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200, + 1400 ], "cameraPosition_c": [ 600, @@ -52,9 +52,9 @@ 600 ], "cameraTarget": [ - 210, - -100, - 150 + 200, + -200, + 180 ], "cameraUpVector": [ 0, diff --git a/data/simulation/Set10/pose.json b/data/simulation/Scene10/pose.json similarity index 100% rename from data/simulation/Set10/pose.json rename to data/simulation/Scene10/pose.json diff --git a/data/simulation/Set10/render_a.json b/data/simulation/Scene10/render_a.json similarity index 100% rename from data/simulation/Set10/render_a.json rename to data/simulation/Scene10/render_a.json diff --git a/data/simulation/Set9/render_a.npz b/data/simulation/Scene10/render_a.npz similarity index 100% rename from data/simulation/Set9/render_a.npz rename to data/simulation/Scene10/render_a.npz diff --git a/data/simulation/Set10/render_a.png b/data/simulation/Scene10/render_a.png similarity index 100% rename from data/simulation/Set10/render_a.png rename to data/simulation/Scene10/render_a.png diff --git 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a/data/simulation/debug/render.png b/data/simulation/debug/render.png index 8c4aeaa..46296c4 100644 Binary files a/data/simulation/debug/render.png and b/data/simulation/debug/render.png differ diff --git a/pipeline/2_Multiview_Trial/2_Multiview.py b/pipeline/2_Multiview_Trial/2_Multiview.py deleted file mode 100644 index 24433e6..0000000 --- a/pipeline/2_Multiview_Trial/2_Multiview.py +++ /dev/null @@ -1,1158 +0,0 @@ -#!/usr/bin/env python3 -""" -Phase 1 — robust multiview robot pose estimation from aruco_detection.json + robot.json - -This version keeps the original kinematic model and optimizer structure, but changes: -- observation weighting to a saturating factor model: min(1, q + f) -- quality indicators are normalized to 0..1 -- blur/sharpness is supported but disabled by default (f=1) -- homography skew quality is added -- summary is built from the final output, so values stay consistent -- duplicate marker ids are warned about instead of being silently overwritten - -Input: - --robot robot.json - --detections render_1a_aruco_detection.json ... - --outDir output - -Output: - multiview_pose.json - multiview_pose_summary.json (optional) -""" - -import argparse -import datetime as _dt -import json -import math -import os -import time -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any, Dict, List, Tuple, Optional - -import cv2 -import numpy as np -from scipy.optimize import least_squares - -STATE_KEYS = ["x", "y", "z", "a", "b", "c", "e"] - - -# ----------------------------------------------------------------------------- -# Small helpers -# ----------------------------------------------------------------------------- - -def clamp01(x: float) -> float: - return float(max(0.0, min(1.0, x))) - - -def load_json(path: str) -> Dict[str, Any]: - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def save_json(data: Dict[str, Any], path: Path) -> None: - with open(path, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2) - - -def resolve_scalar(value: Any, default: float = 0.0) -> float: - if value is None: - return default - if isinstance(value, (int, float)): - return float(value) - try: - return float(str(value).strip()) - except Exception: - return default - - -def resolve_vector(value: Any, default_len: int = 3) -> Tuple[float, ...]: - if value is None: - return tuple(0.0 for _ in range(default_len)) - if isinstance(value, (int, float, str)): - return (resolve_scalar(value),) + tuple(0.0 for _ in range(default_len - 1)) - if isinstance(value, (list, tuple)): - resolved = [resolve_scalar(v) for v in value] - if len(resolved) < default_len: - resolved.extend([0.0] * (default_len - len(resolved))) - return tuple(resolved[:default_len]) - return tuple(0.0 for _ in range(default_len)) - - -def parse_metric_scale(robot: Dict[str, Any]) -> float: - rendering_info = robot.get("renderingInfo", {}) or {} - metric = str(rendering_info.get("metric", "mm")).strip().lower() - return 0.001 if metric == "mm" else 1.0 - - -def normalize_axis(axis: Any) -> np.ndarray: - vec = np.asarray(axis, dtype=np.float64).reshape(-1)[:3] - norm = np.linalg.norm(vec) - if norm < 1e-12: - return np.array([1.0, 0.0, 0.0], dtype=np.float64) - return vec / norm - - -def euler_deg_to_matrix(euler_deg: Any) -> np.ndarray: - x_deg, y_deg, z_deg = resolve_vector(euler_deg, 3) - x = math.radians(x_deg) - y = math.radians(y_deg) - z = math.radians(z_deg) - - cx = math.cos(x) - sx = math.sin(x) - cy = math.cos(y) - sy = math.sin(y) - cz = math.cos(z) - sz = math.sin(z) - - Rx = np.array([[1.0, 0.0, 0.0], [0.0, cx, -sx], [0.0, sx, cx]], dtype=np.float64) - Ry = np.array([[cy, 0.0, sy], [0.0, 1.0, 0.0], [-sy, 0.0, cy]], dtype=np.float64) - Rz = np.array([[cz, -sz, 0.0], [sz, cz, 0.0], [0.0, 0.0, 1.0]], dtype=np.float64) - return Rz @ Ry @ Rx - - -def transform_from_translation_rotation(translation: Any, rotation_deg: Any) -> np.ndarray: - T = np.eye(4, dtype=np.float64) - T[:3, 3] = np.asarray(resolve_vector(translation, 3), dtype=np.float64) - T[:3, :3] = euler_deg_to_matrix(rotation_deg) - return T - - -def axis_angle_matrix(axis: Any, angle_deg: float) -> np.ndarray: - axis_vec = normalize_axis(axis) - theta = math.radians(angle_deg) - kx, ky, kz = axis_vec - c = math.cos(theta) - s = math.sin(theta) - v = 1.0 - c - R = np.array([ - [kx * kx * v + c, kx * ky * v - kz * s, kx * kz * v + ky * s], - [ky * kx * v + kz * s, ky * ky * v + c, ky * kz * v - kx * s], - [kz * kx * v - ky * s, kz * ky * v + kx * s, kz * kz * v + c], - ], dtype=np.float64) - T = np.eye(4, dtype=np.float64) - T[:3, :3] = R - return T - - -# ----------------------------------------------------------------------------- -# Optional quality configuration -# ----------------------------------------------------------------------------- - -@dataclass -class ObservationQualityConfig: - # q indicators are normalized to 0..1 - size_ref_px: float = 50.0 - border_ref_px: float = 120.0 - center_ref_norm: float = 1.0 - sharpness_ref: float = 2500.0 - homography_ref: float = 0.18 - - # factor f scales the effect of each quality indicator: - # f = 1 -> indicator is fully active - # f = 0 -> indicator is ignored (neutral weight = 1) - size_factor: float = 1.0 - aspect_factor: float = 1.0 - border_factor: float = 1.0 - center_factor: float = 1.0 - sharpness_factor: float = 1.0 - homography_factor: float = 1.0 - - # Placeholders for later phases - normal_visibility_factor: float = 1.0 - spin_factor: float = 1.0 - - # Keep a tiny floor for weights so the optimizer remains numerically stable - weight_floor: float = 0.01 - - -def _load_nested_quality_config(src: Dict[str, Any]) -> Dict[str, Any]: - if not isinstance(src, dict): - return {} - for key in ("multiview_quality","multiview_calculation", "multiviewQuality", "quality_config", "multiview"): - v = src.get(key) - if isinstance(v, dict): - return v - return {} - - -def load_quality_config(robot: Dict[str, Any]) -> ObservationQualityConfig: - cfg = ObservationQualityConfig() - candidates = [] - candidates.append(_load_nested_quality_config(robot)) - candidates.append(_load_nested_quality_config(robot.get("vision_config", {}) or {})) - - for src in candidates: - if not src: - continue - for field_name in cfg.