diff --git a/data/evaluations/Scene4/aruco_positions_initial.json b/data/evaluations/Scene4/aruco_positions_initial.json
index 915e6d5..8aae280 100644
--- a/data/evaluations/Scene4/aruco_positions_initial.json
+++ b/data/evaluations/Scene4/aruco_positions_initial.json
@@ -1,7 +1,7 @@
{
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"stage": "initial_triangulation",
- "created_utc": "2026-05-31T08:47:11Z",
+ "created_utc": "2026-06-01T12:30:25Z",
"summary": {
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diff --git a/data/evaluations/Scene4/aruco_positions_optimized.json b/data/evaluations/Scene4/aruco_positions_optimized.json
index 437beb9..240856f 100644
--- a/data/evaluations/Scene4/aruco_positions_optimized.json
+++ b/data/evaluations/Scene4/aruco_positions_optimized.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.2",
- "created_utc": "2026-05-31T08:47:48Z",
+ "created_utc": "2026-06-01T12:31:31Z",
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"num_markers": 50,
diff --git a/data/evaluations/Scene4/render_a_aruco_detection.json b/data/evaluations/Scene4/render_a_aruco_detection.json
index e2d1b1a..820ca3d 100644
--- a/data/evaluations/Scene4/render_a_aruco_detection.json
+++ b/data/evaluations/Scene4/render_a_aruco_detection.json
@@ -1,6 +1,6 @@
{
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+ "created_utc": "2026-06-01T12:38:59Z",
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@@ -46,7 +46,7 @@
},
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+ "observation_id": "44109558-03cb-4d11-bd08-768cc37ee9dc",
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@@ -856,7 +856,7 @@
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@@ -910,7 +910,7 @@
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@@ -964,7 +964,7 @@
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@@ -1018,7 +1018,7 @@
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@@ -1072,7 +1072,7 @@
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@@ -1126,7 +1126,7 @@
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@@ -1180,7 +1180,7 @@
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@@ -1234,7 +1234,7 @@
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@@ -1342,7 +1342,7 @@
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@@ -1450,7 +1450,7 @@
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diff --git a/data/evaluations/Scene4/render_a_camera_pose.json b/data/evaluations/Scene4/render_a_camera_pose.json
index 41a2e9f..b96f84e 100644
--- a/data/evaluations/Scene4/render_a_camera_pose.json
+++ b/data/evaluations/Scene4/render_a_camera_pose.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T08:47:09Z",
+ "created_utc": "2026-06-01T12:39:04Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene4\\render_a_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene4/render_b_aruco_detection.json b/data/evaluations/Scene4/render_b_aruco_detection.json
index 4b27dac..09a38c3 100644
--- a/data/evaluations/Scene4/render_b_aruco_detection.json
+++ b/data/evaluations/Scene4/render_b_aruco_detection.json
@@ -1,6 +1,6 @@
{
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+ "created_utc": "2026-06-01T12:39:00Z",
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"MarkerType": "DICT_4X4_250",
"MarkerSize": 0.025
@@ -46,7 +46,7 @@
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@@ -640,7 +640,7 @@
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@@ -1180,7 +1180,7 @@
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diff --git a/data/evaluations/Scene4/render_b_camera_pose.json b/data/evaluations/Scene4/render_b_camera_pose.json
index 885f282..e89cd3a 100644
--- a/data/evaluations/Scene4/render_b_camera_pose.json
+++ b/data/evaluations/Scene4/render_b_camera_pose.json
@@ -1,6 +1,6 @@
{
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diff --git a/data/evaluations/Scene4/render_c_aruco_detection.json b/data/evaluations/Scene4/render_c_aruco_detection.json
index 16ef633..07102ce 100644
--- a/data/evaluations/Scene4/render_c_aruco_detection.json
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{
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diff --git a/data/evaluations/Scene4/render_c_camera_pose.json b/data/evaluations/Scene4/render_c_camera_pose.json
index 4e8fee5..5104221 100644
--- a/data/evaluations/Scene4/render_c_camera_pose.json
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{
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diff --git a/data/evaluations/Scene4/render_d_aruco_detection.json b/data/evaluations/Scene4/render_d_aruco_detection.json
index f0b19af..7f98765 100644
--- a/data/evaluations/Scene4/render_d_aruco_detection.json
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@@ -856,7 +856,7 @@
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diff --git a/data/evaluations/Scene4/render_d_camera_pose.json b/data/evaluations/Scene4/render_d_camera_pose.json
index 087d7e4..1d12cb7 100644
--- a/data/evaluations/Scene4/render_d_camera_pose.json
+++ b/data/evaluations/Scene4/render_d_camera_pose.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T08:47:10Z",
+ "created_utc": "2026-06-01T12:39:06Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene4\\render_d_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene4/render_e_aruco_detection.json b/data/evaluations/Scene4/render_e_aruco_detection.json
index 8d578fd..fb1799d 100644
--- a/data/evaluations/Scene4/render_e_aruco_detection.json
+++ b/data/evaluations/Scene4/render_e_aruco_detection.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T08:47:08Z",
+ "created_utc": "2026-06-01T12:39:02Z",
"vision_config": {
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"MarkerSize": 0.025
@@ -46,7 +46,7 @@
},
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@@ -100,7 +100,7 @@
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@@ -262,7 +262,7 @@
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@@ -316,7 +316,7 @@
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@@ -478,7 +478,7 @@
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@@ -532,7 +532,7 @@
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@@ -586,7 +586,7 @@
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@@ -640,7 +640,7 @@
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@@ -694,7 +694,7 @@
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@@ -748,7 +748,7 @@
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@@ -802,7 +802,7 @@
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@@ -856,7 +856,7 @@
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@@ -910,7 +910,7 @@
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@@ -964,7 +964,7 @@
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@@ -1072,7 +1072,7 @@
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@@ -1126,7 +1126,7 @@
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@@ -1180,7 +1180,7 @@
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@@ -1234,7 +1234,7 @@
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@@ -1288,7 +1288,7 @@
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@@ -1396,7 +1396,7 @@
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@@ -1450,7 +1450,7 @@
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@@ -1612,7 +1612,7 @@
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diff --git a/data/evaluations/Scene4/render_e_camera_pose.json b/data/evaluations/Scene4/render_e_camera_pose.json
index c753ae6..5e2bf62 100644
--- a/data/evaluations/Scene4/render_e_camera_pose.json
+++ b/data/evaluations/Scene4/render_e_camera_pose.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T08:47:10Z",
+ "created_utc": "2026-06-01T12:39:06Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene4\\render_e_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene4/render_f_aruco_detection.json b/data/evaluations/Scene4/render_f_aruco_detection.json
index 04fd22d..b7c19ac 100644
--- a/data/evaluations/Scene4/render_f_aruco_detection.json
+++ b/data/evaluations/Scene4/render_f_aruco_detection.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T08:47:08Z",
+ "created_utc": "2026-06-01T12:39:03Z",
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@@ -46,7 +46,7 @@
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@@ -100,7 +100,7 @@
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@@ -532,7 +532,7 @@
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@@ -640,7 +640,7 @@
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@@ -1072,7 +1072,7 @@
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@@ -1288,7 +1288,7 @@
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diff --git a/data/evaluations/Scene4/render_f_camera_pose.json b/data/evaluations/Scene4/render_f_camera_pose.json
index b423530..34ba26d 100644
--- a/data/evaluations/Scene4/render_f_camera_pose.json
+++ b/data/evaluations/Scene4/render_f_camera_pose.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T08:47:11Z",
+ "created_utc": "2026-06-01T12:39:07Z",
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"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene4/render_g_aruco_detection.json b/data/evaluations/Scene4/render_g_aruco_detection.json
index 2a4d3cb..acd40c6 100644
--- a/data/evaluations/Scene4/render_g_aruco_detection.json
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{
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@@ -1450,7 +1450,7 @@
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diff --git a/data/evaluations/Scene4/render_g_camera_pose.json b/data/evaluations/Scene4/render_g_camera_pose.json
index 7f04366..32f02de 100644
--- a/data/evaluations/Scene4/render_g_camera_pose.json
+++ b/data/evaluations/Scene4/render_g_camera_pose.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T08:47:11Z",
+ "created_utc": "2026-06-01T12:39:07Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene4\\render_g_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene4/robot_state.json b/data/evaluations/Scene4/robot_state.json
index c78c918..785c915 100644
--- a/data/evaluations/Scene4/robot_state.json
+++ b/data/evaluations/Scene4/robot_state.json
@@ -1,58 +1,58 @@
{
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- "created_utc": "2026-05-31T08:47:48Z",
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+ "value_m": 0.07253974336302962,
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\ No newline at end of file
diff --git a/data/evaluations/Scene9/render_a_aruco_detection.json b/data/evaluations/Scene9/render_a_aruco_detection.json
index 280f842..48add02 100644
--- a/data/evaluations/Scene9/render_a_aruco_detection.json
+++ b/data/evaluations/Scene9/render_a_aruco_detection.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T10:25:20Z",
+ "created_utc": "2026-05-31T20:34:15Z",
"vision_config": {
"MarkerType": "DICT_4X4_250",
"MarkerSize": 0.025
@@ -46,7 +46,7 @@
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diff --git a/data/evaluations/Scene9/render_a_camera_pose.json b/data/evaluations/Scene9/render_a_camera_pose.json
index eb43ffb..6731671 100644
--- a/data/evaluations/Scene9/render_a_camera_pose.json
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{
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diff --git a/data/evaluations/Scene9/render_b_aruco_detection.json b/data/evaluations/Scene9/render_b_aruco_detection.json
index 0a1ec74..b28bbb9 100644
--- a/data/evaluations/Scene9/render_b_aruco_detection.json
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+ "observation_id": "e2e965fe-0688-4956-b5c8-b472a1a866c0",
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@@ -1666,7 +1666,7 @@
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@@ -1720,7 +1720,7 @@
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@@ -1774,7 +1774,7 @@
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@@ -1828,7 +1828,7 @@
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@@ -1882,7 +1882,7 @@
"confidence": 0.22367025973033955
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@@ -1936,7 +1936,7 @@
"confidence": 0.19874429613402733
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@@ -1990,7 +1990,7 @@
"confidence": 0.19627771759033205
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@@ -2044,7 +2044,7 @@
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@@ -2098,7 +2098,7 @@
"confidence": 0.17697203515072252
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@@ -2152,7 +2152,7 @@
"confidence": 0.193995418548584
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@@ -2206,7 +2206,7 @@
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@@ -2260,7 +2260,7 @@
"confidence": 0.17676838843571283
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@@ -2314,7 +2314,7 @@
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diff --git a/data/evaluations/Scene9/render_b_camera_pose.json b/data/evaluations/Scene9/render_b_camera_pose.json
index 504125d..c0a3da8 100644
--- a/data/evaluations/Scene9/render_b_camera_pose.json
+++ b/data/evaluations/Scene9/render_b_camera_pose.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T10:25:24Z",
+ "created_utc": "2026-05-31T20:34:18Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene9\\render_b_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene9/render_c_aruco_detection.json b/data/evaluations/Scene9/render_c_aruco_detection.json
index 6e366d8..da51df3 100644
--- a/data/evaluations/Scene9/render_c_aruco_detection.json
+++ b/data/evaluations/Scene9/render_c_aruco_detection.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T10:25:21Z",
+ "created_utc": "2026-05-31T20:34:16Z",
"vision_config": {
"MarkerType": "DICT_4X4_250",
"MarkerSize": 0.025
@@ -46,7 +46,7 @@
},
"detections": [
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+ "observation_id": "4f5542a8-14df-46c1-82db-1a2d97500448",
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@@ -100,7 +100,7 @@
"confidence": 0.9487615650349993
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+ "observation_id": "13234fdc-5a13-4483-8a76-a2bc6e98df26",
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@@ -154,7 +154,7 @@
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@@ -208,7 +208,7 @@
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@@ -262,7 +262,7 @@
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@@ -316,7 +316,7 @@
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@@ -370,7 +370,7 @@
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{
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@@ -424,7 +424,7 @@
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diff --git a/data/evaluations/Scene9/render_c_camera_pose.json b/data/evaluations/Scene9/render_c_camera_pose.json
index 7283119..09371e0 100644
--- a/data/evaluations/Scene9/render_c_camera_pose.json
+++ b/data/evaluations/Scene9/render_c_camera_pose.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T10:25:24Z",
+ "created_utc": "2026-05-31T20:34:18Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene9\\render_c_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene9/render_d_aruco_detection.json b/data/evaluations/Scene9/render_d_aruco_detection.json
index 81631bb..3300881 100644
--- a/data/evaluations/Scene9/render_d_aruco_detection.json
+++ b/data/evaluations/Scene9/render_d_aruco_detection.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T10:25:21Z",
+ "created_utc": "2026-05-31T20:34:16Z",
"vision_config": {
"MarkerType": "DICT_4X4_250",
"MarkerSize": 0.025
@@ -46,7 +46,7 @@
},
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@@ -100,7 +100,7 @@
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@@ -154,7 +154,7 @@
"confidence": 0.896516741806838
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@@ -208,7 +208,7 @@
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{
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@@ -262,7 +262,7 @@
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@@ -316,7 +316,7 @@
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@@ -370,7 +370,7 @@
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@@ -424,7 +424,7 @@
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+ "observation_id": "5d001e51-cdd2-495c-9a83-b69e0dd68eba",
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@@ -478,7 +478,7 @@
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+ "observation_id": "5bc6c7d7-afd3-4476-90fc-2c5d4f7e419e",
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@@ -532,7 +532,7 @@
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@@ -586,7 +586,7 @@
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@@ -640,7 +640,7 @@
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@@ -694,7 +694,7 @@
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@@ -748,7 +748,7 @@
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@@ -802,7 +802,7 @@
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diff --git a/data/evaluations/Scene9/render_d_camera_pose.json b/data/evaluations/Scene9/render_d_camera_pose.json
index 3ac43de..d83e849 100644
--- a/data/evaluations/Scene9/render_d_camera_pose.json
+++ b/data/evaluations/Scene9/render_d_camera_pose.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T10:25:24Z",
+ "created_utc": "2026-05-31T20:34:19Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene9\\render_d_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene9/render_e_aruco_detection.json b/data/evaluations/Scene9/render_e_aruco_detection.json
index 4737558..a4cf723 100644
--- a/data/evaluations/Scene9/render_e_aruco_detection.json
+++ b/data/evaluations/Scene9/render_e_aruco_detection.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T10:25:22Z",
+ "created_utc": "2026-05-31T20:34:16Z",
"vision_config": {
"MarkerType": "DICT_4X4_250",
"MarkerSize": 0.025
@@ -46,7 +46,7 @@
},
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@@ -100,7 +100,7 @@
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@@ -154,7 +154,7 @@
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@@ -370,7 +370,7 @@
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@@ -532,7 +532,7 @@
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@@ -694,7 +694,7 @@
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@@ -748,7 +748,7 @@
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@@ -910,7 +910,7 @@
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diff --git a/data/evaluations/Scene9/render_e_camera_pose.json b/data/evaluations/Scene9/render_e_camera_pose.json
index f2d769f..bedc912 100644
--- a/data/evaluations/Scene9/render_e_camera_pose.json
+++ b/data/evaluations/Scene9/render_e_camera_pose.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T10:25:25Z",
+ "created_utc": "2026-05-31T20:34:19Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene9\\render_e_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene9/render_f_aruco_detection.json b/data/evaluations/Scene9/render_f_aruco_detection.json
index 5661931..0ebd7ca 100644
--- a/data/evaluations/Scene9/render_f_aruco_detection.json
+++ b/data/evaluations/Scene9/render_f_aruco_detection.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T10:25:22Z",
+ "created_utc": "2026-05-31T20:34:17Z",
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@@ -46,7 +46,7 @@
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@@ -640,7 +640,7 @@
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@@ -856,7 +856,7 @@
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@@ -910,7 +910,7 @@
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@@ -964,7 +964,7 @@
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@@ -1018,7 +1018,7 @@
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@@ -1072,7 +1072,7 @@
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{
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@@ -1126,7 +1126,7 @@
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@@ -1180,7 +1180,7 @@
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@@ -1234,7 +1234,7 @@
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@@ -1288,7 +1288,7 @@
