🟢 Starting Calibration Pipeline

=== Processing MDC044_W027_2025-05-29 ===

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 1338 stationary frames, 224 remain


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→ 1000 Phase 2: corners:  80%|████████  | 180/224 [02:31<00:35,  1.22it/s]

→ 1000 Phase 2: corners:  81%|████████  | 181/224 [02:32<00:35,  1.22it/s]

→ 1000 Phase 2: corners:  81%|████████▏ | 182/224 [02:33<00:34,  1.22it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 183/224 [02:34<00:33,  1.23it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 184/224 [02:35<00:32,  1.25it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 185/224 [02:35<00:31,  1.26it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 186/224 [02:36<00:30,  1.26it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 187/224 [02:37<00:29,  1.27it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 188/224 [02:38<00:28,  1.26it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 189/224 [02:38<00:27,  1.25it/s]

→ 1000 Phase 2: corners:  85%|████████▍ | 190/224 [02:39<00:27,  1.25it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 191/224 [02:40<00:26,  1.26it/s]

→ 1000 Phase 2: corners:  86%|████████▌ | 192/224 [02:41<00:25,  1.23it/s]

→ 1000 Phase 2: corners:  86%|████████▌ | 193/224 [02:42<00:25,  1.24it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 194/224 [02:43<00:24,  1.22it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 195/224 [02:43<00:23,  1.21it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 196/224 [02:44<00:23,  1.21it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 197/224 [02:45<00:22,  1.20it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 198/224 [02:46<00:21,  1.21it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 199/224 [02:47<00:20,  1.20it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 200/224 [02:48<00:19,  1.21it/s]

→ 1000 Phase 2: corners:  90%|████████▉ | 201/224 [02:48<00:19,  1.20it/s]

→ 1000 Phase 2: corners:  90%|█████████ | 202/224 [02:49<00:18,  1.21it/s]

→ 1000 Phase 2: corners:  91%|█████████ | 203/224 [02:50<00:17,  1.23it/s]

→ 1000 Phase 2: corners:  91%|█████████ | 204/224 [02:51<00:16,  1.23it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 205/224 [02:52<00:15,  1.25it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 206/224 [02:52<00:14,  1.26it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 207/224 [02:53<00:13,  1.26it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 208/224 [02:54<00:12,  1.27it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 209/224 [02:55<00:11,  1.27it/s]

→ 1000 Phase 2: corners:  94%|█████████▍| 210/224 [02:55<00:10,  1.28it/s]

→ 1000 Phase 2: corners:  94%|█████████▍| 211/224 [02:56<00:10,  1.29it/s]

→ 1000 Phase 2: corners:  95%|█████████▍| 212/224 [02:57<00:09,  1.30it/s]

→ 1000 Phase 2: corners:  95%|█████████▌| 213/224 [02:58<00:08,  1.30it/s]

→ 1000 Phase 2: corners:  96%|█████████▌| 214/224 [02:59<00:07,  1.28it/s]

→ 1000 Phase 2: corners:  96%|█████████▌| 215/224 [02:59<00:07,  1.28it/s]

→ 1000 Phase 2: corners:  96%|█████████▋| 216/224 [03:00<00:06,  1.25it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 217/224 [03:01<00:05,  1.25it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 218/224 [03:02<00:04,  1.24it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 219/224 [03:03<00:04,  1.25it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 220/224 [03:03<00:03,  1.23it/s]

→ 1000 Phase 2: corners:  99%|█████████▊| 221/224 [03:04<00:02,  1.23it/s]

→ 1000 Phase 2: corners:  99%|█████████▉| 222/224 [03:05<00:01,  1.23it/s]

→ 1000 Phase 2: corners: 100%|█████████▉| 223/224 [03:06<00:00,  1.22it/s]

→ 1000 Phase 2: corners: 100%|██████████| 224/224 [03:07<00:00,  1.23it/s]

                                                                          [PASS2] Frame 0000158: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000159: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000360: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000361: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000378: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000379: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000380: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000381: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000382: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000385: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000520: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000553: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000724: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000726: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001313: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001314: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001315: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001316: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001333: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001334: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001335: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001338: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001339: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001342: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001504: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000360: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000380: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000385: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001315: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001335: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001336: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001337: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001339: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001342: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 17 frames fixed, 13 untrusted, 224 total


→ 1000 Phase 3: finalize:   0%|          | 0/224 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   1%|          | 2/224 [00:00<00:18, 12.03it/s]

→ 1000 Phase 3: finalize:   2%|▏         | 4/224 [00:00<00:17, 12.39it/s]

→ 1000 Phase 3: finalize:   3%|▎         | 6/224 [00:00<00:17, 12.57it/s]

→ 1000 Phase 3: finalize:   4%|▎         | 8/224 [00:00<00:17, 12.65it/s]

→ 1000 Phase 3: finalize:   4%|▍         | 10/224 [00:00<00:16, 12.64it/s]

→ 1000 Phase 3: finalize:   5%|▌         | 12/224 [00:00<00:16, 12.63it/s]

→ 1000 Phase 3: finalize:   6%|▋         | 14/224 [00:01<00:16, 12.66it/s]

→ 1000 Phase 3: finalize:   7%|▋         | 16/224 [00:01<00:16, 12.68it/s]

→ 1000 Phase 3: finalize:   8%|▊         | 18/224 [00:01<00:16, 12.68it/s]

→ 1000 Phase 3: finalize:   9%|▉         | 20/224 [00:01<00:16, 12.67it/s]

→ 1000 Phase 3: finalize:  10%|▉         | 22/224 [00:01<00:15, 12.66it/s]

→ 1000 Phase 3: finalize:  11%|█         | 24/224 [00:01<00:15, 12.67it/s]

→ 1000 Phase 3: finalize:  12%|█▏        | 26/224 [00:02<00:15, 12.80it/s]

→ 1000 Phase 3: finalize:  12%|█▎        | 28/224 [00:02<00:15, 12.93it/s]

→ 1000 Phase 3: finalize:  13%|█▎        | 30/224 [00:02<00:14, 13.00it/s]

→ 1000 Phase 3: finalize:  14%|█▍        | 32/224 [00:02<00:14, 13.06it/s]

→ 1000 Phase 3: finalize:  15%|█▌        | 34/224 [00:02<00:14, 13.06it/s]

→ 1000 Phase 3: finalize:  16%|█▌        | 36/224 [00:02<00:14, 13.09it/s]

→ 1000 Phase 3: finalize:  17%|█▋        | 38/224 [00:02<00:14, 13.08it/s]

→ 1000 Phase 3: finalize:  18%|█▊        | 40/224 [00:03<00:14, 13.07it/s]

→ 1000 Phase 3: finalize:  19%|█▉        | 42/224 [00:03<00:13, 13.04it/s]

→ 1000 Phase 3: finalize:  20%|█▉        | 44/224 [00:03<00:13, 13.05it/s]

→ 1000 Phase 3: finalize:  21%|██        | 46/224 [00:03<00:13, 13.03it/s]

→ 1000 Phase 3: finalize:  21%|██▏       | 48/224 [00:03<00:13, 13.01it/s]

→ 1000 Phase 3: finalize:  22%|██▏       | 50/224 [00:03<00:13, 12.98it/s]

→ 1000 Phase 3: finalize:  23%|██▎       | 52/224 [00:04<00:13, 12.98it/s]

→ 1000 Phase 3: finalize:  24%|██▍       | 54/224 [00:04<00:13, 12.97it/s]

→ 1000 Phase 3: finalize:  25%|██▌       | 56/224 [00:04<00:12, 12.96it/s]

→ 1000 Phase 3: finalize:  26%|██▌       | 58/224 [00:04<00:12, 12.95it/s]

→ 1000 Phase 3: finalize:  27%|██▋       | 60/224 [00:04<00:12, 12.92it/s]

→ 1000 Phase 3: finalize:  28%|██▊       | 62/224 [00:04<00:12, 12.90it/s]

→ 1000 Phase 3: finalize:  29%|██▊       | 64/224 [00:04<00:12, 12.89it/s]

→ 1000 Phase 3: finalize:  29%|██▉       | 66/224 [00:05<00:12, 12.89it/s]

→ 1000 Phase 3: finalize:  30%|███       | 68/224 [00:05<00:12, 12.90it/s]

→ 1000 Phase 3: finalize:  31%|███▏      | 70/224 [00:05<00:11, 12.89it/s]

→ 1000 Phase 3: finalize:  32%|███▏      | 72/224 [00:05<00:11, 12.86it/s]

→ 1000 Phase 3: finalize:  33%|███▎      | 74/224 [00:05<00:11, 12.83it/s]

→ 1000 Phase 3: finalize:  34%|███▍      | 76/224 [00:05<00:11, 12.85it/s]

→ 1000 Phase 3: finalize:  35%|███▍      | 78/224 [00:06<00:11, 12.78it/s]

→ 1000 Phase 3: finalize:  36%|███▌      | 80/224 [00:06<00:11, 12.74it/s]

→ 1000 Phase 3: finalize:  37%|███▋      | 82/224 [00:06<00:11, 12.72it/s]

→ 1000 Phase 3: finalize:  38%|███▊      | 84/224 [00:06<00:11, 12.64it/s]

→ 1000 Phase 3: finalize:  38%|███▊      | 86/224 [00:06<00:10, 12.62it/s]

→ 1000 Phase 3: finalize:  39%|███▉      | 88/224 [00:06<00:10, 12.60it/s]

→ 1000 Phase 3: finalize:  40%|████      | 90/224 [00:07<00:10, 12.60it/s]

→ 1000 Phase 3: finalize:  41%|████      | 92/224 [00:07<00:10, 12.59it/s]

→ 1000 Phase 3: finalize:  42%|████▏     | 94/224 [00:07<00:10, 12.59it/s]

→ 1000 Phase 3: finalize:  43%|████▎     | 96/224 [00:07<00:10, 12.58it/s]

→ 1000 Phase 3: finalize:  44%|████▍     | 98/224 [00:07<00:10, 12.55it/s]

→ 1000 Phase 3: finalize:  45%|████▍     | 100/224 [00:07<00:09, 12.52it/s]

→ 1000 Phase 3: finalize:  46%|████▌     | 102/224 [00:07<00:09, 12.54it/s]

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→ 1000 Phase 3: finalize:  52%|█████▏    | 116/224 [00:09<00:08, 12.65it/s]

→ 1000 Phase 3: finalize:  53%|█████▎    | 118/224 [00:09<00:08, 12.63it/s]

→ 1000 Phase 3: finalize:  54%|█████▎    | 120/224 [00:09<00:08, 12.66it/s]

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→ 1000 Phase 3: finalize:  56%|█████▋    | 126/224 [00:09<00:07, 12.72it/s]

→ 1000 Phase 3: finalize:  57%|█████▋    | 128/224 [00:10<00:07, 12.74it/s]

→ 1000 Phase 3: finalize:  58%|█████▊    | 130/224 [00:10<00:07, 12.79it/s]

→ 1000 Phase 3: finalize:  59%|█████▉    | 132/224 [00:10<00:07, 12.82it/s]

→ 1000 Phase 3: finalize:  60%|█████▉    | 134/224 [00:10<00:07, 12.81it/s]

→ 1000 Phase 3: finalize:  61%|██████    | 136/224 [00:10<00:06, 12.82it/s]

→ 1000 Phase 3: finalize:  62%|██████▏   | 138/224 [00:10<00:06, 12.85it/s]

→ 1000 Phase 3: finalize:  62%|██████▎   | 140/224 [00:10<00:06, 12.88it/s]

→ 1000 Phase 3: finalize:  63%|██████▎   | 142/224 [00:11<00:06, 12.94it/s]

→ 1000 Phase 3: finalize:  64%|██████▍   | 144/224 [00:11<00:06, 13.01it/s]

→ 1000 Phase 3: finalize:  65%|██████▌   | 146/224 [00:11<00:05, 13.05it/s]

→ 1000 Phase 3: finalize:  66%|██████▌   | 148/224 [00:11<00:05, 13.08it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 150/224 [00:11<00:05, 13.08it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 152/224 [00:11<00:05, 13.13it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 154/224 [00:12<00:05, 13.13it/s]