__dataclass_fields__.keys(): - if field_name in src: - setattr(cfg, field_name, resolve_scalar(src.get(field_name), getattr(cfg, field_name))) - return cfg - - -# ----------------------------------------------------------------------------- -# Marker extraction and kinematics -# ----------------------------------------------------------------------------- - -class ConstraintResult: - def __init__(self, name: str, enabled: bool, reason: str = ""): - self.name = name - self.enabled = enabled - self.reason = reason - self.residuals = [] - - def __str__(self) -> str: - status = "✓ ENABLED" if self.enabled else "✗ DISABLED" - return f"{self.name:40s} {status:12s} {self.reason}" - - -def build_link_chain(robot: Dict[str, Any]) -> List[str]: - links = robot.get("links", {}) or {} - ordered: List[str] = [] - remaining = set(links.keys()) - while remaining: - progress = False - for name in list(remaining): - parent = links[name].get("parent") - if not parent or parent in ordered: - ordered.append(name) - remaining.remove(name) - progress = True - if not progress: - raise RuntimeError("Cycle detected in robot link tree or missing parent link") - return ordered - - -def extract_markers(robot: Dict[str, Any], scale: float) -> Dict[int, Dict[str, Any]]: - markers: Dict[int, Dict[str, Any]] = {} - links = robot.get("links", {}) or {} - marker_defaults = (robot.get("renderingInfo", {}) or {}).get("markerDefaults", {}) or {} - default_size_mm = float(marker_defaults.get("size", 25.0)) - - for link_name, link_info in links.items(): - for marker in link_info.get("markers", []) or []: - marker_id = int(marker.get("id", -1)) - if marker_id < 0: - continue - if marker_id in markers: - # Duplicate ids exist in the provided robot.json. Keep the first entry and warn. - print(f"[WARN] Duplicate marker id {marker_id} on link '{link_name}'. Ignoring this duplicate entry.") - continue - - pos = resolve_vector(marker.get("position", [0, 0, 0]), 3) - size_mm = float(marker.get("size", default_size_mm)) - markers[marker_id] = { - "marker_id": marker_id, - "link_name": link_name, - "position_m": np.asarray([pos[0] * scale, pos[1] * scale, pos[2] * scale], dtype=np.float64), - "normal": normalize_axis(resolve_vector(marker.get("normal", [0, 0, 1]), 3)), - "spin_deg": float(marker.get("spin", 0.0)), - "size_m": size_mm * scale, - } - - return markers - - -def compute_link_transforms(robot: Dict[str, Any], state: Dict[str, float], scale: float) -> Dict[str, np.ndarray]: - links = robot.get("links", {}) or {} - ordered_links = build_link_chain(robot) - transforms: Dict[str, np.ndarray] = {} - - for link_name in ordered_links: - link_info = links[link_name] or {} - parent_name = link_info.get("parent") - parent_transform = transforms[parent_name] if parent_name else np.eye(4, dtype=np.float64) - - mount_translation = np.asarray(resolve_vector(link_info.get("mountPosition", [0, 0, 0]), 3), dtype=np.float64) * scale - mount = transform_from_translation_rotation(mount_translation, link_info.get("mountRotation", [0, 0, 0])) - - joint_info = link_info.get("jointToParent", {}) or {} - joint_origin = np.asarray(resolve_vector(joint_info.get("origin", [0, 0, 0]), 3), dtype=np.float64) * scale - joint = transform_from_translation_rotation(joint_origin, joint_info.get("rotation", [0, 0, 0])) - - motion = np.eye(4, dtype=np.float64) - joint_type = str(joint_info.get("type", "fixed")).strip().lower() - control_var = str(joint_info.get("variable", joint_info.get("control", ""))).strip().lower() - axis = resolve_vector(joint_info.get("axis", [1, 0, 0]), 3) - - if joint_type == "linear": - motion[:3, 3] = normalize_axis(axis) * state.get(control_var, 0.0) * scale - elif joint_type == "revolute": - motion = axis_angle_matrix(axis, state.get(control_var, 0.0)) - - transforms[link_name] = parent_transform @ mount @ joint @ motion - - return transforms - - -def compute_marker_world_position(marker: Dict[str, Any], link_transforms: Dict[str, np.ndarray]) -> np.ndarray: - link_transform = link_transforms[marker["link_name"]] - local = np.ones(4, dtype=np.float64) - local[:3] = marker["position_m"] - world = link_transform @ local - return world[:3] - - -def marker_plane_axes(normal: np.ndarray, spin_deg: float) -> Tuple[np.ndarray, np.ndarray]: - n = normalize_axis(normal) - candidate = np.array((0.0, 0.0, 1.0), dtype=np.float64) - if abs(np.dot(n, candidate)) > 0.99: - candidate = np.array((1.0, 0.0, 0.0), dtype=np.float64) - - x_dir = np.cross(candidate, n) - x_dir /= max(np.linalg.norm(x_dir), 1e-9) - y_dir = np.cross(n, x_dir) - - if abs(spin_deg) > 1e-9: - theta = math.radians(spin_deg) - cos_t = math.cos(theta) - sin_t = math.sin(theta) - x_rot = x_dir * cos_t + np.cross(n, x_dir) * sin_t + n * np.dot(n, x_dir) * (1.0 - cos_t) - y_rot = y_dir * cos_t + np.cross(n, y_dir) * sin_t + n * np.dot(n, y_dir) * (1.0 - cos_t) - return x_rot, y_rot - - return x_dir, y_dir - - -def marker_object_corners(marker: Dict[str, Any]) -> np.ndarray: - half = marker["size_m"] * 0.5 - x_dir, y_dir = marker_plane_axes(marker["normal"], marker["spin_deg"]) - corners = np.stack([ - -x_dir * half + y_dir * half, - x_dir * half + y_dir * half, - x_dir * half - y_dir * half, - -x_dir * half - y_dir * half, - ], axis=0) - return marker["position_m"].reshape(1, 3) + corners - - -def compute_marker_world_corners(marker: Dict[str, Any], link_transforms: Dict[str, np.ndarray]) -> np.ndarray: - link_transform = link_transforms[marker["link_name"]] - local = marker_object_corners(marker) - homogeneous = np.concatenate([local, np.ones((local.shape[0], 1), dtype=np.float64)], axis=1) - world = (link_transform @ homogeneous.T).T - return world[:, :3] - - -# ----------------------------------------------------------------------------- -# Quality model -# ----------------------------------------------------------------------------- - -def quality_factor(q: float, f: float) -> float: - # Interpret f in [0, 1] as the per-indicator influence. - # f = 1.0 -> indicator is fully active; q is applied. - # f = 0.0 -> indicator is ignored and contributes a neutral weight of 1.0. - q = clamp01(q) - f = clamp01(f) - return 1.0 - f + f * q - - -def projective_homography_quality(image_points_px: np.ndarray, image_shape: Tuple[int, int], ref: float) -> float: - if image_points_px is None or len(image_points_px) != 4: - return 1.0 - - h, w = image_shape - if h <= 0 or w <= 0: - return 1.0 - - src = np.array([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]], dtype=np.float32) - dst = np.asarray(image_points_px, dtype=np.float32).copy() - dst[:, 0] /= float(w) - dst[:, 1] /= float(h) - - try: - H = cv2.getPerspectiveTransform(src, dst).astype(np.float64) - if abs(H[2, 2]) > 1e-12: - H = H / H[2, 2] - proj_strength = float(abs(H[2, 0]) + abs(H[2, 1])) - q = 1.0 / (1.0 + proj_strength / max(ref, 1e-6)) - return clamp01(q) - except Exception: - return 1.0 - - -def compute_observation_quality( - det: Dict[str, Any], - image_shape: Tuple[int, int], - cfg: ObservationQualityConfig, -) -> Dict[str, Any]: - quality = det.get("quality", {}) or {} - geometry = quality.get("geometry", {}) or {} - sharpness = quality.get("sharpness", {}) or {} - - edge_lengths = quality.get("edge_lengths_px", []) or [] - edge_lengths = [float(x) for x in edge_lengths if x is not None] - mean_edge_px = float(np.mean(edge_lengths)) if len(edge_lengths) else math.sqrt(max(float(quality.get("area_px", 0.0)), 0.0)) - - edge_ratio = float(quality.get("edge_ratio", 1.0) or 1.0) - distance_to_border_px = float(geometry.get("distance_to_border_px", 0.0) or 0.0) - distance_to_center_norm = float(geometry.get("distance_to_center_norm", 1.0) or 1.0) - laplacian_var = float(sharpness.get("laplacian_var", 0.0) or 0.0) - - # q in 0..1 - q_size = clamp01(mean_edge_px / max(cfg.size_ref_px, 1e-6)) - q_aspect = clamp01(2.0 / (1.0 + max(edge_ratio, 1e-6))) - q_border = clamp01(distance_to_border_px / max(cfg.border_ref_px, 