
🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-010_W046(6)_2026-04-08 ===

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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 4 las0.zip files to process
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 512 stationary frames, 10 remain

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                                                                      [A[PASS2] Summary: 0 frames fixed, 9 untrusted, 9 total

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                                                                       [AProcessing Rigs:  25%|██▌       | 1/4 [00:07<00:21,  7.19s/it][INFO] Filtered 713 stationary frames, 615 remain

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                                                                           [A[PASS2] Frame 0000164: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000165: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000166: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000612: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000613: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000614: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000615: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000616: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000617: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000618: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000619: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000620: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000621: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000622: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000623: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000647: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000648: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000649: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000650: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000651: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000653: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000654: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000655: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000728: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000729: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000730: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000800: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000801: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000802: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000803: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001165: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001166: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001167: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001266: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001276: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001281: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001282: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000165: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000166: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000622: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000649: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000650: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000651: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000654: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001165: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001166: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001167: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000165: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000166: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001165: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001166: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001167: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Summary: 16 frames fixed, 44 untrusted, 614 total

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                                                                            [AProcessing Rigs:  50%|█████     | 2/4 [11:11<13:07, 393.59s/it][INFO] Filtered 358 stationary frames, 545 remain

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                                                                           [A[PASS2] Frame 0000118: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000129: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000130: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000131: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000132: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000134: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000194: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000195: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000488: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000489: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000490: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000529: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000541: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000542: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000543: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000544: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000568: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000569: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000570: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000571: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000572: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000574: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000576: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000891: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000892: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000133: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000543: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000570: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000571: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000572: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000573: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000891: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000133: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000891: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Summary: 14 frames fixed, 36 untrusted, 544 total

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                                                                            [AProcessing Rigs:  75%|███████▌  | 3/4 [21:06<08:05, 485.69s/it][INFO] Filtered 268 stationary frames, 444 remain

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                                                                          [A[PASS2] Frame 0000180: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000186: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000194: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000195: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000196: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000512: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000513: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000514: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000515: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000752: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000754: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000755: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000841: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000844: fixed corners {1, 2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000862: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000863: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000864: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000865: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000875: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000876: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000880: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000883: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000194: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000195: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000196: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000752: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000754: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000755: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000862: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000863: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000864: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000875: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000876: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000194: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000195: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000196: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000752: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000754: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000755: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000862: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000863: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000864: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000875: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000876: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Summary: 12 frames fixed, 69 untrusted, 443 total

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                                                                           [AProcessing Rigs: 100%|██████████| 4/4 [29:03<00:00, 482.33s/it]Processing Rigs: 100%|██████████| 4/4 [29:03<00:00, 435.93s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1610 frames from 4 rigs to /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 9 frames selected from 9
[📋] 2000L: 100 frames selected from 614
[📋] 2000R: 100 frames selected from 544
[📋] 1500: 100 frames selected from 443
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Computing max image from bright frames:  10%|█         | 8/80 [00:00<00:00, 79.10it/s]Computing max image from bright frames:  22%|██▎       | 18/80 [00:00<00:00, 89.22it/s]Computing max image from bright frames:  34%|███▍      | 27/80 [00:00<00:00, 88.74it/s]Computing max image from bright frames:  45%|████▌     | 36/80 [00:00<00:00, 88.10it/s]Computing max image from bright frames:  56%|█████▋    | 45/80 [00:00<00:00, 88.51it/s]Computing max image from bright frames:  70%|███████   | 56/80 [00:00<00:00, 92.91it/s]Computing max image from bright frames:  82%|████████▎ | 66/80 [00:00<00:00, 91.82it/s]Computing max image from bright frames:  95%|█████████▌| 76/80 [00:00<00:00, 91.53it/s]                                                                                       [INFO] Computed max image from 80 bright frames
