
🟢 Starting Calibration Pipeline

=== Processing MDC045_W022(6)_2023-10-25 ===

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 4 las0.zip files to process
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 1064 stationary frames, 217 remain

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                                                                          [A[PASS2] Summary: 0 frames fixed, 1 untrusted, 217 total

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                                                                           [AProcessing Rigs:  25%|██▌       | 1/4 [03:48<11:24, 228.04s/it][INFO] Filtered 904 stationary frames, 177 remain

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                                                                           [A[PASS2] Frame 0000299: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000440: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000441: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000442: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000446: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000447: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000448: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000449: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000450: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000441: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000448: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Summary: 8 frames fixed, 7 untrusted, 177 total

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                                                                            [AProcessing Rigs:  50%|█████     | 2/4 [06:58<06:52, 206.15s/it][INFO] Filtered 745 stationary frames, 152 remain

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                                                                          [A[PASS2] Summary: 0 frames fixed, 0 untrusted, 152 total

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                                                                           [AProcessing Rigs:  75%|███████▌  | 3/4 [09:43<03:07, 187.34s/it][INFO] Filtered 1024 stationary frames, 182 remain

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                                                                           [A[PASS2] Frame 0000741: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000743: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000744: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000961: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000962: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000963: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000964: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000984: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000743: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000963: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000743: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Summary: 5 frames fixed, 3 untrusted, 182 total

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                                                                            [AProcessing Rigs: 100%|██████████| 4/4 [12:59<00:00, 190.55s/it]Processing Rigs: 100%|██████████| 4/4 [12:59<00:00, 194.83s/it]
[💾] Saved RigFrameDataset to /working/MDC045_W022(6)_2023-10-25/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 728 frames from 4 rigs to /working/MDC045_W022(6)_2023-10-25/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC045_W022(6)_2023-10-25/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 217
[📋] 2000R: 100 frames selected from 177
[📋] 1000: 100 frames selected from 152
[📋] 2000L: 100 frames selected from 182
[💾] Saved RigFrameDataset to /working/MDC045_W022(6)_2023-10-25/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC045_W022(6)_2023-10-25/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Computing max image from bright frames:  14%|█▍        | 11/80 [00:00<00:00, 104.16it/s]Computing max image from bright frames:  28%|██▊       | 22/80 [00:00<00:00, 107.34it/s]Computing max image from bright frames:  41%|████▏     | 33/80 [00:00<00:00, 101.09it/s]Computing max image from bright frames:  55%|█████▌    | 44/80 [00:00<00:00, 102.11it/s]Computing max image from bright frames:  69%|██████▉   | 55/80 [00:00<00:00, 99.68it/s] Computing max image from bright frames:  82%|████████▎ | 66/80 [00:00<00:00, 101.96it/s]Computing max image from bright frames:  96%|█████████▋| 77/80 [00:00<00:00, 101.33it/s]                                                                                        [INFO] Computed max image from 80 bright frames
[INFO] Corner median intensity: 22, black threshold: 66
[INFO] Saved casing detection debug images to /working/MDC045_W022(6)_2023-10-25/calibration/debug/2_calibration/casing_detection
[INFO] Found 11415 casing boundary candidate points
[RESULT] Detected casing: center=(983, 769), radius=832, inliers=2385/11415
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17.25it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.087 | Best: {'fx': 526.263} (0.089):  33%|███▎      | 33/100 [00:01<00:03, 17.25it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.076 | Best: {'fx': 526.263} (0.089):  34%|███▍      | 34/100 [00:01<00:03, 17.25it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.076 | Best: {'fx': 526.263} (0.089):  35%|███▌      | 35/100 [00:01<00:03, 19.02it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.067 | Best: {'fx': 526.263} (0.089):  35%|███▌      | 35/100 [00:01<00:03, 19.02it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.059 | Best: {'fx': 526.263} (0.089):  36%|███▌      | 36/100 [00:01<00:03, 19.02it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.053 | Best: {'fx': 526.263} (0.089):  37%|███▋      | 37/100 [00:01<00:03, 19.02it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.041 | Best: {'fx': 526.263} (0.089):  38%|███▊      | 38/100 [00:02<00:03, 19.02it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.041 | Best: {'fx': 526.263} (0.089):  39%|███▉      | 39/100 [00:02<00:03, 18.65it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.047 | Best: {'fx': 526.263} (0.089):  39%|███▉      | 39/100 [00:02<00:03, 18.65it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.036 | Best: {'fx': 526.263} (0.089):  40%|████      | 40/100 [00:02<00:03, 18.65it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.035 | Best: {'fx': 526.263} (0.089):  41%|████      | 41/100 [00:02<00:03, 18.65it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.035 | Best: {'fx': 526.263} (0.089):  42%|████▏     | 42/100 [00:02<00:02, 19.98it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.043 | Best: {'fx': 526.263} (0.089):  42%|████▏     | 42/100 [00:02<00:02, 19.98it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.032 | Best: {'fx': 526.263} (0.089):  43%|████▎     | 43/100 [00:02<00:02, 19.98it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.039 | Best: {'fx': 526.263} (0.089):  44%|████▍     | 44/100 [00:02<00:02, 19.98it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.033 | Best: {'fx': 526.263} (0.089):  45%|████▌     | 45/100 [00:02<00:02, 19.98it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.030 | Best: {'fx': 526.263} (0.089):  46%|████▌     | 46/100 [00:02<00:02, 19.98it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.030 | Best: {'fx': 526.263} (0.089):  47%|████▋     | 47/100 [00:02<00:02, 23.43it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.031 | Best: {'fx': 526.263} (0.089):  47%|████▋     | 47/100 [00:02<00:02, 23.43it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.028 | Best: {'fx': 526.263} (0.089):  48%|████▊     | 48/100 [00:02<00:02, 23.43it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.029 | Best: {'fx': 526.263} (0.089):  49%|████▉     | 49/100 [00:02<00:02, 23.43it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.029 | Best: {'fx': 526.263} (0.089):  50%|█████     | 50/100 [00:02<00:02, 20.70it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.027 | Best: {'fx': 526.263} (0.089):  50%|█████     | 50/100 [00:02<00:02, 20.70it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.027 | Best: {'fx': 526.263} (0.089):  51%|█████     | 51/100 [00:02<00:02, 20.70it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.025 | Best: {'fx': 526.263} (0.089):  52%|█████▏    | 52/100 [00:02<00:02, 20.70it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.025 | Best: {'fx': 526.263} (0.089):  53%|█████▎    | 53/100 [00:02<00:02, 21.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.026 | Best: {'fx': 526.263} (0.089):  53%|█████▎    | 53/100 [00:02<00:02, 21.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.025 | Best: {'fx': 526.263} (0.089):  54%|█████▍    | 54/100 [00:02<00:02, 21.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.026 | Best: {'fx': 526.263} (0.089):  55%|█████▌    | 55/100 [00:02<00:02, 21.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 526.263} (0.089):  56%|█████▌    | 56/100 [00:02<00:02, 21.