2026-08-25T02:57:44 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC005_W041_2026-08-11 ===
2026-08-25T02:57:44 - INFO: Camera ID: 12131552
2026-08-25T02:57:44 - INFO: Wand ID: 
2026-08-25T02:57:44 - INFO: Recorded At: 2026-08-11T15:51:32Z

-------------------------------------------------------------------
                        Step 1: Digitization                       
-------------------------------------------------------------------

[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process
Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 579 stationary frames, 791 remain

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                                                                           [A[PASS2] Frame 0000350: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000351: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000352: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000354: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000355: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000356: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000362: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000363: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000364: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000365: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000466: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000476: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000477: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000478: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000479: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000554: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000555: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000556: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000557: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000653: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000654: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000655: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000656: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000657: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000658: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000659: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000660: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000664: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000665: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000666: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000667: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000670: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000671: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000672: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000673: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000691: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000692: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000693: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000694: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001150: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001151: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001152: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001153: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001155: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001156: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001157: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001158: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001159: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001160: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001161: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001162: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001194: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001197: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001226: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001227: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001228: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001229: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001230: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001231: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001232: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001233: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001234: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001235: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001236: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001237: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001282: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001283: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001284: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001329: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000352: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000353: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000355: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000364: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000478: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000556: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000655: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000659: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000666: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000672: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000693: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001152: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001157: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001161: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001194: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001228: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001232: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001236: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001284: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 38 frames fixed, 37 untrusted, 791 total

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                                                                           [A
[TIMING] 1000 — 791 frames
  Phase 1  radial detect   (parallel)  :  11.36s  (14.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.27s  (6.7 ms/frame)
  Phase 2b corner fix      (sequential):   1.87s
  Phase 3  finalize        (sequential):  15.48s  (19.6 ms/frame)
    decode + undistort                 :   7.96s  (10.1 ms/frame)
    corner apply + refinement          :   3.53s  (4.5 ms/frame)
    segment                            :   3.28s  (4.1 ms/frame)
    smooth                             :   0.44s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  33.98s

Processing Rigs:  50%|█████     | 1/2 [00:40<00:40, 40.21s/it][INFO] Filtered 504 stationary frames, 632 remain

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                                                                          [A[PASS2] Frame 0000706: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000707: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000708: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000709: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000726: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000727: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000728: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000729: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000730: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000731: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000778: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000727: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000730: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 6 frames fixed, 10 untrusted, 632 total

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                                                                          [A
[TIMING] 500 — 632 frames
  Phase 1  radial detect   (parallel)  :  10.65s  (16.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.95s  (9.4 ms/frame)
  Phase 2b corner fix      (sequential):   0.56s
  Phase 3  finalize        (sequential):  10.76s  (17.0 ms/frame)
    decode + undistort                 :   5.19s  (8.2 ms/frame)
    corner apply + refinement          :   2.39s  (3.8 ms/frame)
    segment                            :   2.59s  (4.1 ms/frame)
    smooth                             :   0.38s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  27.91s

Processing Rigs: 100%|██████████| 2/2 [01:11<00:00, 35.21s/it]Processing Rigs: 100%|██████████| 2/2 [01:11<00:00, 35.96s/it]
[💾] Saved RigFrameDataset to /working/MDC005_W041_2026-08-11/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1423 frames from 2 rigs to /working/MDC005_W041_2026-08-11/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC005_W041_2026-08-11/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 791
[📋] 500: 100 frames selected from 632
[💾] Saved RigFrameDataset to /working/MDC005_W041_2026-08-11/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC005_W041_2026-08-11/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                      Step 2: Casing Detection                     
-------------------------------------------------------------------

-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]Computing max image from bright frames:   2%|▎         | 1/40 [00:04<02:58,  4.58s/it]Computing max image from bright frames:   5%|▌         | 2/40 [00:04<01:14,  1.97s/it]Computing max image from bright frames:   8%|▊         | 3/40 [00:04<00:42,  1.15s/it]Computing max image from bright frames:  10%|█         | 4/40 [00:05<00:26,  1.35it/s]Computing max image from bright frames:  12%|█▎        | 5/40 [00:05<00:19,  1.84it/s]Computing max image from bright frames:  15%|█▌        | 6/40 [00:05<00:13,  2.46it/s]Computing max image from bright frames:  18%|█▊        | 7/40 [00:05<00:10,  3.20it/s]Computing max image from bright frames:  22%|██▎       | 9/40 [00:05<00:06,  5.07it/s]Computing max image from bright frames:  28%|██▊       | 11/40 [00:05<00:05,  5.65it/s]Computing max image from bright frames:  32%|███▎      | 13/40 [00:06<00:04,  6.52it/s]Computing max image from bright frames:  35%|███▌      | 14/40 [00:06<00:04,  5.49it/s]Computing max image from bright frames:  42%|████▎     | 17/40 [00:06<00:02,  7.74it/s]Computing max image from bright frames:  48%|████▊     | 19/40 [00:06<00:02,  9.53it/s]Computing max image from bright frames:  52%|█████▎    | 21/40 [00:07<00:02,  8.15it/s]Computing max image from bright frames:  57%|█████▊    | 23/40 [00:07<00:01,  9.79it/s]Computing max image from bright frames:  65%|██████▌   | 26/40 [00:07<00:01, 12.67it/s]Computing max image from bright frames:  70%|███████   | 28/40 [00:07<00:00, 12.87it/s]Computing max image from bright frames:  78%|███████▊  | 31/40 [00:07<00:00, 15.32it/s]Computing max image from bright frames:  82%|████████▎ | 33/40 [00:08<00:01,  5.91it/s]Computing max image from bright frames:  88%|████████▊ | 35/40 [00:08<00:00,  6.44it/s]Computing max image from bright frames:  92%|█████████▎| 37/40 [00:08<00:00,  7.95it/s]Computing max image from bright frames:  98%|█████████▊| 39/40 [00:09<00:00,  7.73it/s]                                                                                       [✅] Casing detection complete: cx=1006.0, cy=767.0

-------------------------------------------------------------------
                        Step 3: Calibration                        
-------------------------------------------------------------------