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@@ -1342,7 +1342,7 @@
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@@ -1396,7 +1396,7 @@
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@@ -1450,7 +1450,7 @@
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@@ -1504,7 +1504,7 @@
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@@ -1558,7 +1558,7 @@
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@@ -1612,7 +1612,7 @@
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@@ -1666,7 +1666,7 @@
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@@ -1720,7 +1720,7 @@
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diff --git a/data/evaluations/Scene9/render_f_camera_pose.json b/data/evaluations/Scene9/render_f_camera_pose.json
index f1e22fd..a2efb67 100644
--- a/data/evaluations/Scene9/render_f_camera_pose.json
+++ b/data/evaluations/Scene9/render_f_camera_pose.json
@@ -1,6 +1,6 @@
{
"schema_version": "1.0",
- "created_utc": "2026-05-31T10:25:25Z",
+ "created_utc": "2026-05-31T20:34:19Z",
"source": {
"detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene9\\render_f_aruco_detection.json",
"robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene9/render_g_aruco_detection.json b/data/evaluations/Scene9/render_g_aruco_detection.json
index dfd1919..7ff98fb 100644
--- a/data/evaluations/Scene9/render_g_aruco_detection.json
+++ b/data/evaluations/Scene9/render_g_aruco_detection.json
@@ -1,6 +1,6 @@
{
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- "created_utc": "2026-05-31T10:25:23Z",
+ "created_utc": "2026-05-31T20:34:17Z",
"vision_config": {
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"MarkerSize": 0.025
@@ -46,7 +46,7 @@
},
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@@ -100,7 +100,7 @@
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@@ -154,7 +154,7 @@
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diff --git a/data/evaluations/Scene9/render_g_camera_pose.json b/data/evaluations/Scene9/render_g_camera_pose.json
index 45a2771..8656502 100644
--- a/data/evaluations/Scene9/render_g_camera_pose.json
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diff --git a/data/evaluations/Scene9/robot_state.json b/data/evaluations/Scene9/robot_state.json
index c5d71d6..033c1d7 100644
--- a/data/evaluations/Scene9/robot_state.json
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}
]
}
\ No newline at end of file
diff --git a/data/robot/robot.json b/data/robot/robot.json
index a795fd8..a3ff122 100644
--- a/data/robot/robot.json
+++ b/data/robot/robot.json
@@ -1,89 +1,26 @@
{
- "coordinateSystem": {
- "handedness": "right",
- "x": "right",
- "y": "backward",
- "z": "up"
- },
- "units": {
- "length": "mm",
- "rotation": "degree"
- },
- "vision_config": {
- "MarkerType": "DICT_4X4_250",
- "MarkerSize": 0.025
- },
+ "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": 4896,
"height": 3264,
- "cameraPosition__1": [
- -10,
- -800,
- 500
- ],
- "cameraPosition__2": [
- -500,
- 300,
- 1200
- ],
- "cameraPosition__3": [
- -200,
- -900,
- 200
- ],
- "cameraPosition__4": [
- 1200,
- 200,
- 300
- ],
- "cameraPosition_a": [
- -300,
- -800,
- 500
- ],
- "cameraPosition": [
- -200,
- 200,
- 1400
- ],
- "cameraPosition_c": [
- 600,
- -500,
- 600
- ],
- "cameraTarget": [
- 200,
- -200,
- 180
- ],
- "cameraUpVector": [
- 0,
- 0,
- 1
- ],
- "lightPosition": [
- -500,
- -500,
- 500
- ],
- "lightTarget": [
- 0,
- 0,
- 0
- ],
- "lightUpVector": [
- 0,
- 0,
- 1
- ],
+ "cameraPosition__1": [-10, -800, 500],
+ "cameraPosition__2": [-500, 300, 1200],
+ "cameraPosition__3": [-200, -900, 200],
+ "cameraPosition__4": [1200, 200, 300],
+ "cameraPosition_a": [-300, -800, 500],
+ "cameraPosition": [-200, 200, 1400],
+ "cameraPosition_c": [600, -500, 600],
+ "cameraTarget": [200, -200, 180],
+ "cameraUpVector": [0, 0, 1],
+ "lightPosition": [-500, -500, 500],
+ "lightTarget": [0, 0, 0],
+ "lightUpVector": [0, 0, 1],
"metric": "mm",
"showSkeleton": true,
"showMarkers": true,
- "backgroundColor": [
- 0.7,
- 0.85,
- 1.0
- ],
+ "backgroundColor": [0.7, 0.85, 1.0],
"backgroundStrength": 0.2,
"sunEnergy": 0.35,
"areaEnergy": 120,
@@ -103,141 +40,25 @@
"lensDistortion": true,
"lensDistortionStrength": 0.002,
"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": {
- "baseColor": [
- 0.15,
- 0.25,
- 0.7
- ],
- "roughness": 0.55,
- "metallic": 0.0
- },
- "defaultPlastic": {
- "baseColor": [
- 0.95,
- 0.95,
- 0.95
- ],
- "roughness": 0.4,
- "metallic": 0.0
- },
- "skeletonRed": {
- "baseColor": [
- 0.85,
- 0.2,
- 0.2
- ],
- "roughness": 0.35,
- "metallic": 0.0
- },
- "markerBlack": {
- "baseColor": [
- 0.04,
- 0.04,
- 0.04
- ],
- "roughness": 0.8,
- "metallic": 0.0
- }
+ "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": {"baseColor": [0.15, 0.25, 0.7], "roughness": 0.55, "metallic": 0.0},
+ "defaultPlastic": {"baseColor": [0.95, 0.95, 0.95], "roughness": 0.4, "metallic": 0.0},
+ "skeletonRed": {"baseColor": [0.85, 0.2, 0.2], "roughness": 0.35, "metallic": 0.0},
+ "markerBlack": {"baseColor": [0.04, 0.04, 0.04], "roughness": 0.8, "metallic": 0.0}
},
- "skeletonDefaults": {
- "radius": 4,
- "color": [
- 0.85,
- 0.2,
- 0.2
- ]
- },
- "markerDefaults": {
- "size": 25,
- "thickness": 1,
- "color": [
- 0.04,
- 0.04,
- 0.04
- ]
- },
- "defaultPosition": {
- "x": 80,
- "y": 20,
- "z": 80,
- "a": -120,
- "b": 23,
- "c": 9,
- "e": 3
- }
- },
- "defaultPosition__": {
- "x": 10,
- "y": 4,
- "z": 20,
- "a": 10,
- "b": 2,
- "c": 9,
- "e": 1
- },
- "defaultPosition": {
- "x": 60,
- "y": -2,
- "z": 95,
- "a": 200,
- "b": 60,
- "c": 9,
- "e": 8
- },
- "recognized": {
- "x": null,
- "y": null,
- "z": null,
- "a": null,
- "b": null,
- "c": null,
- "e": null
+ "skeletonDefaults": {"radius": 4, "color": [0.85, 0.2, 0.2]},
+ "markerDefaults": {"size": 25, "thickness": 1, "color": [0.04, 0.04, 0.04]},
+ "defaultPosition": {"x": 80, "y": 20, "z": 80, "a": -120, "b": 23, "c": 9, "e": 3}
},
+ "defaultPosition__": {"x": 10, "y": 4, "z": 20, "a": 10, "b": 2, "c": 9, "e": 1},
+ "defaultPosition": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8},
+ "recognized": {"x": null, "y": null, "z": null, "a": null, "b": null, "c": null, "e": null},
"constraint_rules": {
- "rigid_distance": {
- "enabled": true,
- "mode": "mst",
- "weight": 1.0
- },
- "joint_axis_projection": {
- "enabled": true,
- "max_pairs": 2,
- "weight": 0.35
- },
- "chain_axis_projection": {
- "enabled": false,
- "max_depth": 3,
- "max_pairs": 2,
- "weight": 0.15
- },
+ "rigid_distance": {"enabled": true, "mode": "mst", "weight": 1.0},
+ "joint_axis_projection": {"enabled": true, "max_pairs": 2, "weight": 0.35},
+ "chain_axis_projection": {"enabled": false, "max_depth": 3, "max_pairs": 2, "weight": 0.15},
"axis_alignment_threshold": 0.95
},
"observation_weighting": {
@@ -263,15 +84,20 @@
"spin_factor": 0.3,
"weight_floor": 0.3
},
- "movements": {
- "x": null,
- "y": null,
- "z": null,
- "a": null,
- "b": null,
- "c": null,
- "e": null
- },
+ "robot_test_poses": {
+ "sim04": {"x": 70, "y": 50,"z": -70,"a": 120,"b": 50,"c": 30,"e": 20},
+ "sim05": {"x": 180,"y": 86,"z": -120,"a": -60,"b": 22,"c": 91,"e": 10},
+ "sim06": {"x": 80, "y": 20, "z": 80, "a": -120, "b": 23, "c": 9, "e": 3},
+ "sim07": {"x": 30, "y": -2, "z": 95, "a": 20, "b": 23, "c": 9, "e": 9},
+ "sim08": {"x": 50, "y": -2, "z": 95, "a": 20, "b": 60, "c": 9, "e": 3},
+ "sim09": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8},
+ "sim09a": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8},
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- "rotationOfModelDegree": [
- 0,
- 0,
- -90
- ],
+ "originOfModel": [0, 0, 0],
+ "rotationOfModelDegree": [0, 0, -90],
"material": "wood"
},
{
"stlFile": "surfaces/BoardRail.stl",
- "originOfModel": [
- 0,
- 0,
- 0
- ],
- "rotationOfModelDegree": [
- 0,
- 0,
- -90
- ],
+ "originOfModel": [0, 0, 0],
+ "rotationOfModelDegree": [0, 0, -90],
"material": "steel"
}
]
},
"Base": {
"parent": "Board",
- "size": [
- 150,
- 200,
- 150
- ],
- "mountPosition": [
- 0,
- 0,
- 0
- ],
- "mountRotation": [
- 0,
- 0,
- 0
- ],
+ "size": [150, 200, 150],
+ "mountPosition": [0, 0, 0],
+ "mountRotation": [0, 0, 0],
"jointToParent": {
"name": "Slider",
"type": "linear",
- "axis": [
- 1,
- 0,
- 0
- ],
- "origin": [
- 0,
- 0,
- 16
- ],
- "rotation": [
- 0,
- 0,
- 0
- ],
+ "axis": [1, 0, 0],
+ "origin": [0, 0, 16],
+ "rotation": [0, 0, 0],
"variable": "x"
},
"skeleton": {
- "from": [
- 0,
- 108,
- 45
- ],
- "to": [
- 110,
- 108,
- 45
- ],
+ "from": [0, 108, 45],
+ "to": [110, 108, 45],
"radius": 4,
- "color": [
- 0.2,
- 0.8,
- 0.2
- ]
+ "color": [0.2, 0.8, 0.2]
},
"markers": [],
"model": [
{
"stlFile": "surfaces/Base.stl",
- "originOfModel": [
- -30,
- 0,
- -35
- ],
- "rotationOfModelDegree": [
- 0,
- 0,
- 0
- ],
+ "originOfModel": [-30, 0, -35],
+ "rotationOfModelDegree": [0, 0, 0],
"material": "plaWhite"
}
]
},
"Arm1": {
"parent": "Base",
- "size": [
- 70,
- 250,
- 70
- ],
- "mountPosition": [
- 0,
- 0,
- 0
- ],
- "mountRotation": [
- 0,
- 0,
- 0
- ],
+ "size": [70, 250, 70],
+ "mountPosition": [0, 0, 0],
+ "mountRotation": [0, 0, 0],
"jointToParent": {
"name": "Joint1",
"type": "revolute",
- "axis": [
- -1,
- 0,
- 0
- ],
- "origin": [
- 110,
- 108,
- 45
- ],
- "rotation": [
- 0,
- 0,
- 0
- ],
+ "axis": [-1, 0, 0],
+ "origin": [110, 108, 45],
+ "rotation": [0, 0, 0],
"variable": "y"
},
"skeleton": {
- "from": [
- 0,
- 0,
- 0
- ],
- "to": [
- 0,
- -250,
- 0
- ],
+ "from": [0, 0, 0],
+ "to": [0, -250, 0],
"radius": 4,
- "color": [
- 0.2,
- 0.2,
- 0.9
- ]
+ "color": [0.2, 0.2, 0.9]
},
"markers": [
{
"id": 198,
"name": "aruco_198",
- "position": [
- 0,
- -160,
- 35
- ],
- "normal": [
- 0,
- 0,
- 1
- ],
+ "position": [0, -160, 35],
+ "normal": [0, 0, 1],
"size": 25,
"spin": 0
},
{
"id": 229,
"name": "aruco_229",
- "position": [
- 0,
- -250,
- 35
- ],
- "normal": [
- 0,
- 0,
- 1
- ],
+ "position": [0, -250, 35],
+ "normal": [0, 0, 1],
"size": 25,
"spin": 0
},
{
"id": 242,
"name": "aruco_242",
- "position": [
- 0,
- -250,
- -35
- ],
- "normal": [
- 0,
- 0,
- -1
- ],
+ "position": [0, -250, -35],
+ "normal": [0, 0, -1],
"size": 25,
"spin": 0
},
{
"id": 243,
"name": "aruco_243",
- "position": [
- 0,
- -285,
- 0
- ],
- "normal": [
- 0,
- -1,
- 0
- ],
+ "position": [0, -285, 0],
+ "normal": [0, -1, 0],
"size": 25,
"spin": 0
}
@@ -1508,672 +594,245 @@
"model": [
{
"stlFile": "surfaces/Holm.stl",
- "originOfModel__": [
- -25,
- 29,
- -28.5
- ],
- "originOfModel": [
- -29,
- 25,
- 28.5
- ],
- "rotationOfModelDegree__": [
- 0,
- 0,
- 0
- ],
- "rotationOfModelDegree": [
- 180,
- 0,
- -90
- ],
+ "originOfModel__": [-25, 29, -28.5],
+ "originOfModel": [-29, 25, 28.5],
+ "rotationOfModelDegree__": [0, 0, 0],
+ "rotationOfModelDegree": [180, 0, -90],
"material": "powderCoatBlue"
}
]
},
"Ellbow": {
"parent": "Arm1",
- "mountPosition": [
- 0,
- 0,
- 0
- ],
- "mountRotation": [
- 0,
- 0,
- 0
- ],
+ "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
- ],
+ "axis": [-1, 0, 0],
+ "origin": [0, -250, 0],
+ "rotation": [0, 0, 0],
"variable": "z"
},
"skeleton": {
- "from": [
- 0,
- 0,
- 0
- ],
- "to": [
- 90,
- 0,
- 0
- ],
+ "from": [0, 0, 0],
+ "to": [90, 0, 0],
"radius": 4,
- "color": [
- 0.9,
- 0.2,
- 0.2
- ]
+ "color": [0.9, 0.2, 0.2]
},
"model": [
{
"stlFile": "surfaces/Ellebogen.stl",
- "originOfModel": [
- 90,
- 0,
- 0
- ],
- "rotationOfModelDegree": [
- 0,
- -90,
- -90
- ],
+ "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
- ],
- "normal": [
- 0,
- 0,
- 1
- ],
- "size": 25
- }
+ {"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], "normal": [0, 0, 1], "size": 25},
+ {"id": 248, "name": "aruco_248", "position": [52.5, 0, -35], "normal": [0, 0, -1], "size": 25},
+ {"id": 232, "name": "aruco_232", "position": [90, 24.75, -24.75], "normal": [0, 1, -1], "size": 25},
+ {"id": 231, "name": "aruco_231", "position": [90, 24.75, 24.75], "normal": [0, 1, 1], "size": 25}
]
},
"Arm2": {
"parent": "Ellbow",
- "mountPosition": [
- 0,
- 0,
- 0
- ],
- "mountRotation": [
- 0,
- 0,
- 0
- ],
+ "mountPosition": [0, 0, 0],
+ "mountRotation": [0, 0, 0],
"jointToParent": {
"name": "Joint3",
"type": "revolute",
- "axis": [
- 0,
- -1,
- 0
- ],
- "origin": [
- 90,
- 0,
- 0
- ],
- "rotation": [
- 0,
- 0,
- 0
- ],
+ "axis": [0, -1, 0],
+ "origin": [90, 0, 0],
+ "rotation": [0, 0, 0],
"variable": "a"
},
"skeleton": {
- "from": [
- 0,
- 0,
- 0
- ],
- "to": [
- 0,
- -250,
- 0
- ],
+ "from": [0, 0, 0],
+ "to": [0, -250, 0],
"radius": 4,
- "color": [
- 0.95,
- 0.85,
- 0.2
- ]
+ "color": [0.95, 0.85, 0.2]
},
"model": [
{
"stlFile": "surfaces/Unterarm.stl",
- "originOfModel": [
- 0,
- -250,
- 0
- ],
- "rotationOfModelDegree": [
- 180,
- 0,
- -90
- ],
+ "originOfModel": [0, -250, 0],
+ "rotationOfModelDegree": [180, 0, -90],
"material": "defaultPlastic"
}
],
"markers": [
- {
- "id": 120,
- "position": [
- 24.75,
- -112,
- -24.75
- ],
- "normal": [
- 1,
- 0,
- -1
- ]
- },
+ {"id": 120, "position": [24.75, -112, -24.75], "normal": [1, 0, -1]},
{
"id": 122,
"name": "aruco_122",
- "position": [
- -35,
- -112,
- 0
- ],
- "normal": [
- -1,
- 0,
- 0
- ]
+ "position": [-35, -112, 0],
+ "normal": [-1, 0, 0]
},
{
"id": 218,
"name": "aruco_218",
- "position": [
- 35,
- -112,
- 0
- ],
- "normal": [
- 1,
- 0,
- 0
- ]
+ "position": [35, -112, 0],
+ "normal": [1, 0, 0]
},
{
"id": 113,
"name": "aruco_113",
- "position": [
- 0,
- -182,
- 30
- ],
- "normal": [
- 0,
- 0,
- 1
- ]
+ "position": [0, -182, 30],
+ "normal": [0, 0, 1]
},
{
"id": 114,
"name": "aruco_114",
- "position": [
- 24.75,
- -182,
- -24.75
- ],
- "normal": [
- 1,
- 0,
- -1
- ]
+ "position": [24.75, -182, -24.75],
+ "normal": [1, 0, -1]
},
{
"id": 115,
"name": "aruco_115",
- "position": [
- -24.75,
- -182,
- -24.75
- ],
- "normal": [
- -1,
- 0,
- -1
- ]
+ "position": [-24.75, -182, -24.75],
+ "normal": [-1, 0, -1]
},
{
"id": 124,
"name": "aruco_124",
- "position": [
- -35,
- -219,
- 0
- ],
- "normal": [
- -1,
- 0,
- 0
- ]
+ "position": [-35, -219, 0],
+ "normal": [-1, 0, 0]
},
{
"id": 219,
"name": "aruco_219",
- "position": [
- 35,
- -219,
- 0
- ],
- "normal": [
- 1,
- 0,
- 0
- ]
+ "position": [35, -219, 0],
+ "normal": [1, 0, 0]
}
]
},
"Hand": {
"parent": "Arm2",
- "mountPosition": [
- 0,
- 0,
- 0
- ],
- "mountRotation": [
- 0,
- 0,
- 0
- ],
+ "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
- ],
+ "axis": [1, 0, 0],
+ "origin": [0, -250, 0],
+ "rotation": [0, 0, 0],
"variable": "b"
},
"skeleton": {
- "from": [
- 0,
- 0,
- 0
- ],
- "to": [
- 0,
- -35,
- 0
- ],
+ "from": [0, 0, 0],
+ "to": [0, -35, 0],
"radius": 4,
- "color": [
- 0.95,
- 0.55,
- 0.15
- ]
+ "color": [0.95, 0.55, 0.15]
}
},
"Palm": {
"parent": "Hand",
- "mountPosition": [
- 0,
- 0,
- 0
- ],
- "mountRotation": [
- 0,
- 0,
- 0
- ],
+ "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
- ],
+ "axis": [0, -1, 0],
+ "origin": [0, 0, 0],
+ "rotation": [0, 0, 0],
"variable": "c"
},
"skeleton": {
- "from": [
- -50,
- -35,
- 0
- ],
- "to": [
- 50,
- -35,
- 0
- ],
+ "from": [-50, -35, 0],
+ "to": [50, -35, 0],
"radius": 7,
- "color": [
- 0.95,
- 0.2,
- 0.2
- ]
+ "color": [0.95, 0.2, 0.2]
}
},
"FingerA": {
"parent": "Palm",
- "size": [
- 80,
- 60,
- 20
- ],
- "mountPosition": [
- 0,
- 0,
- 0
- ],
- "mountRotation": [
- 0,
- 0,
- 0
- ],
+ "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
- ],
+ "axis": [1, 0, 0],
+ "origin": [4, -35, 0],
+ "rotation": [0, 0, 0],
"variable": "e"
},
"skeleton": {
- "from": [
- 0,
- 0,
- 0
- ],
- "to": [
- 0,
- -60,
- 0
- ],
+ "from": [0, 0, 0],
+ "to": [0, -60, 0],
"radius": 4,
- "color": [
- 0.2,
- 0.8,
- 0.2
- ]
+ "color": [0.2, 0.8, 0.2]
},
"markers": [
- {
- "id": 40,
- "position": [
- 12,
- -24,
- -17.1
- ],
- "normal": [
- -10.98,
- 0,
- -23.56
- ]
- },
- {
- "id": 41,
- "position": [
- 1.5,
- -2.2,
- 25.8
- ],
- "normal": [
- 0,
- -25.6,
- 9.5
- ]
- },
+ {"id": 40, "position": [12, -24, -17.1], "normal": [-10.98, 0, -23.56]},
+ {"id": 41, "position": [1.5, -2.2, 25.8], "normal": [0, -25.6, 9.5]},
{
"id": 42,
- "position": [
- 13.9,
- -40,
- 0
- ],
- "normal": [
- 1,
- -0.35,
- 0.4
- ],
+ "position": [13.9, -40, 0],
+ "normal": [1, -0.35, 0.4],
"spin": 27
}
],
"model": [
{
"stlFile": "surfaces/Finger.stl",
- "originOfModel": [
- 24,
- 0,
- -9.1
- ],
- "rotationOfModelDegree": [
- 90,
- -90,
- 0
- ],
+ "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
- ],
+ "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
- ],
+ "axis": [-1, 0, 0],
+ "origin": [-4, -35, 0],
+ "rotation": [0, 0, 0],
"variable": "e"
},
"skeleton": {
- "from": [
- 0,
- 0,
- 0
- ],
- "to": [
- 0,
- -60,
- 0
- ],
+ "from": [0, 0, 0],
+ "to": [0, -60, 0],
"radius": 4,
- "color": [
- 0.2,
- 0.8,
- 0.2
- ]
+ "color": [0.2, 0.8, 0.2]
},
"markers": [
{
"id": 43,
- "position": [
- -12,
- -24,
- 17.1
- ],
- "normal": [
- 10.98,
- 0,
- 23.56
- ],
+ "position": [-12, -24, 17.1],
+ "normal": [10.98, 0, 23.56],
"spin": 90
},
{
"id": 44,
- "position": [
- -1.5,
- -2.2,
- -25.8
- ],
- "normal": [
- 0,
- -25.6,
- -9.5
- ],
+ "position": [-1.5, -2.2, -25.8],
+ "normal": [0, -25.6, -9.5],
"spin": 90
},
{
"id": 45,
- "position": [
- -13.9,
- -40,
- 0
- ],
- "normal": [
- -1,
- -0.35,
- -0.4
- ],
+ "position": [-13.9, -40, 0],
+ "normal": [-1, -0.35, -0.4],
"spin": -27
}
],
"model": [
{
"stlFile": "surfaces/Finger.stl",
- "originOfModel": [
- -24,
- 0,
- 9.1
- ],
- "rotationOfModelDegree": [
- 90,
- 90,
- 0
- ],
+ "originOfModel": [-24, 0, 9.1],
+ "rotationOfModelDegree": [90, 90, 0],
"material": "defaultPlastic"
}
]
}
}
-}
\ No newline at end of file
+}
diff --git a/data/robot/robot_r.json b/data/robot/robot_r.json
new file mode 100644
index 0000000..cdcd16d
Binary files /dev/null and b/data/robot/robot_r.json differ
diff --git a/data/robot/robot_running_2026_05_30.json b/data/robot/robot_running_2026_05_30.json
index 49b8b6c..49fbe47 100644
--- a/data/robot/robot_running_2026_05_30.json
+++ b/data/robot/robot_running_2026_05_30.json
@@ -172,6 +172,19 @@
"spin_factor": 0.3,
"weight_floor": 0.3
},
+ "robot_test_poses": {
+ "sim04": {"x": 70, "y": 50,"z": -70,"a": 120,"b": 50,"c": 30,"e": 20},
+ "sim05": {"x": 180,"y": 86,"z": -120,"a": -60,"b": 22,"c": 91,"e": 10},
+ "sim06": {"x": 80, "y": 20, "z": 80, "a": -120, "b": 23, "c": 9, "e": 3},
+ "sim07": {"x": 30, "y": -2, "z": 95, "a": 20, "b": 23, "c": 9, "e": 9},
+ "sim08": {"x": 50, "y": -2, "z": 95, "a": 20, "b": 60, "c": 9, "e": 3},
+ "sim09": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8},
+ "sim09a": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8},
+ "sim09b": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8},
+ "sim010": {"x": 120, "y": 60, "z": -110, "a": 20, "b": 30, "c": 180, "e": 4},
+ "sim011": {"x": 50, "y": 4, "z": 176, "a": 20, "b": 60, "c": 9, "e": 5},
+ "sim012": {"x": 50, "y": 0, "z": 178, "a": 210, "b": 80, "c": 90, "e": 6}
+ },
"movements": {
"x": null,
"y": null,
@@ -183,7 +196,7 @@
},
"state_pose_params":{
"numbers_of_Elements_to_consider_start": 3,
- "numbers_of_Elements_to_consider_final": 5,
+ "numbers_of_Elements_to_consider_final": 4,
"solver_in_between_geometrical": false,
"solver_after_geometrical": false,
"geometric_passes_per_stage": 2,
@@ -416,36 +429,13 @@
}
],
"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],
- "normal": [0, 0, 1],
- "size": 25
- }
+ {"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], "normal": [0, 0, 1], "size": 25},
+ {"id": 248, "name": "aruco_248", "position": [52.5, 0, -35], "normal": [0, 0, -1], "size": 25},
+ {"id": 232, "name": "aruco_232", "position": [90, 24.75, -24.75], "normal": [0, 1, -1], "size": 25},
+ {"id": 231, "name": "aruco_231", "position": [90, 24.75, 24.75], "normal": [0, 1, 1], "size": 25}
]
},
diff --git a/pipeline/3_anchor_patch.py b/pipeline/3_anchor_patch.py
new file mode 100644
index 0000000..e69de29
diff --git a/pipeline/3_multiview_bundle_adjustment_v5.py b/pipeline/3_multiview_bundle_adjustment_v5.py
deleted file mode 100644
index 0381948..0000000
--- a/pipeline/3_multiview_bundle_adjustment_v5.py
+++ /dev/null
@@ -1,1462 +0,0 @@
-#!/usr/bin/env python3
-"""
-3_multiview_bundle_adjustment_v4.py
-
-Multi-view ArUco marker position optimization with explicit, switchable
-degrees-of-freedom constraints.