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 526.263} (0.089):  57%|█████▋    | 57/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.024 | Best: {'fx': 526.263} (0.089):  57%|█████▋    | 57/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.024 | Best: {'fx': 526.263} (0.089):  58%|█████▊    | 58/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 526.263} (0.089):  59%|█████▉    | 59/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.024 | Best: {'fx': 526.263} (0.089):  60%|██████    | 60/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.023 | Best: {'fx': 526.263} (0.089):  61%|██████    | 61/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.023 | Best: {'fx': 526.263} (0.089):  62%|██████▏   | 62/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 526.263} (0.089):  63%|██████▎   | 63/100 [00:02<00:01, 23.30it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 526.263} (0.089):  64%|██████▍   | 64/100 [00:02<00:01, 31.66it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 526.263} (0.089):  64%|██████▍   | 64/100 [00:02<00:01, 31.66it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.023 | Best: {'fx': 526.263} (0.089):  65%|██████▌   | 65/100 [00:03<00:01, 31.66it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 526.263} (0.089):  66%|██████▌   | 66/100 [00:03<00:01, 31.66it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  67%|██████▋   | 67/100 [00:03<00:01, 31.66it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  68%|██████▊   | 68/100 [00:03<00:01, 31.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  68%|██████▊   | 68/100 [00:03<00:01, 31.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  69%|██████▉   | 69/100 [00:03<00:00, 31.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  70%|███████   | 70/100 [00:03<00:00, 31.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  71%|███████   | 71/100 [00:03<00:00, 31.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  72%|███████▏  | 72/100 [00:03<00:00, 30.52it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  72%|███████▏  | 72/100 [00:03<00:00, 30.52it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 526.263} (0.089):  73%|███████▎  | 73/100 [00:03<00:00, 30.52it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  74%|███████▍  | 74/100 [00:03<00:00, 30.52it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  75%|███████▌  | 75/100 [00:03<00:00, 30.52it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  76%|███████▌  | 76/100 [00:03<00:00, 30.52it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  77%|███████▋  | 77/100 [00:03<00:00, 32.10it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  77%|███████▋  | 77/100 [00:03<00:00, 32.10it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  78%|███████▊  | 78/100 [00:03<00:00, 32.10it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  79%|███████▉  | 79/100 [00:03<00:00, 32.10it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  80%|████████  | 80/100 [00:03<00:00, 32.10it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  81%|████████  | 81/100 [00:03<00:00, 29.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  81%|████████  | 81/100 [00:03<00:00, 29.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  82%|████████▏ | 82/100 [00:03<00:00, 29.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  83%|████████▎ | 83/100 [00:03<00:00, 29.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  84%|████████▍ | 84/100 [00:03<00:00, 29.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 526.263} (0.089):  85%|████████▌ | 85/100 [00:03<00:00, 28.69it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  85%|████████▌ | 85/100 [00:03<00:00, 28.69it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  86%|████████▌ | 86/100 [00:03<00:00, 28.69it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  87%|████████▋ | 87/100 [00:03<00:00, 28.69it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  88%|████████▊ | 88/100 [00:03<00:00, 28.69it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  89%|████████▉ | 89/100 [00:03<00:00, 29.48it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  89%|████████▉ | 89/100 [00:03<00:00, 29.48it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  90%|█████████ | 90/100 [00:04<00:00, 29.48it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  91%|█████████ | 91/100 [00:04<00:00, 29.48it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  92%|█████████▏| 92/100 [00:04<00:00, 24.47it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  92%|█████████▏| 92/100 [00:04<00:00, 24.47it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  93%|█████████▎| 93/100 [00:04<00:00, 24.47it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  94%|█████████▍| 94/100 [00:04<00:00, 24.47it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  95%|█████████▌| 95/100 [00:04<00:00, 24.20it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  95%|█████████▌| 95/100 [00:04<00:00, 24.20it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  96%|█████████▌| 96/100 [00:04<00:00, 24.20it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  97%|█████████▋| 97/100 [00:04<00:00, 24.20it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  98%|█████████▊| 98/100 [00:04<00:00, 22.68it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  98%|█████████▊| 98/100 [00:04<00:00, 22.68it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 526.263} (0.089):  99%|█████████▉| 99/100 [00:04<00:00, 22.68it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.041 | Best: {'fx': 501.263} (0.041):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.041 | Best: {'fx': 501.263} (0.041):   2%|▏         | 1/50 [00:00<00:23,  2.13it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.044 | Best: {'fx': 502.283} (0.044):   2%|▏         | 1/50 [00:00<00:23,  2.13it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.045 | Best: {'fx': 503.303} (0.045):   4%|▍         | 2/50 [00:00<00:22,  2.13it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.046 | Best: {'fx': 504.324} (0.046):   6%|▌         | 3/50 [00:00<00:22,  2.13it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.048 | Best: {'fx': 506.365} (0.048):   8%|▊         | 4/50 [00:00<00:21,  2.13it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.048 | Best: {'fx': 506.365} (0.048):  10%|█         | 5/50 [00:00<00:05,  8.70it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.047 | Best: {'fx': 506.365} (0.048):  10%|█         | 5/50 [00:00<00:05,  8.70it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.049 | Best: {'fx': 507.385} (0.049):  12%|█▏        | 6/50 [00:00<00:05,  8.70it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.050 | Best: {'fx': 508.405} (0.050):  14%|█▍        | 7/50 [00:00<00:04,  8.70it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.052 | Best: {'fx': 509.426} (0.052):  16%|█▌        | 8/50 [00:00<00:04,  8.70it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.052 | Best: {'fx': 509.426} (0.052):  18%|█▊        | 9/50 [00:00<00:03, 13.21it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.053 | Best: {'fx': 510.446} (0.053):  18%|█▊        | 9/50 [00:00<00:03, 13.21it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.056 | Best: {'fx': 511.467} (0.056):  20%|██        | 10/50 [00:00<00:03, 13.21it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.058 | Best: {'fx': 512.487} (0.058):  22%|██▏       | 11/50 [00:00<00:02, 13.21it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.058 | Best: {'fx': 512.487} (0.058):  24%|██▍       | 12/50 [00:00<00:02, 16.47it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.059 | Best: {'fx': 513.508} (0.059):  24%|██▍       | 12/50 [00:01<00:02, 16.47it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.061 | Best: {'fx': 514.528} (0.061):  26%|██▌       | 13/50 [00:01<00:02, 16.47it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.063 | Best: {'fx': 515.548} (0.063):  28%|██▊       | 14/50 [00:01<00:02, 16.47it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.063 | Best: {'fx': 515.548} (0.063):  30%|███       | 15/50 [00:01<00:01, 17.65it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.067 | Best: {'fx': 516.569} (0.067):  30%|███       | 15/50 [00:01<00:01, 17.65it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.068 | Best: {'fx': 517.589} (0.068):  32%|███▏      | 16/50 [00:01<00:01, 17.65it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.070 | Best: {'fx': 518.61} (0.070):  34%|███▍      | 17/50 [00:01<00:01, 17.65it/s] Brute | Start: {'fx': 526.263} (0.089) | Current: 0.070 | Best: {'fx': 518.61} (0.070):  36%|███▌      | 18/50 [00:01<00:01, 20.40it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.071 | Best: {'fx': 519.63} (0.071):  36%|███▌      | 18/50 [00:01<00:01, 20.40it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.074 | Best: {'fx': 520.65} (0.074):  38%|███▊      | 19/50 [00:01<00:01, 20.40it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.077 | Best: {'fx': 521.671} (0.077):  40%|████      | 20/50 [00:01<00:01, 20.40it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.077 | Best: {'fx': 521.671} (0.077):  42%|████▏     | 21/50 [00:01<00:01, 20.97it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.078 | Best: {'fx': 522.691} (0.078):  42%|████▏     | 21/50 [00:01<00:01, 20.97it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.081 | Best: {'fx': 523.712} (0.081):  44%|████▍     | 22/50 [00:01<00:01, 20.97it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.083 | Best: {'fx': 524.732} (0.083):  46%|████▌     | 23/50 [00:01<00:01, 20.97it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.083 | Best: {'fx': 524.732} (0.083):  48%|████▊     | 24/50 [00:01<00:01, 20.88it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.086 | Best: {'fx': 525.752} (0.086):  48%|████▊     | 24/50 [00:01<00:01, 20.88it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.089 | Best: {'fx': 526.773} (0.089):  50%|█████     | 25/50 [00:01<00:01, 20.88it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.089 | Best: {'fx': 526.773} (0.089):  52%|█████▏    | 26/50 [00:01<00:01, 20.88it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.089 | Best: {'fx': 526.773} (0.089):  54%|█████▍    | 27/50 [00:01<00:01, 19.67it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.090 | Best: {'fx': 527.793} (0.090):  54%|█████▍    | 27/50 [00:01<00:01, 19.67it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.090 | Best: {'fx': 529.834} (0.090):  56%|█████▌    | 28/50 [00:01<00:01, 19.67it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.086 | Best: {'fx': 529.834} (0.090):  58%|█████▊    | 29/50 [00:01<00:01, 19.67it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.091 | Best: {'fx': 528.814} (0.091):  60%|██████    | 30/50 [00:01<00:01, 19.67it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.089 | Best: {'fx': 528.814} (0.091):  62%|██████▏   | 31/50 [00:01<00:00, 19.67it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.087 | Best: {'fx': 528.814} (0.091):  64%|██████▍   | 32/50 [00:01<00:00, 19.67it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.087 | Best: {'fx': 528.814} (0.091):  66%|██████▌   | 33/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.089 | Best: {'fx': 528.814} (0.091):  66%|██████▌   | 33/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.080 | Best: {'fx': 528.814} (0.091):  68%|██████▊   | 34/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.085 | Best: {'fx': 528.814} (0.091):  70%|███████   | 35/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.078 | Best: {'fx': 528.814} (0.091):  72%|███████▏  | 36/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.085 | Best: {'fx': 528.814} (0.091):  74%|███████▍  | 37/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.083 | Best: {'fx': 528.814} (0.091):  76%|███████▌  | 38/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.075 | Best: {'fx': 528.814} (0.091):  78%|███████▊  | 39/50 [00:01<00:00, 24.78it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.075 | Best: {'fx': 528.814} (0.091):  80%|████████  | 40/50 [00:01<00:00, 33.76it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.071 | Best: {'fx': 528.814} (0.091):  80%|████████  | 40/50 [00:02<00:00, 33.76it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.074 | Best: {'fx': 528.814} (0.091):  82%|████████▏ | 41/50 [00:02<00:00, 33.76it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.073 | Best: {'fx': 528.814} (0.091):  84%|████████▍ | 42/50 [00:02<00:00, 33.76it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.069 | Best: {'fx': 528.814} (0.091):  86%|████████▌ | 43/50 [00:02<00:00, 33.76it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.069 | Best: {'fx': 528.814} (0.091):  88%|████████▊ | 44/50 [00:02<00:00, 32.52it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.069 | Best: {'fx': 528.814} (0.091):  88%|████████▊ | 44/50 [00:02<00:00, 32.52it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.066 | Best: {'fx': 528.814} (0.091):  90%|█████████ | 45/50 [00:02<00:00, 32.52it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.067 | Best: {'fx': 528.814} (0.091):  92%|█████████▏| 46/50 [00:02<00:00, 32.52it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.065 | Best: {'fx': 528.814} (0.091):  94%|█████████▍| 47/50 [00:02<00:00, 32.52it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.063 | Best: {'fx': 528.814} (0.091):  96%|█████████▌| 48/50 [00:02<00:00, 32.52it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.063 | Best: {'fx': 528.814} (0.091):  98%|█████████▊| 49/50 [00:02<00:00, 31.16it/s]Brute | Start: {'fx': 526.263} (0.089) | Current: 0.064 | Best: {'fx': 528.814} (0.091):  98%|█████████▊| 49/50 [00:02<00:00, 31.16it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 528.8136466707896
    px: 983.0
    py: 769.0
    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): 80 frames will be used for optimization
  Rigs: 1500(1500mm):20/20, 2000R(2000mm):20/20, 1000(1000mm):20/20, 2000L(2000mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 44.0262: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 44.0262, picklable_objective(x0) = 44.0390
Best score: 44.0390: : 0iter [00:02, ?iter/s] Best score: 44.0390: : 1iter [00:02,  2.04s/iter]Best score: 44.0390: : 1iter [00:02,  2.04s/iter]Best score: 44.0390: : 2iter [00:02,  1.07iter/s]Best score: 44.0390: : 2iter [00:02,  1.07iter/s]Best score: 44.0390: : 3iter [00:02,  1.72iter/s]Best score: 44.0390: : 3iter [00:02,  1.72iter/s]Best score: 44.0390: : 4iter [00:02,  2.41iter/s]Best score: 44.0390: : 4iter [00:02,  2.41iter/s]Best score: 44.0390: : 5iter [00:02,  3.09iter/s]Best score: 44.0390: : 5iter [00:02,  3.09iter/s]Best score: 44.0390: : 6iter [00:02,  3.74iter/s]Best score: 44.0390: : 6iter [00:03,  3.74iter/s]Best score: 44.0390: : 7iter [00:03,  4.31iter/s]Best score: 44.0390: : 7iter [00:03,  4.31iter/s]Best score: 44.0390: : 8iter [00:03,  4.80iter/s]Best score: 44.0390: : 8iter [00:03,  4.80iter/s]Best score: 44.0390: : 9iter [00:03,  5.19iter/s]Best score: 44.0390: : 9iter [00:03,  5.19iter/s]Best score: 44.0390: : 10iter [00:03,  5.34iter/s]Best score: 44.0390: : 10iter [00:03,  5.34iter/s]Best score: 44.0390: : 11iter [00:03,  5.61iter/s]Best score: 44.0390: : 11iter [00:03,  5.61iter/s]Best score: 44.0390: : 12iter [00:03,  5.81iter/s]Best score: 44.0390: : 12iter [00:03,  5.81iter/s]Best score: 44.0390: : 13iter [00:03,  5.96iter/s]Best score: 44.0390: : 13iter [00:04,  5.96iter/s]Best score: 44.0390: : 14iter [00:04,  6.07iter/s]Best score: 44.0390: : 14iter [00:04,  6.07iter/s]Best score: 44.0390: : 15iter [00:04,  6.13iter/s]Best score: 44.0390: : 15iter [00:04,  6.13iter/s]Best score: 44.0390: : 16iter [00:04,  6.17iter/s]Best score: 44.0390: : 16iter [00:04,  6.17iter/s]Best score: 44.0390: : 17iter [00:04,  6.21iter/s]Best score: 44.0390: : 17iter [00:04,  6.21iter/s]Best score: 44.0390: : 18iter [00:04,  6.23iter/s]Best score: 44.0390: : 18iter [00:04,  6.23iter/s]Best score: 44.0390: : 19iter [00:04,  6.25iter/s]Best score: 17.7211: : 19iter [00:05,  6.25iter/s]Best score: 17.7211: : 20iter [00:05,  6.07iter/s]Best score: 17.7211: : 20iter [00:05,  6.07iter/s]Best score: 17.7211: : 21iter [00:05,  6.14iter/s]Best score: 17.7211: : 21iter [00:05,  6.14iter/s]Best score: 17.7211: : 22iter [00:05,  6.19iter/s]Best score: 17.7211: : 22iter [00:05,  6.19iter/s]Best score: 17.7211: : 23iter [00:05,  6.20iter/s]Best score: 17.7211: : 23iter [00:05,  6.20iter/s]Best score: 17.7211: : 24iter [00:05,  6.24iter/s]Best score: 17.7211: : 24iter [00:05,  6.24iter/s]Best score: 17.7211: : 25iter [00:05,  6.24iter/s]Best score: 17.7211: : 25iter [00:06,  6.24iter/s]Best score: 17.7211: : 26iter [00:06,  6.26iter/s]Best score: 17.7211: : 26iter [00:06,  6.26iter/s]Best score: 17.7211: : 27iter [00:06,  6.29iter/s]Best score: 17.7211: : 27iter [00:06,  6.29iter/s]Best score: 17.7211: : 28iter [00:06,  6.29iter/s]Best score: 17.7211: : 28iter [00:06,  6.29iter/s]Best score: 17.7211: : 29iter [00:06,  6.29iter/s]Best score: 7.9434: : 29iter [00:06,  6.29iter/s] Best score: 7.9434: : 30iter [00:06,  6.10iter/s]Best score: 7.9434: : 30iter [00:06,  6.10iter/s]Best score: 7.9434: : 31iter [00:06,  6.15iter/s]Best score: 7.9434: : 31iter [00:07,  6.15iter/s]Best score: 7.9434: : 32iter [00:07,  6.22iter/s]Best score: 7.9434: : 32iter [00:07,  6.22iter/s]Best score: 7.9434: : 33iter [00:07,  6.27iter/s]Best score: 7.9434: : 33iter [00:07,  6.27iter/s]Best score: 7.9434: : 34iter [00:07,  6.27iter/s]Best score: 7.9434: : 34iter [00:07,  6.27iter/s]Best score: 7.9434: : 35iter [00:07,  6.29iter/s]Best score: 7.9434: : 35iter [00:07,  6.29iter/s]Best score: 7.9434: : 36iter [00:07,  6.28iter/s]Best score: 7.9434: : 36iter [00:07,  6.28iter/s]Best score: 7.9434: : 37iter [00:07,  6.30iter/s]Best score: 7.9434: : 37iter [00:07,  6.30iter/s]Best score: 7.9434: : 38iter [00:07,  6.31iter/s]Best score: 7.9434: : 38iter [00:08,  6.31iter/s]Best score: 7.9434: : 39iter [00:08,  6.33iter/s]Best score: 4.2782: : 39iter [00:08,  6.33iter/s]Best score: 4.2782: : 40iter [00:08,  6.12iter/s]Best score: 4.2782: : 40iter [00:08,  6.12iter/s]Best score: 4.2782: : 41iter [00:08,  6.18iter/s]Best score: 4.2782: : 41iter [00:08,  6.18iter/s]Best score: 4.2782: : 42iter [00:08,  6.21iter/s]Best score: 4.2782: : 42iter [00:08,  6.21iter/s]Best score: 4.2782: : 43iter [00:08,  6.25iter/s]Best score: 4.2782: : 43iter [00:08,  6.25iter/s]Best score: 4.2782: : 44iter [00:08,  6.26iter/s]Best score: 4.2782: : 44iter [00:09,  6.26iter/s]Best score: 4.2782: : 45iter [00:09,  6.28iter/s]Best score: 4.2782: : 45iter [00:09,  6.28iter/s]Best score: 4.2782: : 46iter [00:09,  6.30iter/s]Best score: 4.2782: : 46iter [00:09,  6.30iter/s]Best score: 4.2782: : 47iter [00:09,  6.29iter/s]Best score: 4.2782: : 47iter [00:09,  6.29iter/s]Best score: 4.2782: : 48iter [00:09,  6.30iter/s]Best score: 4.2782: : 48iter [00:09,  6.30iter/s]Best score: 4.2782: : 49iter [00:09,  6.28iter/s]
[ITER 50] Best: 4.2782
[PARAMS] Current best parameters:
  fx: 523.0259
  fy: 519.8952
  px: 986.3327
  py: 782.2142
  k1: 0.02977442
  k2: 0.02174752
  k3: -0.04371791
  k4: 0.01435548
  p1: 0.00601112
  p2: 0.00036003
  p3: 0.00646928
  p4: -0.00400343
  scale: 2.8807
  rotation: 0.5308