→ 1000 Phase 3: finalize:  70%|██████▉   | 156/224 [00:12<00:05, 13.19it/s]

→ 1000 Phase 3: finalize:  71%|███████   | 158/224 [00:12<00:04, 13.23it/s]

→ 1000 Phase 3: finalize:  71%|███████▏  | 160/224 [00:12<00:04, 13.28it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 162/224 [00:12<00:04, 13.26it/s]

→ 1000 Phase 3: finalize:  73%|███████▎  | 164/224 [00:12<00:04, 13.27it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 166/224 [00:12<00:04, 13.30it/s]

→ 1000 Phase 3: finalize:  75%|███████▌  | 168/224 [00:13<00:04, 13.28it/s]

→ 1000 Phase 3: finalize:  76%|███████▌  | 170/224 [00:13<00:04, 13.25it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 172/224 [00:13<00:03, 13.25it/s]

→ 1000 Phase 3: finalize:  78%|███████▊  | 174/224 [00:13<00:03, 13.21it/s]

→ 1000 Phase 3: finalize:  79%|███████▊  | 176/224 [00:13<00:03, 13.19it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 178/224 [00:13<00:03, 13.22it/s]

→ 1000 Phase 3: finalize:  80%|████████  | 180/224 [00:13<00:03, 13.17it/s]

→ 1000 Phase 3: finalize:  81%|████████▏ | 182/224 [00:14<00:03, 13.19it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 184/224 [00:14<00:03, 13.15it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 186/224 [00:14<00:02, 13.12it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 188/224 [00:14<00:02, 13.07it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 190/224 [00:14<00:02, 13.05it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 192/224 [00:14<00:02, 13.02it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 194/224 [00:15<00:02, 12.99it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 196/224 [00:15<00:02, 13.01it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 198/224 [00:15<00:01, 13.06it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 200/224 [00:15<00:01, 13.07it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 202/224 [00:15<00:01, 13.11it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 204/224 [00:15<00:01, 13.12it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 206/224 [00:15<00:01, 13.15it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 208/224 [00:16<00:01, 13.16it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 210/224 [00:16<00:01, 13.16it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 212/224 [00:16<00:00, 13.17it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 214/224 [00:16<00:00, 13.14it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 216/224 [00:16<00:00, 13.14it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 218/224 [00:16<00:00, 13.09it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 220/224 [00:17<00:00, 13.09it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 222/224 [00:17<00:00, 13.09it/s]

→ 1000 Phase 3: finalize: 100%|██████████| 224/224 [00:17<00:00, 13.09it/s]

                                                                           
Processing Rigs:  50%|█████     | 1/2 [03:51<03:51, 231.79s/it][INFO] Filtered 1172 stationary frames, 199 remain


→ 500 (199 images) Phase 1:   0%|          | 0/199 [00:00<?, ?it/s]

→ 500 (199 images) Phase 1:   1%|          | 1/199 [00:03<11:00,  3.34s/it]

→ 500 (199 images) Phase 1:  17%|█▋        | 33/199 [00:06<00:26,  6.32it/s]

→ 500 (199 images) Phase 1:  28%|██▊       | 55/199 [00:06<00:11, 12.05it/s]

→ 500 (199 images) Phase 1:  33%|███▎      | 65/199 [00:08<00:17,  7.83it/s]

→ 500 (199 images) Phase 1:  43%|████▎     | 86/199 [00:09<00:08, 13.11it/s]

→ 500 (199 images) Phase 1:  49%|████▊     | 97/199 [00:11<00:11,  8.62it/s]

→ 500 (199 images) Phase 1:  57%|█████▋    | 113/199 [00:11<00:06, 12.53it/s]

→ 500 (199 images) Phase 1:  62%|██████▏   | 123/199 [00:11<00:04, 15.45it/s]

→ 500 (199 images) Phase 1:  66%|██████▋   | 132/199 [00:14<00:07,  8.76it/s]

→ 500 (199 images) Phase 1:  74%|███████▍  | 147/199 [00:14<00:03, 13.00it/s]

→ 500 (199 images) Phase 1:  78%|███████▊  | 156/199 [00:14<00:02, 15.73it/s]

→ 500 (199 images) Phase 1:  82%|████████▏ | 164/199 [00:17<00:04,  8.35it/s]

→ 500 (199 images) Phase 1:  89%|████████▉ | 177/199 [00:17<00:01, 12.33it/s]

→ 500 (199 images) Phase 1:  93%|█████████▎| 185/199 [00:17<00:00, 15.16it/s]

→ 500 (199 images) Phase 1:  97%|█████████▋| 193/199 [00:18<00:00, 10.55it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/199 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   1%|          | 1/199 [00:01<03:22,  1.02s/it]

→ 500 Phase 2: corners:   1%|          | 2/199 [00:02<03:17,  1.00s/it]

→ 500 Phase 2: corners:   2%|▏         | 3/199 [00:02<03:13,  1.01it/s]

→ 500 Phase 2: corners:   2%|▏         | 4/199 [00:04<03:20,  1.03s/it]

→ 500 Phase 2: corners:   3%|▎         | 5/199 [00:05<03:13,  1.00it/s]

→ 500 Phase 2: corners:   3%|▎         | 6/199 [00:05<03:09,  1.02it/s]

→ 500 Phase 2: corners:   4%|▎         | 7/199 [00:06<03:08,  1.02it/s]

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→ 500 Phase 2: corners:  20%|██        | 40/199 [00:40<02:48,  1.06s/it]

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→ 500 Phase 2: corners:  30%|███       | 60/199 [00:59<02:08,  1.08it/s]

→ 500 Phase 2: corners:  31%|███       | 61/199 [01:00<02:07,  1.08it/s]

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→ 500 Phase 2: corners:  33%|███▎      | 65/199 [01:04<02:07,  1.05it/s]

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→ 500 Phase 2: corners:  40%|████      | 80/199 [01:19<01:58,  1.00it/s]

→ 500 Phase 2: corners:  41%|████      | 81/199 [01:20<01:56,  1.02it/s]

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→ 500 Phase 2: corners:  43%|████▎     | 85/199 [01:24<01:50,  1.03it/s]

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→ 500 Phase 2: corners:  44%|████▍     | 88/199 [01:26<01:45,  1.05it/s]

→ 500 Phase 2: corners:  45%|████▍     | 89/199 [01:27<01:43,  1.06it/s]

→ 500 Phase 2: corners:  45%|████▌     | 90/199 [01:28<01:43,  1.05it/s]

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→ 500 Phase 2: corners:  47%|████▋     | 94/199 [01:32<01:37,  1.07it/s]

→ 500 Phase 2: corners:  48%|████▊     | 95/199 [01:33<01:37,  1.06it/s]

→ 500 Phase 2: corners:  48%|████▊     | 96/199 [01:34<01:38,  1.05it/s]

→ 500 Phase 2: corners:  49%|████▊     | 97/199 [01:35<01:37,  1.05it/s]

→ 500 Phase 2: corners:  49%|████▉     | 98/199 [01:36<01:35,  1.05it/s]

→ 500 Phase 2: corners:  50%|████▉     | 99/199 [01:37<01:35,  1.04it/s]

→ 500 Phase 2: corners:  50%|█████     | 100/199 [01:38<01:34,  1.04it/s]

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→ 500 Phase 2: corners:  52%|█████▏    | 103/199 [01:41<01:34,  1.02it/s]

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→ 500 Phase 2: corners:  53%|█████▎    | 105/199 [01:43<01:33,  1.00it/s]

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→ 500 Phase 2: corners:  54%|█████▍    | 107/199 [01:45<01:31,  1.01it/s]

→ 500 Phase 2: corners:  54%|█████▍    | 108/199 [01:46<01:30,  1.01it/s]

→ 500 Phase 2: corners:  55%|█████▍    | 109/199 [01:47<01:30,  1.00s/it]

→ 500 Phase 2: corners:  55%|█████▌    | 110/199 [01:48<01:29,  1.01s/it]

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→ 500 Phase 2: corners:  56%|█████▋    | 112/199 [01:50<01:28,  1.01s/it]

→ 500 Phase 2: corners:  57%|█████▋    | 113/199 [01:51<01:26,  1.01s/it]

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→ 500 Phase 2: corners:  58%|█████▊    | 115/199 [01:53<01:24,  1.01s/it]

→ 500 Phase 2: corners:  58%|█████▊    | 116/199 [01:54<01:23,  1.01s/it]

→ 500 Phase 2: corners:  59%|█████▉    | 117/199 [01:55<01:22,  1.00s/it]

→ 500 Phase 2: corners:  59%|█████▉    | 118/199 [01:56<01:19,  1.01it/s]

→ 500 Phase 2: corners:  60%|█████▉    | 119/199 [01:57<01:18,  1.03it/s]

→ 500 Phase 2: corners:  60%|██████    | 120/199 [01:58<01:17,  1.02it/s]

→ 500 Phase 2: corners:  61%|██████    | 121/199 [01:59<01:16,  1.03it/s]

→ 500 Phase 2: corners:  61%|██████▏   | 122/199 [02:00<01:14,  1.03it/s]

→ 500 Phase 2: corners:  62%|██████▏   | 123/199 [02:01<01:12,  1.05it/s]

→ 500 Phase 2: corners:  62%|██████▏   | 124/199 [02:02<01:12,  1.04it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 125/199 [02:03<01:11,  1.04it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 126/199 [02:03<01:10,  1.04it/s]

→ 500 Phase 2: corners:  64%|██████▍   | 127/199 [02:04<01:08,  1.04it/s]

→ 500 Phase 2: corners:  64%|██████▍   | 128/199 [02:05<01:07,  1.05it/s]

→ 500 Phase 2: corners:  65%|██████▍   | 129/199 [02:06<01:07,  1.04it/s]

→ 500 Phase 2: corners:  65%|██████▌   | 130/199 [02:07<01:06,  1.03it/s]

→ 500 Phase 2: corners:  66%|██████▌   | 131/199 [02:08<01:06,  1.02it/s]

→ 500 Phase 2: corners:  66%|██████▋   | 132/199 [02:09<01:05,  1.03it/s]

→ 500 Phase 2: corners:  67%|██████▋   | 133/199 [02:10<01:05,  1.01it/s]

→ 500 Phase 2: corners:  67%|██████▋   | 134/199 [02:11<01:03,  1.02it/s]

→ 500 Phase 2: corners:  68%|██████▊   | 135/199 [02:12<01:03,  1.01it/s]

→ 500 Phase 2: corners:  68%|██████▊   | 136/199 [02:13<01:02,  1.02it/s]

→ 500 Phase 2: corners:  69%|██████▉   | 137/199 [02:14<01:01,  1.01it/s]

→ 500 Phase 2: corners:  69%|██████▉   | 138/199 [02:15<00:59,  1.02it/s]

→ 500 Phase 2: corners:  70%|██████▉   | 139/199 [02:16<00:59,  1.01it/s]

→ 500 Phase 2: corners:  70%|███████   | 140/199 [02:17<00:59,  1.01s/it]

→ 500 Phase 2: corners:  71%|███████   | 141/199 [02:18<00:58,  1.01s/it]

→ 500 Phase 2: corners:  71%|███████▏  | 142/199 [02:19<00:58,  1.03s/it]

→ 500 Phase 2: corners:  72%|███████▏  | 143/199 [02:20<00:57,  1.04s/it]

→ 500 Phase 2: corners:  72%|███████▏  | 144/199 [02:21<00:56,  1.03s/it]

→ 500 Phase 2: corners:  73%|███████▎  | 145/199 [02:23<00:56,  1.05s/it]

→ 500 Phase 2: corners:  73%|███████▎  | 146/199 [02:24<00:56,  1.06s/it]

→ 500 Phase 2: corners:  74%|███████▍  | 147/199 [02:25<00:55,  1.06s/it]

→ 500 Phase 2: corners:  74%|███████▍  | 148/199 [02:26<00:54,  1.06s/it]

→ 500 Phase 2: corners:  75%|███████▍  | 149/199 [02:27<00:52,  1.06s/it]

→ 500 Phase 2: corners:  75%|███████▌  | 150/199 [02:28<00:51,  1.06s/it]