  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:38,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:38,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:38,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:38,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:37,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:37,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:36,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:36,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:36,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:35,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:35,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:34,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:34,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:34,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:33,  2.55it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  17%|█▋        | 17/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.059 | Best: {'fx': 526.263} (0.059):  18%|█▊        | 18/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.034 | Best: {'fx': 526.263} (0.059):  19%|█▉        | 19/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.089 | Best: {'fx': 533.333} (0.089):  20%|██        | 20/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.052 | Best: {'fx': 533.333} (0.089):  21%|██        | 21/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  22%|██▏       | 22/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.026 | Best: {'fx': 533.333} (0.089):  23%|██▎       | 23/100 [00:00<00:02, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.022 | Best: {'fx': 533.333} (0.089):  24%|██▍       | 24/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  25%|██▌       | 25/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  26%|██▌       | 26/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.089):  27%|██▋       | 27/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  28%|██▊       | 28/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  29%|██▉       | 29/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.089):  30%|███       | 30/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  31%|███       | 31/100 [00:00<00:01, 38.50it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  32%|███▏      | 32/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  32%|███▏      | 32/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.089):  33%|███▎      | 33/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.089):  34%|███▍      | 34/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.089):  35%|███▌      | 35/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  36%|███▌      | 36/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.089):  37%|███▋      | 37/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  38%|███▊      | 38/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.089):  39%|███▉      | 39/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  40%|████      | 40/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.089):  41%|████      | 41/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.008 | Best: {'fx': 533.333} (0.089):  42%|████▏     | 42/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.089):  43%|████▎     | 43/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.089):  44%|████▍     | 44/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  45%|████▌     | 45/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.089):  46%|████▌     | 46/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.089):  47%|████▋     | 47/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.089):  48%|████▊     | 48/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.089):  49%|████▉     | 49/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.089):  50%|█████     | 50/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.089):  51%|█████     | 51/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.089):  52%|█████▏    | 52/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  53%|█████▎    | 53/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  54%|█████▍    | 54/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  55%|█████▌    | 55/100 [00:00<00:00, 72.49it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  56%|█████▌    | 56/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  56%|█████▌    | 56/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  57%|█████▋    | 57/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  58%|█████▊    | 58/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  59%|█████▉    | 59/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  60%|██████    | 60/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  61%|██████    | 61/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  62%|██████▏   | 62/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  63%|██████▎   | 63/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.089):  64%|██████▍   | 64/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  65%|██████▌   | 65/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  66%|██████▌   | 66/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  67%|██████▋   | 67/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  68%|██████▊   | 68/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  69%|██████▉   | 69/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  70%|███████   | 70/100 [00:00<00:00, 112.42it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  71%|███████   | 71/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  71%|███████   | 71/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  72%|███████▏  | 72/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  73%|███████▎  | 73/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  74%|███████▍  | 74/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  75%|███████▌  | 75/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  76%|███████▌  | 76/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  77%|███████▋  | 77/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  78%|███████▊  | 78/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  79%|███████▉  | 79/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  80%|████████  | 80/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  81%|████████  | 81/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  82%|████████▏ | 82/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  83%|████████▎ | 83/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  84%|████████▍ | 84/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  85%|████████▌ | 85/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  86%|████████▌ | 86/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  87%|████████▋ | 87/100 [00:00<00:00, 120.24it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  88%|████████▊ | 88/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  88%|████████▊ | 88/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  89%|████████▉ | 89/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  90%|█████████ | 90/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  91%|█████████ | 91/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  92%|█████████▏| 92/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  93%|█████████▎| 93/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  94%|█████████▍| 94/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  95%|█████████▌| 95/100 [00:00<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  96%|█████████▌| 96/100 [00:01<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  97%|█████████▋| 97/100 [00:01<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  98%|█████████▊| 98/100 [00:01<00:00, 129.36it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.089):  99%|█████████▉| 99/100 [00:01<00:00, 129.36it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   2%|▏         | 1/50 [00:00<00:05,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.012 | Best: {'fx': 509.354} (0.012):   2%|▏         | 1/50 [00:00<00:05,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.013 | Best: {'fx': 511.395} (0.013):   4%|▍         | 2/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.013 | Best: {'fx': 511.395} (0.013):   6%|▌         | 3/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.018 | Best: {'fx': 513.435} (0.018):   8%|▊         | 4/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.014 | Best: {'fx': 513.435} (0.018):  10%|█         | 5/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.021 | Best: {'fx': 514.456} (0.021):  12%|█▏        | 6/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.027 | Best: {'fx': 516.497} (0.027):  14%|█▍        | 7/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.024 | Best: {'fx': 516.497} (0.027):  16%|█▌        | 8/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.029 | Best: {'fx': 517.517} (0.029):  18%|█▊        | 9/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.036 | Best: {'fx': 520.578} (0.036):  20%|██        | 10/50 [00:00<00:04,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.033 | Best: {'fx': 520.578} (0.036):  22%|██▏       | 11/50 [00:00<00:03,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.031 | Best: {'fx': 520.578} (0.036):  24%|██▍       | 12/50 [00:00<00:03,  9.75it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.031 | Best: {'fx': 520.578} (0.036):  26%|██▌       | 13/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.043 | Best: {'fx': 522.619} (0.043):  26%|██▌       | 13/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.039 | Best: {'fx': 522.619} (0.043):  28%|██▊       | 14/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.055 | Best: {'fx': 525.68} (0.055):  30%|███       | 15/50 [00:00<00:00, 71.08it/s] Brute | Start: {'fx': 533.333} (0.089) | Current: 0.045 | Best: {'fx': 525.68} (0.055):  32%|███▏      | 16/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.050 | Best: {'fx': 525.68} (0.055):  34%|███▍      | 17/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.061 | Best: {'fx': 526.701} (0.061):  36%|███▌      | 18/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.078 | Best: {'fx': 529.762} (0.078):  38%|███▊      | 19/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.073 | Best: {'fx': 529.762} (0.078):  40%|████      | 20/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.069 | Best: {'fx': 529.762} (0.078):  42%|████▏     | 21/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.084 | Best: {'fx': 530.782} (0.084):  44%|████▍     | 22/50 [00:00<00:00, 71.08it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.084 | Best: {'fx': 530.782} (0.084):  46%|████▌     | 23/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.093 | Best: {'fx': 531.803} (0.093):  46%|████▌     | 23/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.088 | Best: {'fx': 531.803} (0.093):  48%|████▊     | 24/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.075 | Best: {'fx': 531.803} (0.093):  50%|█████     | 25/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.088 | Best: {'fx': 531.803} (0.093):  52%|█████▏    | 26/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.068 | Best: {'fx': 531.803} (0.093):  54%|█████▍    | 27/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.081 | Best: {'fx': 531.803} (0.093):  56%|█████▌    | 28/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.054 | Best: {'fx': 531.803} (0.093):  58%|█████▊    | 29/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.060 | Best: {'fx': 531.803} (0.093):  60%|██████    | 30/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.051 | Best: {'fx': 531.803} (0.093):  62%|██████▏   | 31/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.045 | Best: {'fx': 531.803} (0.093):  64%|██████▍   | 32/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.049 | Best: {'fx': 531.803} (0.093):  66%|██████▌   | 33/50 [00:00<00:00, 79.48it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.049 | Best: {'fx': 531.803} (0.093):  68%|██████▊   | 34/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.042 | Best: {'fx': 531.803} (0.093):  68%|██████▊   | 34/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.034 | Best: {'fx': 531.803} (0.093):  70%|███████   | 35/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.040 | Best: {'fx': 531.803} (0.093):  72%|███████▏  | 36/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.038 | Best: {'fx': 531.803} (0.093):  74%|███████▍  | 37/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.035 | Best: {'fx': 531.803} (0.093):  76%|███████▌  | 38/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.032 | Best: {'fx': 531.803} (0.093):  78%|███████▊  | 39/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.030 | Best: {'fx': 531.803} (0.093):  80%|████████  | 40/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.026 | Best: {'fx': 531.803} (0.093):  82%|████████▏ | 41/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.031 | Best: {'fx': 531.803} (0.093):  84%|████████▍ | 42/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.027 | Best: {'fx': 531.803} (0.093):  86%|████████▌ | 43/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.030 | Best: {'fx': 531.803} (0.093):  88%|████████▊ | 44/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.027 | Best: {'fx': 531.803} (0.093):  90%|█████████ | 45/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.023 | Best: {'fx': 531.803} (0.093):  92%|█████████▏| 46/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.025 | Best: {'fx': 531.803} (0.093):  94%|█████████▍| 47/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.025 | Best: {'fx': 531.803} (0.093):  96%|█████████▌| 48/50 [00:00<00:00, 90.59it/s]Brute | Start: {'fx': 533.333} (0.089) | Current: 0.024 | Best: {'fx': 531.803} (0.093):  98%|█████████▊| 49/50 [00:00<00:00, 90.59it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 1006.0
    py: 767.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization
Initial score: 9.5307: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 9.5307, picklable_objective(x0) = 9.5325
Best score: 9.5325: : 0iter [00:01, ?iter/s] Best score: 9.5325: : 1iter [00:01,  1.83s/iter]Best score: 9.5325: : 1iter [00:01,  1.83s/iter]Best score: 9.5325: : 2iter [00:02,  1.83s/iter]Best score: 9.5325: : 3iter [00:02,  1.85iter/s]Best score: 9.5325: : 3iter [00:02,  1.85iter/s]Best score: 9.5325: : 4iter [00:02,  1.85iter/s]Best score: 9.5325: : 5iter [00:02,  1.85iter/s]Best score: 9.5325: : 6iter [00:02,  1.85iter/s]Best score: 9.5325: : 7iter [00:02,  5.25iter/s]Best score: 9.5325: : 7iter [00:02,  5.25iter/s]Best score: 9.5325: : 8iter [00:02,  5.25iter/s]Best score: 9.5325: : 9iter [00:02,  5.25iter/s]Best score: 9.5325: : 10iter [00:02,  5.25iter/s]Best score: 9.5325: : 11iter [00:02,  9.14iter/s]Best score: 9.5325: : 11iter [00:02,  9.14iter/s]Best score: 9.5325: : 12iter [00:02,  9.14iter/s]Best score: 9.5325: : 13iter [00:02,  9.14iter/s]Best score: 9.5325: : 14iter [00:02,  9.14iter/s]Best score: 9.5325: : 15iter [00:02, 13.41iter/s]Best score: 9.5325: : 15iter [00:02, 13.41iter/s]Best score: 9.5325: : 16iter [00:02, 13.41iter/s]Best score: 9.5325: : 17iter [00:02, 13.41iter/s]Best score: 9.5325: : 18iter [00:02, 13.41iter/s]Best score: 9.5325: : 19iter [00:02, 17.79iter/s]Best score: 9.5325: : 19iter [00:02, 17.79iter/s]Best score: 9.5325: : 20iter [00:02, 17.79iter/s]Best score: 9.5325: : 21iter [00:02, 17.79iter/s]Best score: 9.5325: : 22iter [00:02, 17.79iter/s]Best score: 9.5325: : 23iter [00:02, 21.95iter/s]Best score: 9.5325: : 23iter [00:02, 21.95iter/s]Best score: 9.5325: : 24iter [00:02, 21.95iter/s]Best score: 9.5325: : 25iter [00:02, 21.95iter/s]Best score: 9.5325: : 26iter [00:02, 21.95iter/s]Best score: 9.5325: : 27iter [00:02, 25.63iter/s]Best score: 9.5325: : 27iter [00:02, 25.63iter/s]Best score: 9.5325: : 28iter [00:02, 25.63iter/s]Best score: 9.5325: : 29iter [00:02, 25.63iter/s]Best score: 9.5325: : 30iter [00:02, 25.63iter/s]Best score: 9.5325: : 31iter [00:02, 28.72iter/s]Best score: 9.5325: : 31iter [00:02, 28.72iter/s]Best score: 9.5325: : 32iter [00:02, 28.72iter/s]Best score: 9.5325: : 33iter [00:02, 28.72iter/s]Best score: 9.5325: : 34iter [00:02, 28.72iter/s]Best score: 9.5325: : 35iter [00:02, 31.49iter/s]Best score: 9.5325: : 35iter [00:02, 31.49iter/s]Best score: 9.5325: : 36iter [00:02, 31.49iter/s]Best score: 9.5325: : 37iter [00:02, 31.49iter/s]Best score: 9.5325: : 38iter [00:02, 31.49iter/s]Best score: 9.5325: : 39iter [00:02, 33.69iter/s]Best score: 9.5325: : 39iter [00:02, 33.69iter/s]Best score: 9.5325: : 40iter [00:02, 33.69iter/s]Best score: 9.5325: : 41iter [00:02, 33.69iter/s]Best score: 9.5325: : 42iter [00:03, 33.69iter/s]Best score: 9.5325: : 43iter [00:03, 35.16iter/s]Best score: 9.5325: : 43iter [00:03, 35.16iter/s]Best score: 9.5325: : 44iter [00:03, 35.16iter/s]Best score: 9.5325: : 45iter [00:03, 35.16iter/s]Best score: 9.5325: : 46iter [00:03, 35.16iter/s]Best score: 9.5325: : 47iter [00:03, 36.46iter/s]Best score: 9.5325: : 47iter [00:03, 36.46iter/s]Best score: 9.5325: : 48iter [00:03, 36.46iter/s]
[ITER 50] Best: 9.5325
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1006.0000