Best score: 4.2782: : 49iter [00:09,  6.28iter/s]Best score: 4.2782: : 50iter [00:09,  6.10iter/s]Best score: 4.2782: : 50iter [00:10,  6.10iter/s]Best score: 4.2782: : 51iter [00:10,  6.14iter/s]Best score: 4.2782: : 51iter [00:10,  6.14iter/s]Best score: 4.2782: : 52iter [00:10,  6.19iter/s]Best score: 4.2782: : 52iter [00:10,  6.19iter/s]Best score: 4.2782: : 53iter [00:10,  6.24iter/s]Best score: 4.2782: : 53iter [00:10,  6.24iter/s]Best score: 4.2782: : 54iter [00:10,  6.26iter/s]Best score: 4.2782: : 54iter [00:10,  6.26iter/s]Best score: 4.2782: : 55iter [00:10,  6.27iter/s]Best score: 4.2782: : 55iter [00:10,  6.27iter/s]Best score: 4.2782: : 56iter [00:10,  6.29iter/s]Best score: 4.2782: : 56iter [00:11,  6.29iter/s]Best score: 4.2782: : 57iter [00:11,  6.30iter/s]Best score: 4.2782: : 57iter [00:11,  6.30iter/s]Best score: 4.2782: : 58iter [00:11,  6.32iter/s]Best score: 4.2782: : 58iter [00:11,  6.32iter/s]Best score: 4.2782: : 59iter [00:11,  6.32iter/s]Best score: 2.9843: : 59iter [00:11,  6.32iter/s]Best score: 2.9843: : 60iter [00:11,  6.13iter/s]Best score: 2.9843: : 60iter [00:11,  6.13iter/s]Best score: 2.9843: : 61iter [00:11,  6.19iter/s]Best score: 2.9843: : 61iter [00:11,  6.19iter/s]Best score: 2.9843: : 62iter [00:11,  6.22iter/s]Best score: 2.9843: : 62iter [00:11,  6.22iter/s]Best score: 2.9843: : 63iter [00:11,  6.25iter/s]Best score: 2.9843: : 63iter [00:12,  6.25iter/s]Best score: 2.9843: : 64iter [00:12,  6.26iter/s]Best score: 2.9843: : 64iter [00:12,  6.26iter/s]Best score: 2.9843: : 65iter [00:12,  6.28iter/s]Best score: 2.9843: : 65iter [00:12,  6.28iter/s]Best score: 2.9843: : 66iter [00:12,  6.29iter/s]Best score: 2.9843: : 66iter [00:12,  6.29iter/s]Best score: 2.9843: : 67iter [00:12,  6.30iter/s]Best score: 2.9843: : 67iter [00:12,  6.30iter/s]Best score: 2.9843: : 68iter [00:12,  6.31iter/s]Best score: 2.9843: : 68iter [00:12,  6.31iter/s]Best score: 2.9843: : 69iter [00:12,  6.30iter/s]Best score: 2.3992: : 69iter [00:13,  6.30iter/s]Best score: 2.3992: : 70iter [00:13,  6.10iter/s]Best score: 2.3992: : 70iter [00:13,  6.10iter/s]Best score: 2.3992: : 71iter [00:13,  6.18iter/s]Best score: 2.3992: : 71iter [00:13,  6.18iter/s]Best score: 2.3992: : 72iter [00:13,  6.22iter/s]Best score: 2.3992: : 72iter [00:13,  6.22iter/s]Best score: 2.3992: : 73iter [00:13,  6.27iter/s]Best score: 2.3992: : 73iter [00:13,  6.27iter/s]Best score: 2.3992: : 74iter [00:13,  6.30iter/s]Best score: 2.3992: : 74iter [00:13,  6.30iter/s]Best score: 2.3992: : 75iter [00:13,  6.32iter/s]Best score: 2.3992: : 75iter [00:14,  6.32iter/s]Best score: 2.3992: : 76iter [00:14,  6.34iter/s]Best score: 2.3992: : 76iter [00:14,  6.34iter/s]Best score: 2.3992: : 77iter [00:14,  6.36iter/s]Best score: 2.3992: : 77iter [00:14,  6.36iter/s]Best score: 2.3992: : 78iter [00:14,  6.38iter/s]Best score: 2.3992: : 78iter [00:14,  6.38iter/s]Best score: 2.3992: : 79iter [00:14,  6.37iter/s]Best score: 2.3992: : 79iter [00:14,  6.37iter/s]Best score: 2.3992: : 80iter [00:14,  6.17iter/s]Best score: 2.3992: : 80iter [00:14,  6.17iter/s]Best score: 2.3992: : 81iter [00:14,  6.23iter/s]Best score: 2.3992: : 81iter [00:15,  6.23iter/s]Best score: 2.3992: : 82iter [00:15,  6.28iter/s]Best score: 2.3992: : 82iter [00:15,  6.28iter/s]Best score: 2.3992: : 83iter [00:15,  6.30iter/s]Best score: 2.3992: : 83iter [00:15,  6.30iter/s]Best score: 2.3992: : 84iter [00:15,  6.32iter/s]Best score: 2.3992: : 84iter [00:15,  6.32iter/s]Best score: 2.3992: : 85iter [00:15,  6.34iter/s]Best score: 2.3992: : 85iter [00:15,  6.34iter/s]Best score: 2.3992: : 86iter [00:15,  6.33iter/s]Best score: 2.3992: : 86iter [00:15,  6.33iter/s]Best score: 2.3992: : 87iter [00:15,  6.33iter/s]Best score: 2.3992: : 87iter [00:15,  6.33iter/s]Best score: 2.3992: : 88iter [00:15,  6.33iter/s]Best score: 2.3992: : 88iter [00:16,  6.33iter/s]Best score: 2.3992: : 89iter [00:16,  6.34iter/s]Best score: 1.3855: : 89iter [00:16,  6.34iter/s]Best score: 1.3855: : 90iter [00:16,  6.15iter/s]Best score: 1.3855: : 90iter [00:16,  6.15iter/s]Best score: 1.3855: : 91iter [00:16,  6.19iter/s]Best score: 1.3855: : 91iter [00:16,  6.19iter/s]Best score: 1.3855: : 92iter [00:16,  6.23iter/s]Best score: 1.3855: : 92iter [00:16,  6.23iter/s]Best score: 1.3855: : 93iter [00:16,  6.25iter/s]Best score: 1.3855: : 93iter [00:16,  6.25iter/s]Best score: 1.3855: : 94iter [00:16,  6.27iter/s]Best score: 1.3855: : 94iter [00:17,  6.27iter/s]Best score: 1.3855: : 95iter [00:17,  6.29iter/s]Best score: 1.3855: : 95iter [00:17,  6.29iter/s]Best score: 1.3855: : 96iter [00:17,  6.30iter/s]Best score: 1.3855: : 96iter [00:17,  6.30iter/s]Best score: 1.3855: : 97iter [00:17,  6.30iter/s]Best score: 1.3855: : 97iter [00:17,  6.30iter/s]Best score: 1.3855: : 98iter [00:17,  6.31iter/s]Best score: 1.3855: : 98iter [00:17,  6.31iter/s]Best score: 1.3855: : 99iter [00:17,  6.32iter/s]
[ITER 100] Best: 1.3855
[PARAMS] Current best parameters:
  fx: 534.9154
  fy: 535.4136
  px: 981.4662
  py: 777.8447
  k1: -0.00567227
  k2: 0.03380221
  k3: -0.04581799
  k4: 0.01549092
  p1: -0.00539840
  p2: -0.00670072
  p3: 0.00041035
  p4: 0.00451114
  scale: 2.9572
  rotation: 0.2358
Best score: 1.3855: : 99iter [00:17,  6.32iter/s]Best score: 1.3855: : 100iter [00:17,  6.13iter/s]Best score: 1.3855: : 100iter [00:18,  6.13iter/s]Best score: 1.3855: : 101iter [00:18,  6.18iter/s]Best score: 1.3855: : 101iter [00:18,  6.18iter/s]Best score: 1.3855: : 102iter [00:18,  6.23iter/s]Best score: 1.3855: : 102iter [00:18,  6.23iter/s]Best score: 1.3855: : 103iter [00:18,  6.26iter/s]Best score: 1.3855: : 103iter [00:18,  6.26iter/s]Best score: 1.3855: : 104iter [00:18,  6.28iter/s]Best score: 1.3855: : 104iter [00:18,  6.28iter/s]Best score: 1.3855: : 105iter [00:18,  6.30iter/s]Best score: 1.3855: : 105iter [00:18,  6.30iter/s]Best score: 1.3855: : 106iter [00:18,  6.31iter/s]Best score: 1.3855: : 106iter [00:18,  6.31iter/s]Best score: 1.3855: : 107iter [00:18,  6.32iter/s]Best score: 1.3855: : 107iter [00:19,  6.32iter/s]Best score: 1.3855: : 108iter [00:19,  6.33iter/s]Best score: 1.3855: : 108iter [00:19,  6.33iter/s]Best score: 1.3855: : 109iter [00:19,  6.33iter/s]Best score: 1.1796: : 109iter [00:19,  6.33iter/s]Best score: 1.1796: : 110iter [00:19,  6.13iter/s]Best score: 1.1796: : 110iter [00:19,  6.13iter/s]Best score: 1.1796: : 111iter [00:19,  6.20iter/s]Best score: 1.1796: : 111iter [00:19,  6.20iter/s]Best score: 1.1796: : 112iter [00:19,  6.24iter/s]Best score: 1.1796: : 112iter [00:19,  6.24iter/s]Best score: 1.1796: : 113iter [00:19,  6.28iter/s]Best score: 1.1796: : 113iter [00:20,  6.28iter/s]Best score: 1.1796: : 114iter [00:20,  6.31iter/s]Best score: 1.1796: : 114iter [00:20,  6.31iter/s]Best score: 1.1796: : 115iter [00:20,  6.31iter/s]Best score: 1.1796: : 115iter [00:20,  6.31iter/s]Best score: 1.1796: : 116iter [00:20,  6.32iter/s]Best score: 1.1796: : 116iter [00:20,  6.32iter/s]Best score: 1.1796: : 117iter [00:20,  6.33iter/s]Best score: 1.1796: : 117iter [00:20,  6.33iter/s]Best score: 1.1796: : 118iter [00:20,  6.34iter/s]Best score: 1.1796: : 118iter [00:20,  6.34iter/s]Best score: 1.1796: : 119iter [00:20,  6.34iter/s]Best score: 0.4616: : 119iter [00:21,  6.34iter/s]Best score: 0.4616: : 120iter [00:21,  6.16iter/s]Best score: 0.4616: : 120iter [00:21,  6.16iter/s]Best score: 0.4616: : 121iter [00:21,  6.22iter/s]Best score: 0.4616: : 121iter [00:21,  6.22iter/s]Best score: 0.4616: : 122iter [00:21,  6.24iter/s]Best score: 0.4616: : 122iter [00:21,  6.24iter/s]Best score: 0.4616: : 123iter [00:21,  6.28iter/s]Best score: 0.4616: : 123iter [00:21,  6.28iter/s]Best score: 0.4616: : 124iter [00:21,  6.29iter/s]Best score: 0.4616: : 124iter [00:21,  6.29iter/s]Best score: 0.4616: : 125iter [00:21,  6.30iter/s]Best score: 0.4616: : 125iter [00:22,  6.30iter/s]Best score: 0.4616: : 126iter [00:22,  6.30iter/s]Best score: 0.4616: : 126iter [00:22,  6.30iter/s]Best score: 0.4616: : 127iter [00:22,  6.32iter/s]Best score: 0.4616: : 127iter [00:22,  6.32iter/s]Best score: 0.4616: : 128iter [00:22,  6.33iter/s]Best score: 0.4616: : 128iter [00:22,  6.33iter/s]Best score: 0.4616: : 129iter [00:22,  6.35iter/s]Best score: 0.4616: : 129iter [00:22,  6.35iter/s]Best score: 0.4616: : 130iter [00:22,  6.16iter/s]Best score: 0.4616: : 130iter [00:22,  6.16iter/s]Best score: 0.4616: : 131iter [00:22,  6.22iter/s]Best score: 0.4616: : 131iter [00:22,  6.22iter/s]Best score: 0.4616: : 132iter [00:22,  6.28iter/s]Best score: 0.4616: : 132iter [00:23,  6.28iter/s]Best score: 0.4616: : 133iter [00:23,  6.31iter/s]Best score: 0.4616: : 133iter [00:23,  6.31iter/s]Best score: 0.4616: : 134iter [00:23,  6.32iter/s]Best score: 0.4616: : 134iter [00:23,  6.32iter/s]Best score: 0.4616: : 135iter [00:23,  6.35iter/s]Best score: 0.4616: : 135iter [00:23,  6.35iter/s]Best score: 0.4616: : 136iter [00:23,  6.36iter/s]Best score: 0.4616: : 136iter [00:23,  6.36iter/s]Best score: 0.4616: : 137iter [00:23,  6.37iter/s]Best score: 0.4616: : 137iter [00:23,  6.37iter/s]Best score: 0.4616: : 138iter [00:23,  6.38iter/s]Best score: 0.4616: : 138iter [00:24,  6.38iter/s]Best score: 0.4616: : 139iter [00:24,  6.38iter/s]Best score: 0.4322: : 139iter [00:24,  6.38iter/s]Best score: 0.4322: : 140iter [00:24,  6.18iter/s]Best score: 0.4322: : 140iter [00:24,  6.18iter/s]Best score: 0.4322: : 141iter [00:24,  6.25iter/s]Best score: 0.4322: : 141iter [00:24,  6.25iter/s]Best score: 0.4322: : 142iter [00:24,  6.29iter/s]Best score: 0.4322: : 142iter [00:24,  6.29iter/s]Best score: 0.4322: : 143iter [00:24,  6.31iter/s]Best score: 0.4322: : 143iter [00:24,  6.31iter/s]Best score: 0.4322: : 144iter [00:24,  6.32iter/s]Best score: 0.4322: : 144iter [00:25,  6.32iter/s]Best score: 0.4322: : 145iter [00:25,  6.33iter/s]Best score: 0.4322: : 145iter [00:25,  6.33iter/s]Best score: 0.4322: : 146iter [00:25,  6.35iter/s]Best score: 0.4322: : 146iter [00:25,  6.35iter/s]Best score: 0.4322: : 147iter [00:25,  6.37iter/s]Best score: 0.4322: : 147iter [00:25,  6.37iter/s]Best score: 0.4322: : 148iter [00:25,  6.37iter/s]Best score: 0.4322: : 148iter [00:25,  6.37iter/s]Best score: 0.4322: : 149iter [00:25,  6.36iter/s]
[ITER 150] Best: 0.3452
[PARAMS] Current best parameters:
  fx: 520.8328
  fy: 521.0346
  px: 980.3846
  py: 779.7524
  k1: -0.01408956
  k2: 0.04974978
  k3: -0.03635247
  k4: 0.00828033
  p1: 0.00044757
  p2: -0.00771752
  p3: 0.00103991
  p4: 0.00213788
  scale: 2.9836
  rotation: 0.2542
