument(
+        '-robot',
+        '--robot',
+        required=True
+    )
+
+    parser.add_argument(
+        '-cameraId',
+        '--cameraId',
+        required=True,
+        type=str
+    )
+
+    parser.add_argument(
+        '-outDir',
+        '--outDir',
+        required=True
+    )
+
+    args = parser.parse_args()
+
+    os.makedirs(args.outDir, exist_ok=True)
+
+    # --------------------------------------------------------
+    # Load robot vision config
+    # --------------------------------------------------------
+
+    vision_config = load_robot_vision_config(args.robot)
+
+    marker_type = vision_config['MarkerType']
+    marker_size = vision_config['MarkerSize']
+
+    # --------------------------------------------------------
+    # Load image
+    # --------------------------------------------------------
+
+    image = cv2.imread(args.image)
+
+    if image is None:
+        raise RuntimeError(f'Cannot read image: {args.image}')
+
+    gray = cv2.cvtColor(
+        image,
+        cv2.COLOR_BGR2GRAY
+    )
+
+    height, width = gray.shape[:2]
+
+    # --------------------------------------------------------
+    # Intrinsics
+    # --------------------------------------------------------
+
+    K, D = load_intrinsics_npz(args.intrinsics)
+
+    # --------------------------------------------------------
+    # Detection
+    # --------------------------------------------------------
+
+    detector_tuple = get_aruco_detector(marker_type)
+
+    corners_list, ids, rejected = detect_markers(
+        gray,
+        detector_tuple
+    )
+
+    detections = []
+
+    # --------------------------------------------------------
+    # Valid detections
+    # --------------------------------------------------------
+
+    if ids is not None:
+
+        ids = ids.flatten().tolist()
+
+        for i, marker_id in enumerate(ids):
+
+            corners = corners_list[i].reshape((4, 2)).astype(np.float32)
+
+            center = corners.mean(axis=0)
+
+            area_px = float(
+                cv2.contourArea(corners)
+            )
+
+            perimeter_px = float(
+                cv2.arcLength(corners, True)
+            )
+
+            edge_ratio, edge_lengths = compute_edge_ratio(corners)
+
+            sharpness = compute_sharpness(
+                gray,
+                corners
+            )
+
+            contrast = compute_contrast(
+                gray,
+                corners
+            )
+
+            geometry = compute_geometry_metrics(
+                center,
+                corners,
+                width,
+                height
+            )
+
+            confidence = compute_confidence(
+                area_px=area_px,
+                sharpness=sharpness,
+                edge_ratio=edge_ratio,
+                dynamic_range=contrast['dynamic_range'],
+                border_distance_px=geometry['distance_to_border_px']
+            )
+
+            detection = {
+
+                'observation_id': str(uuid.uuid4()),
+
+                'type': 'aruco',
+
+                'marker_id': int(marker_id),
+
+                'marker_size_m': marker_size,
+
+                'image_points_px': corners.tolist(),
+
+                'center_px': center.tolist(),
+
+                'quality': {
+
+                    'area_px': area_px,
+
+                    'perimeter_px': perimeter_px,
+
+                    'sharpness': {
+                        'laplacian_var': sharpness
+                    },
+
+                    'contrast': contrast,
+
+                    'geometry': geometry,
+
+                    'edge_ratio': edge_ratio,
+
+                    'edge_lengths_px': edge_lengths
+                },
+
+                'confidence': confidence
+            }
+
+            detections.append(detection)
+
+    # --------------------------------------------------------
+    # Rejected candidates
+    # --------------------------------------------------------
+
+    rejected_candidates = []
+
+    if rejected is not None:
+
+        for candidate in rejected:
+
+            pts = candidate.reshape((-1, 2)).astype(np.float32)
+
+            center = pts.mean(axis=0)
+
+            area_px = float(
+                cv2.contourArea(pts)
+            )
+
+            rejected_candidates.append({
+
+                'image_points_px': pts.tolist(),
+
+                'center_px': center.tolist(),
+
+                'area_px': area_px
+            })
+
+    # --------------------------------------------------------
+    # Final output
+    # --------------------------------------------------------
+
+    output = {
+
+        'schema_version': '1.0',
+
+        'created_utc': time.strftime(
+            '%Y-%m-%dT%H:%M:%SZ',
+            time.gmtime()
+        ),
+
+        'vision_config': {
+            'MarkerType': marker_type,
+            'MarkerSize': marker_size
+        },
+
+        'camera': {
+
+            'camera_id': args.cameraId,
+
+            'intrinsics_file': os.path.abspath(args.intrinsics),
+
+            'camera_matrix': K.tolist(),
+
+            'distortion_coefficients': D.reshape(-1).tolist()
+        },
+
+        'image': {
+
+            'image_file': os.path.abspath(args.image),
+
+            'image_sha256': hash_file(args.image),
+
+            'width_px': int(width),
+
+            'height_px': int(height)
+        },
+
+        'aruco': {
+
+            'dictionary': marker_type,
+
+            'num_detected_markers': len(detections),
+
+            'num_rejected_candidates': len(rejected_candidates)
+        },
+
+        'detections': detections,
+
+        'rejected_candidates': rejected_candidates
+    }
+
+    # --------------------------------------------------------
+    # Output path
+    # --------------------------------------------------------
+
+    input_filename = os.path.basename(args.image)
+
+    input_base = os.path.splitext(input_filename)[0]
+
+    out_json = os.path.join(
+        args.outDir,
+        f'{input_base}_aruco_detection.json'
+    )
+
+    # --------------------------------------------------------
+    # Save JSON
+    # --------------------------------------------------------
+
+    with open(out_json, 'w', encoding='utf-8') as f:
+
+        json.dump(
+            output,
+            f,
+            indent=2
+        )
+
+    print(f'Saved: {out_json}')
+
+
+# ------------------------------------------------------------
+
+if __name__ == '__main__':
+    main()