1e-6)) - q_center = clamp01(1.0 - (distance_to_center_norm / max(cfg.center_ref_norm, 1e-6))) - q_sharpness = clamp01(laplacian_var / max(cfg.sharpness_ref, 1e-6)) - q_homography = projective_homography_quality(np.asarray(det.get("image_points_px", []), dtype=np.float64), image_shape, cfg.homography_ref) - - factor_map = { - "size": quality_factor(q_size, cfg.size_factor), - "aspect": quality_factor(q_aspect, cfg.aspect_factor), - "border": quality_factor(q_border, cfg.border_factor), - "center": quality_factor(q_center, cfg.center_factor), - "sharpness": quality_factor(q_sharpness, cfg.sharpness_factor), - "homography": quality_factor(q_homography, cfg.homography_factor), - } - - # Currently not active in phase 1, but already supported for later phases. - # They default to f=1, which makes them neutral. - q_normal_visibility = 1.0 - q_spin = 1.0 - factor_map["normal_visibility"] = quality_factor(q_normal_visibility, cfg.normal_visibility_factor) - factor_map["spin"] = quality_factor(q_spin, cfg.spin_factor) - - weight_multiplier = 1.0 - for v in factor_map.values(): - weight_multiplier *= float(v) - - # Conservative default: if no factor is activated in robot.json, - # this remains effectively neutral. - - detector_confidence = clamp01(float(det.get("confidence", 1.0) or 1.0)) - weighted_confidence = detector_confidence * weight_multiplier - weighted_confidence = max(cfg.weight_floor, min(1.0, weighted_confidence)) - - return { - "detector_confidence": detector_confidence, - "weighted_confidence": weighted_confidence, - "q": { - "size": q_size, - "aspect": q_aspect, - "border": q_border, - "center": q_center, - "sharpness": q_sharpness, - "homography": q_homography, - "normal_visibility": q_normal_visibility, - "spin": q_spin, - }, - "factor": factor_map, - "weight_multiplier": weight_multiplier, - "raw": { - "mean_edge_px": mean_edge_px, - "edge_ratio": edge_ratio, - "distance_to_border_px": distance_to_border_px, - "distance_to_center_norm": distance_to_center_norm, - "laplacian_var": laplacian_var, - }, - } - - -# ----------------------------------------------------------------------------- -# Constraints (kept from the existing approach) -# ----------------------------------------------------------------------------- - - -def validate_constraints(robot: Dict[str, Any], robot_markers: Dict[int, Dict[str, Any]]) -> Dict[str, ConstraintResult]: - results = {} - - rigid_body_result = ConstraintResult("RigidBodyDistances", False) - try: - rigid_body_count = 0 - for link_name in ["Arm1", "Ellbow", "Arm2"]: - link_markers = [m for m in robot_markers.values() if m["link_name"] == link_name] - if len(link_markers) >= 2: - rigid_body_count += 1 - if rigid_body_count >= 2: - rigid_body_result.enabled = True - rigid_body_result.reason = f"Found {rigid_body_count} links with 2+ markers each" - else: - rigid_body_result.reason = "Not enough rigid links with multiple markers" - except Exception as e: - rigid_body_result.reason = f"Error: {str(e)}" - results["RigidBodyDistances"] = rigid_body_result - - inter_link_x_result = ConstraintResult("InterLinkXDistances", False) - try: - links_with_markers = set(m["link_name"] for m in robot_markers.values()) - x_rotated_links = [] - for link_name in ["Arm1", "Ellbow"]: - if link_name in links_with_markers: - link_markers = [m for m in robot_markers.values() if m["link_name"] == link_name] - if len(link_markers) >= 1: - x_rotated_links.append(link_name) - if len(x_rotated_links) >= 2: - inter_link_x_result.enabled = True - inter_link_x_result.reason = f"Found {len(x_rotated_links)} X-rotation links: {', '.join(x_rotated_links)}" - else: - inter_link_x_result.reason = "Not enough X-rotation links" - except Exception as e: - inter_link_x_result.reason = f"Error: {str(e)}" - results["InterLinkXDistances"] = inter_link_x_result - - arm2_sina_result = ConstraintResult("Arm2SinADependency", True, "Sanity check only (not enforced)") - try: - arm2_markers = [m for m in robot_markers.values() if m["link_name"] == "Arm2"] - if len(arm2_markers) >= 2: - z_values = set(float(m["position_m"][2]) for m in arm2_markers) - if len(z_values) > 1: - arm2_sina_result.enabled = True - arm2_sina_result.reason = "Multiple Z-values detected; sin(a) dependency confirmed" - else: - arm2_sina_result.enabled = False - arm2_sina_result.reason = "No Z-variation in Arm2 markers (cannot use sin(a) constraint)" - else: - arm2_sina_result.enabled = False - arm2_sina_result.reason = "Not enough Arm2 markers" - except Exception as e: - arm2_sina_result.reason = f"Error: {str(e)}" - results["Arm2SinADependency"] = arm2_sina_result - - return results - - -def compute_soft_constraint_residuals( - robot_state: Dict[str, float], - robot_markers: Dict[int, Dict[str, Any]], - link_transforms: Dict[str, np.ndarray], - robot: Dict[str, Any], - enabled_constraints: Dict[str, ConstraintResult], -) -> List[float]: - residuals = [] - weight_scale = 0.1 - - if enabled_constraints["RigidBodyDistances"].enabled: - for link_name in ["Arm1", "Ellbow", "Arm2"]: - link_markers = [m for m in robot_markers.values() if m["link_name"] == link_name] - if len(link_markers) < 2: - continue - for i in range(len(link_markers)): - for j in range(i + 1, len(link_markers)): - m_i = link_markers[i] - m_j = link_markers[j] - pos_i = compute_marker_world_position(m_i, link_transforms) - pos_j = compute_marker_world_position(m_j, link_transforms) - dist_world = np.linalg.norm(pos_i - pos_j) - dist_local = np.linalg.norm(m_i["position_m"] - m_j["position_m"]) - error = dist_world - dist_local - residuals.append(error * weight_scale * 0.1) - - if enabled_constraints["InterLinkXDistances"].enabled: - arm1_markers = [m for m in robot_markers.values() if m["link_name"] == "Arm1"] - ellbow_markers = [m for m in robot_markers.values() if m["link_name"] == "Ellbow"] - if len(arm1_markers) >= 1 and len(ellbow_markers) >= 1: - m_arm1 = arm1_markers[0] - m_ellbow = ellbow_markers[0] - pos_arm1 = compute_marker_world_position(m_arm1, link_transforms) - pos_ellbow = compute_marker_world_position(m_ellbow, link_transforms) - x_diff_world = pos_ellbow[0] - pos_arm1[0] - x_diff_ref = m_ellbow["position_m"][0] - m_arm1["position_m"][0] - residuals.append((x_diff_world - x_diff_ref) * weight_scale) - - return residuals - - -# ----------------------------------------------------------------------------- -# Camera / observation helpers -# ----------------------------------------------------------------------------- - - -def load_intrinsics(detection_json: Dict[str, Any]) -> Tuple[np.ndarray, np.ndarray]: - cam = detection_json["camera"] - 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 detection_image_shape(detection_json: Dict[str, Any]) -> Tuple[int, int]: - image = detection_json.get("image", {}) or {} - h = int(image.get("height_px", detection_json.get("height_px", 720)) or 720) - w = int(image.get("width_px", detection_json.get("width_px", 1280)) or 1280) - return h, w - - -def collect_views_and_observations( - detection_files: List[str], - robot_markers: Dict[int, Dict[str, Any]], - quality_cfg: ObservationQualityConfig, -) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]: - views: List[Dict[str, Any]] = [] - observations: List[Dict[str, Any]] = [] - - for idx, det_path in enumerate(detection_files): - detection_json = load_json(det_path) - K, D = load_intrinsics(detection_json) - image_shape = detection_image_shape(detection_json) - - views.append({ - "index": idx, - "source_file": os.path.abspath(det_path), - "camera_id": detection_json.get("camera", {}).get("camera_id", f"cam{idx+1}"), - "image_file": detection_json.get("image", {}).get("image_file"), - "image_shape": image_shape, - "K": K, - "D": D, - }) - - for