Best score: 0.3452: : 149iter [00:25,  6.36iter/s]Best score: 0.3452: : 150iter [00:25,  6.16iter/s]Best score: 0.3452: : 150iter [00:25,  6.16iter/s]Best score: 0.3452: : 151iter [00:25,  6.22iter/s]Best score: 0.3452: : 151iter [00:26,  6.22iter/s]Best score: 0.3452: : 152iter [00:26,  6.27iter/s]Best score: 0.3452: : 152iter [00:26,  6.27iter/s]Best score: 0.3452: : 153iter [00:26,  6.31iter/s]Best score: 0.3452: : 153iter [00:26,  6.31iter/s]Best score: 0.3452: : 154iter [00:26,  6.32iter/s]Best score: 0.3452: : 154iter [00:26,  6.32iter/s]Best score: 0.3452: : 155iter [00:26,  6.34iter/s]Best score: 0.3452: : 155iter [00:26,  6.34iter/s]Best score: 0.3452: : 156iter [00:26,  6.35iter/s]Best score: 0.3452: : 156iter [00:26,  6.35iter/s]Best score: 0.3452: : 157iter [00:26,  6.35iter/s]Best score: 0.3452: : 157iter [00:27,  6.35iter/s]Best score: 0.3452: : 158iter [00:27,  6.35iter/s]Best score: 0.3452: : 158iter [00:27,  6.35iter/s]Best score: 0.3452: : 159iter [00:27,  6.36iter/s]Best score: 0.3050: : 159iter [00:27,  6.36iter/s]Best score: 0.3050: : 160iter [00:27,  6.17iter/s]Best score: 0.3050: : 160iter [00:27,  6.17iter/s]Best score: 0.3050: : 161iter [00:27,  6.23iter/s]Best score: 0.3050: : 161iter [00:27,  6.23iter/s]Best score: 0.3050: : 162iter [00:27,  6.28iter/s]Best score: 0.3050: : 162iter [00:27,  6.28iter/s]Best score: 0.3050: : 163iter [00:27,  6.30iter/s]Best score: 0.3050: : 163iter [00:28,  6.30iter/s]Best score: 0.3050: : 164iter [00:28,  6.33iter/s]Best score: 0.3050: : 164iter [00:28,  6.33iter/s]Best score: 0.3050: : 165iter [00:28,  6.35iter/s]Best score: 0.3050: : 165iter [00:28,  6.35iter/s]Best score: 0.3050: : 166iter [00:28,  6.36iter/s]Best score: 0.3050: : 166iter [00:28,  6.36iter/s]Best score: 0.3050: : 167iter [00:28,  6.36iter/s]Best score: 0.3050: : 167iter [00:28,  6.36iter/s]Best score: 0.3050: : 168iter [00:28,  6.36iter/s]Best score: 0.3050: : 168iter [00:28,  6.36iter/s]Best score: 0.3050: : 169iter [00:28,  6.36iter/s]Best score: 0.3050: : 169iter [00:28,  6.36iter/s]Best score: 0.3050: : 170iter [00:28,  6.17iter/s]Best score: 0.3050: : 170iter [00:29,  6.17iter/s]Best score: 0.3050: : 171iter [00:29,  6.22iter/s]Best score: 0.3050: : 171iter [00:29,  6.22iter/s]Best score: 0.3050: : 172iter [00:29,  6.28iter/s]Best score: 0.3050: : 172iter [00:29,  6.28iter/s]Best score: 0.3050: : 173iter [00:29,  6.30iter/s]Best score: 0.3050: : 173iter [00:29,  6.30iter/s]Best score: 0.3050: : 174iter [00:29,  6.30iter/s]Best score: 0.3050: : 174iter [00:29,  6.30iter/s]Best score: 0.3050: : 175iter [00:29,  6.32iter/s]Best score: 0.3050: : 175iter [00:29,  6.32iter/s]Best score: 0.3050: : 176iter [00:29,  6.32iter/s]Best score: 0.3050: : 176iter [00:30,  6.32iter/s]Best score: 0.3050: : 177iter [00:30,  6.33iter/s]Best score: 0.3050: : 177iter [00:30,  6.33iter/s]Best score: 0.3050: : 178iter [00:30,  6.36iter/s]Best score: 0.3050: : 178iter [00:30,  6.36iter/s]Best score: 0.3050: : 179iter [00:30,  6.36iter/s]Best score: 0.2861: : 179iter [00:30,  6.36iter/s]Best score: 0.2861: : 180iter [00:30,  6.15iter/s]Best score: 0.2861: : 180iter [00:30,  6.15iter/s]Best score: 0.2861: : 181iter [00:30,  6.20iter/s]Best score: 0.2861: : 181iter [00:30,  6.20iter/s]Best score: 0.2861: : 182iter [00:30,  6.25iter/s]Best score: 0.2861: : 182iter [00:31,  6.25iter/s]Best score: 0.2861: : 183iter [00:31,  6.29iter/s]Best score: 0.2861: : 183iter [00:31,  6.29iter/s]Best score: 0.2861: : 184iter [00:31,  6.31iter/s]Best score: 0.2861: : 184iter [00:31,  6.31iter/s]Best score: 0.2861: : 185iter [00:31,  6.33iter/s]Best score: 0.2861: : 185iter [00:31,  6.33iter/s]Best score: 0.2861: : 186iter [00:31,  6.34iter/s]Best score: 0.2861: : 186iter [00:31,  6.34iter/s]Best score: 0.2861: : 187iter [00:31,  6.35iter/s]Best score: 0.2861: : 187iter [00:31,  6.35iter/s]Best score: 0.2861: : 188iter [00:31,  6.36iter/s]Best score: 0.2861: : 188iter [00:31,  6.36iter/s]Best score: 0.2861: : 189iter [00:31,  6.36iter/s]Best score: 0.2779: : 189iter [00:32,  6.36iter/s]Best score: 0.2779: : 190iter [00:32,  6.16iter/s]Best score: 0.2779: : 190iter [00:32,  6.16iter/s]Best score: 0.2779: : 191iter [00:32,  6.21iter/s]Best score: 0.2779: : 191iter [00:32,  6.21iter/s]Best score: 0.2779: : 192iter [00:32,  6.26iter/s]Best score: 0.2779: : 192iter [00:32,  6.26iter/s]Best score: 0.2779: : 193iter [00:32,  6.29iter/s]Best score: 0.2779: : 193iter [00:32,  6.29iter/s]Best score: 0.2779: : 194iter [00:32,  6.32iter/s]Best score: 0.2779: : 194iter [00:32,  6.32iter/s]Best score: 0.2779: : 195iter [00:32,  6.34iter/s]Best score: 0.2779: : 195iter [00:33,  6.34iter/s]Best score: 0.2779: : 196iter [00:33,  6.35iter/s]Best score: 0.2779: : 196iter [00:33,  6.35iter/s]Best score: 0.2779: : 197iter [00:33,  6.35iter/s]Best score: 0.2779: : 197iter [00:33,  6.35iter/s]Best score: 0.2779: : 198iter [00:33,  6.36iter/s]Best score: 0.2779: : 198iter [00:33,  6.36iter/s]Best score: 0.2779: : 199iter [00:33,  6.36iter/s]
[ITER 200] Best: 0.2779
[PARAMS] Current best parameters:
  fx: 518.5149
  fy: 518.1661
  px: 980.4226
  py: 779.4351
  k1: -0.01440639
  k2: 0.05314641
  k3: -0.03678973
  k4: 0.00788400
  p1: -0.00053146
  p2: -0.00749541
  p3: 0.00343231
  p4: -0.00032862
  scale: 2.9844
  rotation: 0.2308
Best score: 0.2779: : 199iter [00:33,  6.36iter/s]Best score: 0.2779: : 200iter [00:33,  6.15iter/s]Best score: 0.2779: : 200iter [00:33,  6.15iter/s]Best score: 0.2779: : 201iter [00:33,  6.21iter/s]Best score: 0.2779: : 201iter [00:34,  6.21iter/s]Best score: 0.2779: : 202iter [00:34,  6.25iter/s]Best score: 0.2779: : 202iter [00:34,  6.25iter/s]Best score: 0.2779: : 203iter [00:34,  6.29iter/s]Best score: 0.2779: : 203iter [00:34,  6.29iter/s]Best score: 0.2779: : 204iter [00:34,  6.32iter/s]Best score: 0.2779: : 204iter [00:34,  6.32iter/s]Best score: 0.2779: : 205iter [00:34,  6.34iter/s]Best score: 0.2779: : 205iter [00:34,  6.34iter/s]Best score: 0.2779: : 206iter [00:34,  6.35iter/s]Best score: 0.2779: : 206iter [00:34,  6.35iter/s]Best score: 0.2779: : 207iter [00:34,  6.37iter/s]Best score: 0.2779: : 207iter [00:35,  6.37iter/s]Best score: 0.2779: : 208iter [00:35,  6.38iter/s]Best score: 0.2779: : 208iter [00:35,  6.38iter/s]Best score: 0.2779: : 209iter [00:35,  6.38iter/s]Best score: 0.2779: : 209iter [00:35,  6.38iter/s]Best score: 0.2779: : 210iter [00:35,  6.19iter/s]Best score: 0.2779: : 210iter [00:35,  6.19iter/s]Best score: 0.2779: : 211iter [00:35,  4.96iter/s]Best score: 0.2779: : 211iter [00:35,  4.96iter/s]Best score: 0.2779: : 212iter [00:35,  5.32iter/s]Best score: 0.2779: : 212iter [00:35,  5.32iter/s]Best score: 0.2779: : 213iter [00:35,  5.60iter/s]Best score: 0.2779: : 213iter [00:36,  5.60iter/s]Best score: 0.2779: : 214iter [00:36,  5.82iter/s]Best score: 0.2779: : 214iter [00:36,  5.82iter/s]Best score: 0.2779: : 215iter [00:36,  5.98iter/s]Best score: 0.2779: : 215iter [00:36,  5.98iter/s]Best score: 0.2779: : 216iter [00:36,  6.10iter/s]Best score: 0.2779: : 216iter [00:36,  6.10iter/s]Best score: 0.2779: : 217iter [00:36,  6.16iter/s]Best score: 0.2779: : 217iter [00:36,  6.16iter/s]Best score: 0.2779: : 218iter [00:36,  6.22iter/s]Best score: 0.2779: : 218iter [00:36,  6.22iter/s]Best score: 0.2779: : 219iter [00:36,  6.23iter/s]Best score: 0.2766: : 219iter [00:37,  6.23iter/s]Best score: 0.2766: : 220iter [00:37,  6.07iter/s]Best score: 0.2766: : 220iter [00:37,  6.07iter/s]Best score: 0.2766: : 221iter [00:37,  6.16iter/s]Best score: 0.2766: : 221iter [00:37,  6.16iter/s]Best score: 0.2766: : 222iter [00:37,  6.21iter/s]Best score: 0.2766: : 222iter [00:37,  6.21iter/s]Best score: 0.2766: : 223iter [00:37,  6.26iter/s]Best score: 0.2766: : 223iter [00:37,  6.26iter/s]Best score: 0.2766: : 224iter [00:37,  6.28iter/s]Best score: 0.2766: : 224iter [00:37,  6.28iter/s]Best score: 0.2766: : 225iter [00:37,  6.30iter/s]Best score: 0.2766: : 225iter [00:38,  6.30iter/s]Best score: 0.2766: : 226iter [00:38,  6.31iter/s]Best score: 0.2766: : 226iter [00:38,  6.31iter/s]Best score: 0.2766: : 227iter [00:38,  6.33iter/s]Best score: 0.2766: : 227iter [00:38,  6.33iter/s]Best score: 0.2766: : 228iter [00:38,  6.35iter/s]Best score: 0.2766: : 228iter [00:38,  6.35iter/s]Best score: 0.2766: : 229iter [00:38,  6.35iter/s]Best score: 0.2749: : 229iter [00:38,  6.35iter/s]Best score: 0.2749: : 230iter [00:38,  6.15iter/s]Best score: 0.2749: : 230iter [00:38,  6.15iter/s]Best score: 0.2749: : 231iter [00:38,  6.21iter/s]Best score: 0.2749: : 231iter [00:38,  6.21iter/s]Best score: 0.2749: : 232iter [00:38,  6.26iter/s]Best score: 0.2749: : 232iter [00:39,  6.26iter/s]Best score: 0.2749: : 233iter [00:39,  6.31iter/s]Best score: 0.2749: : 233iter [00:39,  6.31iter/s]Best score: 0.2749: : 234iter [00:39,  6.33iter/s]Best score: 0.2749: : 234iter [00:39,  6.33iter/s]Best score: 0.2749: : 235iter [00:39,  6.35iter/s]Best score: 0.2749: : 235iter [00:39,  6.35iter/s]Best score: 0.2749: : 236iter [00:39,  6.36iter/s]Best score: 0.2749: : 236iter [00:39,  6.36iter/s]Best score: 0.2749: : 237iter [00:39,  6.35iter/s]Best score: 0.2749: : 237iter [00:39,  6.35iter/s]Best score: 0.2749: : 238iter [00:39,  6.36iter/s]Best score: 0.2749: : 238iter [00:40,  6.36iter/s]Best score: 0.2749: : 239iter [00:40,  6.37iter/s]Best score: 0.2698: : 239iter [00:40,  6.37iter/s]Best score: 0.2698: : 240iter [00:40,  6.17iter/s]Best score: 0.2698: : 240iter [00:40,  6.17iter/s]Best score: 0.2698: : 241iter [00:40,  6.22iter/s]Best score: 0.2698: : 241iter [00:40,  6.22iter/s]Best score: 0.2698: : 242iter [00:40,  6.26iter/s]Best score: 0.2698: : 242iter [00:40,  6.26iter/s]Best score: 0.2698: : 243iter [00:40,  6.30iter/s]Best score: 0.2698: : 243iter [00:40,  6.30iter/s]Best score: 0.2698: : 244iter [00:40,  6.33iter/s]Best score: 0.2698: : 244iter [00:41,  6.33iter/s]Best score: 0.2698: : 245iter [00:41,  6.35iter/s]Best score: 0.2698: : 245iter [00:41,  6.35iter/s]Best score: 0.2698: : 246iter [00:41,  6.36iter/s]Best score: 0.2698: : 246iter [00:41,  6.36iter/s]Best score: 0.2698: : 247iter [00:41,  6.36iter/s]Best score: 0.2698: : 247iter [00:41,  6.36iter/s]Best score: 0.2698: : 248iter [00:41,  6.35iter/s]Best score: 0.2698: : 248iter [00:41,  6.35iter/s]Best score: 0.2698: : 249iter [00:41,  6.35iter/s]