pipeline/2_KameraPosition.py

@@ -0,0 +1,562 @@
+#!/usr/bin/env python3
+"""
+Step 2 of the vision pipeline
+=============================
+
+Overall workflow
+----------------
+1) Detect ArUco markers in each image and store 2D corners per marker.
+2) Use the Board markers from robot.json as fixed world references.
+3) Estimate the camera pose for each image with a Perspective-n-Point (PnP)
+   solve from the detected 2D corners and the known 3D Board marker corners.
+4) Use that camera pose later as the fixed extrinsic input for articulated
+   bundle adjustment of the robot joints.
+
+Mathematical methods
+--------------------
+- Homogeneous coordinates and rigid transforms in SE(3)
+- ArUco marker geometry on a known board frame
+- Perspective projection with camera intrinsics K and distortion D
+- Robust pose estimation with RANSAC-PnP
+- Optional nonlinear refinement by minimizing reprojection error
+
+Conventions
+-----------
+- robot.json defines the Board frame as the world frame.
+- The camera pose reported by this script is the pose of the CAMERA in the
+  Board/world frame.
+- The OpenCV rvec/tvec output is the object pose in the camera frame.
+  We invert that transform to obtain world_T_camera.
+
+Expected inputs
+---------------
+- robot.json containing Board marker positions in millimeters.
+- ArUco detection JSON(s) from detect_aruco_observations.py.
+
+Important:
+The detection JSON already contains the camera intrinsics and distortion
+coefficients copied from the calibration file during Step 1.
+Therefore Step 2 no longer requires the original .npz file.
+The detection JSON becomes the self-contained handover format between
+pipeline stages.
+
+Output
+------
+A JSON file per input detection file containing:
+- camera pose (world_T_camera)
+- camera pose inverse (camera_T_world)
+- per-marker and global reprojection errors
+- number of inlier correspondences
+- list of used marker IDs
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
+
+import cv2
+import numpy as np
+
+
+# ----------------------------------------------------------------------------
+# Helpers
+# ----------------------------------------------------------------------------
+
+
+def load_robot_json(robot_json_path: str) -> Dict[str, Any]:
+    with open(robot_json_path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+
+def load_intrinsics_from_detection_json(detection_json: Dict[str, Any]) -> Tuple[np.ndarray, np.ndarray]:
+    cam = detection_json.get("camera", {}) or {}
+
+    if "camera_matrix" not in cam:
+        raise KeyError("camera_matrix missing in detection JSON")
+
+    K = np.asarray(cam["camera_matrix"], dtype=np.float64)
+
+    D = np.asarray(
+        cam.get("distortion_coefficients", [0, 0, 0, 0, 0]),
+        dtype=np.float64,
+    ).reshape(-1, 1)
+
+    return K, D
+
+
+
+def as_float3(v: Any) -> np.ndarray:
+    arr = np.asarray(v, dtype=np.float64).reshape(-1)
+    if arr.size < 3:
+        arr = np.pad(arr, (0, 3 - arr.size))
+    return arr[:3]
+
+
+
+def normalize(v: np.ndarray, eps: float = 1e-12) -> np.ndarray:
+    n = float(np.linalg.norm(v))
+    if n < eps:
+        return v * 0.0
+    return v / n
+
+
+
+def rotation_from_normal(normal: np.ndarray) -> np.ndarray:
+    """Return a 3x3 rotation matrix whose local +Z aligns with the given normal.
+
+    The local x/y axes are chosen deterministically by projecting a stable world
+    reference axis into the tangent plane.
+    """
+    z_axis = normalize(normal)
+    if np.linalg.norm(z_axis) < 1e-12:
+        raise ValueError("Degenerate normal vector")
+
+    ref = np.array([1.0, 0.0, 0.0], dtype=np.float64)
+    if abs(float(np.dot(ref, z_axis))) > 0.9:
+        ref = np.array([0.0, 1.0, 0.0], dtype=np.float64)
+
+    x_axis = ref - np.dot(ref, z_axis) * z_axis
+    x_axis = normalize(x_axis)
+    if np.linalg.norm(x_axis) < 1e-12:
+        ref = np.array([0.0, 1.0, 0.0], dtype=np.float64)
+        x_axis = ref - np.dot(ref, z_axis) * z_axis
+        x_axis = normalize(x_axis)
+
+    y_axis = np.cross(z_axis, x_axis)
+    y_axis = normalize(y_axis)
+
+    # Columns are the basis vectors of the local frame in world coordinates.
+    R = np.column_stack([x_axis, y_axis, z_axis])
+    return R
+
+
+
+def rodrigues_to_matrix(rvec: np.ndarray) -> np.ndarray:
+    R, _ = cv2.Rodrigues(rvec.reshape(3, 1))
+    return R.astype(np.float64)
+
+
+
+def matrix_to_rodrigues(R: np.ndarray) -> np.ndarray:
+    rvec, _ = cv2.Rodrigues(R.astype(np.float64))
+    return rvec.reshape(3)
+
+
+
+def invert_rigid_transform(R: np.ndarray, t: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Invert p_cam = R * p_world + t."""
+    R_inv = R.T
+    t_inv = -R_inv @ t.reshape(3)
+    return R_inv, t_inv
+
+
+
+def make_homogeneous(R: np.ndarray, t: np.ndarray) -> np.ndarray:
+    T = np.eye(4, dtype=np.float64)
+    T[:3, :3] = R
+    T[:3, 3] = t.reshape(3)
+    return T
+
+
+
+def project_points(points_3d: np.ndarray, rvec: np.ndarray, tvec: np.ndarray, K: np.ndarray, D: np.ndarray) -> np.ndarray:
+    pts = points_3d.reshape(-1, 1, 3).astype(np.float64)
+    img_pts, _ = cv2.projectPoints(pts, rvec.reshape(3, 1), tvec.reshape(3, 1), K, D)
+    return img_pts.reshape(-1, 2)
+
+
+
+def rms_error(errors_px: np.ndarray) -> float:
+    if errors_px.size == 0:
+        return float("nan")
+    return float(np.sqrt(np.mean(np.sum(errors_px ** 2, axis=1))))
+
+
+
+def safe_confidence(det: Dict[str, Any]) -> float:
+    try:
+        c = float(det.get("confidence", 1.0))
+    except Exception:
+        c = 1.0
+    return max(0.0, min(1.0, c))
+
+
+# ----------------------------------------------------------------------------
+# Robot board model
+# ----------------------------------------------------------------------------
+
+
+@dataclass
+class BoardMarker:
+    marker_id: int
+    center_world_mm: np.ndarray
+    normal_world: np.ndarray
+    size_mm: float
+    rotation_world: np.ndarray
+
+    def corner_points_world_mm(self) -> np.ndarray:
+        """Return the 4 corner points in board/world coordinates.
+
+        Corner order is OpenCV/ArUco compatible:
+        top-left, top-right, bottom-right, bottom-left in the marker local frame.
+        """
+        s = float(self.size_mm)
