2026-05-17T22:24:14 - INFO: Slick 0.0.8a11

🟢 Starting Calibration Pipeline

=== Processing MDC044_W027_2025-05-29 ===
2026-05-17T22:24:14 - INFO: Camera ID: 6393994
2026-05-17T22:24:14 - INFO: Wand ID: 
2026-05-17T22:24:14 - INFO: Recorded At: 2025-05-29T15:58:13Z

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

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


→ 1000 (224 images) Phase 1:   0%|          | 0/224 [00:00<?, ?it/s]

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→ 1000 (224 images) Phase 1:  28%|██▊       | 63/224 [00:01<00:02, 68.43it/s]

→ 1000 (224 images) Phase 1:  33%|███▎      | 75/224 [00:01<00:02, 54.32it/s]

→ 1000 (224 images) Phase 1:  42%|████▏     | 93/224 [00:01<00:01, 72.83it/s]

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→ 1000 (224 images) Phase 1:  51%|█████▏    | 115/224 [00:02<00:01, 65.49it/s]

→ 1000 (224 images) Phase 1:  58%|█████▊    | 129/224 [00:02<00:01, 65.25it/s]

→ 1000 (224 images) Phase 1:  62%|██████▏   | 138/224 [00:02<00:01, 64.07it/s]

→ 1000 (224 images) Phase 1:  69%|██████▉   | 155/224 [00:02<00:00, 83.79it/s]

→ 1000 (224 images) Phase 1:  74%|███████▍  | 166/224 [00:02<00:00, 68.71it/s]

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→ 1000 (224 images) Phase 1:  86%|████████▌ | 193/224 [00:03<00:00, 69.79it/s]

→ 1000 (224 images) Phase 1:  90%|█████████ | 202/224 [00:03<00:00, 71.76it/s]

→ 1000 (224 images) Phase 1:  98%|█████████▊| 220/224 [00:03<00:00, 92.81it/s]

                                                                              

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→ 1000 Phase 2: corners:  30%|██▉       | 67/224 [00:00<00:01, 117.76it/s]

→ 1000 Phase 2: corners:  37%|███▋      | 82/224 [00:00<00:01, 125.44it/s]

→ 1000 Phase 2: corners:  42%|████▏     | 95/224 [00:00<00:01, 121.59it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 108/224 [00:01<00:00, 117.80it/s]

→ 1000 Phase 2: corners:  54%|█████▎    | 120/224 [00:01<00:00, 111.40it/s]

→ 1000 Phase 2: corners:  60%|█████▉    | 134/224 [00:01<00:00, 118.04it/s]

→ 1000 Phase 2: corners:  65%|██████▌   | 146/224 [00:01<00:00, 118.22it/s]

→ 1000 Phase 2: corners:  72%|███████▏  | 162/224 [00:01<00:00, 126.75it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 177/224 [00:01<00:00, 132.20it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 191/224 [00:01<00:00, 132.73it/s]

                                                                           [PASS2] Frame 0000158: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000159: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000360: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000361: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000378: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000379: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000380: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000381: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000382: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000385: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000520: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000553: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000724: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000726: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001134: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001135: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001136: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001137: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001313: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001314: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001315: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001316: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001333: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001334: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001335: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001338: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001339: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001342: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001504: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001510: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001511: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001512: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001513: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001516: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001517: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001518: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001519: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000360: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000380: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000382: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000385: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001136: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001315: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001335: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001336: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001337: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001339: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001342: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001512: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001518: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 24 frames fixed, 18 untrusted, 224 total


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→ 1000 Phase 3: finalize:  95%|█████████▌| 213/224 [00:04<00:00, 53.66it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 219/224 [00:04<00:00, 53.55it/s]