  py: 767.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 9.5325: : 49iter [00:03, 36.46iter/s]Best score: 9.5325: : 50iter [00:03, 36.46iter/s]Best score: 9.5325: : 51iter [00:03, 37.34iter/s]Best score: 9.5325: : 51iter [00:03, 37.34iter/s]Best score: 9.5325: : 52iter [00:03, 37.34iter/s]Best score: 9.5325: : 53iter [00:03, 37.34iter/s]Best score: 9.5325: : 54iter [00:03, 37.34iter/s]Best score: 9.5325: : 55iter [00:03, 38.04iter/s]Best score: 9.5325: : 55iter [00:03, 38.04iter/s]Best score: 9.5325: : 56iter [00:03, 38.04iter/s]Best score: 9.5325: : 57iter [00:03, 38.04iter/s]Best score: 9.5325: : 58iter [00:03, 38.04iter/s]Best score: 9.5325: : 59iter [00:03, 38.57iter/s]Best score: 9.5325: : 59iter [00:03, 38.57iter/s]Best score: 9.5325: : 60iter [00:03, 38.57iter/s]Best score: 9.5325: : 61iter [00:03, 38.57iter/s]Best score: 9.5325: : 62iter [00:03, 38.57iter/s]Best score: 9.5325: : 63iter [00:03, 38.76iter/s]Best score: 9.5325: : 63iter [00:03, 38.76iter/s]Best score: 9.5325: : 64iter [00:03, 38.76iter/s]Best score: 9.5325: : 65iter [00:03, 38.76iter/s]Best score: 9.5325: : 66iter [00:03, 38.76iter/s]Best score: 9.5325: : 67iter [00:03, 39.00iter/s]Best score: 9.5325: : 67iter [00:03, 39.00iter/s]Best score: 9.5325: : 68iter [00:03, 39.00iter/s]Best score: 9.5325: : 69iter [00:03, 39.00iter/s]Best score: 9.5325: : 70iter [00:03, 39.00iter/s]Best score: 9.5325: : 71iter [00:03, 39.11iter/s]Best score: 9.5325: : 71iter [00:03, 39.11iter/s]Best score: 9.5325: : 72iter [00:03, 39.11iter/s]Best score: 9.5325: : 73iter [00:03, 39.11iter/s]Best score: 9.5325: : 74iter [00:03, 39.11iter/s]Best score: 9.5325: : 75iter [00:03, 39.32iter/s]Best score: 9.5325: : 75iter [00:03, 39.32iter/s]Best score: 9.5325: : 76iter [00:03, 39.32iter/s]Best score: 9.5325: : 77iter [00:03, 39.32iter/s]Best score: 9.5325: : 78iter [00:03, 39.32iter/s]Best score: 9.5325: : 79iter [00:03, 39.51iter/s]Best score: 9.5325: : 79iter [00:03, 39.51iter/s]Best score: 9.5325: : 80iter [00:03, 39.51iter/s]Best score: 9.5325: : 81iter [00:04, 39.51iter/s]Best score: 9.5325: : 82iter [00:04, 39.51iter/s]Best score: 9.5325: : 83iter [00:04, 39.47iter/s]Best score: 9.5325: : 83iter [00:04, 39.47iter/s]Best score: 9.5325: : 84iter [00:04, 39.47iter/s]Best score: 9.5325: : 85iter [00:04, 39.47iter/s]Best score: 9.5325: : 86iter [00:04, 39.47iter/s]Best score: 9.5325: : 87iter [00:04, 39.56iter/s]Best score: 9.5325: : 87iter [00:04, 39.56iter/s]Best score: 9.5325: : 88iter [00:04, 39.56iter/s]Best score: 9.5325: : 89iter [00:04, 39.56iter/s]Best score: 9.5325: : 90iter [00:04, 39.56iter/s]Best score: 9.5325: : 91iter [00:04, 39.48iter/s]Best score: 9.5325: : 91iter [00:04, 39.48iter/s]Best score: 9.5325: : 92iter [00:04, 39.48iter/s]Best score: 9.5325: : 93iter [00:04, 39.48iter/s]Best score: 9.5325: : 94iter [00:04, 39.48iter/s]Best score: 9.5325: : 95iter [00:04, 39.48iter/s]Best score: 9.5325: : 96iter [00:04, 39.73iter/s]Best score: 9.5325: : 96iter [00:04, 39.73iter/s]Best score: 9.5325: : 97iter [00:04, 39.73iter/s]Best score: 9.5325: : 98iter [00:04, 39.73iter/s]
[ITER 100] Best: 9.5325
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1006.0000
  py: 767.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 9.5325: : 99iter [00:04, 39.73iter/s]Best score: 9.5325: : 100iter [00:04, 39.67iter/s]Best score: 9.5325: : 100iter [00:04, 39.67iter/s]Best score: 9.5325: : 101iter [00:04, 39.67iter/s]Best score: 9.5325: : 102iter [00:04, 39.67iter/s]Best score: 9.5325: : 103iter [00:04, 39.67iter/s]Best score: 9.5325: : 104iter [00:04, 37.13iter/s]Best score: 9.5325: : 104iter [00:04, 37.13iter/s]Best score: 9.5325: : 105iter [00:04, 37.13iter/s]Best score: 9.5325: : 106iter [00:04, 37.13iter/s]Best score: 9.5325: : 107iter [00:04, 37.13iter/s]Best score: 9.5325: : 108iter [00:04, 37.82iter/s]Best score: 9.5325: : 108iter [00:04, 37.82iter/s]Best score: 9.5325: : 109iter [00:04, 37.82iter/s]Best score: 9.5325: : 110iter [00:04, 37.82iter/s]Best score: 9.5325: : 111iter [00:04, 37.82iter/s]Best score: 9.5325: : 112iter [00:04, 38.25iter/s]Best score: 9.5325: : 112iter [00:04, 38.25iter/s]Best score: 9.5325: : 113iter [00:04, 38.25iter/s]Best score: 9.5325: : 114iter [00:04, 38.25iter/s]Best score: 9.5325: : 115iter [00:04, 38.25iter/s]Best score: 9.5325: : 116iter [00:04, 38.25iter/s]Best score: 9.5325: : 117iter [00:04, 38.88iter/s]Best score: 9.5325: : 117iter [00:04, 38.88iter/s]Best score: 9.5325: : 118iter [00:04, 38.88iter/s]Best score: 9.5325: : 119iter [00:04, 38.88iter/s]Best score: 9.5325: : 120iter [00:05, 38.88iter/s]Best score: 9.5325: : 121iter [00:05, 39.02iter/s]Best score: 9.5325: : 121iter [00:05, 39.02iter/s]Best score: 9.5325: : 122iter [00:05, 39.02iter/s]Best score: 9.5325: : 123iter [00:05, 39.02iter/s]Best score: 9.5325: : 124iter [00:05, 39.02iter/s]Best score: 9.5325: : 125iter [00:05, 39.23iter/s]Best score: 9.5325: : 125iter [00:05, 39.23iter/s]Best score: 9.5325: : 126iter [00:05, 39.23iter/s]Best score: 9.5325: : 127iter [00:05, 39.23iter/s]Best score: 9.5325: : 128iter [00:05, 39.23iter/s]Best score: 9.5325: : 129iter [00:05, 39.23iter/s]Best score: 9.5325: : 130iter [00:05, 39.43iter/s]Best score: 9.5325: : 130iter [00:05, 39.43iter/s]Best score: 9.5325: : 131iter [00:05, 39.43iter/s]Best score: 9.5325: : 132iter [00:05, 39.43iter/s]Best score: 9.5325: : 133iter [00:05, 39.43iter/s]Best score: 9.5325: : 134iter [00:05, 39.43iter/s]Best score: 9.5325: : 135iter [00:05, 39.79iter/s]Best score: 9.5325: : 135iter [00:05, 39.79iter/s]Best score: 9.5325: : 136iter [00:05, 39.79iter/s]Best score: 9.5325: : 137iter [00:05, 39.79iter/s]Best score: 9.5325: : 138iter [00:05, 39.79iter/s]Best score: 9.5325: : 139iter [00:05, 39.79iter/s]Best score: 9.5325: : 140iter [00:05, 39.79iter/s]Best score: 9.5325: : 140iter [00:05, 39.79iter/s]Best score: 9.5325: : 141iter [00:05, 39.79iter/s]Best score: 9.5325: : 142iter [00:05, 39.79iter/s]Best score: 9.5325: : 143iter [00:05, 39.79iter/s]Best score: 9.5325: : 144iter [00:05, 39.81iter/s]Best score: 9.5325: : 144iter [00:05, 39.81iter/s]Best score: 9.5325: : 145iter [00:05, 39.81iter/s]Best score: 9.5325: : 146iter [00:05, 39.81iter/s]Best score: 9.5325: : 147iter [00:05, 39.81iter/s]Best score: 9.5325: : 148iter [00:05, 39.81iter/s]Best score: 9.5325: : 149iter [00:05, 40.04iter/s]
[ITER 150] Best: 9.5325
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1006.0000
  py: 767.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 9.5325: : 149iter [00:05, 40.04iter/s]Best score: 9.5325: : 150iter [00:05, 40.04iter/s]Best score: 9.5325: : 151iter [00:05, 40.04iter/s]Best score: 9.5325: : 152iter [00:05, 40.04iter/s]Best score: 9.5325: : 153iter [00:05, 40.04iter/s]Best score: 9.5325: : 154iter [00:05, 33.43iter/s]Best score: 9.5325: : 154iter [00:05, 33.43iter/s]Best score: 9.5325: : 155iter [00:05, 33.43iter/s]Best score: 9.5325: : 156iter [00:05, 33.43iter/s]Best score: 9.5325: : 157iter [00:06, 33.43iter/s]Best score: 9.5325: : 158iter [00:06, 33.43iter/s]Best score: 9.5325: : 159iter [00:06, 35.35iter/s]Best score: 9.5325: : 159iter [00:06, 35.35iter/s]Best score: 9.5325: : 160iter [00:06, 35.35iter/s]Best score: 9.5325: : 161iter [00:06, 35.35iter/s]Best score: 9.5325: : 162iter [00:06, 35.35iter/s]Best score: 9.5325: : 163iter [00:06, 36.42iter/s]Best score: 9.5325: : 163iter [00:06, 36.42iter/s]Best score: 9.5325: : 164iter [00:06, 36.42iter/s]Best score: 9.5325: : 165iter [00:06, 36.42iter/s]Best score: 9.5325: : 166iter [00:06, 36.42iter/s]Best score: 9.5325: : 167iter [00:06, 36.42iter/s]Best score: 9.5325: : 168iter [00:06, 37.63iter/s]Best score: 9.5325: : 168iter [00:06, 37.63iter/s]Best score: 9.5325: : 169iter [00:06, 37.63iter/s]Best score: 9.5325: : 170iter [00:06, 37.63iter/s]Best score: 9.5325: : 171iter [00:06, 37.63iter/s]Best score: 9.5325: : 172iter [00:06, 37.63iter/s]Best score: 9.5325: : 173iter [00:06, 38.47iter/s]Best score: 9.5325: : 173iter [00:06, 38.47iter/s]Best score: 9.5325: : 174iter [00:06, 38.47iter/s]Best score: 9.5325: : 175iter [00:06, 38.47iter/s]Best score: 9.5325: : 176iter [00:06, 38.47iter/s]Best score: 9.5325: : 177iter [00:06, 38.47iter/s]Best score: 9.5325: : 178iter [00:06, 39.27iter/s]Best score: 9.5325: : 178iter [00:06, 39.27iter/s]Best score: 9.5325: : 179iter [00:06, 39.27iter/s]Best score: 9.5325: : 180iter [00:06, 39.27iter/s]Best score: 9.5325: : 181iter [00:06, 39.27iter/s]Best score: 9.5325: : 182iter [00:06, 39.45iter/s]Best score: 9.5325: : 182iter [00:06, 39.45iter/s]Best score: 9.5325: : 183iter [00:06, 39.45iter/s]Best score: 9.5325: : 184iter [00:06, 39.45iter/s]Best score: 9.5325: : 185iter [00:06, 39.45iter/s]Best score: 9.5325: : 186iter [00:06, 39.45iter/s]Best score: 9.5325: : 187iter [00:06, 39.94iter/s]Best score: 9.5325: : 187iter [00:06, 39.94iter/s]Best score: 9.5325: : 188iter [00:06, 39.94iter/s]Best score: 9.5325: : 189iter [00:06, 39.94iter/s]Best score: 9.5325: : 190iter [00:06, 39.94iter/s]Best score: 9.5325: : 191iter [00:06, 39.94iter/s]Best score: 9.5325: : 192iter [00:06, 39.99iter/s]Best score: 9.5325: : 192iter [00:06, 39.99iter/s]Best score: 9.5325: : 193iter [00:06, 39.99iter/s]Best score: 9.5325: : 194iter [00:06, 39.99iter/s]Best score: 9.5325: : 195iter [00:06, 39.99iter/s]Best score: 9.5325: : 196iter [00:06, 39.99iter/s]Best score: 9.5325: : 197iter [00:06, 40.24iter/s]Best score: 9.5325: : 197iter [00:07, 40.24iter/s]Best score: 9.5325: : 198iter [00:07, 40.24iter/s]
[ITER 200] Best: 9.5325
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1006.0000
  py: 767.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 9.5325: : 199iter [00:07, 40.24iter/s]Best score: 9.5325: : 200iter [00:07, 40.24iter/s]Best score: 9.5325: : 201iter [00:07, 40.24iter/s]Best score: 9.5325: : 202iter [00:07, 33.79iter/s]Best score: 9.5325: : 202iter [00:07, 33.79iter/s]Best score: 9.5325: : 203iter [00:07, 33.79iter/s]Best score: 9.5325: : 204iter [00:07, 33.79iter/s]Best score: 9.5325: : 205iter [00:07, 33.79iter/s]Best score: 9.5325: : 206iter [00:07, 33.79iter/s]Best score: 9.5325: : 207iter [00:07, 35.68iter/s]Best score: 9.5325: : 207iter [00:07, 35.68iter/s]Best score: 9.5325: : 208iter [00:07, 35.68iter/s]Best score: 6.5948: : 209iter [00:07, 35.68iter/s]Best score: 6.5948: : 210iter [00:07, 35.68iter/s]Best score: 6.5948: : 211iter [00:07, 36.69iter/s]Best score: 6.5948: : 211iter [00:07, 36.69iter/s]Best score: 6.5948: : 212iter [00:07, 36.69iter/s]Best score: 6.5948: : 213iter [00:07, 36.69iter/s]Best score: 6.5948: : 214iter [00:07, 36.69iter/s]Best score: 6.5948: : 215iter [00:07, 36.69iter/s]Best score: 6.5948: : 216iter [00:07, 37.81iter/s]Best score: 6.5948: : 216iter [00:07, 37.81iter/s]Best score: 6.5948: : 217iter [00:07, 37.81iter/s]Best score: 6.5948: : 218iter [00:07, 37.81iter/s]Best score: 3.1694: : 219iter [00:07, 37.81iter/s]Best score: 3.1694: : 220iter [00:07, 38.31iter/s]Best score: 3.1694: : 220iter [00:07, 38.31iter/s]Best score: 3.1694: : 221iter [00:07, 38.31iter/s]Best score: 3.1694: : 222iter [00:07, 38.31iter/s]Best score: 3.1694: : 223iter [00:07, 38.31iter/s]Best score: 3.1694: : 224iter [00:07, 38.31iter/s]Best score: 3.1694: : 225iter [00:07, 39.15iter/s]Best score: 3.1694: : 225iter [00:07, 39.15iter/s]Best score: 3.1694: : 226iter [00:07, 39.15iter/s]Best score: 3.1694: : 227iter [00:07, 39.15iter/s]Best score: 3.1694: : 228iter [00:07, 39.15iter/s]Best score: 3.1694: : 229iter [00:07, 39.15iter/s]Best score: 3.1694: : 230iter [00:07, 39.48iter/s]Best score: 3.1694: : 230iter [00:07, 39.48iter/s]Best score: 3.1694: : 231iter [00:07, 39.48iter/s]Best score: 3.1694: : 232iter [00:07, 39.48iter/s]Best score: 3.1694: : 233iter [00:07, 39.48iter/s]Best score: 3.1694: : 234iter [00:07, 39.48iter/s]Best score: 3.1694: : 235iter [00:07, 39.99iter/s]Best score: 3.1694: : 235iter [00:08, 39.99iter/s]Best score: 3.1694: : 236iter [00:08, 39.99iter/s]Best score: 3.1694: : 237iter [00:08, 39.99iter/s]Best score: 3.1694: : 238iter [00:08, 39.99iter/s]Best score: 1.7709: : 239iter [00:08, 39.99iter/s]Best score: 1.7709: : 240iter [00:08, 40.07iter/s]Best score: 1.7709: : 240iter [00:08, 40.07iter/s]Best score: 1.7709: : 241iter [00:08, 40.07iter/s]Best score: 1.7709: : 242iter [00:08, 40.07iter/s]Best score: 1.7709: : 243iter [00:08, 40.07iter/s]Best score: 1.7709: : 244iter [00:08, 40.07iter/s]Best score: 1.7709: : 245iter [00:08, 40.29iter/s]Best score: 1.7709: : 245iter [00:08, 40.29iter/s]Best score: 1.7709: : 246iter [00:08, 40.29iter/s]Best score: 1.7709: : 247iter [00:08, 40.29iter/s]Best score: 1.7709: : 248iter [00:08, 40.29iter/s]
[ITER 250] Best: 1.7709
[PARAMS] Current best parameters:
  fx: 543.9609
  fy: 543.7912
  px: 1002.0747
  py: 769.0320
  k1: 0.01122569
  k2: -0.00882173
  k3: -0.00613072
  k4: 0.00252531
  p1: -0.00538183
  p2: -0.00238119
  p3: -0.00054542
  p4: 0.00314226
  s1: 0.0059
  s2: -0.0048
  s3: 0.0046
  s4: -0.0051
  tilt_x: 0.0203
  tilt_y: 0.0055
  scale: 2.9380
  rotation: -0.2059
Best score: 1.7709: : 249iter [00:08, 40.29iter/s]Best score: 1.7709: : 250iter [00:08, 40.06iter/s]Best score: 1.7709: : 250iter [00:08, 40.06iter/s]Best score: 1.7709: : 251iter [00:08, 40.06iter/s]Best score: 1.7709: : 252iter [00:08, 40.06iter/s]Best score: 1.7709: : 253iter [00:08, 40.06iter/s]Best score: 1.7709: : 254iter [00:08, 40.06iter/s]Best score: 1.7709: : 255iter [00:08, 33.92iter/s]Best score: 1.7709: : 255iter [00:08, 33.92iter/s]Best score: 1.7709: : 256iter [00:08, 33.92iter/s]Best score: 1.7709: : 257iter [00:08, 33.92iter/s]Best score: 1.7709: : 258iter [00:08, 33.92iter/s]Best score: 1.4464: : 259iter [00:08, 33.92iter/s]Best score: 1.4464: : 260iter [00:08, 35.54iter/s]Best score: 1.4464: : 260iter [00:08, 35.54iter/s]Best score: 1.4464: : 261iter [00:08, 35.54iter/s]Best score: 1.4464: : 262iter [00:08, 35.54iter/s]Best score: 1.4464: : 263iter [00:08, 35.54iter/s]Best score: 1.4464: : 264iter [00:08, 35.54iter/s]Best score: 1.4464: : 265iter [00:08, 36.96iter/s]Best score: 1.4464: : 265iter [00:08, 36.96iter/s]Best score: 1.4464: : 266iter [00:08, 36.96iter/s]Best score: 1.4464: : 267iter [00:08, 36.96iter/s]Best score: 1.4464: : 268iter [00:08, 36.96iter/s]Best score: 1.1375: : 269iter [00:08, 36.96iter/s]Best score: 1.1375: : 270iter [00:08, 37.70iter/s]Best score: 1.1375: : 270iter [00:08, 37.70iter/s]Best score: 1.1375: : 271iter [00:08, 37.70iter/s]Best score: 1.1375: : 272iter [00:09, 37.70iter/s]Best score: 1.1375: : 273iter [00:09, 37.70iter/s]Best score: 1.1375: : 274iter [00:09, 37.70iter/s]Best score: 1.1375: : 275iter [00:09, 38.39iter/s]Best score: 1.1375: : 275iter [00:09, 38.39iter/s]Best score: 1.1375: : 276iter [00:09, 38.39iter/s]Best score: 1.1375: : 277iter [00:09, 38.39iter/s]Best score: 1.1375: : 278iter [00:09, 38.39iter/s]Best score: 0.9714: : 279iter [00:09, 38.39iter/s]Best score: 0.9714: : 280iter [00:09, 38.72iter/s]Best score: 0.9714: : 280iter [00:09, 38.72iter/s]Best score: 0.9714: : 281iter [00:09, 38.72iter/s]Best score: 0.9714: : 282iter [00:09, 38.72iter/s]Best score: 0.9714: : 283iter [00:09, 38.72iter/s]Best score: 0.9714: : 284iter [00:09, 38.72iter/s]Best score: 0.9714: : 285iter [00:09, 39.43iter/s]Best score: 0.9714: : 285iter [00:09, 39.43iter/s]Best score: 0.9714: : 286iter [00:09, 39.43iter/s]Best score: 0.9714: : 287iter [00:09, 39.43iter/s]Best score: 0.9714: : 288iter [00:09, 39.43iter/s]Best score: 0.9714: : 289iter [00:09, 39.43iter/s]Best score: 0.9714: : 290iter [00:09, 39.50iter/s]Best score: 0.9714: : 290iter [00:09, 39.50iter/s]Best score: 0.9714: : 291iter [00:09, 39.50iter/s]Best score: 0.9714: : 292iter [00:09, 39.50iter/s]Best score: 0.9714: : 293iter [00:09, 39.50iter/s]Best score: 0.9714: : 294iter [00:09, 39.50iter/s]Best score: 0.9714: : 295iter [00:09, 40.00iter/s]Best score: 0.9714: : 295iter [00:09, 40.00iter/s]Best score: 0.9714: : 296iter [00:09, 40.00iter/s]Best score: 0.9714: : 297iter [00:09, 40.00iter/s]Best score: 0.9714: : 298iter [00:09, 40.00iter/s]
[ITER 300] Best: 0.9714
[PARAMS] Current best parameters:
  fx: 521.2181
  fy: 521.4193
  px: 1002.7735
  py: 764.8762
  k1: -0.02836502
  k2: 0.02728156
  k3: 0.00428679
  k4: -0.00502310
  p1: 0.01287975
  p2: 0.00711427
  p3: -0.00555306
  p4: -0.01286608
  s1: -0.0001
  s2: 0.0123
  s3: 0.0006
  s4: 0.0063
  tilt_x: -0.0411
  tilt_y: 0.0188
  scale: 3.0849
  rotation: -0.3627
Best score: 0.9714: : 299iter [00:09, 40.00iter/s]Best score: 0.9714: : 300iter [00:09, 40.10iter/s]Best score: 0.9714: : 300iter [00:09, 40.10iter/s]Best score: 0.9714: : 301iter [00:09, 40.10iter/s]Best score: 0.9714: : 302iter [00:09, 40.10iter/s]Best score: 0.9714: : 303iter [00:09, 40.10iter/s]Best score: 0.9714: : 304iter [00:09, 40.10iter/s]Best score: 0.9714: : 305iter [00:09, 34.04iter/s]Best score: 0.9714: : 305iter [00:09, 34.04iter/s]Best score: 0.9714: : 306iter [00:09, 34.04iter/s]Best score: 0.9714: : 307iter [00:09, 34.04iter/s]Best score: 0.9714: : 308iter [00:09, 34.04iter/s]Best score: 0.8413: : 309iter [00:10, 34.04iter/s]Best score: 0.8413: : 310iter [00:10, 35.71iter/s]Best score: 0.8413: : 310iter [00:10, 35.71iter/s]Best score: 0.8413: : 311iter [00:10, 35.71iter/s]Best score: 0.8413: : 312iter [00:10, 35.71iter/s]Best score: 0.8413: : 313iter [00:10, 35.71iter/s]Best score: 0.8413: : 314iter [00:10, 35.71iter/s]Best score: 0.8413: : 315iter [00:10, 36.97iter/s]Best score: 0.8413: : 315iter [00:10, 36.97iter/s]Best score: 0.8413: : 316iter [00:10, 36.97iter/s]Best score: 0.8413: : 317iter [00:10, 36.97iter/s]Best score: 0.8413: : 318iter [00:10, 36.97iter/s]Best score: 0.7294: : 319iter [00:10, 36.97iter/s]Best score: 0.7294: : 320iter [00:10, 37.76iter/s]Best score: 0.7294: : 320iter [00:10, 37.76iter/s]Best score: 0.7294: : 321iter [00:10, 37.76iter/s]Best score: 0.7294: : 322iter [00:10, 37.76iter/s]Best score: 0.7294: : 323iter [00:10, 37.76iter/s]Best score: 0.7294: : 324iter [00:10, 37.76iter/s]Best score: 0.7294: : 325iter [00:10, 38.66iter/s]Best score: 0.7294: : 325iter [00:10, 38.66iter/s]Best score: 0.7294: : 326iter [00:10, 38.66iter/s]Best score: 0.7294: : 327iter [00:10, 38.66iter/s]Best score: 0.7294: : 328iter [00:10, 38.66iter/s]Best score: 0.6740: : 329iter [00:10, 38.66iter/s]Best score: 0.6740: : 330iter [00:10, 39.10iter/s]Best score: 0.6740: : 330iter [00:10, 39.10iter/s]Best score: 0.6740: : 331iter [00:10, 39.10iter/s]Best score: 0.6740: : 332iter [00:10, 39.10iter/s]Best score: 0.6740: : 333iter [00:10, 39.10iter/s]Best score: 0.6740: : 334iter [00:10, 39.10iter/s]Best score: 0.6740: : 335iter [00:10, 39.53iter/s]Best score: 0.6740: : 335iter [00:10, 39.53iter/s]Best score: 0.6740: : 336iter [00:10, 39.53iter/s]Best score: 0.6740: : 337iter [00:10, 39.53iter/s]Best score: 0.6740: : 338iter [00:10, 39.53iter/s]Best score: 0.4810: : 339iter [00:10, 39.53iter/s]Best score: 0.4810: : 340iter [00:10, 39.61iter/s]Best score: 0.4810: : 340iter [00:10, 39.61iter/s]Best score: 0.4810: : 341iter [00:10, 39.61iter/s]Best score: 0.4810: : 342iter [00:10, 39.61iter/s]Best score: 0.4810: : 343iter [00:10, 39.61iter/s]Best score: 0.4810: : 344iter [00:10, 39.61iter/s]Best score: 0.4810: : 345iter [00:10, 40.00iter/s]Best score: 0.4810: : 345iter [00:10, 40.00iter/s]Best score: 0.4810: : 346iter [00:10, 40.00iter/s]Best score: 0.4810: : 347iter [00:10, 40.00iter/s]Best score: 0.4810: : 348iter [00:10, 40.00iter/s]