-
-Mathematical model
-------------------
-We estimate 3D marker positions X_i ∈ R^3 by minimizing
-
- E(X) =
- Σ_{i,c} w_ic || π_c(X_i) - u_ic ||²
- + λ_r Σ_j w_j^r || ||X_a - X_b|| - d_j ||²
- + λ_rev Σ_k w_k^rev || (X_b - X_a)·a_k - t_k ||²
- + λ_pri Σ_m w_m^pri ( ||(X_b - X_a)·u_m - t_u||²
- + ||(X_b - X_a)·v_m - t_v||² )
-
-where:
-- u_ic are observed normalized image coordinates for marker i in camera c
-- π_c(.) is the normalized reprojection model
-- w_ic are observation weights from detection quality / marker priors / range
-- rigid-link constraints preserve internal marker geometry of a link
-- revolute joints keep the projection along the joint axis constant
-- prismatic joints keep the two orthogonal projection components constant
-
-Important design choices
-------------------------
-- robot.json is used as a kinematic description, not as a direct source of
- world-space marker positions.
-- constraint families are explicit, switchable, and easy to compare across
- versions.
-- legacy chain-propagation constraints are retained only as an optional family
- and are OFF by default.
-- observation weighting remains separate from constraint weighting so both can
- be tested independently.
-
-Dependencies:
- numpy, opencv-python, scipy (optional for optimization)
-
-Example:
- python 3_multiview_bundle_adjustment_v4.py ^
- -det cam1_aruco_detection.json cam2_aruco_detection.json cam3_aruco_detection.json ^
- -pose cam1_camera_pose.json cam2_camera_pose.json cam3_camera_pose.json ^
- -robot robot.json ^
- -lambdaWeight 100.0
-"""
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-import time
-from dataclasses import dataclass
-from itertools import combinations
-from typing import Any, Dict, List, Optional, Tuple
-
-import cv2
-import numpy as np
-
-
-# ===================================================================
-# Path / JSON helpers
-# ===================================================================
-
-def resolve_path(path: str) -> str:
- path = os.path.expanduser(path)
- if os.path.isabs(path):
- return path
- return os.path.abspath(path)
-
-
-def load_json(path: str) -> Dict[str, Any]:
- with open(resolve_path(path), "r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def save_json(path: str, data: Dict[str, Any]) -> None:
- with open(resolve_path(path), "w", encoding="utf-8") as f:
- json.dump(data, f, indent=2)
-
-
-# ===================================================================
-# Units
-# ===================================================================
-
-def get_length_scale(robot_data: Dict[str, Any]) -> float:
- units = robot_data.get("units", {}) or {}
- length_unit = str(units.get("length", "")).strip().lower()
- if length_unit in ("mm", "millimeter", "millimeters"):
- return 1.0 / 1000.0
- if length_unit in ("cm", "centimeter", "centimeters"):
- return 1.0 / 100.0
- return 1.0
-
-
-# ===================================================================
-# Small geometry helpers
-# ===================================================================
-
-def safe_norm(v: np.ndarray, eps: float = 1e-12) -> float:
- return float(np.linalg.norm(v) + eps)
-
-
-def normalize_vector(v: np.ndarray, eps: float = 1e-12) -> np.ndarray:
- return np.asarray(v, dtype=np.float64) / safe_norm(v, eps)
-
-
-def clamp(v: float, lo: float, hi: float) -> float:
- return float(max(lo, min(hi, v)))
-
-
-def principal_axis_id(axis: np.ndarray, threshold: float = 0.95) -> Optional[int]:
- """Return 0,1,2 for x,y,z if axis is close enough to a principal axis."""
- a = normalize_vector(np.asarray(axis, dtype=np.float64))
- idx = int(np.argmax(np.abs(a)))
- if abs(a[idx]) >= threshold:
- return idx
- return None
-
-
-def camera_center_from_world_to_cam(R_wc: np.ndarray, t_wc: np.ndarray) -> np.ndarray:
- """world_to_camera: X_cam = R_wc * X_world + t_wc; camera center is -R^T t."""
- return -R_wc.T @ t_wc
-
-
-def principal_axis_vector(axis: np.ndarray) -> np.ndarray:
- """Convert a near-principal axis to an exact signed principal axis vector."""
- a = normalize_vector(axis)
- idx = int(np.argmax(np.abs(a)))
- out = np.zeros(3, dtype=np.float64)
- out[idx] = 1.0 if a[idx] >= 0 else -1.0
- return normalize_vector(out)
-
-
-def orthonormal_basis_from_axis(axis: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
- """
- Build two unit vectors orthogonal to axis, with a deterministic orientation.
- """
- a = normalize_vector(axis)
- ref = np.array([1.0, 0.0, 0.0], dtype=np.float64)
- if abs(float(np.dot(a, ref))) > 0.90:
- ref = np.array([0.0, 1.0, 0.0], dtype=np.float64)
- u = np.cross(a, ref)
- if np.linalg.norm(u) < 1e-12:
- ref = np.array([0.0, 0.0, 1.0], dtype=np.float64)
- u = np.cross(a, ref)
- u = normalize_vector(u)
- v = normalize_vector(np.cross(a, u))
- return u, v
-
-
-# ===================================================================
-# Configuration
-# ===================================================================
-
-@dataclass
-class ConstraintRuleConfig:
- rigid_distance_enabled: bool = True
- rigid_distance_mode: str = "mst" # mst | star | full
- rigid_distance_weight: float = 1.0
-
- # Revolute joints: keep the projection along the axis constant.
- revolute_axis_enabled: bool = True
- revolute_axis_max_pairs: int = 2
- revolute_axis_weight: float = 0.5
-
- # Prismatic joints: keep the two orthogonal projection components constant.
- prismatic_orthogonal_enabled: bool = True
- prismatic_orthogonal_max_pairs: int = 2
- prismatic_orthogonal_weight: float = 0.35
-
- # Legacy / optional chain propagation, disabled by default.
- chain_axis_enabled: bool = False
- chain_axis_max_depth: int = 3
- chain_axis_max_pairs: int = 2
- chain_axis_weight: float = 0.3
-
- axis_alignment_threshold: float = 0.95
-
- strict_unique_marker_ids: bool = False
- show_skipped_constraints: bool = True
-
- enable_observation_weights: bool = True
- weight_floor: float = 0.30
- weight_ceiling: float = 3.00
- ref_distance_m: float = 0.75
- ref_marker_size_px: float = 50.0
- use_detection_confidence: bool = True
- use_detection_size_px: bool = True
- use_initial_range: bool = True
- use_marker_size_prior: bool = True
-
-
-def _bool_or_default(value: Any, default: bool) -> bool:
- if value is None:
- return default
- return bool(value)
-
-
-def _float_or_default(value: Any, default: float) -> float:
- if value is None:
- return default
- return float(value)
-
-
-def _int_or_default(value: Any, default: int) -> int:
- if value is None:
- return default
- return int(value)
-
-
-def load_constraint_rule_config(robot_data: Dict[str, Any], args: argparse.Namespace) -> ConstraintRuleConfig:
- """
- Merge built-in defaults with optional robot.json constraint_rules and CLI flags.
- Backward compatibility:
- - joint_axis_projection -> revolute_axis
- """
- rules = robot_data.get("constraint_rules", {}) or {}
-
- cfg = ConstraintRuleConfig()
- rigid = rules.get("rigid_distance", {}) or {}
- revolute = rules.get("joint_revolute_axis", {}) or rules.get("joint_axis_projection", {}) or {}
- prismatic = rules.get("joint_prismatic_orthogonal", {}) or {}
- chain = rules.get("chain_axis_projection", {}) or {}
- obs = rules.get("observation_weights", {}) or {}
-
- cfg.rigid_distance_enabled = _bool_or_default(rigid.get("enabled"), cfg.rigid_distance_enabled)
- cfg.rigid_distance_mode = str(rigid.get("mode", cfg.rigid_distance_mode)).strip().lower()
- cfg.rigid_distance_weight = _float_or_default(rigid.get("weight"), cfg.rigid_distance_weight)
-
- cfg.revolute_axis_enabled = _bool_or_default(revolute.get("enabled"), cfg.revolute_axis_enabled)
- cfg.revolute_axis_max_pairs = _int_or_default(revolute.get("max_pairs"), cfg.revolute_axis_max_pairs)
- cfg.revolute_axis_weight = _float_or_default(revolute.get("weight"), cfg.revolute_axis_weight)
-
- cfg.prismatic_orthogonal_enabled = _bool_or_default(prismatic.get("enabled"), cfg.prismatic_orthogonal_enabled)
- cfg.prismatic_orthogonal_max_pairs = _int_or_default(prismatic.get("max_pairs"), cfg.prismatic_orthogonal_max_pairs)
- cfg.prismatic_orthogonal_weight = _float_or_default(prismatic.get("weight"), cfg.prismatic_orthogonal_weight)
-
- cfg.chain_axis_enabled = _bool_or_default(chain.get("enabled"), cfg.chain_axis_enabled)
- cfg.chain_axis_max_depth = _int_or_default(chain.get("max_depth"), cfg.chain_axis_max_depth)
- cfg.chain_axis_max_pairs = _int_or_default(chain.get("max_pairs"), cfg.chain_axis_max_pairs)
- cfg.chain_axis_weight = _float_or_default(chain.get("weight"), cfg.chain_axis_weight)
-
- cfg.axis_alignment_threshold = _float_or_default(
- rules.get("axis_alignment_threshold"), cfg.axis_alignment_threshold
- )
-
- cfg.enable_observation_weights = _bool_or_default(obs.get("enabled"), cfg.enable_observation_weights)
- cfg.weight_floor = _float_or_default(obs.get("weight_floor"), cfg.weight_floor)
- cfg.weight_ceiling = _float_or_default(obs.get("weight_ceiling"), cfg.weight_ceiling)
- cfg.ref_distance_m = _float_or_default(obs.get("ref_distance_m"), cfg.ref_distance_m)
- cfg.ref_marker_size_px = _float_or_default(obs.get("ref_marker_size_px"), cfg.ref_marker_size_px)
- cfg.use_detection_confidence = _bool_or_default(obs.get("use_detection_confidence"), cfg.use_detection_confidence)
- cfg.use_detection_size_px = _bool_or_default(obs.get("use_detection_size_px"), cfg.use_detection_size_px)
- cfg.use_initial_range = _bool_or_default(obs.get("use_initial_range"), cfg.use_initial_range)
- cfg.use_marker_size_prior = _bool_or_default(obs.get("use_marker_size_prior"), cfg.use_marker_size_prior)
-
- if getattr(args, "strictUniqueMarkerIds", False):
- cfg.strict_unique_marker_ids = True
- if getattr(args, "showSkippedConstraints", False):
- cfg.show_skipped_constraints = True
- if getattr(args, "noShowSkippedConstraints", False):
- cfg.show_skipped_constraints = False
-
- return cfg
-
-
-# ===================================================================
-# Observation / constraint definitions
-# ===================================================================
-
-@dataclass
-class Observation:
- cam_idx: int
-
- center_norm: np.ndarray
-
- corners_norm: np.ndarray | None
-
- marker_size_m: float | None
-
- meta: Dict[str, Any]
-
-
-@dataclass
-class MarkerDistanceConstraint:
- marker_id_a: int
- marker_id_b: int
- link_name: str
- target_distance_m: float
- weight: float = 1.0
- enabled: bool = True
- source: str = "rigid_distance"
-
-
-@dataclass
-class JointAxisConstraint:
- marker_id_parent: int
- marker_id_child: int
- parent_link: str
- child_link: str
- joint_axis: np.ndarray
- target_delta_along_axis_m: float
- weight: float = 1.0
- enabled: bool = True
- source: str = "joint_axis_projection"
-
-
-Constraint = MarkerDistanceConstraint | JointAxisConstraint
-
-
-# ===================================================================
-# Robot parsing
-# ===================================================================
-
-def parse_robot_markers(
- robot_data: Dict[str, Any],
- length_scale: float,
- strict_unique_marker_ids: bool = False
-) -> Tuple[Dict[int, str], Dict[str, List[Dict[str, Any]]], List[str], Dict[int, Dict[str, Any]]]:
- links = robot_data.get("links", {}) or {}
-
- marker_to_link: Dict[int, str] = {}
- link_markers: Dict[str, List[Dict[str, Any]]] = {}
- issues: List[str] = []
- marker_meta: Dict[int, Dict[str, Any]] = {}
-
- seen_global: Dict[int, str] = {}
-
- for link_name, link_data in links.items():
- markers = link_data.get("markers", []) or []
- collected: List[Dict[str, Any]] = []
- seen_local: set[int] = set()
-
- for idx, marker in enumerate(markers):
- marker_id = int(marker.get("id", -1))
- pos = marker.get("position", None)
-
- if marker_id < 0 or pos is None or len(pos) != 3:
- issues.append(f"[WARN] link={link_name}: skipped invalid marker entry at index {idx}")
- continue
-
- if marker_id in seen_local:
- msg = f"[WARN] duplicate marker id {marker_id} inside link '{link_name}'"
- if strict_unique_marker_ids:
- raise ValueError(msg)
- issues.append(msg + " -> skipped duplicate inside same link")
- continue
-
- if marker_id in seen_global:
- msg = (
- f"[WARN] duplicate marker id {marker_id} appears in link '{link_name}' "
- f"and already in link '{seen_global[marker_id]}'"
- )
- if strict_unique_marker_ids:
- raise ValueError(msg)
- issues.append(msg + " -> skipped duplicate occurrence")
- continue
-
- seen_local.add(marker_id)
- seen_global[marker_id] = link_name
-
- pos_raw = np.array(pos, dtype=np.float64)
- pos_m = pos_raw * float(length_scale)
-
- item = {
- "id": marker_id,
- "name": marker.get("name", f"marker_{marker_id}"),
- "position_raw": pos_raw,
- "position_m": pos_m,
- "normal": np.array(marker.get("normal", [0, 0, 1]), dtype=np.float64),
- "size": marker.get("size", None),
- "spin": marker.get("spin", None),
- }
- collected.append(item)
- marker_to_link[marker_id] = link_name
- marker_meta[marker_id] = {
- "link_name": link_name,
- "name": item["name"],
- "position_m": pos_m,
- "normal": item["normal"],
- "size": item["size"],
- "spin": item["spin"],
- }
-
- link_markers[link_name] = collected
-
- return marker_to_link, link_markers, issues, marker_meta
-
-
-def get_link_parent_map(robot_data: Dict[str, Any]) -> Dict[str, Optional[str]]:
- links = robot_data.get("links", {}) or {}
- return {link_name: (link_data.get("parent", None)) for link_name, link_data in links.items()}
-
-
-def get_joint_info(robot_data: Dict[str, Any], child_link: str) -> Dict[str, Any]:
- links = robot_data.get("links", {}) or {}
- return (links.get(child_link, {}) or {}).get("jointToParent", {}) or {}
-
-
-def get_joint_axis(robot_data: Dict[str, Any], child_link: str) -> Optional[np.ndarray]:
- joint = get_joint_info(robot_data, child_link)
- axis = joint.get("axis", None)
- if axis is None:
- return None
- axis = np.asarray(axis, dtype=np.float64)
- if safe_norm(axis) < 1e-12:
- return None
- return normalize_vector(axis)
-
-
-def get_vision_marker_size_default(robot_data: Dict[str, Any]) -> float:
- vision = robot_data.get("vision_config", {}) or {}
- ms = vision.get("MarkerSize", None)
- if ms is None:
- return 0.025
- return float(ms)
-
-
-# ===================================================================
-# Constraint compilation helpers
-# ===================================================================
-
-def get_enabled_link_rule(
- robot_data: Dict[str, Any],
- link_name: str,
- rule_name: str,
- default_enabled: bool = True
-) -> bool:
- overrides = robot_data.get("constraint_overrides", {}) or {}
- link_override = overrides.get(link_name, {}) or {}
- rule_override = link_override.get(rule_name, {}) or {}
- if "enabled" in rule_override:
- return bool(rule_override["enabled"])
- return default_enabled
-
-
-def select_anchor_marker_ids(
- markers: List[Dict[str, Any]],
- axis: Optional[np.ndarray] = None,
- max_count: int = 2
-) -> List[int]:
- if not markers:
- return []
- if len(markers) == 1:
- return [int(markers[0]["id"])]
-
- ids = [int(m["id"]) for m in markers]
- pos = np.stack([np.asarray(m["position_m"], dtype=np.float64) for m in markers], axis=0)
-
- selected: List[int] = []
-
- if axis is not None and safe_norm(axis) > 1e-12:
- a = normalize_vector(axis)
- proj = pos @ a
- min_idx = int(np.argmin(proj))
- max_idx = int(np.argmax(proj))
- selected = [ids[min_idx], ids[max_idx]]
- else:
- centroid = np.mean(pos, axis=0)
- d = np.linalg.norm(pos - centroid, axis=1)
- min_idx = int(np.argmin(d))
- max_idx = int(np.argmax(d))
- selected = [ids[min_idx], ids[max_idx]]
-
- if len(selected) < max_count:
- for mid in ids:
- if mid not in selected:
- selected.append(mid)
- if len(selected) >= max_count:
- break
-
- out: List[int] = []
- seen: set[int] = set()
- for mid in selected:
- if mid not in seen:
- seen.add(mid)
- out.append(mid)
- if len(out) >= max_count:
- break
- return out
-
-
-def mst_edges_for_link(markers: List[Dict[str, Any]]) -> List[Tuple[int, int]]:
- n = len(markers)
- if n < 2:
- return []
-
- ids = [int(m["id"]) for m in markers]
- pos = np.stack([np.asarray(m["position_m"], dtype=np.float64) for m in markers], axis=0)
- in_tree = np.zeros(n, dtype=bool)
- in_tree[0] = True
- edges: List[Tuple[int, int]] = []
- dist = np.linalg.norm(pos[:, None, :] - pos[None, :, :], axis=2)
-
- for _ in range(n - 1):
- best = None
- best_d = float("inf")
- for i in range(n):
- if not in_tree[i]:
- continue
- for j in range(n):
- if in_tree[j]:
- continue
- d = float(dist[i, j])
- if d < best_d:
- best_d = d
- best = (i, j)
- if best is None:
- break
- i, j = best
- in_tree[j] = True
- edges.append((ids[i], ids[j]))
- return edges
-
-
-def compile_rigid_distance_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[MarkerDistanceConstraint]:
- constraints: List[MarkerDistanceConstraint] = []
-
- for link_name, markers in link_markers.items():
- if not get_enabled_link_rule(robot_data, link_name, "rigid_distance", cfg.rigid_distance_enabled):
- continue
- if len(markers) < 2:
- continue
-
- mode = cfg.rigid_distance_mode
- if mode == "full":
- pairs = [(int(a["id"]), int(b["id"])) for a, b in combinations(markers, 2)]
- elif mode == "star":
- anchor_ids = select_anchor_marker_ids(markers, axis=None, max_count=1)
- anchor_id = anchor_ids[0]
- pairs = []
- for m in markers:
- mid = int(m["id"])
- if mid != anchor_id:
- pairs.append((anchor_id, mid))
- elif mode == "mst":
- pairs = mst_edges_for_link(markers)
- else:
- raise ValueError(f"Unknown rigid_distance_mode='{mode}'. Use mst|star|full.")