→ 500 Phase 2: corners:  76%|███████▌  | 151/199 [02:29<00:51,  1.06s/it]

→ 500 Phase 2: corners:  76%|███████▋  | 152/199 [02:30<00:50,  1.07s/it]

→ 500 Phase 2: corners:  77%|███████▋  | 153/199 [02:31<00:49,  1.07s/it]

→ 500 Phase 2: corners:  77%|███████▋  | 154/199 [02:32<00:47,  1.06s/it]

→ 500 Phase 2: corners:  78%|███████▊  | 155/199 [02:33<00:46,  1.05s/it]

→ 500 Phase 2: corners:  78%|███████▊  | 156/199 [02:34<00:45,  1.05s/it]

→ 500 Phase 2: corners:  79%|███████▉  | 157/199 [02:35<00:44,  1.05s/it]

→ 500 Phase 2: corners:  79%|███████▉  | 158/199 [02:36<00:43,  1.06s/it]

→ 500 Phase 2: corners:  80%|███████▉  | 159/199 [02:37<00:42,  1.06s/it]

→ 500 Phase 2: corners:  80%|████████  | 160/199 [02:38<00:40,  1.05s/it]

→ 500 Phase 2: corners:  81%|████████  | 161/199 [02:40<00:39,  1.05s/it]

→ 500 Phase 2: corners:  81%|████████▏ | 162/199 [02:41<00:38,  1.05s/it]

→ 500 Phase 2: corners:  82%|████████▏ | 163/199 [02:42<00:37,  1.04s/it]

→ 500 Phase 2: corners:  82%|████████▏ | 164/199 [02:43<00:36,  1.03s/it]

→ 500 Phase 2: corners:  83%|████████▎ | 165/199 [02:44<00:35,  1.03s/it]

→ 500 Phase 2: corners:  83%|████████▎ | 166/199 [02:45<00:33,  1.03s/it]

→ 500 Phase 2: corners:  84%|████████▍ | 167/199 [02:46<00:32,  1.03s/it]

→ 500 Phase 2: corners:  84%|████████▍ | 168/199 [02:47<00:31,  1.03s/it]

→ 500 Phase 2: corners:  85%|████████▍ | 169/199 [02:48<00:30,  1.03s/it]

→ 500 Phase 2: corners:  85%|████████▌ | 170/199 [02:49<00:30,  1.04s/it]

→ 500 Phase 2: corners:  86%|████████▌ | 171/199 [02:50<00:28,  1.03s/it]

→ 500 Phase 2: corners:  86%|████████▋ | 172/199 [02:51<00:27,  1.02s/it]

→ 500 Phase 2: corners:  87%|████████▋ | 173/199 [02:52<00:26,  1.02s/it]

→ 500 Phase 2: corners:  87%|████████▋ | 174/199 [02:53<00:25,  1.02s/it]

→ 500 Phase 2: corners:  88%|████████▊ | 175/199 [02:54<00:24,  1.02s/it]

→ 500 Phase 2: corners:  88%|████████▊ | 176/199 [02:55<00:23,  1.01s/it]

→ 500 Phase 2: corners:  89%|████████▉ | 177/199 [02:56<00:21,  1.01it/s]

→ 500 Phase 2: corners:  89%|████████▉ | 178/199 [02:57<00:20,  1.02it/s]

→ 500 Phase 2: corners:  90%|████████▉ | 179/199 [02:58<00:19,  1.02it/s]

→ 500 Phase 2: corners:  90%|█████████ | 180/199 [02:59<00:18,  1.04it/s]

→ 500 Phase 2: corners:  91%|█████████ | 181/199 [03:00<00:17,  1.04it/s]

→ 500 Phase 2: corners:  91%|█████████▏| 182/199 [03:01<00:16,  1.02it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 183/199 [03:02<00:15,  1.03it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 184/199 [03:03<00:14,  1.04it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 185/199 [03:03<00:13,  1.04it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 186/199 [03:04<00:12,  1.05it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 187/199 [03:05<00:11,  1.06it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 188/199 [03:06<00:10,  1.06it/s]

→ 500 Phase 2: corners:  95%|█████████▍| 189/199 [03:07<00:09,  1.05it/s]

→ 500 Phase 2: corners:  95%|█████████▌| 190/199 [03:08<00:08,  1.05it/s]

→ 500 Phase 2: corners:  96%|█████████▌| 191/199 [03:09<00:07,  1.06it/s]

→ 500 Phase 2: corners:  96%|█████████▋| 192/199 [03:10<00:06,  1.06it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 193/199 [03:11<00:05,  1.07it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 194/199 [03:12<00:04,  1.06it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 195/199 [03:13<00:03,  1.04it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 196/199 [03:14<00:02,  1.05it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 197/199 [03:15<00:01,  1.04it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 198/199 [03:16<00:00,  1.05it/s]

→ 500 Phase 2: corners: 100%|██████████| 199/199 [03:17<00:00,  1.06it/s]

                                                                         [PASS2] Frame 0000869: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 0 untrusted, 199 total


→ 500 Phase 3: finalize:   0%|          | 0/199 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   1%|          | 2/199 [00:00<00:15, 12.56it/s]

→ 500 Phase 3: finalize:   2%|▏         | 4/199 [00:00<00:15, 12.84it/s]

→ 500 Phase 3: finalize:   3%|▎         | 6/199 [00:00<00:14, 12.87it/s]

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→ 500 Phase 3: finalize:   5%|▌         | 10/199 [00:00<00:14, 12.89it/s]

→ 500 Phase 3: finalize:   6%|▌         | 12/199 [00:00<00:14, 12.91it/s]

→ 500 Phase 3: finalize:   7%|▋         | 14/199 [00:01<00:14, 12.91it/s]

→ 500 Phase 3: finalize:   8%|▊         | 16/199 [00:01<00:14, 12.90it/s]

→ 500 Phase 3: finalize:   9%|▉         | 18/199 [00:01<00:14, 12.86it/s]

→ 500 Phase 3: finalize:  10%|█         | 20/199 [00:01<00:13, 12.90it/s]

→ 500 Phase 3: finalize:  11%|█         | 22/199 [00:01<00:13, 12.89it/s]

→ 500 Phase 3: finalize:  12%|█▏        | 24/199 [00:01<00:13, 12.82it/s]

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→ 500 Phase 3: finalize:  98%|█████████▊| 196/199 [00:15<00:00, 12.95it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 198/199 [00:15<00:00, 12.95it/s]

                                                                          
Processing Rigs: 100%|██████████| 2/2 [07:47<00:00, 234.24s/it]
Processing Rigs: 100%|██████████| 2/2 [07:47<00:00, 233.87s/it]
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 423 frames from 2 rigs to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 224
[📋] 500: 100 frames selected from 199
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                        Step 2: Calibration                        
-------------------------------------------------------------------

-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:00<00:00, 102.87it/s]
Computing max image from bright frames:  57%|█████▊    | 23/40 [00:00<00:00, 109.95it/s]
Computing max image from bright frames:  88%|████████▊ | 35/40 [00:00<00:00, 113.66it/s]
                                                                                        
[INFO] Computed max image from 40 bright frames
[INFO] Corner median intensity: 19, black threshold: 57
[INFO] Saved casing detection debug images to /working/MDC044_W027_2025-05-29/calibration/debug/2_calibration/casing_detection
[INFO] Found 22702 casing boundary candidate points
[RESULT] Detected casing: center=(1016, 734), radius=828, inliers=1944/22702

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Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 526.263} (0.056):  53%|█████▎    | 53/100 [00:00<00:00, 107.17it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 526.263} (0.056):  54%|█████▍    | 54/100 [00:00<00:00, 107.17it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  55%|█████▌    | 55/100 [00:00<00:00, 107.17it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  56%|█████▌    | 56/100 [00:00<00:00, 107.17it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  57%|█████▋    | 57/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  57%|█████▋    | 57/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  58%|█████▊    | 58/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  59%|█████▉    | 59/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  60%|██████    | 60/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  61%|██████    | 61/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  62%|██████▏   | 62/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  63%|██████▎   | 63/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.056):  64%|██████▍   | 64/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  65%|██████▌   | 65/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  66%|██████▌   | 66/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  67%|██████▋   | 67/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  68%|██████▊   | 68/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  69%|██████▉   | 69/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  70%|███████   | 70/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  71%|███████   | 71/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  72%|███████▏  | 72/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  73%|███████▎  | 73/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  74%|███████▍  | 74/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  75%|███████▌  | 75/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  76%|███████▌  | 76/100 [00:00<00:00, 119.72it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  77%|███████▋  | 77/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  77%|███████▋  | 77/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  78%|███████▊  | 78/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  79%|███████▉  | 79/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  80%|████████  | 80/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  81%|████████  | 81/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  82%|████████▏ | 82/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  83%|████████▎ | 83/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  84%|████████▍ | 84/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  85%|████████▌ | 85/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  86%|████████▌ | 86/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  87%|████████▋ | 87/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  88%|████████▊ | 88/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  89%|████████▉ | 89/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  90%|█████████ | 90/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  91%|█████████ | 91/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  92%|█████████▏| 92/100 [00:00<00:00, 138.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  93%|█████████▎| 93/100 [00:00<00:00, 140.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  93%|█████████▎| 93/100 [00:00<00:00, 140.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  94%|█████████▍| 94/100 [00:00<00:00, 140.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  95%|█████████▌| 95/100 [00:00<00:00, 140.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  96%|█████████▌| 96/100 [00:00<00:00, 140.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  97%|█████████▋| 97/100 [00:00<00:00, 140.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  98%|█████████▊| 98/100 [00:00<00:00, 140.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.056):  99%|█████████▉| 99/100 [00:00<00:00, 140.32it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.004 | Best: {'fx': 501.263} (0.004):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.004 | Best: {'fx': 501.263} (0.004):   2%|▏         | 1/50 [00:00<00:06,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.005 | Best: {'fx': 502.283} (0.005):   2%|▏         | 1/50 [00:00<00:06,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.006 | Best: {'fx': 503.303} (0.006):   4%|▍         | 2/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.006 | Best: {'fx': 503.303} (0.006):   6%|▌         | 3/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.010 | Best: {'fx': 508.405} (0.010):   8%|▊         | 4/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.007 | Best: {'fx': 508.405} (0.010):  10%|█         | 5/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.008 | Best: {'fx': 508.405} (0.010):  12%|█▏        | 6/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.011 | Best: {'fx': 509.426} (0.011):  14%|█▍        | 7/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.009 | Best: {'fx': 509.426} (0.011):  16%|█▌        | 8/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.012 | Best: {'fx': 510.446} (0.012):  18%|█▊        | 9/50 [00:00<00:05,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.013 | Best: {'fx': 511.467} (0.013):  20%|██        | 10/50 [00:00<00:04,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.014 | Best: {'fx': 512.487} (0.014):  22%|██▏       | 11/50 [00:00<00:04,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.027 | Best: {'fx': 516.569} (0.027):  24%|██▍       | 12/50 [00:00<00:04,  8.10it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.027 | Best: {'fx': 516.569} (0.027):  26%|██▌       | 13/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.017 | Best: {'fx': 516.569} (0.027):  26%|██▌       | 13/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.029 | Best: {'fx': 517.589} (0.029):  28%|██▊       | 14/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.022 | Best: {'fx': 517.589} (0.029):  30%|███       | 15/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.024 | Best: {'fx': 517.589} (0.029):  32%|███▏      | 16/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.032 | Best: {'fx': 519.63} (0.032):  34%|███▍      | 17/50 [00:00<00:00, 67.76it/s] 
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.031 | Best: {'fx': 519.63} (0.032):  36%|███▌      | 18/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.034 | Best: {'fx': 520.65} (0.034):  38%|███▊      | 19/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.036 | Best: {'fx': 521.671} (0.036):  40%|████      | 20/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.042 | Best: {'fx': 522.691} (0.042):  42%|████▏     | 21/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.055 | Best: {'fx': 525.752} (0.055):  44%|████▍     | 22/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.044 | Best: {'fx': 525.752} (0.055):  46%|████▌     | 23/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.061 | Best: {'fx': 526.773} (0.061):  48%|████▊     | 24/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.049 | Best: {'fx': 526.773} (0.061):  50%|█████     | 25/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.063 | Best: {'fx': 528.814} (0.063):  52%|█████▏    | 26/50 [00:00<00:00, 67.76it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.063 | Best: {'fx': 528.814} (0.063):  54%|█████▍    | 27/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.068 | Best: {'fx': 527.793} (0.068):  54%|█████▍    | 27/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.065 | Best: {'fx': 527.793} (0.068):  56%|█████▌    | 28/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.060 | Best: {'fx': 527.793} (0.068):  58%|█████▊    | 29/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.051 | Best: {'fx': 527.793} (0.068):  60%|██████    | 30/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.060 | Best: {'fx': 527.793} (0.068):  62%|██████▏   | 31/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.049 | Best: {'fx': 527.793} (0.068):  64%|██████▍   | 32/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.058 | Best: {'fx': 527.793} (0.068):  66%|██████▌   | 33/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.054 | Best: {'fx': 527.793} (0.068):  68%|██████▊   | 34/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.047 | Best: {'fx': 527.793} (0.068):  70%|███████   | 35/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.043 | Best: {'fx': 527.793} (0.068):  72%|███████▏  | 36/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.037 | Best: {'fx': 527.793} (0.068):  74%|███████▍  | 37/50 [00:00<00:00, 97.60it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.037 | Best: {'fx': 527.793} (0.068):  76%|███████▌  | 38/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.033 | Best: {'fx': 527.793} (0.068):  76%|███████▌  | 38/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.042 | Best: {'fx': 527.793} (0.068):  78%|███████▊  | 39/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.037 | Best: {'fx': 527.793} (0.068):  80%|████████  | 40/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.040 | Best: {'fx': 527.793} (0.068):  82%|████████▏ | 41/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.032 | Best: {'fx': 527.793} (0.068):  84%|████████▍ | 42/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.029 | Best: {'fx': 527.793} (0.068):  86%|████████▌ | 43/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.035 | Best: {'fx': 527.793} (0.068):  88%|████████▊ | 44/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.032 | Best: {'fx': 527.793} (0.068):  90%|█████████ | 45/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.028 | Best: {'fx': 527.793} (0.068):  92%|█████████▏| 46/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.031 | Best: {'fx': 527.793} (0.068):  94%|█████████▍| 47/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.029 | Best: {'fx': 527.793} (0.068):  96%|█████████▌| 48/50 [00:00<00:00, 101.43it/s]
Brute | Start: {'fx': 526.263} (0.056) | Current: 0.027 | Best: {'fx': 527.793} (0.068):  98%|█████████▊| 49/50 [00:00<00:00, 101.43it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 527.7932385075243
    px: 1016.0
    py: 734.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization

Initial score: 140.9329: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 140.9329, picklable_objective(x0) = 140.9618

Best score: 140.9618: : 0iter [00:01, ?iter/s] 
Best score: 140.9618: : 1iter [00:01,  1.78s/iter]
Best score: 140.9618: : 1iter [00:01,  1.78s/iter]
Best score: 140.9618: : 2iter [00:01,  1.78s/iter]
Best score: 140.9618: : 3iter [00:01,  1.78s/iter]
Best score: 140.9618: : 4iter [00:01,  2.67iter/s]
Best score: 140.9618: : 4iter [00:01,  2.67iter/s]
Best score: 140.9618: : 5iter [00:02,  2.67iter/s]
Best score: 140.9618: : 6iter [00:02,  2.67iter/s]
Best score: 140.9618: : 7iter [00:02,  5.06iter/s]
Best score: 140.9618: : 7iter [00:02,  5.06iter/s]
Best score: 140.9618: : 8iter [00:02,  5.06iter/s]
Best score: 140.9618: : 9iter [00:02,  6.07iter/s]
Best score: 140.9618: : 9iter [00:02,  6.07iter/s]
Best score: 140.9618: : 10iter [00:02,  6.07iter/s]
Best score: 140.9618: : 11iter [00:02,  6.07iter/s]
Best score: 140.9618: : 12iter [00:02,  8.71iter/s]
Best score: 140.9618: : 12iter [00:02,  8.71iter/s]
Best score: 140.9618: : 13iter [00:02,  8.71iter/s]
Best score: 140.9618: : 14iter [00:02,  8.71iter/s]
Best score: 140.9618: : 15iter [00:02, 11.31iter/s]
Best score: 140.9618: : 15iter [00:02, 11.31iter/s]
Best score: 140.9618: : 16iter [00:02, 11.31iter/s]
Best score: 140.9618: : 17iter [00:02, 11.31iter/s]
Best score: 140.9618: : 18iter [00:02, 13.69iter/s]
Best score: 140.9618: : 18iter [00:02, 13.69iter/s]
Best score: 127.6395: : 19iter [00:02, 13.69iter/s]
Best score: 127.6395: : 20iter [00:02, 13.69iter/s]
Best score: 127.6395: : 21iter [00:02, 15.61iter/s]
Best score: 127.6395: : 21iter [00:02, 15.61iter/s]
Best score: 127.6395: : 22iter [00:02, 15.61iter/s]
Best score: 127.6395: : 23iter [00:02, 15.61iter/s]
Best score: 127.6395: : 24iter [00:02, 17.29iter/s]
Best score: 127.6395: : 24iter [00:02, 17.29iter/s]
Best score: 127.6395: : 25iter [00:03, 17.29iter/s]
Best score: 127.6395: : 26iter [00:03, 17.29iter/s]
Best score: 127.6395: : 27iter [00:03, 18.63iter/s]
Best score: 127.6395: : 27iter [00:03, 18.63iter/s]
Best score: 127.6395: : 28iter [00:03, 18.63iter/s]
Best score: 98.6767: : 29iter [00:03, 18.63iter/s] 
Best score: 98.6767: : 30iter [00:03, 19.46iter/s]
Best score: 98.6767: : 30iter [00:03, 19.46iter/s]
Best score: 98.6767: : 31iter [00:03, 19.46iter/s]
Best score: 98.6767: : 32iter [00:03, 19.46iter/s]
Best score: 98.6767: : 33iter [00:03, 20.28iter/s]
Best score: 98.6767: : 33iter [00:03, 20.28iter/s]
Best score: 98.6767: : 34iter [00:03, 20.28iter/s]
Best score: 98.6767: : 35iter [00:03, 20.28iter/s]
Best score: 98.6767: : 36iter [00:03, 20.86iter/s]
Best score: 98.6767: : 36iter [00:03, 20.86iter/s]
Best score: 98.6767: : 37iter [00:03, 20.86iter/s]
Best score: 98.6767: : 38iter [00:03, 20.86iter/s]
Best score: 98.6767: : 39iter [00:03, 21.30iter/s]
Best score: 98.0809: : 39iter [00:03, 21.30iter/s]
Best score: 98.0809: : 40iter [00:03, 21.30iter/s]
Best score: 98.0809: : 41iter [00:03, 21.30iter/s]
Best score: 98.0809: : 42iter [00:03, 21.41iter/s]
Best score: 98.0809: : 42iter [00:03, 21.41iter/s]
Best score: 98.0809: : 43iter [00:03, 21.41iter/s]
Best score: 98.0809: : 44iter [00:03, 21.41iter/s]
Best score: 98.0809: : 45iter [00:03, 21.61iter/s]
Best score: 98.0809: : 45iter [00:03, 21.61iter/s]
Best score: 98.0809: : 46iter [00:03, 21.61iter/s]
Best score: 98.0809: : 47iter [00:04, 21.61iter/s]
Best score: 98.0809: : 48iter [00:04, 21.78iter/s]
Best score: 98.0809: : 48iter [00:04, 21.78iter/s]
[ITER 50] Best: 94.0838
[PARAMS] Current best parameters:
  fx: 568.5654
  fy: 562.6721
  px: 1018.1081
  py: 748.5838
  k1: 0.03334593
  k2: -0.00209979
  k3: -0.01455331
  k4: -0.00061730
  p1: -0.00335319
  p2: 0.00433434
  p3: -0.00333968
  p4: 0.00507335
  scale: 2.4668
  rotation: -0.3124

Best score: 94.0838: : 49iter [00:04, 21.78iter/s]
Best score: 94.0838: : 50iter [00:04, 21.78iter/s]
Best score: 94.0838: : 51iter [00:04, 21.72iter/s]
Best score: 94.0838: : 51iter [00:04, 21.72iter/s]
Best score: 94.0838: : 52iter [00:04, 21.72iter/s]
Best score: 94.0838: : 53iter [00:04, 21.72iter/s]
Best score: 94.0838: : 54iter [00:04, 21.92iter/s]
Best score: 94.0838: : 54iter [00:04, 21.92iter/s]
Best score: 94.0838: : 55iter [00:04, 21.92iter/s]
Best score: 94.0838: : 56iter [00:04, 21.92iter/s]
Best score: 94.0838: : 57iter [00:04, 22.05iter/s]
Best score: 94.0838: : 57iter [00:04, 22.05iter/s]
Best score: 94.0838: : 58iter [00:04, 22.05iter/s]
Best score: 54.8564: : 59iter [00:04, 22.05iter/s]
Best score: 54.8564: : 60iter [00:04, 22.00iter/s]
Best score: 54.8564: : 60iter [00:04, 22.00iter/s]
Best score: 54.8564: : 61iter [00:04, 22.00iter/s]
Best score: 54.8564: : 62iter [00:04, 22.00iter/s]
Best score: 54.8564: : 63iter [00:04, 22.16iter/s]
Best score: 54.8564: : 63iter [00:04, 22.16iter/s]
Best score: 54.8564: : 64iter [00:04, 22.16iter/s]
Best score: 54.8564: : 65iter [00:04, 22.16iter/s]
Best score: 54.8564: : 66iter [00:04, 22.30iter/s]
Best score: 54.8564: : 66iter [00:04, 22.30iter/s]
Best score: 54.8564: : 67iter [00:04, 22.30iter/s]
Best score: 54.8564: : 68iter [00:04, 22.30iter/s]
Best score: 54.8564: : 69iter [00:04, 22.39iter/s]
Best score: 18.4882: : 69iter [00:05, 22.39iter/s]
Best score: 18.4882: : 70iter [00:05, 22.39iter/s]
Best score: 18.4882: : 71iter [00:05, 22.39iter/s]
Best score: 18.4882: : 72iter [00:05, 22.33iter/s]
Best score: 18.4882: : 72iter [00:05, 22.33iter/s]
Best score: 18.4882: : 73iter [00:05, 22.33iter/s]
Best score: 18.4882: : 74iter [00:05, 22.33iter/s]
Best score: 18.4882: : 75iter [00:05, 22.41iter/s]
Best score: 18.4882: : 75iter [00:05, 22.41iter/s]
Best score: 18.4882: : 76iter [00:05, 22.41iter/s]
Best score: 18.4882: : 77iter [00:05, 22.41iter/s]
Best score: 18.4882: : 78iter [00:05, 22.39iter/s]
Best score: 18.4882: : 78iter [00:05, 22.39iter/s]
Best score: 5.5513: : 79iter [00:05, 22.39iter/s] 
Best score: 5.5513: : 80iter [00:05, 22.39iter/s]
Best score: 5.5513: : 81iter [00:05, 22.31iter/s]
Best score: 5.5513: : 81iter [00:05, 22.31iter/s]
Best score: 5.5513: : 82iter [00:05, 22.31iter/s]
Best score: 5.5513: : 83iter [00:05, 22.31iter/s]
Best score: 5.5513: : 84iter [00:05, 22.35iter/s]
Best score: 5.5513: : 84iter [00:05, 22.35iter/s]
Best score: 5.5513: : 85iter [00:05, 22.35iter/s]
Best score: 5.5513: : 86iter [00:05, 22.35iter/s]
Best score: 5.5513: : 87iter [00:05, 22.45iter/s]
Best score: 5.5513: : 87iter [00:05, 22.45iter/s]
Best score: 5.5513: : 88iter [00:05, 22.45iter/s]
Best score: 5.5513: : 89iter [00:05, 22.45iter/s]
Best score: 5.5513: : 90iter [00:05, 22.22iter/s]
Best score: 5.5513: : 90iter [00:05, 22.22iter/s]
Best score: 5.5513: : 91iter [00:05, 22.22iter/s]
Best score: 5.5513: : 92iter [00:06, 22.22iter/s]
Best score: 5.5513: : 93iter [00:06, 22.36iter/s]
Best score: 5.5513: : 93iter [00:06, 22.36iter/s]
Best score: 5.5513: : 94iter [00:06, 22.36iter/s]
Best score: 5.5513: : 95iter [00:06, 22.36iter/s]
Best score: 5.5513: : 96iter [00:06, 22.44iter/s]
Best score: 5.5513: : 96iter [00:06, 22.44iter/s]
Best score: 5.5513: : 97iter [00:06, 22.44iter/s]
Best score: 5.5513: : 98iter [00:06, 22.44iter/s]
Best score: 5.5513: : 99iter [00:06, 22.54iter/s]
[ITER 100] Best: 5.5513
[PARAMS] Current best parameters:
  fx: 537.5195
  fy: 537.5296
  px: 1012.4971
  py: 741.4383
  k1: 0.03110814
  k2: -0.01917764
  k3: -0.00565591
  k4: 0.00274899
  p1: -0.00494142
  p2: -0.00933804
  p3: -0.00726534
  p4: 0.00586650
  scale: 2.8171
  rotation: -0.0493