det in detection_json.get("detections", []) or []: - if str(det.get("type", "aruco")).lower() != "aruco": - continue - marker_id = int(det.get("marker_id", -1)) - if marker_id < 0 or marker_id not in robot_markers: - continue - - image_points = det.get("image_points_px") - if not (isinstance(image_points, list) and len(image_points) == 4): - # Phase 1 uses full marker corners only. - continue - - image_points = np.asarray(image_points, dtype=np.float64) - marker = robot_markers[marker_id] - obs_quality = compute_observation_quality(det, image_shape, quality_cfg) - - observations.append({ - "view_index": idx, - "marker_id": marker_id, - "marker_link_corners": marker_object_corners(marker), - "image_points_px": image_points, - "confidence_base": obs_quality["detector_confidence"], - "confidence": obs_quality["weighted_confidence"], - "quality": obs_quality, - "raw_detection": det, - }) - - if len(views) == 0: - raise RuntimeError("No valid detection views found") - if len(observations) == 0: - raise RuntimeError("No marker observations matched robot.json markers") - return views, observations - - -def initial_camera_guess( - view: Dict[str, Any], - observations: List[Dict[str, Any]], - robot_markers: Dict[int, Dict[str, Any]], - default_state: Dict[str, float], - scale: float, - robot: Dict[str, Any], -) -> Tuple[np.ndarray, np.ndarray]: - object_points = [] - image_points = [] - link_transforms = compute_link_transforms(robot, default_state, scale) - - for obs in observations: - if obs["view_index"] != view["index"]: - continue - marker = robot_markers[obs["marker_id"]] - object_points.append(compute_marker_world_corners(marker, link_transforms)) - image_points.append(obs["image_points_px"]) - - if len(object_points) == 0: - return np.zeros((3, 1), dtype=np.float64), np.array([[0.0], [0.0], [1.0]], dtype=np.float64) - - object_points = np.vstack(object_points) - image_points = np.vstack(image_points) - - if object_points.shape[0] < 4: - return np.zeros((3, 1), dtype=np.float64), np.array([[0.0], [0.0], [1.0]], dtype=np.float64) - - success, rvec, tvec = cv2.solvePnP( - object_points, - image_points, - view["K"], - view["D"], - flags=cv2.SOLVEPNP_ITERATIVE, - ) - - if not success: - return np.zeros((3, 1), dtype=np.float64), np.array([[0.0], [0.0], [1.0]], dtype=np.float64) - - return rvec, tvec - - -def project_points(points_3d: np.ndarray, rvec: np.ndarray, tvec: np.ndarray, K: np.ndarray, D: np.ndarray) -> np.ndarray: - projected, _ = cv2.projectPoints(points_3d, rvec, tvec, K, D) - return projected.reshape(-1, 2) - - -# ----------------------------------------------------------------------------- -# Optimization -# ----------------------------------------------------------------------------- - - -def pack_parameters(robot_state: Dict[str, float], camera_params: List[Tuple[np.ndarray, np.ndarray]]) -> np.ndarray: - state_vec = np.asarray([robot_state[k] for k in STATE_KEYS], dtype=np.float64) - cams = [] - for rvec, tvec in camera_params: - cams.append(rvec.reshape(3)) - cams.append(tvec.reshape(3)) - return np.concatenate([state_vec] + cams) - - -def unpack_parameters(params: np.ndarray, n_views: int) -> Tuple[Dict[str, float], List[Tuple[np.ndarray, np.ndarray]]]: - robot_state = {STATE_KEYS[i]: float(params[i]) for i in range(len(STATE_KEYS))} - camera_params = [] - offset = len(STATE_KEYS) - for _ in range(n_views): - rvec = params[offset:offset + 3].reshape(3, 1) - tvec = params[offset + 3:offset + 6].reshape(3, 1) - camera_params.append((rvec, tvec)) - offset += 6 - return robot_state, camera_params - - -def residuals_for_parameters( - params: np.ndarray, - views: List[Dict[str, Any]], - observations: List[Dict[str, Any]], - robot_markers: Dict[int, Dict[str, Any]], - robot: Dict[str, Any], - scale: float, - default_state: Dict[str, float], - enabled_constraints: Dict[str, ConstraintResult], -) -> np.ndarray: - robot_state, camera_params = unpack_parameters(params, len(views)) - link_transforms = compute_link_transforms(robot, robot_state, scale) - - residuals = [] - - for obs in observations: - marker = robot_markers[obs["marker_id"]] - world_corners = compute_marker_world_corners(marker, link_transforms) - rvec, tvec = camera_params[obs["view_index"]] - proj = project_points(world_corners, rvec, tvec, views[obs["view_index"]]["K"], views[obs["view_index"]]["D"]) - diffs = proj - obs["image_points_px"] - weight = math.sqrt(max(float(obs["confidence"]), 1e-9)) - residuals.extend((diffs * weight).reshape(-1)) - - for key in STATE_KEYS: - diff = robot_state[key] - default_state.get(key, 0.0) - w = 0.001 if key in ("x", "y", "z", "e") else 0.01 - residuals.append(diff * w) - - residuals.extend(compute_soft_constraint_residuals(robot_state, robot_markers, link_transforms, robot, enabled_constraints)) - - return np.asarray(residuals, dtype=np.float64) - - -def estimate_uncertainty(result: Any, n_params: int) -> np.ndarray: - if result.jac is None: - return np.full(n_params, float("nan"), dtype=np.float64) - J = result.jac - m, n = J.shape - JTJ = J.T @ J - try: - cov = np.linalg.pinv(JTJ) - except np.linalg.LinAlgError: - cov = np.linalg.pinv(JTJ + np.eye(n) * 1e-9) - residuals = result.fun - dof = max(1, m - n) - sigma2 = float(np.sum(residuals ** 2) / dof) - cov *= sigma2 - return np.sqrt(np.diag(cov)) - - -def camera_position_world(rvec: np.ndarray, tvec: np.ndarray) -> np.ndarray: - R, _ = cv2.Rodrigues(rvec) - return (-R.T @ tvec).reshape(3) - - -# ----------------------------------------------------------------------------- -# Output building -# ----------------------------------------------------------------------------- - - -def build_output( - robot_state: Dict[str, float], - state_uncertainty: np.ndarray, - views: List[Dict[str, Any]], - camera_params: List[Tuple[np.ndarray, np.ndarray]], - observations: List[Dict[str, Any]], - robot_markers: Dict[int, Dict[str, Any]], - scale: float, - robot: Dict[str, Any], - robot_json_path: str, - quality_cfg: ObservationQualityConfig, - final_cost: Optional[float] = None, - solver_status: Optional[int] = None, - solver_message: Optional[str] = None, -) -> Dict[str, Any]: - link_transforms = compute_link_transforms(robot, robot_state, scale) - - marker_summary: Dict[int, Dict[str, Any]] = {} - for marker_id, marker in robot_markers.items(): - marker_summary[marker_id] = { - "marker_id": marker_id, - "link_name": marker["link_name"], - "position_world_m": compute_marker_world_position(marker, link_transforms).tolist(), - "size_m": marker["size_m"], - "observation_count": 0, - "mean_confidence": None, - "mean_detector_confidence": None, - "mean_reprojection_error_px": None, - "observations": [], - } - - per_marker_errors: Dict[int, List[float]] = {mid: [] for mid in marker_summary} - per_marker_confidences: Dict[int, List[float]] = {mid: [] for mid in marker_summary} - per_marker_detector_conf: Dict[int, List[float]] = {mid: [] for mid in marker_summary} - - for obs in observations: - marker_id = obs["marker_id"] - marker = robot_markers[marker_id] - object_points_m = compute_marker_world_corners(marker, link_transforms) - rvec, tvec = camera_params[obs["view_index"]] - proj = project_points(object_points_m, rvec, tvec, views[obs["view_index"]]["K"], views[obs["view_index"]]["D"]) - diffs = proj - obs["image_points_px"] - errors = np.linalg.norm(diffs, axis=1) - repro_error = float(np.mean(errors)) - per_marker_errors[marker_id].extend(errors.tolist()) - per_marker_confidences[marker_id].append(float(obs["confidence"])) - per_marker_detector_conf[marker_id].append(float(obs["confidence_base"])) - marker_summary[marker_id]["observation_count"] += 1 - marker_summary[marker_id]["observations"].append({ - "view_index": obs["view_index"], - "source_file": views[obs["view_index"]]["source_file"], - "image_file": views[obs["view_index"]]["image_file"], - "confidence_detector": float(obs["confidence_base"]), - "confidence_weighted": float(obs["confidence"]), - "quality": obs["quality"], - "mean_reprojection_error_px": repro_error, - "corner_reprojection_errors_px": errors.tolist(), - }) - - for marker_id, summary in marker_summary.items(): - if summary["observation_count"] > 0: - summary["mean_confidence"] = float(np.mean(per_marker_confidences[marker_id])) - summary["mean_detector_confidence"] = float(np.mean(per_marker_detector_conf[marker_id])) - summary["mean_reprojection_error_px"] = float(np.mean(per_marker_errors[marker_id])) - - camera_outputs = [] - for idx, view in enumerate(views): - rvec, tvec = camera_params[idx] - cam_pos = camera_position_world(rvec, tvec) - observed_count = sum(1 for obs in observations if obs["view_index"] == idx) - camera_outputs.append({ - "view_index": idx, - "source_file": view["source_file"], - "camera_id": view["camera_id"], - "camera_position_world_m": cam_pos.tolist(), - "rvec": rvec.reshape(-1).tolist(), - "tvec": tvec.reshape(-1).tolist(), - "intrinsics": { - "camera_matrix": view["K"].tolist(), - "distortion_coefficients": view["D"].reshape(-1).tolist(), - }, - "observation_count": observed_count, - }) - - robot_pose_output = { - "state": {k: float(robot_state[k]) for k in STATE_KEYS}, - "uncertainty": { - "x_mm": float(state_uncertainty[0]), - "y_mm": float(state_uncertainty[1]), - "z_mm": float(state_uncertainty[2]), - "a_deg": float(state_uncertainty[3]), - "b_deg": float(state_uncertainty[4]), - "c_deg": float(state_uncertainty[5]), - "e_mm": float(state_uncertainty[6]), - }, - "confidence": { - "x": float(math.exp(-state_uncertainty[0] / 10.0)) if np.isfinite(state_uncertainty[0]) else 0.0, - "y": float(math.exp(-state_uncertainty[1] / 10.0)) if np.isfinite(state_uncertainty[1]) else 0.0, - "z": float(math.exp(-state_uncertainty[2] / 10.0)) if np.isfinite(state_uncertainty[2]) else 0.0, - "a": float(math.exp(-state_uncertainty[3] / 10.0)) if np.isfinite(state_uncertainty[3]) else 0.0, - "b": float(math.exp(-state_uncertainty[4] / 10.0)) if np.isfinite(state_uncertainty[4]) else 0.0, - "c": float(math.exp(-state_uncertainty[5] / 10.0)) if np.isfinite(state_uncertainty[5]) else 0.0, - "e": float(math.exp(-state_uncertainty[6] / max(1.0, state_uncertainty[6]))) if np.isfinite(state_uncertainty[6]) else 0.0, - }, - } - - all_conf = np.asarray([obs["confidence"] for obs in observations], dtype=np.float64) - all_det_conf = np.asarray([obs["confidence_base"] for obs in observations], dtype=np.float64) - all_q_size = np.asarray([obs["quality"]["q"]["size"] for obs in observations], dtype=np.float64) - all_q_aspect = np.asarray([obs["quality"]["q"]["aspect"] for obs in observations], dtype=np.float64) - all_q_border = np.asarray([obs["quality"]["q"]["border"] for obs in observations], dtype=np.float64) - all_q_homography = np.asarray([obs["quality"]["q"]["homography"] for obs in observations], dtype=np.float64) - - all_errors = [] - for marker in marker_summary.values(): - if marker["mean_reprojection_error_px"] is not None: - all_errors.append(marker["mean_reprojection_error_px"]) - - statistics = { - "observation_count": len(observations), - "camera_count": len(views), - "marker_count": len(robot_markers), - "observed_marker_count": int(sum(1 for m in marker_summary.values() if m["observation_count"] > 0)), - "mean_detector_confidence": float(np.mean(all_det_conf)) if len(all_det_conf) else None, - "mean_weighted_confidence": float(np.mean(all_conf)) if len(all_conf) else None, - "mean_reprojection_error_px": float(np.mean(all_errors)) if len(all_errors) else None, - "quality_means": { - "size": float(np.mean(all_q_size)) if len(all_q_size) else None, - "aspect": float(np.mean(all_q_aspect)) if len(all_q_aspect) else None, - "border": float(np.mean(all_q_border)) if len(all_q_border) else None, - "homography": float(np.mean(all_q_homography)) if len(all_q_homography) else None, - }, - "quality_config": { - "size_ref_px": quality_cfg.size_ref_px, - "border_ref_px": quality_cfg.border_ref_px, - "center_ref_norm": quality_cfg.center_ref_norm, - "sharpness_ref": quality_cfg.sharpness_ref, - "homography_ref": quality_cfg.homography_ref, - "size_factor": quality_cfg.size_factor, - "aspect_factor": quality_cfg.aspect_factor, - "border_factor": quality_cfg.border_factor, - "center_factor": quality_cfg.center_factor, - "sharpness_factor": quality_cfg.sharpness_factor, - "homography_factor": quality_cfg.homography_factor, - }, - } - - output = { - "schema_version": "1.0", - "created_utc": _dt.datetime.utcnow().isoformat() + "Z", - "source_robot_json": os.path.abspath(robot_json_path), - "source_detections": [view["source_file"] for view in views], - "robot_pose": robot_pose_output, - "camera_poses": camera_outputs, - "marker_positions": list(marker_summary.values()), - "statistics": statistics, - "solver": { - "final_cost": final_cost, - "status": solver_status, - "message": solver_message, - }, - } - return output - - -def build_summary(output: Dict[str, Any]) -> Dict[str, Any]: - return { - "schema_version": output.get("schema_version"), - "created_utc": output.get("created_utc"), - "source_robot_json": output.get("source_robot_json"), - "source_detections": output.get("source_detections"), - "solver": output.get("solver", {}), - "robot_pose": output.get("robot_pose"), - "statistics": output.get("statistics", {}), - } - - -# ----------------------------------------------------------------------------- -# Diagnostics -# ----------------------------------------------------------------------------- - - -def print_constraint_sanity_check( - robot_state: Dict[str, float], - robot_markers: Dict[int, Dict[str, Any]], - link_transforms: Dict[str, np.ndarray], - robot: Dict[str, Any], - enabled_constraints: Dict[str, ConstraintResult], - scale: float, -) -> None: - print("\n" + "=" * 70) - print("CONSTRAINT SANITY CHECKS (after optimization)") - print("=" * 70) - - if enabled_constraints["RigidBodyDistances"].enabled: - print("\n1. RIGID BODY DISTANCES") - for link_name in ["Arm1", "Ellbow", "Arm2"]: - link_markers = [m for m in robot_markers.values() if m["link_name"] == link_name] - if len(link_markers) < 2: - continue - max_error = 0.0 - for i in range(len(link_markers)): - for j in range(i + 1, len(link_markers)): - m_i = link_markers[i] - m_j = link_markers[j] - pos_i = compute_marker_world_position(m_i, link_transforms) - pos_j = compute_marker_world_position(m_j, link_transforms) - dist_world = np.linalg.norm(pos_i - pos_j) - dist_local = np.linalg.norm(m_i["position_m"] - m_j["position_m"]) - max_error = max(max_error, abs(dist_world - dist_local)) - status = "✓" if max_error < 1.0 else "⚠" if max_error < 5.0 else "✗" - print(f" {link_name:10s}: max_error = {max_error:.3f} mm {status}") - - if enabled_constraints["InterLinkXDistances"].enabled: - print("\n2. INTER-LINK X-DISTANCES") - arm1_markers = [m for m in robot_markers.values() if m["link_name"] == "Arm1"] - ellbow_markers = [m for m in robot_markers.values() if m["link_name"] == "Ellbow"] - if len(arm1_markers) >= 1 and len(ellbow_markers) >= 1: - m_arm1 = arm1_markers[0] - m_ellbow = ellbow_markers[0] - pos_arm1 = compute_marker_world_position(m_arm1, link_transforms) - pos_ellbow = compute_marker_world_position(m_ellbow, link_transforms) - x_diff_world = pos_ellbow[0] - pos_arm1[0] - x_diff_ref = m_ellbow["position_m"][0] - m_arm1["position_m"][0] - error = abs(x_diff_world - x_diff_ref) - status = "✓" if error < 1.0 else "⚠" if error < 5.0 else "✗" - print(f" Arm1 <-> Ellbow: error = {error:.3f} mm {status}") - - if enabled_constraints["Arm2SinADependency"].enabled: - print("\n3. ARM2 sin(a) DEPENDENCY (sanity check)") - arm2_markers = [m for m in robot_markers.values() if m["link_name"] == "Arm2"] - if len(arm2_markers) >= 2: - a_rad = math.radians(robot_state["a"]) - sin_a = math.sin(a_rad) - cos_a = math.cos(a_rad) - max_error = 0.0 - # This remains only a qualitative check. - for m in arm2_markers: - pos_world = compute_marker_world_position(m, link_transforms) - x_world = pos_world[0] - x_local = m["position_m"][0] - z_local = m["position_m"][2] - x_expected = (90.0 * scale) + x_local * cos_a - z_local * sin_a - max_error = max(max_error, abs(x_world - x_expected)) - status = "✓" if max_error < 5.0 else "⚠" - print(f" X-consistency with sin(a): max_error = {max_error:.3f} mm {status}") - print(" (Note: this is a consistency check, not a hard constraint)") - - print("=" * 70) - - -# ----------------------------------------------------------------------------- -# Main -# ----------------------------------------------------------------------------- - - -def main() -> None: - parser = argparse.ArgumentParser(description="Multiview optimization of robot pose and camera extrinsics") - parser.add_argument("--robot", required=True, help="Path to robot.json") - parser.add_argument("--detections", required=True, nargs="+", help="List of detection JSON files") - parser.add_argument("--outDir", required=True, help="Output directory") - parser.add_argument("--write-summary", action="store_true", help="Write summary file") - parser.add_argument("--max-iter", type=int, default=500, help="Maximum optimizer iterations") - args = parser.parse_args() - - os.makedirs(args.outDir, exist_ok=True) - - robot_json_path = os.path.abspath(args.robot) - robot = load_json(robot_json_path) - scale = parse_metric_scale(robot) - quality_cfg = load_quality_config(robot) - - default_state = {k: float(robot.get("defaultPosition", {}).get(k, 0.0) or 0.0) for k in STATE_KEYS} - robot_markers = extract_markers(robot, scale) - - print("\n" + "=" * 70) - print("CONSTRAINT VALIDATION") - print("=" * 70) - enabled_constraints = validate_constraints(robot, robot_markers) - for _, result in enabled_constraints.items(): - print(result) - print("=" * 70) - - views, observations = collect_views_and_observations(args.detections, robot_markers, quality_cfg) - - print("\n" + "=" * 70) - print("OBSERVATION QUALITY SUMMARY") - print("=" * 70) - print(f"Total observations: {len(observations)}") - print() - - quality_by_marker: Dict[int, List[Dict[str, Any]]] = {} - for obs in observations: - quality_by_marker.setdefault(obs["marker_id"], []).append(obs["quality"]) - - print(f"{'Marker':>8} {'Link':>12} {'Count':>6} {'Avg Size':>10} {'Avg Aspec':>10} {'Avg Hmg.':>10} {'Avg Conf.':>10}") - print("-" * 74) - for marker_id in sorted(quality_by_marker.keys()): - marker = robot_markers[marker_id] - qlist = quality_by_marker[marker_id] - avg_size = float(np.mean([q["q"]["size"] for q in qlist])) - avg_aspect = float(np.mean([q["q"]["aspect"] for q in qlist])) - avg_homog = float(np.mean([q["q"]["homography"] for q in qlist])) - obs_for_marker = [o for o in observations if o["marker_id"] == marker_id] - avg_conf = float(np.mean([o["confidence"] for o in obs_for_marker])) - print(f"{marker_id:8d} {marker['link_name']:>12} {len(qlist):6d} {avg_size:10.3f} {avg_aspect:10.3f} {avg_homog:10.3f} {avg_conf:10.3f}") - print("=" * 70) - - camera_guesses = [] - for view in views: - rvec, tvec = initial_camera_guess(view, observations, robot_markers, default_state, scale, robot) - camera_guesses.append((rvec, tvec)) - - x0 = pack_parameters(default_state, camera_guesses) - - progress = { - "iter": 0, - "last_cost": None, - "last_print": time.time(), - "prev_x": x0.copy(), - } - - def progress_callback(xk: np.ndarray) -> None: - progress["iter"] += 1 - now = time.time() - if progress["iter"] == 1 or now - progress["last_print"] >= 1.0: - res = residuals_for_parameters(xk, views, observations, robot_markers, robot, scale, default_state, enabled_constraints) - cost = 0.5 * float(np.dot(res, res)) - delta_cost = None - convergence = "" - if progress["last_cost"] is not None: - delta_cost = cost - progress["last_cost"] - if abs(delta_cost) < 1e-3: - convergence = " stable" - elif delta_cost < 0: - convergence = " improving" - else: - convergence = " worsening" - step_norm = float(np.linalg.norm(xk - progress["prev_x"])) - print(f'[Multiview] iter={progress["iter"]:4d} cost={cost:.4f}' + (f' delta={delta_cost:.4g}' if delta_cost is not None else "") + f' step={step_norm:.4g}' + convergence) - progress["last_cost"] = cost - progress["last_print"] = now - progress["prev_x"] = xk.copy() - - result = least_squares( - residuals_for_parameters, - x0, - args=(views, observations, robot_markers, robot, scale, default_state, enabled_constraints), - jac="2-point", - method="trf", - loss="soft_l1", - f_scale=1.0, - max_nfev=args.max_iter, - callback=progress_callback, - ) - - robot_state, camera_params = unpack_parameters(result.x, len(views)) - uncertainties = estimate_uncertainty(result, len(result.x)) - - link_transforms = compute_link_transforms(robot, robot_state, scale) - print_constraint_sanity_check(robot_state, robot_markers, link_transforms, robot, enabled_constraints, scale) - - output = build_output( - robot_state, - uncertainties[:len(STATE_KEYS)], - views, - camera_params, - observations, - robot_markers, - scale, - robot, - robot_json_path, - quality_cfg, - final_cost=float(result.cost), - solver_status=int(result.status), - solver_message=str(result.message), - ) - - out_path = Path(args.outDir) / "multiview_pose.json" - save_json(output, out_path) - print(f"Saved: {out_path}") - - if args.write_summary: - summary_path = Path(args.outDir) / "multiview_pose_summary.json" - summary = build_summary(output) - save_json(summary, summary_path) - print(f"Saved: {summary_path}") - - -if __name__ == "__main__": - main() diff --git a/pipeline/2_Multiview_Trial/multiview_pose.json b/pipeline/2_Multiview_Trial/multiview_pose.json deleted file mode 100644 index fba7e38..0000000 --- a/pipeline/2_Multiview_Trial/multiview_pose.json +++ /dev/null @@ -1,2471 +0,0 @@ -{ - "schema_version": "1.0", - "created_utc": "2026-05-28T21:58:12.453984Z", - "source_robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\robot.json", - "source_detections": [ - "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2a_aruco_detection.json", - "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2b_aruco_detection.json", - "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2c_aruco_detection.json", - "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2d_aruco_detection.json" - ], - "robot_pose": { - "state": { - "x": 6.55912456537666, - "y": 4.460952073670867, - "z": 30.7175200969605, - "a": 11.566185446462232, - "b": 1.999999999999993, - "c": 8.999999999999904, - "e": 0.9999999999999776 - }, - "uncertainty": { - "x_mm": 3481.5587672468964, - "y_mm": 1721.1687436205234, - "z_mm": 230.0328185419544, - "a_deg": 559.8212894814748, - "b_deg": 10506.72751291286, - "c_deg": 10506.727245747321, - "e_mm": 105067.37089893252 - }, - "confidence": { - "x": 6.278037366676565e-152, - "y": 1.7807019001431863e-75, - "z": 