+        half = s * 0.5
+
+        # Local marker corners in the marker's own frame, z=0.
+        corners_local = np.array([
+            [-half, +half, 0.0],
+            [+half, +half, 0.0],
+            [+half, -half, 0.0],
+            [-half, -half, 0.0],
+        ], dtype=np.float64)
+
+        corners_world = (self.rotation_world @ corners_local.T).T + self.center_world_mm.reshape(1, 3)
+        return corners_world
+
+
+
+def extract_board_markers(robot: Dict[str, Any]) -> Dict[int, BoardMarker]:
+    links = robot.get("links", {})
+    if "Board" not in links:
+        raise KeyError("robot.json must contain links.Board for the world reference frame")
+
+    board = links["Board"]
+    marker_defaults = robot.get("renderingInfo", {}).get("markerDefaults", {}) or {}
+    default_size_mm = float(marker_defaults.get("size", 25.0))
+
+    markers: Dict[int, BoardMarker] = {}
+
+    for m in board.get("markers", []):
+        if not isinstance(m, dict):
+            continue
+        if "id" not in m or "position" not in m:
+            continue
+
+        marker_id = int(m["id"])
+        center = as_float3(m["position"])
+        normal = as_float3(m.get("normal", [0, 0, 1]))
+        size_mm = float(m.get("size", default_size_mm))
+
+        # If a spin is present in the future, it can be added here.
+        # For the current Board markers it is not needed.
+        R = rotation_from_normal(normal)
+
+        markers[marker_id] = BoardMarker(
+            marker_id=marker_id,
+            center_world_mm=center,
+            normal_world=normalize(normal),
+            size_mm=size_mm,
+            rotation_world=R,
+        )
+
+    if not markers:
+        raise ValueError("No Board markers found in robot.json")
+
+    return markers
+
+
+# ----------------------------------------------------------------------------
+# Observation loading
+# ----------------------------------------------------------------------------
+
+
+def load_detection_json(path: str) -> Dict[str, Any]:
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+
+def build_pnp_correspondences(
+    detections: Sequence[Dict[str, Any]],
+    board_markers: Dict[int, BoardMarker],
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[int]]:
+    """Build object/image correspondences from all detected Board markers.
+
+    Returns:
+        object_points_mm: (N, 3)
+        image_points_px: (N, 2)
+        weights: (N,)
+        used_marker_ids: list of marker ids that contributed at least one corner
+    """
+    object_points: List[np.ndarray] = []
+    image_points: List[np.ndarray] = []
+    weights: List[np.ndarray] = []
+    used_marker_ids: List[int] = []
+
+    for det in detections:
+        if str(det.get("type", "")).lower() != "aruco":
+            continue
+
+        try:
+            marker_id = int(det["marker_id"])
+        except Exception:
+            continue
+
+        if marker_id not in board_markers:
+            continue
+
+        corners_px = np.asarray(det.get("image_points_px", []), dtype=np.float64)
+        if corners_px.shape != (4, 2):
+            continue
+
+        marker = board_markers[marker_id]
+        corners_world_mm = marker.corner_points_world_mm()
+
+        object_points.append(corners_world_mm)
+        image_points.append(corners_px)
+
+        conf = safe_confidence(det)
+        # Repeat the marker confidence for all 4 corners.
+        weights.append(np.full((4,), conf, dtype=np.float64))
+        used_marker_ids.append(marker_id)
+
+    if not object_points:
+        raise ValueError("No usable Board marker correspondences found in detections")
+
+    obj = np.concatenate(object_points, axis=0).astype(np.float64)
+    img = np.concatenate(image_points, axis=0).astype(np.float64)
+    w = np.concatenate(weights, axis=0).astype(np.float64)
+
+    return obj, img, w, sorted(set(used_marker_ids))
+
+
+# ----------------------------------------------------------------------------
+# Pose estimation
+# ----------------------------------------------------------------------------
+
+
+def pnp_initial_pose(object_points_mm: np.ndarray, image_points_px: np.ndarray, K: np.ndarray, D: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Robust initial pose estimate using RANSAC-PnP when possible."""
+    obj = object_points_mm.reshape(-1, 1, 3).astype(np.float64)
+    img = image_points_px.reshape(-1, 1, 2).astype(np.float64)
+
+    # Prefer RANSAC for robustness.
+    ok, rvec, tvec, inliers = cv2.solvePnPRansac(
+        obj,
+        img,
+        K,
+        D,
+        iterationsCount=200,
+        reprojectionError=4.0,
+        confidence=0.999,
+        flags=cv2.SOLVEPNP_EPNP,
+    )
+
+    if not ok or rvec is None or tvec is None:
+        # Fallback to a direct solve.
+        ok, rvec, tvec = cv2.solvePnP(
+            obj,
+            img,
+            K,
+            D,
+            flags=cv2.SOLVEPNP_ITERATIVE,
+        )
+        if not ok:
+            raise RuntimeError("cv2.solvePnP failed")
+        inliers = np.arange(len(object_points_mm), dtype=np.int32).reshape(-1, 1)
+
+    rvec = rvec.reshape(3)
+    tvec = tvec.reshape(3)
+
+    if inliers is None:
+        inliers = np.arange(len(object_points_mm), dtype=np.int32).reshape(-1, 1)
+
+    return rvec, tvec, inliers
+
+
+
+def refine_pose_with_weights(
+    rvec_init: np.ndarray,
+    tvec_init: np.ndarray,
+    object_points_mm: np.ndarray,
+    image_points_px: np.ndarray,
+    weights: np.ndarray,
+    K: np.ndarray,
+    D: np.ndarray,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Optional weighted nonlinear refinement.
+
+    Uses OpenCV's LM refinement if available; otherwise falls back to the initial pose.
+    """
+    rvec = rvec_init.reshape(3, 1).astype(np.float64)
+    tvec = tvec_init.reshape(3, 1).astype(np.float64)
+
+    # Build a diagonal weighting by repeating stronger correspondences more often is not ideal.
+    # Instead, use a conservative weighted refinement via OpenCV if present.
+    if hasattr(cv2, "solvePnPRefineLM"):
+        try:
+            cv2.solvePnPRefineLM(
+                object_points_mm.reshape(-1, 1, 3).astype(np.float64),
+                image_points_px.reshape(-1, 1, 2).astype(np.float64),
