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b/data/evaluations/Scene4/aruco_positions_optimized.json index 6d836f5..437beb9 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": "2026-05-31T08:09:09Z", + "created_utc": "2026-05-31T08:47:48Z", "summary": { "num_cameras": 7, "num_markers": 50, diff --git a/data/evaluations/Scene4/render_a_aruco_detection.json b/data/evaluations/Scene4/render_a_aruco_detection.json index 76e5869..e2d1b1a 100644 --- a/data/evaluations/Scene4/render_a_aruco_detection.json +++ b/data/evaluations/Scene4/render_a_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:14Z", + "created_utc": "2026-05-31T08:47:06Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "572dad67-cef1-4eb9-a3ef-6f1b862657bb", + "observation_id": 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@@ -532,7 +532,7 @@ "confidence": 0.12146268776527554 }, { - "observation_id": "43cc331e-a209-476a-9af4-e9f3bf9ce8a5", + "observation_id": "9f1b0912-d591-4371-bfb9-a5fa6c571e60", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.46829700346999537 }, { - "observation_id": "948a2a5b-019e-4af7-b852-a8dbd9a3706a", + "observation_id": "d5647595-eed8-4487-b564-5e6bd14436a8", "type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.4560525484643318 }, { - "observation_id": "03ee6043-e293-4be1-8106-b8a8c7fba356", + "observation_id": "3a2b7364-a8bd-484d-aed5-314fbbc12d50", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.5734281513866332 }, { - "observation_id": "870a8dcf-ce08-405a-b022-275c73f9ce12", + "observation_id": "0a981d4c-f32c-42bc-ba1c-7db45aca4f09", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.525542829627259 }, { - "observation_id": "ff220989-48ef-4a7e-8ede-2a5ccb8d4ec0", + "observation_id": "4ba55d56-3956-4f58-9000-8c9e9b9ea4a0", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.456911083882938 }, { - "observation_id": "cda69275-e2e1-47b5-8b25-aa850808135d", + "observation_id": "8800627a-400c-4cca-b4e2-89a1661fbcad", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.43622925667631085 }, { - "observation_id": "591eca48-d9cf-4d63-b08a-904634515322", + "observation_id": "077b0333-5b5a-4b9b-adb4-6b5a01517280", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.4417935296159658 }, { - "observation_id": "a629b604-4c3c-41b3-82bf-2aac4ca68bed", + "observation_id": "c6a375f4-8084-490f-84f2-f59d68badae7", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.3826078272694969 }, { - "observation_id": "4997f2a3-786b-44cb-b986-f3afff8ee8e8", + "observation_id": "af422433-75e2-4f3a-b2c1-d94d366d249d", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.29844141482805986 }, { - "observation_id": "5acad86b-556b-4353-9768-8f068de27715", + "observation_id": "33e5f248-80ea-47b3-82d5-89d0865f9cf8", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.2521714913889437 }, { - "observation_id": "07dadeea-2280-4200-9cb4-bd83cf02897b", + "observation_id": "df611a6c-c7bf-46c6-9751-9a4c1df50115", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.38467027802270787 }, { - "observation_id": "46186156-0e4a-411f-b621-8fd6562593fe", + "observation_id": "7769664a-962f-4d15-83a0-353f114f5be1", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.31777455864589965 }, { - "observation_id": "8780f748-5b72-4a7e-ad00-5a4d93726e69", + "observation_id": "203a986b-496b-4aa9-b09d-92f87f1e9c96", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.3029173397022545 }, { - "observation_id": "10868182-1027-4dda-9023-6af4eb1b560b", + "observation_id": "e714abfe-bd7f-4f26-a013-7e8d30fc3e86", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.1527342148240039 }, { - "observation_id": "74d7358b-3481-4afd-a600-0b45e2fc6e19", + "observation_id": "10d67411-73d3-4a16-95fc-4b6fb66ce9d2", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.19743498130596515 }, { - "observation_id": "79da89a7-aa8d-4fdb-aeb9-6198b4971ce9", + "observation_id": "fe02d1ed-cbfe-4986-b8c4-07822592c803", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.2849187633475991 }, { - "observation_id": "19490037-34e6-4c60-96e0-f663abbf8b7d", + "observation_id": "a64c5eb5-f28a-4443-b1b1-e38533229ae0", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.24627951083020566 }, { - "observation_id": "190f175b-f68a-4ff0-8e07-5407a1ad3901", + "observation_id": "921da138-9354-401f-b719-aad7dcb82b95", "type": "aruco", "marker_id": 93, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene4/render_a_camera_pose.json b/data/evaluations/Scene4/render_a_camera_pose.json index 1aff639..41a2e9f 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": "2026-05-31T08:08:17Z", + "created_utc": "2026-05-31T08:47:09Z", "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 b360c7c..4b27dac 100644 --- a/data/evaluations/Scene4/render_b_aruco_detection.json +++ b/data/evaluations/Scene4/render_b_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:14Z", + "created_utc": "2026-05-31T08:47:07Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "3d177433-5bbf-432b-a8e2-81bd4e86e6d6", + "observation_id": "6aab51d3-8f8e-4b78-b4d4-cca9fa9b98ae", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.80343703773924 }, { - "observation_id": "1fcb259d-2b63-4253-99d9-db0c691a3059", + "observation_id": "082bf1b8-a4c4-4c38-88fd-0cd995c7e479", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.7694115459687173 }, { - "observation_id": "5cb50e23-4539-4b6e-b8e9-6fb36d95ea77", + "observation_id": "16876b6c-315f-4a34-81c0-242a4fe4d482", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.770675991358623 }, { - "observation_id": "f8ed5b83-265d-4513-b045-c82c436fdfb0", + "observation_id": "9e8d2195-7a17-44c9-9ce6-7c84adbaa920", "type": "aruco", "marker_id": 219, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.6459779993693033 }, { - "observation_id": "7b697d5f-ca71-48cb-8ff4-bd9d95b501ac", + "observation_id": "7f252298-54c7-448d-a3de-d72e687ce77f", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.6729142857142857 }, { - "observation_id": "e7e791c0-c5db-4803-ab83-fb3ce977e21c", + "observation_id": "2643b320-0e47-46a8-8379-129e66bbee84", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.6507344589435812 }, { - "observation_id": "47b1b05f-9862-40b2-a003-fc3ea8f0735b", + "observation_id": "8a11802b-a1ad-4f63-8f4b-2ee6d490eef3", "type": "aruco", "marker_id": 218, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.5993310137356028 }, { - "observation_id": "70e2f84d-cea9-4ccf-afcc-8b823795c095", + "observation_id": "c1d9cbb1-18fd-473f-a177-bf60900ada21", "type": "aruco", "marker_id": 44, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.46011560227730974 }, { - "observation_id": "020afc16-0cec-4ce3-bafd-372c4988e417", + "observation_id": "626151f8-b54e-48ce-8ec1-104711123983", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.0456345257443131 }, { - "observation_id": "def07359-63b7-41bf-a1bc-c1529ca0c800", + "observation_id": "95a84990-d6a4-42c9-9e9d-9dff35ca6309", "type": "aruco", "marker_id": 63, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.1539593300495616 }, { - "observation_id": "503d5173-e8eb-42fa-9a0d-bef1ad855beb", + "observation_id": "ee9e53b4-e504-4389-bacf-0d4f4fad0888", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.39637168760361213 }, { - "observation_id": "4e53b5a7-e894-4d9f-bd64-0b0fbd9ae5e4", + "observation_id": "6940a210-9090-4489-972a-fc96cd0dbff6", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.4633649853376546 }, { - "observation_id": "0f83af84-b858-4c95-85a1-03440aa58ea5", + "observation_id": "070b352d-fd05-4038-8290-5beadbbfef88", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.3638956157928849 }, { - "observation_id": "a1e94425-7e95-4fd6-b643-869ee3c68d0c", + "observation_id": "78223644-d61c-4a3c-8e97-418d3249791f", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.46896024883690896 }, { - "observation_id": "f09b2f9b-12e1-4351-a2be-9bc96a10c508", + "observation_id": "02e00e55-85f9-4f0a-8216-776d9caf8300", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.44372569385651617 }, { - "observation_id": "6b58a86e-bb3f-4c56-8394-81e0cba7d714", + "observation_id": "df5fb02f-0ad6-447c-afa4-63b614af09ae", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.45199802144368495 }, { - "observation_id": "b3ab5d10-97f9-4e56-b24b-f8ef4fa52726", + "observation_id": "e8214723-46f6-4d85-96ac-e27f720ffd85", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.45199802144368495 }, { - "observation_id": "83178dc0-6991-4360-9f51-5e5ace5db0ab", + "observation_id": "e23e2c6d-d0f1-40b0-922a-7c51e2dbd83e", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.44348322079976393 }, { - "observation_id": "8aba8b43-7f99-4f35-952a-e47a161a0515", + "observation_id": "506cca57-5935-4563-8f31-c13b4925889a", "type": "aruco", "marker_id": 205, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.3958181457519531 }, { - "observation_id": "d6275504-8817-4ae5-9e13-37e966d7e8d1", + "observation_id": "a0fcaf48-6973-4616-afd3-5c528ea43e9a", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.4015868133874269 }, { - "observation_id": "fa968b7f-dc5d-4127-9001-17cb00ed839c", + "observation_id": "b0e0409b-cd3c-4fb7-8280-441914fb7924", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.3776 }, { - "observation_id": "cc07a88b-9c2f-41a3-9f7e-6533e22217ba", + "observation_id": "f76e08ae-1b93-43e6-8415-4f02895c9ab1", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.3320910299473707 }, { - "observation_id": "4d8d32eb-758d-43d8-a8cd-35323b720060", + "observation_id": "15018bcd-f7be-46b3-9ca5-0106eb98ab2a", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.3579602826436361 }, { - "observation_id": "96fd0207-f9a1-45da-9f9f-01a41aabdd77", + "observation_id": "14a99a02-7aa5-4239-9d1a-0532822098ba", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.343462485354258 }, { - "observation_id": "e18ff3bb-35e2-4ab9-ab07-0e24b5c8c45f", + "observation_id": "85986399-de4c-4d08-a06d-0b502e6b2b6e", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.3244367816091954 }, { - "observation_id": "393e91a0-0bb0-426d-a3a3-fa0817b8e198", + "observation_id": "28819d00-f6b7-4f9a-b84b-0fa65f93567b", "type": "aruco", "marker_id": 94, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.06057156860351561 }, { - "observation_id": "0abfee60-c2a9-4985-b8e2-1cf663dafb68", + "observation_id": "19751dd7-e8f6-40d3-87fe-a317bb39fc64", "type": "aruco", "marker_id": 76, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.2459421895345052 }, { - "observation_id": "6d9ba5ef-3bcb-444b-a4a8-4c367798e511", + "observation_id": "b05b3442-249a-4049-9ee9-52fab70fd806", "type": "aruco", "marker_id": 100, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.24407473894265982 }, { - "observation_id": "620f5c7e-fe82-40ca-a2de-3c187714e940", + "observation_id": "7fcc6be4-ba8c-47db-822d-7b1166e7687f", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.23896265492072474 }, { - "observation_id": "3101549b-1b77-417a-be73-925ec1e06c34", + "observation_id": "8203bb0e-9610-4fcb-a657-07da2e5efa4f", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.24909137483284013 }, { - "observation_id": "8b3e49f9-a0d7-4a85-9c24-b82b0340c218", + "observation_id": "997abfa1-eb73-4059-a522-4c3a809e30b4", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.23734254719660833 }, { - "observation_id": "c54860c6-5ecd-44c5-9d3c-88edf2ea0711", + "observation_id": "dffbadef-3d8a-40a4-b2af-0b4c6716fe72", "type": "aruco", "marker_id": 50, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.21965114789131363 }, { - "observation_id": "4e23f3ae-56a7-450d-bb4d-068b266b00c8", + "observation_id": "8798463c-9700-4b8f-a92f-c95a20f50fa9", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.21841319450965294 }, { - "observation_id": "04e72aaf-8092-4b4d-ad29-3aa3c57488c2", + "observation_id": "f5547d61-969f-43cd-b1ab-2c675642f73c", "type": "aruco", "marker_id": 104, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.22417849731445313 }, { - "observation_id": "2b2959dd-fd76-485d-a350-42dbe261955f", + "observation_id": "9e1204a9-d33a-41b6-84e1-c937808249ac", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.22367025973033955 }, { - "observation_id": "39abc342-2e93-4044-90f4-c201912631d2", + "observation_id": "2f9601fd-2ea6-4b1e-9012-9c3b1cdbb7e8", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1990,7 +1990,7 @@ "confidence": 0.21633306884765624 }, { - "observation_id": "77b77378-e37c-46f8-9d15-61469b1ae501", + "observation_id": "224abf5b-2d4d-46ee-80a1-2865fd7cdcb2", "type": "aruco", "marker_id": 60, "marker_size_m": 0.025, @@ -2044,7 +2044,7 @@ "confidence": 0.2000031054889577 }, { - "observation_id": "644e544c-3a68-46f2-b56f-fae9f457b502", + "observation_id": "6207cccf-06ae-4761-bd81-361e3c22f8bf", "type": "aruco", "marker_id": 67, "marker_size_m": 0.025, @@ -2098,7 +2098,7 @@ "confidence": 0.19627771759033205 }, { - "observation_id": "74b1699a-6704-42d3-ad5a-48993869f65a", + "observation_id": "e8cf49ff-9083-465e-a4f7-b755ba2fcad9", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -2152,7 +2152,7 @@ "confidence": 0.18966921411877496 }, { - "observation_id": "d32cf87d-b0bc-44fd-8aa2-2b3e07bd98b2", + "observation_id": "885fc5c0-30dd-4c72-b8b8-f7fd8cf97d19", "type": "aruco", "marker_id": 70, "marker_size_m": 0.025, @@ -2206,7 +2206,7 @@ "confidence": 0.193995418548584 }, { - "observation_id": "3370271d-8c13-4c52-bc16-837277327296", + "observation_id": "905aec40-0624-4f4b-8ff5-4b21f3ef65fa", "type": "aruco", "marker_id": 88, "marker_size_m": 0.025, @@ -2260,7 +2260,7 @@ "confidence": 0.2032033303021305 }, { - "observation_id": "1654f1c2-c3b2-4828-8f6f-af154107ab65", + "observation_id": "670f78f0-1bf6-4695-b0e2-adcfb9605369", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -2314,7 +2314,7 @@ "confidence": 0.1929997138977051 }, { - "observation_id": "48dba7c2-031c-4f70-8f22-4c43cc7292c5", + "observation_id": "89894577-9d7a-4e8a-aa6c-eab38b66716c", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -2368,7 +2368,7 @@ "confidence": 0.19249963767972095 }, { - "observation_id": "c1d4b619-a6a4-49a2-81aa-a0d604deb6f0", + "observation_id": "ebee6900-dc82-44da-a887-7656afde3cdb", "type": "aruco", "marker_id": 