[ITER 350] Best: 0.4810
[PARAMS] Current best parameters:
  fx: 522.6750
  fy: 522.5855
  px: 1003.1761
  py: 766.6340
  k1: -0.00977248
  k2: 0.02227079
  k3: -0.00706448
  k4: -0.00030640
  p1: 0.00823238
  p2: -0.00104591
  p3: -0.00752611
  p4: -0.00555926
  s1: 0.0047
  s2: 0.0070
  s3: -0.0018
  s4: 0.0042
  tilt_x: -0.0227
  tilt_y: 0.0007
  scale: 3.0345
  rotation: -0.4050
Best score: 0.4810: : 349iter [00:11, 40.00iter/s]Best score: 0.4810: : 350iter [00:11, 39.97iter/s]Best score: 0.4810: : 350iter [00:11, 39.97iter/s]Best score: 0.4810: : 351iter [00:11, 39.97iter/s]Best score: 0.4810: : 352iter [00:11, 39.97iter/s]Best score: 0.4810: : 353iter [00:11, 39.97iter/s]Best score: 0.4810: : 354iter [00:11, 39.97iter/s]Best score: 0.4810: : 355iter [00:11, 33.63iter/s]Best score: 0.4810: : 355iter [00:11, 33.63iter/s]Best score: 0.4810: : 356iter [00:11, 33.63iter/s]Best score: 0.4810: : 357iter [00:11, 33.63iter/s]Best score: 0.4810: : 358iter [00:11, 33.63iter/s]Best score: 0.4810: : 359iter [00:11, 33.63iter/s]Best score: 0.4810: : 360iter [00:11, 35.31iter/s]Best score: 0.4810: : 360iter [00:11, 35.31iter/s]Best score: 0.4810: : 361iter [00:11, 35.31iter/s]Best score: 0.4810: : 362iter [00:11, 35.31iter/s]Best score: 0.4810: : 363iter [00:11, 35.31iter/s]Best score: 0.4810: : 364iter [00:11, 29.70iter/s]Best score: 0.4810: : 364iter [00:11, 29.70iter/s]Best score: 0.4810: : 365iter [00:11, 29.70iter/s]Best score: 0.4810: : 366iter [00:11, 29.70iter/s]Best score: 0.4810: : 367iter [00:11, 29.70iter/s]Best score: 0.4810: : 368iter [00:11, 29.70iter/s]Best score: 0.4810: : 369iter [00:11, 32.45iter/s]Best score: 0.3523: : 369iter [00:11, 32.45iter/s]Best score: 0.3523: : 370iter [00:11, 32.45iter/s]Best score: 0.3523: : 371iter [00:11, 32.45iter/s]Best score: 0.3523: : 372iter [00:11, 32.45iter/s]Best score: 0.3523: : 373iter [00:11, 34.05iter/s]Best score: 0.3523: : 373iter [00:11, 34.05iter/s]Best score: 0.3523: : 374iter [00:11, 34.05iter/s]Best score: 0.3523: : 375iter [00:11, 34.05iter/s]Best score: 0.3523: : 376iter [00:11, 34.05iter/s]Best score: 0.3523: : 377iter [00:11, 34.05iter/s]Best score: 0.3523: : 378iter [00:11, 35.84iter/s]Best score: 0.3523: : 378iter [00:11, 35.84iter/s]Best score: 0.3460: : 379iter [00:11, 35.84iter/s]Best score: 0.3460: : 380iter [00:11, 35.84iter/s]Best score: 0.3460: : 381iter [00:11, 35.84iter/s]Best score: 0.3460: : 382iter [00:11, 36.76iter/s]Best score: 0.3460: : 382iter [00:12, 36.76iter/s]Best score: 0.3460: : 383iter [00:12, 36.76iter/s]Best score: 0.3460: : 384iter [00:12, 36.76iter/s]Best score: 0.3460: : 385iter [00:12, 36.76iter/s]Best score: 0.3460: : 386iter [00:12, 36.76iter/s]Best score: 0.3460: : 387iter [00:12, 37.89iter/s]Best score: 0.3460: : 387iter [00:12, 37.89iter/s]Best score: 0.3460: : 388iter [00:12, 37.89iter/s]Best score: 0.2997: : 389iter [00:12, 37.89iter/s]Best score: 0.2997: : 390iter [00:12, 37.89iter/s]Best score: 0.2997: : 391iter [00:12, 38.28iter/s]Best score: 0.2997: : 391iter [00:12, 38.28iter/s]Best score: 0.2997: : 392iter [00:12, 38.28iter/s]Best score: 0.2997: : 393iter [00:12, 38.28iter/s]Best score: 0.2997: : 394iter [00:12, 38.28iter/s]Best score: 0.2997: : 395iter [00:12, 38.28iter/s]Best score: 0.2997: : 396iter [00:12, 39.02iter/s]Best score: 0.2997: : 396iter [00:12, 39.02iter/s]Best score: 0.2997: : 397iter [00:12, 39.02iter/s]Best score: 0.2997: : 398iter [00:12, 39.02iter/s]
[ITER 400] Best: 0.2997
[PARAMS] Current best parameters:
  fx: 523.8126
  fy: 523.6567
  px: 1001.1555
  py: 767.8294
  k1: -0.01090880
  k2: 0.02717260
  k3: -0.01159182
  k4: 0.00083542
  p1: 0.00472515
  p2: -0.00704838
  p3: -0.00468669
  p4: -0.00021144
  s1: 0.0072
  s2: 0.0021
  s3: -0.0023
  s4: 0.0004
  tilt_x: -0.0097
  tilt_y: 0.0004
  scale: 3.0179
  rotation: -0.4124
Best score: 0.2997: : 399iter [00:12, 39.02iter/s]Best score: 0.2997: : 400iter [00:12, 39.18iter/s]Best score: 0.2997: : 400iter [00:12, 39.18iter/s]Best score: 0.2997: : 401iter [00:12, 39.18iter/s]Best score: 0.2997: : 402iter [00:12, 39.18iter/s]Best score: 0.2997: : 403iter [00:12, 39.18iter/s]Best score: 0.2997: : 404iter [00:12, 32.58iter/s]Best score: 0.2997: : 404iter [00:12, 32.58iter/s]Best score: 0.2997: : 405iter [00:12, 32.58iter/s]Best score: 0.2997: : 406iter [00:12, 32.58iter/s]Best score: 0.2997: : 407iter [00:12, 32.58iter/s]Best score: 0.2997: : 408iter [00:12, 34.33iter/s]Best score: 0.2997: : 408iter [00:12, 34.33iter/s]Best score: 0.2997: : 409iter [00:12, 34.33iter/s]Best score: 0.2997: : 410iter [00:12, 34.33iter/s]Best score: 0.2997: : 411iter [00:12, 34.33iter/s]Best score: 0.2997: : 412iter [00:12, 35.64iter/s]Best score: 0.2997: : 412iter [00:12, 35.64iter/s]Best score: 0.2997: : 413iter [00:12, 35.64iter/s]Best score: 0.2997: : 414iter [00:12, 35.64iter/s]Best score: 0.2997: : 415iter [00:12, 35.64iter/s]Best score: 0.2997: : 416iter [00:12, 35.64iter/s]Best score: 0.2997: : 417iter [00:12, 37.26iter/s]Best score: 0.2997: : 417iter [00:12, 37.26iter/s]Best score: 0.2997: : 418iter [00:12, 37.26iter/s]Best score: 0.2997: : 419iter [00:13, 37.26iter/s]Best score: 0.2997: : 420iter [00:13, 37.26iter/s]Best score: 0.2997: : 421iter [00:13, 37.93iter/s]Best score: 0.2997: : 421iter [00:13, 37.93iter/s]Best score: 0.2997: : 422iter [00:13, 37.93iter/s]Best score: 0.2997: : 423iter [00:13, 37.93iter/s]Best score: 0.2997: : 424iter [00:13, 37.93iter/s]Best score: 0.2997: : 425iter [00:13, 37.93iter/s]Best score: 0.2997: : 426iter [00:13, 38.77iter/s]Best score: 0.2997: : 426iter [00:13, 38.77iter/s]Best score: 0.2997: : 427iter [00:13, 38.77iter/s]Best score: 0.2997: : 428iter [00:13, 38.77iter/s]Best score: 0.2997: : 429iter [00:13, 38.77iter/s]Best score: 0.2997: : 430iter [00:13, 39.01iter/s]Best score: 0.2997: : 430iter [00:13, 39.01iter/s]Best score: 0.2997: : 431iter [00:13, 39.01iter/s]Best score: 0.2997: : 432iter [00:13, 39.01iter/s]Best score: 0.2997: : 433iter [00:13, 39.01iter/s]Best score: 0.2997: : 434iter [00:13, 39.01iter/s]Best score: 0.2997: : 435iter [00:13, 39.63iter/s]Best score: 0.2997: : 435iter [00:13, 39.63iter/s]Best score: 0.2997: : 436iter [00:13, 39.63iter/s]Best score: 0.2997: : 437iter [00:13, 39.63iter/s]Best score: 0.2997: : 438iter [00:13, 39.63iter/s]Best score: 0.2997: : 439iter [00:13, 39.63iter/s]Best score: 0.2997: : 440iter [00:13, 39.62iter/s]Best score: 0.2997: : 440iter [00:13, 39.62iter/s]Best score: 0.2997: : 441iter [00:13, 39.62iter/s]Best score: 0.2997: : 442iter [00:13, 39.62iter/s]Best score: 0.2997: : 443iter [00:13, 39.62iter/s]Best score: 0.2997: : 444iter [00:13, 39.62iter/s]Best score: 0.2997: : 445iter [00:13, 39.90iter/s]Best score: 0.2997: : 445iter [00:13, 39.90iter/s]Best score: 0.2997: : 446iter [00:13, 39.90iter/s]Best score: 0.2997: : 447iter [00:13, 39.90iter/s]Best score: 0.2997: : 448iter [00:13, 39.90iter/s]
[ITER 450] Best: 0.2932
[PARAMS] Current best parameters:
  fx: 525.4558
  fy: 525.3386
  px: 1000.5813
  py: 767.9480
  k1: -0.00596608
  k2: 0.02272592
  k3: -0.01181107
  k4: 0.00130707
  p1: 0.00354930
  p2: -0.00798339
  p3: -0.00324018
  p4: 0.00071197
  s1: 0.0054
  s2: 0.0015
  s3: -0.0021
  s4: -0.0004
  tilt_x: -0.0074
  tilt_y: 0.0014
  scale: 3.0018
  rotation: -0.3953
Best score: 0.2932: : 449iter [00:13, 39.90iter/s]Best score: 0.2932: : 450iter [00:13, 39.81iter/s]Best score: 0.2932: : 450iter [00:13, 39.81iter/s]Best score: 0.2932: : 451iter [00:13, 39.81iter/s]Best score: 0.2932: : 452iter [00:13, 39.81iter/s]Best score: 0.2932: : 453iter [00:13, 39.81iter/s]Best score: 0.2932: : 454iter [00:13, 33.16iter/s]Best score: 0.2932: : 454iter [00:13, 33.16iter/s]Best score: 0.2932: : 455iter [00:13, 33.16iter/s]Best score: 0.2932: : 456iter [00:13, 33.16iter/s]Best score: 0.2932: : 457iter [00:14, 33.16iter/s]Best score: 0.2932: : 458iter [00:14, 33.16iter/s]Best score: 0.2932: : 459iter [00:14, 35.29iter/s]Best score: 0.2932: : 459iter [00:14, 35.29iter/s]Best score: 0.2932: : 460iter [00:14, 35.29iter/s]Best score: 0.2932: : 461iter [00:14, 35.29iter/s]Best score: 0.2932: : 462iter [00:14, 35.29iter/s]Best score: 0.2932: : 463iter [00:14, 36.38iter/s]Best score: 0.2932: : 463iter [00:14, 36.38iter/s]Best score: 0.2932: : 464iter [00:14, 36.38iter/s]Best score: 0.2932: : 465iter [00:14, 36.38iter/s]Best score: 0.2932: : 466iter [00:14, 36.38iter/s]Best score: 0.2932: : 467iter [00:14, 36.38iter/s]Best score: 0.2932: : 468iter [00:14, 37.71iter/s]Best score: 0.2932: : 468iter [00:14, 37.71iter/s]Best score: 0.2897: : 469iter [00:14, 37.71iter/s]Best score: 0.2897: : 470iter [00:14, 37.71iter/s]Best score: 0.2897: : 471iter [00:14, 37.71iter/s]Best score: 0.2897: : 472iter [00:14, 38.22iter/s]Best score: 0.2897: : 472iter [00:14, 38.22iter/s]Best score: 0.2897: : 473iter [00:14, 38.22iter/s]Best score: 0.2897: : 474iter [00:14, 38.22iter/s]Best score: 0.2897: : 475iter [00:14, 38.22iter/s]Best score: 0.2897: : 476iter [00:14, 38.22iter/s]Best score: 0.2897: : 477iter [00:14, 38.91iter/s]Best score: 0.2897: : 477iter [00:14, 38.91iter/s]Best score: 0.2897: : 478iter [00:14, 38.91iter/s]Best score: 0.2844: : 479iter [00:14, 38.91iter/s]Best score: 0.2844: : 480iter [00:14, 38.91iter/s]Best score: 0.2844: : 481iter [00:14, 38.94iter/s]Best score: 0.2844: : 481iter [00:14, 38.94iter/s]Best score: 0.2844: : 482iter [00:14, 38.94iter/s]Best score: 0.2844: : 483iter [00:14, 38.94iter/s]Best score: 0.2844: : 484iter [00:14, 38.94iter/s]Best score: 0.2844: : 485iter [00:14, 38.94iter/s]Best score: 0.2844: : 486iter [00:14, 39.49iter/s]Best score: 0.2844: : 486iter [00:14, 39.49iter/s]Best score: 0.2844: : 487iter [00:14, 39.49iter/s]Best score: 0.2844: : 488iter [00:14, 39.49iter/s]Best score: 0.2836: : 489iter [00:14, 39.49iter/s]Best score: 0.2836: : 490iter [00:14, 39.57iter/s]Best score: 0.2836: : 490iter [00:14, 39.57iter/s]Best score: 0.2836: : 491iter [00:14, 39.57iter/s]Best score: 0.2836: : 492iter [00:14, 39.57iter/s]Best score: 0.2836: : 493iter [00:14, 39.57iter/s]Best score: 0.2836: : 494iter [00:14, 39.57iter/s]Best score: 0.2836: : 495iter [00:14, 39.94iter/s]Best score: 0.2836: : 495iter [00:14, 39.94iter/s]Best score: 0.2836: : 496iter [00:14, 39.94iter/s]Best score: 0.2836: : 497iter [00:15, 39.94iter/s]Best score: 0.2836: : 498iter [00:15, 39.94iter/s]
[ITER 500] Best: 0.2801
[PARAMS] Current best parameters:
  fx: 522.0898
  fy: 521.9477
  px: 1001.5175
  py: 767.9261
  k1: -0.01231883
  k2: 0.03239889
  k3: -0.01478918
  k4: 0.00149533
  p1: 0.00690149
  p2: -0.00915933
  p3: -0.00816712
  p4: 0.00284377
  s1: 0.0151
  s2: -0.0020
  s3: 0.0002
  s4: -0.0030
  tilt_x: -0.0138
  tilt_y: -0.0055
  scale: 3.0199
  rotation: -0.3834
Best score: 0.2801: : 499iter [00:15, 39.94iter/s]Best score: 0.2801: : 500iter [00:15, 39.86iter/s]Best score: 0.2801: : 500iter [00:15, 39.86iter/s]Best score: 0.2801: : 501iter [00:15, 39.86iter/s]Best score: 0.2801: : 502iter [00:15, 39.86iter/s]Best score: 0.2801: : 503iter [00:15, 39.86iter/s]Best score: 0.2801: : 504iter [00:15, 33.30iter/s]Best score: 0.2801: : 504iter [00:15, 33.30iter/s]Best score: 0.2801: : 505iter [00:15, 33.30iter/s]Best score: 0.2801: : 506iter [00:15, 33.30iter/s]Best score: 0.2801: : 507iter [00:15, 33.30iter/s]Best score: 0.2801: : 508iter [00:15, 33.30iter/s]Best score: 0.2801: : 509iter [00:15, 35.43iter/s]Best score: 0.2745: : 509iter [00:15, 35.43iter/s]Best score: 0.2745: : 510iter [00:15, 35.43iter/s]Best score: 0.2745: : 511iter [00:15, 35.43iter/s]Best score: 0.2745: : 512iter [00:15, 35.43iter/s]Best score: 0.2745: : 513iter [00:15, 36.52iter/s]Best score: 0.2745: : 513iter [00:15, 36.52iter/s]Best score: 0.2745: : 514iter [00:15, 36.52iter/s]Best score: 0.2745: : 515iter [00:15, 36.52iter/s]Best score: 0.2745: : 516iter [00:15, 36.52iter/s]Best score: 0.2745: : 517iter [00:15, 36.52iter/s]Best score: 0.2745: : 518iter [00:15, 37.87iter/s]Best score: 0.2745: : 518iter [00:15, 37.87iter/s]Best score: 0.2745: : 519iter [00:15, 37.87iter/s]Best score: 0.2745: : 520iter [00:15, 37.87iter/s]Best score: 0.2745: : 521iter [00:15, 37.87iter/s]Best score: 0.2745: : 522iter [00:15, 38.41iter/s]Best score: 0.2745: : 522iter [00:15, 38.41iter/s]Best score: 0.2745: : 523iter [00:15, 38.41iter/s]Best score: 0.2745: : 524iter [00:15, 38.41iter/s]Best score: 0.2745: : 525iter [00:15, 38.41iter/s]Best score: 0.2745: : 526iter [00:15, 38.41iter/s]Best score: 0.2745: : 527iter [00:15, 39.08iter/s]Best score: 0.2745: : 527iter [00:15, 39.08iter/s]Best score: 0.2745: : 528iter [00:15, 39.08iter/s]Best score: 0.2725: : 529iter [00:15, 39.08iter/s]Best score: 0.2725: : 530iter [00:15, 39.08iter/s]Best score: 0.2725: : 531iter [00:15, 39.23iter/s]Best score: 0.2725: : 531iter [00:15, 39.23iter/s]Best score: 0.2725: : 532iter [00:15, 39.23iter/s]Best score: 0.2725: : 533iter [00:15, 39.23iter/s]Best score: 0.2725: : 534iter [00:16, 39.23iter/s]Best score: 0.2725: : 535iter [00:16, 39.23iter/s]Best score: 0.2725: : 536iter [00:16, 39.61iter/s]Best score: 0.2725: : 536iter [00:16, 39.61iter/s]Best score: 0.2725: : 537iter [00:16, 39.61iter/s]Best score: 0.2725: : 538iter [00:16, 39.61iter/s]Best score: 0.2717: : 539iter [00:16, 39.61iter/s]Best score: 0.2717: : 540iter [00:16, 39.55iter/s]Best score: 0.2717: : 540iter [00:16, 39.55iter/s]Best score: 0.2717: : 541iter [00:16, 39.55iter/s]Best score: 0.2717: : 542iter [00:16, 39.55iter/s]Best score: 0.2717: : 543iter [00:16, 39.55iter/s]Best score: 0.2717: : 544iter [00:16, 39.55iter/s]Best score: 0.2717: : 545iter [00:16, 39.96iter/s]Best score: 0.2717: : 545iter [00:16, 39.96iter/s]Best score: 0.2717: : 546iter [00:16, 39.96iter/s]Best score: 0.2717: : 547iter [00:16, 39.96iter/s]Best score: 0.2717: : 548iter [00:16, 39.96iter/s]
[ITER 550] Best: 0.2686
[PARAMS] Current best parameters:
  fx: 522.2224
  fy: 522.1811
  px: 999.5968
  py: 767.5969
  k1: -0.01134511
  k2: 0.03019377
  k3: -0.01333651
  k4: 0.00115427
  p1: 0.00513621
  p2: -0.01074368
  p3: -0.00491306
  p4: 0.00160006
  s1: 0.0111
  s2: -0.0003
  s3: -0.0014
  s4: -0.0020
  tilt_x: -0.0116
  tilt_y: 0.0010
  scale: 3.0200
  rotation: -0.3801
Best score: 0.2686: : 549iter [00:16, 39.96iter/s]Best score: 0.2686: : 550iter [00:16, 39.89iter/s]Best score: 0.2686: : 550iter [00:16, 39.89iter/s]Best score: 0.2686: : 551iter [00:16, 39.89iter/s]Best score: 0.2686: : 552iter [00:16, 39.89iter/s]Best score: 0.2686: : 553iter [00:16, 39.89iter/s]Best score: 0.2686: : 554iter [00:16, 33.31iter/s]Best score: 0.2686: : 554iter [00:16, 33.31iter/s]Best score: 0.2686: : 555iter [00:16, 33.31iter/s]Best score: 0.2686: : 556iter [00:16, 33.31iter/s]Best score: 0.2686: : 557iter [00:16, 33.31iter/s]Best score: 0.2686: : 558iter [00:16, 33.31iter/s]Best score: 0.2686: : 559iter [00:16, 35.41iter/s]Best score: 0.2667: : 559iter [00:16, 35.41iter/s]Best score: 0.2667: : 560iter [00:16, 35.41iter/s]Best score: 0.2667: : 561iter [00:16, 35.41iter/s]Best score: 0.2667: : 562iter [00:16, 35.41iter/s]Best score: 0.2667: : 563iter [00:16, 36.46iter/s]Best score: 0.2667: : 563iter [00:16, 36.46iter/s]Best score: 0.2667: : 564iter [00:16, 36.46iter/s]Best score: 0.2667: : 565iter [00:16, 36.46iter/s]Best score: 0.2667: : 566iter [00:16, 36.46iter/s]Best score: 0.2667: : 567iter [00:16, 36.46iter/s]Best score: 0.2667: : 568iter [00:16, 37.77iter/s]Best score: 0.2667: : 568iter [00:16, 37.77iter/s]Best score: 0.2645: : 569iter [00:16, 37.77iter/s]Best score: 0.2645: : 570iter [00:16, 37.77iter/s]Best score: 0.2645: : 571iter [00:17, 37.77iter/s]Best score: 0.2645: : 572iter [00:17, 38.33iter/s]Best score: 0.2645: : 572iter [00:17, 38.33iter/s]Best score: 0.2645: : 573iter [00:17, 38.33iter/s]Best score: 0.2645: : 574iter [00:17, 38.33iter/s]Best score: 0.2645: : 575iter [00:17, 38.33iter/s]Best score: 0.2645: : 576iter [00:17, 38.33iter/s]Best score: 0.2645: : 577iter [00:17, 39.01iter/s]Best score: 0.2645: : 577iter [00:17, 39.01iter/s]Best score: 0.2645: : 578iter [00:17, 39.01iter/s]Best score: 0.2628: : 579iter [00:17, 39.01iter/s]Best score: 0.2628: : 580iter [00:17, 39.01iter/s]Best score: 0.2628: : 581iter [00:17, 39.02iter/s]Best score: 0.2628: : 581iter [00:17, 39.02iter/s]Best score: 0.2628: : 582iter [00:17, 39.02iter/s]Best score: 0.2628: : 583iter [00:17, 39.02iter/s]Best score: 0.2628: : 584iter [00:17, 39.02iter/s]Best score: 0.2628: : 585iter [00:17, 39.02iter/s]Best score: 0.2628: : 586iter [00:17, 39.68iter/s]Best score: 0.2628: : 586iter [00:17, 39.68iter/s]Best score: 0.2628: : 587iter [00:17, 39.68iter/s]Best score: 0.2628: : 588iter [00:17, 39.68iter/s]Best score: 0.2628: : 589iter [00:17, 39.68iter/s]Best score: 0.2628: : 590iter [00:17, 39.68iter/s]Best score: 0.2628: : 591iter [00:17, 39.87iter/s]Best score: 0.2628: : 591iter [00:17, 39.87iter/s]Best score: 0.2628: : 592iter [00:17, 39.87iter/s]Best score: 0.2628: : 593iter [00:17, 39.87iter/s]Best score: 0.2628: : 594iter [00:17, 39.87iter/s]Best score: 0.2628: : 595iter [00:17, 39.87iter/s]Best score: 0.2628: : 596iter [00:17, 40.15iter/s]Best score: 0.2628: : 596iter [00:17, 40.15iter/s]Best score: 0.2628: : 597iter [00:17, 40.15iter/s]Best score: 0.2628: : 598iter [00:17, 40.15iter/s]