-
- pos_map = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in markers}
- seen_pairs: set[Tuple[int, int]] = set()
-
- for mid_a, mid_b in pairs:
- if mid_a == mid_b:
- continue
- key = tuple(sorted((int(mid_a), int(mid_b))))
- if key in seen_pairs:
- continue
- seen_pairs.add(key)
-
- pos_a = pos_map[mid_a]
- pos_b = pos_map[mid_b]
- target = float(np.linalg.norm(pos_b - pos_a))
-
- constraints.append(
- MarkerDistanceConstraint(
- marker_id_a=mid_a,
- marker_id_b=mid_b,
- link_name=link_name,
- target_distance_m=target,
- weight=cfg.rigid_distance_weight,
- enabled=True,
- source=f"rigid_distance:{mode}",
- )
- )
-
- return constraints
-
-
-def compile_joint_dof_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[JointAxisConstraint]:
- """
- Compile local joint constraints from robot.json.
-
- Revolute joints: one scalar constraint per anchor pair
- (projection along the joint axis stays constant)
-
- Prismatic joints: two scalar constraints per anchor pair
- (the orthogonal projections stay constant)
-
- Both are emitted as JointAxisConstraint objects so the rest of the
- optimization pipeline remains unchanged.
- """
- constraints: List[JointAxisConstraint] = []
- links = robot_data.get("links", {}) or {}
-
- for child_link, child_data in links.items():
- parent_link = child_data.get("parent", None)
- if not parent_link:
- continue
-
- joint_info = child_data.get("jointToParent", {}) or {}
- joint_type = str(joint_info.get("type", "")).strip().lower()
-
- joint_axis = get_joint_axis(robot_data, child_link)
- if joint_axis is None:
- continue
-
- axis_vec = principal_axis_vector(joint_axis)
-
- parent_markers = link_markers.get(parent_link, [])
- child_markers = link_markers.get(child_link, [])
- if len(parent_markers) == 0 or len(child_markers) == 0:
- continue
-
- parent_pos = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in parent_markers}
- child_pos = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in child_markers}
-
- seen: set[Tuple[int, int]] = set()
-
- if joint_type == "revolute":
- if not get_enabled_link_rule(
- robot_data, child_link, "joint_revolute_axis", cfg.revolute_axis_enabled
- ):
- continue
-
- max_pairs = max(1, int(cfg.revolute_axis_max_pairs))
- parent_anchor_ids = select_anchor_marker_ids(parent_markers, axis=axis_vec, max_count=max_pairs)
- child_anchor_ids = select_anchor_marker_ids(child_markers, axis=axis_vec, max_count=max_pairs)
-
- for mid_p in parent_anchor_ids:
- for mid_c in child_anchor_ids:
- if mid_p == mid_c:
- continue
- key = (mid_p, mid_c)
- if key in seen:
- continue
- seen.add(key)
-
- delta = child_pos[mid_c] - parent_pos[mid_p]
- target = float(np.dot(delta, axis_vec))
-
- constraints.append(
- JointAxisConstraint(
- marker_id_parent=mid_p,
- marker_id_child=mid_c,
- parent_link=parent_link,
- child_link=child_link,
- joint_axis=axis_vec,
- target_delta_along_axis_m=target,
- weight=cfg.revolute_axis_weight,
- enabled=True,
- source="revolute_axis_projection",
- )
- )
-
- elif joint_type == "linear":
- if not get_enabled_link_rule(
- robot_data, child_link, "joint_prismatic_orthogonal", cfg.prismatic_orthogonal_enabled
- ):
- continue
-
- max_pairs = max(1, int(cfg.prismatic_orthogonal_max_pairs))
- parent_anchor_ids = select_anchor_marker_ids(parent_markers, axis=axis_vec, max_count=max_pairs)
- child_anchor_ids = select_anchor_marker_ids(child_markers, axis=axis_vec, max_count=max_pairs)
- basis_u, basis_v = orthonormal_basis_from_axis(axis_vec)
-
- for mid_p in parent_anchor_ids:
- for mid_c in child_anchor_ids:
- if mid_p == mid_c:
- continue
- key = (mid_p, mid_c)
- if key in seen:
- continue
- seen.add(key)
-
- delta = child_pos[mid_c] - parent_pos[mid_p]
-
- constraints.append(
- JointAxisConstraint(
- marker_id_parent=mid_p,
- marker_id_child=mid_c,
- parent_link=parent_link,
- child_link=child_link,
- joint_axis=basis_u,
- target_delta_along_axis_m=float(np.dot(delta, basis_u)),
- weight=cfg.prismatic_orthogonal_weight,
- enabled=True,
- source="prismatic_orthogonal_projection:u",
- )
- )
- constraints.append(
- JointAxisConstraint(
- marker_id_parent=mid_p,
- marker_id_child=mid_c,
- parent_link=parent_link,
- child_link=child_link,
- joint_axis=basis_v,
- target_delta_along_axis_m=float(np.dot(delta, basis_v)),
- weight=cfg.prismatic_orthogonal_weight,
- enabled=True,
- source="prismatic_orthogonal_projection:v",
- )
- )
-
- else:
- continue
-
- return constraints
-
-
-
-
-def compile_constraints(
- robot_data: Dict[str, Any],
- length_scale: float,
- cfg: ConstraintRuleConfig
-) -> Tuple[Dict[int, str], Dict[str, List[Dict[str, Any]]], List[Constraint], List[str], Dict[int, Dict[str, Any]]]:
- marker_to_link, link_markers, issues, marker_meta = parse_robot_markers(
- robot_data,
- length_scale=length_scale,
- strict_unique_marker_ids=cfg.strict_unique_marker_ids,
- )
-
- constraints: List[Constraint] = []
- constraints.extend(compile_rigid_distance_constraints(robot_data, link_markers, cfg))
- constraints.extend(compile_joint_dof_constraints(robot_data, link_markers, cfg))
-
- # Legacy optional family, OFF by default.
- if cfg.chain_axis_enabled:
- constraints.extend(compile_chain_axis_constraints(robot_data, link_markers, cfg))
-
- unique_constraints: List[Constraint] = []
- seen_keys: set[Tuple[Any, ...]] = set()
-
- for c in constraints:
- if isinstance(c, MarkerDistanceConstraint):
- key = (
- "d",
- min(c.marker_id_a, c.marker_id_b),
- max(c.marker_id_a, c.marker_id_b),
- c.link_name,
- round(c.target_distance_m, 9),
- )
- else:
- key = (
- "j",
- c.parent_link,
- c.child_link,
- c.marker_id_parent,
- c.marker_id_child,
- tuple(np.round(c.joint_axis, 9).tolist()),
- round(c.target_delta_along_axis_m, 9),
- )
- if key in seen_keys:
- continue
- seen_keys.add(key)
- unique_constraints.append(c)
-
- return marker_to_link, link_markers, unique_constraints, issues, marker_meta
-
-
-# ===================================================================
-# Observation quality / weighting
-# ===================================================================
-
-def _optional_float(meta: Dict[str, Any], keys: List[str]) -> Optional[float]:
- for k in keys:
- if k in meta and meta[k] is not None:
- try:
- return float(meta[k])
- except Exception:
- pass
- return None
-
-
-def detection_quality_from_metadata(det_obj: Dict[str, Any], cfg: ConstraintRuleConfig) -> float:
- q = 1.0
-
- if cfg.use_detection_confidence:
- conf = _optional_float(det_obj, ["confidence", "score", "quality", "det_confidence"])
- if conf is not None:
- q *= clamp(conf, 0.1, 1.0)
-
- if cfg.use_detection_size_px:
- size_px = _optional_float(det_obj, ["size_px", "marker_size_px", "side_px", "side_length_px"])
- if size_px is None and "corners_px" in det_obj and isinstance(det_obj["corners_px"], list):
- try:
- corners = np.asarray(det_obj["corners_px"], dtype=np.float64).reshape(-1, 2)
- if len(corners) >= 4:
- edges = []
- for i in range(len(corners)):
- p = corners[i]
- q2 = corners[(i + 1) % len(corners)]
- edges.append(float(np.linalg.norm(q2 - p)))
- size_px = float(np.mean(edges))
- except Exception:
- size_px = None
- if size_px is not None:
- q *= clamp(size_px / max(cfg.ref_marker_size_px, 1e-6), 0.25, 3.0)
-
- sharpness = _optional_float(det_obj, ["sharpness", "corner_sharpness"])
- if sharpness is not None:
- q *= clamp(sharpness / 2500.0, 0.5, 1.5)
-
- normal_alignment = _optional_float(det_obj, ["normal_alignment", "view_cosine", "cos_to_camera"])
- if normal_alignment is not None:
- q *= clamp(normal_alignment, 0.3, 1.0)
-
- return float(q)
-
-
-def marker_size_prior_factor(marker_meta: Dict[str, Any], default_marker_size_m: float) -> float:
- size_val = marker_meta.get("size", None)
- if size_val is None:
- return 1.0
-
- try:
- size_val = float(size_val)
- except Exception:
- return 1.0
-
- size_m = size_val / 1000.0 if size_val > 1.0 else size_val
- ref = max(default_marker_size_m, 1e-6)
- return clamp(size_m / ref, 0.7, 1.3)
-
-
-def compute_observation_weights(
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- initial_positions: Dict[int, np.ndarray],
- marker_meta: Dict[int, Dict[str, Any]],
- cfg: ConstraintRuleConfig,
- robot_data: Dict[str, Any]
-) -> Dict[Tuple[int, int], float]:
- weights: Dict[Tuple[int, int], float] = {}
- default_marker_size_m = get_vision_marker_size_default(robot_data)
-
- for marker_id, obs_list in marker_observations.items():
- X = initial_positions.get(marker_id, None)
- size_prior = marker_size_prior_factor(marker_meta.get(marker_id, {}), default_marker_size_m)
-
- for obs_idx, obs in enumerate(obs_list):
- w = 1.0
- q = detection_quality_from_metadata(obs.meta, cfg)
- w *= q
-
- if cfg.use_marker_size_prior:
- w *= size_prior
-
- if cfg.use_initial_range and X is not None:
- _, _, R_wc, t_wc = cameras[obs.cam_idx]
- C = camera_center_from_world_to_cam(R_wc, t_wc)
- dist = float(np.linalg.norm(X - C))
- if np.isfinite(dist):
- w *= clamp(cfg.ref_distance_m / max(dist, 1e-6), 0.4, 2.0)
-
- weights[(marker_id, obs_idx)] = clamp(w, cfg.weight_floor, cfg.weight_ceiling)
-
- return weights
-
-
-# ===================================================================
-# Multi-view loading
-# ===================================================================
-
-def load_observations_and_poses(
- detection_files: List[str],
- pose_files: List[str]
-) -> Tuple[
- Dict[int, List[Observation]],
- List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- List[Dict[str, Any]]
-]:
- if len(detection_files) != len(pose_files):
- raise ValueError(f"Mismatch: {len(detection_files)} detections vs {len(pose_files)} poses")
-
- marker_observations: Dict[int, List[Observation]] = {}
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = []
- obs_metadata: List[Dict[str, Any]] = []
-
- for cam_idx, (det_file, pose_file) in enumerate(zip(detection_files, pose_files)):
- det = load_json(det_file)
- pose_data = load_json(pose_file)
-
- cam_section = det.get("camera", {}) or {}
- K = np.array(cam_section.get("camera_matrix", []), dtype=np.float64).reshape(3, 3)
- D = np.array(cam_section.get("distortion_coefficients", []), dtype=np.float64).reshape(-1, 1)
-
- pose_section = pose_data.get("camera_pose", {}) or {}
- world_to_cam = pose_section.get("world_to_camera", {}) or {}
- R_wc = np.array(world_to_cam.get("rotation_matrix", []), dtype=np.float64).reshape(3, 3)
- t_wc = np.array(world_to_cam.get("translation_m", []), dtype=np.float64).reshape(3)
-
- cameras.append((K, D, R_wc, t_wc))
-
- detections = det.get("detections", []) or []
- for det_obj in detections:
- marker_id = int(det_obj.get("marker_id", -1))
- if marker_id < 0:
- continue
-
- center_px = np.array(det_obj.get("center_px", []), dtype=np.float64)
- if center_px.shape != (2,):
- continue
-
- pts = center_px.reshape(1, 1, 2).astype(np.float32)
- und = cv2.undistortPoints(pts, K.astype(np.float32), D.astype(np.float32), P=None)
- center_norm = und.reshape(2).astype(np.float64)
-
- obs = Observation(
- cam_idx=cam_idx,
- center_norm=center_norm,
- meta=dict(det_obj),
- )
- marker_observations.setdefault(marker_id, []).append(obs)
-
- obs_metadata.append(
- {
- "detection_file": det_file,
- "pose_file": pose_file,
- "num_detections": len(detections),
- }
- )
-
- return marker_observations, cameras, obs_metadata
-
-
-# ===================================================================
-# Initial triangulation
-# ===================================================================
-
-def triangulate_marker_initial(
- marker_id: int,
- observations: List[Observation],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]
-) -> Optional[np.ndarray]:
- """
- Multi-view linear triangulation from all available normalized observations.
- Uses all cameras instead of only the best pair.
- """
- if len(observations) < 2:
- return None
-
- A = []
-
- for obs in observations:
- cam_idx = obs.cam_idx
- _, _, R_wc, t_wc = cameras[cam_idx]
-
- # world -> camera projection matrix without intrinsics,
- # because obs.center_norm is already undistorted / normalized
- P = np.hstack([R_wc, t_wc.reshape(3, 1)]) # 3x4
-
- x = float(obs.center_norm[0])
- y = float(obs.center_norm[1])
-
- A.append(x * P[2, :] - P[0, :])
- A.append(y * P[2, :] - P[1, :])
-
- A = np.asarray(A, dtype=np.float64)
-
- if A.shape[0] < 4:
- return None
-
- try:
- _, _, Vt = np.linalg.svd(A)
- X_h = Vt[-1, :]
- if abs(X_h[3]) < 1e-12:
- return None
- X = X_h[:3] / X_h[3]
- if not np.all(np.isfinite(X)):
- return None
- return X.astype(np.float64)
- except Exception:
- return None
-
-
-def initial_triangulation(
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]
-) -> Dict[int, np.ndarray]:
- triangulated: Dict[int, np.ndarray] = {}
- for marker_id, observations in marker_observations.items():
- X = triangulate_marker_initial(marker_id, observations, cameras)
- if X is not None:
- triangulated[marker_id] = X
- return triangulated
-
-
-# ===================================================================
-# Weighted residuals / optimization
-# ===================================================================
-
-def bundle_adjustment_residuals(
- marker_positions_flat: np.ndarray,
- marker_ids: List[int],
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- constraints: List[Constraint],
- obs_weights: Dict[Tuple[int, int], float],
- lambda_constraint: float = 100.0
-) -> np.ndarray:
- marker_dict: Dict[int, np.ndarray] = {}
- for i, marker_id in enumerate(marker_ids):
- marker_dict[marker_id] = marker_positions_flat[i * 3:(i + 1) * 3]
-
- residuals: List[float] = []
-
- for marker_id, observations in marker_observations.items():
- if marker_id not in marker_dict:
- continue
-
- X_world = marker_dict[marker_id]
- for obs_idx, obs in enumerate(observations):
- cam_idx, norm_coords_obs = obs.cam_idx, obs.norm_coords
- K, D, R_wc, t_wc = cameras[cam_idx]
- X_cam = R_wc @ X_world + t_wc
- if X_cam[2] > 1e-6:
- proj_norm = X_cam[:2] / X_cam[2]
- r = proj_norm - norm_coords_obs
- w = float(np.sqrt(obs_weights.get((marker_id, obs_idx), 1.0)))
- residuals.append(w * float(r[0]))
- residuals.append(w * float(r[1]))
-
- for constraint in constraints:
- if isinstance(constraint, MarkerDistanceConstraint):
- if constraint.marker_id_a in marker_dict and constraint.marker_id_b in marker_dict:
- pos_a = marker_dict[constraint.marker_id_a]
- pos_b = marker_dict[constraint.marker_id_b]
- actual_dist = float(np.linalg.norm(pos_b - pos_a))
- residuals.append((actual_dist - constraint.target_distance_m) * constraint.weight * lambda_constraint)
-
- elif isinstance(constraint, JointAxisConstraint):
- if constraint.marker_id_parent in marker_dict and constraint.marker_id_child in marker_dict:
- pos_parent = marker_dict[constraint.marker_id_parent]
- pos_child = marker_dict[constraint.marker_id_child]
- delta = pos_child - pos_parent
- actual_delta = float(np.dot(delta, constraint.joint_axis))
- residuals.append((actual_delta - constraint.target_delta_along_axis_m) * constraint.weight * lambda_constraint)
-
- return np.asarray(residuals, dtype=np.float64)
-
-
-def optimize_with_constraints(
- initial_positions: Dict[int, np.ndarray],
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- constraints: List[Constraint],
- obs_weights: Dict[Tuple[int, int], float],
- lambda_constraint: float = 100.0,
- max_iterations: int = 50
-) -> Dict[int, np.ndarray]:
- try:
- from scipy.optimize import least_squares
- except ImportError:
- print("[WARN] scipy not available, skipping optimization.")