98, "marker_size_m": 0.025, @@ -2422,7 +2422,7 @@ "confidence": 0.1899538473288218 }, { - "observation_id": "2ef06a6b-9b2b-4e24-8255-b67d8480e7c0", + "observation_id": "2aef8bd4-d3eb-4131-a6c0-ba06153ad499", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -2476,7 +2476,7 @@ "confidence": 0.17676838843571283 }, { - "observation_id": "33344c7f-6293-48bf-91ac-9d4debbfc7f5", + "observation_id": "9568032e-d400-4bfb-8776-acd22ca10e90", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -2530,7 +2530,7 @@ "confidence": 0.1668050360320266 }, { - "observation_id": "d3edf18e-154c-46e3-ba87-05236a11d844", + "observation_id": "0ffbe58e-4317-4b1f-847d-454ee59bc667", "type": "aruco", "marker_id": 90, "marker_size_m": 0.025, @@ -2584,7 +2584,7 @@ "confidence": 0.17612778902053833 }, { - "observation_id": "02c0eb34-079c-48e6-b180-e56d51e742b2", + "observation_id": "2dc36177-77bc-4b16-98f1-2ebae1bb9806", "type": "aruco", "marker_id": 91, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene4/render_b_camera_pose.json b/data/evaluations/Scene4/render_b_camera_pose.json index 4d7236f..885f282 100644 --- a/data/evaluations/Scene4/render_b_camera_pose.json +++ b/data/evaluations/Scene4/render_b_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:18Z", + "created_utc": "2026-05-31T08:47:10Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene4\\render_b_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene4/render_c_aruco_detection.json b/data/evaluations/Scene4/render_c_aruco_detection.json index 79d3430..16ef633 100644 --- a/data/evaluations/Scene4/render_c_aruco_detection.json +++ b/data/evaluations/Scene4/render_c_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:15Z", + "created_utc": "2026-05-31T08:47:07Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "cfd3e1f7-1116-4537-82a3-f43a0b4fd879", + "observation_id": "28b40528-3f47-4b34-bed6-9cbffc75f09d", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.8343550641259049 }, { - "observation_id": "b8c8c989-1826-44bb-a481-ff3a25e5f7ae", + "observation_id": "bd049558-ecd2-4a34-ade7-1be26c010823", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.7789736723135801 }, { - "observation_id": "18aa02bf-4aea-468c-b3cd-ebf9e172fbf1", + "observation_id": "481a9285-9060-409b-9e13-f225f1befe11", "type": "aruco", "marker_id": 44, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.734766387939453 }, { - "observation_id": "3ae5ba69-63f3-4279-bacc-f346c2db1062", + "observation_id": "c1291740-3e8a-43ef-8f5f-d12025f3219e", "type": "aruco", "marker_id": 219, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.6138985440690424 }, { - "observation_id": "1abbd54b-3140-4fcd-a521-51088e6b6857", + "observation_id": "d082660a-f4f3-44c8-a6a6-62f07f35087b", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.5645808242627047 }, { - "observation_id": "f6e5c97d-5073-4cb2-b4fd-79fe81bdc104", + "observation_id": "7f1b7764-c669-4fd4-bf3e-9727533f6499", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.755998822943334 }, { - "observation_id": "22381067-8d68-46db-9e28-f1aae67f4090", + "observation_id": "fb63a2c0-f203-4787-a7c6-49bae65ef133", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.5284366390074502 }, { - "observation_id": "aa66cfbf-e965-42d3-8421-a5b71f55d459", + "observation_id": "91412bdc-d88b-470a-a8bc-d33ce48c8598", "type": "aruco", "marker_id": 218, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.5315859761486594 }, { - "observation_id": "dda37639-6e32-476d-9c69-0a9df44c8e54", + "observation_id": "021daa16-c95f-49cc-a61d-f8d093fae96d", "type": "aruco", "marker_id": 242, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.6006520365292345 }, { - "observation_id": "1579b967-61f0-4472-9bd4-92d23e879064", + "observation_id": "55ba6a69-7bb2-4231-9b5e-170fda6b841c", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.3823850960994706 }, { - "observation_id": "553de8ac-abf2-4437-ba44-a7b6e0daa073", + "observation_id": "82fbe71a-39ad-42da-8187-b3c3e012ec68", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.3808358629990202 }, { - "observation_id": "10fc0339-a27f-4ebe-8230-dbe44adf7bee", + "observation_id": "a7b5c93a-3346-49cb-8745-29550d51c781", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.2964796698261984 }, { - "observation_id": "4fb220ec-7544-4ab7-aa9c-07b9e9f1c424", + "observation_id": "5c788bd2-e930-4250-ab34-432ff4be26b5", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.2743742952602821 }, { - "observation_id": "2c36b227-cf13-483a-b29d-9d9fbbb18f80", + "observation_id": "8ca922db-623b-4acf-8457-1c29629bd2c3", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.233781757000696 }, { - "observation_id": "31171e1b-ee52-4f31-9127-739e95631b2f", + "observation_id": "bfe2089b-34da-4273-bc5e-530c81aa42db", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene4/render_c_camera_pose.json b/data/evaluations/Scene4/render_c_camera_pose.json index f889d9c..4e8fee5 100644 --- a/data/evaluations/Scene4/render_c_camera_pose.json +++ b/data/evaluations/Scene4/render_c_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:18Z", + "created_utc": "2026-05-31T08:47:10Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene4\\render_c_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene4/render_d_aruco_detection.json b/data/evaluations/Scene4/render_d_aruco_detection.json index 0003b18..f0b19af 100644 --- a/data/evaluations/Scene4/render_d_aruco_detection.json +++ b/data/evaluations/Scene4/render_d_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:15Z", + "created_utc": "2026-05-31T08:47:07Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "c63c6b01-043e-4d0a-a64d-a9ce31cb4c6a", + "observation_id": "c7800982-7597-49f0-a539-664a5a7b9f0f", "type": "aruco", "marker_id": 44, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9829005790795233 }, { - "observation_id": "c885e543-1468-4cc5-90e7-ac0dbb7a4644", + "observation_id": "2e5134ba-500f-494c-8250-ec316939eed2", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.7952853072848569 }, { - "observation_id": "db666a01-75eb-4a0b-b516-4eba4f94f83b", + "observation_id": "c78334cf-0a73-4de6-a876-1b9ac6558381", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.7534617346649917 }, { - "observation_id": "b0007c2b-b17d-42b7-934d-0dd57714c050", + "observation_id": "693762cb-1dcf-4784-97f8-1831334569ed", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.7282777200221999 }, { - "observation_id": "166c4313-10da-433d-8fb2-ed25fd9df360", + "observation_id": "177177d6-6f49-46ac-a9df-40327a4e0e53", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.5518116657099041 }, { - "observation_id": "228b5495-cdc5-48fc-bb98-6505e7e7a93d", + "observation_id": "30cb20f9-6091-43ab-b7e4-3a700987491e", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.589171450268867 }, { - "observation_id": "61ca68fb-c720-4a4e-a9a5-1f6bc2ee7a12", + "observation_id": "08d4c176-3b7d-4366-a0c6-782ee6acd47d", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.4569666605043164 }, { - "observation_id": "c072fc31-459d-4f89-a57f-af13d2628662", + "observation_id": "4021c02f-cfa4-4e8f-9592-a4c05f5f799f", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.5412868278516675 }, { - "observation_id": "2f808d4a-e40f-4966-8e81-92e509434059", + "observation_id": "6b51617e-c611-413c-b849-7d7fbf2c8aea", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.5367694875460974 }, { - "observation_id": "fa42128d-aec8-45fe-942c-64ad68321110", + "observation_id": "cd3418b9-2ab6-4687-865f-7ec70111db52", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.5683985238307451 }, { - "observation_id": "d96ef7b0-e261-4aba-9c13-ea5362181944", + "observation_id": "e1a9f903-9fb4-4495-9aca-9dc90140f462", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.5703994452422884 }, { - "observation_id": "71c86178-c1e8-4644-8a00-c1ba1051d9a7", + "observation_id": "dbf23df8-d025-4344-adc6-afefebfa866a", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.408432388349621 }, { - "observation_id": "72b2e5a8-8003-49a2-a56f-2b526d49a548", + "observation_id": "3b17118d-d631-49e9-91e5-4394e2ce4586", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.32753550791646946 }, { - "observation_id": "a91573d9-60bc-4211-b355-b0d05594f518", + "observation_id": "9af71b24-2089-4df1-bfad-0d556b02cd27", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.4466430195499723 }, { - "observation_id": "4581e5ee-9f8d-4092-a4e3-abc32e3f8814", + "observation_id": "795aa7d5-5dca-4baf-9200-069c52e314ca", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.29690586628373444 }, { - "observation_id": "d47ecefb-601d-4cfd-8520-c45f6bf43466", + "observation_id": "960cb8db-1da7-4fee-91ee-3e49f768a39a", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.03479010216782576 }, { - "observation_id": "d78e93f5-5ab3-44ba-873e-c8ffb8ae9a7a", + "observation_id": "6dfc58e6-a930-48a0-9738-e3bdc8a8e44b", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.09228958358804855 }, { - "observation_id": "bbfdce9d-36c4-48bc-827f-1cfb39f5fc81", + "observation_id": "7e29173d-d504-457e-a775-9cef4f094361", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.12403848180952393 }, { - "observation_id": "e182d87f-19aa-45a1-a499-7494242b90c6", + "observation_id": "31f1d5f6-96b5-4610-aac5-9656d693736f", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene4/render_d_camera_pose.json b/data/evaluations/Scene4/render_d_camera_pose.json index 46e2e5d..087d7e4 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": "2026-05-31T08:08:18Z", + "created_utc": "2026-05-31T08:47:10Z", "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 8390019..8d578fd 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": "2026-05-31T08:08:16Z", + "created_utc": "2026-05-31T08:47:08Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "259b220c-3c08-4b30-a0ff-6fa61a04731c", + "observation_id": "e1959889-bda0-4a15-889c-dcb237998151", "type": "aruco", "marker_id": 44, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9774738327603469 }, { - "observation_id": "b074ca3a-a898-45cd-bb31-1af2c1b4092c", + "observation_id": "3e54f3ff-6015-4c97-ace8-76df1f832966", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.15369374989294518 }, { - "observation_id": "5e4b9b75-96ff-43b9-9265-b75234311ffd", + "observation_id": "dc12e7da-78a8-4898-ad88-3e9ec94c08cd", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.3233651345714012 }, { - "observation_id": "ffcfec3d-1bdb-4019-9a16-f1f597d69a11", + "observation_id": "2aa4875f-083f-4a99-8c01-d6b220b69ac3", "type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.3248781943257027 }, { - "observation_id": "58f81a0e-f462-4ac7-a92b-a51255dcc91e", + "observation_id": "7a8cb838-ba44-48de-a105-4bf5918a6873", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.5419921532895585 }, { - "observation_id": "7a873be7-6272-4367-b5f2-48f687472cc3", + "observation_id": "e6a2a887-92c1-42f6-824f-b37a2e9e7bb7", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.42546318190651866 }, { - "observation_id": "ae0b827c-b350-4021-871a-83bd6a405ca1", + "observation_id": "70957fc9-2b92-4587-b8cb-96d9159b4171", "type": "aruco", "marker_id": 244, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.4483121499334993 }, { - "observation_id": "7569b864-b200-4def-a21a-0a4acc395545", + "observation_id": "ae62e780-0a88-4645-9cc6-c9207f8c5a99", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.15136171428561934 }, { - "observation_id": "28293cc0-9ac6-4fcc-8f84-6b0ffd38d1a9", + "observation_id": "810be360-dc4c-4095-8831-c140c5b60957", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.430473829601915 }, { - "observation_id": "834f6d41-11e6-44ed-8910-567cdf4f7b3f", + "observation_id": "36adc3e6-d127-456d-aff9-785a052bac4f", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.40218814597808433 }, { - "observation_id": "3006fb7c-ddc8-4285-9e74-a267027297dd", + "observation_id": "d4b087ec-b495-4b5f-998b-74f31f8d5dcb", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.22596816505744277 }, { - "observation_id": "dcc5885c-3c69-4319-9cd8-f96a5c869912", + "observation_id": "a7d2f473-331a-43c3-a140-4aa5a0c2b219", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.24626971873720385 }, { - "observation_id": "0b37475e-365c-4e9f-be6e-0c6486693c5c", + "observation_id": "be5118c7-495e-41d0-881a-02b544045b0f", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.28140192555287236 }, { - "observation_id": "8727713b-0420-4cd2-b3e9-34ece1f71744", + "observation_id": "ec8c2388-adac-4e80-a55b-0cc149da6a78", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.20905304008181452 }, { - "observation_id": "8448acae-1f89-47db-bbb9-a21312403ed5", + "observation_id": "f37486dc-e295-43f5-823c-c453e3e90e4a", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.2664122029520185 }, { - "observation_id": "459d8838-04d4-4054-802a-55f86b35af72", + "observation_id": "fc85af2e-53ff-4320-9de5-f6e2077e6954", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.16725963004706537 }, { - "observation_id": "78a400ce-dfcc-488f-b26b-057112210a6c", + "observation_id": "2c3d8edb-15fb-424f-a4a7-c05e75337a26", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.22298824455832925 }, { - "observation_id": "bdff49d0-4b99-4d5a-90ad-93efede7bcc7", + "observation_id": "7f41ae4a-a449-4226-92b7-445670832ebe", "type": "aruco", "marker_id": 40, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.06522200557737874 }, { - "observation_id": "e7bce243-b54a-4fa8-b143-d7f0732d6498", + "observation_id": "9d0fe286-bd31-4572-80d6-b09a8a074018", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.19757108839994864 }, { - "observation_id": "46b4ba1d-39c0-4522-82ca-0bd47c0af378", + "observation_id": "ef206c3f-3edb-408b-9d6c-c4e6c2f24213", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.15675000342230294 }, { - "observation_id": "72543e7b-7d21-45d2-a8ed-02e126e3b557", + "observation_id": "1e64dc44-786e-4705-b77b-5b962cce7411", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.13595850692924102 }, { - "observation_id": "7a0dce36-e539-45a0-90f0-89eb2102baf9", + "observation_id": "bf4376b5-55ad-4fa9-baac-43b0a7205448", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.12157403976480062 }, { - "observation_id": "5aa8028b-c5b0-4d6f-8027-6abf708e1e26", + "observation_id": "030ffa08-f2ba-499d-b757-331688a40990", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.14922985097126407 }, { - "observation_id": "ab8ecef5-e99d-4cb3-b517-fa9982f98e39", + "observation_id": "86ee27a0-bc7a-4601-a165-61a43687dc70", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.1099085657112018 }, { - "observation_id": "6bb8d934-6ede-4bb8-b5f1-d597f619d6bb", + "observation_id": "2d16ba79-b6df-43bd-8b90-c520fa0773a0", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.13220570535010506 }, { - "observation_id": "b4155a69-3fe7-4d0e-9068-b6a61cf73c42", + "observation_id": "58e3279c-d241-4d55-ad32-dab6234d8d84", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.11373255626245794 }, { - "observation_id": "7d31d9d4-673a-4851-8a35-99b6e01e1d72", + "observation_id": "3b94ef95-ff5d-41f8-b56e-83f7830db2ca", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.13255097071329755 }, { - "observation_id": "0303f540-e076-44ab-b89c-bfa24c7571eb", + "observation_id": "f194a7cb-b7f6-464d-aad2-c3cde87ab14f", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.09620577544552089 }, { - "observation_id": "abb77b5a-7128-425b-b36e-5ab3385b69bf", + "observation_id": "68a654aa-ba54-4ee9-a5f7-1b496106bc28", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.11774828338549573 }, { - "observation_id": "2138af9a-cd55-4449-90ed-bb591eef3dcd", + "observation_id": "c16cfc52-0ccf-41f6-bbac-cd224e4151ca", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene4/render_e_camera_pose.json b/data/evaluations/Scene4/render_e_camera_pose.json index 9818583..c753ae6 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": "2026-05-31T08:08:19Z", + "created_utc": "2026-05-31T08:47:10Z", "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 6e2452c..04fd22d 100644 --- a/data/evaluations/Scene4/render_f_aruco_detection.json +++ b/data/evaluations/Scene4/render_f_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:16Z", + "created_utc": "2026-05-31T08:47:08Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "d3072cfc-6a7b-4039-8f38-0f01e91d56b8", + "observation_id": "9fb92c22-8274-4e34-89d0-6cc9b25a21cf", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.8595541333255614 }, { - "observation_id": "a72a160c-420b-4461-b40f-ca567e04f1da", + "observation_id": "40e5dd35-505f-413e-a0c4-76f33dac19a4", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.8446693980390577 }, { - "observation_id": "721b6f69-52d5-424d-93b6-0a9c8d064a5c", + "observation_id": "ede8e441-7f04-478c-b47a-5a98e0336dfe", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.8383841261229988 }, { - "observation_id": "a9264c32-4b29-4570-84c6-3daa49c2700e", + "observation_id": "5564bf05-32cc-4b27-96f4-b450d7646ccb", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.7722445599677091 }, { - "observation_id": "8990fd93-428f-44a2-b82e-db4fc86b4177", + "observation_id": "dd04b27d-fefc-4a86-a792-1c74b98ceed5", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.6913219978931935 }, { - "observation_id": "2ada01ed-8cc9-4707-bd5d-ea25f772096f", + "observation_id": "280457bc-1662-4659-af7d-3736ca0fa43d", "type": "aruco", "marker_id": 40, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.46826993269571887 }, { - "observation_id": "963eac71-f6c6-4128-84f7-dd353279af93", + "observation_id": "11eabbd5-188e-459e-b227-43c2bb616731", "type": "aruco", "marker_id": 218, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.45659746024893416 }, { - "observation_id": "00fa99d9-78ed-4dd6-ab64-040e8b90adb1", + "observation_id": "8116fb34-059b-4e13-88c1-cc998bbca2c7", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.2988751797371847 }, { - "observation_id": "092dd314-ecf5-4486-a5ff-471b33a276cb", + "observation_id": "81c7a325-dfde-4532-87e0-d903afbd975c", "type": "aruco", "marker_id": 219, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.4719563350095902 }, { - "observation_id": "684fd4d1-171c-4a1b-9528-1c56e372ac7a", + "observation_id": "13f934d5-4384-42db-90d0-07bfdd026205", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.5954364184044425 }, { - "observation_id": "248d9772-0386-42cc-810c-41e6b0bef85b", + "observation_id": "2738e43b-1bc7-423b-a63b-dd01c1fda2e0", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.5697620483284124 }, { - "observation_id": "7bf57f9c-ce46-4fed-888d-522231e9467a", + "observation_id": "8c22fb4b-c2b3-450a-9d07-4f887eb80578", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.5711737708098855 }, { - "observation_id": "48d3646c-566b-4031-825a-61b431caef89", + "observation_id": "87718790-ff49-452d-90f8-695a69a78c9d", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.5768583617164568 }, { - "observation_id": "afd30247-cab6-4b3b-8965-7f3c9828d051", + "observation_id": "b6929125-2ff5-4c97-9c46-04137d0a0853", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.570786168435231 }, { - "observation_id": "c24e97b0-c888-4437-bdb0-55e433a41329", + "observation_id": "1adc35ff-a482-4884-b26d-8697d5fab01b", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.21364841201201337 }, { - "observation_id": "78bd3cb3-89d8-4063-acc8-03a8babcc260", + "observation_id": "efc60dbd-a3ca-4c35-ab3c-a53b9df9fbd5", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.5705029634947052 }, { - "observation_id": "062d153f-fd0a-49b4-bfab-84da21e97d21", + "observation_id": "8631c7f3-70cb-4c3f-bb78-db524b3c102a", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.5440516905787338 }, { - "observation_id": "9f2ad2f8-d8be-4cc7-9552-05b14a1d169d", + "observation_id": "7829d9fc-9b12-4272-8906-2d7a742c1516", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.06519221426443914 }, { - "observation_id": "c40672b7-5fc9-45a9-99bb-f2aabcdcab33", + "observation_id": "17370d70-c669-47a8-ae50-8c63225e88f7", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.5460974493377216 }, { - "observation_id": "5b2c575b-7caf-4958-b1b4-309dfe0ab702", + "observation_id": "4e29c39d-d70d-47f8-9b21-969f38ad9e6f", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.4817209906880129 }, { - "observation_id": "ad310f87-c45f-4df2-8c9d-7ebf863745f3", + "observation_id": "abe73a5b-a80f-49e0-9640-f23b5c98d52f", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.5046333482067283 }, { - "observation_id": "6f89e611-aac5-4e5f-9ead-83dad7057135", + "observation_id": "bff5c572-91a0-4451-9b12-55d41d03dfe8", "type": "aruco", "marker_id": 44, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.31387842129667237 }, { - "observation_id": "0d2d9cb7-c622-4baf-aec0-e2bc0d5975e1", + "observation_id": "293eeee8-c372-42c5-aed4-9a2d95724362", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.13607001327160623 }, { - "observation_id": "f07db2f9-912d-4fde-8abf-ecf7f94abd09", + "observation_id": "e7f3630f-1503-4b1d-b065-f32d4efe6048", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.46988879526830113 }, { - "observation_id": "9e8b248e-6ffd-42e6-98f8-013b96e684ff", + "observation_id": "3b2d82c4-0038-4bc5-b496-cf1d3d2933a1", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "2a2f5287-7914-4fad-ae61-1585b2767f27", + "observation_id": "94a8ab5c-920b-483f-b557-9e798350b435", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "1c5c3b8d-a2cf-4270-a5bb-eab0ea1b4a1a", + "observation_id": "af1459fc-5bee-451a-bb1a-a6850d9369fe", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.49001748422717006 }, { - "observation_id": "76d27234-9cec-4922-ad8b-8ba62130d64d", + "observation_id": "94734722-fda2-4f02-9f67-ab68b5b0841e", "type": "aruco", "marker_id": 101, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.46225891142368675 }, { - "observation_id": "ed07d0c9-cd7b-4859-b40e-0cc7959340fa", + "observation_id": "0edd8b80-f56d-4d9d-9fd4-d799eb0c919c", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.46225891142368675 }, { - "observation_id": "b26ff251-9700-4adb-9b18-a015395cdf6f", + "observation_id": "032ff99b-d91d-4016-a0cd-75ae11b1c198", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.43211414091114625 }, { - "observation_id": "2c931e6b-0f8f-48fd-8f3b-92e562c501e7", + "observation_id": "7895db9d-08fa-47fc-ad50-e23ee7f32328", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.4377857805109236 }, { - "observation_id": "53c199fc-25ef-48ee-b65e-c57a3cdd79f1", + "observation_id": "ceb9c06b-47cf-40d0-b2cf-adee1e079dad", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene4/render_f_camera_pose.json b/data/evaluations/Scene4/render_f_camera_pose.json index 29f20a4..b423530 100644 --- a/data/evaluations/Scene4/render_f_camera_pose.json +++ b/data/evaluations/Scene4/render_f_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:19Z", + "created_utc": "2026-05-31T08:47:11Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene4\\render_f_aruco_detection.json", "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 6521e95..2a4d3cb 100644 --- a/data/evaluations/Scene4/render_g_aruco_detection.json +++ b/data/evaluations/Scene4/render_g_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:08:17Z", + "created_utc": "2026-05-31T08:47:09Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "9b5e01d5-7a7b-4405-9fbf-4f3480b53781", + "observation_id": "6f9e7654-a6e3-4795-99f3-d1098a899578", "type": "aruco", "marker_id": 229, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.5315105976452267 }, { - "observation_id": "8db0c0cf-3e08-4b88-b66b-a3cae07deade", + "observation_id": "a4292486-d21a-4af7-819c-1ed79e415381", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.5729494608527742 }, { - "observation_id": "a5fbab5b-2c65-4cca-805f-202381bfc235", + "observation_id": "03baadaa-520c-4abb-b8a7-4fada5328682", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.5765483710806963 }, { - "observation_id": "e514ea81-f091-4d0a-b68f-19a585da7354", + "observation_id": "25476efb-763e-460a-91d9-56bafa8fc456", "type": "aruco", "marker_id": 218, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.4621566714491005 }, { - "observation_id": "53f5ba0b-2685-486c-924e-34e6d4a90b3e", + "observation_id": "b6d0798e-baf1-4f7f-af0e-acddb709b755", "type": "aruco", "marker_id": 198, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.43078987568380794 }, { - "observation_id": "5c648b7d-e04d-4a72-8111-43e513c2773a", + "observation_id": "5e5f3558-11ec-4a2c-8be6-c59adbdf28d1", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.3490767193505014 }, { - "observation_id": "595f203f-2483-4dca-ae1e-11f0404a54c4", + "observation_id": "4012cbf6-af42-4029-911e-720d9601dc1e", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.5331747682067169 }, { - "observation_id": "f1d00a02-9e48-48f0-abab-e92aa6540cec", + "observation_id": "25901d5b-2a1c-4531-8299-5dc07448851d", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.43254677700170213 }, { - "observation_id": "47102b04-4e16-4b47-a10f-72871bc50f70", + "observation_id": "5cfa5600-5262-4a7d-a69d-03db75b552fc", "type": "aruco", "marker_id": 219, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.4062135721795724 }, { - "observation_id": "32d999e6-2d0d-42e8-803d-a5322f2715c7", + "observation_id": "1c33de07-23d0-4572-93c7-e3acf913a7a7", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.27711751756945874 }, { - "observation_id": "53242e2d-427b-4ddc-8750-51638d4e6930", + "observation_id": "4ab41dd3-62fb-480b-8c66-702389b226aa", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.48073279309443956 }, { - "observation_id": "c9989489-359c-445a-a01a-3ecdd39b0866", + "observation_id": "6b6d8a74-4f99-445a-82e5-091d8fc067df", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.29073788994886046 }, { - "observation_id": "f6a02c82-4b71-47e2-b774-54847e4a3823", + "observation_id": "0e8e4398-31d2-4fe2-82ce-40afd0d3dc24", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.45546210617301425 }, { - "observation_id": "e7a1927c-588c-4c51-b44b-2aa9cd080dff", + "observation_id": "e48f8c4c-995c-4d9d-b87b-dbd77a3da6e2", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.47991555158957017 }, { - "observation_id": "147a2751-f622-4a89-ad18-96f9b2b3aabe", + "observation_id": "192f4fa2-f958-42bc-82a0-59df612ac1fb", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.4668259742313624 }, { - "observation_id": "eeb536d0-5c3e-4e71-a14b-68968432c989", + "observation_id": "a16d40c2-4c9c-4ecb-beeb-4ab023808ee6", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.39662908657084783 }, { - "observation_id": "63b0bbdd-a1ae-43e9-b377-9be96b4269cb", + "observation_id": "b9e74a92-cd82-4835-b29d-151c3fb8c6c5", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.3851482516876627 }, { - "observation_id": 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a/data/evaluations/Scene5/aruco_positions_initial.json b/data/evaluations/Scene5/aruco_positions_initial.json index e860573..8ccb8d9 100644 --- a/data/evaluations/Scene5/aruco_positions_initial.json +++ b/data/evaluations/Scene5/aruco_positions_initial.json @@ -1,7 +1,7 @@ { "schema_version": "1.2", "stage": "initial_triangulation", - "created_utc": "2026-05-31T08:21:19Z", + "created_utc": "2026-05-31T08:42:49Z", "summary": { "num_cameras": 7, "num_markers": 55, diff --git a/data/evaluations/Scene5/aruco_positions_optimized.json b/data/evaluations/Scene5/aruco_positions_optimized.json index 631f4f5..75033cb 100644 --- a/data/evaluations/Scene5/aruco_positions_optimized.json +++ b/data/evaluations/Scene5/aruco_positions_optimized.json @@ -1,6 +1,6 @@ { "schema_version": "1.2", - "created_utc": "2026-05-31T08:21:30Z", + "created_utc": "2026-05-31T08:42:58Z", "summary": { "num_cameras": 7, "num_markers": 55, diff --git a/data/evaluations/Scene5/render_a_aruco_detection.json b/data/evaluations/Scene5/render_a_aruco_detection.json index fe447b5..77abbc6 100644 --- a/data/evaluations/Scene5/render_a_aruco_detection.json +++ b/data/evaluations/Scene5/render_a_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:13Z", + "created_utc": "2026-05-31T08:42:43Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "24b5ca3a-41db-473f-9635-06b99f7b47a7", + "observation_id": "c761f4cb-430b-4b63-a282-f658feb6b5bb", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9534814647781058 }, { - "observation_id": "ccfd3e28-670a-44cf-8eab-d8c1b5948ecc", + "observation_id": "28b422c4-49b0-4d12-90ab-891b182a2c1c", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.8810013242977345 }, { - "observation_id": "ccc4c2f3-7f35-47a7-bbd2-b482d15d45e3", + "observation_id": 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"1e49e9a2-cd08-40a1-9e25-89b13f5e9b3c", + "observation_id": "0245de0e-0180-48ad-a460-e92adf2f7abd", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.35589175865715006 }, { - "observation_id": "5c0e3145-d909-4b52-8e57-764f90fc102b", + "observation_id": "68b9db20-b3bb-4986-88f4-56d77372404b", "type": "aruco", "marker_id": 74, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.27942076592245474 }, { - "observation_id": "eb06e749-ad89-48c5-a364-c67b6faa7e30", + "observation_id": "f1e7ff82-9b89-443f-a1de-2cc0a6ec9910", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.31777455864589965 }, { - "observation_id": "cdf5958f-7743-49e9-85d3-451ffcdc9f57", + "observation_id": "13ae671f-af20-413b-a7f9-c9be9f4959e0", "type": "aruco", "marker_id": 81, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.2741936898356918 }, { - "observation_id": "57eaf3f2-99b1-4c47-abf8-0ade458ed6f8", + "observation_id": "6f91a102-1243-451e-bf38-1d787952fe08", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.3029173397022545 }, { - "observation_id": "2c8c1bf1-83ea-4a2a-afee-8371e0f1515d", + "observation_id": "05f40d51-1b97-404f-b6c1-e9dc3c9e1733", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.1527342148240039 }, { - "observation_id": "877b5c22-5df9-4685-8467-9b47739d7a67", + "observation_id": "bab681b9-5d5a-45e0-91f1-442117627c22", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.21946523336267398 }, { - "observation_id": "16daedea-a8a5-4028-b89b-bcb78db188a9", + "observation_id": "58784339-7959-4005-b901-1ce87ff4eb3d", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.19743498130596515 }, { - "observation_id": "700e5483-d144-4b28-a834-5bef3ee0c2f1", + "observation_id": "f98163c0-2af4-4030-899f-196273508359", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.2849187633475991 }, { - "observation_id": "45fe6f65-c879-4fb3-b44d-32b07a9f4bb0", + "observation_id": "94a9bb89-dd33-46b8-8a9d-5bf3d746c43d", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.20388582934250105 }, { - "observation_id": "2ec17798-a730-44e3-b6b6-909dafd58094", + "observation_id": "1544f57d-15b6-4f17-a9d5-2bff57eb344c", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.24627951083020566 }, { - "observation_id": "bcd5cc51-3714-4432-b493-5c4231fc11f8", + "observation_id": "44420ebd-cb9b-4c84-bca5-7e835a11c136", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.2345502390230936 }, { - "observation_id": "2689d9c1-9a2d-4199-aa68-92f9c4d7f65d", + "observation_id": "1596039b-76ad-4133-ad68-a47d8d29795e", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.17208874842045807 }, { - "observation_id": "53fe06a9-0ffe-413c-aaef-ba42cab2372d", + "observation_id": "f3bb4632-5a88-4a13-8df9-f2310bb2941b", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.19579935125865724 }, { - "observation_id": "799b654f-8cf0-4d85-ad7e-597eee275e27", + "observation_id": "006893d0-f0d9-4686-aae7-f6e6f7f55c46", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.2119841467910161 }, { - "observation_id": "9fcdd3f8-ddc7-44d6-af39-cdc55729a972", + "observation_id": "4d8b707e-fe2d-40fc-8574-d2a94ddd16c4", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.18860386268790535 }, { - "observation_id": "cf9baaf3-c327-465e-9217-84b5579c8f23", + "observation_id": "2ebe8283-ec4c-4771-a1bf-294cf55abb57", "type": "aruco", "marker_id": 93, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_a_camera_pose.json b/data/evaluations/Scene5/render_a_camera_pose.json index 7d1e499..28acb8d 100644 --- a/data/evaluations/Scene5/render_a_camera_pose.json +++ b/data/evaluations/Scene5/render_a_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:17Z", + "created_utc": "2026-05-31T08:42:46Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_a_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_b_aruco_detection.json b/data/evaluations/Scene5/render_b_aruco_detection.json index f71e6a0..5412818 100644 --- a/data/evaluations/Scene5/render_b_aruco_detection.json +++ b/data/evaluations/Scene5/render_b_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:14Z", + "created_utc": "2026-05-31T08:42:43Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "588c0d46-1f3d-4d19-a75a-ae3a42a55a3a", + "observation_id": "d11b7353-ad3d-4198-8af4-86cad918f5ce", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.5544269284959954 }, { - "observation_id": "63f5e118-42cb-4344-bec1-dbc2156a64ac", + "observation_id": "3f62861e-fdc1-452e-90d2-5af05032890c", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.5466352941176471 }, { - "observation_id": "bae3277b-58da-49a5-8644-213aa711cc80", + "observation_id": "4e6ac51f-1c0b-4bf0-905d-b6e7b3286411", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.6668638701841824 }, { - "observation_id": "e725136a-0073-40a0-b002-d7ee8c0fefb8", + "observation_id": "f5b6e6be-889e-4e8d-b5ef-ce07c237f6c6", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.5937058777020776 }, { - "observation_id": "9ed23b90-e1a1-49c8-9e42-4f59262f104d", + "observation_id": "1b021305-8aa5-4b8f-b668-d173327e187f", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.6074766727947272 }, { - "observation_id": "0f0a703f-e0fd-4682-8824-056e22b5549e", + "observation_id": "32e803f1-dc92-4a84-807d-75245e08a34e", "type": "aruco", "marker_id": 63, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.1539593300495616 }, { - "observation_id": "7ba72bdf-e6ee-422e-9b06-3c67588a6b42", + "observation_id": "c0c4d373-afc1-4743-b255-fb1af651a5b3", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.39637168760361213 }, { - "observation_id": "0620817b-77cf-4c7d-a42a-e90eff29a54b", + "observation_id": "d366451f-c999-4c35-8484-7ad52360a485", "type": 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}, { - "observation_id": "1f2d2c52-4946-4fba-a8b6-199e41c98fae", + "observation_id": "19492b51-c1ef-4c04-83ea-96c9a7c358f1", "type": "aruco", "marker_id": 242, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.3252974596452519 }, { - "observation_id": "05236c3b-a91e-4e84-a170-7fac365cc727", + "observation_id": "564c1897-13a4-4f9f-b4d4-d854ab9d1bcb", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.41815762699787656 }, { - "observation_id": "9021c19e-b309-4819-af69-d71d07acaf46", + "observation_id": "822e8efd-0dfa-445a-8552-1867bb7f96f9", "type": "aruco", "marker_id": 205, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.3958181457519531 }, { - "observation_id": "64631c0b-a483-47d5-9b33-75705f6adccf", + "observation_id": "bdae3987-ec60-460f-aec7-445001761e76", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.4015868133874269 }, { - "observation_id": "f3d8b0a0-5f98-4c90-a011-d306ad55e869", + "observation_id": "af6d1183-bdcb-4910-b9ce-7b0d1fbb6e63", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.38377189776715764 }, { - "observation_id": "96c486a1-37b2-4bf0-a9c8-8b96f7d802d6", + "observation_id": "b2fc322e-ec72-4e0e-8807-3ba56ada5f06", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.3320910299473707 }, { - "observation_id": "64f75683-fe20-4e84-b664-714931438b59", + "observation_id": "fdd4a323-88a4-4aad-b966-1a6118f1b8c7", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.3579602826436361 }, { - "observation_id": "deb89b6e-5b3e-41ac-bf7a-c21bf3e846ef", + "observation_id": "755f2b20-1679-4b89-b45c-4838530fb0e7", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.343462485354258 }, { - "observation_id": "233882bd-da72-4372-9baf-d94de676405a", + "observation_id": "a8601e00-7142-49e4-a639-4d6ed1f8628b", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.3524419738837484 }, { - "observation_id": "bc25662f-f096-450f-89c0-6fb262916011", + "observation_id": "7f8e0cbd-6ebb-4fe0-8c0c-cd0dc7edd2f3", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.1293030348501416 }, { - "observation_id": "907472c1-6607-44aa-b799-8a71254c6d2d", + "observation_id": "605dc35c-d23e-4d90-8568-d8506c0e01c7", "type": "aruco", "marker_id": 94, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.06057156860351561 }, { - "observation_id": "78f958f7-678f-4421-ace6-d19153025c0d", + "observation_id": "10d01448-c813-4a5b-8b7f-af8e62690328", "type": "aruco", "marker_id": 76, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.2459421895345052 }, { - "observation_id": "c38db3ac-f170-41c5-ac60-fc5f369b4836", + "observation_id": "b421a4da-8d76-4841-aedf-0cdbca381afc", "type": "aruco", "marker_id": 100, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.24407473894265982 }, { - "observation_id": "ba948344-e8fb-404b-b4e8-3f458aa9a511", + "observation_id": "af8676dc-dbf0-4f2b-8a58-1de7527216eb", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.060547802731330126 }, { - "observation_id": "ad60cc0e-7cca-4e7c-8e35-5570a21a742f", + "observation_id": "37bd7c8b-7fbe-4818-9a0a-ec50adaeadab", "type": "aruco", "marker_id": 81, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.24033422651029265 }, { - "observation_id": "2da01679-458e-4cf5-a226-1a61be705f15", + "observation_id": "ae90470e-c172-4c81-8cae-901dc216242b", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.23896265492072474 }, { - "observation_id": "414f9b98-cacf-4ae5-8258-7cbf53781ca4", + "observation_id": "c5ca9202-3d49-47d6-95d3-f739561a9a1b", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.24909137483284013 }, { - "observation_id": "f49c2933-14fa-4649-a2c4-744d6b371bca", + "observation_id": "eb60556e-e5cd-4ea9-88ee-eb2e45d84e47", "type": "aruco", "marker_id": 77, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.23734254719660833 }, { - "observation_id": "d12571b5-ca88-42d6-b584-12b65190ec16", + "observation_id": "637e755d-5f1b-47ac-b2a4-43f863dc9d84", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.23734254719660833 }, { - "observation_id": "3f486603-ed59-4ce3-ae3c-1b6adfcf06c8", + "observation_id": "ae1c8262-c038-4d4b-8806-d24950ce7d64", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -1990,7 +1990,7 @@ "confidence": 0.236365170112023 }, { - "observation_id": "f6af25f0-37e3-453d-be23-19f3c4543766", + "observation_id": "7c1858de-2fb9-4225-a5b6-df0d46047e0d", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -2044,7 +2044,7 @@ "confidence": 0.21925684197423947 }, { - "observation_id": "0b287fed-e401-4aa1-b682-4f93bf96e049", + "observation_id": "55bef921-c8fa-40c7-ab89-10ae3fbc61e0", "type": "aruco", "marker_id": 50, "marker_size_m": 0.025, @@ -2098,7 +2098,7 @@ "confidence": 0.21921579106955483 }, { - "observation_id": "885250a5-3580-4841-8f82-a4ef84120ee8", + "observation_id": "cf70e9d2-a952-42d6-8ec7-247c3f8b81c1", "type": "aruco", "marker_id": 104, "marker_size_m": 0.025, @@ -2152,7 +2152,7 @@ "confidence": 0.22417849731445313 }, { - "observation_id": "35e9156a-8879-4574-bce1-d77b277375f8", + "observation_id": "3ad2e99f-5014-4aa6-a85b-4af1ff3e6fa5", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -2206,7 +2206,7 @@ "confidence": 0.21217281752366285 }, { - "observation_id": "98fc5c06-c9f4-46db-8eaa-597194e19253", + "observation_id": "252303c9-04cd-41ef-a7d5-b24930d1446c", "type": "aruco", "marker_id": 67, "marker_size_m": 0.025, @@ -2260,7 +2260,7 @@ "confidence": 0.19627771759033205 }, { - "observation_id": "915f6a0f-9d48-4e3f-b87b-dbc292cfa8d5", + "observation_id": "712e48f6-7cfa-4b42-b431-e85dc7998b28", "type": "aruco", "marker_id": 88, "marker_size_m": 0.025, @@ -2314,7 +2314,7 @@ "confidence": 0.20782158780899712 }, { - "observation_id": "6eaacb2b-9e39-46fd-855d-ef3069328a3d", + "observation_id": "b5dd6078-a60f-4839-a291-9d6c973c2991", "type": "aruco", "marker_id": 70, "marker_size_m": 0.025, @@ -2368,7 +2368,7 @@ "confidence": 0.193995418548584 }, { - "observation_id": "5fe745e8-9dd1-4099-863e-067e8f491990", + "observation_id": "1dbfdb0a-6131-46a0-a382-0e319aaba49a", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -2422,7 +2422,7 @@ "confidence": 0.1722355942663244 }, { - "observation_id": "774a894f-1d02-48d7-9395-e9dbafd0171a", + "observation_id": "54b837a5-3cba-4b39-b150-37ec4435fb6b", "type": "aruco", "marker_id": 98, "marker_size_m": 0.025, @@ -2476,7 +2476,7 @@ "confidence": 0.1899538473288218 }, { - "observation_id": "0eeb1caa-299a-4708-8011-3c2b6367b5cf", + "observation_id": "35b16fa1-e00f-4cc4-ae26-1b516b8e5505", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -2530,7 +2530,7 @@ "confidence": 0.17676838843571283 }, { - "observation_id": "0abeffec-dde1-46e3-bb22-3bc301fbcc15", + "observation_id": "8f290ad0-b46b-4976-820b-7690a7a98ad7", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -2584,7 +2584,7 @@ "confidence": 0.1668050360320266 }, { - "observation_id": "ffb7d80d-339b-486f-8c58-43b81eeda069", + "observation_id": "ef4c7f94-d7fb-4155-ac61-6d9fca7943fc", "type": "aruco", "marker_id": 90, "marker_size_m": 0.025, @@ -2638,7 +2638,7 @@ "confidence": 0.17612778902053833 }, { - "observation_id": "74762d9c-a369-466a-b515-f04522b69912", + "observation_id": "8958b42c-5c59-43bb-8c83-a98457f1a9de", "type": "aruco", "marker_id": 91, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_b_camera_pose.json b/data/evaluations/Scene5/render_b_camera_pose.json index 2fa4f6e..0bb389d 100644 --- a/data/evaluations/Scene5/render_b_camera_pose.json +++ b/data/evaluations/Scene5/render_b_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:17Z", + "created_utc": "2026-05-31T08:42:47Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_b_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_c_aruco_detection.json b/data/evaluations/Scene5/render_c_aruco_detection.json index 02f336f..0cbbdb8 100644 --- a/data/evaluations/Scene5/render_c_aruco_detection.json +++ b/data/evaluations/Scene5/render_c_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:14Z", + "created_utc": "2026-05-31T08:42:44Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "db8f00a0-e0fe-48ee-bdc3-a17ffcdbca51", + "observation_id": "84d3c4bf-348d-4dde-ab3b-ba6d271c0c8d", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9034560154563519 }, { - "observation_id": "2653d539-e1a8-467d-bbe5-fb9af489ff18", + "observation_id": "dfac5e00-c922-4acf-9803-e61f35c1eaa1", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.9035563368771673 }, { - "observation_id": "a79bbe12-a08f-4a69-a0b9-1c847890331c", + "observation_id": "954c6ed2-4b69-4a90-8b7b-8a0a8f1cc371", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.9539329593288292 }, { - "observation_id": "46d3ac23-bffd-42c4-90b3-e72080eca4f6", + "observation_id": "400fd45a-ce58-4132-b1b6-ea6a6bdbf3cc", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.7440653293809782 }, { - "observation_id": "b3c96815-f471-4190-a3f6-4ef8e598850e", + "observation_id": "cf08dc4f-e93e-44b2-8221-e2e19dd752f2", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.663361454729631 }, { - "observation_id": "a85c2369-e152-4787-87f9-533f416103f7", + "observation_id": "3bef45f1-d79d-4998-b3b0-aa48b9d5a75c", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.386044632761102 }, { - "observation_id": "67690216-1fd6-494f-9383-44ec5c2fee83", + "observation_id": "a39ae8a0-dd0b-4cc0-999d-2bbe7834afc9", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.2964796698261984 }, { - "observation_id": "4d29957d-cb38-4851-a4bf-0eb159d121a3", + "observation_id": "c13ca70d-141d-45c4-abd2-1612e155990f", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.2743742952602821 }, { - "observation_id": "c7372f1a-aa97-4c18-b4ee-6a65987cbaec", + "observation_id": "146e68ea-d15b-41c5-8843-bd09bfbbbf22", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.21966133686197709 }, { - "observation_id": "4b8906c8-d1e4-4b63-8c1e-4887357b3752", + "observation_id": "42d7bd53-d94e-4084-a638-c6e112c091f4", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.233781757000696 }, { - "observation_id": "7d533fbf-16bf-457a-845a-cf3afb997e75", + "observation_id": "3e328dfd-91c0-4592-b6c2-b8b40473ab9e", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.21604275474161502 }, { - "observation_id": "7a620a20-9b11-4166-9161-b98970c99a54", + "observation_id": "2da6b99b-6c71-420d-b0ca-e633f9f7c37e", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_c_camera_pose.json b/data/evaluations/Scene5/render_c_camera_pose.json index a7ab89f..2247fa3 100644 --- a/data/evaluations/Scene5/render_c_camera_pose.json +++ b/data/evaluations/Scene5/render_c_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:17Z", + "created_utc": "2026-05-31T08:42:47Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_c_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_d_aruco_detection.json b/data/evaluations/Scene5/render_d_aruco_detection.json index f1751bf..2cda55b 100644 --- a/data/evaluations/Scene5/render_d_aruco_detection.json +++ b/data/evaluations/Scene5/render_d_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:15Z", + "created_utc": "2026-05-31T08:42:44Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "b66ea928-3b92-4519-89fa-e86cac61cfd8", + "observation_id": "8794ca46-4f25-467d-8806-a8d0eaf97c3d", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9823660345732603 }, { - "observation_id": "7010029e-a997-48c8-866f-44617da9be05", + "observation_id": "57f8ff84-d709-45fd-b5ba-4375cd358f22", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.8483779676539408 }, { - "observation_id": "5e1d9835-afef-42e7-b046-8f02dc0825a0", + "observation_id": "582e4cae-b349-46dd-a590-c9ca8c9a18fd", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.773184725081227 }, { - "observation_id": "99c4b0ba-b48d-4e67-a111-05670100fb47", + "observation_id": "1db548fa-3725-4e39-8e0c-ce552c942477", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.6280285196076234 }, { - "observation_id": "1560d728-86c5-4997-996d-fc069cadfbcd", + "observation_id": "a7ea4401-253f-401a-b951-a64fc67917e5", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.3116200930635104 }, { - "observation_id": "176325ed-058a-4f62-bb62-dce778504ec4", + "observation_id": "41541d46-c85c-4dc7-aeb0-f3c9b7bbad0c", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.5885341085448238 }, { - "observation_id": "95c22ac2-e0e7-4c0d-a0a0-0b8bd999e240", + "observation_id": "2b77bcde-9e81-4d77-898f-22df89bde662", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.06739671544456982 }, { - "observation_id": "2adfbca2-746e-46bb-ab76-8cf8f748e1f3", + "observation_id": "0d6e296d-05c6-4ddb-b18b-66bf75813e60", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.17416420855033052 }, { - "observation_id": "56e69824-1e13-482c-b765-0ab93f399fd6", + "observation_id": "ff7a8a79-ff09-444e-af95-95c99540721b", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.24540823915500745 }, { - "observation_id": "72d7b7f3-c06f-4548-90cb-a13465420c79", + "observation_id": "d2135a4c-4e4e-4954-b7e4-357c77e784d2", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.43828628344822296 }, { - "observation_id": "19f966ba-ca36-47cf-a2ba-80a903059aa5", + "observation_id": "1e50399a-c642-429e-8925-f8ba4336d21e", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.32624039952470835 }, { - "observation_id": "6773dc75-678e-4d64-8536-ab158134abaf", + "observation_id": "470a817f-3fb6-4d24-b213-c0b3c74d831d", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.4432762787121197 }, { - "observation_id": "c7a41861-ca38-429d-b549-1e8eac217c12", + "observation_id": "9d07062c-fdbb-4d10-8d57-aa7972229a43", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.3071912693949274 }, { - "observation_id": "9a9987dc-e8f9-480c-ac22-fe76f20da185", + "observation_id": "1ed8b283-8343-42b6-8414-ac77750675bc", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.018333813047618722 }, { - "observation_id": "da77754a-385a-49cd-8bb8-ebc52e941a31", + "observation_id": "abe4fe3f-b920-4b60-ac6f-9be70a900070", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.2483175954884472 }, { - "observation_id": "3f23332a-b3f3-4727-b6e0-354b1c25edf3", + "observation_id": "f3e7de1f-21cd-4564-a230-6b4796bc01d8", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.07285288505606423 }, { - "observation_id": "b8c37868-8110-4f6e-a657-9a9805ff66f4", + "observation_id": "7fd80950-5c71-4e32-9ec7-6ead0dea1c14", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_d_camera_pose.json b/data/evaluations/Scene5/render_d_camera_pose.json index 1c256e7..8d3d7f8 100644 --- a/data/evaluations/Scene5/render_d_camera_pose.json +++ b/data/evaluations/Scene5/render_d_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:18Z", + "created_utc": "2026-05-31T08:42:47Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_d_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_e_aruco_detection.json b/data/evaluations/Scene5/render_e_aruco_detection.json index d73fffe..2b4ad00 100644 --- a/data/evaluations/Scene5/render_e_aruco_detection.json +++ b/data/evaluations/Scene5/render_e_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:15Z", + "created_utc": "2026-05-31T08:42:45Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "c0e94a71-dcea-45b4-9762-baf58d2cf453", + "observation_id": "0560b2a5-8ef9-41fa-9ac0-454b73f9841c", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.8333996992934779 }, { - "observation_id": "05205186-a5cc-4ad5-a169-a11e30b31088", + "observation_id": "6911feae-cce2-4899-8dbf-e5e6e359fca7", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.7377098688391879 }, { - "observation_id": "46e65d5e-e3c5-4a2d-8845-cdef81f22218", + "observation_id": "3086c23f-bc27-4917-9f2d-19f60d4de959", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.15369374989294518 }, { - "observation_id": "54703b58-3f0e-4b76-af46-ce2b48ee776b", + "observation_id": "16ac011b-74d5-4cb5-9386-651052cbdbe1", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.3233651345714012 }, { - "observation_id": "c67efa6a-3ae2-4232-bd93-b60ac81a993f", + "observation_id": "4d6714b0-7acc-4fbc-8ae6-1a97cd36c5a4", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.420622587927407 }, { - "observation_id": "c0327186-7474-44db-a32a-16d73d73397b", + "observation_id": "22bdddbc-79ff-455c-a144-26e18c3a1b1c", "type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.3128949822399185 }, { - "observation_id": "05d7c0cd-50f7-41c0-9aef-13975f511d76", + "observation_id": "ef8479f3-6c0f-49a9-a433-720daab97167", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.15136171428561934 }, { - "observation_id": "cca86284-8bbb-4dce-ad41-89316a2c396e", + "observation_id": "8dc2a83d-17de-47d0-b389-af9dce053f85", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.22596816505744277 }, { - "observation_id": "d5744d0a-646b-4ddf-a6c9-61b0eb2903c6", + "observation_id": "5e3256bb-f2d6-4e66-a115-23507a477416", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.24626971873720385 }, { - "observation_id": "b0d95d89-9ab5-45d9-b0b7-c4ef04bb9a1b", + "observation_id": "af011a41-b969-47df-bb30-3fe5331c5a7d", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.2205630261207473 }, { - "observation_id": "f56654d5-7459-40ae-a14d-82b29ccdb6c9", + "observation_id": "13bc6144-af72-4682-9b48-6d15714e4971", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.2664122029520185 }, { - "observation_id": "52e4ca3d-e0c9-46ad-a511-1883a532d51d", + "observation_id": "8b5d29c8-6044-4d33-9a69-bc80b8e584c7", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.2093822185726055 }, { - "observation_id": "5ff502f5-e5b6-4228-ba6e-f0104abd79ff", + "observation_id": "3adf62da-cfee-41dd-97d0-6680a58e6afd", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.16725963004706537 }, { - "observation_id": "c8b00936-e558-4bd9-8dcf-cc7f76f8def3", + "observation_id": "503d318f-9f46-4d8f-8d44-601728b0ed16", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.22298824455832925 }, { - "observation_id": "a1701a39-ac58-4f11-b95b-641b75ba3e95", + "observation_id": "2f72821e-062d-499c-8ec7-26d0ddb5da1e", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.16812950449251388 }, { - "observation_id": "d7b923d6-c6a5-436a-bc99-5a56d8073983", + "observation_id": "3b4b8dd6-3d0c-4add-82bd-a728c455f6eb", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.1697872859143744 }, { - "observation_id": "1f6b51b6-4c4d-4fc5-b261-02a5cabca6d7", + "observation_id": "b827a9f8-f4f2-4f3b-b18c-e311d9d1f998", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.12157403976480062 }, { - "observation_id": "849e0cd6-d007-4331-b2e0-1d8ddee78e38", + "observation_id": "0619d55f-f80b-4ab0-b321-d85489c60901", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.14922985097126407 }, { - "observation_id": "c50f5244-d5c4-4d10-887f-0c8f2ad5e41c", + "observation_id": "fde38de1-33bc-4a4c-9ad6-2f68998a4665", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.12136831092318832 }, { - "observation_id": "e5bbe289-68e3-43b9-829f-657910b1fa83", + "observation_id": "b520c887-a82c-47aa-a811-922c306ed643", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.16137616188441373 }, { - "observation_id": "90bc6b9b-8e41-4159-bdd1-e692fa0813ec", + "observation_id": "d5e14ff1-73f6-44fd-b09a-0e6df54f7aa3", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.13595850692924102 }, { - "observation_id": "c6e01981-feec-41d7-a39a-1000df8edb23", + "observation_id": "831ac4df-c7c6-450d-bfa4-3bde9e893fab", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.09610181165120435 }, { - "observation_id": "59b1df44-e7e7-4535-8c81-7ddac3172ef5", + "observation_id": "3fd6f467-4e76-4716-8772-de9e96cbd9a4", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.13307488759358727 }, { - "observation_id": "833dc3f2-88f0-4b40-b493-3be7c5c341a2", + "observation_id": "9d250265-fac3-4cd1-9502-bfcac1f4c76d", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.09071710124676984 }, { - "observation_id": "e39d85d3-5d63-422c-92ab-fdb3107425eb", + "observation_id": "93069c05-9e48-436f-a65c-a1e1f631cedf", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.1273664826027396 }, { - "observation_id": "ec2ff593-dd48-40f4-b4dd-a830e14e1274", + "observation_id": "ce65c581-87b0-488d-9f43-30f1e022dc3e", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.09857192946593334 }, { - "observation_id": "c6ab9bc6-f84f-413e-9c23-7c5bf83c402f", + "observation_id": "582b3c33-526b-4d81-a560-caf25ffa2acf", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.1006702290166984 }, { - "observation_id": "3c9251ed-f116-426b-ab6b-9e9e154e8e25", + "observation_id": "e3335908-fc50-461c-a159-cacfddfc967b", "type": "aruco", "marker_id": 116, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_e_camera_pose.json b/data/evaluations/Scene5/render_e_camera_pose.json index 86bc080..253b035 100644 --- a/data/evaluations/Scene5/render_e_camera_pose.json +++ b/data/evaluations/Scene5/render_e_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:18Z", + "created_utc": "2026-05-31T08:42:48Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_e_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_f_aruco_detection.json b/data/evaluations/Scene5/render_f_aruco_detection.json index 73e63cd..1c6bffc 100644 --- a/data/evaluations/Scene5/render_f_aruco_detection.json +++ b/data/evaluations/Scene5/render_f_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:16Z", + "created_utc": "2026-05-31T08:42:45Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "e95e8090-a2db-4c50-ae45-597814af932c", + "observation_id": "c18cec63-a094-4791-a70a-20031af5cf70", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9768332578354781 }, { - "observation_id": "f67cc46d-d0b3-4058-954e-a5d643bfff45", + "observation_id": "dc78f01c-8180-4506-9e4c-b7a4f66ac6b0", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 1.0 }, { - "observation_id": "d57f16ed-7857-4fee-b8ae-62a1acec4677", + "observation_id": "62075a0a-1f58-4270-b029-028a08b639f1", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.9517473802485018 }, { - "observation_id": "30f76638-b703-4a2a-9be5-1091ae206c50", + "observation_id": "694e007e-304b-4b69-8242-c48ab5cbaad8", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.5995617283681293 }, { - "observation_id": "99b26086-57e7-4332-82f9-819191ab12fe", + "observation_id": "a6bb6030-d055-41a2-948e-95347c1951b3", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.569827819836196 }, { - "observation_id": "50bf6744-5827-4674-a6ea-f1cd40286b62", + "observation_id": "fe289c19-f3b8-49a6-9ddc-f2f41456d9a7", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.2988751797371847 }, { - "observation_id": "ec2f3120-64c3-4f9c-990e-7f5cd0bc0d56", + "observation_id": "9ba72aa4-9e67-4e44-aab8-c82d3e9236da", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.5697620483284124 }, { - "observation_id": "8bae909a-1b6f-41b5-99d3-e023b0e89be1", + "observation_id": "7a0f80a2-ba38-4c47-a5ac-7cf04100cc93", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.5768583617164568 }, { - "observation_id": "87f4538c-1ded-4577-b8f0-a4c8e3d33675", + "observation_id": "16847af8-d13b-4d36-a410-c869021b102d", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.5593704450665263 }, { - "observation_id": "077f1fda-6644-48ec-8c75-d8358e74e232", + "observation_id": "7f5916a3-afe2-4090-944d-fe50b32f3a14", "type": "aruco", "marker_id": 54, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.5766374039623903 }, { - "observation_id": "f97119e2-611b-4f5e-83d9-ff7abc01f022", + "observation_id": "6a8bfd89-66d3-47ce-8545-901f04d04a8c", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.21364841201201337 }, { - "observation_id": "55b1461d-e905-4a5b-a1ac-22e9ad47d2d8", + "observation_id": "7d4224fd-b358-4131-9223-016886ea9f8e", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.44403921568627447 }, { - "observation_id": "e61fcf99-3266-48be-b9fd-a75d35e5b090", + "observation_id": "70940107-9231-4606-814c-b40e29869a3f", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.5705029634947052 }, { - "observation_id": "052b4c90-e020-448e-9e41-94ef2bdd0eb4", + "observation_id": "cf8f995e-abee-4f4d-a5f4-1f3f2b94c8d2", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.5440516905787338 }, { - "observation_id": "1f35a840-c4cc-476a-94e8-d01acb01b62f", + "observation_id": "f1df1cdb-1bea-4acb-9e20-f93319a963ca", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.551983363957988 }, { - "observation_id": "cea78b54-c01c-4832-aafc-911f2748ff2c", + "observation_id": "26d1092b-3ffa-44af-833b-2296cf3e9c34", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.5459402660246769 }, { - "observation_id": "c5f0544a-3393-46d4-bbd7-c197c61480d5", + "observation_id": "a0f8200c-6831-4ce6-b86a-c7def9956675", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.06519221426443914 }, { - "observation_id": "9fb88644-be26-4e2d-80ac-9ef6dd485933", + "observation_id": "aab66f46-28ae-47f3-8afd-d568b7bb8405", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.5349107016469481 }, { - "observation_id": "ac6a09bc-efa9-4554-a8da-fd5cc32461ef", + "observation_id": "2edd0ed2-186a-4dd2-9d4a-6b1ce173d0bc", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.31811398390251094 }, { - "observation_id": "91c2bb05-18e7-4b47-8006-7b566660d3eb", + "observation_id": "9f1aac2d-1482-4410-a05d-235c5aaa3b5a", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.5227826547122322 }, { - "observation_id": "d5b1871e-6658-4dbe-b139-59412cc047de", + "observation_id": "ef377148-e90e-4feb-be16-e092bf11cac6", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.5111500432161571 }, { - "observation_id": "065f3bf9-94ec-4132-b5da-d22946ac0b1c", + "observation_id": "c3b5ff89-5183-4afb-8636-99b2fe696748", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.5046333482067283 }, { - "observation_id": "57e33bd2-4ae0-4731-8fe4-f5eb9212a704", + "observation_id": "915f6c2f-bbf2-4816-9aa9-270d271d58f5", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.13607001327160623 }, { - "observation_id": "8d7fccea-d735-4ae9-baf6-81a9b6ea3c89", + "observation_id": "4d729cfb-9bf3-4304-8959-63919bdffd88", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.46988879526830113 }, { - "observation_id": "9ac45d35-3bdd-41cd-a162-3046f88eefc5", + "observation_id": "71997848-bcbf-4cf2-a6d4-d96c9d7e42ba", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "fbed1729-0a73-47ae-a98d-91359d622e19", + "observation_id": "7435797c-2233-48c7-b35e-186faf659d40", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "5603547d-e7d7-4809-ac51-d9b34159916d", + "observation_id": "df7e2358-01d3-4ab0-ad95-17492c02cf94", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.49001748422717006 }, { - "observation_id": "7e8e1d38-bf1f-4225-9a40-f7b0fa0b5ca2", + "observation_id": "1e81fe90-ec7c-4d87-9665-d51c5340f873", "type": "aruco", "marker_id": 101, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.46225891142368675 }, { - "observation_id": "84a8c914-1c59-40ed-9b70-f4798f1b8a09", + "observation_id": "59e9f6c4-1345-4730-95d6-c047a79976fe", "type": "aruco", "marker_id": 74, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.4548532843889077 }, { - "observation_id": "1e35273c-1c4b-47c4-9af0-3a7d3ce71fb4", + "observation_id": "e0d31f97-08f9-47c0-a926-606c29069161", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.43211414091114625 }, { - "observation_id": "13cad369-6b3e-457f-8d1b-68e972053939", + "observation_id": "1dc06cab-7395-47e6-a8d1-86788068cb33", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.45818248585417487 }, { - "observation_id": "ab5f28e5-f1e0-4073-954f-83829db72851", + "observation_id": "139aa570-7cef-4ee1-90be-54cd72ca05af", "type": "aruco", "marker_id": 77, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.422956152719696 }, { - "observation_id": "02ea673d-a180-4f24-b503-f602b965d103", + "observation_id": "8201b8e1-9cbc-47dc-af62-5a877ba4c381", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.4377857805109236 }, { - "observation_id": "1a1312ca-6953-4035-8576-26438a217671", + "observation_id": "052e70d7-fd4b-4d2a-bb4f-718b21000a90", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_f_camera_pose.json b/data/evaluations/Scene5/render_f_camera_pose.json index f44ef4b..c262b67 100644 --- a/data/evaluations/Scene5/render_f_camera_pose.json +++ b/data/evaluations/Scene5/render_f_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:18Z", + "created_utc": "2026-05-31T08:42:48Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_f_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_g_aruco_detection.json b/data/evaluations/Scene5/render_g_aruco_detection.json index 8b4cfbe..4b392ec 100644 --- a/data/evaluations/Scene5/render_g_aruco_detection.json +++ b/data/evaluations/Scene5/render_g_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:16Z", + "created_utc": "2026-05-31T08:42:46Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "4fe9c897-0f72-4ed9-8a06-f554c33da25e", + "observation_id": "49b8d799-5520-486c-9559-da08558b6b1c", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.7460587402140983 }, { - "observation_id": "e5cb4b7f-15b7-4db9-b823-461ad8df1ec4", + "observation_id": "48790d3a-1f5f-4eb9-81cb-023e5f5f3457", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.5903116652341175 }, { - "observation_id": "5a8a5328-1d2f-4bcf-b4bc-90b9fc8707a3", + "observation_id": 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@@ -640,7 +640,7 @@ "confidence": 0.45546210617301425 }, { - "observation_id": "cee68598-3fd8-4bef-864a-5e13793c1c23", + "observation_id": "35449c11-ef4c-4d9e-ad8f-d9953b1f6d84", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.4795917219326271 }, { - "observation_id": "81b84eca-b0fc-4075-b3bc-fb56ecba370e", + "observation_id": "fe5d7871-0f7a-474d-8b0c-6469210c184c", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.4668259742313624 }, { - "observation_id": "de8cd73d-3cf0-4a93-bb69-df03b22e4952", + "observation_id": "bfa68a7d-cce8-4397-97ea-df7e4a8ec5a2", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.39662908657084783 }, { - "observation_id": "5a74b66a-157e-42c6-93d6-6bcc27969a51", + "observation_id": "dffa60a0-4146-47fd-8d9f-cc49052b0990", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.4561015937422635 }, { - "observation_id": "3df74bbc-66e1-4221-b673-47f7c82df41d", + "observation_id": "aa1c19f6-7bba-413e-bfe9-8505e17a679f", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.1698001278724927 }, { - "observation_id": "b47310cf-ceb2-4aaa-9d5d-f9d546b7f955", + "observation_id": "0e99b62e-8d91-4ffc-9781-9337d4c1110c", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.07718187630567852 }, { - "observation_id": "760c36b2-e124-4f30-a7e1-62647eb2cf26", + "observation_id": "a5fb70f5-d3f0-430c-a099-fcdf2631833b", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.4316478958478293 }, { - "observation_id": "0dd3cb1e-4e09-4dbb-b8ee-5bc9da27d73a", + "observation_id": "1b175bc7-145b-4aee-9f70-67e4ef1be3f3", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.34608319473201704 }, { - "observation_id": "e75c5ba0-8988-4ee9-abf3-4de81afc4cf0", + "observation_id": "c8f7c863-be70-466f-b10b-39036bb11a6f", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.40793535669071385 }, { - "observation_id": "2b5f838b-d458-4e89-865d-a1a722955e1c", + "observation_id": "33d594d7-1868-4076-b5ff-7c250935b0b7", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.4181682027393045 }, { - "observation_id": "b4844ee9-6c7f-4fd7-a7c8-c1e2e361d9d0", + "observation_id": "dd642fc0-076b-4d38-a65c-b989b7f7e3af", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.39668368657567366 }, { - "observation_id": "f9d98aee-a33b-4ec2-a927-54893f8d8977", + "observation_id": "275d5708-f277-4063-94e2-d3f95e315f1f", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.3577321830843512 }, { - "observation_id": "084e2a6b-0a51-4725-9e1c-4f4654a03566", + "observation_id": "daeae7dd-8029-47f0-86a9-28fa078f88b2", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.3829765144945479 }, { - "observation_id": "255faa60-7211-41b5-a2e9-cd14315fb057", + "observation_id": "e6a252f1-f603-4956-b0e5-38657db6fc54", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.3382587531306593 }, { - "observation_id": "668da36f-a13f-40cb-b9bb-545cc52074ac", + "observation_id": "9d3d5995-3496-40f8-9278-fad09371401e", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.3623898971459892 }, { - "observation_id": "5afbce54-d68b-49b0-b40b-e6465bc2ef80", + "observation_id": "cf05eaea-ba81-40e2-9796-9ee8ea44a111", "type": "aruco", "marker_id": 54, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.3590718541167963 }, { - "observation_id": "7ed4ec9f-51b5-40ae-a067-ab36a05bf225", + "observation_id": 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"type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.2969848480224609 }, { - "observation_id": "8e3b2080-b4d7-4c1a-8bd8-62a3d70c9a0a", + "observation_id": "397d2732-8d26-4988-8b07-ac19b226b192", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.3170967427530611 }, { - "observation_id": "e17eee5c-f43e-4c24-8f05-91b066e9c678", + "observation_id": "958c396b-a150-45bd-97bc-48f6e23d340a", "type": "aruco", "marker_id": 59, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.3170967427530611 }, { - "observation_id": "6483bd61-d1a9-4317-9e3a-6d9841b82c9b", + "observation_id": "2774c487-6eea-40f7-982b-5144dc470505", "type": "aruco", "marker_id": 48, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.17828567290874034 }, { - "observation_id": "05ddf5d6-1889-4604-a5c4-1e7960adb5b5", + "observation_id": "193bc817-0696-4aff-9a28-4d1190d20f7c", "type": "aruco", "marker_id": 57, "marker_size_m": 0.025, @@ -1990,7 +1990,7 @@ "confidence": 0.3167426670523156 }, { - "observation_id": "9a9b9c50-c029-42fb-82c8-fe72f30f5dc2", + "observation_id": "0bb777e3-4efa-47ec-8572-15d8a4230a72", "type": "aruco", "marker_id": 63, "marker_size_m": 0.025, @@ -2044,7 +2044,7 @@ "confidence": 0.15449597168691093 }, { - "observation_id": "a2cb5bb9-b055-40ee-afb8-79a8e5e14ecc", + "observation_id": "f1548dcd-6fa7-49cd-8352-3f3a5d756999", "type": "aruco", "marker_id": 71, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_g_camera_pose.json b/data/evaluations/Scene5/render_g_camera_pose.json index 341016b..dfb1cbe 100644 --- a/data/evaluations/Scene5/render_g_camera_pose.json +++ b/data/evaluations/Scene5/render_g_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:21:19Z", + "created_utc": "2026-05-31T08:42:48Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_g_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/robot_state.json b/data/evaluations/Scene5/robot_state.json new file mode 100644 index 0000000..fa4e74d --- /dev/null +++ b/data/evaluations/Scene5/robot_state.json @@ -0,0 +1,5313 @@ +{ + "schema_version": "1.0", + "method": "deterministic_geometric_sequential_prefix", + "created_utc": "2026-05-31T08:42:59Z", + "root_pose": { + "translation_m": [ + 0.0010717433490066708, + 0.002672965009129998, + -0.0023728043812698517 + ], + "rotation_matrix": [ + [ + 0.9999894046982626, + 0.0043895703201625455, + -0.0013864211549890536 + ], + [ + -0.004374191049197013, + 0.9999309432865576, + 0.010907571256674815 + ], + [ + 0.0014342049643540089, + -0.01090139121665954, + 0.9999395495357014 + ] + ], + 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0.21405166387557983, + -0.0764177069067955, + 0.34054282307624817 ], "position_mm": [ - 311.91036105155945, - -186.46296858787537, - 8.502699434757233 + 214.05166387557983, + -76.4177069067955, + 340.54282307624817 ], "rotation_quaternion": [ - 0.3547179698944092, - 0.08097859472036362, - -0.8743175268173218, - -0.3212265372276306 + 0.2647150754928589, + 0.453290730714798, + -0.008107217960059643, + -0.8511097431182861 ], "normal": [ - -0.6722972393035889, - 0.5042588710784912, - -0.5419773459434509 + -0.7758923768997192, + -0.22618548572063446, + 0.5889236330986023 ] }, { @@ -2292,25 +2292,25 @@ "id": 43, "link": "FingerB", "position_m": [ - 0.35388419032096863, - -0.17790545523166656, - 0.03092682547867298 + 0.26951199769973755, + -0.07608000934123993, + 0.34051966667175293 ], "position_mm": [ - 353.8841903209686, - -177.90545523166656, - 30.92682547867298 + 269.51199769973755, + -76.08000934123993, + 340.51966667175293 ], "rotation_quaternion": [ - 0.4389708638191223, - 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+ 0.12353380024433136 ] }, { @@ -2344,25 +2344,25 @@ "id": 45, "link": "FingerB", "position_m": [ - 0.35379403829574585, - -0.19850373268127441, - 0.01837557554244995 + 0.2614270746707916, + -0.055022913962602615, + 0.3319719433784485 ], "position_mm": [ - 353.79403829574585, - -198.5037326812744, - 18.37557554244995 + 261.4270746707916, + -55.022913962602615, + 331.9719433784485 ], "rotation_quaternion": [ - 0.6868346929550171, - 0.43043267726898193, - 0.5849026441574097, - -0.029576977714896202 + 0.18035854399204254, + -0.6729050278663635, + -0.44777974486351013, + -0.5605023503303528 ], "normal": [ - 0.7780009508132935, - -0.6258714199066162, - -0.05476672202348709 + 0.592807948589325, + 0.7446916103363037, + -0.30661579966545105 ] } ] \ No newline at end of file diff --git a/data/simulation/debug/render.npz b/data/simulation/debug/render.npz index 6243600..c76f892 100644 Binary files a/data/simulation/debug/render.npz and b/data/simulation/debug/render.npz differ diff --git a/data/simulation/debug/render.png b/data/simulation/debug/render.png index 46296c4..e5e5987 100644 Binary files a/data/simulation/debug/render.png and b/data/simulation/debug/render.png differ diff --git a/pipeline/3_multiview_bundle_adjustment_v5.py b/pipeline/3_multiview_bundle_adjustment_v5.py new file mode 100644 index 0000000..0381948 --- /dev/null +++ b/pipeline/3_multiview_bundle_adjustment_v5.py @@ -0,0 +1,1462 @@ +#!