1.0228256841704706e-10, - "a": 4.8671004844006695e-25, - "b": 0.0, - "c": 0.0, - "e": 0.36787944117144233 - } - }, - "camera_poses": [ - { - "view_index": 0, - "source_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2a_aruco_detection.json", - "camera_id": "cam1", - "camera_position_world_m": [ - -0.48713303571291616, - -0.7715360575930439, - 0.14953088354489824 - ], - "rvec": [ - 1.9541362695264402, - 0.8822677917940757, - 1.5766838362137556 - ], - "tvec": [ - 0.061087566675323, - -0.1719235401502643, - 0.906441709831749 - ], - "intrinsics": { - "camera_matrix": [ - [ - 1777.77783203125, - 0.0, - 640.0 - ], - [ - 0.0, - 1500.0, - 360.0 - ], - [ - 0.0, - 0.0, - 1.0 - ] - ], - "distortion_coefficients": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - "observation_count": 14 - }, - { - "view_index": 1, - "source_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2b_aruco_detection.json", - "camera_id": "cam1", - "camera_position_world_m": [ - -1.6303219880576203, - 3.51287453308301, - 3.2153725079243105 - ], - "rvec": [ - -4.922763361494977, - -1.9372228726605645, - -0.8634745374702567 - ], - "tvec": [ - 0.40274693422709124, - 0.40259001405514877, - -5.001257224158397 - ], - "intrinsics": { - "camera_matrix": [ - [ - 1777.77783203125, - 0.0, - 640.0 - ], - [ - 0.0, - 1500.0, - 360.0 - ], - [ - 0.0, - 0.0, - 1.0 - ] - ], - "distortion_coefficients": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - "observation_count": 11 - }, - { - "view_index": 2, - "source_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2c_aruco_detection.json", - "camera_id": "cam1", - "camera_position_world_m": [ - 9.875554551448467, - -19.520795422275746, - 4.064291036134082 - ], - "rvec": [ - -3.070508302109151, - -3.013701571556592, - -1.023299829029131 - ], - "tvec": [ - 0.7571981809096362, - -0.24288397753597846, - 22.23677475560832 - ], - "intrinsics": { - "camera_matrix": [ - [ - 1777.77783203125, - 0.0, - 640.0 - ], - [ - 0.0, - 1500.0, - 360.0 - ], - [ - 0.0, - 0.0, - 1.0 - ] - ], - "distortion_coefficients": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - "observation_count": 9 - }, - { - "view_index": 3, - "source_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2d_aruco_detection.json", - "camera_id": "cam1", - "camera_position_world_m": [ - -0.07894709786604179, - -0.5930070260577565, - 1.0455215751225158 - ], - "rvec": [ - -3.1591117140290805, - -1.5673895105365265, - -0.5417451235673743 - ], - "tvec": [ - -0.008681949901032555, - -0.008067751015250885, - 1.2045185247232495 - ], - "intrinsics": { - "camera_matrix": [ - [ - 1777.77783203125, - 0.0, - 640.0 - ], - [ - 0.0, - 1500.0, - 360.0 - ], - [ - 0.0, - 0.0, - 1.0 - ] - ], - "distortion_coefficients": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - "observation_count": 6 - } - ], - "marker_positions": [ - { - "marker_id": 210, - "link_name": "Board", - "position_world_m": [ - 0.02, - -0.02, - 0.0003 - ], - "size_m": 0.025, - "observation_count": 1, - "mean_confidence": 0.3483281753274218, - "mean_detector_confidence": 0.4416265006808409, - "mean_reprojection_error_px": 758.8503648611904, - "observations": [ - { - "view_index": 0, - "source_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2a_aruco_detection.json", - "image_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2a.png", - "confidence_detector": 0.4416265006808409, - "confidence_weighted": 0.3483281753274218, - "quality": { - "detector_confidence": 0.4416265006808409, - "weighted_confidence": 0.3483281753274218, - "q": { - "size": 0.7631931686401368, - "aspect": 0.6720365161330434, - "border": 0.8083333333333333, - "center": 0.0, - "sharpness": 1.0, - "homography": 0.7652561321016037, - "normal_visibility": 1.0, - "spin": 1.0 - }, - "factor": { - "size": 0.928957950592041, - "aspect": 0.9016109548399129, - "border": 0.9980833333333333, - "center": 0.99, - "sharpness": 1.0, - "homography": 0.9530512264203208, - "normal_visibility": 1.0, - "spin": 1.0 - }, - "weight_multiplier": 0.7887392961935387, - "raw": { - "mean_edge_px": 38.159658432006836, - "edge_ratio": 1.9760287603241775, - "distance_to_border_px": 97.0, - "distance_to_center_norm": 0.7171543836593628, - "laplacian_var": 2874.586081340775 - } - }, - "mean_reprojection_error_px": 758.8503648611904, - "corner_reprojection_errors_px": [ - 775.0797309842314, - 711.6798482787276, - 742.306747358212, - 806.3351328235907 - ] - } - ] - }, - { - "marker_id": 211, - "link_name": "Board", - "position_world_m": [ - 0.25, - -0.01, - 0.0003 - ], - "size_m": 0.025, - "observation_count": 2, - "mean_confidence": 0.3056927898732239, - "mean_detector_confidence": 0.23870369098208438, - "mean_reprojection_error_px": 290.9103483736556, - "observations": [ - { - "view_index": 0, - "source_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2a_aruco_detection.json", - "image_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\pipeline\\render_2a.png", - "confidence_detector": 0.38796888201791047, - "confidence_weighted": 0.3113855797464478, - "quality": { - "detector_confidence": 0.38796888201791047, - "weighted_confidence": 0.3113855797464478, - "q": { - "size": 0.6876940250396728, - "aspect": 0.7318081135752206, - "border": 1.0, - "center": 0.0, - "sharpness": 1.0, - "homography": 0.8639457741983584, - "normal_visibility": 1.0, - "spin": 1.0 - }, - "factor": { - "size": 0.9063082075119018, - "aspect": 0.9195424340725661, - "border": 1.0, - "center": 0.99, - "sharpness": 1.0, - "homography": 0.9727891548396718, - 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"coordinateSystem": { - "handedness": "right", - "x": "right", - "y": "backward", - "z": "up" - }, - "units": { - "length": "mm", - "rotation": "degree" - }, - "vision_config": { - "MarkerType": "DICT_4X4_250", - "MarkerSize": 0.025 - }, - "renderingInfo": { - "width": 1280, - "height": 720, - "cameraPosition": [-200, -900, 200], - "cameraTarget": [210, -180, 180], - "cameraUpVector": [0, 0, 1], - "lightPosition": [-500, -500, 500], - "lightTarget": [0, 0, 0], - "lightUpVector": [0, 0, 1], - "metric": "mm", - "showSkeleton": true, - "showMarkers": true, - "backgroundColor": [0.70, 0.85, 1.0], - "backgroundStrength": 0.20, - "sunEnergy": 0.35, - "areaEnergy": 120, - "exposure": -1.5, - "materials": { - "wood": { - "baseColor": [0.72, 0.52, 0.33], - "roughness": 0.85, - "metallic": 0.0 - }, - "plaWhite": { - "baseColor": [0.95, 0.95, 0.95], - "roughness": 0.45, - "metallic": 0.0 - }, - "steel": { - "baseColor": [0.72, 0.72, 0.75], - "roughness": 0.25, - "metallic": 1.0 - }, - "powderCoatBlue": { - 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"material": "powderCoatBlue" - } - ] - }, - "Ellbow": { - "parent": "Arm1", - - "mountPosition": [0, 0, 0], - "mountRotation": [0, 0, 0], - - "jointToParent": { - "name": "Joint2", - "type": "revolute", - "axis": [-1, 0, 0], - "origin": [0, -250, 0], - "rotation": [0, 0, 0], - "variable": "z" - }, - - "skeleton": { - "from": [0, 0, 0], - "to": [90, 0, 0], - "radius": 4, - "color": [0.90, 0.20, 0.20] - }, - "model": [ - { - "stlFile": "surfaces/Ellebogen.stl", - "originOfModel": [90,0,0], - "rotationOfModelDegree": [0,-90,-90], - "material": "defaultPlastic" - } - ], - "markers": [ - { - "id": 244, - "name": "aruco_244", - "position": [125, 0, 0], - "normal": [1, 0, 0], - "size": 25, - "spin": 0 - }, - { - "id": 245, - "name": "aruco_245", - "position": [90, 0, -35], - "normal": [0, 0, -1], - "size": 25, - "spin": 0 - }, - { - "id": 246, - "name": "aruco_246", - "position": [90, 0, 35], - "normal": [0, 0, 1], - "size": 25 - }, - { - "id": 247, - "name": "aruco_247", - "position": [52.5, 