[INFO] Corner median intensity: 9, black threshold: 27
[INFO] Saved casing detection debug images to /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/2_calibration/casing_detection
[INFO] Found 4423 casing boundary candidate points
[RESULT] Detected casing: center=(855, 986), radius=907, inliers=2542/4423
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 300.0} (0.003):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 300.0} (0.003):   1%|          | 1/100 [00:00<00:22,  4.47it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.002 | Best: {'fx': 300.0} (0.003):   1%|          | 1/100 [00:00<00:22,  4.47it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 300.0} (0.003):   2%|▏         | 2/100 [00:00<00:21,  4.47it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 321.212} (0.003):   3%|▎         | 3/100 [00:00<00:21,  4.47it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 328.283} (0.003):   4%|▍         | 4/100 [00:00<00:21,  4.47it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 328.283} (0.003):   5%|▌         | 5/100 [00:00<00:21,  4.47it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 328.283} (0.003):   6%|▌         | 6/100 [00:00<00:05, 18.58it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 356.566} (0.004):   6%|▌         | 6/100 [00:00<00:05, 18.58it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 356.566} (0.004):   7%|▋         | 7/100 [00:00<00:05, 18.58it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 356.566} (0.004):   8%|▊         | 8/100 [00:00<00:04, 18.58it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 356.566} (0.004):   9%|▉         | 9/100 [00:00<00:04, 21.52it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 356.566} (0.004):   9%|▉         | 9/100 [00:00<00:04, 21.52it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 356.566} (0.004):  10%|█         | 10/100 [00:00<00:04, 21.52it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.003 | Best: {'fx': 356.566} (0.004):  11%|█         | 11/100 [00:00<00:04, 21.52it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:04, 21.52it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 26.63it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 26.63it/s] Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:03, 26.63it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  15%|█▌        | 15/100 [00:00<00:03, 26.63it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  16%|█▌        | 16/100 [00:00<00:03, 26.63it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  17%|█▋        | 17/100 [00:00<00:02, 29.80it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  17%|█▋        | 17/100 [00:00<00:02, 29.80it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.005 | Best: {'fx': 427.273} (0.005):  18%|█▊        | 18/100 [00:00<00:02, 29.80it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.007 | Best: {'fx': 441.414} (0.007):  19%|█▉        | 19/100 [00:00<00:02, 29.80it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.006 | Best: {'fx': 441.414} (0.007):  20%|██        | 20/100 [00:00<00:02, 29.80it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.006 | Best: {'fx': 441.414} (0.007):  21%|██        | 21/100 [00:00<00:02, 29.50it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.008 | Best: {'fx': 448.485} (0.008):  21%|██        | 21/100 [00:00<00:02, 29.50it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.011 | Best: {'fx': 462.626} (0.011):  22%|██▏       | 22/100 [00:00<00:02, 29.50it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.009 | Best: {'fx': 462.626} (0.011):  23%|██▎       | 23/100 [00:00<00:02, 29.50it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.016 | Best: {'fx': 476.768} (0.016):  24%|██▍       | 24/100 [00:00<00:02, 29.50it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.016 | Best: {'fx': 476.768} (0.016):  25%|██▌       | 25/100 [00:00<00:02, 32.05it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.014 | Best: {'fx': 476.768} (0.016):  25%|██▌       | 25/100 [00:01<00:02, 32.05it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.035 | Best: {'fx': 505.051} (0.035):  26%|██▌       | 26/100 [00:01<00:02, 32.05it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 505.051} (0.035):  27%|██▋       | 27/100 [00:01<00:02, 32.05it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.021 | Best: {'fx': 505.051} (0.035):  28%|██▊       | 28/100 [00:01<00:02, 32.05it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.021 | Best: {'fx': 505.051} (0.035):  29%|██▉       | 29/100 [00:01<00:02, 29.37it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.042 | Best: {'fx': 512.121} (0.042):  29%|██▉       | 29/100 [00:01<00:02, 29.37it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.027 | Best: {'fx': 512.121} (0.042):  30%|███       | 30/100 [00:01<00:02, 29.37it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.051 | Best: {'fx': 519.192} (0.051):  31%|███       | 31/100 [00:01<00:02, 29.37it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.053 | Best: {'fx': 540.404} (0.053):  32%|███▏      | 32/100 [00:01<00:02, 29.37it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.053 | Best: {'fx': 540.404} (0.053):  33%|███▎      | 33/100 [00:01<00:03, 22.09it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.061 | Best: {'fx': 533.333} (0.061):  33%|███▎      | 33/100 [00:01<00:03, 22.09it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.061 | Best: {'fx': 526.263} (0.061):  34%|███▍      | 34/100 [00:01<00:02, 22.09it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.040 | Best: {'fx': 526.263} (0.061):  35%|███▌      | 35/100 [00:01<00:02, 22.09it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.045 | Best: {'fx': 526.263} (0.061):  36%|███▌      | 36/100 [00:01<00:02, 22.09it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.045 | Best: {'fx': 526.263} (0.061):  37%|███▋      | 37/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.028 | Best: {'fx': 526.263} (0.061):  37%|███▋      | 37/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.033 | Best: {'fx': 526.263} (0.061):  38%|███▊      | 38/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.031 | Best: {'fx': 526.263} (0.061):  39%|███▉      | 39/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.036 | Best: {'fx': 526.263} (0.061):  40%|████      | 40/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.036 | Best: {'fx': 526.263} (0.061):  41%|████      | 41/100 [00:01<00:02, 27.85it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.027 | Best: {'fx': 526.263} (0.061):  41%|████      | 41/100 [00:01<00:02, 27.85it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.026 | Best: {'fx': 526.263} (0.061):  42%|████▏     | 42/100 [00:01<00:02, 27.85it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.029 | Best: {'fx': 526.263} (0.061):  43%|████▎     | 43/100 [00:01<00:02, 27.85it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.024 | Best: {'fx': 526.263} (0.061):  44%|████▍     | 44/100 [00:01<00:02, 27.85it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.024 | Best: {'fx': 526.263} (0.061):  45%|████▌     | 45/100 [00:01<00:01, 30.46it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.025 | Best: {'fx': 526.263} (0.061):  45%|████▌     | 45/100 [00:01<00:01, 30.46it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.023 | Best: {'fx': 526.263} (0.061):  46%|████▌     | 46/100 [00:01<00:01, 30.46it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  47%|████▋     | 47/100 [00:01<00:01, 30.46it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.023 | Best: {'fx': 526.263} (0.061):  48%|████▊     | 48/100 [00:01<00:01, 30.46it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.023 | Best: {'fx': 526.263} (0.061):  49%|████▉     | 49/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.021 | Best: {'fx': 526.263} (0.061):  49%|████▉     | 49/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.022 | Best: {'fx': 526.263} (0.061):  50%|█████     | 50/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  51%|█████     | 51/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.020 | Best: {'fx': 526.263} (0.061):  52%|█████▏    | 52/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.019 | Best: {'fx': 526.263} (0.061):  53%|█████▎    | 53/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.021 | Best: {'fx': 526.263} (0.061):  54%|█████▍    | 54/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  55%|█████▌    | 55/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.020 | Best: {'fx': 526.263} (0.061):  56%|█████▌    | 56/100 [00:01<00:01, 27.95it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.020 | Best: {'fx': 526.263} (0.061):  57%|█████▋    | 57/100 [00:01<00:01, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.019 | Best: {'fx': 526.263} (0.061):  57%|█████▋    | 57/100 [00:01<00:01, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.020 | Best: {'fx': 526.263} (0.061):  58%|█████▊    | 58/100 [00:01<00:01, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.019 | Best: {'fx': 526.263} (0.061):  59%|█████▉    | 59/100 [00:01<00:01, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  60%|██████    | 60/100 [00:01<00:01, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  61%|██████    | 61/100 [00:01<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  62%|██████▏   | 62/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  63%|██████▎   | 63/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.019 | Best: {'fx': 526.263} (0.061):  64%|██████▍   | 64/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.019 | Best: {'fx': 526.263} (0.061):  65%|██████▌   | 65/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.019 | Best: {'fx': 526.263} (0.061):  66%|██████▌   | 66/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.019 | Best: {'fx': 526.263} (0.061):  67%|██████▋   | 67/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  68%|██████▊   | 68/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  69%|██████▉   | 69/100 [00:02<00:00, 39.90it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  70%|███████   | 70/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  70%|███████   | 70/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  71%|███████   | 71/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  72%|███████▏  | 72/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  73%|███████▎  | 73/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  74%|███████▍  | 74/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  75%|███████▌  | 75/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  76%|███████▌  | 76/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  77%|███████▋  | 77/100 [00:02<00:00, 59.48it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  78%|███████▊  | 