Best score: 5.5513: : 99iter [00:06, 22.54iter/s]
Best score: 5.5513: : 100iter [00:06, 22.54iter/s]
Best score: 5.5513: : 101iter [00:06, 22.54iter/s]
Best score: 5.5513: : 102iter [00:06, 22.33iter/s]
Best score: 5.5513: : 102iter [00:06, 22.33iter/s]
Best score: 5.5513: : 103iter [00:06, 22.33iter/s]
Best score: 5.5513: : 104iter [00:06, 22.33iter/s]
Best score: 5.5513: : 105iter [00:06, 22.45iter/s]
Best score: 5.5513: : 105iter [00:06, 22.45iter/s]
Best score: 5.5513: : 106iter [00:06, 22.45iter/s]
Best score: 5.5513: : 107iter [00:06, 22.45iter/s]
Best score: 5.5513: : 108iter [00:06, 22.51iter/s]
Best score: 5.5513: : 108iter [00:06, 22.51iter/s]
Best score: 5.5513: : 109iter [00:06, 22.51iter/s]
Best score: 5.5513: : 110iter [00:06, 22.51iter/s]
Best score: 5.5513: : 111iter [00:06, 22.31iter/s]
Best score: 5.5513: : 111iter [00:06, 22.31iter/s]
Best score: 5.5513: : 112iter [00:06, 22.31iter/s]
Best score: 5.5513: : 113iter [00:06, 22.31iter/s]
Best score: 5.5513: : 114iter [00:06, 22.37iter/s]
Best score: 5.5513: : 114iter [00:07, 22.37iter/s]
Best score: 5.5513: : 115iter [00:07, 22.37iter/s]
Best score: 5.5513: : 116iter [00:07, 22.37iter/s]
Best score: 5.5513: : 117iter [00:07, 22.40iter/s]
Best score: 5.5513: : 117iter [00:07, 22.40iter/s]
Best score: 5.5513: : 118iter [00:07, 22.40iter/s]
Best score: 5.5513: : 119iter [00:07, 22.40iter/s]
Best score: 5.5513: : 120iter [00:07, 22.19iter/s]
Best score: 5.5513: : 120iter [00:07, 22.19iter/s]
Best score: 5.5513: : 121iter [00:07, 22.19iter/s]
Best score: 5.5513: : 122iter [00:07, 22.19iter/s]
Best score: 5.5513: : 123iter [00:07, 22.14iter/s]
Best score: 5.5513: : 123iter [00:07, 22.14iter/s]
Best score: 5.5513: : 124iter [00:07, 22.14iter/s]
Best score: 5.5513: : 125iter [00:07, 22.14iter/s]
Best score: 5.5513: : 126iter [00:07, 22.34iter/s]
Best score: 5.5513: : 126iter [00:07, 22.34iter/s]
Best score: 5.5513: : 127iter [00:07, 22.34iter/s]
Best score: 5.5513: : 128iter [00:07, 22.34iter/s]
Best score: 5.5513: : 129iter [00:07, 22.46iter/s]
Best score: 4.3308: : 129iter [00:07, 22.46iter/s]
Best score: 4.3308: : 130iter [00:07, 22.46iter/s]
Best score: 4.3308: : 131iter [00:07, 22.46iter/s]
Best score: 4.3308: : 132iter [00:07, 22.23iter/s]
Best score: 4.3308: : 132iter [00:07, 22.23iter/s]
Best score: 4.3308: : 133iter [00:07, 22.23iter/s]
Best score: 4.3308: : 134iter [00:07, 22.23iter/s]
Best score: 4.3308: : 135iter [00:07, 22.31iter/s]
Best score: 4.3308: : 135iter [00:07, 22.31iter/s]
Best score: 4.3308: : 136iter [00:08, 22.31iter/s]
Best score: 4.3308: : 137iter [00:08, 22.31iter/s]
Best score: 4.3308: : 138iter [00:08, 22.39iter/s]
Best score: 4.3308: : 138iter [00:08, 22.39iter/s]
Best score: 2.6976: : 139iter [00:08, 22.39iter/s]
Best score: 2.6976: : 140iter [00:08, 22.39iter/s]
Best score: 2.6976: : 141iter [00:08, 22.20iter/s]
Best score: 2.6976: : 141iter [00:08, 22.20iter/s]
Best score: 2.6976: : 142iter [00:08, 22.20iter/s]
Best score: 2.6976: : 143iter [00:08, 22.20iter/s]
Best score: 2.6976: : 144iter [00:08, 22.36iter/s]
Best score: 2.6976: : 144iter [00:08, 22.36iter/s]
Best score: 2.6976: : 145iter [00:08, 22.36iter/s]
Best score: 2.6976: : 146iter [00:08, 22.36iter/s]
Best score: 2.6976: : 147iter [00:08, 22.45iter/s]
Best score: 2.6976: : 147iter [00:08, 22.45iter/s]
Best score: 2.6976: : 148iter [00:08, 22.45iter/s]
[ITER 150] Best: 1.4833
[PARAMS] Current best parameters:
  fx: 529.6620
  fy: 529.2864
  px: 1013.7085
  py: 745.6135
  k1: 0.03276856
  k2: -0.00785027
  k3: -0.01521055
  k4: 0.00579332
  p1: 0.00408511
  p2: -0.00618489
  p3: -0.00655714
  p4: 0.00312703
  scale: 2.8137
  rotation: 0.1546

Best score: 1.4833: : 149iter [00:08, 22.45iter/s]
Best score: 1.4833: : 150iter [00:08, 22.26iter/s]
Best score: 1.4833: : 150iter [00:08, 22.26iter/s]
Best score: 1.4833: : 151iter [00:08, 22.26iter/s]
Best score: 1.4833: : 152iter [00:08, 22.26iter/s]
Best score: 1.4833: : 153iter [00:08, 22.38iter/s]
Best score: 1.4833: : 153iter [00:08, 22.38iter/s]
Best score: 1.4833: : 154iter [00:08, 22.38iter/s]
Best score: 1.4833: : 155iter [00:08, 22.38iter/s]
Best score: 1.4833: : 156iter [00:08, 22.44iter/s]
Best score: 1.4833: : 156iter [00:08, 22.44iter/s]
Best score: 1.4833: : 157iter [00:08, 22.44iter/s]
Best score: 1.4833: : 158iter [00:08, 22.44iter/s]
Best score: 1.4833: : 159iter [00:08, 22.54iter/s]
Best score: 1.4833: : 159iter [00:09, 22.54iter/s]
Best score: 1.4833: : 160iter [00:09, 22.54iter/s]
Best score: 1.4833: : 161iter [00:09, 22.54iter/s]
Best score: 1.4833: : 162iter [00:09, 22.33iter/s]
Best score: 1.4833: : 162iter [00:09, 22.33iter/s]
Best score: 1.4833: : 163iter [00:09, 22.33iter/s]
Best score: 1.4833: : 164iter [00:09, 22.33iter/s]
Best score: 1.4833: : 165iter [00:09, 22.45iter/s]
Best score: 1.4833: : 165iter [00:09, 22.45iter/s]
Best score: 1.4833: : 166iter [00:09, 22.45iter/s]
Best score: 1.4833: : 167iter [00:09, 22.45iter/s]
Best score: 1.4833: : 168iter [00:09, 22.49iter/s]
Best score: 1.4833: : 168iter [00:09, 22.49iter/s]
Best score: 1.1853: : 169iter [00:09, 22.49iter/s]
Best score: 1.1853: : 170iter [00:09, 22.49iter/s]
Best score: 1.1853: : 171iter [00:09, 22.30iter/s]
Best score: 1.1853: : 171iter [00:09, 22.30iter/s]
Best score: 1.1853: : 172iter [00:09, 22.30iter/s]
Best score: 1.1853: : 173iter [00:09, 22.30iter/s]
Best score: 1.1853: : 174iter [00:09, 22.39iter/s]
Best score: 1.1853: : 174iter [00:09, 22.39iter/s]
Best score: 1.1853: : 175iter [00:09, 22.39iter/s]
Best score: 1.1853: : 176iter [00:09, 22.39iter/s]
Best score: 1.1853: : 177iter [00:09, 22.41iter/s]
Best score: 1.1853: : 177iter [00:09, 22.41iter/s]
Best score: 1.1853: : 178iter [00:09, 22.41iter/s]
Best score: 1.1853: : 179iter [00:09, 22.41iter/s]
Best score: 1.1853: : 180iter [00:09, 22.22iter/s]
Best score: 1.1853: : 180iter [00:09, 22.22iter/s]
Best score: 1.1853: : 181iter [00:10, 22.22iter/s]
Best score: 1.1853: : 182iter [00:10, 22.22iter/s]
Best score: 1.1853: : 183iter [00:10, 22.36iter/s]
Best score: 1.1853: : 183iter [00:10, 22.36iter/s]
Best score: 1.1853: : 184iter [00:10, 22.36iter/s]
Best score: 1.1853: : 185iter [00:10, 22.36iter/s]
Best score: 1.1853: : 186iter [00:10, 22.41iter/s]
Best score: 1.1853: : 186iter [00:10, 22.41iter/s]
Best score: 1.1853: : 187iter [00:10, 22.41iter/s]
Best score: 1.1853: : 188iter [00:10, 22.41iter/s]
Best score: 1.1853: : 189iter [00:10, 22.48iter/s]
Best score: 0.8647: : 189iter [00:10, 22.48iter/s]
Best score: 0.8647: : 190iter [00:10, 22.48iter/s]
Best score: 0.8647: : 191iter [00:10, 22.48iter/s]
Best score: 0.8647: : 192iter [00:10, 22.37iter/s]
Best score: 0.8647: : 192iter [00:10, 22.37iter/s]
Best score: 0.8647: : 193iter [00:10, 22.37iter/s]
Best score: 0.8647: : 194iter [00:10, 22.37iter/s]
Best score: 0.8647: : 195iter [00:10, 22.43iter/s]
Best score: 0.8647: : 195iter [00:10, 22.43iter/s]
Best score: 0.8647: : 196iter [00:10, 22.43iter/s]
Best score: 0.8647: : 197iter [00:10, 22.43iter/s]
Best score: 0.8647: : 198iter [00:10, 22.48iter/s]
Best score: 0.8647: : 198iter [00:10, 22.48iter/s]
[ITER 200] Best: 0.8647
[PARAMS] Current best parameters:
  fx: 514.0819
  fy: 513.7505
  px: 1013.3560
  py: 743.3537
  k1: 0.03486639
  k2: 0.00169295
  k3: -0.01248682
  k4: 0.00320738
  p1: -0.00063006
  p2: -0.00686554
  p3: -0.00379143
  p4: 0.00211880
  scale: 2.8351
  rotation: 0.0505