[ITER 600] Best: 0.2628
[PARAMS] Current best parameters:
  fx: 524.9369
  fy: 524.8499
  px: 999.6910
  py: 767.1368
  k1: -0.01311577
  k2: 0.02980363
  k3: -0.01391881
  k4: 0.00150099
  p1: 0.00341621
  p2: -0.00953369
  p3: -0.00482297
  p4: 0.00259645
  s1: 0.0093
  s2: -0.0010
  s3: -0.0006
  s4: -0.0028
  tilt_x: -0.0106
  tilt_y: 0.0029
  scale: 3.0195
  rotation: -0.3807
Best score: 0.2628: : 599iter [00:17, 40.15iter/s]Best score: 0.2628: : 600iter [00:17, 40.15iter/s]Best score: 0.2628: : 601iter [00:17, 33.76iter/s]Best score: 0.2628: : 601iter [00:17, 33.76iter/s]Best score: 0.2628: : 602iter [00:17, 33.76iter/s]Best score: 0.2628: : 603iter [00:17, 33.76iter/s]Best score: 0.2628: : 604iter [00:17, 33.76iter/s]Best score: 0.2628: : 605iter [00:17, 33.76iter/s]Best score: 0.2628: : 606iter [00:17, 35.66iter/s]Best score: 0.2628: : 606iter [00:17, 35.66iter/s]Best score: 0.2628: : 607iter [00:17, 35.66iter/s]Best score: 0.2628: : 608iter [00:17, 35.66iter/s]Best score: 0.2622: : 609iter [00:18, 35.66iter/s]Best score: 0.2622: : 610iter [00:18, 36.58iter/s]Best score: 0.2622: : 610iter [00:18, 36.58iter/s]Best score: 0.2622: : 611iter [00:18, 36.58iter/s]Best score: 0.2622: : 612iter [00:18, 36.58iter/s]Best score: 0.2622: : 613iter [00:18, 36.58iter/s]Best score: 0.2622: : 614iter [00:18, 36.58iter/s]Best score: 0.2622: : 615iter [00:18, 37.64iter/s]Best score: 0.2622: : 615iter [00:18, 37.64iter/s]Best score: 0.2622: : 616iter [00:18, 37.64iter/s]Best score: 0.2622: : 617iter [00:18, 37.64iter/s]Best score: 0.2622: : 618iter [00:18, 37.64iter/s]Best score: 0.2600: : 619iter [00:18, 37.64iter/s]Best score: 0.2600: : 620iter [00:18, 38.24iter/s]Best score: 0.2600: : 620iter [00:18, 38.24iter/s]Best score: 0.2600: : 621iter [00:18, 38.24iter/s]Best score: 0.2600: : 622iter [00:18, 38.24iter/s]Best score: 0.2600: : 623iter [00:18, 38.24iter/s]Best score: 0.2600: : 624iter [00:18, 38.24iter/s]Best score: 0.2600: : 625iter [00:18, 38.89iter/s]Best score: 0.2600: : 625iter [00:18, 38.89iter/s]Best score: 0.2600: : 626iter [00:18, 38.89iter/s]Best score: 0.2600: : 627iter [00:18, 38.89iter/s]Best score: 0.2600: : 628iter [00:18, 38.89iter/s]Best score: 0.2564: : 629iter [00:18, 38.89iter/s]Best score: 0.2564: : 630iter [00:18, 39.16iter/s]Best score: 0.2564: : 630iter [00:18, 39.16iter/s]Best score: 0.2564: : 631iter [00:18, 39.16iter/s]Best score: 0.2564: : 632iter [00:18, 39.16iter/s]Best score: 0.2564: : 633iter [00:18, 39.16iter/s]Best score: 0.2564: : 634iter [00:18, 39.16iter/s]Best score: 0.2564: : 635iter [00:18, 39.68iter/s]Best score: 0.2564: : 635iter [00:18, 39.68iter/s]Best score: 0.2564: : 636iter [00:18, 39.68iter/s]Best score: 0.2564: : 637iter [00:18, 39.68iter/s]Best score: 0.2564: : 638iter [00:18, 39.68iter/s]Best score: 0.2559: : 639iter [00:18, 39.68iter/s]Best score: 0.2559: : 640iter [00:18, 39.63iter/s]Best score: 0.2559: : 640iter [00:18, 39.63iter/s]Best score: 0.2559: : 641iter [00:18, 39.63iter/s]Best score: 0.2559: : 642iter [00:18, 39.63iter/s]Best score: 0.2559: : 643iter [00:18, 39.63iter/s]Best score: 0.2559: : 644iter [00:18, 39.63iter/s]Best score: 0.2559: : 645iter [00:18, 39.93iter/s]Best score: 0.2559: : 645iter [00:18, 39.93iter/s]Best score: 0.2559: : 646iter [00:18, 39.93iter/s]Best score: 0.2559: : 647iter [00:18, 39.93iter/s]Best score: 0.2559: : 648iter [00:18, 39.93iter/s]
[ITER 650] Best: 0.2551
[PARAMS] Current best parameters:
  fx: 528.4880
  fy: 528.4377
  px: 998.1464
  py: 765.4625
  k1: -0.01158529
  k2: 0.02426063
  k3: -0.01175197
  k4: 0.00126978
  p1: 0.00190055
  p2: -0.01018904
  p3: -0.00315644
  p4: 0.00229242
  s1: 0.0077
  s2: -0.0012
  s3: -0.0031
  s4: -0.0018
  tilt_x: -0.0158
  tilt_y: 0.0091
  scale: 3.0085
  rotation: -0.3735
Best score: 0.2551: : 649iter [00:19, 39.93iter/s]Best score: 0.2551: : 650iter [00:19, 39.94iter/s]Best score: 0.2551: : 650iter [00:19, 39.94iter/s]Best score: 0.2551: : 651iter [00:19, 39.94iter/s]Best score: 0.2551: : 652iter [00:19, 39.94iter/s]Best score: 0.2551: : 653iter [00:19, 39.94iter/s]Best score: 0.2551: : 654iter [00:19, 39.94iter/s]Best score: 0.2551: : 655iter [00:19, 33.86iter/s]Best score: 0.2551: : 655iter [00:19, 33.86iter/s]Best score: 0.2551: : 656iter [00:19, 33.86iter/s]Best score: 0.2551: : 657iter [00:19, 33.86iter/s]Best score: 0.2551: : 658iter [00:19, 33.86iter/s]Best score: 0.2523: : 659iter [00:19, 33.86iter/s]Best score: 0.2523: : 660iter [00:19, 35.41iter/s]Best score: 0.2523: : 660iter [00:19, 35.41iter/s]Best score: 0.2523: : 661iter [00:19, 35.41iter/s]Best score: 0.2523: : 662iter [00:19, 35.41iter/s]Best score: 0.2523: : 663iter [00:19, 35.41iter/s]Best score: 0.2523: : 664iter [00:19, 35.41iter/s]Best score: 0.2523: : 665iter [00:19, 36.90iter/s]Best score: 0.2523: : 665iter [00:19, 36.90iter/s]Best score: 0.2523: : 666iter [00:19, 36.90iter/s]Best score: 0.2523: : 667iter [00:19, 36.90iter/s]Best score: 0.2523: : 668iter [00:19, 36.90iter/s]Best score: 0.2523: : 669iter [00:19, 36.90iter/s]Best score: 0.2523: : 670iter [00:19, 37.87iter/s]Best score: 0.2523: : 670iter [00:19, 37.87iter/s]Best score: 0.2523: : 671iter [00:19, 37.87iter/s]Best score: 0.2523: : 672iter [00:19, 37.87iter/s]Best score: 0.2523: : 673iter [00:19, 37.87iter/s]Best score: 0.2523: : 674iter [00:19, 37.87iter/s]Best score: 0.2523: : 675iter [00:19, 38.64iter/s]Best score: 0.2523: : 675iter [00:19, 38.64iter/s]Best score: 0.2523: : 676iter [00:19, 38.64iter/s]Best score: 0.2523: : 677iter [00:19, 38.64iter/s]Best score: 0.2523: : 678iter [00:19, 38.64iter/s]Best score: 0.2523: : 679iter [00:19, 38.64iter/s]Best score: 0.2523: : 680iter [00:19, 38.82iter/s]Best score: 0.2523: : 680iter [00:19, 38.82iter/s]Best score: 0.2523: : 681iter [00:19, 38.82iter/s]Best score: 0.2523: : 682iter [00:19, 38.82iter/s]Best score: 0.2523: : 683iter [00:20, 38.82iter/s]Best score: 0.2523: : 684iter [00:20, 31.69iter/s]Best score: 0.2523: : 684iter [00:20, 31.69iter/s]Best score: 0.2523: : 685iter [00:20, 31.69iter/s]Best score: 0.2523: : 686iter [00:20, 31.69iter/s]Best score: 0.2523: : 687iter [00:20, 31.69iter/s]Best score: 0.2523: : 688iter [00:20, 31.69iter/s]Best score: 0.2523: : 689iter [00:20, 33.99iter/s]Best score: 0.2523: : 689iter [00:20, 33.99iter/s]Best score: 0.2523: : 690iter [00:20, 33.99iter/s]Best score: 0.2523: : 691iter [00:20, 33.99iter/s]Best score: 0.2523: : 692iter [00:20, 33.99iter/s]Best score: 0.2523: : 693iter [00:20, 35.26iter/s]Best score: 0.2523: : 693iter [00:20, 35.26iter/s]Best score: 0.2523: : 694iter [00:20, 35.26iter/s]Best score: 0.2523: : 695iter [00:20, 35.26iter/s]Best score: 0.2523: : 696iter [00:20, 35.26iter/s]Best score: 0.2523: : 697iter [00:20, 35.26iter/s]Best score: 0.2523: : 698iter [00:20, 36.77iter/s]Best score: 0.2523: : 698iter [00:20, 36.77iter/s]
[ITER 700] Best: 0.2512
[PARAMS] Current best parameters:
  fx: 527.8233
  fy: 527.8210
  px: 997.8998
  py: 765.5532
  k1: -0.01093000
  k2: 0.02328180
  k3: -0.01066237
  k4: 0.00094188
  p1: 0.00195367
  p2: -0.01111164
  p3: -0.00207591
  p4: 0.00166141
  s1: 0.0073
  s2: -0.0005
  s3: -0.0039
  s4: -0.0015
  tilt_x: -0.0155
  tilt_y: 0.0084
  scale: 3.0095
  rotation: -0.3748
Best score: 0.2512: : 699iter [00:20, 36.77iter/s]Best score: 0.2512: : 700iter [00:20, 36.77iter/s]Best score: 0.2512: : 701iter [00:20, 36.77iter/s]Best score: 0.2512: : 702iter [00:20, 31.44iter/s]Best score: 0.2512: : 702iter [00:20, 31.44iter/s]Best score: 0.2512: : 703iter [00:20, 31.44iter/s]Best score: 0.2512: : 704iter [00:20, 31.44iter/s]Best score: 0.2512: : 705iter [00:20, 31.44iter/s]Best score: 0.2512: : 706iter [00:20, 33.39iter/s]Best score: 0.2512: : 706iter [00:20, 33.39iter/s]Best score: 0.2512: : 707iter [00:20, 33.39iter/s]Best score: 0.2512: : 708iter [00:20, 33.39iter/s]Best score: 0.2512: : 709iter [00:20, 33.39iter/s]Best score: 0.2512: : 710iter [00:20, 34.96iter/s]Best score: 0.2512: : 710iter [00:20, 34.96iter/s]Best score: 0.2512: : 711iter [00:20, 34.96iter/s]Best score: 0.2512: : 712iter [00:20, 34.96iter/s]Best score: 0.2512: : 713iter [00:20, 34.96iter/s]Best score: 0.2512: : 714iter [00:20, 34.96iter/s]Best score: 0.2512: : 715iter [00:20, 36.76iter/s]Best score: 0.2512: : 715iter [00:20, 36.76iter/s]Best score: 0.2512: : 716iter [00:20, 36.76iter/s]Best score: 0.2512: : 717iter [00:20, 36.76iter/s]Best score: 0.2512: : 718iter [00:20, 36.76iter/s]Best score: 0.2504: : 719iter [00:21, 36.76iter/s]Best score: 0.2504: : 720iter [00:21, 37.69iter/s]Best score: 0.2504: : 720iter [00:21, 37.69iter/s]Best score: 0.2504: : 721iter [00:21, 37.69iter/s]Best score: 0.2504: : 722iter [00:21, 37.69iter/s]Best score: 0.2504: : 723iter [00:21, 37.69iter/s]Best score: 0.2504: : 724iter [00:21, 37.69iter/s]Best score: 0.2504: : 725iter [00:21, 38.60iter/s]Best score: 0.2504: : 725iter [00:21, 38.60iter/s]Best score: 0.2504: : 726iter [00:21, 38.60iter/s]Best score: 0.2504: : 727iter [00:21, 38.60iter/s]Best score: 0.2504: : 728iter [00:21, 38.60iter/s]Best score: 0.2497: : 729iter [00:21, 38.60iter/s]Best score: 0.2497: : 730iter [00:21, 38.91iter/s]Best score: 0.2497: : 730iter [00:21, 38.91iter/s]Best score: 0.2497: : 731iter [00:21, 38.91iter/s]Best score: 0.2497: : 732iter [00:21, 38.91iter/s]Best score: 0.2497: : 733iter [00:21, 38.91iter/s]Best score: 0.2497: : 734iter [00:21, 38.91iter/s]Best score: 0.2497: : 735iter [00:21, 39.41iter/s]Best score: 0.2497: : 735iter [00:21, 39.41iter/s]Best score: 0.2497: : 736iter [00:21, 39.41iter/s]Best score: 0.2497: : 737iter [00:21, 39.41iter/s]Best score: 0.2497: : 738iter [00:21, 39.41iter/s]Best score: 0.2497: : 739iter [00:21, 39.41iter/s]Best score: 0.2497: : 740iter [00:21, 39.54iter/s]Best score: 0.2497: : 740iter [00:21, 39.54iter/s]Best score: 0.2497: : 741iter [00:21, 39.54iter/s]Best score: 0.2497: : 742iter [00:21, 39.54iter/s]Best score: 0.2497: : 743iter [00:21, 39.54iter/s]Best score: 0.2497: : 744iter [00:21, 39.54iter/s]Best score: 0.2497: : 745iter [00:21, 39.82iter/s]Best score: 0.2497: : 745iter [00:21, 39.82iter/s]Best score: 0.2497: : 746iter [00:21, 39.82iter/s]Best score: 0.2497: : 747iter [00:21, 39.82iter/s]Best score: 0.2497: : 748iter [00:21, 39.82iter/s]
[ITER 750] Best: 0.2497
[PARAMS] Current best parameters:
  fx: 527.3546
  fy: 527.3975
  px: 997.8902
  py: 765.3913
  k1: -0.01102749
  k2: 0.02445591
  k3: -0.01159590
  k4: 0.00117484
  p1: 0.00262434
  p2: -0.01104746
  p3: -0.00243888
  p4: 0.00113626
  s1: 0.0072
  s2: 0.0002
  s3: -0.0040
  s4: -0.0011
  tilt_x: -0.0177
  tilt_y: 0.0085
  scale: 3.0106
  rotation: -0.3782
Best score: 0.2497: : 749iter [00:21, 39.82iter/s]Best score: 0.2497: : 750iter [00:21, 39.85iter/s]Best score: 0.2497: : 750iter [00:21, 39.85iter/s]Best score: 0.2497: : 751iter [00:21, 39.85iter/s]Best score: 0.2497: : 752iter [00:21, 39.85iter/s]Best score: 0.2497: : 753iter [00:21, 39.85iter/s]Best score: 0.2497: : 754iter [00:21, 33.42iter/s]Best score: 0.2497: : 754iter [00:21, 33.42iter/s]Best score: 0.2497: : 755iter [00:21, 33.42iter/s]Best score: 0.2497: : 756iter [00:22, 33.42iter/s]Best score: 0.2497: : 757iter [00:22, 33.42iter/s]Best score: 0.2497: : 758iter [00:22, 33.42iter/s]Best score: 0.2497: : 759iter [00:22, 35.29iter/s]Best score: 0.2497: : 759iter [00:22, 35.29iter/s]Best score: 0.2497: : 760iter [00:22, 35.29iter/s]Best score: 0.2497: : 761iter [00:22, 35.29iter/s]Best score: 0.2497: : 762iter [00:22, 35.29iter/s]Best score: 0.2497: : 763iter [00:22, 36.13iter/s]Best score: 0.2497: : 763iter [00:22, 36.13iter/s]Best score: 0.2497: : 764iter [00:22, 36.13iter/s]Best score: 0.2497: : 765iter [00:22, 36.13iter/s]Best score: 0.2497: : 766iter [00:22, 36.13iter/s]Best score: 0.2497: : 767iter [00:22, 36.13iter/s]Best score: 0.2497: : 768iter [00:22, 37.37iter/s]Best score: 0.2497: : 768iter [00:22, 37.37iter/s]Best score: 0.2494: : 769iter [00:22, 37.37iter/s]Best score: 0.2494: : 770iter [00:22, 37.37iter/s]Best score: 0.2494: : 771iter [00:22, 37.37iter/s]Best score: 0.2494: : 772iter [00:22, 37.91iter/s]Best score: 0.2494: : 772iter [00:22, 37.91iter/s]Best score: 0.2494: : 773iter [00:22, 37.91iter/s]Best score: 0.2494: : 774iter [00:22, 37.91iter/s]Best score: 0.2494: : 775iter [00:22, 37.91iter/s]Best score: 0.2494: : 776iter [00:22, 37.91iter/s]Best score: 0.2494: : 777iter [00:22, 38.72iter/s]Best score: 0.2494: : 777iter [00:22, 38.72iter/s]Best score: 0.2494: : 778iter [00:22, 38.72iter/s]Best score: 0.2494: : 779iter [00:22, 38.72iter/s]Best score: 0.2494: : 780iter [00:22, 38.72iter/s]Best score: 0.2494: : 781iter [00:22, 39.00iter/s]Best score: 0.2494: : 781iter [00:22, 39.00iter/s]Best score: 0.2494: : 782iter [00:22, 39.00iter/s]Best score: 0.2494: : 783iter [00:22, 39.00iter/s]Best score: 0.2494: : 784iter [00:22, 39.00iter/s]Best score: 0.2494: : 785iter [00:22, 39.25iter/s]Best score: 0.2494: : 785iter [00:22, 39.25iter/s]Best score: 0.2494: : 786iter [00:22, 39.25iter/s]Best score: 0.2494: : 787iter [00:22, 39.25iter/s]Best score: 0.2494: : 788iter [00:22, 39.25iter/s]Best score: 0.2488: : 789iter [00:22, 39.25iter/s]Best score: 0.2488: : 790iter [00:22, 39.40iter/s]Best score: 0.2488: : 790iter [00:22, 39.40iter/s]Best score: 0.2488: : 791iter [00:22, 39.40iter/s]Best score: 0.2488: : 792iter [00:22, 39.40iter/s]Best score: 0.2488: : 793iter [00:22, 39.40iter/s]Best score: 0.2488: : 794iter [00:22, 39.47iter/s]Best score: 0.2488: : 794iter [00:22, 39.47iter/s]Best score: 0.2488: : 795iter [00:22, 39.47iter/s]Best score: 0.2488: : 796iter [00:23, 39.47iter/s]Best score: 0.2488: : 797iter [00:23, 39.47iter/s]Best score: 0.2488: : 798iter [00:23, 39.47iter/s]Best score: 0.2488: : 799iter [00:23, 39.77iter/s]
[ITER 800] Best: 0.2488
[PARAMS] Current best parameters:
  fx: 527.6145
  fy: 527.6254
  px: 997.6600
  py: 765.3927
  k1: -0.01044765
  k2: 0.02323731
  k3: -0.01096063
  k4: 0.00106723
  p1: 0.00291452
  p2: -0.01069307
  p3: -0.00257322
  p4: 0.00110753
  s1: 0.0071
  s2: 0.0000
  s3: -0.0041
  s4: -0.0011
  tilt_x: -0.0183
  tilt_y: 0.0103
  scale: 3.0098
  rotation: -0.3832
Best score: 0.2488: : 799iter [00:23, 39.77iter/s]Best score: 0.2488: : 800iter [00:23, 39.77iter/s]Best score: 0.2488: : 801iter [00:23, 39.77iter/s]Best score: 0.2488: : 802iter [00:23, 39.77iter/s]Best score: 0.2488: : 803iter [00:23, 32.92iter/s]Best score: 0.2488: : 803iter [00:23, 32.92iter/s]Best score: 0.2488: : 804iter [00:23, 32.92iter/s]Best score: 0.2488: : 805iter [00:23, 32.92iter/s]Best score: 0.2488: : 806iter [00:23, 32.92iter/s]Best score: 0.2488: : 807iter [00:23, 32.92iter/s]Best score: 0.2488: : 808iter [00:23, 35.13iter/s]Best score: 0.2488: : 808iter [00:23, 35.13iter/s]Best score: 0.2488: : 809iter [00:23, 35.13iter/s]Best score: 0.2488: : 810iter [00:23, 35.13iter/s]Best score: 0.2488: : 811iter [00:23, 35.13iter/s]Best score: 0.2488: : 812iter [00:23, 35.13iter/s]Best score: 0.2488: : 813iter [00:23, 36.60iter/s]Best score: 0.2488: : 813iter [00:23, 36.60iter/s]Best score: 0.2488: : 814iter [00:23, 36.60iter/s]Best score: 0.2488: : 815iter [00:23, 36.60iter/s]Best score: 0.2488: : 816iter [00:23, 36.60iter/s]Best score: 0.2488: : 817iter [00:23, 36.60iter/s]Best score: 0.2488: : 818iter [00:23, 37.82iter/s]Best score: 0.2488: : 818iter [00:23, 37.82iter/s]Best score: 0.2488: : 819iter [00:23, 37.82iter/s]Best score: 0.2488: : 820iter [00:23, 37.82iter/s]Best score: 0.2488: : 821iter [00:23, 37.82iter/s]Best score: 0.2488: : 822iter [00:23, 38.09iter/s]Best score: 0.2488: : 822iter [00:23, 38.09iter/s]Best score: 0.2488: : 823iter [00:23, 38.09iter/s]Best score: 0.2488: : 824iter [00:23, 38.09iter/s]Best score: 0.2488: : 825iter [00:23, 38.09iter/s]Best score: 0.2488: : 826iter [00:23, 38.09iter/s]Best score: 0.2488: : 827iter [00:23, 38.76iter/s]Best score: 0.2488: : 827iter [00:23, 38.76iter/s]Best score: 0.2488: : 828iter [00:23, 38.76iter/s]Best score: 0.2488: : 829iter [00:23, 38.76iter/s]Best score: 0.2488: : 830iter [00:23, 38.76iter/s]Best score: 0.2488: : 831iter [00:23, 38.85iter/s]Best score: 0.2488: : 831iter [00:23, 38.85iter/s]Best score: 0.2488: : 832iter [00:23, 38.85iter/s]Best score: 0.2488: : 833iter [00:24, 38.85iter/s]Best score: 0.2488: : 834iter [00:24, 38.85iter/s]Best score: 0.2488: : 835iter [00:24, 39.15iter/s]Best score: 0.2488: : 835iter [00:24, 39.15iter/s]Best score: 0.2488: : 836iter [00:24, 39.15iter/s]Best score: 0.2488: : 837iter [00:24, 39.15iter/s]Best score: 0.2488: : 838iter [00:24, 39.15iter/s]Best score: 0.2488: : 839iter [00:24, 39.12iter/s]Best score: 0.2488: : 839iter [00:24, 39.12iter/s]Best score: 0.2488: : 840iter [00:24, 39.12iter/s]Best score: 0.2488: : 841iter [00:24, 39.12iter/s]Best score: 0.2488: : 842iter [00:24, 39.12iter/s]Best score: 0.2488: : 843iter [00:24, 39.32iter/s]Best score: 0.2488: : 843iter [00:24, 39.32iter/s]Best score: 0.2488: : 844iter [00:24, 39.32iter/s]Best score: 0.2488: : 845iter [00:24, 39.32iter/s]Best score: 0.2488: : 846iter [00:24, 39.32iter/s]Best score: 0.2488: : 847iter [00:24, 39.32iter/s]Best score: 0.2488: : 848iter [00:24, 39.88iter/s]Best score: 0.2488: : 848iter [00:24, 39.88iter/s]
[ITER 850] Best: 0.2485
[PARAMS] Current best parameters:
  fx: 526.6910
  fy: 526.6811