[ITER 250] Best: 0.2653
[PARAMS] Current best parameters:
  fx: 533.5404
  fy: 533.2494
  px: 980.1474
  py: 778.6955
  k1: -0.02410222
  k2: 0.04612509
  k3: -0.03348283
  k4: 0.00768613
  p1: -0.00220421
  p2: -0.00867160
  p3: 0.00474299
  p4: -0.00029544
  scale: 2.9727
  rotation: 0.2506
Best score: 0.2653: : 249iter [00:41,  6.35iter/s]Best score: 0.2653: : 250iter [00:41,  6.16iter/s]Best score: 0.2653: : 250iter [00:41,  6.16iter/s]Best score: 0.2653: : 251iter [00:41,  6.22iter/s]Best score: 0.2653: : 251iter [00:42,  6.22iter/s]Best score: 0.2653: : 252iter [00:42,  6.27iter/s]Best score: 0.2653: : 252iter [00:42,  6.27iter/s]Best score: 0.2653: : 253iter [00:42,  6.31iter/s]Best score: 0.2653: : 253iter [00:42,  6.31iter/s]Best score: 0.2653: : 254iter [00:42,  6.33iter/s]Best score: 0.2653: : 254iter [00:42,  6.33iter/s]Best score: 0.2653: : 255iter [00:42,  6.34iter/s]Best score: 0.2653: : 255iter [00:42,  6.34iter/s]Best score: 0.2653: : 256iter [00:42,  6.35iter/s]Best score: 0.2653: : 256iter [00:42,  6.35iter/s]Best score: 0.2653: : 257iter [00:42,  6.36iter/s]Best score: 0.2653: : 257iter [00:43,  6.36iter/s]Best score: 0.2653: : 258iter [00:43,  6.37iter/s]Best score: 0.2653: : 258iter [00:43,  6.37iter/s]Best score: 0.2653: : 259iter [00:43,  6.36iter/s]Best score: 0.2631: : 259iter [00:43,  6.36iter/s]Best score: 0.2631: : 260iter [00:43,  6.17iter/s]Best score: 0.2631: : 260iter [00:43,  6.17iter/s]Best score: 0.2631: : 261iter [00:43,  6.23iter/s]Best score: 0.2631: : 261iter [00:43,  6.23iter/s]Best score: 0.2631: : 262iter [00:43,  6.28iter/s]Best score: 0.2631: : 262iter [00:43,  6.28iter/s]Best score: 0.2631: : 263iter [00:43,  6.30iter/s]Best score: 0.2631: : 263iter [00:44,  6.30iter/s]Best score: 0.2631: : 264iter [00:44,  6.32iter/s]Best score: 0.2631: : 264iter [00:44,  6.32iter/s]Best score: 0.2631: : 265iter [00:44,  6.34iter/s]Best score: 0.2631: : 265iter [00:44,  6.34iter/s]Best score: 0.2631: : 266iter [00:44,  6.34iter/s]Best score: 0.2631: : 266iter [00:44,  6.34iter/s]Best score: 0.2631: : 267iter [00:44,  6.36iter/s]Best score: 0.2631: : 267iter [00:44,  6.36iter/s]Best score: 0.2631: : 268iter [00:44,  6.35iter/s]Best score: 0.2631: : 268iter [00:44,  6.35iter/s]Best score: 0.2631: : 269iter [00:44,  6.35iter/s]Best score: 0.2622: : 269iter [00:45,  6.35iter/s]Best score: 0.2622: : 270iter [00:45,  6.17iter/s]Best score: 0.2622: : 270iter [00:45,  6.17iter/s]Best score: 0.2622: : 271iter [00:45,  6.23iter/s]Best score: 0.2622: : 271iter [00:45,  6.23iter/s]Best score: 0.2622: : 272iter [00:45,  6.27iter/s]Best score: 0.2622: : 272iter [00:45,  6.27iter/s]Best score: 0.2622: : 273iter [00:45,  6.30iter/s]Best score: 0.2622: : 273iter [00:45,  6.30iter/s]Best score: 0.2622: : 274iter [00:45,  6.30iter/s]Best score: 0.2622: : 274iter [00:45,  6.30iter/s]Best score: 0.2622: : 275iter [00:45,  6.31iter/s]Best score: 0.2622: : 275iter [00:45,  6.31iter/s]Best score: 0.2622: : 276iter [00:45,  6.32iter/s]Best score: 0.2622: : 276iter [00:46,  6.32iter/s]Best score: 0.2622: : 277iter [00:46,  6.34iter/s]Best score: 0.2622: : 277iter [00:46,  6.34iter/s]Best score: 0.2622: : 278iter [00:46,  6.34iter/s]Best score: 0.2622: : 278iter [00:46,  6.34iter/s]Best score: 0.2622: : 279iter [00:46,  6.35iter/s]Best score: 0.2614: : 279iter [00:46,  6.35iter/s]Best score: 0.2614: : 280iter [00:46,  6.15iter/s]Best score: 0.2614: : 280iter [00:46,  6.15iter/s]Best score: 0.2614: : 281iter [00:46,  6.21iter/s]Best score: 0.2614: : 281iter [00:46,  6.21iter/s]Best score: 0.2614: : 282iter [00:46,  6.27iter/s]Best score: 0.2614: : 282iter [00:47,  6.27iter/s]Best score: 0.2614: : 283iter [00:47,  6.31iter/s]Best score: 0.2614: : 283iter [00:47,  6.31iter/s]Best score: 0.2614: : 284iter [00:47,  6.34iter/s]Best score: 0.2614: : 284iter [00:47,  6.34iter/s]Best score: 0.2614: : 285iter [00:47,  6.35iter/s]Best score: 0.2614: : 285iter [00:47,  6.35iter/s]Best score: 0.2614: : 286iter [00:47,  6.36iter/s]Best score: 0.2614: : 286iter [00:47,  6.36iter/s]Best score: 0.2614: : 287iter [00:47,  6.36iter/s]Best score: 0.2614: : 287iter [00:47,  6.36iter/s]Best score: 0.2614: : 288iter [00:47,  6.37iter/s]Best score: 0.2614: : 288iter [00:48,  6.37iter/s]Best score: 0.2614: : 289iter [00:48,  6.36iter/s]Best score: 0.2611: : 289iter [00:48,  6.36iter/s]Best score: 0.2611: : 290iter [00:48,  6.17iter/s]Best score: 0.2611: : 290iter [00:48,  6.17iter/s]Best score: 0.2611: : 291iter [00:48,  6.22iter/s]Best score: 0.2611: : 291iter [00:48,  6.22iter/s]Best score: 0.2611: : 292iter [00:48,  6.25iter/s]Best score: 0.2611: : 292iter [00:48,  6.25iter/s]Best score: 0.2611: : 293iter [00:48,  6.28iter/s]Best score: 0.2611: : 293iter [00:48,  6.28iter/s]Best score: 0.2611: : 294iter [00:48,  6.31iter/s]Best score: 0.2611: : 294iter [00:48,  6.31iter/s]Best score: 0.2611: : 295iter [00:48,  6.34iter/s]Best score: 0.2611: : 295iter [00:49,  6.34iter/s]Best score: 0.2611: : 296iter [00:49,  6.34iter/s]Best score: 0.2611: : 296iter [00:49,  6.34iter/s]Best score: 0.2611: : 297iter [00:49,  6.36iter/s]Best score: 0.2611: : 297iter [00:49,  6.36iter/s]Best score: 0.2611: : 298iter [00:49,  6.37iter/s]Best score: 0.2611: : 298iter [00:49,  6.37iter/s]Best score: 0.2611: : 299iter [00:49,  6.37iter/s]
[ITER 300] Best: 0.2602
[PARAMS] Current best parameters:
  fx: 528.7363
  fy: 528.5291
  px: 979.5882
  py: 778.5626
  k1: -0.01961518
  k2: 0.04409604
  k3: -0.03107856
  k4: 0.00687770
  p1: -0.00255932
  p2: -0.00968796
  p3: 0.00451846
  p4: 0.00001861
  scale: 2.9777
  rotation: 0.2294
Best score: 0.2602: : 299iter [00:49,  6.37iter/s]Best score: 0.2602: : 300iter [00:49,  6.18iter/s]Best score: 0.2602: : 300iter [00:49,  6.18iter/s]Best score: 0.2602: : 301iter [00:49,  6.24iter/s]Best score: 0.2602: : 301iter [00:50,  6.24iter/s]Best score: 0.2602: : 302iter [00:50,  6.28iter/s]Best score: 0.2602: : 302iter [00:50,  6.28iter/s]Best score: 0.2602: : 303iter [00:50,  6.30iter/s]Best score: 0.2602: : 303iter [00:50,  6.30iter/s]Best score: 0.2602: : 304iter [00:50,  6.31iter/s]Best score: 0.2602: : 304iter [00:50,  6.31iter/s]Best score: 0.2602: : 305iter [00:50,  6.32iter/s]Best score: 0.2602: : 305iter [00:50,  6.32iter/s]Best score: 0.2602: : 306iter [00:50,  6.34iter/s]Best score: 0.2602: : 306iter [00:50,  6.34iter/s]Best score: 0.2602: : 307iter [00:50,  6.35iter/s]Best score: 0.2602: : 307iter [00:51,  6.35iter/s]Best score: 0.2602: : 308iter [00:51,  6.36iter/s]Best score: 0.2602: : 308iter [00:51,  6.36iter/s]Best score: 0.2602: : 309iter [00:51,  6.38iter/s]Best score: 0.2591: : 309iter [00:51,  6.38iter/s]Best score: 0.2591: : 310iter [00:51,  6.18iter/s]Best score: 0.2591: : 310iter [00:51,  6.18iter/s]Best score: 0.2591: : 311iter [00:51,  6.24iter/s]Best score: 0.2591: : 311iter [00:51,  6.24iter/s]Best score: 0.2591: : 312iter [00:51,  6.27iter/s]Best score: 0.2591: : 312iter [00:51,  6.27iter/s]Best score: 0.2591: : 313iter [00:51,  6.30iter/s]Best score: 0.2591: : 313iter [00:51,  6.30iter/s]Best score: 0.2591: : 314iter [00:51,  6.33iter/s]Best score: 0.2591: : 314iter [00:52,  6.33iter/s]Best score: 0.2591: : 315iter [00:52,  6.36iter/s]Best score: 0.2591: : 315iter [00:52,  6.36iter/s]Best score: 0.2591: : 316iter [00:52,  6.37iter/s]Best score: 0.2591: : 316iter [00:52,  6.37iter/s]Best score: 0.2591: : 317iter [00:52,  6.36iter/s]Best score: 0.2591: : 317iter [00:52,  6.36iter/s]Best score: 0.2591: : 318iter [00:52,  6.36iter/s]Best score: 0.2591: : 318iter [00:52,  6.36iter/s]Best score: 0.2591: : 319iter [00:52,  6.36iter/s]Best score: 0.2583: : 319iter [00:52,  6.36iter/s]Best score: 0.2583: : 320iter [00:52,  6.17iter/s]Best score: 0.2583: : 320iter [00:53,  6.17iter/s]Best score: 0.2583: : 321iter [00:53,  6.24iter/s]Best score: 0.2583: : 321iter [00:53,  6.24iter/s]Best score: 0.2583: : 322iter [00:53,  6.29iter/s]Best score: 0.2583: : 322iter [00:53,  6.29iter/s]Best score: 0.2583: : 323iter [00:53,  6.33iter/s]Best score: 0.2583: : 323iter [00:53,  6.33iter/s]Best score: 0.2583: : 324iter [00:53,  6.33iter/s]Best score: 0.2583: : 324iter [00:53,  6.33iter/s]Best score: 0.2583: : 325iter [00:53,  6.35iter/s]Best score: 0.2583: : 325iter [00:53,  6.35iter/s]Best score: 0.2583: : 326iter [00:53,  6.37iter/s]Best score: 0.2583: : 326iter [00:54,  6.37iter/s]Best score: 0.2583: : 327iter [00:54,  6.38iter/s]Best score: 0.2583: : 327iter [00:54,  6.38iter/s]Best score: 0.2583: : 328iter [00:54,  6.38iter/s]Best score: 0.2583: : 328iter [00:54,  6.38iter/s]Best score: 0.2583: : 329iter [00:54,  6.39iter/s]Best score: 0.2579: : 329iter [00:54,  6.39iter/s]Best score: 0.2579: : 330iter [00:54,  6.18iter/s]Best score: 0.2579: : 330iter [00:54,  6.18iter/s]Best score: 0.2579: : 331iter [00:54,  6.24iter/s]Best score: 0.2579: : 331iter [00:54,  6.24iter/s]Best score: 0.2579: : 332iter [00:54,  6.28iter/s]Best score: 0.2579: : 332iter [00:54,  6.28iter/s]Best score: 0.2579: : 333iter [00:54,  6.31iter/s]Best score: 0.2579: : 333iter [00:55,  6.31iter/s]Best score: 0.2579: : 334iter [00:55,  6.34iter/s]Best score: 0.2579: : 334iter [00:55,  6.34iter/s]Best score: 0.2579: : 335iter [00:55,  6.36iter/s]Best score: 0.2579: : 335iter [00:55,  6.36iter/s]Best score: 0.2579: : 336iter [00:55,  6.36iter/s]Best score: 0.2579: : 336iter [00:55,  6.36iter/s]Best score: 0.2579: : 337iter [00:55,  6.37iter/s]Best score: 0.2579: : 337iter [00:55,  6.37iter/s]Best score: 0.2579: : 338iter [00:55,  6.38iter/s]Best score: 0.2579: : 338iter [00:55,  6.38iter/s]Best score: 0.2579: : 339iter [00:55,  6.39iter/s]Best score: 0.2574: : 339iter [00:56,  6.39iter/s]Best score: 0.2574: : 340iter [00:56,  6.20iter/s]Best score: 0.2574: : 340iter [00:56,  6.20iter/s]Best score: 0.2574: : 341iter [00:56,  6.26iter/s]Best score: 0.2574: : 341iter [00:56,  6.26iter/s]Best score: 0.2574: : 342iter [00:56,  6.31iter/s]Best score: 0.2574: : 342iter [00:56,  6.31iter/s]Best score: 0.2574: : 343iter [00:56,  6.32iter/s]Best score: 0.2574: : 343iter [00:56,  6.32iter/s]Best score: 0.2574: : 344iter [00:56,  6.35iter/s]Best score: 0.2574: : 344iter [00:56,  6.35iter/s]Best score: 0.2574: : 345iter [00:56,  6.36iter/s]Best score: 0.2574: : 345iter [00:57,  6.36iter/s]Best score: 0.2574: : 346iter [00:57,  6.34iter/s]Best score: 0.2574: : 346iter [00:57,  6.34iter/s]Best score: 0.2574: : 347iter [00:57,  6.36iter/s]Best score: 0.2574: : 347iter [00:57,  6.36iter/s]Best score: 0.2574: : 348iter [00:57,  6.37iter/s]Best score: 0.2574: : 348iter [00:57,  6.37iter/s]Best score: 0.2574: : 349iter [00:57,  6.38iter/s]