- return initial_positions
-
- marker_ids = sorted(initial_positions.keys())
- if not marker_ids:
- return {}
-
- x0 = np.concatenate([initial_positions[mid] for mid in marker_ids])
-
- def residuals_fn(x: np.ndarray) -> np.ndarray:
- return bundle_adjustment_residuals(
- x, marker_ids, marker_observations, cameras, constraints, obs_weights, lambda_constraint
- )
-
- print(f"[INFO] Starting optimization with {len(x0)} variables and {len(constraints)} constraints...")
-
- result = least_squares(
- residuals_fn,
- x0,
- max_nfev=max_iterations * max(1, len(marker_ids)),
- verbose=1,
- )
-
- optimized = {}
- for i, marker_id in enumerate(marker_ids):
- optimized[marker_id] = result.x[i * 3:(i + 1) * 3]
-
- print(f"[INFO] Optimization complete. Final cost: {float(np.sum(result.fun ** 2)):.6f}")
- return optimized
-
-
-# ===================================================================
-# Reporting helpers
-# ===================================================================
-
-def print_constraint_summary(constraints: List[Constraint]) -> None:
- num_dist = sum(isinstance(c, MarkerDistanceConstraint) for c in constraints)
- num_joint = sum(isinstance(c, JointAxisConstraint) for c in constraints)
- print(f"[INFO] Constraint summary: total={len(constraints)} distance={num_dist} joint/chain={num_joint}")
-
-
-def print_constraint_list(constraints: List[Constraint]) -> None:
- print("\n[INFO] Constraint list:")
- for idx, constraint in enumerate(constraints):
- if isinstance(constraint, MarkerDistanceConstraint):
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | "
- f"Target={constraint.target_distance_m:.6f} m | "
- f"Weight={constraint.weight} | "
- f"Source={constraint.source}"
- )
- else:
- axis_str = np.array2string(constraint.joint_axis, precision=3, suppress_small=True)
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}(M{constraint.marker_id_parent}) -> "
- f"{constraint.child_link}(M{constraint.marker_id_child}) | "
- f"Axis={axis_str} | "
- f"TargetDelta={constraint.target_delta_along_axis_m:.6f} m | "
- f"Weight={constraint.weight} | "
- f"Source={constraint.source}"
- )
-
-
-def print_constraints_with_errors(
- title: str,
- constraints: List[Constraint],
- positions: Dict[int, np.ndarray],
- show_skipped: bool = True
-) -> None:
- print(f"\n[INFO] {title}")
-
- active = 0
- skipped = 0
-
- for idx, constraint in enumerate(constraints):
- if isinstance(constraint, MarkerDistanceConstraint):
- if constraint.marker_id_a not in positions or constraint.marker_id_b not in positions:
- skipped += 1
- if show_skipped:
- print(
- f" [{idx:03d}] DISTANCE | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | SKIPPED (missing marker)"
- )
- continue
-
- pos_a = positions[constraint.marker_id_a]
- pos_b = positions[constraint.marker_id_b]
- actual = float(np.linalg.norm(pos_b - pos_a))
- error = actual - constraint.target_distance_m
- active += 1
-
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | "
- f"target={constraint.target_distance_m*1000:.2f} mm | "
- f"actual={actual*1000:.2f} mm | "
- f"error={error*1000:+.2f} mm"
- )
-
- elif isinstance(constraint, JointAxisConstraint):
- if constraint.marker_id_parent not in positions or constraint.marker_id_child not in positions:
- skipped += 1
- if show_skipped:
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"M{constraint.marker_id_parent} -> M{constraint.marker_id_child} | SKIPPED (missing marker)"
- )
- continue
-
- pos_parent = positions[constraint.marker_id_parent]
- pos_child = positions[constraint.marker_id_child]
- delta = pos_child - pos_parent
- actual = float(np.dot(delta, constraint.joint_axis))
- error = actual - constraint.target_delta_along_axis_m
- active += 1
-
- axis_str = np.array2string(constraint.joint_axis, precision=2, suppress_small=True)
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}(M{constraint.marker_id_parent}) -> "
- f"{constraint.child_link}(M{constraint.marker_id_child}) | "
- f"axis={axis_str} | "
- f"target={constraint.target_delta_along_axis_m*1000:.2f} mm | "
- f"actual={actual*1000:.2f} mm | "
- f"error={error*1000:+.2f} mm"
- )
-
- print(f"[INFO] Active constraints: {active} | Skipped: {skipped}")
-
-
-def print_observation_weight_summary(obs_weights: Dict[Tuple[int, int], float]) -> None:
- if not obs_weights:
- print("[INFO] Observation weighting: disabled or empty")
- return
- values = np.array(list(obs_weights.values()), dtype=np.float64)
- print(
- "[INFO] Observation weights: "
- f"min={values.min():.3f} mean={values.mean():.3f} "
- f"median={np.median(values):.3f} max={values.max():.3f}"
- )
-
-
-# ===================================================================
-# Main
-# ===================================================================
-
-def main() -> None:
- parser = argparse.ArgumentParser(
- description="Multi-view bundle adjustment with rule-based geometric constraints"
- )
- parser.add_argument(
- "-det", "--detections",
- action="append",
- required=True,
- help="*_aruco_detection.json files"
- )
- parser.add_argument(
- "-pose", "--poses",
- action="append",
- required=True,
- help="*_camera_pose.json files"
- )
- parser.add_argument(
- "-robot", "--robot",
- required=True,
- help="robot.json"
- )
- parser.add_argument(
- "-outDir", "--outDir",
- default=None,
- help="Output directory"
- )
- parser.add_argument(
- "-lambdaWeight", "--lambdaWeight",
- type=float,
- default=100.0,
- help="Constraint weight multiplier"
- )
- parser.add_argument(
- "--strictUniqueMarkerIds",
- action="store_true",
- help="Fail if a marker ID appears more than once in robot.json"
- )
- parser.add_argument(
- "--showSkippedConstraints",
- action="store_true",
- help="Print skipped constraints in the report"
- )
- parser.add_argument(
- "--noShowSkippedConstraints",
- action="store_true",
- help="Hide skipped constraints in the report"
- )
- parser.add_argument(
- "--saveConstraintReport",
- action="store_true",
- help="Save constraint report JSON files"
- )
- parser.add_argument(
- "--saveObservationWeightReport",
- action="store_true",
- help="Save observation-weight report JSON file"
- )
-
- args = parser.parse_args()
-
- if args.showSkippedConstraints and args.noShowSkippedConstraints:
- print("[ERROR] Choose only one of --showSkippedConstraints or --noShowSkippedConstraints")
- sys.exit(1)
-
- if len(args.detections) != len(args.poses):
- print(f"[ERROR] Mismatch: {len(args.detections)} detection files vs {len(args.poses)} pose files")
- sys.exit(1)
-
- robot_data = load_json(args.robot)
- length_scale = get_length_scale(robot_data)
- cfg = load_constraint_rule_config(robot_data, args)
-
- print("[STEP 1] Compile constraints from robot.json structure...")
- print(
- "[INFO] Constraint families: "
- f"rigid_distance={'on' if cfg.rigid_distance_enabled else 'off'}, "
- f"revolute={'on' if cfg.revolute_axis_enabled else 'off'}, "
- f"prismatic={'on' if cfg.prismatic_orthogonal_enabled else 'off'}, "
- f"chain_legacy={'on' if cfg.chain_axis_enabled else 'off'}, "
- f"observation_weights={'on' if cfg.enable_observation_weights else 'off'}"
- )
- marker_to_link, link_markers, constraints, issues, marker_meta = compile_constraints(robot_data, length_scale, cfg)
-
- for issue in issues:
- print(issue)
-
- print(f"[INFO] Links with markers: {sum(1 for v in link_markers.values() if len(v) > 0)}")
- print(f"[INFO] Unique marker IDs: {len(marker_to_link)}")
- print_constraint_summary(constraints)
- print_constraint_list(constraints)
-
- print("\n[STEP 2] Load observations and camera poses...")
- marker_observations, cameras, obs_metadata = load_observations_and_poses(args.detections, args.poses)
- print(f"[INFO] {len(cameras)} cameras, {len(marker_observations)} observed markers")
- print(f"[INFO] Detection files loaded: {len(obs_metadata)}")
-
- print("\n[STEP 3] Initial triangulation...")
- initial_pos = initial_triangulation(marker_observations, cameras)
- print(f"[INFO] Triangulated {len(initial_pos)} markers")
-
- out_dir = args.outDir or os.path.dirname(args.detections[0]) or "."
- os.makedirs(resolve_path(out_dir), exist_ok=True)
-
- initial_output_markers = []
- for marker_id, position in sorted(initial_pos.items()):
- initial_output_markers.append(
- {
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown"),
- }
- )
-
- initial_output = {
- "schema_version": "1.2",
- "stage": "initial_triangulation",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_cameras": len(cameras),
- "num_markers": len(initial_pos),
- "num_constraints": len(constraints),
- },
- "markers": initial_output_markers,
- }
- initial_out_file = os.path.join(out_dir, "aruco_positions_initial.json")
- save_json(initial_out_file, initial_output)
- print(f"[INFO] Initial triangulation saved to {initial_out_file}")
-
- obs_weights = compute_observation_weights(
- marker_observations=marker_observations,
- cameras=cameras,
- initial_positions=initial_pos,
- marker_meta=marker_meta,
- cfg=cfg,
- robot_data=robot_data,
- )
- print_observation_weight_summary(obs_weights)
-
- print_constraints_with_errors(
- "Constraint list BEFORE optimization",
- constraints,
- initial_pos,
- show_skipped=cfg.show_skipped_constraints,
- )
-
- print("\n[STEP 4] Bundle adjustment with constraints...")
- optimized_pos = optimize_with_constraints(
- initial_pos,
- marker_observations,
- cameras,
- constraints,
- obs_weights,
- lambda_constraint=args.lambdaWeight,
- )
-
- print_constraints_with_errors(
- "Constraint list AFTER optimization",
- constraints,
- optimized_pos,
- show_skipped=cfg.show_skipped_constraints,
- )
-
- output_markers = []
- for marker_id, position in sorted(optimized_pos.items()):
- output_markers.append(
- {
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown"),
- }
- )
-
- output = {
- "schema_version": "1.2",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_cameras": len(cameras),
- "num_markers": len(optimized_pos),
- "num_constraints": len(constraints),
- },
- "markers": output_markers,
- }
- out_file = os.path.join(out_dir, "aruco_positions_optimized.json")
- save_json(out_file, output)
- print(f"\n[INFO] Saved to {out_file}")
-
- if args.saveConstraintReport:
- report = {
- "schema_version": "1.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_constraints": len(constraints),
- "num_links_with_markers": sum(1 for v in link_markers.values() if len(v) > 0),
- "num_observed_markers": len(marker_observations),
- "num_triangulated_markers": len(initial_pos),
- "num_optimized_markers": len(optimized_pos),
- },
- "constraints": [],
- }
- for c in constraints:
- if isinstance(c, MarkerDistanceConstraint):
- report["constraints"].append(
- {
- "kind": "distance",
- "link_name": c.link_name,
- "marker_id_a": c.marker_id_a,
- "marker_id_b": c.marker_id_b,
- "target_distance_m": c.target_distance_m,
- "weight": c.weight,
- "source": c.source,
- }
- )
- else:
- report["constraints"].append(
- {
- "kind": "joint_axis",
- "parent_link": c.parent_link,
- "child_link": c.child_link,
- "marker_id_parent": c.marker_id_parent,
- "marker_id_child": c.marker_id_child,
- "joint_axis": [float(x) for x in c.joint_axis],
- "target_delta_along_axis_m": c.target_delta_along_axis_m,
- "weight": c.weight,
- "source": c.source,
- }
- )
- report_file = os.path.join(out_dir, "constraint_report.json")
- save_json(report_file, report)
- print(f"[INFO] Constraint report saved to {report_file}")
-
- if args.saveObservationWeightReport:
- obs_report = {
- "schema_version": "1.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_weighted_observations": len(obs_weights),
- },
- "observation_weights": [
- {
- "marker_id": int(mid),
- "observation_index": int(obs_idx),
- "weight": float(w),
- }
- for (mid, obs_idx), w in sorted(obs_weights.items())
- ],
- }
- obs_file = os.path.join(out_dir, "observation_weight_report.json")
- save_json(obs_file, obs_report)
- print(f"[INFO] Observation-weight report saved to {obs_file}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/pipeline/3_multiview_bundle_adjustment_v5_board_anchor_patch.py b/pipeline/3_multiview_bundle_adjustment_v5_board_anchor_patch.py
new file mode 100644
index 0000000..1cba4c6
--- /dev/null
+++ b/pipeline/3_multiview_bundle_adjustment_v5_board_anchor_patch.py
@@ -0,0 +1,229 @@
+#!/usr/bin/env python3
+"""
+3_board_anchor_patch.py
+-----------------------
+DROP-IN PATCH for 3_multiview_bundle_adjustment_v4.py
+
+What it adds
+------------
+Board markers have KNOWN world positions (they are fixed to the physical board,
+which defines the coordinate frame). Letting the optimizer move them freely
+makes them drift, which degrades all arm-marker estimates.
+
+This patch adds a PositionAnchorConstraint that pins each observed Board
+marker to its robot.json world position during bundle adjustment.
+
+How to integrate
+----------------
+Copy the three sections labelled [PATCH – copy into v4] into the v4 file:
+
+ 1. The PositionAnchorConstraint dataclass → after the JointAxisConstraint dataclass
+ 2. Update the Constraint type alias → replace the existing line
+ 3. The load_board_anchors() function → anywhere before main()
+ 4. The change to bundle_adjustment_residuals → add one branch inside the for-loop
+ 5. The three lines in main() → just before "STEP 4"
+
+Quick-test without editing v4
+------------------------------
+You can also run this file standalone — it imports from v4 and patches it at
+runtime. Then call main_anchored() instead of main() for the patched run.
+
+ python 3_board_anchor_patch.py -det ... -pose ... -robot ... -outDir ...
+"""
+
+from __future__ import annotations
+
+# ══════════════════════════════════════════════════════════════
+# [PATCH section 1 – copy into v4 after JointAxisConstraint]
+# ══════════════════════════════════════════════════════════════
+from dataclasses import dataclass
+import numpy as np
+from typing import Dict, Any, List, Optional, Tuple
+
+
+@dataclass
+class PositionAnchorConstraint:
+ """
+ Pins a single marker to a known world-space position.
+ Used to anchor Board markers whose world positions are read from robot.json.
+ """
+ marker_id: int
+ target_world_m: np.ndarray # shape (3,), in metres
+ weight: float = 100.0
+ source: str = "board_anchor"
+
+
+# ══════════════════════════════════════════════════════════════
+# [PATCH section 2 – replace the Constraint type alias in v4]
+# ══════════════════════════════════════════════════════════════
+# OLD: Constraint = MarkerDistanceConstraint | JointAxisConstraint
+# NEW:
+# from 3_board_anchor_patch import PositionAnchorConstraint
+# Constraint = MarkerDistanceConstraint | JointAxisConstraint | PositionAnchorConstraint
+
+
+# ══════════════════════════════════════════════════════════════
+# [PATCH section 3 – new function, copy anywhere before main()]
+# ══════════════════════════════════════════════════════════════
+
+def load_board_anchors(
+ robot_data: Dict[str, Any],
+ length_scale: float,
+ weight: float = 100.0,
+) -> List[PositionAnchorConstraint]:
+ """
+ Build PositionAnchorConstraints for every marker on the 'Board' link.
+
+ Board is the root link; its marker positions are in the Board (world) frame.
+ length_scale = 1/1000 when robot.json uses mm (the standard).
+
+ Parameters
+ ----------
+ robot_data : loaded robot.json
+ length_scale : float (1/1000 to convert mm → m)
+ weight : constraint weight (100 ≈ 1 mm anchor tolerance at λ=100)
+ """
+ links = robot_data.get("links", {}) or {}
+ board = links.get("Board", {}) or {}
+ anchors: List[PositionAnchorConstraint] = []
+
+ for m in board.get("markers", []):
+ mid = int(m.get("id", -1))
+ pos = m.get("position", None)
+ if mid < 0 or pos is None or len(pos) != 3:
+ continue
+ world_m = np.array(pos, dtype=np.float64) * float(length_scale)
+ anchors.append(
+ PositionAnchorConstraint(
+ marker_id=mid,
+ target_world_m=world_m,
+ weight=weight,
+ source="board_anchor",
+ )
+ )
+
+ return anchors
+
+
+# ══════════════════════════════════════════════════════════════
+# [PATCH section 4 – add this branch inside bundle_adjustment_residuals,
+# in the "for constraint in constraints:" loop, after the JointAxisConstraint branch]
+# ══════════════════════════════════════════════════════════════
+
+def _anchor_residuals(constraint: PositionAnchorConstraint,
+ marker_dict: Dict[int, np.ndarray],
+ lambda_constraint: float) -> List[float]:
+ """
+ Returns 3 residual components w·λ·(X_observed - X_target) for each axis.
+
+ Paste the body of this function into bundle_adjustment_residuals like this:
+
+ elif isinstance(constraint, PositionAnchorConstraint):
+ if constraint.marker_id in marker_dict:
+ diff = marker_dict[constraint.marker_id] - constraint.target_world_m
+ for d in diff:
+ residuals.append(float(d) * constraint.weight * lambda_constraint)
+ """
+ residuals: List[float] = []
+ if constraint.marker_id in marker_dict:
+ diff = marker_dict[constraint.marker_id] - constraint.target_world_m
+ for d in diff:
+ residuals.append(float(d) * constraint.weight * lambda_constraint)
+ return residuals
+
+
+# ══════════════════════════════════════════════════════════════
+# [PATCH section 5 – add these lines in main(), just BEFORE
+# "print('\n[STEP 4] Bundle adjustment with constraints...')"]
+# ══════════════════════════════════════════════════════════════
+#
+# board_anchors = load_board_anchors(robot_data, length_scale, weight=100.0)
+# constraints += board_anchors
+# print(f"[INFO] Board anchors added: {len(board_anchors)}")
+#
+# ══════════════════════════════════════════════════════════════
+
+
+# ──────────────────────────────────────────────────────────────
+# STANDALONE RUNTIME PATCH (alternative to editing v4)
+# Run this file directly and it patches v4 in memory.
+# ──────────────────────────────────────────────────────────────
+
+def _monkey_patch_v4():
+ """
+ Import the v4 module under an alias and patch it so Board markers are
+ anchored without touching the original file.
+ """
+ import importlib.util, sys, types, pathlib
+
+ v4_path = pathlib.Path(__file__).parent / "3_multiview_bundle_adjustment_v4.py"
+ if not v4_path.exists():
+ raise FileNotFoundError(
+ f"Expected v4 at {v4_path}.\n"
+ "Place this patch file alongside 3_multiview_bundle_adjustment_v4.py."