  px: 997.8023
  py: 765.7126
  k1: -0.01290267
  k2: 0.02839748
  k3: -0.01378993
  k4: 0.00159361
  p1: 0.00383377
  p2: -0.01003014
  p3: -0.00367594
  p4: 0.00125966
  s1: 0.0078
  s2: -0.0002
  s3: -0.0036
  s4: -0.0013
  tilt_x: -0.0185
  tilt_y: 0.0110
  scale: 3.0132
  rotation: -0.3878
Best score: 0.2485: : 849iter [00:24, 39.88iter/s]Best score: 0.2485: : 850iter [00:24, 39.88iter/s]Best score: 0.2485: : 851iter [00:24, 39.88iter/s]Best score: 0.2485: : 852iter [00:24, 32.72iter/s]Best score: 0.2485: : 852iter [00:24, 32.72iter/s]Best score: 0.2485: : 853iter [00:24, 32.72iter/s]Best score: 0.2485: : 854iter [00:24, 32.72iter/s]Best score: 0.2485: : 855iter [00:24, 32.72iter/s]Best score: 0.2485: : 856iter [00:24, 32.72iter/s]Best score: 0.2485: : 857iter [00:24, 34.96iter/s]Best score: 0.2485: : 857iter [00:24, 34.96iter/s]Best score: 0.2485: : 858iter [00:24, 34.96iter/s]Best score: 0.2485: : 859iter [00:24, 34.96iter/s]Best score: 0.2485: : 860iter [00:24, 34.96iter/s]Best score: 0.2485: : 861iter [00:24, 36.02iter/s]Best score: 0.2485: : 861iter [00:24, 36.02iter/s]Best score: 0.2485: : 862iter [00:24, 36.02iter/s]Best score: 0.2485: : 863iter [00:24, 36.02iter/s]Best score: 0.2485: : 864iter [00:24, 36.02iter/s]Best score: 0.2485: : 865iter [00:24, 36.02iter/s]Best score: 0.2485: : 866iter [00:24, 37.41iter/s]Best score: 0.2485: : 866iter [00:24, 37.41iter/s]Best score: 0.2485: : 867iter [00:24, 37.41iter/s]Best score: 0.2485: : 868iter [00:24, 37.41iter/s]Best score: 0.2485: : 869iter [00:24, 37.41iter/s]Best score: 0.2485: : 870iter [00:25, 37.41iter/s]Best score: 0.2485: : 871iter [00:25, 38.21iter/s]Best score: 0.2485: : 871iter [00:25, 38.21iter/s]Best score: 0.2485: : 872iter [00:25, 38.21iter/s]Best score: 0.2485: : 873iter [00:25, 38.21iter/s]Best score: 0.2485: : 874iter [00:25, 38.21iter/s]Best score: 0.2485: : 875iter [00:25, 38.21iter/s]Best score: 0.2485: : 876iter [00:25, 38.91iter/s]Best score: 0.2485: : 876iter [00:25, 38.91iter/s]Best score: 0.2485: : 877iter [00:25, 38.91iter/s]Best score: 0.2485: : 878iter [00:25, 38.91iter/s]Best score: 0.2485: : 879iter [00:25, 38.91iter/s]Best score: 0.2485: : 880iter [00:25, 39.11iter/s]Best score: 0.2485: : 880iter [00:25, 39.11iter/s]Best score: 0.2485: : 881iter [00:25, 39.11iter/s]Best score: 0.2485: : 882iter [00:25, 39.11iter/s]Best score: 0.2485: : 883iter [00:25, 39.11iter/s]Best score: 0.2485: : 884iter [00:25, 39.11iter/s]Best score: 0.2485: : 885iter [00:25, 39.50iter/s]Best score: 0.2485: : 885iter [00:25, 39.50iter/s]Best score: 0.2485: : 886iter [00:25, 39.50iter/s]Best score: 0.2485: : 887iter [00:25, 39.50iter/s]Best score: 0.2485: : 888iter [00:25, 39.50iter/s]Best score: 0.2485: : 889iter [00:25, 39.50iter/s]Best score: 0.2485: : 890iter [00:25, 39.49iter/s]Best score: 0.2485: : 890iter [00:25, 39.49iter/s]Best score: 0.2485: : 891iter [00:25, 39.49iter/s]Best score: 0.2485: : 892iter [00:25, 39.49iter/s]Best score: 0.2485: : 893iter [00:25, 39.49iter/s]Best score: 0.2485: : 894iter [00:25, 39.49iter/s]Best score: 0.2485: : 895iter [00:25, 39.84iter/s]Best score: 0.2485: : 895iter [00:25, 39.84iter/s]Best score: 0.2485: : 896iter [00:25, 39.84iter/s]Best score: 0.2485: : 897iter [00:25, 39.84iter/s]Best score: 0.2485: : 898iter [00:25, 39.84iter/s]
[ITER 900] Best: 0.2485
[PARAMS] Current best parameters:
  fx: 526.6910
  fy: 526.6811
  px: 997.8023
  py: 765.7126
  k1: -0.01290267
  k2: 0.02839748
  k3: -0.01378993
  k4: 0.00159361
  p1: 0.00383377
  p2: -0.01003014
  p3: -0.00367594
  p4: 0.00125966
  s1: 0.0078
  s2: -0.0002
  s3: -0.0036
  s4: -0.0013
  tilt_x: -0.0185
  tilt_y: 0.0110
  scale: 3.0132
  rotation: -0.3878
Best score: 0.2485: : 899iter [00:25, 39.84iter/s]Best score: 0.2485: : 900iter [00:25, 39.91iter/s]Best score: 0.2485: : 900iter [00:25, 39.91iter/s]Best score: 0.2485: : 901iter [00:25, 39.91iter/s]Best score: 0.2485: : 902iter [00:25, 39.91iter/s]Best score: 0.2485: : 903iter [00:25, 39.91iter/s]Best score: 0.2485: : 904iter [00:25, 33.46iter/s]Best score: 0.2485: : 904iter [00:25, 33.46iter/s]Best score: 0.2485: : 905iter [00:25, 33.46iter/s]Best score: 0.2485: : 906iter [00:25, 33.46iter/s]Best score: 0.2485: : 907iter [00:26, 33.46iter/s]Best score: 0.2485: : 908iter [00:26, 33.46iter/s]Best score: 0.2485: : 909iter [00:26, 35.37iter/s]Best score: 0.2485: : 909iter [00:26, 35.37iter/s]Best score: 0.2485: : 910iter [00:26, 35.37iter/s]Best score: 0.2485: : 911iter [00:26, 35.37iter/s]Best score: 0.2485: : 912iter [00:26, 35.37iter/s]Best score: 0.2485: : 913iter [00:26, 36.30iter/s]Best score: 0.2485: : 913iter [00:26, 36.30iter/s]Best score: 0.2485: : 914iter [00:26, 36.30iter/s]Best score: 0.2485: : 915iter [00:26, 36.30iter/s]Best score: 0.2485: : 916iter [00:26, 36.30iter/s]Best score: 0.2485: : 917iter [00:26, 36.30iter/s]Best score: 0.2485: : 918iter [00:26, 37.50iter/s]Best score: 0.2485: : 918iter [00:26, 37.50iter/s]Best score: 0.2484: : 919iter [00:26, 37.50iter/s]Best score: 0.2484: : 920iter [00:26, 37.50iter/s]Best score: 0.2484: : 921iter [00:26, 37.50iter/s]Best score: 0.2484: : 922iter [00:26, 37.71iter/s]Best score: 0.2484: : 922iter [00:26, 37.71iter/s]Best score: 0.2484: : 923iter [00:26, 37.71iter/s]Best score: 0.2484: : 924iter [00:26, 37.71iter/s]Best score: 0.2484: : 925iter [00:26, 37.71iter/s]Best score: 0.2484: : 926iter [00:26, 37.71iter/s]Best score: 0.2484: : 927iter [00:26, 38.63iter/s]Best score: 0.2484: : 927iter [00:26, 38.63iter/s]Best score: 0.2484: : 928iter [00:26, 38.63iter/s]Best score: 0.2483: : 929iter [00:26, 38.63iter/s]Best score: 0.2483: : 930iter [00:26, 38.63iter/s]Best score: 0.2483: : 931iter [00:26, 38.88iter/s]Best score: 0.2483: : 931iter [00:26, 38.88iter/s]Best score: 0.2483: : 932iter [00:26, 38.88iter/s]Best score: 0.2483: : 933iter [00:26, 38.88iter/s]Best score: 0.2483: : 934iter [00:26, 38.88iter/s]Best score: 0.2483: : 935iter [00:26, 38.88iter/s]Best score: 0.2483: : 936iter [00:26, 39.44iter/s]Best score: 0.2483: : 936iter [00:26, 39.44iter/s]Best score: 0.2483: : 937iter [00:26, 39.44iter/s]Best score: 0.2483: : 938iter [00:26, 39.44iter/s]Best score: 0.2480: : 939iter [00:26, 39.44iter/s]Best score: 0.2480: : 940iter [00:26, 39.36iter/s]Best score: 0.2480: : 940iter [00:26, 39.36iter/s]Best score: 0.2480: : 941iter [00:26, 39.36iter/s]Best score: 0.2480: : 942iter [00:26, 39.36iter/s]Best score: 0.2480: : 943iter [00:26, 39.36iter/s]Best score: 0.2480: : 944iter [00:26, 39.36iter/s]Best score: 0.2480: : 945iter [00:26, 39.60iter/s]Best score: 0.2480: : 945iter [00:26, 39.60iter/s]Best score: 0.2480: : 946iter [00:26, 39.60iter/s]Best score: 0.2480: : 947iter [00:27, 39.60iter/s]Best score: 0.2480: : 948iter [00:27, 39.60iter/s]
[ITER 950] Best: 0.2480
[PARAMS] Current best parameters:
  fx: 525.7815
  fy: 525.7886
  px: 997.5076
  py: 765.6737
  k1: -0.01569972
  k2: 0.03371105
  k3: -0.01668868
  k4: 0.00215615
  p1: 0.00363967
  p2: -0.01032512
  p3: -0.00368663
  p4: 0.00134953
  s1: 0.0075
  s2: -0.0001
  s3: -0.0037
  s4: -0.0012
  tilt_x: -0.0183
  tilt_y: 0.0117
  scale: 3.0183
  rotation: -0.3822
Best score: 0.2480: : 949iter [00:27, 39.60iter/s]Best score: 0.2480: : 950iter [00:27, 39.59iter/s]Best score: 0.2480: : 950iter [00:27, 39.59iter/s]Best score: 0.2480: : 951iter [00:27, 39.59iter/s]Best score: 0.2480: : 952iter [00:27, 39.59iter/s]Best score: 0.2480: : 953iter [00:27, 39.59iter/s]Best score: 0.2480: : 954iter [00:27, 33.07iter/s]Best score: 0.2480: : 954iter [00:27, 33.07iter/s]Best score: 0.2480: : 955iter [00:27, 33.07iter/s]Best score: 0.2480: : 956iter [00:27, 33.07iter/s]Best score: 0.2480: : 957iter [00:27, 33.07iter/s]Best score: 0.2480: : 958iter [00:27, 33.07iter/s]Best score: 0.2480: : 959iter [00:27, 35.30iter/s]Best score: 0.2477: : 959iter [00:27, 35.30iter/s]Best score: 0.2477: : 960iter [00:27, 35.30iter/s]Best score: 0.2477: : 961iter [00:27, 35.30iter/s]Best score: 0.2477: : 962iter [00:27, 35.30iter/s]Best score: 0.2477: : 963iter [00:27, 36.36iter/s]Best score: 0.2477: : 963iter [00:27, 36.36iter/s]Best score: 0.2477: : 964iter [00:27, 36.36iter/s]Best score: 0.2477: : 965iter [00:27, 36.36iter/s]Best score: 0.2477: : 966iter [00:27, 36.36iter/s]Best score: 0.2477: : 967iter [00:27, 36.36iter/s]Best score: 0.2477: : 968iter [00:27, 37.56iter/s]Best score: 0.2477: : 968iter [00:27, 37.56iter/s]Best score: 0.2477: : 969iter [00:27, 37.56iter/s]Best score: 0.2477: : 970iter [00:27, 37.56iter/s]Best score: 0.2477: : 971iter [00:27, 37.56iter/s]Best score: 0.2477: : 972iter [00:27, 38.00iter/s]Best score: 0.2477: : 972iter [00:27, 38.00iter/s]Best score: 0.2477: : 973iter [00:27, 38.00iter/s]Best score: 0.2477: : 974iter [00:27, 38.00iter/s]Best score: 0.2477: : 975iter [00:27, 38.00iter/s]Best score: 0.2477: : 976iter [00:27, 38.00iter/s]Best score: 0.2477: : 977iter [00:27, 38.82iter/s]Best score: 0.2477: : 977iter [00:27, 38.82iter/s]Best score: 0.2477: : 978iter [00:27, 38.82iter/s]Best score: 0.2476: : 979iter [00:27, 38.82iter/s]Best score: 0.2476: : 980iter [00:27, 38.82iter/s]Best score: 0.2476: : 981iter [00:27, 38.98iter/s]Best score: 0.2476: : 981iter [00:27, 38.98iter/s]Best score: 0.2476: : 982iter [00:27, 38.98iter/s]Best score: 0.2476: : 983iter [00:27, 38.98iter/s]Best score: 0.2476: : 984iter [00:28, 38.98iter/s]Best score: 0.2476: : 985iter [00:28, 38.98iter/s]Best score: 0.2476: : 986iter [00:28, 39.50iter/s]Best score: 0.2476: : 986iter [00:28, 39.50iter/s]Best score: 0.2476: : 987iter [00:28, 39.50iter/s]Best score: 0.2476: : 988iter [00:28, 39.50iter/s]Best score: 0.2476: : 989iter [00:28, 39.50iter/s]Best score: 0.2476: : 990iter [00:28, 39.51iter/s]Best score: 0.2476: : 990iter [00:28, 39.51iter/s]Best score: 0.2476: : 991iter [00:28, 39.51iter/s]Best score: 0.2476: : 992iter [00:28, 39.51iter/s]Best score: 0.2476: : 993iter [00:28, 39.51iter/s]Best score: 0.2476: : 994iter [00:28, 39.51iter/s]Best score: 0.2476: : 995iter [00:28, 39.80iter/s]Best score: 0.2476: : 995iter [00:28, 39.80iter/s]Best score: 0.2476: : 996iter [00:28, 39.80iter/s]Best score: 0.2476: : 997iter [00:28, 39.80iter/s]Best score: 0.2476: : 998iter [00:28, 39.80iter/s]
[ITER 1000] Best: 0.2475
[PARAMS] Current best parameters:
  fx: 525.8769
  fy: 525.8728
  px: 997.3366
  py: 765.6598
  k1: -0.01508031
  k2: 0.03331287
  k3: -0.01671965
  k4: 0.00219106
  p1: 0.00378814
  p2: -0.01000016
  p3: -0.00336110
  p4: 0.00112515
  s1: 0.0067
  s2: 0.0001
  s3: -0.0042
  s4: -0.0010
  tilt_x: -0.0187
  tilt_y: 0.0132
  scale: 3.0158
  rotation: -0.3835
Best score: 0.2475: : 999iter [00:28, 39.80iter/s]Best score: 0.2475: : 1000iter [00:28, 39.78iter/s]Best score: 0.2475: : 1000iter [00:28, 39.78iter/s]Best score: 0.2475: : 1001iter [00:28, 39.78iter/s]Best score: 0.2475: : 1002iter [00:28, 39.78iter/s]Best score: 0.2475: : 1003iter [00:28, 39.78iter/s]Best score: 0.2475: : 1004iter [00:28, 33.20iter/s]Best score: 0.2475: : 1004iter [00:28, 33.20iter/s]Best score: 0.2475: : 1005iter [00:28, 33.20iter/s]Best score: 0.2475: : 1006iter [00:28, 33.20iter/s]Best score: 0.2475: : 1007iter [00:28, 33.20iter/s]Best score: 0.2475: : 1008iter [00:28, 28.06iter/s]Best score: 0.2475: : 1008iter [00:28, 28.06iter/s]Best score: 0.2475: : 1009iter [00:28, 28.06iter/s]Best score: 0.2475: : 1010iter [00:28, 28.06iter/s]Best score: 0.2475: : 1011iter [00:28, 28.06iter/s]Best score: 0.2475: : 1012iter [00:28, 30.56iter/s]Best score: 0.2475: : 1012iter [00:28, 30.56iter/s]Best score: 0.2475: : 1013iter [00:28, 30.56iter/s]Best score: 0.2475: : 1014iter [00:28, 30.56iter/s]Best score: 0.2475: : 1015iter [00:28, 30.56iter/s]Best score: 0.2475: : 1016iter [00:28, 30.56iter/s]Best score: 0.2475: : 1017iter [00:28, 33.31iter/s]Best score: 0.2475: : 1017iter [00:29, 33.31iter/s]Best score: 0.2475: : 1018iter [00:29, 33.31iter/s]Best score: 0.2475: : 1019iter [00:29, 33.31iter/s]Best score: 0.2475: : 1020iter [00:29, 33.31iter/s]Best score: 0.2475: : 1021iter [00:29, 34.76iter/s]Best score: 0.2475: : 1021iter [00:29, 34.76iter/s]Best score: 0.2475: : 1022iter [00:29, 34.76iter/s]Best score: 0.2475: : 1023iter [00:29, 34.76iter/s]Best score: 0.2475: : 1024iter [00:29, 34.76iter/s]Best score: 0.2475: : 1025iter [00:29, 34.76iter/s]Best score: 0.2475: : 1026iter [00:29, 36.45iter/s]Best score: 0.2475: : 1026iter [00:29, 36.45iter/s]Best score: 0.2475: : 1027iter [00:29, 36.45iter/s]Best score: 0.2475: : 1028iter [00:29, 36.45iter/s]Best score: 0.2475: : 1029iter [00:29, 36.45iter/s]Best score: 0.2475: : 1030iter [00:29, 37.29iter/s]Best score: 0.2475: : 1030iter [00:29, 37.29iter/s]Best score: 0.2475: : 1031iter [00:29, 37.29iter/s]Best score: 0.2475: : 1032iter [00:29, 37.29iter/s]Best score: 0.2475: : 1033iter [00:29, 37.29iter/s]Best score: 0.2475: : 1034iter [00:29, 37.29iter/s]Best score: 0.2475: : 1035iter [00:29, 38.28iter/s]Best score: 0.2475: : 1035iter [00:29, 38.28iter/s]Best score: 0.2475: : 1036iter [00:29, 38.28iter/s]Best score: 0.2475: : 1037iter [00:29, 38.28iter/s]Best score: 0.2475: : 1038iter [00:29, 38.28iter/s]Best score: 0.2475: : 1039iter [00:29, 38.28iter/s]Best score: 0.2475: : 1040iter [00:29, 38.81iter/s]Best score: 0.2475: : 1040iter [00:29, 38.81iter/s]Best score: 0.2475: : 1041iter [00:29, 38.81iter/s]Best score: 0.2475: : 1042iter [00:29, 38.81iter/s]Best score: 0.2475: : 1043iter [00:29, 38.81iter/s]Best score: 0.2475: : 1044iter [00:29, 38.81iter/s]Best score: 0.2475: : 1045iter [00:29, 39.31iter/s]Best score: 0.2475: : 1045iter [00:29, 39.31iter/s]Best score: 0.2475: : 1046iter [00:29, 39.31iter/s]Best score: 0.2475: : 1047iter [00:29, 39.31iter/s]Best score: 0.2475: : 1048iter [00:29, 39.31iter/s]Best score: 0.2475: : 1049iter [00:29, 39.41iter/s]
[ITER 1050] Best: 0.2475
[PARAMS] Current best parameters:
  fx: 525.8769
  fy: 525.8728
  px: 997.3366
  py: 765.6598
  k1: -0.01508031
  k2: 0.03331287
  k3: -0.01671965
  k4: 0.00219106
  p1: 0.00378814
  p2: -0.01000016
  p3: -0.00336110
  p4: 0.00112515
  s1: 0.0067
  s2: 0.0001
  s3: -0.0042
  s4: -0.0010
  tilt_x: -0.0187
  tilt_y: 0.0132
  scale: 3.0158
  rotation: -0.3835
Best score: 0.2475: : 1049iter [00:29, 39.41iter/s]Best score: 0.2475: : 1050iter [00:29, 39.41iter/s]Best score: 0.2475: : 1051iter [00:29, 39.41iter/s]Best score: 0.2475: : 1052iter [00:29, 39.41iter/s]Best score: 0.2475: : 1053iter [00:29, 32.63iter/s]Best score: 0.2475: : 1053iter [00:29, 32.63iter/s]Best score: 0.2475: : 1054iter [00:30, 32.63iter/s]Best score: 0.2475: : 1055iter [00:30, 32.63iter/s]Best score: 0.2475: : 1056iter [00:30, 32.63iter/s]Best score: 0.2475: : 1057iter [00:30, 34.40iter/s]Best score: 0.2475: : 1057iter [00:30, 34.40iter/s]Best score: 0.2475: : 1058iter [00:30, 34.40iter/s]Best score: 0.2475: : 1059iter [00:30, 34.40iter/s]Best score: 0.2475: : 1060iter [00:30, 34.40iter/s]Best score: 0.2475: : 1061iter [00:30, 35.44iter/s]Best score: 0.2475: : 1061iter [00:30, 35.44iter/s]Best score: 0.2475: : 1062iter [00:30, 35.44iter/s]Best score: 0.2475: : 1063iter [00:30, 35.44iter/s]Best score: 0.2475: : 1064iter [00:30, 35.44iter/s]Best score: 0.2475: : 1065iter [00:30, 35.44iter/s]Best score: 0.2475: : 1066iter [00:30, 36.99iter/s]Best score: 0.2475: : 1066iter [00:30, 36.99iter/s]Best score: 0.2475: : 1067iter [00:30, 36.99iter/s]Best score: 0.2475: : 1068iter [00:30, 36.99iter/s]Best score: 0.2475: : 1069iter [00:30, 36.99iter/s]Best score: 0.2475: : 1070iter [00:30, 37.61iter/s]Best score: 0.2475: : 1070iter [00:30, 37.61iter/s]Best score: 0.2475: : 1071iter [00:30, 37.61iter/s]Best score: 0.2475: : 1072iter [00:30, 37.61iter/s]Best score: 0.2475: : 1073iter [00:30, 37.61iter/s]Best score: 0.2475: : 1074iter [00:30, 37.61iter/s]Best score: 0.2475: : 1075iter [00:30, 38.53iter/s]Best score: 0.2475: : 1075iter [00:30, 38.53iter/s]Best score: 0.2475: : 1076iter [00:30, 38.53iter/s]Best score: 0.2475: : 1077iter [00:30, 38.53iter/s]Best score: 0.2475: : 1078iter [00:30, 38.53iter/s]Best score: 0.2475: : 1079iter [00:30, 38.53iter/s]Best score: 0.2475: : 1080iter [00:30, 38.87iter/s]Best score: 0.2475: : 1080iter [00:30, 38.87iter/s]Best score: 0.2475: : 1081iter [00:30, 38.87iter/s]Best score: 0.2475: : 1082iter [00:30, 38.87iter/s]Best score: 0.2475: : 1083iter [00:30, 38.87iter/s]Best score: 0.2475: : 1084iter [00:30, 38.87iter/s]Best score: 0.2475: : 1085iter [00:30, 39.45iter/s]Best score: 0.2475: : 1085iter [00:30, 39.45iter/s]Best score: 0.2475: : 1086iter [00:30, 39.45iter/s]Best score: 0.2475: : 1087iter [00:30, 39.45iter/s]Best score: 0.2475: : 1088iter [00:30, 39.45iter/s]Best score: 0.2475: : 1089iter [00:30, 39.45iter/s]Best score: 0.2475: : 1090iter [00:30, 39.65iter/s]Best score: 0.2475: : 1090iter [00:30, 39.65iter/s]Best score: 0.2475: : 1091iter [00:30, 39.65iter/s]Best score: 0.2475: : 1092iter [00:30, 39.65iter/s]Best score: 0.2475: : 1093iter [00:30, 39.65iter/s]Best score: 0.2475: : 1094iter [00:31, 39.65iter/s]Best score: 0.2475: : 1095iter [00:31, 39.92iter/s]Best score: 0.2475: : 1095iter [00:31, 39.92iter/s]Best score: 0.2475: : 1096iter [00:31, 39.92iter/s]Best score: 0.2475: : 1097iter [00:31, 39.92iter/s]Best score: 0.2475: : 1098iter [00:31, 39.92iter/s]
[ITER 1100] Best: 0.2475
[PARAMS] Current best parameters:
  fx: 525.4198
  fy: 525.4043
  px: 997.3307
  py: 765.6654
  k1: -0.01497959
  k2: 0.03369185
  k3: -0.01690086
  k4: 0.00222255
  p1: 0.00376478
  p2: -0.00980503
  p3: -0.00319328
  p4: 0.00110735
  s1: 0.0063
  s2: 0.0002
  s3: -0.0043
  s4: -0.0010
  tilt_x: -0.0186
  tilt_y: 0.0135
  scale: 3.0170
  rotation: -0.3831
Best score: 0.2475: : 1099iter [00:31, 39.92iter/s]Best score: 0.2475: : 1100iter [00:31, 39.75iter/s]Best score: 0.2475: : 1100iter [00:31, 35.23iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.247456 -> 0.247374