78/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  78%|███████▊  | 78/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  79%|███████▉  | 79/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  80%|████████  | 80/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  81%|████████  | 81/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.018 | Best: {'fx': 526.263} (0.061):  82%|████████▏ | 82/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  83%|████████▎ | 83/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  84%|████████▍ | 84/100 [00:02<00:00, 58.92it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  85%|████████▌ | 85/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  85%|████████▌ | 85/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  86%|████████▌ | 86/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  87%|████████▋ | 87/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  88%|████████▊ | 88/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  89%|████████▉ | 89/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  90%|█████████ | 90/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  91%|█████████ | 91/100 [00:02<00:00, 51.00it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  92%|█████████▏| 92/100 [00:02<00:00, 51.62it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  92%|█████████▏| 92/100 [00:02<00:00, 51.62it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.016 | Best: {'fx': 526.263} (0.061):  93%|█████████▎| 93/100 [00:02<00:00, 51.62it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  94%|█████████▍| 94/100 [00:02<00:00, 51.62it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.016 | Best: {'fx': 526.263} (0.061):  95%|█████████▌| 95/100 [00:02<00:00, 51.62it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  96%|█████████▌| 96/100 [00:02<00:00, 51.62it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  97%|█████████▋| 97/100 [00:02<00:00, 51.62it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  98%|█████████▊| 98/100 [00:02<00:00, 49.97it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  98%|█████████▊| 98/100 [00:02<00:00, 49.97it/s]Brute | Start: {'fx': 500.0} (0.029) | Current: 0.017 | Best: {'fx': 526.263} (0.061):  99%|█████████▉| 99/100 [00:02<00:00, 49.97it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.031 | Best: {'fx': 501.263} (0.031):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.031 | Best: {'fx': 501.263} (0.031):   2%|▏         | 1/50 [00:00<00:23,  2.05it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.032 | Best: {'fx': 502.283} (0.032):   2%|▏         | 1/50 [00:00<00:23,  2.05it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.034 | Best: {'fx': 504.324} (0.034):   4%|▍         | 2/50 [00:00<00:23,  2.05it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.034 | Best: {'fx': 504.324} (0.034):   6%|▌         | 3/50 [00:00<00:07,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.033 | Best: {'fx': 504.324} (0.034):   6%|▌         | 3/50 [00:00<00:07,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.035 | Best: {'fx': 505.344} (0.035):   8%|▊         | 4/50 [00:00<00:07,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.036 | Best: {'fx': 506.365} (0.036):  10%|█         | 5/50 [00:00<00:07,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.038 | Best: {'fx': 508.405} (0.038):  12%|█▏        | 6/50 [00:00<00:07,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.037 | Best: {'fx': 508.405} (0.038):  14%|█▍        | 7/50 [00:00<00:07,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.040 | Best: {'fx': 509.426} (0.040):  16%|█▌        | 8/50 [00:00<00:07,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.040 | Best: {'fx': 510.446} (0.040):  18%|█▊        | 9/50 [00:00<00:06,  5.94it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.040 | Best: {'fx': 510.446} (0.040):  20%|██        | 10/50 [00:00<00:02, 17.83it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.044 | Best: {'fx': 513.508} (0.044):  20%|██        | 10/50 [00:00<00:02, 17.83it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.043 | Best: {'fx': 513.508} (0.044):  22%|██▏       | 11/50 [00:00<00:02, 17.83it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.041 | Best: {'fx': 513.508} (0.044):  24%|██▍       | 12/50 [00:00<00:02, 17.83it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.041 | Best: {'fx': 513.508} (0.044):  26%|██▌       | 13/50 [00:00<00:01, 19.46it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.046 | Best: {'fx': 515.548} (0.046):  26%|██▌       | 13/50 [00:00<00:01, 19.46it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.047 | Best: {'fx': 516.569} (0.047):  28%|██▊       | 14/50 [00:00<00:01, 19.46it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.045 | Best: {'fx': 516.569} (0.047):  30%|███       | 15/50 [00:00<00:01, 19.46it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.048 | Best: {'fx': 517.589} (0.048):  32%|███▏      | 16/50 [00:00<00:01, 19.46it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.053 | Best: {'fx': 520.65} (0.053):  34%|███▍      | 17/50 [00:01<00:01, 19.46it/s] Brute | Start: {'fx': 526.263} (0.061) | Current: 0.053 | Best: {'fx': 520.65} (0.053):  36%|███▌      | 18/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.055 | Best: {'fx': 521.671} (0.055):  36%|███▌      | 18/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.059 | Best: {'fx': 522.691} (0.059):  38%|███▊      | 19/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.059 | Best: {'fx': 523.712} (0.059):  40%|████      | 20/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.050 | Best: {'fx': 523.712} (0.059):  42%|████▏     | 21/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.061 | Best: {'fx': 532.895} (0.061):  44%|████▍     | 22/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.052 | Best: {'fx': 532.895} (0.061):  46%|████▌     | 23/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.060 | Best: {'fx': 532.895} (0.061):  48%|████▊     | 24/50 [00:01<00:01, 20.52it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.060 | Best: {'fx': 532.895} (0.061):  50%|█████     | 25/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.061 | Best: {'fx': 527.793} (0.061):  50%|█████     | 25/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.058 | Best: {'fx': 527.793} (0.061):  52%|█████▏    | 26/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.052 | Best: {'fx': 527.793} (0.061):  54%|█████▍    | 27/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.055 | Best: {'fx': 527.793} (0.061):  56%|█████▌    | 28/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.056 | Best: {'fx': 527.793} (0.061):  58%|█████▊    | 29/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.054 | Best: {'fx': 527.793} (0.061):  60%|██████    | 30/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.058 | Best: {'fx': 527.793} (0.061):  62%|██████▏   | 31/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.061 | Best: {'fx': 527.793} (0.061):  64%|██████▍   | 32/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.056 | Best: {'fx': 527.793} (0.061):  66%|██████▌   | 33/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.059 | Best: {'fx': 527.793} (0.061):  68%|██████▊   | 34/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.062 | Best: {'fx': 525.752} (0.062):  70%|███████   | 35/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.062 | Best: {'fx': 525.752} (0.062):  72%|███████▏  | 36/50 [00:01<00:00, 25.90it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.062 | Best: {'fx': 525.752} (0.062):  74%|███████▍  | 37/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.062 | Best: {'fx': 525.752} (0.062):  74%|███████▍  | 37/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.049 | Best: {'fx': 525.752} (0.062):  76%|███████▌  | 38/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.061 | Best: {'fx': 525.752} (0.062):  78%|███████▊  | 39/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.061 | Best: {'fx': 525.752} (0.062):  80%|████████  | 40/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.047 | Best: {'fx': 525.752} (0.062):  82%|████████▏ | 41/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.050 | Best: {'fx': 525.752} (0.062):  84%|████████▍ | 42/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.048 | Best: {'fx': 525.752} (0.062):  86%|████████▌ | 43/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.046 | Best: {'fx': 525.752} (0.062):  88%|████████▊ | 44/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.045 | Best: {'fx': 525.752} (0.062):  90%|█████████ | 45/50 [00:01<00:00, 42.71it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.045 | Best: {'fx': 525.752} (0.062):  92%|█████████▏| 46/50 [00:01<00:00, 51.40it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.045 | Best: {'fx': 525.752} (0.062):  92%|█████████▏| 46/50 [00:01<00:00, 51.40it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.043 | Best: {'fx': 525.752} (0.062):  94%|█████████▍| 47/50 [00:01<00:00, 51.40it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.044 | Best: {'fx': 525.752} (0.062):  96%|█████████▌| 48/50 [00:01<00:00, 51.40it/s]Brute | Start: {'fx': 526.263} (0.061) | Current: 0.042 | Best: {'fx': 525.752} (0.062):  98%|█████████▊| 49/50 [00:01<00:00, 51.40it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 525.7524221809937
    px: 855.0
    py: 986.0
    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

  [Ring preference] diameter=3500 mm  →  radius=202.9 px

[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 14 incomplete frames): 55 frames will be used for optimization
  Rigs: 1000(1000mm):0/9, 2000L(2000mm):18/20, 2000R(2000mm):19/20, 1500(1500mm):18/20
  Total: 55/69 frames used for optimization
Initial score: 146.8223: 0iter [00:00, ?iter/s]  [Ring mask] 19,438/70,586 points within ring radius 202.9 px
[DEBUG] objective(x0) = 146.8223, picklable_objective(x0) = 31180.8886