/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/4_robotState_estimation_v7.py b/pipeline/4_robotState_estimation_v7.py new file mode 100644 index 0000000..1e8dc83 --- /dev/null +++ b/pipeline/4_robotState_estimation_v7.py @@ -0,0 +1,484 @@ +#!/usr/bin/env python3 +""" +4_robotState_estimation_v7.py + +Estimate the Arm1 joint angle from ArUco marker positions. + +What this script does +--------------------- +1. Loads a robot configuration JSON and extracts the local marker positions of Arm1. +2. Loads an observation JSON with marker positions (e.g. aruco_positions_optimized.json). +3. Matches observed markers to Arm1 markers by marker_id. +4. Estimates the Arm1 angle in two ways: + + Option 1 (single marker, optional): + ---------------------------------- + If a world-space joint origin is known, estimate angle from one marker by comparing + the vector from joint origin -> marker in the model and in the observation. + + Option 2 (marker pairs, default and robust): + ------------------------------------------ + For every pair of Arm1 markers, estimate the rotation angle from the relative vector + between the two markers. This is translation-invariant and usually the preferred method. + +5. Aggregates all valid estimates using a weighted circular mean and reports: + mean angle, circular variance, circular std, linear std on wrapped samples, and per-estimate details. + +Coordinate conventions +---------------------- +The provided robot config says: +- right-handed coordinate system +- x right, y backward, z up + +Arm1 rotates around the x-axis (joint axis [-1, 0, 0] in the supplied config). +The script therefore works in 3D and automatically respects the joint axis sign. + +Usage +----- + python 4_robotState_estimation_v7.py \ + --robot-json robot_running_2026_05_30.json \ + --aruco-json aruco_positions_optimized.json + +Optional: + --joint-origin-world X Y Z # enables single-marker estimates + --output-json result.json + --arm-link Arm1 + +The script prints a concise report and can also write a JSON summary. + +Notes +----- +- This script is intentionally defensive: it accepts positions in meters or millimeters. +- It does not require any additional robot state beyond the Arm1 marker geometry. +- If only a subset of Arm1 markers is observed, the pairwise estimator still works. +""" + +from __future__ import annotations + +import argparse +import json +import math +from dataclasses import dataclass, asdict +from itertools import combinations +from pathlib import Path +from typing import Dict, Iterable, List, Optional, Sequence, Tuple + +import numpy as np + + +# ---------------------------- +# Data structures +# ---------------------------- + +@dataclass +class MarkerSpec: + marker_id: int + local_position_mm: np.ndarray + + +@dataclass +class MarkerObservation: + marker_id: int + position_mm: np.ndarray + link: Optional[str] = None + + +@dataclass +class AngleEstimate: + source: str + angle_rad: float + angle_deg: float + weight: float + marker_ids: Tuple[int, ...] + + +# ---------------------------- +# Helpers +# ---------------------------- + +def _as_np3(value: Sequence[float]) -> np.ndarray: + arr = np.asarray(value, dtype=float).reshape(3) + return arr + + +def load_json(path: Path) -> dict: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + +def unit_scale_to_mm(units: Optional[str]) -> float: + if not units: + return 1.0 + u = str(units).strip().lower() + if u in {"mm", "millimeter", "millimeters"}: + return 1.0 + if u in {"m", "meter", "meters"}: + return 1000.0 + if u in {"cm", "centimeter", "centimeters"}: + return 10.0 + # conservative default + return 1.0 + + +def normalize_angle_rad(angle: float) -> float: + """Wrap to (-pi, pi].""" + return (angle + math.pi) % (2 * math.pi) - math.pi + + +def angle_between_vectors(v_from: np.ndarray, v_to: np.ndarray, axis: np.ndarray) -> float: + """ + Signed angle rotating v_from onto v_to around axis. + + Uses atan2( axis_hat · (v_from × v_to), v_from · v_to ). + """ + a = np.asarray(axis, dtype=float).reshape(3) + an = np.linalg.norm(a) + if an == 0: + raise ValueError("Rotation axis must be non-zero.") + a = a / an + + v1 = np.asarray(v_from, dtype=float).reshape(3) + v2 = np.asarray(v_to, dtype=float).reshape(3) + + n1 = np.linalg.norm(v1) + n2 = np.linalg.norm(v2) + if n1 == 0 or n2 == 0: + raise ValueError("Cannot compute angle for zero-length vector.") + cross_term = float(np.dot(a, np.cross(v1, v2))) + dot_term = float(np.dot(v1, v2)) + return math.atan2(cross_term, dot_term) + + +def weighted_circular_mean(angles_rad: Sequence[float], weights: Sequence[float]) -> Tuple[float, float, float]: + """ + Returns: + mean_angle_rad, circular_variance, circular_std_rad + + circular_variance = 1 - R, where R is the normalized resultant length. + circular_std_rad = sqrt(-2 ln R) if R>0 else pi. + """ + if len(angles_rad) == 0: + raise ValueError("No angles provided.") + + ang = np.asarray(angles_rad, dtype=float) + w = np.asarray(weights, dtype=float) + w = np.clip(w, 0.0, None) + + if np.sum(w) <= 0: + w = np.ones_like(w) + + s = np.sum(w * np.sin(ang)) + c = np.sum(w * np.cos(ang)) + mean = math.atan2(s, c) + + R = math.sqrt(s * s + c * c) / float(np.sum(w)) + R = min(max(R, 0.0), 1.0) + + circular_variance = 1.0 - R + circular_std = math.sqrt(max(0.0, -2.0 * math.log(max(R, 1e-15)))) if R > 0 else math.pi + return mean, circular_variance, circular_std + + +def weighted_linear_std_rad(angles_rad: Sequence[float], mean_rad: float, weights: Sequence[float]) -> float: + if len(angles_rad) == 0: + return float("nan") + ang = np.asarray([normalize_angle_rad(a - mean_rad) for a in angles_rad], dtype=float) + w = np.asarray(weights, dtype=float) + w = np.clip(w, 0.0, None) + if np.sum(w) <= 0: + w = np.ones_like(w) + mu = np.sum(w * ang) / np.sum(w) + var = np.sum(w * (ang - mu) ** 2) / np.sum(w) + return float(math.sqrt(max(0.0, var))) + + +def extract_arm_markers(robot: dict, arm_link_name: str) -> Tuple[np.ndarray, List[MarkerSpec], np.ndarray]: + """ + Returns: + joint_origin_mm, markers, joint_axis + """ + links = robot.get("links", {}) + if arm_link_name not in links: + # case-insensitive fallback + lookup = {k.lower(): k for k in links.keys()} + if arm_link_name.lower() not in lookup: + raise KeyError(f"Link '{arm_link_name}' not found in robot.json.") + arm_link_name = lookup[arm_link_name.lower()] + + arm = links[arm_link_name] + jt = arm.get("jointToParent", {}) + joint_origin_mm = _as_np3(jt.get("origin", [0, 0, 0])) + joint_axis = _as_np3(jt.get("axis", [1, 0, 0])) + + markers = [] + for m in arm.get("markers", []): + if "id" not in m or "position" not in m: + continue + markers.append(MarkerSpec(marker_id=int(m["id"]), local_position_mm=_as_np3(m["position"]))) + + if not markers: + raise ValueError(f"No markers found on link '{arm_link_name}'.") + + return joint_origin_mm, markers, joint_axis + + +def load_observations(observations_json: dict) -> List[MarkerObservation]: + markers = observations_json.get("markers", []) + units = observations_json.get("units", {}) + scale = 1.0 + if isinstance(units, dict): + scale = unit_scale_to_mm(units.get("length")) + else: + scale = unit_scale_to_mm(units) + + obs: List[MarkerObservation] = [] + for m in markers: + if "marker_id" not in m and "id" not in m: + continue + marker_id = int(m.get("marker_id", m.get("id"))) + if "position_mm" in m: + pos = _as_np3(m["position_mm"]) + elif "position_m" in m: + pos = _as_np3(m["position_m"]) * 1000.0 + elif "position" in m: + pos = _as_np3(m["position"]) * scale + else: + continue + obs.append(MarkerObservation(marker_id=marker_id, position_mm=pos, link=m.get("link"))) + return obs + + +def match_markers(specs: Sequence[MarkerSpec], observations: Sequence[MarkerObservation]) -> Dict[int, Tuple[np.ndarray, np.ndarray]]: + """ + Returns marker_id -> (local_position_mm, observed_position_mm) + """ + spec_map = {m.marker_id: m.local_position_mm for m in specs} + out = {} + for o in observations: + if o.marker_id in spec_map: + out[o.marker_id] = (spec_map[o.marker_id], o.position_mm) + return out + + +def estimate_pairwise_angles( + matched: Dict[int, Tuple[np.ndarray, np.ndarray]], + axis: np.ndarray, + min_baseline_mm: float = 1.0, +) -> List[AngleEstimate]: + estimates: List[AngleEstimate] = [] + ids = sorted(matched.keys()) + + for id1, id2 in combinations(ids, 2): + p1_local, p1_obs = matched[id1] + p2_local, p2_obs = matched[id2] + + v_local = p2_local - p1_local + v_obs = p2_obs - p1_obs + + baseline_local = float(np.linalg.norm(v_local)) + baseline_obs = float(np.linalg.norm(v_obs)) + if baseline_local < min_baseline_mm or baseline_obs < min_baseline_mm: + continue + + try: + angle = angle_between_vectors(v_local, v_obs, axis) + except ValueError: + continue + + # Weight long baselines a little more strongly; if a pair is very short, it becomes noisy. + weight = max(1e-6, baseline_local * baseline_obs) + estimates.append( + AngleEstimate( + source="pair", + angle_rad=normalize_angle_rad(angle), + angle_deg=math.degrees(normalize_angle_rad(angle)), + weight=weight, + marker_ids=(id1, id2), + ) + ) + return estimates + + +def estimate_single_marker_angles( + matched: Dict[int, Tuple[np.ndarray, np.ndarray]], + joint_origin_world_mm: np.ndarray, + axis: np.ndarray, +) -> List[AngleEstimate]: + """ + Single-marker estimates require a known world-space joint origin. + + For each marker, compare: + v_local = marker_local - joint_origin_local + v_obs = marker_obs - joint_origin_world + + In the provided Arm1 config the joint origin is the local origin of Arm1, so + joint_origin_local is taken as [0,0,0]. + """ + estimates: List[AngleEstimate] = [] + joint_origin_world_mm = _as_np3(joint_origin_world_mm) + + for marker_id, (p_local, p_obs) in matched.items(): + v_local = p_local # Arm1 local origin is at the joint + v_obs = p_obs - joint_origin_world_mm + + if np.linalg.norm(v_local) < 1e-9 or np.linalg.norm(v_obs) < 1e-9: + continue + + try: + angle = angle_between_vectors(v_local, v_obs, axis) + except ValueError: + continue + + weight = float(np.linalg.norm(v_local)) + estimates.append( + AngleEstimate( + source="single", + angle_rad=normalize_angle_rad(angle), + angle_deg=math.degrees(normalize_angle_rad(angle)), + weight=weight, + marker_ids=(marker_id,), + ) + ) + return estimates + + +def build_report( + arm_link_name: str, + joint_origin_mm: np.ndarray, + joint_axis: np.ndarray, + matched: Dict[int, Tuple[np.ndarray, np.ndarray]], + estimates: List[AngleEstimate], +) -> dict: + if not estimates: + raise ValueError("No valid angle estimates could be computed.") + + mean_rad, circ_var, circ_std_rad = weighted_circular_mean( + [e.angle_rad for e in estimates], + [e.weight for e in estimates], + ) + lin_std_rad = weighted_linear_std_rad( + [e.angle_rad for e in estimates], + mean_rad, + [e.weight for e in estimates], + ) + + # unwrap around mean for a stable arithmetic mean/std reporting + unwrapped_deg = [] + for e in estimates: + d = math.degrees(normalize_angle_rad(e.angle_rad - mean_rad)) + unwrapped_deg.append(d) + + report = { + "link": arm_link_name, + "joint_origin_mm": joint_origin_mm.tolist(), + "joint_axis": joint_axis.tolist(), + "num_matched_markers": len(matched), + "num_estimates": len(estimates), + "mean_angle_rad": mean_rad, + "mean_angle_deg": math.degrees(mean_rad), + "circular_variance": circ_var, + "circular_std_rad": circ_std_rad, + "circular_std_deg": math.degrees(circ_std_rad), + "linear_std_rad": lin_std_rad, + "linear_std_deg": math.degrees(lin_std_rad), + "estimate_spread_deg_unwrapped": { + "min": float(np.min(unwrapped_deg)) if unwrapped_deg else None, + "max": float(np.max(unwrapped_deg)) if unwrapped_deg else None, + "mean_abs": float(np.mean(np.abs(unwrapped_deg))) if unwrapped_deg else None, + }, + "estimates": [asdict(e) for e in estimates], + } + return report + + +def pretty_print_report(report: dict) -> None: + print("\nArm angle estimation report") + print("=" * 32) + print(f"Link : {report['link']}") + print(f"Matched markers : {report['num_matched_markers']}") + print(f"Valid estimates : {report['num_estimates']}") + print(f"Mean angle : {report['mean_angle_deg']:.3f} deg") + print(f"Circular variance : {report['circular_variance']:.6f}") + print(f"Circular std : {report['circular_std_deg']:.3f} deg") + print(f"Linear std (wrapped): {report['linear_std_deg']:.3f} deg") + + print("\nIndividual estimates") + print("-" * 32) + for e in report["estimates"]: + mids = ",".join(str(x) for x in e["marker_ids"]) + print(f"{e['source']:>6s} markers [{mids:<8s}] angle={e['angle_deg']:>8.3f} deg