Best score: 0.8647: : 199iter [00:10, 22.48iter/s]
Best score: 0.8647: : 200iter [00:10, 22.48iter/s]
Best score: 0.8647: : 201iter [00:10, 22.28iter/s]
Best score: 0.8647: : 201iter [00:10, 22.28iter/s]
Best score: 0.8647: : 202iter [00:10, 22.28iter/s]
Best score: 0.8647: : 203iter [00:10, 22.28iter/s]
Best score: 0.8647: : 204iter [00:10, 22.38iter/s]
Best score: 0.8647: : 204iter [00:11, 22.38iter/s]
Best score: 0.8647: : 205iter [00:11, 22.38iter/s]
Best score: 0.8647: : 206iter [00:11, 22.38iter/s]
Best score: 0.8647: : 207iter [00:11, 22.43iter/s]
Best score: 0.8647: : 207iter [00:11, 22.43iter/s]
Best score: 0.8647: : 208iter [00:11, 22.43iter/s]
Best score: 0.8438: : 209iter [00:11, 22.43iter/s]
Best score: 0.8438: : 210iter [00:11, 22.29iter/s]
Best score: 0.8438: : 210iter [00:11, 22.29iter/s]
Best score: 0.8438: : 211iter [00:11, 22.29iter/s]
Best score: 0.8438: : 212iter [00:11, 22.29iter/s]
Best score: 0.8438: : 213iter [00:11, 22.44iter/s]
Best score: 0.8438: : 213iter [00:11, 22.44iter/s]
Best score: 0.8438: : 214iter [00:11, 22.44iter/s]
Best score: 0.8438: : 215iter [00:11, 22.44iter/s]
Best score: 0.8438: : 216iter [00:11, 22.52iter/s]
Best score: 0.8438: : 216iter [00:11, 22.52iter/s]
Best score: 0.8438: : 217iter [00:11, 22.52iter/s]
Best score: 0.8438: : 218iter [00:11, 22.52iter/s]
Best score: 0.8438: : 219iter [00:11, 22.54iter/s]
Best score: 0.8438: : 219iter [00:11, 22.54iter/s]
Best score: 0.8438: : 220iter [00:11, 22.54iter/s]
Best score: 0.8438: : 221iter [00:11, 22.54iter/s]
Best score: 0.8438: : 222iter [00:11, 22.39iter/s]
Best score: 0.8438: : 222iter [00:11, 22.39iter/s]
Best score: 0.8438: : 223iter [00:11, 22.39iter/s]
Best score: 0.8438: : 224iter [00:11, 22.39iter/s]
Best score: 0.8438: : 225iter [00:11, 22.41iter/s]
Best score: 0.8438: : 225iter [00:11, 22.41iter/s]
Best score: 0.8438: : 226iter [00:12, 22.41iter/s]
Best score: 0.8438: : 227iter [00:12, 22.41iter/s]
Best score: 0.8438: : 228iter [00:12, 22.45iter/s]
Best score: 0.8438: : 228iter [00:12, 22.45iter/s]
Best score: 0.7607: : 229iter [00:12, 22.45iter/s]
Best score: 0.7607: : 230iter [00:12, 22.45iter/s]
Best score: 0.7607: : 231iter [00:12, 22.28iter/s]
Best score: 0.7607: : 231iter [00:12, 22.28iter/s]
Best score: 0.7607: : 232iter [00:12, 22.28iter/s]
Best score: 0.7607: : 233iter [00:12, 22.28iter/s]
Best score: 0.7607: : 234iter [00:12, 22.43iter/s]
Best score: 0.7607: : 234iter [00:12, 22.43iter/s]
Best score: 0.7607: : 235iter [00:12, 22.43iter/s]
Best score: 0.7607: : 236iter [00:12, 22.43iter/s]
Best score: 0.7607: : 237iter [00:12, 22.48iter/s]
Best score: 0.7607: : 237iter [00:12, 22.48iter/s]
Best score: 0.7607: : 238iter [00:12, 22.48iter/s]
Best score: 0.6529: : 239iter [00:12, 22.48iter/s]
Best score: 0.6529: : 240iter [00:12, 22.25iter/s]
Best score: 0.6529: : 240iter [00:12, 22.25iter/s]
Best score: 0.6529: : 241iter [00:12, 22.25iter/s]
Best score: 0.6529: : 242iter [00:12, 22.25iter/s]
Best score: 0.6529: : 243iter [00:12, 22.36iter/s]
Best score: 0.6529: : 243iter [00:12, 22.36iter/s]
Best score: 0.6529: : 244iter [00:12, 22.36iter/s]
Best score: 0.6529: : 245iter [00:12, 22.36iter/s]
Best score: 0.6529: : 246iter [00:12, 22.41iter/s]
Best score: 0.6529: : 246iter [00:12, 22.41iter/s]
Best score: 0.6529: : 247iter [00:12, 22.41iter/s]
Best score: 0.6529: : 248iter [00:13, 22.41iter/s]
Best score: 0.6529: : 249iter [00:13, 22.46iter/s]
[ITER 250] Best: 0.5641
[PARAMS] Current best parameters:
  fx: 511.0493
  fy: 510.6600
  px: 1015.8794
  py: 743.4489
  k1: 0.03304145
  k2: 0.00083338
  k3: -0.00888088
  k4: 0.00211038
  p1: 0.00013101
  p2: -0.00245585
  p3: -0.00209892
  p4: 0.00076022
  scale: 2.8624
  rotation: 0.0635

Best score: 0.5641: : 249iter [00:13, 22.46iter/s]
Best score: 0.5641: : 250iter [00:13, 22.46iter/s]
Best score: 0.5641: : 251iter [00:13, 22.46iter/s]
Best score: 0.5641: : 252iter [00:13, 22.33iter/s]
Best score: 0.5641: : 252iter [00:13, 22.33iter/s]
Best score: 0.5641: : 253iter [00:13, 22.33iter/s]
Best score: 0.5641: : 254iter [00:13, 22.33iter/s]
Best score: 0.5641: : 255iter [00:13, 22.47iter/s]
Best score: 0.5641: : 255iter [00:13, 22.47iter/s]
Best score: 0.5641: : 256iter [00:13, 22.47iter/s]
Best score: 0.5641: : 257iter [00:13, 22.47iter/s]
Best score: 0.5641: : 258iter [00:13, 22.58iter/s]
Best score: 0.5641: : 258iter [00:13, 22.58iter/s]
Best score: 0.5368: : 259iter [00:13, 22.58iter/s]
Best score: 0.5368: : 260iter [00:13, 22.58iter/s]
Best score: 0.5368: : 261iter [00:13, 22.42iter/s]
Best score: 0.5368: : 261iter [00:13, 22.42iter/s]
Best score: 0.5368: : 262iter [00:13, 22.42iter/s]
Best score: 0.5368: : 263iter [00:13, 22.42iter/s]
Best score: 0.5368: : 264iter [00:13, 22.35iter/s]
Best score: 0.5368: : 264iter [00:13, 22.35iter/s]
Best score: 0.5368: : 265iter [00:13, 22.35iter/s]
Best score: 0.5368: : 266iter [00:13, 22.35iter/s]
Best score: 0.5368: : 267iter [00:13, 22.46iter/s]
Best score: 0.5368: : 267iter [00:13, 22.46iter/s]
Best score: 0.5368: : 268iter [00:13, 22.46iter/s]
Best score: 0.4649: : 269iter [00:13, 22.46iter/s]
Best score: 0.4649: : 270iter [00:13, 22.23iter/s]
Best score: 0.4649: : 270iter [00:13, 22.23iter/s]
Best score: 0.4649: : 271iter [00:14, 22.23iter/s]
Best score: 0.4649: : 272iter [00:14, 22.23iter/s]
Best score: 0.4649: : 273iter [00:14, 22.23iter/s]
Best score: 0.4649: : 273iter [00:14, 22.23iter/s]
Best score: 0.4649: : 274iter [00:14, 22.23iter/s]
Best score: 0.4649: : 275iter [00:14, 22.23iter/s]
Best score: 0.4649: : 276iter [00:14, 22.34iter/s]
Best score: 0.4649: : 276iter [00:14, 22.34iter/s]
Best score: 0.4649: : 277iter [00:14, 22.34iter/s]
Best score: 0.4649: : 278iter [00:14, 22.34iter/s]
Best score: 0.4649: : 279iter [00:14, 22.44iter/s]
Best score: 0.4649: : 279iter [00:14, 22.44iter/s]
Best score: 0.4649: : 280iter [00:14, 22.44iter/s]
Best score: 0.4649: : 281iter [00:14, 22.44iter/s]
Best score: 0.4649: : 282iter [00:14, 22.25iter/s]
Best score: 0.4649: : 282iter [00:14, 22.25iter/s]
Best score: 0.4649: : 283iter [00:14, 22.25iter/s]
Best score: 0.4649: : 284iter [00:14, 22.25iter/s]
Best score: 0.4649: : 285iter [00:14, 22.31iter/s]
Best score: 0.4649: : 285iter [00:14, 22.31iter/s]
Best score: 0.4649: : 286iter [00:14, 22.31iter/s]
Best score: 0.4649: : 287iter [00:14, 22.31iter/s]
Best score: 0.4649: : 288iter [00:14, 22.38iter/s]
Best score: 0.4649: : 288iter [00:14, 22.38iter/s]
Best score: 0.4649: : 289iter [00:14, 22.38iter/s]
Best score: 0.4649: : 290iter [00:14, 22.38iter/s]
Best score: 0.4649: : 291iter [00:14, 22.20iter/s]
Best score: 0.4649: : 291iter [00:14, 22.20iter/s]
Best score: 0.4649: : 292iter [00:14, 22.20iter/s]
Best score: 0.4649: : 293iter [00:15, 22.20iter/s]
Best score: 0.4649: : 294iter [00:15, 22.35iter/s]
Best score: 0.4649: : 294iter [00:15, 22.35iter/s]
Best score: 0.4649: : 295iter [00:15, 22.35iter/s]
Best score: 0.4649: : 296iter [00:15, 22.35iter/s]
Best score: 0.4649: : 297iter [00:15, 22.46iter/s]
Best score: 0.4649: : 297iter [00:15, 22.46iter/s]
Best score: 0.4649: : 298iter [00:15, 22.46iter/s]
[ITER 300] Best: 0.4649
[PARAMS] Current best parameters:
  fx: 508.8697
  fy: 508.5736
  px: 1016.0029
  py: 743.9689
  k1: 0.03047150
  k2: 0.00456016
  k3: -0.00967763
  k4: 0.00209821
  p1: 0.00047198
  p2: -0.00243609
  p3: -0.00012911
  p4: 0.00040346
  scale: 2.8763
  rotation: 0.0661