Best score: 31180.8886: : 0iter [00:02, ?iter/s]Best score: 31180.8886: : 1iter [00:02,  2.15s/iter]Best score: 31180.8886: : 1iter [00:02,  2.15s/iter]Best score: 31180.8886: : 2iter [00:02,  1.05iter/s]Best score: 31180.8886: : 2iter [00:02,  1.05iter/s]Best score: 31180.8886: : 3iter [00:02,  1.76iter/s]Best score: 31180.8886: : 3iter [00:02,  1.76iter/s]Best score: 31180.8886: : 4iter [00:02,  2.57iter/s]Best score: 31180.8886: : 4iter [00:02,  2.57iter/s]Best score: 31180.8886: : 5iter [00:02,  3.44iter/s]Best score: 31180.8886: : 5iter [00:02,  3.44iter/s]Best score: 31180.8886: : 6iter [00:02,  4.34iter/s]Best score: 31180.8886: : 6iter [00:02,  4.34iter/s]Best score: 31180.8886: : 7iter [00:02,  5.20iter/s]Best score: 31180.8886: : 7iter [00:02,  5.20iter/s]Best score: 31180.8886: : 8iter [00:02,  5.97iter/s]Best score: 31180.8886: : 8iter [00:03,  5.97iter/s]Best score: 31180.8886: : 9iter [00:03,  6.63iter/s]Best score: 40.2161: : 9iter [00:03,  6.63iter/s]   Best score: 40.2161: : 10iter [00:03,  6.98iter/s]Best score: 40.2161: : 10iter [00:03,  6.98iter/s]Best score: 40.2161: : 11iter [00:03,  7.43iter/s]Best score: 40.2161: : 11iter [00:03,  7.43iter/s]Best score: 40.2161: : 12iter [00:03,  7.78iter/s]Best score: 40.2161: : 12iter [00:03,  7.78iter/s]Best score: 40.2161: : 13iter [00:03,  8.06iter/s]Best score: 40.2161: : 13iter [00:03,  8.06iter/s]Best score: 40.2161: : 14iter [00:03,  8.25iter/s]Best score: 40.2161: : 14iter [00:03,  8.25iter/s]Best score: 40.2161: : 15iter [00:03,  8.40iter/s]Best score: 40.2161: : 15iter [00:03,  8.40iter/s]Best score: 40.2161: : 16iter [00:03,  8.51iter/s]Best score: 40.2161: : 16iter [00:03,  8.51iter/s]Best score: 40.2161: : 17iter [00:03,  8.59iter/s]Best score: 40.2161: : 17iter [00:04,  8.59iter/s]Best score: 40.2161: : 18iter [00:04,  8.65iter/s]Best score: 40.2161: : 18iter [00:04,  8.65iter/s]Best score: 40.2161: : 19iter [00:04,  8.69iter/s]Best score: 36.8489: : 19iter [00:04,  8.69iter/s]Best score: 36.8489: : 20iter [00:04,  8.45iter/s]Best score: 36.8489: : 20iter [00:04,  8.45iter/s]Best score: 36.8489: : 21iter [00:04,  8.55iter/s]Best score: 36.8489: : 21iter [00:04,  8.55iter/s]Best score: 36.8489: : 22iter [00:04,  8.62iter/s]Best score: 36.8489: : 22iter [00:04,  8.62iter/s]Best score: 36.8489: : 23iter [00:04,  8.66iter/s]Best score: 36.8489: : 23iter [00:04,  8.66iter/s]Best score: 36.8489: : 24iter [00:04,  8.69iter/s]Best score: 36.8489: : 24iter [00:04,  8.69iter/s]Best score: 36.8489: : 25iter [00:04,  8.73iter/s]Best score: 36.8489: : 25iter [00:05,  8.73iter/s]Best score: 36.8489: : 26iter [00:05,  8.75iter/s]Best score: 36.8489: : 26iter [00:05,  8.75iter/s]Best score: 36.8489: : 27iter [00:05,  8.76iter/s]Best score: 36.8489: : 27iter [00:05,  8.76iter/s]Best score: 36.8489: : 28iter [00:05,  8.76iter/s]Best score: 36.8489: : 28iter [00:05,  8.76iter/s]Best score: 36.8489: : 29iter [00:05,  8.76iter/s]Best score: 23.4855: : 29iter [00:05,  8.76iter/s]Best score: 23.4855: : 30iter [00:05,  8.49iter/s]Best score: 23.4855: : 30iter [00:05,  8.49iter/s]Best score: 23.4855: : 31iter [00:05,  8.57iter/s]Best score: 23.4855: : 31iter [00:05,  8.57iter/s]Best score: 23.4855: : 32iter [00:05,  8.63iter/s]Best score: 23.4855: : 32iter [00:05,  8.63iter/s]Best score: 23.4855: : 33iter [00:05,  8.67iter/s]Best score: 23.4855: : 33iter [00:05,  8.67iter/s]Best score: 23.4855: : 34iter [00:05,  8.70iter/s]Best score: 23.4855: : 34iter [00:06,  8.70iter/s]Best score: 23.4855: : 35iter [00:06,  8.72iter/s]Best score: 23.4855: : 35iter [00:06,  8.72iter/s]Best score: 23.4855: : 36iter [00:06,  8.74iter/s]Best score: 23.4855: : 36iter [00:06,  8.74iter/s]Best score: 23.4855: : 37iter [00:06,  8.75iter/s]Best score: 23.4855: : 37iter [00:06,  8.75iter/s]Best score: 23.4855: : 38iter [00:06,  8.75iter/s]Best score: 23.4855: : 38iter [00:06,  8.75iter/s]Best score: 23.4855: : 39iter [00:06,  8.75iter/s]Best score: 22.7050: : 39iter [00:06,  8.75iter/s]Best score: 22.7050: : 40iter [00:06,  8.48iter/s]Best score: 22.7050: : 40iter [00:06,  8.48iter/s]Best score: 22.7050: : 41iter [00:06,  8.55iter/s]Best score: 22.7050: : 41iter [00:06,  8.55iter/s]Best score: 22.7050: : 42iter [00:06,  8.59iter/s]Best score: 22.7050: : 42iter [00:06,  8.59iter/s]Best score: 22.7050: : 43iter [00:06,  8.64iter/s]Best score: 22.7050: : 43iter [00:07,  8.64iter/s]Best score: 22.7050: : 44iter [00:07,  8.67iter/s]Best score: 22.7050: : 44iter [00:07,  8.67iter/s]Best score: 22.7050: : 45iter [00:07,  8.69iter/s]Best score: 22.7050: : 45iter [00:07,  8.69iter/s]Best score: 22.7050: : 46iter [00:07,  8.70iter/s]Best score: 22.7050: : 46iter [00:07,  8.70iter/s]Best score: 22.7050: : 47iter [00:07,  8.69iter/s]Best score: 22.7050: : 47iter [00:07,  8.69iter/s]Best score: 22.7050: : 48iter [00:07,  8.71iter/s]Best score: 22.7050: : 48iter [00:07,  8.71iter/s]Best score: 22.7050: : 49iter [00:07,  8.72iter/s]
[ITER 50] Best: 20.5047
[PARAMS] Current best parameters:
  fx: 480.8294
  fy: 479.6634
  px: 849.2436
  py: 1002.4862
  k1: 0.03252839
  k2: 0.06401048
  k3: 0.03912313
  k4: 0.00910502
  p1: -0.00178659
  p2: -0.00596139
  p3: -0.00083517
  p4: -0.00213486
  scale: 5.9742
  rotation: -2.4523
Best score: 20.5047: : 49iter [00:07,  8.72iter/s]Best score: 20.5047: : 50iter [00:07,  8.46iter/s]Best score: 20.5047: : 50iter [00:07,  8.46iter/s]Best score: 20.5047: : 51iter [00:07,  8.54iter/s]Best score: 20.5047: : 51iter [00:08,  8.54iter/s]Best score: 20.5047: : 52iter [00:08,  8.59iter/s]Best score: 20.5047: : 52iter [00:08,  8.59iter/s]Best score: 20.5047: : 53iter [00:08,  8.63iter/s]Best score: 20.5047: : 53iter [00:08,  8.63iter/s]Best score: 20.5047: : 54iter [00:08,  8.67iter/s]Best score: 20.5047: : 54iter [00:08,  8.67iter/s]Best score: 20.5047: : 55iter [00:08,  8.69iter/s]Best score: 20.5047: : 55iter [00:08,  8.69iter/s]Best score: 20.5047: : 56iter [00:08,  8.72iter/s]Best score: 20.5047: : 56iter [00:08,  8.72iter/s]Best score: 20.5047: : 57iter [00:08,  8.73iter/s]Best score: 20.5047: : 57iter [00:08,  8.73iter/s]Best score: 20.5047: : 58iter [00:08,  8.74iter/s]Best score: 20.5047: : 58iter [00:08,  8.74iter/s]Best score: 20.5047: : 59iter [00:08,  8.75iter/s]Best score: 16.8395: : 59iter [00:08,  8.75iter/s]Best score: 16.8395: : 60iter [00:08,  8.49iter/s]Best score: 16.8395: : 60iter [00:09,  8.49iter/s]Best score: 16.8395: : 61iter [00:09,  8.58iter/s]Best score: 16.8395: : 61iter [00:09,  8.58iter/s]Best score: 16.8395: : 62iter [00:09,  8.64iter/s]Best score: 16.8395: : 62iter [00:09,  8.64iter/s]Best score: 16.8395: : 63iter [00:09,  8.68iter/s]Best score: 16.8395: : 63iter [00:09,  8.68iter/s]Best score: 16.8395: : 64iter [00:09,  8.71iter/s]Best score: 16.8395: : 64iter [00:09,  8.71iter/s]Best score: 16.8395: : 65iter [00:09,  8.73iter/s]Best score: 16.8395: : 65iter [00:09,  8.73iter/s]Best score: 16.8395: : 66iter [00:09,  8.75iter/s]Best score: 16.8395: : 66iter [00:09,  8.75iter/s]Best score: 16.8395: : 67iter [00:09,  8.74iter/s]Best score: 16.8395: : 67iter [00:09,  8.74iter/s]Best score: 16.8395: : 68iter [00:09,  8.74iter/s]Best score: 16.8395: : 68iter [00:09,  8.74iter/s]Best score: 16.8395: : 69iter [00:09,  8.75iter/s]Best score: 15.2221: : 69iter [00:10,  8.75iter/s]Best score: 15.2221: : 70iter [00:10,  8.50iter/s]Best score: 15.2221: : 70iter [00:10,  8.50iter/s]Best score: 15.2221: : 71iter [00:10,  8.57iter/s]Best score: 15.2221: : 71iter [00:10,  8.57iter/s]Best score: 15.2221: : 72iter [00:10,  8.63iter/s]Best score: 15.2221: : 72iter [00:10,  8.63iter/s]Best score: 15.2221: : 73iter [00:10,  8.67iter/s]Best score: 15.2221: : 73iter [00:10,  8.67iter/s]Best score: 15.2221: : 74iter [00:10,  8.71iter/s]Best score: 15.2221: : 74iter [00:10,  8.71iter/s]Best score: 15.2221: : 75iter [00:10,  8.72iter/s]Best score: 15.2221: : 75iter [00:10,  8.72iter/s]Best score: 15.2221: : 76iter [00:10,  8.74iter/s]Best score: 15.2221: : 76iter [00:10,  8.74iter/s]Best score: 15.2221: : 77iter [00:10,  8.75iter/s]Best score: 15.2221: : 77iter [00:11,  8.75iter/s]Best score: 15.2221: : 78iter [00:11,  8.75iter/s]Best score: 15.2221: : 78iter [00:11,  8.75iter/s]Best score: 15.2221: : 79iter [00:11,  8.76iter/s]Best score: 12.3730: : 79iter [00:11,  8.76iter/s]Best score: 12.3730: : 80iter [00:11,  8.49iter/s]Best score: 12.3730: : 80iter [00:11,  8.49iter/s]Best score: 12.3730: : 81iter [00:11,  8.56iter/s]Best score: 12.3730: : 81iter [00:11,  8.56iter/s]Best score: 12.3730: : 82iter [00:11,  8.61iter/s]Best score: 12.3730: : 82iter [00:11,  8.61iter/s]Best score: 12.3730: : 83iter [00:11,  8.59iter/s]Best score: 12.3730: : 83iter [00:11,  8.59iter/s]Best score: 12.3730: : 84iter [00:11,  8.65iter/s]Best score: 12.3730: : 84iter [00:11,  8.65iter/s]Best score: 12.3730: : 85iter [00:11,  8.68iter/s]Best score: 12.3730: : 85iter [00:11,  8.68iter/s]Best score: 12.3730: : 86iter [00:11,  8.70iter/s]Best score: 12.3730: : 86iter [00:12,  8.70iter/s]Best score: 12.3730: : 87iter [00:12,  8.71iter/s]Best score: 12.3730: : 87iter [00:12,  8.71iter/s]Best score: 12.3730: : 88iter [00:12,  8.72iter/s]Best score: 12.3730: : 88iter [00:12,  8.72iter/s]Best score: 12.3730: : 89iter [00:12,  8.72iter/s]Best score: 11.3012: : 89iter [00:12,  8.72iter/s]Best score: 11.3012: : 90iter [00:12,  8.46iter/s]Best score: 11.3012: : 90iter [00:12,  8.46iter/s]Best score: 11.3012: : 91iter [00:12,  8.54iter/s]Best score: 11.3012: : 91iter [00:12,  8.54iter/s]Best score: 11.3012: : 92iter [00:12,  8.60iter/s]Best score: 11.3012: : 92iter [00:12,  8.60iter/s]Best score: 11.3012: : 93iter [00:12,  8.64iter/s]Best score: 11.3012: : 93iter [00:12,  8.64iter/s]Best score: 11.3012: : 94iter [00:12,  8.65iter/s]Best score: 11.3012: : 94iter [00:12,  8.65iter/s]Best score: 11.3012: : 95iter [00:12,  8.67iter/s]Best score: 11.3012: : 95iter [00:13,  8.67iter/s]Best score: 11.3012: : 96iter [00:13,  8.69iter/s]Best score: 11.3012: : 96iter [00:13,  8.69iter/s]Best score: 11.3012: : 97iter [00:13,  8.70iter/s]Best score: 11.3012: : 97iter [00:13,  8.70iter/s]Best score: 11.3012: : 98iter [00:13,  8.70iter/s]Best score: 11.3012: : 98iter [00:13,  8.70iter/s]Best score: 11.3012: : 99iter [00:13,  8.72iter/s]
[ITER 100] Best: 10.9367
[PARAMS] Current best parameters:
  fx: 482.6234
  fy: 483.5861
  px: 853.6024
  py: 1003.7272
  k1: 0.04628033
  k2: 0.07803283
  k3: 0.03644088
  k4: 0.01741233
  p1: 0.00468686
  p2: 0.00179816
  p3: -0.00973341
  p4: 0.00179552
  scale: 5.9851
  rotation: -0.7633