+                K,
+                D,
+                rvec,
+                tvec,
+            )
+            return rvec.reshape(3), tvec.reshape(3)
+        except Exception:
+            pass
+
+    return rvec_init.reshape(3), tvec_init.reshape(3)
+
+
+
+def compute_reprojection_statistics(
+    object_points_mm: np.ndarray,
+    image_points_px: np.ndarray,
+    rvec: np.ndarray,
+    tvec: np.ndarray,
+    K: np.ndarray,
+    D: np.ndarray,
+) -> Dict[str, Any]:
+    pred = project_points(object_points_mm, rvec, tvec, K, D)
+    residual = image_points_px - pred
+    err = np.linalg.norm(residual, axis=1)
+
+    return {
+        "rmse_px": float(np.sqrt(np.mean(err ** 2))) if err.size else float("nan"),
+        "mean_px": float(np.mean(err)) if err.size else float("nan"),
+        "median_px": float(np.median(err)) if err.size else float("nan"),
+        "max_px": float(np.max(err)) if err.size else float("nan"),
+        "per_corner_errors_px": err.tolist(),
+        "per_corner_residuals_px": residual.tolist(),
+    }
+
+
+
+def pose_to_camera_in_world(rvec: np.ndarray, tvec: np.ndarray) -> Dict[str, Any]:
+    """Convert object-to-camera pose into camera pose in world coordinates."""
+    R_wc_obj = rodrigues_to_matrix(rvec)
+    t_wc_obj = tvec.reshape(3)
+
+    # object/world -> camera is: p_cam = R * p_world + t
+    # therefore camera in world is inverse transform.
+    R_cw, t_cw = invert_rigid_transform(R_wc_obj, t_wc_obj)
+    T_world_camera = make_homogeneous(R_cw, t_cw)
+    T_camera_world = make_homogeneous(R_wc_obj, t_wc_obj)
+
+    return {
+        "rvec_world_to_camera": rvec.reshape(3).tolist(),
+        "tvec_world_to_camera_mm": tvec.reshape(3).tolist(),
+        "R_world_to_camera": R_wc_obj.tolist(),
+        "T_camera_world": T_camera_world.tolist(),
+        "R_camera_to_world": R_cw.tolist(),
+        "t_camera_in_world_mm": t_cw.tolist(),
+        "T_world_camera": T_world_camera.tolist(),
+    }
+
+
+
+def estimate_camera_pose_from_detection(
+    detection_json: Dict[str, Any],
+    robot: Dict[str, Any],
+) -> Dict[str, Any]:
+    board_markers = extract_board_markers(robot)
+    K, D = load_intrinsics_from_detection_json(detection_json)
+
+    detections = detection_json.get("detections", [])
+    object_points_mm, image_points_px, weights, used_marker_ids = build_pnp_correspondences(detections, board_markers)
+
+    rvec_init, tvec_init, inliers = pnp_initial_pose(object_points_mm, image_points_px, K, D)
+    rvec, tvec = refine_pose_with_weights(rvec_init, tvec_init, object_points_mm, image_points_px, weights, K, D)
+
+    stats = compute_reprojection_statistics(object_points_mm, image_points_px, rvec, tvec, K, D)
+    pose = pose_to_camera_in_world(rvec, tvec)
+
+    # Add a few diagnostics useful for checking against the real world.
+    camera_pose = {
+        **pose,
+        "statistics": {
+            **stats,
+            "num_correspondences": int(len(object_points_mm)),
+            "num_inliers": int(len(inliers)) if inliers is not None else int(len(object_points_mm)),
+            "used_marker_ids": used_marker_ids,
+        },
+        "input": {
+            "detection_image_file": detection_json.get("image", {}).get("image_file"),
+            "camera_id": detection_json.get("camera", {}).get("camera_id"),
+            "marker_dictionary": detection_json.get("vision_config", {}).get("MarkerType"),
+        },
+    }
+
+    return camera_pose
+
+
+# ----------------------------------------------------------------------------
+# Main
+# ----------------------------------------------------------------------------
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Estimate camera pose from Board ArUco markers using PnP.")
+    parser.add_argument("--robot", required=True, help="Path to robot.json")
+    parser.add_argument(
+        "--detections",
+        required=True,
+        nargs="+",
+        help="One or more detection JSON files created by detect_aruco_observations.py",
+    )
+    parser.add_argument("--outdir", required=True, help="Directory for the pose JSON outputs")
+    parser.add_argument("--write-summary", action="store_true", help="Write one combined summary JSON as well")
+    args = parser.parse_args()
+
+    os.makedirs(args.outdir, exist_ok=True)
+
+    robot = load_robot_json(args.robot)
+
+    summary: Dict[str, Any] = {
+        "schema_version": "1.0",
+        "algorithm": "board_pnp_camera_pose",
+        "robot_file": os.path.abspath(args.robot),
+        "intrinsics_source": "embedded_in_detection_json",
+        "results": [],
+    }
+
+    for det_path in args.detections:
+        detection_json = load_detection_json(det_path)
+        pose = estimate_camera_pose_from_detection(detection_json, robot)
+
+        base = Path(det_path).stem
+        out_path = Path(args.outdir) / f"{base}_camera_pose.json"
+
+        payload = {
+            "schema_version": "1.0",
+            "created_utc": __import__("time").strftime("%Y-%m-%dT%H:%M:%SZ", __import__("time").gmtime()),
+            "source_detection_file": os.path.abspath(det_path),
+            "camera_pose": pose,
+        }
+
+        with open(out_path, "w", encoding="utf-8") as f:
+            json.dump(payload, f, indent=2)
+
+        summary["results"].append({
+            "detection_file": os.path.abspath(det_path),
+            "output_file": str(out_path),
+            "rmse_px": pose["statistics"]["rmse_px"],
+            "median_px": pose["statistics"]["median_px"],
+            "num_correspondences": pose["statistics"]["num_correspondences"],
+            "used_marker_ids": pose["statistics"]["used_marker_ids"],
+        })
+
+        print(f"Saved: {out_path}")
+        print(f"  RMSE: {pose['statistics']['rmse_px']:.3f} px")
+        print(f"  Median: {pose['statistics']['median_px']:.3f} px")
+        print(f"  Used markers: {pose['statistics']['used_marker_ids']}")
+
+    if args.write_summary:
+        summary_path = Path(args.outdir) / "camera_pose_summary.json"
+        with open(summary_path, "w", encoding="utf-8") as f:
+            json.dump(summary, f, indent=2)
+        print(f"Saved summary: {summary_path}")
+
+
+if __name__ == "__main__":
+    main()