Best score: 0.4649: : 299iter [00:15, 22.46iter/s]
Best score: 0.4649: : 300iter [00:15, 22.33iter/s]
Best score: 0.4649: : 300iter [00:15, 22.33iter/s]
Best score: 0.4649: : 301iter [00:15, 22.33iter/s]
Best score: 0.4649: : 302iter [00:15, 22.33iter/s]
Best score: 0.4649: : 303iter [00:15, 22.42iter/s]
Best score: 0.4649: : 303iter [00:15, 22.42iter/s]
Best score: 0.4649: : 304iter [00:15, 22.42iter/s]
Best score: 0.4649: : 305iter [00:15, 22.42iter/s]
Best score: 0.4649: : 306iter [00:15, 22.47iter/s]
Best score: 0.4649: : 306iter [00:15, 22.47iter/s]
Best score: 0.4649: : 307iter [00:15, 22.47iter/s]
Best score: 0.4649: : 308iter [00:15, 22.47iter/s]
Best score: 0.4649: : 309iter [00:15, 22.55iter/s]
Best score: 0.4649: : 309iter [00:15, 22.55iter/s]
Best score: 0.4649: : 310iter [00:15, 22.55iter/s]
Best score: 0.4649: : 311iter [00:15, 22.55iter/s]
Best score: 0.4649: : 312iter [00:15, 22.35iter/s]
Best score: 0.4649: : 312iter [00:15, 22.35iter/s]
Best score: 0.4649: : 313iter [00:15, 22.35iter/s]
Best score: 0.4649: : 314iter [00:15, 22.35iter/s]
Best score: 0.4649: : 315iter [00:15, 22.33iter/s]
Best score: 0.4649: : 315iter [00:15, 22.33iter/s]
Best score: 0.4649: : 316iter [00:16, 22.33iter/s]
Best score: 0.4649: : 317iter [00:16, 22.33iter/s]
Best score: 0.4649: : 318iter [00:16, 22.39iter/s]
Best score: 0.4649: : 318iter [00:16, 22.39iter/s]
Best score: 0.4482: : 319iter [00:16, 22.39iter/s]
Best score: 0.4482: : 320iter [00:16, 22.39iter/s]
Best score: 0.4482: : 321iter [00:16, 22.27iter/s]
Best score: 0.4482: : 321iter [00:16, 22.27iter/s]
Best score: 0.4482: : 322iter [00:16, 22.27iter/s]
Best score: 0.4482: : 323iter [00:16, 22.27iter/s]
Best score: 0.4482: : 324iter [00:16, 22.39iter/s]
Best score: 0.4482: : 324iter [00:16, 22.39iter/s]
Best score: 0.4482: : 325iter [00:16, 22.39iter/s]
Best score: 0.4482: : 326iter [00:16, 22.39iter/s]
Best score: 0.4482: : 327iter [00:16, 22.44iter/s]
Best score: 0.4482: : 327iter [00:16, 22.44iter/s]
Best score: 0.4482: : 328iter [00:16, 22.44iter/s]
Best score: 0.4482: : 329iter [00:16, 22.44iter/s]
Best score: 0.4482: : 330iter [00:16, 22.22iter/s]
Best score: 0.4482: : 330iter [00:16, 22.22iter/s]
Best score: 0.4482: : 331iter [00:16, 22.22iter/s]
Best score: 0.4482: : 332iter [00:16, 22.22iter/s]
Best score: 0.4482: : 333iter [00:16, 22.31iter/s]
Best score: 0.4482: : 333iter [00:16, 22.31iter/s]
Best score: 0.4482: : 334iter [00:16, 22.31iter/s]
Best score: 0.4482: : 335iter [00:16, 22.31iter/s]
Best score: 0.4482: : 336iter [00:16, 22.43iter/s]
Best score: 0.4482: : 336iter [00:16, 22.43iter/s]
Best score: 0.4482: : 337iter [00:16, 22.43iter/s]
Best score: 0.4482: : 338iter [00:17, 22.43iter/s]
Best score: 0.4482: : 339iter [00:17, 22.52iter/s]
Best score: 0.4456: : 339iter [00:17, 22.52iter/s]
Best score: 0.4456: : 340iter [00:17, 22.52iter/s]
Best score: 0.4456: : 341iter [00:17, 22.52iter/s]
Best score: 0.4456: : 342iter [00:17, 21.95iter/s]
Best score: 0.4456: : 342iter [00:17, 21.95iter/s]
Best score: 0.4456: : 343iter [00:17, 21.95iter/s]
Best score: 0.4456: : 344iter [00:17, 21.95iter/s]
Best score: 0.4456: : 345iter [00:17, 22.03iter/s]
Best score: 0.4456: : 345iter [00:17, 22.03iter/s]
Best score: 0.4456: : 346iter [00:17, 22.03iter/s]
Best score: 0.4456: : 347iter [00:17, 22.03iter/s]
Best score: 0.4456: : 348iter [00:17, 22.17iter/s]
Best score: 0.4456: : 348iter [00:17, 22.17iter/s]
[ITER 350] Best: 0.4456
[PARAMS] Current best parameters:
  fx: 511.8635
  fy: 511.4869
  px: 1015.5110
  py: 743.2736
  k1: 0.02649079
  k2: 0.00575576
  k3: -0.00902350
  k4: 0.00163212
  p1: -0.00107888
  p2: -0.00361240
  p3: -0.00024174
  p4: 0.00066170
  scale: 2.8668
  rotation: 0.0655

Best score: 0.4456: : 349iter [00:17, 22.17iter/s]
Best score: 0.4456: : 350iter [00:17, 22.17iter/s]
Best score: 0.4456: : 351iter [00:17, 22.06iter/s]
Best score: 0.4456: : 351iter [00:17, 22.06iter/s]
Best score: 0.4456: : 352iter [00:17, 22.06iter/s]
Best score: 0.4456: : 353iter [00:17, 22.06iter/s]
Best score: 0.4456: : 354iter [00:17, 22.22iter/s]
Best score: 0.4456: : 354iter [00:17, 22.22iter/s]
Best score: 0.4456: : 355iter [00:17, 22.22iter/s]
Best score: 0.4456: : 356iter [00:17, 22.22iter/s]
Best score: 0.4456: : 357iter [00:17, 22.19iter/s]
Best score: 0.4456: : 357iter [00:17, 22.19iter/s]
Best score: 0.4456: : 358iter [00:17, 22.19iter/s]
Best score: 0.4417: : 359iter [00:17, 22.19iter/s]
Best score: 0.4417: : 360iter [00:17, 22.14iter/s]
Best score: 0.4417: : 360iter [00:18, 22.14iter/s]
Best score: 0.4417: : 361iter [00:18, 22.14iter/s]
Best score: 0.4417: : 362iter [00:18, 22.14iter/s]
Best score: 0.4417: : 363iter [00:18, 22.30iter/s]
Best score: 0.4417: : 363iter [00:18, 22.30iter/s]
Best score: 0.4417: : 364iter [00:18, 22.30iter/s]
Best score: 0.4417: : 365iter [00:18, 22.30iter/s]
Best score: 0.4417: : 366iter [00:18, 22.36iter/s]
Best score: 0.4417: : 366iter [00:18, 22.36iter/s]
Best score: 0.4417: : 367iter [00:18, 22.36iter/s]
Best score: 0.4417: : 368iter [00:18, 22.36iter/s]
Best score: 0.4417: : 369iter [00:18, 22.43iter/s]
Best score: 0.4417: : 369iter [00:18, 22.43iter/s]
Best score: 0.4417: : 370iter [00:18, 22.43iter/s]
Best score: 0.4417: : 371iter [00:18, 22.43iter/s]
Best score: 0.4417: : 372iter [00:18, 22.27iter/s]
Best score: 0.4417: : 372iter [00:18, 22.27iter/s]
Best score: 0.4417: : 373iter [00:18, 22.27iter/s]
Best score: 0.4417: : 374iter [00:18, 22.27iter/s]
Best score: 0.4417: : 375iter [00:18, 22.37iter/s]
Best score: 0.4417: : 375iter [00:18, 22.37iter/s]
Best score: 0.4417: : 376iter [00:18, 22.37iter/s]
Best score: 0.4417: : 377iter [00:18, 22.37iter/s]
Best score: 0.4417: : 378iter [00:18, 22.51iter/s]
Best score: 0.4417: : 378iter [00:18, 22.51iter/s]
Best score: 0.4373: : 379iter [00:18, 22.51iter/s]
Best score: 0.4373: : 380iter [00:18, 22.51iter/s]
Best score: 0.4373: : 381iter [00:18, 22.31iter/s]
Best score: 0.4373: : 381iter [00:18, 22.31iter/s]
Best score: 0.4373: : 382iter [00:19, 22.31iter/s]
Best score: 0.4373: : 383iter [00:19, 22.31iter/s]
Best score: 0.4373: : 384iter [00:19, 22.41iter/s]
Best score: 0.4373: : 384iter [00:19, 22.41iter/s]
Best score: 0.4373: : 385iter [00:19, 22.41iter/s]
Best score: 0.4373: : 386iter [00:19, 22.41iter/s]
Best score: 0.4373: : 387iter [00:19, 22.39iter/s]
Best score: 0.4373: : 387iter [00:19, 22.39iter/s]
Best score: 0.4373: : 388iter [00:19, 22.39iter/s]
Best score: 0.4357: : 389iter [00:19, 22.39iter/s]
Best score: 0.4357: : 390iter [00:19, 22.25iter/s]
Best score: 0.4357: : 390iter [00:19, 22.25iter/s]
Best score: 0.4357: : 391iter [00:19, 22.25iter/s]
Best score: 0.4357: : 392iter [00:19, 22.25iter/s]
Best score: 0.4357: : 393iter [00:19, 22.39iter/s]
Best score: 0.4357: : 393iter [00:19, 22.39iter/s]
Best score: 0.4357: : 394iter [00:19, 22.39iter/s]
Best score: 0.4357: : 395iter [00:19, 22.39iter/s]
Best score: 0.4357: : 396iter [00:19, 22.49iter/s]
Best score: 0.4357: : 396iter [00:19, 22.49iter/s]
Best score: 0.4357: : 397iter [00:19, 22.49iter/s]
Best score: 0.4357: : 398iter [00:19, 22.49iter/s]
Best score: 0.4357: : 399iter [00:19, 22.55iter/s]
[ITER 400] Best: 0.4357
[PARAMS] Current best parameters:
  fx: 509.6907
  fy: 509.3162
  px: 1015.6417
  py: 743.3764
  k1: 0.02367067
  k2: 0.00810009
  k3: -0.00830526
  k4: 0.00123638
  p1: -0.00082960
  p2: -0.00327197
  p3: -0.00008423
  p4: 0.00048949
  scale: 2.8845
  rotation: 0.0519

Best score: 0.4357: : 399iter [00:19, 22.55iter/s]
Best score: 0.4357: : 400iter [00:19, 22.55iter/s]
Best score: 0.4357: : 401iter [00:19, 22.55iter/s]
Best score: 0.4357: : 402iter [00:19, 22.31iter/s]
Best score: 0.4357: : 402iter [00:19, 22.31iter/s]
Best score: 0.4357: : 403iter [00:19, 22.31iter/s]
Best score: 0.4357: : 404iter [00:19, 22.31iter/s]
Best score: 0.4357: : 405iter [00:19, 22.38iter/s]
Best score: 0.4357: : 405iter [00:20, 22.38iter/s]
Best score: 0.4357: : 406iter [00:20, 22.38iter/s]
Best score: 0.4357: : 407iter [00:20, 22.38iter/s]
Best score: 0.4357: : 408iter [00:20, 22.43iter/s]
Best score: 0.4357: : 408iter [00:20, 22.43iter/s]
Best score: 0.4346: : 409iter [00:20, 22.43iter/s]
Best score: 0.4346: : 410iter [00:20, 22.43iter/s]
Best score: 0.4346: : 411iter [00:20, 22.30iter/s]
Best score: 0.4346: : 411iter [00:20, 22.30iter/s]
Best score: 0.4346: : 412iter [00:20, 22.30iter/s]
Best score: 0.4346: : 413iter [00:20, 22.30iter/s]
Best score: 0.4346: : 414iter [00:20, 22.42iter/s]
Best score: 0.4346: : 414iter [00:20, 22.42iter/s]
Best score: 0.4346: : 415iter [00:20, 22.42iter/s]
Best score: 0.4346: : 416iter [00:20, 22.42iter/s]
Best score: 0.4346: : 417iter [00:20, 22.52iter/s]
Best score: 0.4346: : 417iter [00:20, 22.52iter/s]
Best score: 0.4346: : 418iter [00:20, 22.52iter/s]
Best score: 0.4340: : 419iter [00:20, 22.52iter/s]
Best score: 0.4340: : 420iter [00:20, 22.35iter/s]
Best score: 0.4340: : 420iter [00:20, 22.35iter/s]
Best score: 0.4340: : 421iter [00:20, 22.35iter/s]
Best score: 0.4340: : 422iter [00:20, 22.35iter/s]
Best score: 0.4340: : 423iter [00:20, 22.44iter/s]
Best score: 0.4340: : 423iter [00:20, 22.44iter/s]
Best score: 0.4340: : 424iter [00:20, 22.44iter/s]
Best score: 0.4340: : 425iter [00:20, 22.44iter/s]
Best score: 0.4340: : 426iter [00:20, 22.47iter/s]
Best score: 0.4340: : 426iter [00:20, 22.47iter/s]
Best score: 0.4340: : 427iter [00:21, 22.47iter/s]
Best score: 0.4340: : 428iter [00:21, 22.47iter/s]
Best score: 0.4340: : 429iter [00:21, 22.56iter/s]
Best score: 0.4335: : 429iter [00:21, 22.56iter/s]
Best score: 0.4335: : 430iter [00:21, 22.56iter/s]
Best score: 0.4335: : 431iter [00:21, 22.56iter/s]
Best score: 0.4335: : 432iter [00:21, 22.37iter/s]
Best score: 0.4335: : 432iter [00:21, 22.37iter/s]
Best score: 0.4335: : 433iter [00:21, 22.37iter/s]
Best score: 0.4335: : 434iter [00:21, 22.37iter/s]
Best score: 0.4335: : 435iter [00:21, 22.49iter/s]
Best score: 0.4335: : 435iter [00:21, 22.49iter/s]
Best score: 0.4335: : 436iter [00:21, 22.49iter/s]
Best score: 0.4335: : 437iter [00:21, 22.49iter/s]
Best score: 0.4335: : 438iter [00:21, 22.61iter/s]
Best score: 0.4335: : 438iter [00:21, 22.61iter/s]
Best score: 0.4332: : 439iter [00:21, 22.61iter/s]
Best score: 0.4332: : 440iter [00:21, 22.61iter/s]
Best score: 0.4332: : 441iter [00:21, 22.42iter/s]
Best score: 0.4332: : 441iter [00:21, 22.42iter/s]
Best score: 0.4332: : 442iter [00:21, 22.42iter/s]
Best score: 0.4332: : 443iter [00:21, 22.42iter/s]
Best score: 0.4332: : 444iter [00:21, 22.51iter/s]
Best score: 0.4332: : 444iter [00:21, 22.51iter/s]
Best score: 0.4332: : 445iter [00:21, 22.51iter/s]
Best score: 0.4332: : 446iter [00:21, 22.51iter/s]
Best score: 0.4332: : 447iter [00:21, 22.54iter/s]
Best score: 0.4332: : 447iter [00:21, 22.54iter/s]
Best score: 0.4332: : 448iter [00:21, 22.54iter/s]
[ITER 450] Best: 0.4328
[PARAMS] Current best parameters:
  fx: 506.1541
  fy: 505.7937
  px: 1015.6591
  py: 743.2433
  k1: 0.02331636
  k2: 0.01412759
  k3: -0.01149425
  k4: 0.00175339
  p1: -0.00110476
  p2: -0.00329322
  p3: -0.00003255
  p4: 0.00052755
  scale: 2.8888
  rotation: 0.0500