Best score: 10.9367: : 99iter [00:13,  8.72iter/s]Best score: 10.9367: : 100iter [00:13,  8.46iter/s]Best score: 10.9367: : 100iter [00:13,  8.46iter/s]Best score: 10.9367: : 101iter [00:13,  8.54iter/s]Best score: 10.9367: : 101iter [00:13,  8.54iter/s]Best score: 10.9367: : 102iter [00:13,  8.59iter/s]Best score: 10.9367: : 102iter [00:13,  8.59iter/s]Best score: 10.9367: : 103iter [00:13,  8.64iter/s]Best score: 10.9367: : 103iter [00:14,  8.64iter/s]Best score: 10.9367: : 104iter [00:14,  8.67iter/s]Best score: 10.9367: : 104iter [00:14,  8.67iter/s]Best score: 10.9367: : 105iter [00:14,  8.69iter/s]Best score: 10.9367: : 105iter [00:14,  8.69iter/s]Best score: 10.9367: : 106iter [00:14,  8.71iter/s]Best score: 10.9367: : 106iter [00:14,  8.71iter/s]Best score: 10.9367: : 107iter [00:14,  8.71iter/s]Best score: 10.9367: : 107iter [00:14,  8.71iter/s]Best score: 10.9367: : 108iter [00:14,  8.71iter/s]Best score: 10.9367: : 108iter [00:14,  8.71iter/s]Best score: 10.9367: : 109iter [00:14,  8.73iter/s]Best score: 10.8370: : 109iter [00:14,  8.73iter/s]Best score: 10.8370: : 110iter [00:14,  8.46iter/s]Best score: 10.8370: : 110iter [00:14,  8.46iter/s]Best score: 10.8370: : 111iter [00:14,  8.55iter/s]Best score: 10.8370: : 111iter [00:14,  8.55iter/s]Best score: 10.8370: : 112iter [00:14,  8.60iter/s]Best score: 10.8370: : 112iter [00:15,  8.60iter/s]Best score: 10.8370: : 113iter [00:15,  8.65iter/s]Best score: 10.8370: : 113iter [00:15,  8.65iter/s]Best score: 10.8370: : 114iter [00:15,  8.68iter/s]Best score: 10.8370: : 114iter [00:15,  8.68iter/s]Best score: 10.8370: : 115iter [00:15,  8.69iter/s]Best score: 10.8370: : 115iter [00:15,  8.69iter/s]Best score: 10.8370: : 116iter [00:15,  8.70iter/s]Best score: 10.8370: : 116iter [00:15,  8.70iter/s]Best score: 10.8370: : 117iter [00:15,  8.71iter/s]Best score: 10.8370: : 117iter [00:15,  8.71iter/s]Best score: 10.8370: : 118iter [00:15,  8.70iter/s]Best score: 10.8370: : 118iter [00:15,  8.70iter/s]Best score: 10.8370: : 119iter [00:15,  8.72iter/s]Best score: 10.8280: : 119iter [00:15,  8.72iter/s]Best score: 10.8280: : 120iter [00:15,  8.46iter/s]Best score: 10.8280: : 120iter [00:16,  8.46iter/s]Best score: 10.8280: : 121iter [00:16,  8.54iter/s]Best score: 10.8280: : 121iter [00:16,  8.54iter/s]Best score: 10.8280: : 122iter [00:16,  8.58iter/s]Best score: 10.8280: : 122iter [00:16,  8.58iter/s]Best score: 10.8280: : 123iter [00:16,  8.62iter/s]Best score: 10.8280: : 123iter [00:16,  8.62iter/s]Best score: 10.8280: : 124iter [00:16,  8.64iter/s]Best score: 10.8280: : 124iter [00:16,  8.64iter/s]Best score: 10.8280: : 125iter [00:16,  8.66iter/s]Best score: 10.8280: : 125iter [00:16,  8.66iter/s]Best score: 10.8280: : 126iter [00:16,  8.66iter/s]Best score: 10.8280: : 126iter [00:16,  8.66iter/s]Best score: 10.8280: : 127iter [00:16,  8.68iter/s]Best score: 10.8280: : 127iter [00:16,  8.68iter/s]Best score: 10.8280: : 128iter [00:16,  8.70iter/s]Best score: 10.8280: : 128iter [00:16,  8.70iter/s]Best score: 10.8280: : 129iter [00:16,  8.67iter/s]Best score: 10.6234: : 129iter [00:17,  8.67iter/s]Best score: 10.6234: : 130iter [00:17,  8.40iter/s]Best score: 10.6234: : 130iter [00:17,  8.40iter/s]Best score: 10.6234: : 131iter [00:17,  8.50iter/s]Best score: 10.6234: : 131iter [00:17,  8.50iter/s]Best score: 10.6234: : 132iter [00:17,  8.56iter/s]Best score: 10.6234: : 132iter [00:17,  8.56iter/s]Best score: 10.6234: : 133iter [00:17,  8.59iter/s]Best score: 10.6234: : 133iter [00:17,  8.59iter/s]Best score: 10.6234: : 134iter [00:17,  8.60iter/s]Best score: 10.6234: : 134iter [00:17,  8.60iter/s]Best score: 10.6234: : 135iter [00:17,  8.63iter/s]Best score: 10.6234: : 135iter [00:17,  8.63iter/s]Best score: 10.6234: : 136iter [00:17,  8.65iter/s]Best score: 10.6234: : 136iter [00:17,  8.65iter/s]Best score: 10.6234: : 137iter [00:17,  8.65iter/s]Best score: 10.6234: : 137iter [00:17,  8.65iter/s]Best score: 10.6234: : 138iter [00:17,  8.67iter/s]Best score: 10.6234: : 138iter [00:18,  8.67iter/s]Best score: 10.6234: : 139iter [00:18,  8.68iter/s]Best score: 10.5476: : 139iter [00:18,  8.68iter/s]Best score: 10.5476: : 140iter [00:18,  8.42iter/s]Best score: 10.5476: : 140iter [00:18,  8.42iter/s]Best score: 10.5476: : 141iter [00:18,  8.51iter/s]Best score: 10.5476: : 141iter [00:18,  8.51iter/s]Best score: 10.5476: : 142iter [00:18,  8.58iter/s]Best score: 10.5476: : 142iter [00:18,  8.58iter/s]Best score: 10.5476: : 143iter [00:18,  8.62iter/s]Best score: 10.5476: : 143iter [00:18,  8.62iter/s]Best score: 10.5476: : 144iter [00:18,  8.66iter/s]Best score: 10.5476: : 144iter [00:18,  8.66iter/s]Best score: 10.5476: : 145iter [00:18,  8.68iter/s]Best score: 10.5476: : 145iter [00:18,  8.68iter/s]Best score: 10.5476: : 146iter [00:18,  8.70iter/s]Best score: 10.5476: : 146iter [00:19,  8.70iter/s]Best score: 10.5476: : 147iter [00:19,  8.71iter/s]Best score: 10.5476: : 147iter [00:19,  8.71iter/s]Best score: 10.5476: : 148iter [00:19,  8.72iter/s]Best score: 10.5476: : 148iter [00:19,  8.72iter/s]Best score: 10.5476: : 149iter [00:19,  8.72iter/s]
[ITER 150] Best: 10.4057
[PARAMS] Current best parameters:
  fx: 482.8468
  fy: 483.1498
  px: 853.9817
  py: 1003.9149
  k1: 0.04686452
  k2: 0.07670270
  k3: 0.04656132
  k4: 0.01315841
  p1: 0.00431286
  p2: 0.00525515
  p3: 0.00299235
  p4: -0.00173117
  scale: 5.9998
  rotation: -0.5901
Best score: 10.4057: : 149iter [00:19,  8.72iter/s]Best score: 10.4057: : 150iter [00:19,  8.46iter/s]Best score: 10.4057: : 150iter [00:19,  8.46iter/s]Best score: 10.4057: : 151iter [00:19,  8.54iter/s]Best score: 10.4057: : 151iter [00:19,  8.54iter/s]Best score: 10.4057: : 152iter [00:19,  8.59iter/s]Best score: 10.4057: : 152iter [00:19,  8.59iter/s]Best score: 10.4057: : 153iter [00:19,  8.62iter/s]Best score: 10.4057: : 153iter [00:19,  8.62iter/s]Best score: 10.4057: : 154iter [00:19,  8.65iter/s]Best score: 10.4057: : 154iter [00:19,  8.65iter/s]Best score: 10.4057: : 155iter [00:19,  8.67iter/s]Best score: 10.4057: : 155iter [00:20,  8.67iter/s]Best score: 10.4057: : 156iter [00:20,  8.69iter/s]Best score: 10.4057: : 156iter [00:20,  8.69iter/s]Best score: 10.4057: : 157iter [00:20,  8.70iter/s]Best score: 10.4057: : 157iter [00:20,  8.70iter/s]Best score: 10.4057: : 158iter [00:20,  8.70iter/s]Best score: 10.4057: : 158iter [00:20,  8.70iter/s]Best score: 10.4057: : 159iter [00:20,  8.71iter/s]Best score: 10.3984: : 159iter [00:20,  8.71iter/s]Best score: 10.3984: : 160iter [00:20,  8.44iter/s]Best score: 10.3984: : 160iter [00:20,  8.44iter/s]Best score: 10.3984: : 161iter [00:20,  8.52iter/s]Best score: 10.3984: : 161iter [00:20,  8.52iter/s]Best score: 10.3984: : 162iter [00:20,  8.58iter/s]Best score: 10.3984: : 162iter [00:20,  8.58iter/s]Best score: 10.3984: : 163iter [00:20,  8.62iter/s]Best score: 10.3984: : 163iter [00:20,  8.62iter/s]Best score: 10.3984: : 164iter [00:20,  8.65iter/s]Best score: 10.3984: : 164iter [00:21,  8.65iter/s]Best score: 10.3984: : 165iter [00:21,  8.66iter/s]Best score: 10.3984: : 165iter [00:21,  8.66iter/s]Best score: 10.3984: : 166iter [00:21,  8.68iter/s]Best score: 10.3984: : 166iter [00:21,  8.68iter/s]Best score: 10.3984: : 167iter [00:21,  8.68iter/s]Best score: 10.3984: : 167iter [00:21,  8.68iter/s]Best score: 10.3984: : 168iter [00:21,  8.69iter/s]Best score: 10.3984: : 168iter [00:21,  8.69iter/s]Best score: 10.3984: : 169iter [00:21,  8.70iter/s]Best score: 10.3150: : 169iter [00:21,  8.70iter/s]Best score: 10.3150: : 170iter [00:21,  8.45iter/s]Best score: 10.3150: : 170iter [00:21,  8.45iter/s]Best score: 10.3150: : 171iter [00:21,  8.54iter/s]Best score: 10.3150: : 171iter [00:21,  8.54iter/s]Best score: 10.3150: : 172iter [00:21,  8.53iter/s]Best score: 10.3150: : 172iter [00:22,  8.53iter/s]Best score: 10.3150: : 173iter [00:22,  8.60iter/s]Best score: 10.3150: : 173iter [00:22,  8.60iter/s]Best score: 10.3150: : 174iter [00:22,  8.63iter/s]Best score: 10.3150: : 174iter [00:22,  8.63iter/s]Best score: 10.3150: : 175iter [00:22,  8.65iter/s]Best score: 10.3150: : 175iter [00:22,  8.65iter/s]Best score: 10.3150: : 176iter [00:22,  8.67iter/s]Best score: 10.3150: : 176iter [00:22,  8.67iter/s]Best score: 10.3150: : 177iter [00:22,  8.69iter/s]Best score: 10.3150: : 177iter [00:22,  8.69iter/s]Best score: 10.3150: : 178iter [00:22,  8.68iter/s]Best score: 10.3150: : 178iter [00:22,  8.68iter/s]Best score: 10.3150: : 179iter [00:22,  8.70iter/s]Best score: 10.2148: : 179iter [00:22,  8.70iter/s]Best score: 10.2148: : 180iter [00:22,  8.44iter/s]Best score: 10.2148: : 180iter [00:22,  8.44iter/s]Best score: 10.2148: : 181iter [00:22,  8.52iter/s]Best score: 10.2148: : 181iter [00:23,  8.52iter/s]Best score: 10.2148: : 182iter [00:23,  8.59iter/s]Best score: 10.2148: : 182iter [00:23,  8.59iter/s]Best score: 10.2148: : 183iter [00:23,  8.63iter/s]Best score: 10.2148: : 183iter [00:23,  8.63iter/s]Best score: 10.2148: : 184iter [00:23,  8.66iter/s]Best score: 10.2148: : 184iter [00:23,  8.66iter/s]Best score: 10.2148: : 185iter [00:23,  8.67iter/s]Best score: 10.2148: : 185iter [00:23,  8.67iter/s]Best score: 10.2148: : 186iter [00:23,  8.68iter/s]Best score: 10.2148: : 186iter [00:23,  8.68iter/s]Best score: 10.2148: : 187iter [00:23,  8.69iter/s]Best score: 10.2148: : 187iter [00:23,  8.69iter/s]Best score: 10.2148: : 188iter [00:23,  8.70iter/s]Best score: 10.2148: : 188iter [00:23,  8.70iter/s]Best score: 10.2148: : 189iter [00:23,  8.70iter/s]Best score: 10.1664: : 189iter [00:24,  8.70iter/s]Best score: 10.1664: : 190iter [00:24,  8.44iter/s]Best score: 10.1664: : 190iter [00:24,  8.44iter/s]Best score: 10.1664: : 191iter [00:24,  8.52iter/s]Best score: 10.1664: : 191iter [00:24,  8.52iter/s]Best score: 10.1664: : 192iter [00:24,  8.58iter/s]Best score: 10.1664: : 192iter [00:24,  8.58iter/s]Best score: 10.1664: : 193iter [00:24,  8.62iter/s]Best score: 10.1664: : 193iter [00:24,  8.62iter/s]Best score: 10.1664: : 194iter [00:24,  8.65iter/s]Best score: 10.1664: : 194iter [00:24,  8.65iter/s]Best score: 10.1664: : 195iter [00:24,  8.67iter/s]Best score: 10.1664: : 195iter [00:24,  8.67iter/s]Best score: 10.1664: : 196iter [00:24,  8.69iter/s]Best score: 10.1664: : 196iter [00:24,  8.69iter/s]Best score: 10.1664: : 197iter [00:24,  8.70iter/s]Best score: 10.1664: : 197iter [00:24,  8.70iter/s]Best score: 10.1664: : 198iter [00:24,  8.69iter/s]Best score: 10.1664: : 198iter [00:25,  8.69iter/s]Best score: 10.1664: : 199iter [00:25,  8.70iter/s]
[ITER 200] Best: 10.0779
[PARAMS] Current best parameters:
  fx: 477.1378
  fy: 476.7878
  px: 853.6681
  py: 1003.8050
  k1: 0.04620099
  k2: 0.07974270
  k3: 0.04957353
  k4: 0.00648346
  p1: 0.00830457
  p2: 0.00754735
  p3: -0.00967568
  p4: -0.00078090
  scale: 5.9942
  rotation: -0.7815