                                                                           
[TIMING] 1000 — 224 frames
  Phase 1  radial detect   (parallel)  :   3.50s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.79s  (8.0 ms/frame)
  Phase 2b corner fix      (sequential):   0.92s
  Phase 3  finalize        (sequential):   4.24s  (18.9 ms/frame)
    decode + undistort                 :   2.09s  (9.3 ms/frame)
    corner apply + refinement          :   1.04s  (4.7 ms/frame)
    segment                            :   0.91s  (4.0 ms/frame)
    smooth                             :   0.13s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.46s


Processing Rigs:  50%|█████     | 1/2 [00:15<00:15, 15.32s/it][INFO] Filtered 1172 stationary frames, 199 remain


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→ 500 (199 images) Phase 1:  79%|███████▉  | 157/199 [00:02<00:00, 81.03it/s]

→ 500 (199 images) Phase 1:  85%|████████▍ | 169/199 [00:02<00:00, 63.48it/s]

→ 500 (199 images) Phase 1:  95%|█████████▌| 190/199 [00:02<00:00, 85.01it/s]

                                                                             

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

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→ 500 Phase 2: corners:   8%|▊         | 15/199 [00:00<00:03, 53.17it/s]

→ 500 Phase 2: corners:  15%|█▍        | 29/199 [00:00<00:02, 80.79it/s]

→ 500 Phase 2: corners:  21%|██        | 42/199 [00:00<00:01, 95.37it/s]

→ 500 Phase 2: corners:  28%|██▊       | 56/199 [00:00<00:01, 104.64it/s]

→ 500 Phase 2: corners:  35%|███▌      | 70/199 [00:00<00:01, 110.73it/s]

→ 500 Phase 2: corners:  41%|████      | 82/199 [00:00<00:01, 113.21it/s]

→ 500 Phase 2: corners:  48%|████▊     | 96/199 [00:00<00:00, 118.09it/s]

→ 500 Phase 2: corners:  56%|█████▌    | 111/199 [00:01<00:00, 125.80it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 126/199 [00:01<00:00, 132.33it/s]

→ 500 Phase 2: corners:  73%|███████▎  | 145/199 [00:01<00:00, 146.72it/s]

→ 500 Phase 2: corners:  80%|████████  | 160/199 [00:01<00:00, 136.70it/s]

→ 500 Phase 2: corners:  89%|████████▉ | 177/199 [00:01<00:00, 141.18it/s]

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


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

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→ 500 Phase 3: finalize:  59%|█████▉    | 118/199 [00:02<00:01, 53.72it/s]

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→ 500 Phase 3: finalize:  71%|███████▏  | 142/199 [00:02<00:01, 54.00it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 148/199 [00:02<00:00, 54.15it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 154/199 [00:02<00:00, 54.17it/s]

→ 500 Phase 3: finalize:  80%|████████  | 160/199 [00:02<00:00, 53.77it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 166/199 [00:02<00:00, 53.74it/s]

→ 500 Phase 3: finalize:  86%|████████▋ | 172/199 [00:03<00:00, 53.84it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 178/199 [00:03<00:00, 54.02it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 184/199 [00:03<00:00, 53.86it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 190/199 [00:03<00:00, 53.76it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 196/199 [00:03<00:00, 53.63it/s]

                                                                          
[TIMING] 500 — 199 frames
  Phase 1  radial detect   (parallel)  :   3.20s  (16.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.67s  (8.4 ms/frame)
  Phase 2b corner fix      (sequential):   0.09s
  Phase 3  finalize        (sequential):   3.54s  (17.8 ms/frame)
    decode + undistort                 :   1.78s  (8.9 ms/frame)
    corner apply + refinement          :   0.79s  (4.0 ms/frame)
    segment                            :   0.80s  (4.0 ms/frame)
    smooth                             :   0.11s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.52s