+ )
+
+ spec = importlib.util.spec_from_file_location("ba_v4", v4_path)
+ mod = importlib.util.module_from_spec(spec)
+ spec.loader.exec_module(mod)
+
+ # Inject PositionAnchorConstraint into the module namespace
+ mod.PositionAnchorConstraint = PositionAnchorConstraint
+
+ # Patch bundle_adjustment_residuals ─────────────────────────
+ _orig_residuals = mod.bundle_adjustment_residuals
+
+ def _patched_residuals(marker_positions_flat, marker_ids, marker_observations,
+ cameras, constraints, obs_weights, lambda_constraint=100.0):
+ r = _orig_residuals(marker_positions_flat, marker_ids, marker_observations,
+ cameras, constraints, obs_weights, lambda_constraint)
+ # Rebuild marker_dict (same as inside the original function)
+ marker_dict = {
+ mid: marker_positions_flat[i*3:(i+1)*3]
+ for i, mid in enumerate(marker_ids)
+ }
+ extra = []
+ for c in constraints:
+ if isinstance(c, PositionAnchorConstraint):
+ extra.extend(_anchor_residuals(c, marker_dict, lambda_constraint))
+ if extra:
+ r = np.concatenate([r, np.array(extra, dtype=np.float64)])
+ return r
+
+ mod.bundle_adjustment_residuals = _patched_residuals
+
+ # Patch main to inject board anchors ────────────────────────
+ _orig_main = mod.main
+
+ def _patched_main():
+ import sys as _sys
+ # We hijack the module-level function calls by patching compile_constraints
+ _orig_compile = mod.compile_constraints
+
+ def _compile_with_anchors(robot_data, length_scale, cfg):
+ marker_to_link, link_markers, constraints, issues, marker_meta = \
+ _orig_compile(robot_data, length_scale, cfg)
+
+ anchors = load_board_anchors(robot_data, length_scale, weight=100.0)
+ print(f"[PATCH] Board anchors added: {len(anchors)}")
+ constraints = constraints + anchors
+ return marker_to_link, link_markers, constraints, issues, marker_meta
+
+ mod.compile_constraints = _compile_with_anchors
+ _orig_main()
+ mod.compile_constraints = _orig_compile # restore
+
+ mod.main = _patched_main
+ return mod
+
+
+def main():
+ """Run the patched v4 pipeline with Board marker anchoring."""
+ import sys
+ mod = _monkey_patch_v4()
+ sys.argv[0] = "3_multiview_bundle_adjustment_v4.py" # cosmetic
+ mod.main()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/pipeline/4_v8_4a_base_slider.py b/pipeline/4_v8_4a_base_slider.py
new file mode 100644
index 0000000..7720f74
--- /dev/null
+++ b/pipeline/4_v8_4a_base_slider.py
@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+"""
+4a_base_slider.py
+-----------------
+Estimate the Base linear-slider position (variable 'x', in mm) from
+observed world-space marker positions produced by step 3.
+
+Mathematical principle
+----------------------
+The Base slides along the world x-axis.
+Every joint between Base and the observable arm markers rotates around
+the x-axis (axis ≈ [-1,0,0] for Arm1, Ellbow, Arm2).
+
+Rotation around the x-axis NEVER changes the x-coordinate of any point.
+Therefore, for every marker on Arm1, Ellbow, or Arm2:
+
+ world_x = x_slider + link_frame_x_at_zero + local_x
+
+where link_frame_x_at_zero is the x of the link-frame origin in world
+when all joint variables are zero (constant, readable from robot.json).
+
+Rearranging:
+ x_slider = world_x - link_frame_x_at_zero - local_x
+
+This holds for ANY rotation angle y, z, a of the arm links.
+
+This gives one independent estimate per observed marker.
+We report their mean (and statistics) as the final estimate.
+
+Usage
+-----
+ python 4a_base_slider.py --robot robot.json --aruco aruco_positions_optimized.json
+
+ python 4a_base_slider.py --robot robot.json --aruco aruco_positions_optimized.json \\
+ --links Arm1 Ellbow Arm2 --output 4a_result.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import numpy as np
+from robot_fk import RobotFK
+
+
+# ──────────────────────────────────────────────────────────────
+# I/O helpers
+# ──────────────────────────────────────────────────────────────
+
+def _load_json(path: Path) -> dict:
+ with path.open("r", encoding="utf-8") as f:
+ return json.load(f)
+
+
+def _load_observed_mm(aruco_json: dict) -> Dict[int, np.ndarray]:
+ """marker_id → world position in mm, from aruco_positions_optimized.json."""
+ result: Dict[int, np.ndarray] = {}
+ for m in aruco_json.get("markers", []):
+ mid = int(m.get("marker_id", m.get("id", -1)))
+ if mid < 0:
+ continue
+ if "position_mm" in m:
+ result[mid] = np.array(m["position_mm"], dtype=float)
+ elif "position_m" in m:
+ result[mid] = np.array(m["position_m"], dtype=float) * 1000.0
+ return result
+
+
+# ──────────────────────────────────────────────────────────────
+# Core estimator
+# ──────────────────────────────────────────────────────────────
+
+def estimate_base_slider(
+ fk: RobotFK,
+ observed_mm: Dict[int, np.ndarray],
+ use_links: Optional[List[str]] = None,
+ verbose: bool = True,
+) -> dict:
+ """
+ Parameters
+ ----------
+ fk : RobotFK instance
+ observed_mm : dict marker_id -> world position [mm]
+ use_links : links whose markers are used (default: Arm1, Ellbow, Arm2)
+ All must be connected to Base via only x-axis rotations.
+ verbose : print breakdown
+
+ Returns
+ -------
+ dict with keys:
+ status, x_mean_mm, x_median_mm, x_std_mm,
+ num_markers, per_marker
+ """
+ if use_links is None:
+ use_links = ["Arm1", "Ellbow", "Arm2"]
+
+ # Pre-compute link_frame_x at zero-state for each target link
+ link_x0: Dict[str, float] = {}
+ for lname in use_links:
+ if lname in fk.links:
+ link_x0[lname] = fk.link_x_at_zero_state(lname)
+
+ estimates: List[float] = []
+ per_marker: List[dict] = []
+
+ for lname in use_links:
+ if lname not in fk.links or lname not in link_x0:
+ continue
+ x0 = link_x0[lname]
+
+ for m in fk.links[lname].get("markers", []):
+ mid = int(m.get("id", -1))
+ if mid < 0 or "position" not in m:
+ continue
+ if mid not in observed_mm:
+ continue # marker not seen in this pose
+
+ local_x = float(m["position"][0])
+ world_x = float(observed_mm[mid][0])
+ x_est = world_x - x0 - local_x
+
+ estimates.append(x_est)
+ per_marker.append({
+ "marker_id": mid,
+ "link": lname,
+ "local_x_mm": local_x,
+ "link_x0_mm": x0,
+ "world_x_mm": world_x,
+ "x_estimate_mm": x_est,
+ })
+
+ if not estimates:
+ msg = (f"No observed markers found on links {use_links}. "
+ "Check that aruco_positions_optimized.json contains "
+ "markers from those links.")
+ if verbose:
+ print(f"[4a FAILED] {msg}")
+ return {"status": "failed", "reason": msg, "use_links": use_links}
+
+ arr = np.array(estimates)
+ x_mean = float(np.mean(arr))
+ x_median = float(np.median(arr))
+ x_std = float(np.std(arr, ddof=0))
+
+ if verbose:
+ print("\n── 4a: Base slider (x) ──────────────────────────────────")
+ print(f" Links used: {use_links}")
+ print(f" Markers used: {len(estimates)}")
+ print(f" x = {x_mean:+.2f} mm (median {x_median:+.2f}, σ {x_std:.2f})")
+ if x_std > 5.0:
+ print(f" [WARN] high spread ({x_std:.1f} mm) – step-3 errors or wrong link list")
+ print(f"\n Per-marker:")
+ print(f" {'ID':>5} {'Link':8} {'local_x':>8} {'world_x':>8} {'x_est':>8}")
+ print(f" " + "-"*46)
+ for pm in per_marker:
+ print(f" {pm['marker_id']:>5} {pm['link']:8} "
+ f"{pm['local_x_mm']:>8.2f} {pm['world_x_mm']:>8.2f} {pm['x_estimate_mm']:>8.2f}")
+
+ return {
+ "status": "ok",
+ "joint": "x",
+ "description": "Base linear slider",
+ "x_mean_mm": x_mean,
+ "x_median_mm": x_median,
+ "x_std_mm": x_std,
+ "num_markers": len(estimates),
+ "use_links": use_links,
+ "per_marker": per_marker,
+ }
+
+
+# ──────────────────────────────────────────────────────────────
+# CLI
+# ──────────────────────────────────────────────────────────────
+
+def main() -> int:
+ p = argparse.ArgumentParser(description="4a: estimate Base slider (x)")
+ p.add_argument("--robot", type=Path, default=Path("robot.json"))
+ p.add_argument("--aruco", type=Path, default=Path("aruco_positions_optimized.json"))
+ p.add_argument("--links", nargs="+", default=["Arm1", "Ellbow", "Arm2"],
+ dest="links",
+ help="Links whose markers to use (must be x-axis-rotation chain)")
+ p.add_argument("--output", type=Path, default=None,
+ help="Optional JSON output path (e.g. 4a_result.json)")
+ args = p.parse_args()
+
+ fk = RobotFK.from_file(args.robot)
+ aruco_json = _load_json(args.aruco)
+ observed_mm = _load_observed_mm(aruco_json)
+
+ result = estimate_base_slider(fk, observed_mm, use_links=args.links)
+
+ if args.output and result["status"] == "ok":
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ with args.output.open("w", encoding="utf-8") as f:
+ json.dump(result, f, indent=2)
+ print(f"\n Saved → {args.output}")
+
+ return 0 if result["status"] == "ok" else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/pipeline/4_v8_4b_revolute_angle.py b/pipeline/4_v8_4b_revolute_angle.py
new file mode 100644
index 0000000..18fb439
--- /dev/null
+++ b/pipeline/4_v8_4b_revolute_angle.py
@@ -0,0 +1,360 @@
+#!/usr/bin/env python3
+"""
+4b_revolute_angle.py
+--------------------
+Generic revolute-joint angle estimator.
+
+For each movable link (Arm1, Ellbow, Arm2 …) whose joint type is 'revolute',
+this script estimates the rotation angle using the pairwise-vector method:
+
+ For every PAIR (m1, m2) of markers belonging to the target link:
+ v_model = local_pos_m2 - local_pos_m1 (in link's own frame)
+ v_obs = world_pos_m2 - world_pos_m1 (in world frame)
+
+ Both vectors are projected perpendicular to the joint axis (in world frame),
+ and the signed angle from v_model_perp to v_obs_perp is measured.
+
+ The joint axis in world frame is computed via FK using the already-known
+ joint values (from 4a, 4b-prev …), so it is ALWAYS correct — even for
+ deeply-nested joints whose world-frame axis differs from their local axis.
+
+ Pair weights = baseline_model × baseline_obs (longer baselines → more reliable).
+
+How to use sequentially
+-----------------------
+Run 4b once per revolute joint, from root to tip:
+
+ python 4b_revolute_angle.py --robot r.json --aruco obs.json --link Arm1 --x-mm 180
+ python 4b_revolute_angle.py --robot r.json --aruco obs.json --link Ellbow --from-state state.json
+ python 4b_revolute_angle.py --robot r.json --aruco obs.json --link Arm2 --from-state state.json
+
+The --from-state flag reads the accumulated joint state JSON so you don't have
+to pass every preceding value on the command line.
+
+Output JSON
+-----------
+{
+ "link": "Arm1",
+ "joint": "y",
+ "mean_angle_deg": 86.3,
+ "circular_std_deg": 0.7,
+ "num_pairs": 6,
+ "joint_origin_world_mm": [290, 108, 61],
+ "joint_axis_world": [-1, 0, 0],
+ ...
+}
+
+The file also contains the full accumulated state so the next 4b invocation
+can read it via --from-state.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from itertools import combinations
+from pathlib import Path
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import numpy as np
+from robot_fk import RobotFK
+
+STATE_KEYS = ("x", "y", "z", "a", "b", "c", "e")
+
+
+# ──────────────────────────────────────────────────────────────
+# I/O
+# ──────────────────────────────────────────────────────────────
+
+def _load_json(path: Path) -> dict:
+ with path.open("r", encoding="utf-8") as f:
+ return json.load(f)
+
+
+def _load_observed_mm(aruco_json: dict) -> Dict[int, np.ndarray]:
+ result: Dict[int, np.ndarray] = {}
+ for m in aruco_json.get("markers", []):
+ mid = int(m.get("marker_id", m.get("id", -1)))
+ if mid < 0:
+ continue
+ if "position_mm" in m:
+ result[mid] = np.array(m["position_mm"], dtype=float)
+ elif "position_m" in m:
+ result[mid] = np.array(m["position_m"], dtype=float) * 1000.0
+ return result
+
+
+def _load_state(path: Path) -> Dict[str, float]:
+ """Load accumulated joint state from a previous 4b output JSON."""
+ raw = _load_json(path)
+ state = raw.get("accumulated_state", raw.get("state", {}))
+ return {k: float(v) for k, v in state.items() if k in STATE_KEYS}
+
+
+# ──────────────────────────────────────────────────────────────
+# Angle maths
+# ──────────────────────────────────────────────────────────────
+
+def _project_perp(v: np.ndarray, axis: np.ndarray) -> np.ndarray:
+ """Remove the component of v along axis."""
+ a = axis / (np.linalg.norm(axis) + 1e-15)
+ return v - np.dot(v, a) * a
+
+
+def _signed_angle_rad(v_from: np.ndarray, v_to: np.ndarray,
+ axis: np.ndarray) -> float:
+ """Signed angle rotating v_from onto v_to around axis (radians)."""
+ a = axis / (np.linalg.norm(axis) + 1e-15)
+ return math.atan2(float(np.dot(a, np.cross(v_from, v_to))),
+ float(np.dot(v_from, v_to)))
+
+
+def _wrap(angle: float) -> float:
+ """Wrap to (−π, π]."""
+ return (angle + math.pi) % (2.0 * math.pi) - math.pi
+
+
+def _circular_mean_deg(angles_rad: np.ndarray,
+ weights: np.ndarray) -> Tuple[float, float, float]:
+ """Returns mean_deg, circular_variance, circular_std_deg."""
+ w = np.clip(weights, 0, None)
+ if w.sum() < 1e-15:
+ w = np.ones_like(w)
+ s, c = np.sum(w * np.sin(angles_rad)), np.sum(w * np.cos(angles_rad))
+ mean = math.atan2(s, c)
+ R = math.sqrt(s*s + c*c) / w.sum()
+ R = float(np.clip(R, 0, 1))
+ c_var = 1.0 - R
+ c_std = math.sqrt(max(0.0, -2.0 * math.log(max(R, 1e-15))))
+ return math.degrees(mean), c_var, math.degrees(c_std)
+
+
+# ──────────────────────────────────────────────────────────────
+# Core estimator (generic — works for any revolute joint)
+# ──────────────────────────────────────────────────────────────
+
+def estimate_revolute_angle(
+ fk: RobotFK,
+ observed_mm: Dict[int, np.ndarray],
+ link_name: str,
+ known_state: Dict[str, float],
+ min_baseline_mm: float = 15.0,
+ verbose: bool = True,
+) -> dict:
+ """
+ Estimate the revolute joint angle for `link_name`.
+
+ Parameters
+ ----------
+ fk : RobotFK
+ observed_mm : world marker positions from step 3
+ link_name : e.g. "Arm1", "Ellbow", "Arm2"
+ known_state : already-estimated joint values (e.g. {"x": 180.0, "y": 86.0})
+ The target joint variable should NOT be in this dict.
+ min_baseline_mm : skip pairs with model or observed baseline shorter than this
+ verbose : print report
+
+ Returns
+ -------
+ dict with estimation results + updated accumulated_state
+ """
+
+ # ── sanity checks ─────────────────────────────────────────
+ links = fk.links
+ if link_name not in links:
+ return {"status": "failed",
+ "reason": f"Link '{link_name}' not in robot.json"}
+
+ ji = links[link_name].get("jointToParent", {}) or {}
+ jtype = str(ji.get("type", "")).lower()
+ if jtype != "revolute":
+ return {"status": "failed",
+ "reason": f"Joint of '{link_name}' is not revolute (type='{jtype}')"}
+
+ var = str(ji.get("variable", "")).lower()
+
+ # ── FK: joint origin and axis in world ───────────────────
+ # Use known_state with the TARGET joint at 0
+ zero_state = dict(known_state)
+ zero_state[var] = 0.0
+
+ origin_world = fk.joint_origin_world(link_name, zero_state)
+ axis_world = fk.joint_axis_world(link_name, zero_state)
+
+ # ── collect matched markers ───────────────────────────────
+ model_local: Dict[int, np.ndarray] = {}
+ for m in links[link_name].get("markers", []):
+ mid = int(m.get("id", -1))
+ if mid >= 0 and "position" in m:
+ model_local[mid] = np.array(m["position"], dtype=float)
+
+ matched = {mid: (model_local[mid], observed_mm[mid])
+ for mid in model_local if mid in observed_mm}
+
+ if len(matched) < 2:
+ return {
+ "status": "failed",
+ "reason": (f"Need ≥2 matched markers, found {len(matched)}: "
+ f"{list(matched.keys())}. "
+ f"Model marker IDs: {list(model_local.keys())}"),
+ }
+
+ # ── pairwise estimation ───────────────────────────────────
+ ids = sorted(matched.keys())
+ angle_rad_list: List[float] = []
+ weight_list: List[float] = []
+ per_pair: List[dict] = []
+
+ for id1, id2 in combinations(ids, 2):
+ l1, o1 = matched[id1]
+ l2, o2 = matched[id2]
+
+ v_model = l2 - l1 # local frame, both in same link
+ v_obs = o2 - o1 # world frame
+
+ v_model_perp = _project_perp(v_model, axis_world)
+ v_obs_perp = _project_perp(v_obs, axis_world)
+
+ bl_model = float(np.linalg.norm(v_model_perp))
+ bl_obs = float(np.linalg.norm(v_obs_perp))
+
+ if bl_model < min_baseline_mm or bl_obs < min_baseline_mm:
+ per_pair.append({
+ "marker_ids": [id1, id2],
+ "skipped": True,
+ "reason": f"bl_model={bl_model:.1f} bl_obs={bl_obs:.1f} < {min_baseline_mm}",
+ })
+ continue
+
+ angle = _wrap(_signed_angle_rad(v_model_perp, v_obs_perp, axis_world))
+ w = bl_model * bl_obs
+
+ angle_rad_list.append(angle)
+ weight_list.append(w)
+ per_pair.append({
+ "marker_ids": [id1, id2],
+ "skipped": False,
+ "angle_deg": math.degrees(angle),
+ "baseline_model_mm": bl_model,
+ "baseline_obs_mm": bl_obs,
+ "weight": w,
+ })
+
+ if not angle_rad_list:
+ return {
+ "status": "failed",
+ "reason": "All pairs below min_baseline_mm. "
+ "Try --min-baseline 5 or check step-3 output.",
+ }
+
+ mean_deg, c_var, c_std_deg = _circular_mean_deg(
+ np.array(angle_rad_list), np.array(weight_list)
+ )
+
+ # ── verbose report ────────────────────────────────────────
+ if verbose:
+ print(f"\n── 4b: '{link_name}' angle ({var}) ──────────────────────")
+ print(f" Joint origin (world): [{', '.join(f'{v:.1f}' for v in origin_world)}] mm")
+ print(f" Joint axis (world): [{', '.join(f'{v:.3f}' for v in axis_world)}]")
+ print(f" Matched markers: {list(matched.keys())}")
+ print(f" Pairs used: {len(angle_rad_list)} / {len(list(combinations(ids, 2)))}")
+ print(f" Angle: {mean_deg:+.2f} ° circular_σ {c_std_deg:.2f} °")
+ if c_std_deg > 5.0:
+ print(f" [WARN] high spread – step-3 errors or marker overlap")
+ print(f"\n Pair detail:")
+ for pp in per_pair:
+ if pp["skipped"]:
+ print(f" M{pp['marker_ids'][0]}↔M{pp['marker_ids'][1]}: SKIPPED – {pp['reason']}")
+ else:
+ print(f" M{pp['marker_ids'][0]}↔M{pp['marker_ids'][1]}: "
+ f"{pp['angle_deg']:+7.2f}° "
+ f"bl_model={pp['baseline_model_mm']:.1f} "
+ f"bl_obs={pp['baseline_obs_mm']:.1f}")
+
+ # ── build accumulated state ───────────────────────────────
+ accumulated = dict(known_state)
+ accumulated[var] = mean_deg
+
+ return {
+ "status": "ok",
+ "link": link_name,
+ "joint": var,
+ "joint_origin_world_mm": origin_world.tolist(),
+ "joint_axis_world": axis_world.tolist(),
+ "mean_angle_deg": mean_deg,
+ "circular_variance": c_var,
+ "circular_std_deg": c_std_deg,
+ "num_pairs_used": len(angle_rad_list),
+ "num_markers_matched": len(matched),
+ "per_pair": per_pair,
+ "accumulated_state": accumulated,
+ }
+
+
+# ──────────────────────────────────────────────────────────────
+# CLI
+# ──────────────────────────────────────────────────────────────
+
+def main() -> int:
+ p = argparse.ArgumentParser(
+ description="4b: estimate revolute joint angle for any link")
+ p.add_argument("--robot", type=Path, default=Path("robot.json"))
+ p.add_argument("--aruco", type=Path,
+ default=Path("aruco_positions_optimized.json"))
+ p.add_argument("--link", type=str, required=True,
+ help="Link name, e.g. Arm1, Ellbow, Arm2")
+ p.add_argument("--from-state", type=Path, default=None,
+ help="JSON from a previous 4b run (carries accumulated state)")
+
+ # Manual joint-value overrides (for use without --from-state)
+ for k in STATE_KEYS:
+ p.add_argument(f"--{k}-mm" if k in ("x", "e") else f"--{k}-deg",
+ type=float, default=None,
+ help=f"Known value for joint '{k}'"
+ + (" (mm)" if k in ("x", "e") else " (deg)"))
+
+ p.add_argument("--min-baseline", type=float, default=15.0,
+ help="Skip pairs with perpendicular baseline < this (mm)")
+ p.add_argument("--output", type=Path, default=None,
+ help="Save result JSON (readable by next 4b as --from-state)")
+ args = p.parse_args()
+
+ # Assemble known state
+ if args.from_state:
+ known_state = _load_state(args.from_state)
+ print(f" Loaded state from {args.from_state}: {known_state}")
+ else:
+ known_state = {}
+
+ # CLI overrides
+ for k in STATE_KEYS:
+ attr = f"{k}_mm" if k in ("x", "e") else f"{k}_deg"
+ v = getattr(args, attr, None)
+ if v is not None:
+ known_state[k] = float(v)
+
+ fk = RobotFK.from_file(args.robot)
+ aruco_json = _load_json(args.aruco)
+ observed_mm = _load_observed_mm(aruco_json)
+
+ result = estimate_revolute_angle(
+ fk = fk,
+ observed_mm = observed_mm,
+ link_name = args.link,
+ known_state = known_state,
+ min_baseline_mm = args.min_baseline,
+ verbose = True,
+ )
+
+ if args.output and result["status"] == "ok":
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ with args.output.open("w", encoding="utf-8") as f:
+ json.dump(result, f, indent=2)
+ print(f"\n Saved → {args.output}")
+
+ return 0 if result["status"] == "ok" else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/pipeline/4_v8_4d_direct_hand_pose.py b/pipeline/4_v8_4d_direct_hand_pose.py
new file mode 100644
index 0000000..81e436d
--- /dev/null
+++ b/pipeline/4_v8_4d_direct_hand_pose.py
@@ -0,0 +1,464 @@
+#!/usr/bin/env python3
+"""
+4d_direct_hand_pose.py
+----------------------
+Estimate Hand/Palm/Finger joint angles (b, c, e) directly from ArUco
+marker observations — WITHOUT relying on step-3 triangulated positions.