Best score: 10.0779: : 199iter [00:25,  8.70iter/s]Best score: 10.0779: : 200iter [00:25,  8.43iter/s]Best score: 10.0779: : 200iter [00:25,  8.43iter/s]Best score: 10.0779: : 201iter [00:25,  8.51iter/s]Best score: 10.0779: : 201iter [00:25,  8.51iter/s]Best score: 10.0779: : 202iter [00:25,  8.57iter/s]Best score: 10.0779: : 202iter [00:25,  8.57iter/s]Best score: 10.0779: : 203iter [00:25,  8.59iter/s]Best score: 10.0779: : 203iter [00:25,  8.59iter/s]Best score: 10.0779: : 204iter [00:25,  8.59iter/s]Best score: 10.0779: : 204iter [00:25,  8.59iter/s]Best score: 10.0779: : 205iter [00:25,  8.63iter/s]Best score: 10.0779: : 205iter [00:25,  8.63iter/s]Best score: 10.0779: : 206iter [00:25,  8.66iter/s]Best score: 10.0779: : 206iter [00:25,  8.66iter/s]Best score: 10.0779: : 207iter [00:25,  8.67iter/s]Best score: 10.0779: : 207iter [00:26,  8.67iter/s]Best score: 10.0779: : 208iter [00:26,  8.68iter/s]Best score: 10.0779: : 208iter [00:26,  8.68iter/s]Best score: 10.0779: : 209iter [00:26,  8.69iter/s]Best score: 9.9983: : 209iter [00:26,  8.69iter/s] Best score: 9.9983: : 210iter [00:26,  8.42iter/s]Best score: 9.9983: : 210iter [00:26,  8.42iter/s]Best score: 9.9983: : 211iter [00:26,  8.51iter/s]Best score: 9.9983: : 211iter [00:26,  8.51iter/s]Best score: 9.9983: : 212iter [00:26,  8.55iter/s]Best score: 9.9983: : 212iter [00:26,  8.55iter/s]Best score: 9.9983: : 213iter [00:26,  8.58iter/s]Best score: 9.9983: : 213iter [00:26,  8.58iter/s]Best score: 9.9983: : 214iter [00:26,  8.61iter/s]Best score: 9.9983: : 214iter [00:26,  8.61iter/s]Best score: 9.9983: : 215iter [00:26,  8.64iter/s]Best score: 9.9983: : 215iter [00:27,  8.64iter/s]Best score: 9.9983: : 216iter [00:27,  8.67iter/s]Best score: 9.9983: : 216iter [00:27,  8.67iter/s]Best score: 9.9983: : 217iter [00:27,  8.69iter/s]Best score: 9.9983: : 217iter [00:27,  8.69iter/s]Best score: 9.9983: : 218iter [00:27,  8.71iter/s]Best score: 9.9983: : 218iter [00:27,  8.71iter/s]Best score: 9.9983: : 219iter [00:27,  8.71iter/s]Best score: 9.9939: : 219iter [00:27,  8.71iter/s]Best score: 9.9939: : 220iter [00:27,  8.44iter/s]Best score: 9.9939: : 220iter [00:27,  8.44iter/s]Best score: 9.9939: : 221iter [00:27,  8.51iter/s]Best score: 9.9939: : 221iter [00:27,  8.51iter/s]Best score: 9.9939: : 222iter [00:27,  8.56iter/s]Best score: 9.9939: : 222iter [00:27,  8.56iter/s]Best score: 9.9939: : 223iter [00:27,  8.61iter/s]Best score: 9.9939: : 223iter [00:27,  8.61iter/s]Best score: 9.9939: : 224iter [00:27,  8.63iter/s]Best score: 9.9939: : 224iter [00:28,  8.63iter/s]Best score: 9.9939: : 225iter [00:28,  8.64iter/s]Best score: 9.9939: : 225iter [00:28,  8.64iter/s]Best score: 9.9939: : 226iter [00:28,  8.66iter/s]Best score: 9.9939: : 226iter [00:28,  8.66iter/s]Best score: 9.9939: : 227iter [00:28,  8.66iter/s]Best score: 9.9939: : 227iter [00:28,  8.66iter/s]Best score: 9.9939: : 228iter [00:28,  8.66iter/s]Best score: 9.9939: : 228iter [00:28,  8.66iter/s]Best score: 9.9939: : 229iter [00:28,  8.66iter/s]Best score: 9.9883: : 229iter [00:28,  8.66iter/s]Best score: 9.9883: : 230iter [00:28,  8.39iter/s]Best score: 9.9883: : 230iter [00:28,  8.39iter/s]Best score: 9.9883: : 231iter [00:28,  8.48iter/s]Best score: 9.9883: : 231iter [00:28,  8.48iter/s]Best score: 9.9883: : 232iter [00:28,  8.55iter/s]Best score: 9.9883: : 232iter [00:29,  8.55iter/s]Best score: 9.9883: : 233iter [00:29,  8.59iter/s]Best score: 9.9883: : 233iter [00:29,  8.59iter/s]Best score: 9.9883: : 234iter [00:29,  8.62iter/s]Best score: 9.9883: : 234iter [00:29,  8.62iter/s]Best score: 9.9883: : 235iter [00:29,  8.65iter/s]Best score: 9.9883: : 235iter [00:29,  8.65iter/s]Best score: 9.9883: : 236iter [00:29,  8.66iter/s]Best score: 9.9883: : 236iter [00:29,  8.66iter/s]Best score: 9.9883: : 237iter [00:29,  8.68iter/s]Best score: 9.9883: : 237iter [00:29,  8.68iter/s]Best score: 9.9883: : 238iter [00:29,  8.69iter/s]Best score: 9.9883: : 238iter [00:29,  8.69iter/s]Best score: 9.9883: : 239iter [00:29,  8.70iter/s]Best score: 9.9332: : 239iter [00:29,  8.70iter/s]Best score: 9.9332: : 240iter [00:29,  8.42iter/s]Best score: 9.9332: : 240iter [00:29,  8.42iter/s]Best score: 9.9332: : 241iter [00:29,  8.51iter/s]Best score: 9.9332: : 241iter [00:30,  8.51iter/s]Best score: 9.9332: : 242iter [00:30,  8.58iter/s]Best score: 9.9332: : 242iter [00:30,  8.58iter/s]Best score: 9.9332: : 243iter [00:30,  8.62iter/s]Best score: 9.9332: : 243iter [00:30,  8.62iter/s]Best score: 9.9332: : 244iter [00:30,  8.64iter/s]Best score: 9.9332: : 244iter [00:30,  8.64iter/s]Best score: 9.9332: : 245iter [00:30,  8.65iter/s]Best score: 9.9332: : 245iter [00:30,  8.65iter/s]Best score: 9.9332: : 246iter [00:30,  8.66iter/s]Best score: 9.9332: : 246iter [00:30,  8.66iter/s]Best score: 9.9332: : 247iter [00:30,  8.67iter/s]Best score: 9.9332: : 247iter [00:30,  8.67iter/s]Best score: 9.9332: : 248iter [00:30,  8.67iter/s]Best score: 9.9332: : 248iter [00:30,  8.67iter/s]Best score: 9.9332: : 249iter [00:30,  8.68iter/s]
[ITER 250] Best: 9.9332
[PARAMS] Current best parameters:
  fx: 473.7082
  fy: 473.9985
  px: 853.9416
  py: 1004.2107
  k1: 0.04979360
  k2: 0.07999397
  k3: 0.04945417
  k4: 0.00476876
  p1: 0.00915567
  p2: 0.00619738
  p3: -0.00402902
  p4: -0.00339644
  scale: 5.9988
  rotation: -0.5864
Best score: 9.9332: : 249iter [00:30,  8.68iter/s]Best score: 9.9332: : 250iter [00:30,  8.42iter/s]Best score: 9.9332: : 250iter [00:31,  8.42iter/s]Best score: 9.9332: : 251iter [00:31,  8.51iter/s]Best score: 9.9332: : 251iter [00:31,  8.51iter/s]Best score: 9.9332: : 252iter [00:31,  8.56iter/s]Best score: 9.9332: : 252iter [00:31,  8.56iter/s]Best score: 9.9332: : 253iter [00:31,  8.60iter/s]Best score: 9.9332: : 253iter [00:31,  8.60iter/s]Best score: 9.9332: : 254iter [00:31,  8.63iter/s]Best score: 9.9332: : 254iter [00:31,  8.63iter/s]Best score: 9.9332: : 255iter [00:31,  8.64iter/s]Best score: 9.9332: : 255iter [00:31,  8.64iter/s]Best score: 9.9332: : 256iter [00:31,  8.66iter/s]Best score: 9.9332: : 256iter [00:31,  8.66iter/s]Best score: 9.9332: : 257iter [00:31,  8.66iter/s]Best score: 9.9332: : 257iter [00:31,  8.66iter/s]Best score: 9.9332: : 258iter [00:31,  8.67iter/s]Best score: 9.9332: : 258iter [00:32,  8.67iter/s]Best score: 9.9332: : 259iter [00:32,  8.68iter/s]Best score: 9.9332: : 259iter [00:32,  8.68iter/s]Best score: 9.9332: : 260iter [00:32,  8.40iter/s]Best score: 9.9332: : 260iter [00:32,  8.40iter/s]Best score: 9.9332: : 261iter [00:32,  8.48iter/s]Best score: 9.9332: : 261iter [00:32,  8.48iter/s]Best score: 9.9332: : 262iter [00:32,  8.54iter/s]Best score: 9.9332: : 262iter [00:32,  8.54iter/s]Best score: 9.9332: : 263iter [00:32,  8.60iter/s]Best score: 9.9332: : 263iter [00:32,  8.60iter/s]Best score: 9.9332: : 264iter [00:32,  8.64iter/s]Best score: 9.9332: : 264iter [00:32,  8.64iter/s]Best score: 9.9332: : 265iter [00:32,  8.67iter/s]Best score: 9.9332: : 265iter [00:32,  8.67iter/s]Best score: 9.9332: : 266iter [00:32,  8.68iter/s]Best score: 9.9332: : 266iter [00:32,  8.68iter/s]Best score: 9.9332: : 267iter [00:32,  8.68iter/s]Best score: 9.9332: : 267iter [00:33,  8.68iter/s]Best score: 9.9332: : 268iter [00:33,  8.68iter/s]Best score: 9.9332: : 268iter [00:33,  8.68iter/s]Best score: 9.9332: : 269iter [00:33,  8.70iter/s]Best score: 9.9332: : 269iter [00:33,  8.70iter/s]Best score: 9.9332: : 270iter [00:33,  8.42iter/s]Best score: 9.9332: : 270iter [00:33,  8.42iter/s]Best score: 9.9332: : 271iter [00:33,  8.50iter/s]Best score: 9.9332: : 271iter [00:33,  8.50iter/s]Best score: 9.9332: : 272iter [00:33,  8.56iter/s]Best score: 9.9332: : 272iter [00:33,  8.56iter/s]Best score: 9.9332: : 273iter [00:33,  8.60iter/s]Best score: 9.9332: : 273iter [00:33,  8.60iter/s]Best score: 9.9332: : 274iter [00:33,  8.63iter/s]Best score: 9.9332: : 274iter [00:33,  8.63iter/s]Best score: 9.9332: : 275iter [00:33,  8.64iter/s]Best score: 9.9332: : 275iter [00:34,  8.64iter/s]Best score: 9.9332: : 276iter [00:34,  8.66iter/s]Best score: 9.9332: : 276iter [00:34,  8.66iter/s]Best score: 9.9332: : 277iter [00:34,  8.68iter/s]Best score: 9.9332: : 277iter [00:34,  8.68iter/s]Best score: 9.9332: : 278iter [00:34,  8.69iter/s]Best score: 9.9332: : 278iter [00:34,  8.69iter/s]Best score: 9.9332: : 279iter [00:34,  8.68iter/s]Best score: 9.8818: : 279iter [00:34,  8.68iter/s]Best score: 9.8818: : 280iter [00:34,  8.42iter/s]Best score: 9.8818: : 280iter [00:34,  8.42iter/s]Best score: 9.8818: : 281iter [00:34,  8.49iter/s]Best score: 9.8818: : 281iter [00:34,  8.49iter/s]Best score: 9.8818: : 282iter [00:34,  8.56iter/s]Best score: 9.8818: : 282iter [00:34,  8.56iter/s]Best score: 9.8818: : 283iter [00:34,  8.61iter/s]Best score: 9.8818: : 283iter [00:34,  8.61iter/s]Best score: 9.8818: : 284iter [00:34,  8.65iter/s]Best score: 9.8818: : 284iter [00:35,  8.65iter/s]Best score: 9.8818: : 285iter [00:35,  8.68iter/s]Best score: 9.8818: : 285iter [00:35,  8.68iter/s]Best score: 9.8818: : 286iter [00:35,  8.69iter/s]Best score: 9.8818: : 286iter [00:35,  8.69iter/s]Best score: 9.8818: : 287iter [00:35,  8.70iter/s]Best score: 9.8818: : 287iter [00:35,  8.70iter/s]Best score: 9.8818: : 288iter [00:35,  8.72iter/s]Best score: 9.8818: : 288iter [00:35,  8.72iter/s]Best score: 9.8818: : 289iter [00:35,  8.72iter/s]Best score: 9.8752: : 289iter [00:35,  8.72iter/s]Best score: 9.8752: : 290iter [00:35,  8.45iter/s]Best score: 9.8752: : 290iter [00:35,  8.45iter/s]Best score: 9.8752: : 291iter [00:35,  8.53iter/s]Best score: 9.8752: : 291iter [00:35,  8.53iter/s]Best score: 9.8752: : 292iter [00:35,  8.59iter/s]Best score: 9.8752: : 292iter [00:35,  8.59iter/s]Best score: 9.8752: : 293iter [00:35,  8.62iter/s]Best score: 9.8752: : 293iter [00:36,  8.62iter/s]Best score: 9.8752: : 294iter [00:36,  8.65iter/s]Best score: 9.8752: : 294iter [00:36,  8.65iter/s]Best score: 9.8752: : 295iter [00:36,  8.66iter/s]Best score: 9.8752: : 295iter [00:36,  8.66iter/s]Best score: 9.8752: : 296iter [00:36,  8.68iter/s]Best score: 9.8752: : 296iter [00:36,  8.68iter/s]Best score: 9.8752: : 297iter [00:36,  8.69iter/s]Best score: 9.8752: : 297iter [00:36,  8.69iter/s]Best score: 9.8752: : 298iter [00:36,  8.68iter/s]Best score: 9.8752: : 298iter [00:36,  8.68iter/s]Best score: 9.8752: : 299iter [00:36,  8.68iter/s]
[ITER 300] Best: 9.8752
[PARAMS] Current best parameters:
  fx: 473.2353
  fy: 473.3205
  px: 854.2910
  py: 1003.7965
  k1: 0.04918694
  k2: 0.07993763
  k3: 0.04999679
  k4: 0.00419246
  p1: 0.00716531
  p2: 0.00958469
  p3: -0.00540337
  p4: -0.00240133
  scale: 5.9986
  rotation: -0.6715
Best score: 9.8752: : 299iter [00:36,  8.68iter/s]Best score: 9.8752: : 300iter [00:36,  8.41iter/s]Best score: 9.8752: : 300iter [00:36,  8.41iter/s]Best score: 9.8752: : 301iter [00:36,  8.50iter/s]Best score: 9.8752: : 301iter [00:37,  8.50iter/s]Best score: 9.8752: : 302iter [00:37,  8.56iter/s]Best score: 9.8752: : 302iter [00:37,  8.56iter/s]Best score: 9.8752: : 303iter [00:37,  8.59iter/s]Best score: 9.8752: : 303iter [00:37,  8.59iter/s]Best score: 9.8752: : 304iter [00:37,  8.56iter/s]Best score: 9.8752: : 304iter [00:37,  8.56iter/s]Best score: 9.8752: : 305iter [00:37,  8.59iter/s]Best score: 9.8752: : 305iter [00:37,  8.59iter/s]Best score: 9.8752: : 306iter [00:37,  8.62iter/s]Best score: 9.8752: : 306iter [00:37,  8.62iter/s]Best score: 9.8752: : 307iter [00:37,  8.64iter/s]Best score: 9.8752: : 307iter [00:37,  8.64iter/s]Best score: 9.8752: : 308iter [00:37,  8.65iter/s]Best score: 9.8752: : 308iter [00:37,  8.65iter/s]Best score: 9.8752: : 309iter [00:37,  8.65iter/s]Best score: 9.8646: : 309iter [00:37,  8.65iter/s]Best score: 9.8646: : 310iter [00:37,  8.40iter/s]Best score: 9.8646: : 310iter [00:38,  8.40iter/s]Best score: 9.8646: : 311iter [00:38,  8.47iter/s]Best score: 9.8646: : 311iter [00:38,  8.47iter/s]Best score: 9.8646: : 312iter [00:38,  8.53iter/s]Best score: 9.8646: : 312iter [00:38,  8.53iter/s]Best score: 9.8646: : 313iter [00:38,  8.58iter/s]Best score: 9.8646: : 313iter [00:38,  8.58iter/s]Best score: 9.8646: : 314iter [00:38,  8.61iter/s]Best score: 9.8646: : 314iter [00:38,  8.61iter/s]Best score: 9.8646: : 315iter [00:38,  8.63iter/s]Best score: 9.8646: : 315iter [00:38,  8.63iter/s]Best score: 9.8646: : 316iter [00:38,  8.63iter/s]Best score: 9.8646: : 316iter [00:38,  8.63iter/s]Best score: 9.8646: : 317iter [00:38,  8.65iter/s]Best score: 9.8646: : 317iter [00:38,  8.65iter/s]Best score: 9.8646: : 318iter [00:38,  8.67iter/s]Best score: 9.8646: : 318iter [00:38,  8.67iter/s]Best score: 9.8646: : 319iter [00:38,  8.68iter/s]Best score: 9.8453: : 319iter [00:39,  8.68iter/s]Best score: 9.8453: : 320iter [00:39,  8.42iter/s]Best score: 9.8453: : 320iter [00:39,  8.42iter/s]Best score: 9.8453: : 321iter [00:39,  8.52iter/s]Best score: 9.8453: : 321iter [00:39,  8.52iter/s]Best score: 9.8453: : 322iter [00:39,  8.58iter/s]Best score: 9.8453: : 322iter [00:39,  8.58iter/s]Best score: 9.8453: : 323iter [00:39,  8.62iter/s]Best score: 9.8453: : 323iter [00:39,  8.62iter/s]Best score: 9.8453: : 324iter [00:39,  8.65iter/s]Best score: 9.8453: : 324iter [00:39,  8.65iter/s]Best score: 9.8453: : 325iter [00:39,  8.67iter/s]Best score: 9.8453: : 325iter [00:39,  8.67iter/s]Best score: 9.8453: : 326iter [00:39,  8.68iter/s]Best score: 9.8453: : 326iter [00:39,  8.68iter/s]Best score: 9.8453: : 327iter [00:39,  8.68iter/s]Best score: 9.8453: : 327iter [00:40,  8.68iter/s]Best score: 9.8453: : 328iter [00:40,  8.69iter/s]Best score: 9.8453: : 328iter [00:40,  8.69iter/s]Best score: 9.8453: : 329iter [00:40,  8.70iter/s]Best score: 9.8453: : 329iter [00:40,  8.70iter/s]Best score: 9.8453: : 330iter [00:40,  8.43iter/s]Best score: 9.8453: : 330iter [00:40,  8.43iter/s]Best score: 9.8453: : 331iter [00:40,  8.52iter/s]Best score: 9.8453: : 331iter [00:40,  8.52iter/s]Best score: 9.8453: : 332iter [00:40,  8.58iter/s]Best score: 9.8453: : 332iter [00:40,  8.58iter/s]Best score: 9.8453: : 333iter [00:40,  8.61iter/s]Best score: 9.8453: : 333iter [00:40,  8.61iter/s]Best score: 9.8453: : 334iter [00:40,  8.64iter/s]Best score: 9.8453: : 334iter [00:40,  8.64iter/s]Best score: 9.8453: : 335iter [00:40,  8.67iter/s]Best score: 9.8453: : 335iter [00:40,  8.67iter/s]Best score: 9.8453: : 336iter [00:40,  8.69iter/s]Best score: 9.8453: : 336iter [00:41,  8.69iter/s]Best score: 9.8453: : 337iter [00:41,  8.70iter/s]Best score: 9.8453: : 337iter [00:41,  8.70iter/s]Best score: 9.8453: : 338iter [00:41,  8.71iter/s]Best score: 9.8453: : 338iter [00:41,  8.71iter/s]Best score: 9.8453: : 339iter [00:41,  8.72iter/s]Best score: 9.8441: : 339iter [00:41,  8.72iter/s]Best score: 9.8441: : 340iter [00:41,  8.45iter/s]Best score: 9.8441: : 340iter [00:41,  8.45iter/s]Best score: 9.8441: : 341iter [00:41,  8.52iter/s]Best score: 9.8441: : 341iter [00:41,  8.52iter/s]Best score: 9.8441: : 342iter [00:41,  8.57iter/s]Best score: 9.8441: : 342iter [00:41,  8.57iter/s]Best score: 9.8441: : 343iter [00:41,  8.55iter/s]Best score: 9.8441: : 343iter [00:41,  8.55iter/s]Best score: 9.8441: : 344iter [00:41,  8.59iter/s]Best score: 9.8441: : 344iter [00:42,  8.59iter/s]Best score: 9.8441: : 345iter [00:42,  8.61iter/s]Best score: 9.8441: : 345iter [00:42,  8.61iter/s]Best score: 9.8441: : 346iter [00:42,  8.63iter/s]Best score: 9.8441: : 346iter [00:42,  8.63iter/s]Best score: 9.8441: : 347iter [00:42,  8.61iter/s]Best score: 9.8441: : 347iter [00:42,  8.61iter/s]Best score: 9.8441: : 348iter [00:42,  8.64iter/s]Best score: 9.8441: : 348iter [00:42,  8.64iter/s]Best score: 9.8441: : 349iter [00:42,  8.67iter/s]
[ITER 350] Best: 9.8239
[PARAMS] Current best parameters:
  fx: 473.7389
  fy: 473.3270
  px: 853.9633
  py: 1003.9711
  k1: 0.04990673
  k2: 0.07995274
  k3: 0.04991822
  k4: 0.00447492
  p1: 0.00812489
  p2: 0.00979874
  p3: -0.00815617
  p4: -0.00238880
  scale: 5.9998
  rotation: -0.6233
Best score: 9.8239: : 349iter [00:42,  8.67iter/s]Best score: 9.8239: : 350iter [00:42,  8.40iter/s]Best score: 9.8239: : 350iter [00:42,  8.40iter/s]Best score: 9.8239: : 351iter [00:42,  8.49iter/s]Best score: 9.8239: : 351iter [00:42,  8.49iter/s]Best score: 9.8239: : 352iter [00:42,  8.55iter/s]Best score: 9.8239: : 352iter [00:42,  8.55iter/s]Best score: 9.8239: : 353iter [00:42,  8.57iter/s]Best score: 9.8239: : 353iter [00:43,  8.57iter/s]Best score: 9.8239: : 354iter [00:43,  8.62iter/s]Best score: 9.8239: : 354iter [00:43,  8.62iter/s]Best score: 9.8239: : 355iter [00:43,  8.65iter/s]Best score: 9.8239: : 355iter [00:43,  8.65iter/s]Best score: 9.8239: : 356iter [00:43,  8.67iter/s]Best score: 9.8239: : 356iter [00:43,  8.67iter/s]Best score: 9.8239: : 357iter [00:43,  8.68iter/s]Best score: 9.8239: : 357iter [00:43,  8.68iter/s]Best score: 9.8239: : 358iter [00:43,  8.68iter/s]Best score: 9.8239: : 358iter [00:43,  8.68iter/s]Best score: 9.8239: : 359iter [00:43,  8.69iter/s]Best score: 9.8220: : 359iter [00:43,  8.69iter/s]Best score: 9.8220: : 360iter [00:43,  8.44iter/s]Best score: 9.8220: : 360iter [00:43,  8.44iter/s]Best score: 9.8220: : 361iter [00:43,  8.52iter/s]Best score: 9.8220: : 361iter [00:43,  8.52iter/s]Best score: 9.8220: : 362iter [00:43,  8.58iter/s]Best score: 9.8220: : 362iter [00:44,  8.58iter/s]Best score: 9.8220: : 363iter [00:44,  8.63iter/s]Best score: 9.8220: : 363iter [00:44,  8.63iter/s]Best score: 9.8220: : 364iter [00:44,  8.66iter/s]Best score: 9.8220: : 364iter [00:44,  8.66iter/s]Best score: 9.8220: : 365iter [00:44,  8.66iter/s]Best score: 9.8220: : 365iter [00:44,  8.66iter/s]Best score: 9.8220: : 366iter [00:44,  8.67iter/s]Best score: 9.8220: : 366iter [00:44,  8.67iter/s]Best score: 9.8220: : 367iter [00:44,  8.66iter/s]Best score: 9.8220: : 367iter [00:44,  8.66iter/s]Best score: 9.8220: : 368iter [00:44,  8.67iter/s]Best score: 9.8220: : 368iter [00:44,  8.67iter/s]Best score: 9.8220: : 369iter [00:44,  8.69iter/s]Best score: 9.8175: : 369iter [00:44,  8.69iter/s]Best score: 9.8175: : 370iter [00:44,  8.43iter/s]Best score: 9.8175: : 370iter [00:45,  8.43iter/s]Best score: 9.8175: : 371iter [00:45,  8.52iter/s]Best score: 9.8175: : 371iter [00:45,  8.52iter/s]Best score: 9.8175: : 372iter [00:45,  8.58iter/s]Best score: 9.8175: : 372iter [00:45,  8.58iter/s]Best score: 9.8175: : 373iter [00:45,  8.63iter/s]Best score: 9.8175: : 373iter [00:45,  8.63iter/s]Best score: 9.8175: : 374iter [00:45,  8.65iter/s]Best score: 9.8175: : 374iter [00:45,  8.65iter/s]Best score: 9.8175: : 375iter [00:45,  8.68iter/s]Best score: 9.8175: : 375iter [00:45,  8.68iter/s]Best score: 9.8175: : 376iter [00:45,  8.70iter/s]Best score: 9.8175: : 376iter [00:45,  8.70iter/s]Best score: 9.8175: : 377iter [00:45,  8.71iter/s]Best score: 9.8175: : 377iter [00:45,  8.71iter/s]Best score: 9.8175: : 378iter [00:45,  8.71iter/s]Best score: 9.8175: : 378iter [00:45,  8.71iter/s]Best score: 9.8175: : 379iter [00:45,  8.71iter/s]Best score: 9.8129: : 379iter [00:46,  8.71iter/s]Best score: 9.8129: : 380iter [00:46,  8.44iter/s]Best score: 9.8129: : 380iter [00:46,  8.44iter/s]Best score: 9.8129: : 381iter [00:46,  8.52iter/s]Best score: 9.8129: : 381iter [00:46,  8.52iter/s]Best score: 9.8129: : 382iter [00:46,  8.58iter/s]Best score: 9.8129: : 382iter [00:46,  8.58iter/s]Best score: 9.8129: : 383iter [00:46,  8.62iter/s]Best score: 9.8129: : 383iter [00:46,  8.62iter/s]Best score: 9.8129: : 384iter [00:46,  8.65iter/s]Best score: 9.8129: : 384iter [00:46,  8.65iter/s]Best score: 9.8129: : 385iter [00:46,  8.67iter/s]Best score: 9.8129: : 385iter [00:46,  8.67iter/s]Best score: 9.8129: : 386iter [00:46,  8.67iter/s]Best score: 9.8129: : 386iter [00:46,  8.67iter/s]Best score: 9.8129: : 387iter [00:46,  8.68iter/s]Best score: 9.8129: : 387iter [00:47,  8.68iter/s]Best score: 9.8129: : 388iter [00:47,  8.70iter/s]Best score: 9.8129: : 388iter [00:47,  8.70iter/s]Best score: 9.8129: : 389iter [00:47,  8.71iter/s]Best score: 9.8067: : 389iter [00:47,  8.71iter/s]Best score: 9.8067: : 390iter [00:47,  8.45iter/s]Best score: 9.8067: : 390iter [00:47,  8.45iter/s]Best score: 9.8067: : 391iter [00:47,  8.51iter/s]Best score: 9.8067: : 391iter [00:47,  8.51iter/s]Best score: 9.8067: : 392iter [00:47,  8.57iter/s]Best score: 9.8067: : 392iter [00:47,  8.57iter/s]Best score: 9.8067: : 393iter [00:47,  8.61iter/s]Best score: 9.8067: : 393iter [00:47,  8.61iter/s]Best score: 9.8067: : 394iter [00:47,  8.64iter/s]Best score: 9.8067: : 394iter [00:47,  8.64iter/s]Best score: 9.8067: : 395iter [00:47,  8.67iter/s]Best score: 9.8067: : 395iter [00:47,  8.67iter/s]Best score: 9.8067: : 396iter [00:47,  8.69iter/s]Best score: 9.8067: : 396iter [00:48,  8.69iter/s]Best score: 9.8067: : 397iter [00:48,  8.70iter/s]Best score: 9.8067: : 397iter [00:48,  8.70iter/s]Best score: 9.8067: : 398iter [00:48,  8.68iter/s]Best score: 9.8067: : 398iter [00:48,  8.68iter/s]Best score: 9.8067: : 399iter [00:48,  8.69iter/s]
[ITER 400] Best: 9.8067
[PARAMS] Current best parameters:
  fx: 473.8663
  fy: 473.4386
  px: 854.1181
  py: 1004.1079
  k1: 0.04988487
  k2: 0.07994269
  k3: 0.04995302
  k4: 0.00453427
  p1: 0.00899717
  p2: 0.00999539
  p3: -0.00394451
  p4: -0.00987186
  scale: 5.9998
  rotation: -0.6619
Best score: 9.8067: : 399iter [00:48,  8.69iter/s]Best score: 9.8067: : 400iter [00:48,  8.43iter/s]Best score: 9.8067: : 400iter [00:48,  8.43iter/s]Best score: 9.8067: : 401iter [00:48,  8.52iter/s]Best score: 9.8067: : 401iter [00:48,  8.52iter/s]Best score: 9.8067: : 402iter [00:48,  8.57iter/s]Best score: 9.8067: : 402iter [00:48,  8.57iter/s]Best score: 9.8067: : 403iter [00:48,  8.59iter/s]Best score: 9.8067: : 403iter [00:48,  8.59iter/s]Best score: 9.8067: : 404iter [00:48,  8.63iter/s]Best score: 9.8067: : 404iter [00:48,  8.63iter/s]Best score: 9.8067: : 405iter [00:48,  8.64iter/s]Best score: 9.8067: : 405iter [00:49,  8.64iter/s]Best score: 9.8067: : 406iter [00:49,  8.66iter/s]Best score: 9.8067: : 406iter [00:49,  8.66iter/s]Best score: 9.8067: : 407iter [00:49,  8.68iter/s]Best score: 9.8067: : 407iter [00:49,  8.68iter/s]Best score: 9.8067: : 408iter [00:49,  8.69iter/s]Best score: 9.8067: : 408iter [00:49,  8.69iter/s]Best score: 9.8067: : 409iter [00:49,  8.69iter/s]Best score: 9.8052: : 409iter [00:49,  8.69iter/s]Best score: 9.8052: : 410iter [00:49,  8.41iter/s]Best score: 9.8052: : 410iter [00:49,  8.41iter/s]Best score: 9.8052: : 411iter [00:49,  8.50iter/s]Best score: 9.8052: : 411iter [00:49,  8.50iter/s]Best score: 9.8052: : 412iter [00:49,  8.57iter/s]Best score: 9.8052: : 412iter [00:49,  8.57iter/s]Best score: 9.8052: : 413iter [00:49,  8.62iter/s]Best score: 9.8052: : 413iter [00:50,  8.62iter/s]Best score: 9.8052: : 414iter [00:50,  8.65iter/s]Best score: 9.8052: : 414iter [00:50,  8.65iter/s]Best score: 9.8052: : 415iter [00:50,  8.68iter/s]Best score: 9.8052: : 415iter [00:50,  8.68iter/s]Best score: 9.8052: : 416iter [00:50,  8.70iter/s]Best score: 9.8052: : 416iter [00:50,  8.70iter/s]Best score: 9.8052: : 417iter [00:50,  8.70iter/s]Best score: 9.8052: : 417iter [00:50,  8.70iter/s]Best score: 9.8052: : 418iter [00:50,  8.71iter/s]Best score: 9.8052: : 418iter [00:50,  8.71iter/s]Best score: 9.8052: : 419iter [00:50,  8.71iter/s]Best score: 9.7992: : 419iter [00:50,  8.71iter/s]Best score: 9.7992: : 420iter [00:50,  8.45iter/s]Best score: 9.7992: : 420iter [00:50,  8.45iter/s]Best score: 9.7992: : 421iter [00:50,  8.53iter/s]Best score: 9.7992: : 