[ITER 350] Best: 0.2570
[PARAMS] Current best parameters:
  fx: 537.3917
  fy: 537.1958
  px: 979.6225
  py: 778.5629
  k1: -0.01844595
  k2: 0.02651640
  k3: -0.01959339
  k4: 0.00448456
  p1: -0.00260232
  p2: -0.00978630
  p3: 0.00472134
  p4: -0.00021042
  scale: 2.9676
  rotation: 0.2334
Best score: 0.2570: : 349iter [00:57,  6.38iter/s]Best score: 0.2570: : 350iter [00:57,  6.19iter/s]Best score: 0.2570: : 350iter [00:57,  6.19iter/s]Best score: 0.2570: : 351iter [00:57,  6.25iter/s]Best score: 0.2570: : 351iter [00:57,  6.25iter/s]Best score: 0.2570: : 352iter [00:57,  6.28iter/s]Best score: 0.2570: : 352iter [00:58,  6.28iter/s]Best score: 0.2570: : 353iter [00:58,  6.32iter/s]Best score: 0.2570: : 353iter [00:58,  6.32iter/s]Best score: 0.2570: : 354iter [00:58,  6.35iter/s]Best score: 0.2570: : 354iter [00:58,  6.35iter/s]Best score: 0.2570: : 355iter [00:58,  6.35iter/s]Best score: 0.2570: : 355iter [00:58,  6.35iter/s]Best score: 0.2570: : 356iter [00:58,  6.36iter/s]Best score: 0.2570: : 356iter [00:58,  6.36iter/s]Best score: 0.2570: : 357iter [00:58,  6.36iter/s]Best score: 0.2570: : 357iter [00:58,  6.36iter/s]Best score: 0.2570: : 358iter [00:58,  6.37iter/s]Best score: 0.2570: : 358iter [00:59,  6.37iter/s]Best score: 0.2570: : 359iter [00:59,  6.38iter/s]Best score: 0.2568: : 359iter [00:59,  6.38iter/s]Best score: 0.2568: : 360iter [00:59,  6.19iter/s]Best score: 0.2568: : 360iter [00:59,  6.19iter/s]Best score: 0.2568: : 361iter [00:59,  6.25iter/s]Best score: 0.2568: : 361iter [00:59,  6.25iter/s]Best score: 0.2568: : 362iter [00:59,  6.28iter/s]Best score: 0.2568: : 362iter [00:59,  6.28iter/s]Best score: 0.2568: : 363iter [00:59,  6.31iter/s]Best score: 0.2568: : 363iter [00:59,  6.31iter/s]Best score: 0.2568: : 364iter [00:59,  6.32iter/s]Best score: 0.2568: : 364iter [01:00,  6.32iter/s]Best score: 0.2568: : 365iter [01:00,  6.34iter/s]Best score: 0.2568: : 365iter [01:00,  6.34iter/s]Best score: 0.2568: : 366iter [01:00,  6.36iter/s]Best score: 0.2568: : 366iter [01:00,  6.36iter/s]Best score: 0.2568: : 367iter [01:00,  6.34iter/s]Best score: 0.2568: : 367iter [01:00,  6.34iter/s]Best score: 0.2568: : 368iter [01:00,  6.34iter/s]Best score: 0.2568: : 368iter [01:00,  6.34iter/s]Best score: 0.2568: : 369iter [01:00,  6.34iter/s]Best score: 0.2563: : 369iter [01:00,  6.34iter/s]Best score: 0.2563: : 370iter [01:00,  6.14iter/s]Best score: 0.2563: : 370iter [01:01,  6.14iter/s]Best score: 0.2563: : 371iter [01:01,  6.20iter/s]Best score: 0.2563: : 371iter [01:01,  6.20iter/s]Best score: 0.2563: : 372iter [01:01,  6.25iter/s]Best score: 0.2563: : 372iter [01:01,  6.25iter/s]Best score: 0.2563: : 373iter [01:01,  6.28iter/s]Best score: 0.2563: : 373iter [01:01,  6.28iter/s]Best score: 0.2563: : 374iter [01:01,  6.30iter/s]Best score: 0.2563: : 374iter [01:01,  6.30iter/s]Best score: 0.2563: : 375iter [01:01,  6.32iter/s]Best score: 0.2563: : 375iter [01:01,  6.32iter/s]Best score: 0.2563: : 376iter [01:01,  6.35iter/s]Best score: 0.2563: : 376iter [01:01,  6.35iter/s]Best score: 0.2563: : 377iter [01:01,  6.36iter/s]Best score: 0.2563: : 377iter [01:02,  6.36iter/s]Best score: 0.2563: : 378iter [01:02,  6.38iter/s]Best score: 0.2563: : 378iter [01:02,  6.38iter/s]Best score: 0.2563: : 379iter [01:02,  6.39iter/s]Best score: 0.2562: : 379iter [01:02,  6.39iter/s]Best score: 0.2562: : 380iter [01:02,  6.19iter/s]Best score: 0.2562: : 380iter [01:02,  6.19iter/s]Best score: 0.2562: : 381iter [01:02,  6.25iter/s]Best score: 0.2562: : 381iter [01:02,  6.25iter/s]Best score: 0.2562: : 382iter [01:02,  6.30iter/s]Best score: 0.2562: : 382iter [01:02,  6.30iter/s]Best score: 0.2562: : 383iter [01:02,  6.33iter/s]Best score: 0.2562: : 383iter [01:03,  6.33iter/s]Best score: 0.2562: : 384iter [01:03,  6.36iter/s]Best score: 0.2562: : 384iter [01:03,  6.36iter/s]Best score: 0.2562: : 385iter [01:03,  6.37iter/s]Best score: 0.2562: : 385iter [01:03,  6.37iter/s]Best score: 0.2562: : 386iter [01:03,  6.37iter/s]Best score: 0.2562: : 386iter [01:03,  6.37iter/s]Best score: 0.2562: : 387iter [01:03,  6.37iter/s]Best score: 0.2562: : 387iter [01:03,  6.37iter/s]Best score: 0.2562: : 388iter [01:03,  6.37iter/s]Best score: 0.2562: : 388iter [01:03,  6.37iter/s]Best score: 0.2562: : 389iter [01:03,  6.39iter/s]Best score: 0.2561: : 389iter [01:04,  6.39iter/s]Best score: 0.2561: : 390iter [01:04,  6.18iter/s]Best score: 0.2561: : 390iter [01:04,  6.18iter/s]Best score: 0.2561: : 391iter [01:04,  6.24iter/s]Best score: 0.2561: : 391iter [01:04,  6.24iter/s]Best score: 0.2561: : 392iter [01:04,  6.28iter/s]Best score: 0.2561: : 392iter [01:04,  6.28iter/s]Best score: 0.2561: : 393iter [01:04,  6.30iter/s]Best score: 0.2561: : 393iter [01:04,  6.30iter/s]Best score: 0.2561: : 394iter [01:04,  6.33iter/s]Best score: 0.2561: : 394iter [01:04,  6.33iter/s]Best score: 0.2561: : 395iter [01:04,  6.33iter/s]Best score: 0.2561: : 395iter [01:04,  6.33iter/s]Best score: 0.2561: : 396iter [01:04,  6.35iter/s]Best score: 0.2561: : 396iter [01:05,  6.35iter/s]Best score: 0.2561: : 397iter [01:05,  6.37iter/s]Best score: 0.2561: : 397iter [01:05,  6.37iter/s]Best score: 0.2561: : 398iter [01:05,  6.37iter/s]Best score: 0.2561: : 398iter [01:05,  6.37iter/s]Best score: 0.2561: : 399iter [01:05,  6.38iter/s]
[ITER 400] Best: 0.2559
[PARAMS] Current best parameters:
  fx: 537.9955
  fy: 537.7651
  px: 979.5924
  py: 778.3587
  k1: -0.01561486
  k2: 0.02000621
  k3: -0.01515054
  k4: 0.00345572
  p1: -0.00305476
  p2: -0.00993973
  p3: 0.00480937
  p4: -0.00005871
  scale: 2.9654
  rotation: 0.2448
Best score: 0.2559: : 399iter [01:05,  6.38iter/s]Best score: 0.2559: : 400iter [01:05,  6.17iter/s]Best score: 0.2559: : 400iter [01:05,  6.17iter/s]Best score: 0.2559: : 401iter [01:05,  6.23iter/s]Best score: 0.2559: : 401iter [01:05,  6.23iter/s]Best score: 0.2559: : 402iter [01:05,  6.27iter/s]Best score: 0.2559: : 402iter [01:06,  6.27iter/s]Best score: 0.2559: : 403iter [01:06,  6.30iter/s]Best score: 0.2559: : 403iter [01:06,  6.30iter/s]Best score: 0.2559: : 404iter [01:06,  6.33iter/s]Best score: 0.2559: : 404iter [01:06,  6.33iter/s]Best score: 0.2559: : 405iter [01:06,  6.35iter/s]Best score: 0.2559: : 405iter [01:06,  6.35iter/s]Best score: 0.2559: : 406iter [01:06,  6.36iter/s]Best score: 0.2559: : 406iter [01:06,  6.36iter/s]Best score: 0.2559: : 407iter [01:06,  6.36iter/s]Best score: 0.2559: : 407iter [01:06,  6.36iter/s]Best score: 0.2559: : 408iter [01:06,  6.37iter/s]Best score: 0.2559: : 408iter [01:07,  6.37iter/s]Best score: 0.2559: : 409iter [01:07,  6.36iter/s]Best score: 0.2559: : 409iter [01:07,  6.36iter/s]Best score: 0.2559: : 410iter [01:07,  6.17iter/s]Best score: 0.2559: : 410iter [01:07,  6.17iter/s]Best score: 0.2559: : 411iter [01:07,  6.24iter/s]Best score: 0.2559: : 411iter [01:07,  6.24iter/s]Best score: 0.2559: : 412iter [01:07,  6.29iter/s]Best score: 0.2559: : 412iter [01:07,  6.29iter/s]Best score: 0.2559: : 413iter [01:07,  6.31iter/s]Best score: 0.2559: : 413iter [01:07,  6.31iter/s]Best score: 0.2559: : 414iter [01:07,  6.34iter/s]Best score: 0.2559: : 414iter [01:07,  6.34iter/s]Best score: 0.2559: : 415iter [01:07,  6.34iter/s]Best score: 0.2559: : 415iter [01:08,  6.34iter/s]Best score: 0.2559: : 416iter [01:08,  6.34iter/s]Best score: 0.2559: : 416iter [01:08,  6.34iter/s]Best score: 0.2559: : 417iter [01:08,  6.35iter/s]Best score: 0.2559: : 417iter [01:08,  6.35iter/s]Best score: 0.2559: : 418iter [01:08,  6.35iter/s]Best score: 0.2559: : 418iter [01:08,  6.35iter/s]Best score: 0.2559: : 419iter [01:08,  6.34iter/s]Best score: 0.2558: : 419iter [01:08,  6.34iter/s]Best score: 0.2558: : 420iter [01:08,  6.15iter/s]Best score: 0.2558: : 420iter [01:08,  6.15iter/s]Best score: 0.2558: : 421iter [01:08,  6.20iter/s]Best score: 0.2558: : 421iter [01:09,  6.20iter/s]Best score: 0.2558: : 422iter [01:09,  6.26iter/s]Best score: 0.2558: : 422iter [01:09,  6.26iter/s]Best score: 0.2558: : 423iter [01:09,  6.28iter/s]Best score: 0.2558: : 423iter [01:09,  6.28iter/s]Best score: 0.2558: : 424iter [01:09,  6.31iter/s]Best score: 0.2558: : 424iter [01:09,  6.31iter/s]Best score: 0.2558: : 425iter [01:09,  6.32iter/s]Best score: 0.2558: : 425iter [01:09,  6.32iter/s]Best score: 0.2558: : 426iter [01:09,  6.34iter/s]Best score: 0.2558: : 426iter [01:09,  6.34iter/s]Best score: 0.2558: : 427iter [01:09,  6.36iter/s]Best score: 0.2558: : 427iter [01:10,  6.36iter/s]Best score: 0.2558: : 428iter [01:10,  6.37iter/s]Best score: 0.2558: : 428iter [01:10,  6.37iter/s]Best score: 0.2558: : 429iter [01:10,  6.39iter/s]Best score: 0.2558: : 429iter [01:10,  6.39iter/s]Best score: 0.2558: : 430iter [01:10,  6.20iter/s]Best score: 0.2558: : 430iter [01:10,  6.20iter/s]Best score: 0.2558: : 431iter [01:10,  6.26iter/s]Best score: 0.2558: : 431iter [01:10,  6.26iter/s]Best score: 0.2558: : 432iter [01:10,  6.29iter/s]Best score: 0.2558: : 432iter [01:10,  6.29iter/s]Best score: 0.2558: : 433iter [01:10,  6.31iter/s]Best