+
+Why this is needed
+------------------
+Step 3 triangulates marker centres from multiple cameras. For markers
+far above the board (arm extended in Z), the depth estimation from
+triangulation degrades: all reference Board markers lie in a flat plane,
+so the cameras are poorly conditioned for Z estimation of out-of-plane points.
+
+This script takes a completely different route:
+
+ 1. For each camera that sees a finger or hand marker, run cv2.solvePnP
+ on the four detected corners. solvePnP uses the apparent size /
+ aspect ratio of the marker to recover depth — it is NOT affected
+ by the flat-board reference plane problem.
+
+ 2. Use the known camera-in-world pose (from step 2) to convert the
+ per-camera marker pose to world frame.
+
+ 3. Fuse all world-frame marker poses with quality weighting.
+
+ 4. Given world-frame positions for all visible finger markers, and the
+ known local positions in the FingerA/FingerB frames, solve a small
+ nonlinear LS problem for (b, c, e) using scipy.
+
+What you need
+-------------
+ - robot.json
+ - Step-2 camera-pose JSONs for all cameras
+ - Step-1 aruco-detection JSONs for all cameras
+ - Already-estimated x, y, z, a from 4a/4b (pass via --state-json or CLI)
+
+Usage
+-----
+ python 4d_direct_hand_pose.py \\
+ --robot robot.json \\
+ --det cam1_aruco_detection.json cam2_aruco_detection.json ... \\
+ --pose cam1_camera_pose.json cam2_camera_pose.json ... \\
+ --state 4b_arm2_result.json \\
+ --output 4d_result.json
+
+Output
+------
+ {
+ "b_deg": ..., "c_deg": ..., "e_mm": ...,
+ "rms_mm": ...,
+ "accumulated_state": {"x":..., "y":..., "z":..., "a":..., "b":..., "c":..., "e":...}
+ }
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Sequence, Tuple
+
+import cv2
+import numpy as np
+
+try:
+ from scipy.optimize import least_squares
+ _HAS_SCIPY = True
+except ImportError:
+ _HAS_SCIPY = False
+
+from robot_fk import RobotFK, STATE_KEYS
+
+
+# ──────────────────────────────────────────────────────────────
+# I/O
+# ──────────────────────────────────────────────────────────────
+
+def _load(path: Path) -> dict:
+ with path.open("r", encoding="utf-8") as f:
+ return json.load(f)
+
+
+def _load_state(path: Path) -> Dict[str, float]:
+ raw = _load(path)
+ state = raw.get("accumulated_state", raw.get("state", {}))
+ return {k: float(v) for k, v in state.items() if k in STATE_KEYS}
+
+
+# ──────────────────────────────────────────────────────────────
+# ArUco / PnP helpers
+# ──────────────────────────────────────────────────────────────
+
+def _marker_local_corners(size_m: float) -> np.ndarray:
+ """Four local corners of a marker (same order as OpenCV ArUco)."""
+ h = size_m / 2.0
+ return np.array([[-h, h, 0], [h, h, 0], [h, -h, 0], [-h, -h, 0]], dtype=np.float32)
+
+
+def _camera_pose_from_json(pose_json: dict) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ Load world-to-camera rotation and translation from step-2 JSON.
+ Returns rvec (3,), tvec (3,) — the same convention used in step 2.
+ """
+ wtc = pose_json["camera_pose"]["world_to_camera"]
+ R = np.array(wtc["rotation_matrix"], dtype=np.float32).reshape(3, 3)
+ t = np.array(wtc["translation_m"], dtype=np.float32).reshape(3)
+ rvec, _ = cv2.Rodrigues(R)
+ return rvec.reshape(3), t
+
+
+def _intrinsics_from_detection(det: dict) -> Tuple[np.ndarray, np.ndarray]:
+ cam = det["camera"]
+ K = np.array(cam["camera_matrix"], dtype=np.float32).reshape(3, 3)
+ D = np.array(cam["distortion_coefficients"], dtype=np.float32).reshape(-1, 1)
+ return K, D
+
+
+def _solve_marker_pose_in_camera(
+ corners_px: np.ndarray,
+ marker_size_m: float,
+ K: np.ndarray,
+ D: np.ndarray
+) -> Optional[Tuple[np.ndarray, np.ndarray]]:
+ """
+ Estimate marker-in-camera pose via solvePnP (IPPE_SQUARE).
+ Returns (rvec_cm, tvec_cm) or None on failure.
+ """
+ obj = _marker_local_corners(marker_size_m)
+ img = corners_px.astype(np.float32)
+ ok, rvec, tvec = cv2.solvePnP(obj, img, K, D, flags=cv2.SOLVEPNP_IPPE_SQUARE)
+ if not ok:
+ ok, rvec, tvec = cv2.solvePnP(obj, img, K, D, flags=cv2.SOLVEPNP_ITERATIVE)
+ if not ok:
+ return None
+ return rvec.reshape(3), tvec.reshape(3)
+
+
+def _marker_world_pose(
+ rvec_cm: np.ndarray,
+ tvec_cm: np.ndarray,
+ rvec_wc: np.ndarray,
+ tvec_wc: np.ndarray
+) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ Convert marker-in-camera pose to marker-in-world pose.
+
+ Convention (same as step 2):
+ X_cam = R_wc @ X_world + t_wc
+ So:
+ R_wc = rvec→R(rvec_wc), camera origin in world = -R_wc^T @ t_wc
+
+ Marker pose in world:
+ R_mw = R_wc^T @ R_cm (world_to_marker rotation)
+ t_mw = R_wc^T @ (tvec_cm - tvec_wc)
+ → marker centre in world = -R_mw^T @ t_mw = R_mw^T @ (-t_mw)
+
+ We return (pos_world_m, R_marker_in_world) where
+ R_marker_in_world maps local marker axes to world axes.
+ """
+ R_wc, _ = cv2.Rodrigues(rvec_wc.reshape(3, 1))
+ R_cm, _ = cv2.Rodrigues(rvec_cm.reshape(3, 1))
+
+ R_cw = R_wc.T # camera-to-world rotation
+
+ # Marker centre in world frame
+ pos_w = R_cw @ tvec_cm.reshape(3) - R_cw @ tvec_wc.reshape(3)
+
+ # Marker orientation in world frame (columns = marker x,y,z axes in world)
+ R_mw = R_cw @ R_cm
+
+ return pos_w.astype(np.float64), R_mw.astype(np.float64)
+
+
+# ──────────────────────────────────────────────────────────────
+# Multi-camera marker pose fusion
+# ──────────────────────────────────────────────────────────────
+
+def _observation_weight(corners_px: np.ndarray, img_w: int, img_h: int) -> float:
+ """
+ Simple quality weight: larger markers, more central = higher weight.
+ """
+ area = float(cv2.contourArea(corners_px.astype(np.float32)))
+ cx, cy = corners_px.mean(axis=0)
+ dx = abs(cx - img_w / 2.0) / (img_w / 2.0)
+ dy = abs(cy - img_h / 2.0) / (img_h / 2.0)
+ centrality = 1.0 - 0.5 * (dx + dy)
+ return max(0.05, (area / 500.0) * centrality)
+
+
+def collect_marker_world_poses(
+ detection_files: List[Path],
+ pose_files: List[Path],
+ target_marker_ids: set,
+ marker_size_m: float,
+ verbose: bool = True,
+) -> Dict[int, List[Dict[str, Any]]]:
+ """
+ For each target marker, collect world-frame pose estimates from all cameras.
+
+ Returns: marker_id → list of {pos_m, R, weight, camera_id}
+ """
+ result: Dict[int, List[Dict[str, Any]]] = {}
+
+ for det_path, pose_path in zip(detection_files, pose_files):
+ det = _load(det_path)
+ pose_json = _load(pose_path)
+
+ K, D = _intrinsics_from_detection(det)
+ rvec_wc, tvec_wc = _camera_pose_from_json(pose_json)
+
+ cam_id = det.get("camera", {}).get("camera_id", str(det_path))
+ img_w = int(det.get("image", {}).get("width_px", 1280))
+ img_h = int(det.get("image", {}).get("height_px", 720))
+
+ for d in det.get("detections", []):
+ mid = int(d.get("marker_id", -1))
+ if mid not in target_marker_ids:
+ continue
+ corners = np.array(d.get("image_points_px", []), dtype=np.float32).reshape(4, 2)
+ if corners.shape != (4, 2):
+ continue
+
+ pnp = _solve_marker_pose_in_camera(corners, marker_size_m, K, D)
+ if pnp is None:
+ continue
+ rvec_cm, tvec_cm = pnp
+
+ pos_w, R_w = _marker_world_pose(rvec_cm, tvec_cm, rvec_wc, tvec_wc)
+ w = _observation_weight(corners, img_w, img_h)
+
+ result.setdefault(mid, []).append({
+ "pos_m": pos_w,
+ "R": R_w,
+ "weight": w,
+ "camera_id": cam_id,
+ })
+
+ if verbose:
+ for mid, obs in result.items():
+ print(f" M{mid}: {len(obs)} camera(s) — weights "
+ f"{[f'{o[\"weight\"]:.2f}' for o in obs]}")
+
+ return result
+
+
+def fuse_marker_positions(
+ observations: Dict[int, List[Dict[str, Any]]]
+) -> Dict[int, np.ndarray]:
+ """
+ Weighted-mean fusion of world-frame marker centre positions (metres).
+ """
+ result: Dict[int, np.ndarray] = {}
+ for mid, obs_list in observations.items():
+ total_w = sum(o["weight"] for o in obs_list)
+ if total_w < 1e-12:
+ continue
+ pos = sum(o["pos_m"] * o["weight"] for o in obs_list) / total_w
+ result[mid] = pos.astype(np.float64)
+ return result
+
+
+# ──────────────────────────────────────────────────────────────
+# FK-based optimizer for b, c, e
+# ──────────────────────────────────────────────────────────────
+
+def _fk_finger_positions(
+ fk: RobotFK,
+ base_state: Dict[str, float],
+ b: float,
+ c: float,
+ e: float
+) -> Dict[int, np.ndarray]:
+ """
+ Compute world positions (m) of all FingerA and FingerB markers.
+ base_state already contains x, y, z, a.
+ """
+ state = dict(base_state)
+ state.update({"b": b, "c": c, "e": e})
+ transforms = fk.compute(state)
+ result = {}
+ for link_name in ("FingerA", "FingerB", "Hand", "Palm"):
+ if link_name not in fk.links:
+ continue
+ for m in fk.links[link_name].get("markers", []):
+ mid = int(m.get("id", -1))
+ if mid < 0 or "position" not in m:
+ continue
+ # position in METRES
+ local_mm = np.array(m["position"], dtype=float)
+ world_m = fk.marker_world(transforms, link_name, local_mm) / 1000.0
+ result[mid] = world_m
+ return result
+
+
+def optimize_bce(
+ fk: RobotFK,
+ base_state: Dict[str, float],
+ observed_m: Dict[int, np.ndarray],
+ b_init: float = 0.0,
+ c_init: float = 0.0,
+ e_init: float = 0.0,
+ verbose: bool = True,
+) -> dict:
+ """
+ Find (b, c, e) minimising reprojection of FK finger-marker positions
+ to observed world positions.
+
+ Parameters
+ ----------
+ fk : RobotFK
+ base_state : already-estimated {x, y, z, a} (all in mm/deg)
+ observed_m : {marker_id: world_position_m}
+ """
+
+ if not _HAS_SCIPY:
+ return {"status": "failed", "reason": "scipy not available (pip install scipy)"}
+
+ def residuals(p):
+ b, c, e = float(p[0]), float(p[1]), float(p[2])
+ fk_pos = _fk_finger_positions(fk, base_state, b, c, e)
+ r = []
+ for mid, obs_p in observed_m.items():
+ if mid in fk_pos:
+ diff = fk_pos[mid] - obs_p
+ r.extend(diff.tolist())
+ return r
+
+ # Initial residual
+ r0 = residuals([b_init, c_init, e_init])
+ if len(r0) < 3:
+ return {
+ "status": "failed",
+ "reason": f"Only {len(r0)//3} finger markers matched. Need ≥1.",
+ }
+
+ res = least_squares(
+ residuals,
+ x0=[b_init, c_init, e_init],
+ method="lm",
+ max_nfev=2000,
+ )
+
+ b_est, c_est, e_est = float(res.x[0]), float(res.x[1]), float(res.x[2])
+
+ rms_m = float(np.sqrt(np.mean(np.array(res.fun) ** 2)))
+ rms_mm = rms_m * 1000.0
+
+ accum = dict(base_state)
+ accum.update({"b": b_est, "c": c_est, "e": e_est})
+
+ if verbose:
+ print(f"\n── 4d: Hand/Palm/Finger (b, c, e) ──────────────────────")
+ print(f" Markers used: {len(observed_m)} fused positions")
+ print(f" b = {b_est:+.2f} ° c = {c_est:+.2f} ° e = {e_est:+.2f} mm")
+ print(f" RMS position error: {rms_mm:.2f} mm")
+ if not res.success:
+ print(f" [WARN] Optimizer: {res.message}")
+
+ return {
+ "status": "ok",
+ "b_deg": b_est,
+ "c_deg": c_est,
+ "e_mm": e_est,
+ "rms_mm": rms_mm,
+ "optimizer_success": bool(res.success),
+ "num_markers": len(observed_m),
+ "accumulated_state": accum,
+ }
+
+
+# ──────────────────────────────────────────────────────────────
+# Main
+# ──────────────────────────────────────────────────────────────
+
+def main() -> int:
+ p = argparse.ArgumentParser(
+ description="4d: Direct hand/finger pose from per-camera solvePnP")
+ p.add_argument("--robot", type=Path, required=True)
+ p.add_argument("--det", "-d", type=Path, nargs="+", required=True,
+ help="*_aruco_detection.json files (one per camera)")
+ p.add_argument("--pose", "-p", type=Path, nargs="+", required=True,
+ help="*_camera_pose.json files (same order as --det)")
+ p.add_argument("--state", type=Path, default=None,
+ help="JSON from previous 4b step (accumulated_state)")
+ for k in ("x", "y", "z", "a"):
+ p.add_argument(f"--{k}", type=float, default=None,
+ help=f"Known {k} value (overrides --state)")
+ p.add_argument("--b-init", type=float, default=0.0)
+ p.add_argument("--c-init", type=float, default=0.0)
+ p.add_argument("--e-init", type=float, default=0.0)
+ p.add_argument("--output", type=Path, default=None)
+ args = p.parse_args()
+
+ if len(args.det) != len(args.pose):
+ print("[ERROR] --det and --pose must have the same number of files")
+ return 1
+
+ # Base state from file or CLI
+ base_state: Dict[str, float] = {}
+ if args.state:
+ base_state = _load_state(args.state)
+ print(f" Loaded state: {base_state}")
+ for k in ("x", "y", "z", "a"):
+ v = getattr(args, k, None)
+ if v is not None:
+ base_state[k] = float(v)
+
+ # Load FK
+ fk = RobotFK.from_file(args.robot)
+
+ # Which markers belong to Hand/Palm/FingerA/FingerB?
+ target_ids: set = set()
+ for link_name in ("Hand", "Palm", "FingerA", "FingerB"):
+ for m in fk.links.get(link_name, {}).get("markers", []):
+ mid = int(m.get("id", -1))
+ if mid >= 0:
+ target_ids.add(mid)
+
+ marker_size_m = float(
+ fk.robot.get("vision_config", {}).get("MarkerSize", 0.025)
+ )
+
+ print(f"\n Target marker IDs: {sorted(target_ids)}")
+ print(f" Marker size: {marker_size_m*1000:.1f} mm")
+ print(f" Cameras: {len(args.det)}")
+
+ # Collect world-frame poses from all cameras via solvePnP
+ print("\n Per-camera solvePnP:")
+ world_pose_obs = collect_marker_world_poses(
+ args.det, args.pose, target_ids, marker_size_m, verbose=True
+ )
+
+ if not world_pose_obs:
+ print("[ERROR] No target markers found in any detection file.")