0, 35], - 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{"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]} - - ] - }, - "Hand": { - "parent": "Arm2", - - "mountPosition": [0, 0, 0], - "mountRotation": [0, 0, 0], - - "jointToParent": { - "name": "Joint4", - "type": "revolute", - "axis": [1, 0, 0], - "origin": [0, -250, 0], - "rotation": [0, 0, 0], - "variable": "b" - }, - - "skeleton": { - "from": [0, 0, 0], - "to": [0, -35, 0], - "radius": 4, - "color": [0.95, 0.55, 0.15] - } - }, - "Palm": { - "parent": "Hand", - - "mountPosition": [0, 0, 0], - "mountRotation": [0, 0, 0], - - "jointToParent": { - "name": "Joint3", - "type": "revolute", - "axis": [0, -1, 0], - "origin": [0, 0, 0], - "rotation": [0, 0, 0], - "variable": "c" - }, - - "skeleton": { - "from": [-50, -35, 0], - "to": [50, -35, 0], - "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" - } - ] - } - } -} \ No newline at end of file diff --git a/pipeline/2_Multiview_Trial/run_pipeline_1.bat b/pipeline/2_Multiview_Trial/run_pipeline_1.bat deleted file mode 100644 index 645e352..0000000 --- a/pipeline/2_Multiview_Trial/run_pipeline_1.bat +++ /dev/null @@ -1,7 +0,0 @@ - -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 1_detect_aruco_observations.py --image render_1d.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir . - -python3 2_Multiview.py --robot ../robot.json --detections render_1a_aruco_detection.json render_1b_aruco_detection.json render_1c_aruco_detection.json render_1d_aruco_detection.json --outDir . --write-summary --max-iter 120 diff --git a/pipeline/2_Multiview_Trial/run_pipeline_2.bat b/pipeline/2_Multiview_Trial/run_pipeline_2.bat deleted file mode 100644 index fb751bc..0000000 --- a/pipeline/2_Multiview_Trial/run_pipeline_2.bat +++ /dev/null @@ -1,7 +0,0 @@ - -python3 1_detect_aruco_observations.py --image render_2a.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir . -python3 1_detect_aruco_observations.py --image render_2b.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir . -python3 1_detect_aruco_observations.py --image render_2c.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir . -python3 1_detect_aruco_observations.py --image render_2d.png -npz render.npz -robot ../robot.json -cameraId cam1 -outDir . - -python3 2_Multiview.py --robot ../robot.json --detections render_2a_aruco_detection.json render_2b_aruco_detection.json render_2c_aruco_detection.json render_2d_aruco_detection.json --outDir . --write-summary --max-iter 120 diff --git a/pipeline/4_robotState_estimation_seq.py b/pipeline/4_robotState_estimation_v4_seq.py similarity index 100% rename from pipeline/4_robotState_estimation_seq.py rename to pipeline/4_robotState_estimation_v4_seq.py diff --git a/run/run_pipeline.bat b/run/run_pipeline.bat new file mode 100644 index 0000000..0b372cd --- /dev/null +++ b/run/run_pipeline.bat @@ -0,0 +1,77 @@ +@echo off +setlocal EnableDelayedExpansion +REM 3_pipeline_multiview.bat +REM Multi-camera ArUco detection and pose estimation pipeline +REM Parametr e.g. +REM run_pipeline.bat ../data/simulation/Scene4 + + + + +set IMAGES=%~1 + + +set ROBOT_JSON=..\data\robot\robot.json +set BASE_OUT_DIR=C:\Users\kech\SynologyDrive\2026-AppServer-AppRobot\appRobotRendering\data\evaluations +set SCENE_NAME=%~nx1 +set OUT_DIR=%BASE_OUT_DIR%\%SCENE_NAME% + +echo. +echo [STEP 1] Detect ArUco markers from all cameras in the folder %IMAGES% + +for %%F in ("%IMAGES%\*.png" "%IMAGES%\*.PNG" "%IMAGES%\*.jpg" "%IMAGES%\*.jpeg" "%IMAGES%\*.JPG" "%IMAGES%\*.JPEG") do ( + + REM Dateiname ohne Pfad und ohne .png + set "NAME=%%~nF" + echo Bearbeite: !NAME! + + REM Split bei "_" → nehme 2. Teil (die ID) + for /f "tokens=2 delims=_" %%A in ("%%~nF") do ( + set "CAMID=%%A" + + REM Takes files and detected arucos output to render_c_aruco_detection.json + python3 ../pipeline/1_detect_aruco_observations.py ^ + -i "%%F" ^ + -npz "%IMAGES%\render_a.npz" ^ + -outDir %OUT_DIR% ^ + -robot %ROBOT_JSON% ^ + -cameraId !CAMID! + ) +) + +echo. +echo [STEP 2] Estimate camera poses from detections + +for %%F in ("%OUT_DIR%\*_aruco_detection.json") do ( + echo Bearbeite: %%F + + python3 ../pipeline/2_estimate_camera_from_observations.py ^ + -i "%%F" ^ + -robot %ROBOT_JSON% ^ + -outDir %OUT_DIR% +) + +echo. +echo [STEP 3] Triangulate marker positions from multi-view observations + + +REM Alle detection files sammeln +for %%F in ("%OUT_DIR%\*_aruco_detection.json") do ( + set DET_ARGS=!DET_ARGS! -det "%%F" +) + +REM Alle pose files sammeln +for %%F in ("%OUT_DIR%\*_camera_pose.json") do ( + set POSE_ARGS=!POSE_ARGS! -pose "%%F" +) + +REM Debug-Ausgabe +echo DET_ARGS: !DET_ARGS! +echo POSE_ARGS: !POSE_ARGS! + +REM EINMAL Python aufrufen +python3 "..\pipeline\3_multiview_bundle_adjustment_v4.py" ^ + -robot "%ROBOT_JSON%" ^ + -lambdaWeight 100.0 ^ + !DET_ARGS! ^ + !POSE_ARGS! diff --git a/setup/generateSets/render_Loop.py b/setup/generateSets/render_Loop.py index 6ac10cf..8201ea2 100644 --- a/setup/generateSets/render_Loop.py +++ b/setup/generateSets/render_Loop.py @@ -103,19 +103,19 @@ def main(): # Robot-Pose-Dictionary robot_poses = { - #"4": {"x": 70, "y": 50,"z": -70,"a": 120,"b": 50,"c": 30,"e": 20}, - #"5": {"x": 180,"y": 86,"z": -120,"a": -60,"b": 22,"c": 91,"e": 10}, - #"6": {"x": 80, "y": 20, "z": 80, "a": -120, "b": 23, "c": 9, "e": 3}, - #"7": {"x": 30, "y": -2, "z": 95, "a": 20, "b": 23, "c": 9, "e": 9}, - #"8": {"x": 50, "y": -2, "z": 95, "a": 20, "b": 60, "c": 9, "e": 3}, + "4": {"x": 70, "y": 50,"z": -70,"a": 120,"b": 50,"c": 30,"e": 20}, + "5": {"x": 180,"y": 86,"z": -120,"a": -60,"b": 22,"c": 91,"e": 10}, + "6": {"x": 80, "y": 20, "z": 80, "a": -120, "b": 23, "c": 9, "e": 3}, + "7": {"x": 30, "y": -2, "z": 95, "a": 20, "b": 23, "c": 9, "e": 9}, + "8": {"x": 50, "y": -2, "z": 95, "a": 20, "b": 60, "c": 9, "e": 3}, "9": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8}, "10": {"x": 120, "y": 60, "z": -110, "a": 20, "b": 30, "c": 180, "e": 4}, - #"11": {"x": 50, "y": 4, "z": 176, "a": 20, "b": 60, "c": 9, "e": 5}, - #"12": {"x": 50, "y": 0, "z": 178, "a": 210, "b": 80, "c": 90, "e": 6}, + "11": {"x": 50, "y": 4, "z": 176, "a": 20, "b": 60, "c": 9, "e": 5}, + "12": {"x": 50, "y": 0, "z": 178, "a": 210, "b": 80, "c": 90, "e": 6}, } for pose_name, default_position in robot_poses.items(): - new_folder = Path(args.output_set) / f"Set{pose_name}" + new_folder = Path(args.output_set) / f"Scene{pose_name}" os.makedirs(new_folder, exist_ok=True) @@ -147,7 +147,7 @@ def main(): if not copy_and_rename_file(args.render_png.replace("render.png","markers.json"), new_folder, new_filename.replace(".png",".json")): continue # Gehe zum nächsten Schleifendurchlauf - print(f"Rendering für Frame {frame_name}, Set {pose_name} erfolgreich abgeschlossen.") + print(f"Rendering für Frame {frame_name}, Scene {pose_name} erfolgreich abgeschlossen.") if __name__ == "__main__": main() \ No newline at end of file diff --git a/setup/generateSets/render_robot.py b/setup/generateSets/render_robot.py index 5cf3620..e89caaf 100644 --- a/setup/generateSets/render_robot.py +++ b/setup/generateSets/render_robot.py @@ -12,10 +12,7 @@ from mathutils import Matrix # PATHS # ============================================================ -ROBOT_JSON_FILE = r"C:\Users\kech\SynologyDrive\2026-AppServer-AppRobot\appRobotRendering\data\robot\robot.json" -OUTPUT_FILE = r"C:\Users\kech\SynologyDrive\2026-AppServer-AppRobot\appRobotRendering\data\simulation\debug\render.png" -from pathlib import Path # Holt dynamisch den Pfad zum aktuellen Benutzerverzeichnis (z.B. C:\Users\Name) USER_HOME = Path.home()