score: 0.2558: : 433iter [01:10,  6.31iter/s]Best score: 0.2558: : 434iter [01:10,  6.32iter/s]Best score: 0.2558: : 434iter [01:11,  6.32iter/s]Best score: 0.2558: : 435iter [01:11,  6.34iter/s]Best score: 0.2558: : 435iter [01:11,  6.34iter/s]Best score: 0.2558: : 436iter [01:11,  6.36iter/s]Best score: 0.2558: : 436iter [01:11,  6.36iter/s]Best score: 0.2558: : 437iter [01:11,  6.36iter/s]Best score: 0.2558: : 437iter [01:11,  6.36iter/s]Best score: 0.2558: : 438iter [01:11,  6.35iter/s]Best score: 0.2558: : 438iter [01:11,  6.35iter/s]Best score: 0.2558: : 439iter [01:11,  6.37iter/s]Best score: 0.2557: : 439iter [01:11,  6.37iter/s]Best score: 0.2557: : 440iter [01:11,  6.17iter/s]Best score: 0.2557: : 440iter [01:12,  6.17iter/s]Best score: 0.2557: : 441iter [01:12,  6.23iter/s]Best score: 0.2557: : 441iter [01:12,  6.23iter/s]Best score: 0.2557: : 442iter [01:12,  6.27iter/s]Best score: 0.2557: : 442iter [01:12,  6.27iter/s]Best score: 0.2557: : 443iter [01:12,  6.31iter/s]Best score: 0.2557: : 443iter [01:12,  6.31iter/s]Best score: 0.2557: : 444iter [01:12,  6.33iter/s]Best score: 0.2557: : 444iter [01:12,  6.33iter/s]Best score: 0.2557: : 445iter [01:12,  6.34iter/s]Best score: 0.2557: : 445iter [01:12,  6.34iter/s]Best score: 0.2557: : 446iter [01:12,  6.35iter/s]Best score: 0.2557: : 446iter [01:13,  6.35iter/s]Best score: 0.2557: : 447iter [01:13,  6.35iter/s]Best score: 0.2557: : 447iter [01:13,  6.35iter/s]Best score: 0.2557: : 448iter [01:13,  6.36iter/s]Best score: 0.2557: : 448iter [01:13,  6.36iter/s]Best score: 0.2557: : 449iter [01:13,  6.38iter/s]
[ITER 450] Best: 0.2556
[PARAMS] Current best parameters:
  fx: 537.8861
  fy: 537.6599
  px: 979.5968
  py: 778.2417
  k1: -0.01229285
  k2: 0.01425013
  k3: -0.01142159
  k4: 0.00260968
  p1: -0.00332447
  p2: -0.00996762
  p3: 0.00498971
  p4: -0.00011783
  scale: 2.9632
  rotation: 0.2422
Best score: 0.2556: : 449iter [01:13,  6.38iter/s]Best score: 0.2556: : 450iter [01:13,  6.19iter/s]Best score: 0.2556: : 450iter [01:13,  6.19iter/s]Best score: 0.2556: : 451iter [01:13,  6.25iter/s]Best score: 0.2556: : 451iter [01:13,  6.25iter/s]Best score: 0.2556: : 452iter [01:13,  6.28iter/s]Best score: 0.2556: : 452iter [01:13,  6.28iter/s]Best score: 0.2556: : 453iter [01:13,  6.32iter/s]Best score: 0.2556: : 453iter [01:14,  6.32iter/s]Best score: 0.2556: : 454iter [01:14,  6.35iter/s]Best score: 0.2556: : 454iter [01:14,  6.35iter/s]Best score: 0.2556: : 455iter [01:14,  6.38iter/s]Best score: 0.2556: : 455iter [01:14,  6.38iter/s]Best score: 0.2556: : 456iter [01:14,  6.39iter/s]Best score: 0.2556: : 456iter [01:14,  6.39iter/s]Best score: 0.2556: : 457iter [01:14,  6.38iter/s]Best score: 0.2556: : 457iter [01:14,  6.38iter/s]Best score: 0.2556: : 458iter [01:14,  6.39iter/s]Best score: 0.2556: : 458iter [01:14,  6.39iter/s]Best score: 0.2556: : 459iter [01:14,  6.40iter/s]Best score: 0.2555: : 459iter [01:15,  6.40iter/s]Best score: 0.2555: : 460iter [01:15,  6.19iter/s]Best score: 0.2555: : 460iter [01:15,  6.19iter/s]Best score: 0.2555: : 461iter [01:15,  6.25iter/s]Best score: 0.2555: : 461iter [01:15,  6.25iter/s]Best score: 0.2555: : 462iter [01:15,  6.29iter/s]Best score: 0.2555: : 462iter [01:15,  6.29iter/s]Best score: 0.2555: : 463iter [01:15,  5.11iter/s]Best score: 0.2555: : 463iter [01:15,  5.11iter/s]Best score: 0.2555: : 464iter [01:15,  5.44iter/s]Best score: 0.2555: : 464iter [01:16,  5.44iter/s]Best score: 0.2555: : 465iter [01:16,  5.69iter/s]Best score: 0.2555: : 465iter [01:16,  5.69iter/s]Best score: 0.2555: : 466iter [01:16,  5.88iter/s]Best score: 0.2555: : 466iter [01:16,  5.88iter/s]Best score: 0.2555: : 467iter [01:16,  6.01iter/s]Best score: 0.2555: : 467iter [01:16,  6.01iter/s]Best score: 0.2555: : 468iter [01:16,  6.13iter/s]Best score: 0.2555: : 468iter [01:16,  6.13iter/s]Best score: 0.2555: : 469iter [01:16,  6.19iter/s]Best score: 0.2555: : 469iter [01:16,  6.19iter/s]Best score: 0.2555: : 470iter [01:16,  6.05iter/s]Best score: 0.2555: : 470iter [01:16,  6.05iter/s]Best score: 0.2555: : 471iter [01:16,  6.14iter/s]Best score: 0.2555: : 471iter [01:17,  6.14iter/s]Best score: 0.2555: : 472iter [01:17,  6.20iter/s]Best score: 0.2555: : 472iter [01:17,  6.20iter/s]Best score: 0.2555: : 473iter [01:17,  6.27iter/s]Best score: 0.2555: : 473iter [01:17,  6.27iter/s]Best score: 0.2555: : 474iter [01:17,  6.30iter/s]Best score: 0.2555: : 474iter [01:17,  6.30iter/s]Best score: 0.2555: : 475iter [01:17,  6.33iter/s]Best score: 0.2555: : 475iter [01:17,  6.33iter/s]Best score: 0.2555: : 476iter [01:17,  6.35iter/s]Best score: 0.2555: : 476iter [01:17,  6.35iter/s]Best score: 0.2555: : 477iter [01:17,  6.38iter/s]Best score: 0.2555: : 477iter [01:18,  6.38iter/s]Best score: 0.2555: : 478iter [01:18,  6.37iter/s]Best score: 0.2555: : 478iter [01:18,  6.37iter/s]Best score: 0.2555: : 479iter [01:18,  6.37iter/s]Best score: 0.2554: : 479iter [01:18,  6.37iter/s]Best score: 0.2554: : 480iter [01:18,  6.17iter/s]Best score: 0.2554: : 480iter [01:18,  6.17iter/s]Best score: 0.2554: : 481iter [01:18,  6.22iter/s]Best score: 0.2554: : 481iter [01:18,  6.22iter/s]Best score: 0.2554: : 482iter [01:18,  6.27iter/s]Best score: 0.2554: : 482iter [01:18,  6.27iter/s]Best score: 0.2554: : 483iter [01:18,  6.30iter/s]Best score: 0.2554: : 483iter [01:19,  6.30iter/s]Best score: 0.2554: : 484iter [01:19,  6.31iter/s]Best score: 0.2554: : 484iter [01:19,  6.31iter/s]Best score: 0.2554: : 485iter [01:19,  6.33iter/s]Best score: 0.2554: : 485iter [01:19,  6.33iter/s]Best score: 0.2554: : 486iter [01:19,  6.35iter/s]Best score: 0.2554: : 486iter [01:19,  6.35iter/s]Best score: 0.2554: : 487iter [01:19,  6.35iter/s]Best score: 0.2554: : 487iter [01:19,  6.35iter/s]Best score: 0.2554: : 488iter [01:19,  6.35iter/s]Best score: 0.2554: : 488iter [01:19,  6.35iter/s]Best score: 0.2554: : 489iter [01:19,  6.36iter/s]Best score: 0.2554: : 489iter [01:19,  6.36iter/s]Best score: 0.2554: : 490iter [01:19,  6.17iter/s]Best score: 0.2554: : 490iter [01:20,  6.17iter/s]Best score: 0.2554: : 491iter [01:20,  6.23iter/s]Best score: 0.2554: : 491iter [01:20,  6.23iter/s]Best score: 0.2554: : 492iter [01:20,  6.27iter/s]Best score: 0.2554: : 492iter [01:20,  6.27iter/s]Best score: 0.2554: : 493iter [01:20,  6.29iter/s]Best score: 0.2554: : 493iter [01:20,  6.29iter/s]Best score: 0.2554: : 494iter [01:20,  6.32iter/s]Best score: 0.2554: : 494iter [01:20,  6.32iter/s]Best score: 0.2554: : 495iter [01:20,  6.33iter/s]Best score: 0.2554: : 495iter [01:20,  6.33iter/s]Best score: 0.2554: : 496iter [01:20,  6.35iter/s]Best score: 0.2554: : 496iter [01:21,  6.35iter/s]Best score: 0.2554: : 497iter [01:21,  6.36iter/s]Best score: 0.2554: : 497iter [01:21,  6.36iter/s]Best score: 0.2554: : 498iter [01:21,  6.37iter/s]Best score: 0.2554: : 498iter [01:21,  6.37iter/s]Best score: 0.2554: : 499iter [01:21,  6.37iter/s]
[ITER 500] Best: 0.2554
[PARAMS] Current best parameters:
  fx: 534.7959
  fy: 534.5733
  px: 979.5533
  py: 778.2514
  k1: -0.00848768
  k2: 0.01365605
  k3: -0.01147824
  k4: 0.00262660
  p1: -0.00329947
  p2: -0.00999664
  p3: 0.00504195
  p4: -0.00017753
  scale: 2.9632
  rotation: 0.2420
Best score: 0.2554: : 499iter [01:21,  6.37iter/s]Best score: 0.2554: : 500iter [01:21,  6.17iter/s]Best score: 0.2554: : 500iter [01:21,  6.13iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.255393 -> 0.255370
  Success: True
  Tangential distortion: p1=-0.003298, p2=-0.009992, p3=0.005045, p4=-0.000172
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2554
[✅] Parameters copied to: /working/MDC045_W022(6)_2023-10-25/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 534.7958595847065
    fy: 534.5732878999228
    px: 979.5532579425657
    py: 778.2513638267532
    k1: -0.008500312271099146
    k2: 0.013662476503193596
    k3: -0.011473148804558481
    k4: 0.0026247505747488977
    p1: -0.0032975276424794917
    p2: -0.009992065142110334
    p3: 0.005044995532433096
    p4: -0.00017209177694937862
    scale: 2.9631216183450486
    rotation: 0.2420438727753562
    mmpp: 3.39708353874032
    nmpp: 3397083.53874032

[🔄] Converting 728 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDC045_W022(6)_2023-10-25/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC045_W022(6)_2023-10-25/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
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[✅] LUT written to: /working/MDC045_W022(6)_2023-10-25/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC045_W022(6)_2023-10-25' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC045_W022(6)_2023-10-25/calibration/MDC045_W022(6)_2023-10-25_unknown.lut

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

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                                                                 [A[✅] 1500: /working/MDC045_W022(6)_2023-10-25/calibration/debug/4_heatmap/1500_heatmap.png
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                                                                  [A[✅] 2000R: /working/MDC045_W022(6)_2023-10-25/calibration/debug/4_heatmap/2000R_heatmap.png
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                                                                 [A[✅] 1000: /working/MDC045_W022(6)_2023-10-25/calibration/debug/4_heatmap/1000_heatmap.png
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                                                                  [A[✅] 2000L: /working/MDC045_W022(6)_2023-10-25/calibration/debug/4_heatmap/2000L_heatmap.png
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[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC045_W022(6)_2023-10-25/calibration/debug/4_heatmap
[🏁] Finished processing MDC045_W022(6)_2023-10-25


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