pipeline/camera_pose_summary.json

@@ -0,0 +1,47 @@
+{
+  "schema_version": "1.0",
+  "algorithm": "board_pnp_camera_pose",
+  "robot_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\robot.json",
+  "intrinsics_source": "embedded_in_detection_json",
+  "results": [
+    {
+      "detection_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1a_aruco_detection.json",
+      "output_file": "render_1a_aruco_detection_camera_pose.json",
+      "rmse_px": 53.69444650385422,
+      "median_px": 32.08915387366267,
+      "num_correspondences": 32,
+      "used_marker_ids": [
+        205,
+        206,
+        207,
+        208,
+        210,
+        211,
+        215,
+        217
+      ]
+    },
+    {
+      "detection_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1b_aruco_detection.json",
+      "output_file": "render_1b_aruco_detection_camera_pose.json",
+      "rmse_px": 0.9028284747812563,
+      "median_px": 0.87428115526563,
+      "num_correspondences": 8,
+      "used_marker_ids": [
+        210,
+        215
+      ]
+    },
+    {
+      "detection_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1c_aruco_detection.json",
+      "output_file": "render_1c_aruco_detection_camera_pose.json",
+      "rmse_px": 1.013497154241286,
+      "median_px": 0.98147711476875,
+      "num_correspondences": 8,
+      "used_marker_ids": [
+        210,
+        214
+      ]
+    }
+  ]
+}