+ return 1
+
+ # Fuse positions
+ observed_m = fuse_marker_positions(world_pose_obs)
+ print(f"\n Fused {len(observed_m)} marker world positions")
+
+ # Optimise b, c, e
+ result = optimize_bce(
+ fk = fk,
+ base_state = base_state,
+ observed_m = observed_m,
+ b_init = args.b_init,
+ c_init = args.c_init,
+ e_init = args.e_init,
+ verbose = True,
+ )
+
+ if args.output and result["status"] == "ok":
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ with args.output.open("w", encoding="utf-8") as f:
+ json.dump(result, f, indent=2)
+ print(f"\n Saved → {args.output}")
+
+ return 0 if result["status"] == "ok" else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/pipeline/__pycache__/3_multiview_bundle_adjustment_v4.cpython-311.pyc b/pipeline/__pycache__/3_multiview_bundle_adjustment_v4.cpython-311.pyc
new file mode 100644
index 0000000..7d7478a
Binary files /dev/null and b/pipeline/__pycache__/3_multiview_bundle_adjustment_v4.cpython-311.pyc differ
diff --git a/pipeline/__pycache__/robot_fk.cpython-311.pyc b/pipeline/__pycache__/robot_fk.cpython-311.pyc
new file mode 100644
index 0000000..c47a056
Binary files /dev/null and b/pipeline/__pycache__/robot_fk.cpython-311.pyc differ
diff --git a/pipeline/robot_fk.py b/pipeline/robot_fk.py
new file mode 100644
index 0000000..2f9a376
--- /dev/null
+++ b/pipeline/robot_fk.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+"""
+robot_fk.py
+-----------
+Minimal forward kinematics engine for the robot.json format.
+
+Matches the Blender hierarchy used by render_robot.py exactly:
+ world_T_link = world_T_parent
+ @ Translate(mountPosition) @ Rotate_xyz(mountRotation)
+ @ Translate(jointOrigin) @ Rotate_xyz(joint.rotation)
+ @ T_motion
+
+ T_motion = Rotate(axis, value_deg) for revolute joints
+ Translate(axis * value_mm) for linear joints
+
+Units throughout: millimetres, degrees.
+
+Public API
+----------
+fk = RobotFK.from_file("robot.json")
+
+transforms = fk.compute({"x": 180, "y": 86, "z": -120,
+ "a": -60, "b": 22, "c": 91, "e": 10})
+# → dict link_name -> 4×4 np.ndarray (world frame, mm)
+
+p_world = fk.marker_world(transforms, "Arm1", [0, -160, 35])
+# → np.ndarray shape (3,), in mm
+
+all_m = fk.all_markers_world(transforms)
+# → dict marker_id -> {"world_mm", "link", "local_mm"}
+
+# Cumulative x-offset for a link at all-zero state
+# (useful for 4a: x_slider = world_x - local_x - link_x_at_zero)
+x0 = fk.link_x_at_zero_state("Arm1") # → float mm
+"""
+
+from __future__ import annotations
+
+import json
+import math
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Sequence, Tuple
+
+import numpy as np
+
+STATE_KEYS = ("x", "y", "z", "a", "b", "c", "e")
+
+
+# ──────────────────────────────────────────────────────────────
+# Low-level matrix helpers
+# ──────────────────────────────────────────────────────────────
+
+def _rot_axis_angle(axis: Sequence[float], angle_deg: float) -> np.ndarray:
+ """3×3 rotation matrix via Rodrigues (axis need not be normalised)."""
+ ax = np.asarray(axis, dtype=float)
+ n = float(np.linalg.norm(ax))
+ if n < 1e-12:
+ return np.eye(3)
+ ax = ax / n
+ c = math.cos(math.radians(angle_deg))
+ s = math.sin(math.radians(angle_deg))
+ t = 1.0 - c
+ x, y, z = ax
+ return np.array([
+ [t*x*x + c, t*x*y - s*z, t*x*z + s*y],
+ [t*x*y + s*z, t*y*y + c, t*y*z - s*x],
+ [t*x*z - s*y, t*y*z + s*x, t*z*z + c ],
+ ])
+
+
+def _rot_xyz_euler(rx: float, ry: float, rz: float) -> np.ndarray:
+ """XYZ Euler angles (degrees) → 3×3 — matches Blender XYZ Euler mode."""
+ return (_rot_axis_angle([0, 0, 1], rz)
+ @ _rot_axis_angle([0, 1, 0], ry)
+ @ _rot_axis_angle([1, 0, 0], rx))
+
+
+def make_T(R: np.ndarray, t: Sequence[float]) -> np.ndarray:
+ """4×4 homogeneous transform."""
+ T = np.eye(4)
+ T[:3, :3] = R
+ T[:3, 3] = np.asarray(t, dtype=float)
+ return T
+
+
+def transform_point(T: np.ndarray, p: Sequence[float]) -> np.ndarray:
+ """Apply 4×4 transform to a 3-D point."""
+ h = np.array([p[0], p[1], p[2], 1.0])
+ return (T @ h)[:3]
+
+
+# ──────────────────────────────────────────────────────────────
+# FK engine
+# ──────────────────────────────────────────────────────────────
+
+class RobotFK:
+ """Forward kinematics for the robot.json format."""
+
+ def __init__(self, robot_data: Dict[str, Any]):
+ self.robot = robot_data
+ self.links: Dict[str, Any] = robot_data.get("links", {})
+ self._topo: List[str] = self._topological_sort()
+
+ # ── construction ─────────────────────────────────────────
+
+ @classmethod
+ def from_file(cls, path) -> "RobotFK":
+ with open(path, "r", encoding="utf-8") as f:
+ return cls(json.load(f))
+
+ def _topological_sort(self) -> List[str]:
+ parent_map = {n: d.get("parent") for n, d in self.links.items()}
+ visited, order = set(), []
+
+ def visit(name: str) -> None:
+ if name in visited:
+ return
+ visited.add(name)
+ p = parent_map.get(name)
+ if p and p in self.links:
+ visit(p)
+ order.append(name)
+
+ for name in self.links:
+ visit(name)
+ return order
+
+ # ── core computation ──────────────────────────────────────
+
+ def compute(self, joint_values: Dict[str, float]) -> Dict[str, np.ndarray]:
+ """
+ Compute link world transforms for the given joint state.
+
+ Parameters
+ ----------
+ joint_values : dict variable_name -> value
+ Linear joints (x, e): mm
+ Revolute joints (y, z, a, b, c): degrees
+
+ Returns
+ -------
+ dict link_name -> 4×4 np.ndarray (world frame, mm)
+ """
+ state = {k: 0.0 for k in STATE_KEYS}
+ for k, v in joint_values.items():
+ if k in state:
+ state[k] = float(v)
+
+ transforms: Dict[str, np.ndarray] = {}
+
+ for link_name in self._topo:
+ d = self.links[link_name]
+ parent = d.get("parent")
+ T_parent = transforms.get(parent, np.eye(4)) if parent else np.eye(4)
+
+ # 1 · Mount (static in parent frame)
+ mp = d.get("mountPosition", [0, 0, 0])
+ mr = d.get("mountRotation", [0, 0, 0])
+ T_m = make_T(_rot_xyz_euler(*mr), mp)
+
+ # 2 · Joint origin (pivot point in mount frame)
+ ji = d.get("jointToParent", {}) or {}
+ jp = ji.get("origin", [0, 0, 0])
+ jr = ji.get("rotation", [0, 0, 0])
+ T_j = make_T(_rot_xyz_euler(*jr), jp)
+
+ # 3 · Motion
+ jtype = str(ji.get("type", "fixed")).lower()
+ var = str(ji.get("variable", "")).lower()
+ axis = np.asarray(ji.get("axis", [1, 0, 0]), dtype=float)
+ val = state.get(var, 0.0)
+
+ if jtype == "revolute":
+ T_mot = make_T(_rot_axis_angle(axis, val), [0, 0, 0])
+ elif jtype == "linear":
+ n = float(np.linalg.norm(axis))
+ u = axis / n if n > 1e-12 else np.array([1.0, 0, 0])
+ T_mot = make_T(np.eye(3), u * val)
+ else:
+ T_mot = np.eye(4)
+
+ transforms[link_name] = T_parent @ T_m @ T_j @ T_mot
+
+ return transforms
+
+ # ── marker helpers ────────────────────────────────────────
+
+ @staticmethod
+ def marker_world(transforms: Dict[str, np.ndarray],
+ link_name: str,
+ local_pos: Sequence[float]) -> np.ndarray:
+ """Transform a local marker position → world (mm)."""
+ return transform_point(transforms.get(link_name, np.eye(4)), local_pos)
+
+ def all_markers_world(self,
+ transforms: Dict[str, np.ndarray]
+ ) -> Dict[int, Dict[str, Any]]:
+ """
+ Returns
+ -------
+ dict marker_id -> {world_mm, local_mm, link, normal_world}
+ """
+ result: Dict[int, Dict[str, Any]] = {}
+ for lname, ldata in self.links.items():
+ T = transforms.get(lname, np.eye(4))
+ R = T[:3, :3]
+ for m in ldata.get("markers", []):
+ mid = int(m.get("id", -1))
+ if mid < 0 or "position" not in m:
+ continue
+ loc = np.array(m["position"], dtype=float)
+ nor = np.array(m.get("normal", [0, 0, 1]), dtype=float)
+ result[mid] = {
+ "world_mm": transform_point(T, loc),
+ "local_mm": loc,
+ "link": lname,
+ "normal_world": (R @ nor) / max(np.linalg.norm(R @ nor), 1e-12),
+ }
+ return result
+
+ # ── x-axis invariant helpers (used by 4a) ────────────────
+
+ def link_x_at_zero_state(self, link_name: str) -> float:
+ """
+ Return the world x-coordinate of the link-frame origin
+ when ALL joint variables are zero.
+
+ For any link reachable via only x-axis rotations (Arm1, Ellbow, Arm2),
+ this value is constant regardless of the actual revolute angles.
+ Adding the slider value x_mm gives the true link origin x:
+ link_origin_world_x = x_mm + link_x_at_zero_state(link_name)
+
+ And for any marker in that link:
+ marker_world_x = x_mm + link_x_at_zero_state(link_name) + marker_local_x
+ """
+ T = self.compute({k: 0.0 for k in STATE_KEYS})
+ return float(T[link_name][0, 3])
+
+ def joint_origin_world(self,
+ link_name: str,
+ joint_state: Dict[str, float]) -> np.ndarray:
+ """
+ World position of the pivot that link_name rotates / slides around.
+ """
+ d = self.links[link_name]
+ parent = d.get("parent")
+ T_all = self.compute(joint_state)
+ T_parent = T_all.get(parent, np.eye(4)) if parent else np.eye(4)
+
+ mp = d.get("mountPosition", [0, 0, 0])
+ mr = d.get("mountRotation", [0, 0, 0])
+ T_m = make_T(_rot_xyz_euler(*mr), mp)
+
+ ji = d.get("jointToParent", {}) or {}
+ jp = ji.get("origin", [0, 0, 0])
+ jr = ji.get("rotation", [0, 0, 0])
+ T_j = make_T(_rot_xyz_euler(*jr), jp)
+
+ return transform_point(T_parent @ T_m @ T_j, [0, 0, 0])
+
+ def joint_axis_world(self,
+ link_name: str,
+ joint_state: Dict[str, float]) -> np.ndarray:
+ """
+ Joint axis of link_name expressed in world frame.
+ """
+ d = self.links[link_name]
+ parent = d.get("parent")
+ T_all = self.compute(joint_state)
+ T_parent = T_all.get(parent, np.eye(4)) if parent else np.eye(4)
+
+ mp = d.get("mountPosition", [0, 0, 0])
+ mr = d.get("mountRotation", [0, 0, 0])
+ T_m = make_T(_rot_xyz_euler(*mr), mp)
+
+ ji = d.get("jointToParent", {}) or {}
+ jp = ji.get("origin", [0, 0, 0])
+ jr = ji.get("rotation", [0, 0, 0])
+ T_j = make_T(_rot_xyz_euler(*jr), jp)
+
+ R_to_pivot = (T_parent @ T_m @ T_j)[:3, :3]
+ axis_local = np.asarray(ji.get("axis", [1, 0, 0]), dtype=float)
+ world = R_to_pivot @ axis_local
+ n = float(np.linalg.norm(world))
+ return world / n if n > 1e-12 else world
+
+ # ── utility ───────────────────────────────────────────────
+
+ def chain(self, link_name: str) -> List[str]:
+ """Return chain from root to link_name (inclusive)."""
+ out, cur = [], link_name
+ while cur:
+ out.append(cur)
+ cur = self.links.get(cur, {}).get("parent")
+ return list(reversed(out))
+
+ def board_marker_world_positions(self, length_scale: float = 1.0) -> Dict[int, np.ndarray]:
+ """
+ Return known world positions for all Board markers (in mm).
+ Board is the root, so its marker positions ARE world positions.
+ length_scale: 1/1000 if robot.json uses mm.
+ """
+ board = self.links.get("Board", {})
+ result: Dict[int, np.ndarray] = {}
+ for m in board.get("markers", []):
+ mid = int(m.get("id", -1))
+ if mid >= 0 and "position" in m:
+ p = np.array(m["position"], dtype=float) * length_scale
+ result[mid] = p
+ return result
diff --git a/robot_viewer.html b/robot_viewer.html
new file mode 100644
index 0000000..6837d94
--- /dev/null
+++ b/robot_viewer.html
@@ -0,0 +1,751 @@
+
+
+
+
+Robot FK Viewer
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/run/robot_viewer.html b/run/robot_viewer.html
new file mode 100644
index 0000000..867c53f
--- /dev/null
+++ b/run/robot_viewer.html
@@ -0,0 +1,894 @@
+
+
+
+
+Robot FK Viewer
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/run/run_pipeline.bat b/run/run_pipeline.bat
index 619248e..89c2112 100644
--- a/run/run_pipeline.bat
+++ b/run/run_pipeline.bat
@@ -95,6 +95,6 @@ echo [STEP 4] Robotics Pose calculation of angles and joint positions
python3 "..\pipeline\4_robotState_estimation_v6.py" ^
- "%OUT_DIR%\aruco_positions_optimized.json" ^
+ "%OUT_DIR%\aruco_positions_initial.json" ^
-robot "%ROBOT_JSON%" ^
-out "%OUT_DIR%\robot_state.json"
diff --git a/run/run_pipeline_v8.bat b/run/run_pipeline_v8.bat
new file mode 100644
index 0000000..a06de4f
--- /dev/null
+++ b/run/run_pipeline_v8.bat
@@ -0,0 +1,100 @@
+@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
+
+
+
+
+REM Wenn kein Argument übergeben wurde
+if "%1"=="" (
+ echo.
+ echo [32m[INFO] Aufruf fehlt![0m
+ echo [32mBeispiel: .\run_pipeline.bat ../data/simulation/Scene4[0m
+ echo.
+ exit /b
+)
+
+
+set "IMAGES=%~1"
+
+REM trailing slash entfernen
+if "%IMAGES:~-1%"=="\" set "IMAGES=%IMAGES:~0,-1%"
+if "%IMAGES:~-1%"=="/" set "IMAGES=%IMAGES:~0,-1%"
+
+set ROBOT_JSON=..\data\robot\robot.json
+set BASE_OUT_DIR=C:\Users\kech\SynologyDrive\2026-AppServer-AppRobot\appRobotRendering\data\evaluations
+
+REM ✅ richtigen Namen extrahieren
+for %%I in ("%IMAGES%") do set "SCENE_NAME=%%~nxI"
+
+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"
+)
+
+python3 "..\pipeline\3_multiview_bundle_adjustment_v4.py" ^
+ -robot "%ROBOT_JSON%" ^
+ -lambdaWeight 100.0 ^
+ !DET_ARGS! ^
+ !POSE_ARGS!
+
+
+echo.
+echo [STEP 4] Robotics Pose calculation of angles and joint positions
+
+
+python3 "..\pipeline\4_v8_4a_base_slider.py" ^
+ "%OUT_DIR%\aruco_positions_initial.json" ^
+ -robot "%ROBOT_JSON%" ^
+ -out "%OUT_DIR%\robot_state.json"
diff --git a/setup/precheck/jsonReadable.py b/setup/precheck/jsonReadable.py
new file mode 100644
index 0000000..00244db
--- /dev/null
+++ b/setup/precheck/jsonReadable.py
@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+"""
+Pretty-print JSON with a simple rule:
+
+- If an object/array fits on one line within 80 characters, keep it on one line.
+- Otherwise format it across multiple lines with 2-space indentation.
+- Preserve key order as read from the file.
+
+Usage:
+ python jsonReadable input.json > output.json
+ python jsonReadable.py input.json --inplace
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+
+MAX_WIDTH = 120
+INDENT_STEP = 2
+
+
+def compact_json(value: Any) -> str:
+ """Return a compact single-line JSON representation."""
+ if value is None or isinstance(value, (bool, int, float, str)):
+ return json.dumps(value, ensure_ascii=False, separators=(",", ": "))
+ if isinstance(value, list):
+ return "[" + ", ".join(compact_json(v) for v in value) + "]"
+ if isinstance(value, dict):
+ items = []
+ for k, v in value.items():
+ key = json.dumps(k, ensure_ascii=False)
+ items.append(f"{key}: {compact_json(v)}")
+ return "{" + ", ".join(items) + "}"
+ raise TypeError(f"Unsupported JSON type: {type(value)!r}")
+
+
+def pretty_json(value: Any, indent: int = 0, width: int = MAX_WIDTH) -> str:
+ """Pretty-print JSON with line-length awareness."""
+ if value is None or isinstance(value, (bool, int, float, str)):
+ return json.dumps(value, ensure_ascii=False)
+
+ if isinstance(value, list):
+ if not value:
+ return "[]"
+
+ single_line = compact_json(value)
+ if indent + len(single_line) <= width:
+ return single_line
+
+ inner_indent = " " * (indent + INDENT_STEP)
+ lines = ["["]
+ for i, item in enumerate(value):
+ rendered = pretty_json(item, indent + INDENT_STEP, width)
+ comma = "," if i < len(value) - 1 else ""
+ lines.append(f"{inner_indent}{rendered}{comma}")
+ lines.append(" " * indent + "]")
+ return "\n".join(lines)
+
+ if isinstance(value, dict):
+ if not value:
+ return "{}"
+
+ single_line = compact_json(value)
+ if indent + len(single_line) <= width:
+ return single_line
+
+ inner_indent = " " * (indent + INDENT_STEP)
+ lines = ["{"]
+ items = list(value.items())
+ for i, (k, v) in enumerate(items):
+ key = json.dumps(k, ensure_ascii=False)
+ rendered = pretty_json(v, indent + INDENT_STEP, width)
+ comma = "," if i < len(items) - 1 else ""
+ lines.append(f"{inner_indent}{key}: {rendered}{comma}")
+ lines.append(" " * indent + "}")
+ return "\n".join(lines)
+
+ raise TypeError(f"Unsupported JSON type: {type(value)!r}")
+
+
+def format_json_text(text: str) -> str:
+ data = json.loads(text)
+ return pretty_json(data, indent=0, width=MAX_WIDTH) + "\n"
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(
+ description="Format JSON so short objects/arrays stay on one line."
+ )
+ parser.add_argument("file", type=Path, help="Path to the .json file")
+ parser.add_argument(
+ "--inplace",
+ action="store_true",
+ help="Overwrite the input file instead of printing to stdout",
+ )
+ args = parser.parse_args()
+
+ input_path: Path = args.file
+ text = input_path.read_text(encoding="utf-8")
+ formatted = format_json_text(text)
+
+ if args.inplace:
+ input_path.write_text(formatted, encoding="utf-8")
+ else:
+ print(formatted, end="")
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file