421iter [00:50,  8.53iter/s]Best score: 9.7992: : 422iter [00:50,  8.59iter/s]Best score: 9.7992: : 422iter [00:51,  8.59iter/s]Best score: 9.7992: : 423iter [00:51,  8.63iter/s]Best score: 9.7992: : 423iter [00:51,  8.63iter/s]Best score: 9.7992: : 424iter [00:51,  8.66iter/s]Best score: 9.7992: : 424iter [00:51,  8.66iter/s]Best score: 9.7992: : 425iter [00:51,  8.68iter/s]Best score: 9.7992: : 425iter [00:51,  8.68iter/s]Best score: 9.7992: : 426iter [00:51,  8.69iter/s]Best score: 9.7992: : 426iter [00:51,  8.69iter/s]Best score: 9.7992: : 427iter [00:51,  8.70iter/s]Best score: 9.7992: : 427iter [00:51,  8.70iter/s]Best score: 9.7992: : 428iter [00:51,  8.71iter/s]Best score: 9.7992: : 428iter [00:51,  8.71iter/s]Best score: 9.7992: : 429iter [00:51,  8.70iter/s]Best score: 9.7984: : 429iter [00:51,  8.70iter/s]Best score: 9.7984: : 430iter [00:51,  8.43iter/s]Best score: 9.7984: : 430iter [00:52,  8.43iter/s]Best score: 9.7984: : 431iter [00:52,  8.51iter/s]Best score: 9.7984: : 431iter [00:52,  8.51iter/s]Best score: 9.7984: : 432iter [00:52,  8.57iter/s]Best score: 9.7984: : 432iter [00:52,  8.57iter/s]Best score: 9.7984: : 433iter [00:52,  8.61iter/s]Best score: 9.7984: : 433iter [00:52,  8.61iter/s]Best score: 9.7984: : 434iter [00:52,  8.65iter/s]Best score: 9.7984: : 434iter [00:52,  8.65iter/s]Best score: 9.7984: : 435iter [00:52,  8.68iter/s]Best score: 9.7984: : 435iter [00:52,  8.68iter/s]Best score: 9.7984: : 436iter [00:52,  8.70iter/s]Best score: 9.7984: : 436iter [00:52,  8.70iter/s]Best score: 9.7984: : 437iter [00:52,  8.69iter/s]Best score: 9.7984: : 437iter [00:52,  8.69iter/s]Best score: 9.7984: : 438iter [00:52,  8.69iter/s]Best score: 9.7984: : 438iter [00:52,  8.69iter/s]Best score: 9.7984: : 439iter [00:52,  8.69iter/s]Best score: 9.7953: : 439iter [00:53,  8.69iter/s]Best score: 9.7953: : 440iter [00:53,  8.43iter/s]Best score: 9.7953: : 440iter [00:53,  8.43iter/s]Best score: 9.7953: : 441iter [00:53,  8.51iter/s]Best score: 9.7953: : 441iter [00:53,  8.51iter/s]Best score: 9.7953: : 442iter [00:53,  8.58iter/s]Best score: 9.7953: : 442iter [00:53,  8.58iter/s]Best score: 9.7953: : 443iter [00:53,  8.62iter/s]Best score: 9.7953: : 443iter [00:53,  8.62iter/s]Best score: 9.7953: : 444iter [00:53,  8.64iter/s]Best score: 9.7953: : 444iter [00:53,  8.64iter/s]Best score: 9.7953: : 445iter [00:53,  8.66iter/s]Best score: 9.7953: : 445iter [00:53,  8.66iter/s]Best score: 9.7953: : 446iter [00:53,  8.68iter/s]Best score: 9.7953: : 446iter [00:53,  8.68iter/s]Best score: 9.7953: : 447iter [00:53,  8.69iter/s]Best score: 9.7953: : 447iter [00:53,  8.69iter/s]Best score: 9.7953: : 448iter [00:53,  8.70iter/s]Best score: 9.7953: : 448iter [00:54,  8.70iter/s]Best score: 9.7953: : 449iter [00:54,  8.71iter/s]
[ITER 450] Best: 9.7945
[PARAMS] Current best parameters:
  fx: 473.6069
  fy: 473.1906
  px: 854.1209
  py: 1004.1381
  k1: 0.04998259
  k2: 0.07995055
  k3: 0.04992242
  k4: 0.00435384
  p1: 0.00993760
  p2: 0.00997382
  p3: -0.00652825
  p4: -0.00730572
  scale: 5.9999
  rotation: -0.6386
Best score: 9.7945: : 449iter [00:54,  8.71iter/s]Best score: 9.7945: : 450iter [00:54,  8.45iter/s]Best score: 9.7945: : 450iter [00:54,  8.45iter/s]Best score: 9.7945: : 451iter [00:54,  8.52iter/s]Best score: 9.7945: : 451iter [00:54,  8.52iter/s]Best score: 9.7945: : 452iter [00:54,  8.58iter/s]Best score: 9.7945: : 452iter [00:54,  8.58iter/s]Best score: 9.7945: : 453iter [00:54,  8.63iter/s]Best score: 9.7945: : 453iter [00:54,  8.63iter/s]Best score: 9.7945: : 454iter [00:54,  8.66iter/s]Best score: 9.7945: : 454iter [00:54,  8.66iter/s]Best score: 9.7945: : 455iter [00:54,  8.68iter/s]Best score: 9.7945: : 455iter [00:54,  8.68iter/s]Best score: 9.7945: : 456iter [00:54,  8.70iter/s]Best score: 9.7945: : 456iter [00:55,  8.70iter/s]Best score: 9.7945: : 457iter [00:55,  8.71iter/s]Best score: 9.7945: : 457iter [00:55,  8.71iter/s]Best score: 9.7945: : 458iter [00:55,  8.71iter/s]Best score: 9.7945: : 458iter [00:55,  8.71iter/s]Best score: 9.7945: : 459iter [00:55,  8.63iter/s]Best score: 9.7923: : 459iter [00:55,  8.63iter/s]Best score: 9.7923: : 460iter [00:55,  8.40iter/s]Best score: 9.7923: : 460iter [00:55,  8.40iter/s]Best score: 9.7923: : 461iter [00:55,  8.49iter/s]Best score: 9.7923: : 461iter [00:55,  8.49iter/s]Best score: 9.7923: : 462iter [00:55,  8.55iter/s]Best score: 9.7923: : 462iter [00:55,  8.55iter/s]Best score: 9.7923: : 463iter [00:55,  8.59iter/s]Best score: 9.7923: : 463iter [00:55,  8.59iter/s]Best score: 9.7923: : 464iter [00:55,  8.63iter/s]Best score: 9.7923: : 464iter [00:55,  8.63iter/s]Best score: 9.7923: : 465iter [00:55,  8.68iter/s]Best score: 9.7923: : 465iter [00:56,  8.68iter/s]Best score: 9.7923: : 466iter [00:56,  8.70iter/s]Best score: 9.7923: : 466iter [00:56,  8.70iter/s]Best score: 9.7923: : 467iter [00:56,  8.71iter/s]Best score: 9.7923: : 467iter [00:56,  8.71iter/s]Best score: 9.7923: : 468iter [00:56,  8.70iter/s]Best score: 9.7923: : 468iter [00:56,  8.70iter/s]Best score: 9.7923: : 469iter [00:56,  8.69iter/s]Best score: 9.7920: : 469iter [00:56,  8.69iter/s]Best score: 9.7920: : 470iter [00:56,  8.44iter/s]Best score: 9.7920: : 470iter [00:56,  8.44iter/s]Best score: 9.7920: : 471iter [00:56,  8.52iter/s]Best score: 9.7920: : 471iter [00:56,  8.52iter/s]Best score: 9.7920: : 472iter [00:56,  8.58iter/s]Best score: 9.7920: : 472iter [00:56,  8.58iter/s]Best score: 9.7920: : 473iter [00:56,  8.63iter/s]Best score: 9.7920: : 473iter [00:56,  8.63iter/s]Best score: 9.7920: : 474iter [00:56,  8.66iter/s]Best score: 9.7920: : 474iter [00:57,  8.66iter/s]Best score: 9.7920: : 475iter [00:57,  8.68iter/s]Best score: 9.7920: : 475iter [00:57,  8.68iter/s]Best score: 9.7920: : 476iter [00:57,  8.69iter/s]Best score: 9.7920: : 476iter [00:57,  8.69iter/s]Best score: 9.7920: : 477iter [00:57,  8.70iter/s]Best score: 9.7920: : 477iter [00:57,  8.70iter/s]Best score: 9.7920: : 478iter [00:57,  8.71iter/s]Best score: 9.7920: : 478iter [00:57,  8.71iter/s]Best score: 9.7920: : 479iter [00:57,  8.71iter/s]Best score: 9.7908: : 479iter [00:57,  8.71iter/s]Best score: 9.7908: : 480iter [00:57,  8.45iter/s]Best score: 9.7908: : 480iter [00:57,  8.45iter/s]Best score: 9.7908: : 481iter [00:57,  8.52iter/s]Best score: 9.7908: : 481iter [00:57,  8.52iter/s]Best score: 9.7908: : 482iter [00:57,  8.57iter/s]Best score: 9.7908: : 482iter [00:58,  8.57iter/s]Best score: 9.7908: : 483iter [00:58,  8.62iter/s]Best score: 9.7908: : 483iter [00:58,  8.62iter/s]Best score: 9.7908: : 484iter [00:58,  8.64iter/s]Best score: 9.7908: : 484iter [00:58,  8.64iter/s]Best score: 9.7908: : 485iter [00:58,  8.65iter/s]Best score: 9.7908: : 485iter [00:58,  8.65iter/s]Best score: 9.7908: : 486iter [00:58,  8.67iter/s]Best score: 9.7908: : 486iter [00:58,  8.67iter/s]Best score: 9.7908: : 487iter [00:58,  8.69iter/s]Best score: 9.7908: : 487iter [00:58,  8.69iter/s]Best score: 9.7908: : 488iter [00:58,  8.70iter/s]Best score: 9.7908: : 488iter [00:58,  8.70iter/s]Best score: 9.7908: : 489iter [00:58,  8.69iter/s]Best score: 9.7893: : 489iter [00:58,  8.69iter/s]Best score: 9.7893: : 490iter [00:58,  8.43iter/s]Best score: 9.7893: : 490iter [00:58,  8.43iter/s]Best score: 9.7893: : 491iter [00:58,  8.51iter/s]Best score: 9.7893: : 491iter [00:59,  8.51iter/s]Best score: 9.7893: : 492iter [00:59,  8.57iter/s]Best score: 9.7893: : 492iter [00:59,  8.57iter/s]Best score: 9.7893: : 493iter [00:59,  8.61iter/s]Best score: 9.7893: : 493iter [00:59,  8.61iter/s]Best score: 9.7893: : 494iter [00:59,  8.64iter/s]Best score: 9.7893: : 494iter [00:59,  8.64iter/s]Best score: 9.7893: : 495iter [00:59,  8.67iter/s]Best score: 9.7893: : 495iter [00:59,  8.67iter/s]Best score: 9.7893: : 496iter [00:59,  8.68iter/s]Best score: 9.7893: : 496iter [00:59,  8.68iter/s]Best score: 9.7893: : 497iter [00:59,  8.69iter/s]Best score: 9.7893: : 497iter [00:59,  8.69iter/s]Best score: 9.7893: : 498iter [00:59,  8.69iter/s]Best score: 9.7893: : 498iter [00:59,  8.69iter/s]Best score: 9.7893: : 499iter [00:59,  8.69iter/s]
[ITER 500] Best: 9.7891
[PARAMS] Current best parameters:
  fx: 473.5119
  fy: 473.1772
  px: 854.1470
  py: 1004.1328
  k1: 0.04998847
  k2: 0.07999733
  k3: 0.04996522
  k4: 0.00433354
  p1: 0.00990392
  p2: 0.00998728
  p3: -0.00591183
  p4: -0.00824518
  scale: 6.0000
  rotation: -0.6386
Best score: 9.7891: : 499iter [01:00,  8.69iter/s]Best score: 9.7891: : 500iter [01:00,  8.43iter/s]Best score: 9.7891: : 500iter [01:00,  8.33iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 9.789149 -> 9.786889
  Success: True
  Tangential distortion: p1=0.009912, p2=0.010000, p3=-0.005917, p4=-0.008249
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 9.7869
[✅] Parameters copied to: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 473.5119574976825
    fy: 473.1771891171793
    px: 854.1469914719323
    py: 1004.1327946742412
    k1: 0.05
    k2: 0.08
    k3: 0.05
    k4: 0.004312554782331323
    p1: 0.00991154516180043
    p2: 0.01
    p3: -0.005917441555632546
    p4: -0.00824851057859633
    scale: 6.0
    rotation: -0.6386256420543577
    mmpp: 8.62615385486272
    nmpp: 8626153.85486272

[🔄] Converting 1610 frames to PTD, creating debug images for 309 calibration frames...
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[✅] Saved PTDs: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDCP3D-SL-010_W046(6)_2026-04-08/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.
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[✅] LUT written to: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDCP3D-SL-010_W046(6)_2026-04-08' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/MDCP3D-SL-010_W046(6)_2026-04-08_unknown.lut

-------------------------------------------------------------------
                          Step 4: Heatmap                          
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                                                                  [A[✅] 2000L: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/4_heatmap/2000L_heatmap.png
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                                                                  [A[✅] 2000R: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/4_heatmap/2000R_heatmap.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:07<00:02,  2.69s/rig]
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                                                                 [A[✅] 1500: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/4_heatmap/1500_heatmap.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:10<00:00,  2.77s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:10<00:00,  2.65s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDCP3D-SL-010_W046(6)_2026-04-08/calibration/debug/4_heatmap
[🏁] Finished processing MDCP3D-SL-010_W046(6)_2026-04-08


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