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,854 → 25,854 within 3500mm ring
  Ring polish did not improve score: 0.247374
  Success: True
  Tangential distortion: p1=0.003767, p2=-0.009808, p3=-0.003194, p4=0.001105
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2474
[✅] Parameters copied to: /working/MDC005_W041_2026-08-11/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


-------------------------------------------------------------------
                        Step 4: Undistortion                       
-------------------------------------------------------------------


[DEBUG] Parameters being applied to PTD conversion:
    fx: 525.4198013679928
    fy: 525.404266574752
    px: 997.3306720908724
    py: 765.6654287179326
    k1: -0.014978184744837761
    k2: 0.033692577973225446
    k3: -0.01689936689119515
    k4: 0.0022214704298140924
    p1: 0.0037668725121450604
    p2: -0.009807818861032417
    p3: -0.0031936796234116487
    p4: 0.001105288680613529
    scale: 3.017046401731401
    rotation: -0.38307188069040204
    mmpp: 1.2676296184940847

[🔄] Converting 1423 frames to PTD, creating debug images for 200 calibration frames...
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   🔄 500:  97%|█████████▋| 614/632 [00:03<00:00, 175.46frame/s][A
   🔄 500: 100%|██████████| 632/632 [00:03<00:00, 175.21frame/s][A
                                                                [A📁 Rigs: 100%|██████████| 2/2 [00:14<00:00,  7.04s/rig]📁 Rigs: 100%|██████████| 2/2 [00:14<00:00,  7.21s/rig]
[✅] Saved PTDs: /working/MDC005_W041_2026-08-11/calibration/debug/4_undistorted/output/ptd
📁 Rigs:   0%|          | 0/2 [00:00<?, ?rig/s]
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   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 174.75frame/s][A
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   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 174.89frame/s][A
                                                                [A📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.03s/rig]
   🔄 500:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 172.69frame/s][A
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   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 173.21frame/s][A
   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 172.95frame/s][A
   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 173.47frame/s][A
                                                               [A📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]
[✅] Saved PTDs: /working/MDC005_W041_2026-08-11/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 80812/3080448 [00:00<00:03, 808118.84px/s]📤 Writing LUT:   6%|▌         | 173131/3080448 [00:00<00:03, 875806.98px/s]📤 Writing LUT:   9%|▊         | 262925/3080448 [00:00<00:03, 885911.83px/s]📤 Writing LUT:  12%|█▏        | 355108/3080448 [00:00<00:03, 900087.16px/s]📤 Writing LUT:  15%|█▍        | 447196/3080448 [00:00<00:02, 907581.58px/s]📤 Writing LUT:  17%|█▋        | 538010/3080448 [00:00<00:02, 907768.80px/s]📤 Writing LUT:  20%|██        | 629295/3080448 [00:00<00:02, 909428.82px/s]📤 Writing LUT:  23%|██▎       | 721181/3080448 [00:00<00:02, 912427.36px/s]📤 Writing LUT:  26%|██▋       | 812424/3080448 [00:00<00:02, 907945.21px/s]📤 Writing LUT:  29%|██▉       | 903224/3080448 [00:01<00:02, 903420.88px/s]📤 Writing LUT:  32%|███▏      | 994284/3080448 [00:01<00:02, 905604.18px/s]📤 Writing LUT:  35%|███▌      | 1084851/3080448 [00:01<00:02, 905132.06px/s]📤 Writing LUT:  38%|███▊      | 1179012/3080448 [00:01<00:02, 916145.23px/s]📤 Writing LUT:  41%|████▏     | 1271666/3080448 [00:01<00:01, 919275.51px/s]📤 Writing LUT:  44%|████▍     | 1363599/3080448 [00:01<00:01, 918483.53px/s]📤 Writing LUT:  47%|████▋     | 1455451/3080448 [00:01<00:01, 911999.00px/s]📤 Writing LUT:  50%|█████     | 1546663/3080448 [00:01<00:01, 911660.62px/s]📤 Writing LUT:  53%|█████▎    | 1639892/3080448 [00:01<00:01, 917829.32px/s]📤 Writing LUT:  56%|█████▋    | 1733487/3080448 [00:01<00:01, 923252.47px/s]📤 Writing LUT:  59%|█████▉    | 1825820/3080448 [00:02<00:01, 918957.97px/s]📤 Writing LUT:  62%|██████▏   | 1918643/3080448 [00:02<00:01, 921725.25px/s]📤 Writing LUT:  65%|██████▌   | 2010824/3080448 [00:02<00:01, 915085.75px/s]📤 Writing LUT:  68%|██████▊   | 2103035/3080448 [00:02<00:01, 917176.86px/s]📤 Writing LUT:  71%|███████▏  | 2197207/3080448 [00:02<00:00, 924502.32px/s]📤 Writing LUT:  74%|███████▍  | 2289842/3080448 [00:02<00:00, 925054.27px/s]📤 Writing LUT:  77%|███████▋  | 2382949/3080448 [00:02<00:00, 926852.80px/s]📤 Writing LUT:  80%|████████  | 2476800/3080448 [00:02<00:00, 930342.17px/s]📤 Writing LUT:  83%|████████▎ | 2570851/3080448 [00:02<00:00, 933387.74px/s]📤 Writing LUT:  86%|████████▋ | 2664194/3080448 [00:02<00:00, 933027.94px/s]📤 Writing LUT:  90%|████████▉ | 2758533/3080448 [00:03<00:00, 936133.43px/s]📤 Writing LUT:  93%|█████████▎| 2852265/3080448 [00:03<00:00, 936488.11px/s]📤 Writing LUT:  96%|█████████▌| 2945916/3080448 [00:03<00:00, 931716.80px/s]📤 Writing LUT:  99%|█████████▊| 3039319/3080448 [00:03<00:00, 932405.68px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 918146.09px/s]
[✅] LUT written to: /working/MDC005_W041_2026-08-11/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC005_W041_2026-08-11' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC005_W041_2026-08-11/calibration/MDC005_W041_2026-08-11_unknown.lut