Best score: 0.4328: : 449iter [00:21, 22.54iter/s]
Best score: 0.4328: : 450iter [00:21, 22.31iter/s]
Best score: 0.4328: : 450iter [00:22, 22.31iter/s]
Best score: 0.4328: : 451iter [00:22, 22.31iter/s]
Best score: 0.4328: : 452iter [00:22, 22.31iter/s]
Best score: 0.4328: : 453iter [00:22, 22.40iter/s]
Best score: 0.4328: : 453iter [00:22, 22.40iter/s]
Best score: 0.4328: : 454iter [00:22, 22.40iter/s]
Best score: 0.4328: : 455iter [00:22, 22.40iter/s]
Best score: 0.4328: : 456iter [00:22, 22.47iter/s]
Best score: 0.4328: : 456iter [00:22, 22.47iter/s]
Best score: 0.4328: : 457iter [00:22, 22.47iter/s]
Best score: 0.4328: : 458iter [00:22, 22.47iter/s]
Best score: 0.4328: : 459iter [00:22, 22.54iter/s]
Best score: 0.4324: : 459iter [00:22, 22.54iter/s]
Best score: 0.4324: : 460iter [00:22, 22.54iter/s]
Best score: 0.4324: : 461iter [00:22, 22.54iter/s]
Best score: 0.4324: : 462iter [00:22, 22.39iter/s]
Best score: 0.4324: : 462iter [00:22, 22.39iter/s]
Best score: 0.4324: : 463iter [00:22, 22.39iter/s]
Best score: 0.4324: : 464iter [00:22, 22.39iter/s]
Best score: 0.4324: : 465iter [00:22, 22.52iter/s]
Best score: 0.4324: : 465iter [00:22, 22.52iter/s]
Best score: 0.4324: : 466iter [00:22, 22.52iter/s]
Best score: 0.4324: : 467iter [00:22, 22.52iter/s]
Best score: 0.4324: : 468iter [00:22, 22.60iter/s]
Best score: 0.4324: : 468iter [00:22, 22.60iter/s]
Best score: 0.4323: : 469iter [00:22, 22.60iter/s]
Best score: 0.4323: : 470iter [00:22, 22.60iter/s]
Best score: 0.4323: : 471iter [00:22, 22.36iter/s]
Best score: 0.4323: : 471iter [00:22, 22.36iter/s]
Best score: 0.4323: : 472iter [00:23, 22.36iter/s]
Best score: 0.4323: : 473iter [00:23, 22.36iter/s]
Best score: 0.4323: : 474iter [00:23, 22.41iter/s]
Best score: 0.4323: : 474iter [00:23, 22.41iter/s]
Best score: 0.4323: : 475iter [00:23, 22.41iter/s]
Best score: 0.4323: : 476iter [00:23, 22.41iter/s]
Best score: 0.4323: : 477iter [00:23, 22.50iter/s]
Best score: 0.4323: : 477iter [00:23, 22.50iter/s]
Best score: 0.4323: : 478iter [00:23, 22.50iter/s]
Best score: 0.4319: : 479iter [00:23, 22.50iter/s]
Best score: 0.4319: : 480iter [00:23, 22.26iter/s]
Best score: 0.4319: : 480iter [00:23, 22.26iter/s]
Best score: 0.4319: : 481iter [00:23, 22.26iter/s]
Best score: 0.4319: : 482iter [00:23, 22.26iter/s]
Best score: 0.4319: : 483iter [00:23, 22.37iter/s]
Best score: 0.4319: : 483iter [00:23, 22.37iter/s]
Best score: 0.4319: : 484iter [00:23, 22.37iter/s]
Best score: 0.4319: : 485iter [00:23, 22.37iter/s]
Best score: 0.4319: : 486iter [00:23, 22.45iter/s]
Best score: 0.4319: : 486iter [00:23, 22.45iter/s]
Best score: 0.4319: : 487iter [00:23, 22.45iter/s]
Best score: 0.4319: : 488iter [00:23, 22.45iter/s]
Best score: 0.4319: : 489iter [00:23, 22.54iter/s]
Best score: 0.4315: : 489iter [00:23, 22.54iter/s]
Best score: 0.4315: : 490iter [00:23, 22.54iter/s]
Best score: 0.4315: : 491iter [00:23, 22.54iter/s]
Best score: 0.4315: : 492iter [00:23, 22.33iter/s]
Best score: 0.4315: : 492iter [00:23, 22.33iter/s]
Best score: 0.4315: : 493iter [00:23, 22.33iter/s]
Best score: 0.4315: : 494iter [00:23, 22.33iter/s]
Best score: 0.4315: : 495iter [00:23, 22.43iter/s]
Best score: 0.4315: : 495iter [00:24, 22.43iter/s]
Best score: 0.4315: : 496iter [00:24, 22.43iter/s]
Best score: 0.4315: : 497iter [00:24, 22.43iter/s]
Best score: 0.4315: : 498iter [00:24, 22.40iter/s]
Best score: 0.4315: : 498iter [00:24, 22.40iter/s]
[ITER 500] Best: 0.4304
[PARAMS] Current best parameters:
  fx: 503.9618
  fy: 503.6080
  px: 1015.5616
  py: 743.1128
  k1: -0.00201845
  k2: 0.05139378
  k3: -0.02981536
  k4: 0.00494742
  p1: -0.00138881
  p2: -0.00350524
  p3: 0.00026307
  p4: 0.00050771
  scale: 2.9210
  rotation: 0.0516

Best score: 0.4304: : 499iter [00:24, 22.40iter/s]
Best score: 0.4304: : 500iter [00:24, 20.64iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish did not improve score: 0.430385
  Success: False
  Tangential distortion: p1=-0.001389, p2=-0.003505, p3=0.000263, p4=0.000508
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.4304
[✅] Parameters copied to: /working/MDC044_W027_2025-05-29/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


-------------------------------------------------------------------
                        Step 3: Undistortion                       
-------------------------------------------------------------------


[DEBUG] Parameters being applied to PTD conversion:
    fx: 503.9617982446449
    fy: 503.60804140337694
    px: 1015.5616002096658
    py: 743.112793576004
    k1: -0.0020184486248824643
    k2: 0.051393776232055835
    k3: -0.029815357573290815
    k4: 0.0049474217680136245
    p1: -0.0013888134976307843
    p2: -0.0035052367078970714
    p3: 0.00026306546789474974
    p4: 0.0005077126318835235
    scale: 2.9209967964494927
    rotation: 0.05157749438409409
    mmpp: 1.2263997211258055
    nmpp: 1226399.7211258055

[🔄] Converting 423 frames to PTD, creating debug images for 200 calibration frames...

📁 Rigs:   0%|          | 0/2 [00:00<?, ?rig/s]

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   🔄 1000:  96%|█████████▋| 216/224 [00:01<00:00, 177.01frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.30s/rig]

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   🔄 500:  92%|█████████▏| 183/199 [00:01<00:00, 174.39frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.18s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.20s/rig]
[✅] Saved PTDs: /working/MDC044_W027_2025-05-29/calibration/debug/3_undistorted/output/ptd

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📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.09rig/s]

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   🔄 500:  73%|███████▎  | 73/100 [00:00<00:00, 179.31frame/s]

   🔄 500:  91%|█████████ | 91/100 [00:00<00:00, 178.53frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.02rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.03rig/s]
[✅] Saved PTDs: /working/MDC044_W027_2025-05-29/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
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📤 Writing LUT:  21%|██▏       | 656199/3080448 [00:00<00:02, 827527.80px/s]
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📤 Writing LUT:  62%|██████▏   | 1900416/3080448 [00:02<00:01, 826541.50px/s]
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📤 Writing LUT:  67%|██████▋   | 2066517/3080448 [00:02<00:01, 827923.82px/s]
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📤 Writing LUT:  81%|████████  | 2482527/3080448 [00:03<00:00, 829548.46px/s]
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📤 Writing LUT:  86%|████████▌ | 2649159/3080448 [00:03<00:00, 831619.10px/s]
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📤 Writing LUT:  91%|█████████▏| 2816658/3080448 [00:03<00:00, 834574.34px/s]
📤 Writing LUT:  94%|█████████▍| 2900352/3080448 [00:03<00:00, 835283.45px/s]
📤 Writing LUT:  97%|█████████▋| 2984439/3080448 [00:03<00:00, 836956.87px/s]
📤 Writing LUT: 100%|█████████▉| 3068624/3080448 [00:03<00:00, 838424.21px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 828532.17px/s]
[✅] LUT written to: /working/MDC044_W027_2025-05-29/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC044_W027_2025-05-29' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC044_W027_2025-05-29/calibration/MDC044_W027_2025-05-29_unknown.lut

-------------------------------------------------------------------
                          Step 4: Heatmap                          
-------------------------------------------------------------------


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   🔥 1000:  91%|█████████ | 203/224 [00:01<00:00, 165.64frame/s]

   🔥 1000:  98%|█████████▊| 220/224 [00:01<00:00, 166.11frame/s]

                                                                 [✅] 1000: /working/MDC044_W027_2025-05-29/calibration/debug/4_heatmap/1000_heatmap.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:02<00:02,  2.00s/rig]

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   🔥 500:  26%|██▌       | 51/199 [00:00<00:00, 165.05frame/s]

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   🔥 500:  79%|███████▉  | 158/199 [00:00<00:00, 170.09frame/s]

   🔥 500:  88%|████████▊ | 176/199 [00:01<00:00, 169.14frame/s]

   🔥 500:  97%|█████████▋| 193/199 [00:01<00:00, 168.02frame/s]

                                                                [✅] 500: /working/MDC044_W027_2025-05-29/calibration/debug/4_heatmap/500_heatmap.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:03<00:00,  1.78s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:03<00:00,  1.82s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC044_W027_2025-05-29/calibration/debug/4_heatmap
[🏁] Finished processing MDC044_W027_2025-05-29


================================================================================================================================
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