pipeline/render_1a_aruco_detection_camera_pose.json

@@ -0,0 +1,295 @@
+{
+  "schema_version": "1.0",
+  "created_utc": "2026-05-28T14:19:28Z",
+  "source_detection_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1a_aruco_detection.json",
+  "camera_pose": {
+    "rvec_world_to_camera": [
+      -2.305496407968605,
+      1.065207351740574,
+      0.34998974882711126
+    ],
+    "tvec_world_to_camera_mm": [
+      -315.2306259816743,
+      224.77488089057,
+      1383.92954200185
+    ],
+    "R_world_to_camera": [
+      [
+        0.6485090690393105,
+        -0.7612056883925573,
+        0.0013738022347707657
+      ],
+      [
+        -0.6120529322947699,
+        -0.5203636712935098,
+        0.5954937931392015
+      ],
+      [
+        -0.45257838596550426,
+        -0.38702396509357573,
+        -0.8033587336925552
+      ]
+    ],
+    "T_camera_world": [
+      [
+        0.6485090690393105,
+        -0.7612056883925573,
+        0.0013738022347707657,
+        -315.2306259816743
+      ],
+      [
+        -0.6120529322947699,
+        -0.5203636712935098,
+        0.5954937931392015,
+        224.77488089057
+      ],
+      [
+        -0.45257838596550426,
+        -0.38702396509357573,
+        -0.8033587336925552,
+        1383.92954200185
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        1.0
+      ]
+    ],
+    "R_camera_to_world": [
+      [
+        0.6485090690393105,
+        -0.6120529322947699,
+        -0.45257838596550426
+      ],
+      [
+        -0.7612056883925573,
+        -0.5203636712935098,
+        -0.38702396509357573
+      ],
+      [
+        0.0013738022347707657,
+        0.5954937931392015,
+        -0.8033587336925552
+      ]
+    ],
+    "t_camera_in_world_mm": [
+      968.3406431525125,
+      412.6232353376735,
+      978.3729024968281
+    ],
+    "T_world_camera": [
+      [
+        0.6485090690393105,
+        -0.6120529322947699,
+        -0.45257838596550426,
+        968.3406431525125
+      ],
+      [
+        -0.7612056883925573,
+        -0.5203636712935098,
+        -0.38702396509357573,
+        412.6232353376735
+      ],
+      [
+        0.0013738022347707657,
+        0.5954937931392015,
+        -0.8033587336925552,
+        978.3729024968281
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        1.0
+      ]
+    ],
+    "statistics": {
+      "rmse_px": 53.69444650385422,
+      "mean_px": 43.49018862623262,
+      "median_px": 32.08915387366267,
+      "max_px": 131.0132247190388,
+      "per_corner_errors_px": [
+        125.9940054900667,
+        105.07352467385594,
+        109.87324347189022,
+        131.0132247190388,
+        21.010672127270144,
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+        42.69531998291903,
+        60.42053547730011,
+        53.73185743455883,
+        36.347500219111076
+      ],
+      "per_corner_residuals_px": [
+        [
+          -111.43384038596344,
+          58.79616174774151
+        ],
+        [
+          -94.21398419609403,
+          46.520648848501196
+        ],
+        [
+          -93.31959118739167,
+          57.996409644488836
+        ],
+        [
+          -110.44277538824701,
+          70.4759421066691
+        ],
+        [
+          19.119375834118955,
+          -8.711934971827702
+        ],
+        [
+          26.07607978564579,
+          -15.190070993800134
+        ],
+        [
+          28.267108793747752,
+          -9.249292511446242
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+        [
+          21.386189257435035,
+          -2.528783645053636
+        ],
+        [
+          14.391649857517677,
+          -27.089066604665845
+        ],
+        [
+          22.623064650364768,
+          -29.75405987987233
+        ],
+        [
+          20.48965174053592,
+          -26.507848239232487
+        ],
+        [
+          14.349578701348491,
+          -22.58240253890682
+        ],
+        [
+          42.68032051979753,
+          -30.845460489754316
+        ],
+        [
+          45.092250608114796,
+          -33.11073786135313
+        ],
+        [
+          46.99580232025039,
+          -31.20599140474883
+        ],
+        [
+          45.653152604068396,
+          -27.671246754794254
+        ],
+        [
+          16.64076508745518,
+          -17.488257920248316
+        ],
+        [
+          7.241353209464364,
+          -10.190837448506784
+        ],
+        [
+          7.5129531818588475,
+          -16.185050796483836
+        ],
+        [
+          16.94668756026067,
+          -22.102990240127752
+        ],
+        [
+          19.553105326365085,
+          0.2911463137773467
+        ],
+        [
+          11.302035895770928,
+          9.871591186511665
+        ],
+        [
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+          1.780632287325858
+        ],
+        [
+          17.27663470149446,
+          -6.38720056084361
+        ],
+        [
+          -23.527458763101663,
+          12.504814483751204
+        ],
+        [
+          -37.370264800659925,
+          22.720633378253694
+        ],
+        [
+          -37.18919281759213,
+          14.086486679897916
+        ],
+        [
+          -23.33139615759319,
+          4.299976277948048
+        ],
+        [
+          -18.10794101712986,
+          38.66513701726649
+        ],
+        [
+          -31.863312707890373,
+          51.33585891599432
+        ],
+        [
+          -35.3592943065546,
+          40.45779046747623
+        ],
+        [
+          -21.569770185610196,
+          29.255525739904954
+        ]
+      ],
+      "num_correspondences": 32,
+      "num_inliers": 12,
+      "used_marker_ids": [
+        205,
+        206,
+        207,
+        208,
+        210,
+        211,
+        215,
+        217
+      ]
+    },
+    "input": {
+      "detection_image_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1a.png",
+      "camera_id": "cam1",
+      "marker_dictionary": "DICT_4X4_250"
+    }
+  }
+}

pipeline/render_1b_aruco_detection_camera_pose.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "1.0",
+  "created_utc": "2026-05-28T14:19:28Z",
+  "source_detection_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1b_aruco_detection.json",
+  "camera_pose": {
+    "rvec_world_to_camera": [
+      -3.4928668596759627,
+      -0.11465199431472019,
+      0.06891346455898573
+    ],
+    "tvec_world_to_camera_mm": [
+      -218.99274893021345,
+      -66.7998001007427,
+      991.433880749304
+    ],
+    "R_world_to_camera": [
+      [
+        0.99716158725115,
+        0.07035390512768569,
+        -0.026815982996183867
+      ],
+      [
+        0.0566912850549916,
+        -0.9359657619679411,
+        -0.3474970368543954
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+      [
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+    "t_camera_in_world_mm": [
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+      900.1833170218047
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+    "T_world_camera": [
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+        271.2803169327997
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+      ],
+      [
+        0.0,
+        0.0,
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+        1.0
+      ]
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+    "statistics": {
+      "rmse_px": 0.9028284747812563,
+      "mean_px": 0.7517926037815363,
+      "median_px": 0.87428115526563,
+      "max_px": 1.5028790321047885,
+      "per_corner_errors_px": [
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+        ],
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+          -0.8849670156405409
+        ],
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+          0.03208204880576204,
+          0.2181330653094733
+        ]
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+      "num_inliers": 5,
+      "used_marker_ids": [
+        210,
+        215
+      ]
+    },
+    "input": {
+      "detection_image_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1b.png",
+      "camera_id": "cam1",
+      "marker_dictionary": "DICT_4X4_250"
+    }
+  }
+}