-------------------------------------------------------------------
                          Step 5: Heatmap                          
-------------------------------------------------------------------

[ℹ️] Detected device type: MD (rigs: [500, 1000]mm)
📁 Rigs (heatmap):   0%|          | 0/2 [00:00<?, ?rig/s]
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   🔥 1000:  11%|█▏        | 89/791 [00:01<00:08, 87.18frame/s][A
   🔥 1000:  12%|█▏        | 98/791 [00:01<00:07, 87.19frame/s][A
   🔥 1000:  14%|█▎        | 107/791 [00:01<00:07, 87.91frame/s][A
   🔥 1000:  15%|█▍        | 116/791 [00:01<00:07, 88.00frame/s][A
   🔥 1000:  16%|█▌        | 125/791 [00:01<00:07, 87.44frame/s][A
   🔥 1000:  17%|█▋        | 134/791 [00:01<00:07, 87.86frame/s][A
   🔥 1000:  18%|█▊        | 144/791 [00:01<00:07, 88.70frame/s][A
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   🔥 1000:  20%|██        | 162/791 [00:01<00:07, 86.06frame/s][A
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                                                                [A[✅] 1000: /working/MDC005_W041_2026-08-11/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC005_W041_2026-08-11/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap):  50%|█████     | 1/2 [00:12<00:12, 12.28s/rig]
   🔥 500:   0%|          | 0/632 [00:00<?, ?frame/s][A
   🔥 500:   1%|▏         | 8/632 [00:00<00:08, 74.97frame/s][A
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                                                               [A[✅] 500: /working/MDC005_W041_2026-08-11/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC005_W041_2026-08-11/calibration/debug/5_heatmap/500_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:21<00:00, 10.36s/rig]📁 Rigs (heatmap): 100%|██████████| 2/2 [00:21<00:00, 10.65s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC005_W041_2026-08-11/calibration/debug/5_heatmap
[🏁] Finished processing MDC005_W041_2026-08-11


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