weight={e['weight']:.3f}") + + +def estimate_arm1_state( + robot_json: dict, + observations_json: dict, + arm_link_name: str = "Arm1", + joint_origin_world_mm: Optional[Sequence[float]] = None, +) -> dict: + joint_origin_mm, arm_markers, joint_axis = extract_arm_markers(robot_json, arm_link_name) + observations = load_observations(observations_json) + matched = match_markers(arm_markers, observations) + + if not matched: + available = sorted(m.marker_id for m in arm_markers) + observed = sorted(o.marker_id for o in observations) + raise ValueError( + f"No Arm1 marker matches found. Arm markers: {available}. Observed IDs present in file: {observed[:25]}" + + ("..." if len(observed) > 25 else "") + ) + + estimates = estimate_pairwise_angles(matched, axis=joint_axis) + + # Optional single-marker estimates only if a world-space joint origin is supplied. + if joint_origin_world_mm is not None: + estimates.extend(estimate_single_marker_angles(matched, _as_np3(joint_origin_world_mm), axis=joint_axis)) + + report = build_report( + arm_link_name=arm_link_name, + joint_origin_mm=joint_origin_mm, + joint_axis=joint_axis, + matched=matched, + estimates=estimates, + ) + return report + + +def main() -> int: + parser = argparse.ArgumentParser(description="Estimate Arm1 angle from ArUco marker positions.") + parser.add_argument("--robot-json", type=Path, default=Path("robot.json"), help="Path to robot config JSON.") + parser.add_argument("--aruco-json", type=Path, default=Path("aruco_positions_optimized.json"), help="Path to marker observation JSON.") + parser.add_argument("--arm-link", type=str, default="Arm1", help="Name of the link to estimate.") + parser.add_argument( + "--joint-origin-world", + type=float, + nargs=3, + default=None, + metavar=("X", "Y", "Z"), + help="Optional world-space joint origin for single-marker estimates.", + ) + parser.add_argument("--output-json", type=Path, default=None, help="Optional path to write the result JSON.") + args = parser.parse_args() + + robot_json = load_json(args.robot_json) + observations_json = load_json(args.aruco_json) + + report = estimate_arm1_state( + robot_json=robot_json, + observations_json=observations_json, + arm_link_name=args.arm_link, + joint_origin_world_mm=args.joint_origin_world, + ) + + pretty_print_report(report) + + if args.output_json: + args.output_json.parent.mkdir(parents=True, exist_ok=True) + with args.output_json.open("w", encoding="utf-8") as f: + json.dump(report, f, indent=2, ensure_ascii=False) + print(f"\nWrote JSON summary to: {args.output_json}") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/pipeline/9_poseEvaluation.py b/pipeline/9_evaluateMarker.py similarity index 100% rename from pipeline/9_poseEvaluation.py rename to pipeline/9_evaluateMarker.py diff --git a/pipeline/aruco_positions_initial.json b/pipeline/aruco_positions_initial.json deleted file mode 100644 index 2ad1968..0000000 --- a/pipeline/aruco_positions_initial.json +++ /dev/null @@ -1,124 +0,0 @@ -{ - 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], - "link": "Arm1" - } - ] -} \ No newline at end of file diff --git a/pipeline/aruco_positions_optimized.json b/pipeline/aruco_positions_optimized.json deleted file mode 100644 index d435c4f..0000000 --- a/pipeline/aruco_positions_optimized.json +++ /dev/null @@ -1,123 +0,0 @@ -{ - "schema_version": "1.2", - "created_utc": "2026-05-29T17:30:30Z", - "summary": { - "num_cameras": 3, - "num_markers": 8, - "num_constraints": 25 - }, - "markers": [ - { - "marker_id": 122, - "position_m": [ - 0.16408414603046484, - -0.2598816877789887, - 0.17784668754227578 - ], - "position_mm": [ - 164.08414603046484, - -259.8816877789887, - 177.8466875422758 - ], - "link": "Arm2" - }, - { - "marker_id": 198, - "position_m": [ - 0.11704671868929717, - -0.05006352997427808, - 0.13545744846179997 - ], - "position_mm": [ - 117.04671868929717, - -50.063529974278076, - 135.45744846179997 - ], - "link": "Arm1" - }, - { - "marker_id": 210, - "position_m": [ - 0.019883267288547467, - -0.019568887683375974, - -6.02159742251129e-05 - 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-0.08525115996599197, - 0.018120698630809784 - ], - "normal": [ - -0.1736481487751007, - 0.40055757761001587, - 0.8996666669845581 - ] - }, - { - "name": "aruco_101", - "id": 101, - "link": "Arm2", - "position_m": [ - 0.23908136785030365, - -0.31593865156173706, - 0.13386270403862 - ], - "position_mm": [ - 239.08136785030365, - -315.93865156173706, - 133.86270403862 - ], - "rotation_quaternion": [ - 0.4516582787036896, - -0.09600294381380081, - 0.8676275014877319, - -0.18441995978355408 - ], - "normal": [ - 0.8191521167755127, - -0.23329465091228485, - -0.5239881277084351 - ] - }, - { - "name": "aruco_102", - "id": 102, - "link": "Arm2", - "position_m": [ - 0.18963702023029327, - -0.31948474049568176, - 0.1258980929851532 - ], - "position_mm": [ - 189.63702023029327, - -319.48474049568176, - 125.8980929851532 - ], - "rotation_quaternion": [ - 0.2941346764564514, - -0.06252026557922363, - -0.93287593126297, - 0.1982889324426651 - ], - "normal": [ - -0.5735765099525452, - -0.33317920565605164, - -0.7483325600624084 - ] - }, - { - "name": "aruco_124", - "id": 124, - "link": "Arm2", - "position_m": [ - 0.17503933608531952, - -0.3439647853374481, - 0.1618829369544983 - ], - "position_mm": [ - 175.03933608531952, - -343.9647853374481, - 161.8829369544983 - ], - "rotation_quaternion": [ - 0.6287412643432617, - -0.13364310562610626, - -0.749304473400116, - 0.15926963090896606 - ], - "normal": [ - -0.9848078489303589, - -0.07062903046607971, - -0.15863528847694397 - ] - }, - { - "name": "aruco_219", - "id": 219, - "link": "Arm2", - "position_m": [ - 0.24496068060398102, - -0.33895012736320496, - 0.17314603924751282 - ], - "position_mm": [ - 244.96068060398102, - -338.95012736320496, - 173.14603924751282 - ], - "rotation_quaternion": [ - 0.749304473400116, - -0.15926964581012726, - 0.6287412643432617, - -0.13364310562610626 - ], - "normal": [ - 0.9848078489303589, - 0.0706290453672409, - 0.15863528847694397 - ] - } -] \ No newline at end of file diff --git a/pipeline/run_pipeline_multiview.bat b/pipeline/run_pipeline_multiview.bat deleted file mode 100644 index 8d804b9..0000000 --- a/pipeline/run_pipeline_multiview.bat +++ /dev/null @@ -1,53 +0,0 @@ -@echo off -REM 3_pipeline_multiview.bat -REM Multi-camera ArUco detection and pose estimation pipeline - -REM Example: 3 camera views -REM Usage: run_pipeline_multiview.bat - -cd /d "%~dp0" - -set ROBOT_JSON=..\robot.json -set OUT_DIR=. - -REM Camera 1 -set IMG_1=render_1a.png -set NPZ_1=render.npz -set CAM_ID_1=cam1 - -REM Camera 2 (example - you need actual second camera image) -set IMG_2=render_1b.png -set NPZ_2=render.npz -set CAM_ID_2=cam2 - -REM Camera 3 (example) -set IMG_3=render_1c.png -set NPZ_3=render.npz -set CAM_ID_3=cam3 - - - -echo [STEP 1] Detect ArUco markers from all cameras -python 1_detect_aruco_observations.py -i %IMG_1% -npz %NPZ_1% -robot %ROBOT_JSON% -cameraId %CAM_ID_1% -outDir %OUT_DIR% -python 1_detect_aruco_observations.py -i %IMG_2% -npz %NPZ_2% -robot %ROBOT_JSON% -cameraId %CAM_ID_2% -outDir %OUT_DIR% -python 1_detect_aruco_observations.py -i %IMG_3% -npz %NPZ_3% -robot %ROBOT_JSON% -cameraId %CAM_ID_3% -outDir %OUT_DIR% - -echo. -echo [STEP 2] Estimate camera poses from detections -python 2_estimate_camera_from_observations.py -i render_3a_aruco_detection.json -robot %ROBOT_JSON% -outDir %OUT_DIR% -python 2_estimate_camera_from_observations.py -i render_3b_aruco_detection.json -robot %ROBOT_JSON% -outDir %OUT_DIR% -python 2_estimate_camera_from_observations.py -i render_3c_aruco_detection.json -robot %ROBOT_JSON% -outDir %OUT_DIR% - -echo. -echo [STEP 3] Triangulate marker positions from multi-view observations -python 3_multiview_bundle_adjustment_v6.py ^ - -det render_3a_aruco_detection.json ^ - -det render_3b_aruco_detection.json ^ - -det render_3c_aruco_detection.json ^ - -pose render_3a_camera_pose.json ^ - -pose render_3b_camera_pose.json ^ - -pose render_3c_camera_pose.json ^ - -robot %ROBOT_JSON% -lambdaWeight 100.0 - -echo. -echo [DONE] Pipeline complete diff --git a/run/run_evaluateMarker.bat b/run/run_evaluateMarker.bat new file mode 100644 index 0000000..71b8a79 --- /dev/null +++ b/run/run_evaluateMarker.bat @@ -0,0 +1,41 @@ + + + + +REM Wenn kein Argument übergeben wurde +if "%1"=="" ( + echo. + echo [INFO] Aufruf fehlt! + echo Beispiel: .\run_evaluateMarker.bat ../data/evaluations/Scene4/ + 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 BASE_SIM=C:\Users\kech\SynologyDrive\2026-AppServer-AppRobot\appRobotRendering\data\simulation + +REM ✅ richtigen Namen extrahieren +for %%I in ("%IMAGES%") do set "SCENE_NAME=%%~nxI" + + +set ORIGINAL=%BASE_SIM%\%SCENE_NAME%\render_a.json + + +ECHO ==================================== +ECHO INITIAL: +python3 ../pipeline/9_evaluateMarker.py ^ + "%IMAGES%\aruco_positions_initial.json" ^ + "%ORIGINAL%" + +ECHO ==================================== +ECHO OPTIMIZED: +python3 ../pipeline/9_evaluateMarker.py ^ + "%IMAGES%\aruco_positions_optimized.json" ^ + "%ORIGINAL%" diff --git a/run/run_pipeline.bat b/run/run_pipeline.bat index 0b372cd..619248e 100644 --- a/run/run_pipeline.bat +++ b/run/run_pipeline.bat @@ -8,14 +8,32 @@ REM run_pipeline.bat ../data/simulation/Scene4 -set IMAGES=%~1 +REM Wenn kein Argument übergeben wurde +if "%1"=="" ( + echo. + echo [INFO] Aufruf fehlt! + echo Beispiel: .\run_pipeline.bat ../data/simulation/Scene4 + 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 -set SCENE_NAME=%~nx1 + +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% @@ -65,13 +83,18 @@ for %%F in ("%OUT_DIR%\*_camera_pose.json") do ( set POSE_ARGS=!POSE_ARGS! -pose "%%F" ) -REM Debug-Ausgabe -echo DET_ARGS: !DET_ARGS! -echo POSE_ARGS: !POSE_ARGS! - -REM EINMAL Python aufrufen -python3 "..\pipeline\3_multiview_bundle_adjustment_v4.py" ^ +python3 "..\pipeline\3_multiview_bundle_adjustment_v5.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_robotState_estimation_v6.py" ^ + "%OUT_DIR%\aruco_positions_optimized.json" ^ + -robot "%ROBOT_JSON%" ^ + -out "%OUT_DIR%\robot_state.json" diff --git a/setup/generateSets/render_Loop.py b/setup/generateSets/render_Loop.py index 8201ea2..ad49214 100644 --- a/setup/generateSets/render_Loop.py +++ b/setup/generateSets/render_Loop.py @@ -5,7 +5,7 @@ import shutil from pathlib import Path import argparse -def update_robot_json(robot_json_file, camera_position, camera_target, default_position): +def update_robot_json(robot_json_file, camera_position, camera_target, default_position, width, height, fstop): """Aktualisiert die cameraPosition und defaultPosition in der robot.json-Datei.""" try: with open(robot_json_file, 'r') as f: @@ -13,6 +13,9 @@ def update_robot_json(robot_json_file, camera_position, camera_target, default_p data['renderingInfo']['cameraPosition'] = camera_position data['renderingInfo']['cameraTarget'] = camera_target + data['renderingInfo']['width'] = width + data['renderingInfo']['height'] = height + data['renderingInfo']['dofFStop'] = fstop data['defaultPosition'] = default_position with open(robot_json_file, 'w') as f: @@ -100,24 +103,43 @@ def main(): "f": [200, -200, 180], "g": [200, -200, 180], } + # Robot-Pose-Dictionary robot_poses = { - "4": {"x": 70, "y": 50,"z": -70,"a": 120,"b": 50,"c": 30,"e": 20}, - "5": {"x": 180,"y": 86,"z": -120,"a": -60,"b": 22,"c": 91,"e": 10}, - "6": {"x": 80, "y": 20, "z": 80, "a": -120, "b": 23, "c": 9, "e": 3}, - "7": {"x": 30, "y": -2, "z": 95, "a": 20, "b": 23, "c": 9, "e": 9}, - "8": {"x": 50, "y": -2, "z": 95, "a": 20, "b": 60, "c": 9, "e": 3}, - "9": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8}, - "10": {"x": 120, "y": 60, "z": -110, "a": 20, "b": 30, "c": 180, "e": 4}, - "11": {"x": 50, "y": 4, "z": 176, "a": 20, "b": 60, "c": 9, "e": 5}, - "12": {"x": 50, "y": 0, "z": 178, "a": 210, "b": 80, "c": 90, "e": 6}, + #"4": {"x": 70, "y": 50,"z": -70,"a": 120,"b": 50,"c": 30,"e": 20}, + #"5": {"x": 180,"y": 86,"z": -120,"a": -60,"b": 22,"c": 91,"e": 10}, + #"6": {"x": 80, "y": 20, "z": 80, "a": -120, "b": 23, "c": 9, "e": 3}, + #"7": {"x": 30, "y": -2, "z": 95, "a": 20, "b": 23, "c": 9, "e": 9}, + #"8": {"x": 50, "y": -2, "z": 95, "a": 20, "b": 60, "c": 9, "e": 3}, + #"9": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8}, + #"9a": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8}, + "9b": {"x": 60, "y": -2, "z": 95, "a": 200, "b": 60, "c": 9, "e": 8}, + #"10": {"x": 120, "y": 60, "z": -110, "a": 20, "b": 30, "c": 180, "e": 4}, + #"11": {"x": 50, "y": 4, "z": 176, "a": 20, "b": 60, "c": 9, "e": 5}, + #"12": {"x": 50, "y": 0, "z": 178, "a": 210, "b": 80, "c": 90, "e": 6}, + } + + renderingInfos = { + "4": {"width":1280, "height":720, "dofFStop":11}, + "5": {"width":1280, "height":720, "dofFStop":11}, + "6": {"width":1280, "height":720, "dofFStop":11}, + "7": {"width":1280, "height":720, "dofFStop":11}, + "8": {"width":1280, "height":720, "dofFStop":11}, + "9": {"width":1280, "height":720, "dofFStop":11}, + "9a": {"width":1440, "height":1080, "dofFStop":11}, + "9b": {"width":4896, "height":3264, "dofFStop":5.6}, + "10": {"width":1280, "height":720, "dofFStop":11}, + "11": {"width":1280, "height":720, "dofFStop":11}, + "12": {"width":1280, "height":720, "dofFStop":11}, } for pose_name, default_position in robot_poses.items(): new_folder = Path(args.output_set) / f"Scene{pose_name}" os.makedirs(new_folder, exist_ok=True) + renderingInfo = renderingInfos[pose_name] + print(renderingInfo) pose_file = new_folder / "pose.json" pose_data = {"name": pose_name,"position": default_position, "camera_positions": camera_positions} @@ -129,7 +151,7 @@ def main(): camera_target = camera_targets[frame_name] # 1. JSON aktualisieren - if not update_robot_json(args.robot_json, camera_position, camera_target, default_position): + if not update_robot_json(args.robot_json, camera_position, camera_target, default_position, renderingInfo["width"], renderingInfo["height"], renderingInfo["dofFStop"]): continue # Gehe zum nächsten Schleifendurchlauf # 2. Blender-Skript ausführen