pipeline/render_1c_aruco_detection_camera_pose.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "1.0",
+  "created_utc": "2026-05-28T14:19:28Z",
+  "source_detection_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1c_aruco_detection.json",
+  "camera_pose": {
+    "rvec_world_to_camera": [
+      2.263468911707335,
+      0.4185279076232256,
+      -0.15618269854175848
+    ],
+    "tvec_world_to_camera_mm": [
+      -131.52071411361564,
+      6.261008191633575,
+      845.2175730094486
+    ],
+    "R_world_to_camera": [
+      [
+        0.9373310430689994,
+        0.34765394455176385,
+        0.023393386603497504
+      ],
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+        0.24733770712755493,
+        -0.6165675091799039,
+        -0.7474413456964855
+      ],
+      [
+        -0.24542733004306072,
+        0.7063860427990407,
+        -0.6639157960213375
+      ]
+    ],
+    "T_camera_world": [
+      [
+        0.9373310430689994,
+        0.34765394455176385,
+        0.023393386603497504,
+        -131.52071411361564
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+        0.0,
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+        1.0
+      ]
+    ],
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+        0.24733770712755493,
+        -0.24542733004306072
+      ],
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+        -0.7474413456964855,
+        -0.6639157960213375
+      ]
+    ],
+    "t_camera_in_world_mm": [
+      329.1693569840543,
+      -547.46586742482,
+      568.9097490955903
+    ],
+    "T_world_camera": [
+      [
+        0.9373310430689994,
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+      ],
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+        0.0,
+        0.0,
+        1.0
+      ]
+    ],
+    "statistics": {
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+      "mean_px": 0.959948878595011,
+      "median_px": 0.98147711476875,
+      "max_px": 1.4670431124238368,
+      "per_corner_errors_px": [
+        0.7453912348426285,
+        0.7217726973711857,
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+        1.4670431124238368,
+        1.0780299889909544,
+        1.3026787892805474
+      ],
+      "per_corner_residuals_px": [
+        [
+          -0.14935206506288523,
+          -0.7302753272853124
+        ],
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+        ],
+        [
+          0.5489523368783011,
+          0.9469388196853288
+        ],
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+          0.3286776763773105,
+          0.20087317349123168
+        ],
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+          0.6565456329204267,
+          0.5933285290629442
+        ],
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+          -1.440736791251254,
+          0.27657366477171763
+        ],
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+          -0.6545732662175396,
+          -0.8565526815772273
+        ],
+        [
+          1.3003287403698778,
+          -0.07821249906197636
+        ]
+      ],
+      "num_correspondences": 8,
+      "num_inliers": 8,
+      "used_marker_ids": [
+        210,
+        214
+      ]
+    },
+    "input": {
+      "detection_image_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_1c.png",
+      "camera_id": "cam1",
+      "marker_dictionary": "DICT_4X4_250"
+    }
+  }
+}

pipeline/run.bat

@@ -0,0 +1,6 @@
+
+python3 1_detect_aruco_observations.py --image render_1a.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir .
+python3 1_detect_aruco_observations.py --image render_1b.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir .
+python3 1_detect_aruco_observations.py --image render_1c.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir .
+
+python3 2_KameraPosition.py  --robot ../robot.json --detections render_1a_aruco_detection.json render_1b_aruco_detection.json render_1c_aruco_detection.json --outdir . --write-summary

render_robot.py

@@ -649,11 +649,6 @@ for link_name, link_info in links_def.items():
             else:
                 marker_obj.data.materials[0] = marker_mat    
 
-            print("Marker:", marker_name)
-            print("WORLD POS:", marker_obj.matrix_world.translation)
-            print("LOCAL POS:", marker_obj.location)
-
-
             # --------------------------------------------------------
             # --------------------------------------------------------
             # BACKING PLATE (white PLA behind marker)

robot.json

@@ -16,8 +16,8 @@
   "renderingInfo": {
     "width": 1280,
     "height": 720,
-    "cameraPosition": [-150, -400, 800],
-    "cameraTarget": [230, -180, 180],
+    "cameraPosition": [370, -600, 500],
+    "cameraTarget": [210, -180, 180],
     "cameraUpVector": [0, 0, 1],
     "lightPosition": [-500, -500, 500],
     "lightTarget": [0, 0, 0],
@@ -78,13 +78,13 @@
     }
   },
   "defaultPosition": {
-    "x": 150,
+    "x": 100,
     "y": 30,
-    "z": -40,
-    "a": 260,
-    "b": 0,
-    "c": 0,
-    "e": 0
+    "z": -30,
+    "a": -120,
+    "b": 22,
+    "c": 91,
+    "e": 10
   },
   "recognized": {
     "x": null,
@@ -117,15 +117,15 @@
         "color": [0.85, 0.20, 0.20]
       },
       "markers":[         
-        {"id":205,"position":[800, -90, 0.3], "normal":[0,0,1]},
-        {"id":206,"position":[600,   0, 0.3], "normal":[0,0,1]},
-        {"id":207,"position":[800,   0, 0.3], "normal":[0,0,1]},        
-        {"id":208,"position":[500, -90, 0.3], "normal":[0,0,1]},
-        {"id":210,"position":[0,   0.0, 0.3], "normal":[0,0,1]},
-        {"id":211,"position":[200, 0.0, 0.3], "normal":[0,0,1]},
-        {"id":214,"position":[400, 0.0, 0.3], "normal":[0,0,1]},
-        {"id":215,"position":[200, -90, 0.3], "normal":[0,0,1]},
-        {"id":217,"position":[600, -90, 0.3], "normal":[0,0,1]}
+        {"id":210,"position":[20,  -20, 0.3], "normal":[0,0,1]},
+        {"id":211,"position":[250, -10, 0.3], "normal":[0,0,1]},
+        {"id":215,"position":[250, -90, 0.3], "normal":[0,0,1]},
+        {"id":214,"position":[350, -10, 0.3], "normal":[0,0,1]},        
+        {"id":208,"position":[350, -90, 0.3], "normal":[0,0,1]},
+        {"id":206,"position":[650, -10, 0.3], "normal":[0,0,1]},
+        {"id":205,"position":[750, -90, 0.3], "normal":[0,0,1]},
+        {"id":207,"position":[750, -10, 0.3], "normal":[0,0,1]},
+        {"id":217,"position":[650, -90, 0.3], "normal":[0,0,1]}
       ],
       "model": [
         {
@@ -330,13 +330,14 @@
       ], 
       "markers":[
                      
-        {"id":228,  "position":[-24.75, -112, 24.75],"normal":[-1,0,1],  "relPosSource":["Fusion","Fusion","Fusion"]},
-        {"id": 122, "name": "aruco_122", "position":[-24.75, -182, 24.75],"normal":[-1,0,1],  "relPosSource":["Fusion","Fusion","Fusion"]},
-        {"id": 122, "name": "aruco_122", "position":[-35,-112,0], "normal":[-1,0,0],          "relPosSource":["Fusion","Fusion","Fusion"]},
-        {"id": 124, "name": "aruco_124", "position":[-35,-219,0], "normal":[-1,0,0],          "relPosSource":["Fusion","Fusion","Fusion"]},
-        {"id":223,"name": "aruco_223", "position":[-28.67,-112,-20.08], "normal":[-28.67,0,-20.08],   "relPosSource":["Fusion","Fusion","Fusion"]},
-        {"id":218,"name": "aruco_218", "position":[35,-112,0], "normal":[1,0,0],           "relPosSource":["Fusion","Fusion","Fusion"]},
-        {"id":219, "name": "aruco_219", "position":[35,-219,0], "normal":[1,0,0],           "relPosSource":["Fusion","Fusion","Fusion"]}
+        {"id":124,                      "position":[24.75, -112, -24.75],"normal":[1,0,-1]},
+        {"id":122, "name": "aruco_122", "position":[-35,-112,0], "normal":[-1,0,0]},
+        {"id":218, "name": "aruco_218", "position":[35,-112,0], "normal":[1,0,0]},
+        {"id":122, "name": "aruco_122", "position":[0, -182, 30],"normal":[0,0,1]},
+        {"id":101, "name": "aruco_122", "position":[ 24.75, -182, -24.75],"normal":[ 1,0,-1]},
+        {"id":102, "name": "aruco_122", "position":[-24.75, -182, -24.75],"normal":[-1,0,-1]},
+        {"id":124, "name": "aruco_124", "position":[-35,-219,0], "normal":[-1,0,0]},
+        {"id":219, "name": "aruco_219", "position":[35,-219,0], "normal":[1,0,0]}
 
       ]
     },
@@ -383,6 +384,62 @@
         "radius": 7,
         "color": [0.95, 0.20, 0.20]
       }
+    },
+    "FingerA": {
+      "parent": "Palm",
+      "size": [80, 60, 20],
+      "mountPosition": [0, 0, 0],
+      "mountRotation": [0, 0, 0],
+      "jointToParent": {
+        "name": "Slider",
+        "type": "linear",
+        "axis": [1, 0, 0],
+        "origin": [4, -35,0],
+        "rotation": [0, 0, 0],
+        "variable": "e"
+      },
+      "skeleton": {
+        "from": [0, 0,0],
+        "to": [0, -60, 0],
+        "radius": 4,
+        "color": [0.20, 0.80, 0.20]
+      },
+      "model": [
+        {
+          "stlFile": "surfaces/Finger.stl",
+          "originOfModel": [24,0,-9.1],
+          "rotationOfModelDegree": [90, -90,0],
+          "material": "defaultPlastic"
+        }
+      ]
+    },
+    "FingerB": {
+      "parent": "Palm",
+      "size": [80, 60, 20],
+      "mountPosition": [0, 0, 0],
+      "mountRotation": [0, 0, 0],
+      "jointToParent": {
+        "name": "Slider",
+        "type": "linear",
+        "axis": [-1, 0, 0],
+        "origin": [-4, -35,0],
+        "rotation": [0, 0, 0],
+        "variable": "e"
+      },
+      "skeleton": {
+        "from": [0, 0,0],
+        "to": [0, -60, 0],
+        "radius": 4,
+        "color": [0.20, 0.80, 0.20]
+      },
+      "model": [
+        {
+          "stlFile": "surfaces/Finger.stl",
+          "originOfModel": [-24,0,9.1],
+          "rotationOfModelDegree": [90, 90,0],
+          "material": "defaultPlastic"
+        }
+      ]
     }
   }
 }