Processing Rigs: 100%|██████████| 2/2 [00:28<00:00, 13.83s/it]
Processing Rigs: 100%|██████████| 2/2 [00:28<00:00, 14.05s/it]
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 423 frames from 2 rigs to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 224
[📋] 500: 100 frames selected from 199
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                      Step 2: 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 [00:03<02:06,  3.25s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:02,  3.22s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:58,  3.19s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:54,  3.19s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:16<01:52,  3.21s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:19<01:48,  3.20s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:45,  3.18s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:42,  3.22s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:40,  3.25s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:32<01:38,  3.27s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:35<01:35,  3.28s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:38<01:32,  3.31s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:42<01:29,  3.32s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:45<01:26,  3.33s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:48<01:22,  3.28s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:52<01:17,  3.25s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:55<01:14,  3.23s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:58<01:10,  3.22s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:01<01:07,  3.22s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:03<00:54,  2.74s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:06<00:55,  2.90s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:09<00:53,  3.00s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:12<00:52,  3.07s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:16<00:50,  3.13s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:19<00:47,  3.16s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:22<00:44,  3.18s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:25<00:41,  3.20s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:29<00:38,  3.23s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:32<00:35,  3.25s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:35<00:32,  3.27s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:39<00:29,  3.29s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:42<00:26,  3.31s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:45<00:23,  3.31s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:49<00:19,  3.30s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:52<00:16,  3.28s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:55<00:13,  3.26s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:58<00:09,  3.25s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:02<00:06,  3.25s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:05<00:03,  3.25s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:08<00:00,  3.22s/it]
                                                                                       
[✅] Casing detection complete: cx=1018.0, cy=745.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:12,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:12,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:12,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:12,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:12,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:11,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:10,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:10,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:10,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:10,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:10,  7.97it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:01, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:01, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 490.909} (0.001):  27%|██▋       | 27/100 [00:00<00:00, 76.91it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 490.909} (0.001):  28%|██▊       | 28/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 512.121} (0.022):  28%|██▊       | 28/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 512.121} (0.022):  29%|██▉       | 29/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.005 | Best: {'fx': 512.121} (0.022):  30%|███       | 30/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.035 | Best: {'fx': 519.192} (0.035):  31%|███       | 31/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.066 | Best: {'fx': 526.263} (0.066):  32%|███▏      | 32/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.081 | Best: {'fx': 533.333} (0.081):  33%|███▎      | 33/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.033 | Best: {'fx': 533.333} (0.081):  34%|███▍      | 34/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.049 | Best: {'fx': 533.333} (0.081):  35%|███▌      | 35/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.026 | Best: {'fx': 533.333} (0.081):  36%|███▌      | 36/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.081):  37%|███▋      | 37/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.081):  38%|███▊      | 38/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.081):  39%|███▉      | 39/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  40%|████      | 40/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.020 | Best: {'fx': 533.333} (0.081):  41%|████      | 41/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  42%|████▏     | 42/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.081):  43%|████▎     | 43/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.081):  44%|████▍     | 44/100 [00:00<00:00, 81.79it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.081):  45%|████▌     | 45/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.081):  45%|████▌     | 45/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  46%|████▌     | 46/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  47%|████▋     | 47/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  48%|████▊     | 48/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  49%|████▉     | 49/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  50%|█████     | 50/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  51%|█████     | 51/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  52%|█████▏    | 52/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  53%|█████▎    | 53/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  54%|█████▍    | 54/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  55%|█████▌    | 55/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  56%|█████▌    | 56/100 [00:00<00:00, 110.96it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  57%|█████▋    | 57/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  57%|█████▋    | 57/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  58%|█████▊    | 58/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  59%|█████▉    | 59/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  60%|██████    | 60/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  61%|██████    | 61/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  62%|██████▏   | 62/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  63%|██████▎   | 63/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  64%|██████▍   | 64/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  65%|██████▌   | 65/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  66%|██████▌   | 66/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  67%|██████▋   | 67/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  68%|██████▊   | 68/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  69%|██████▉   | 69/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  70%|███████   | 70/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  71%|███████   | 71/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  72%|███████▏  | 72/100 [00:00<00:00, 113.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  73%|███████▎  | 73/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  73%|███████▎  | 73/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  74%|███████▍  | 74/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  75%|███████▌  | 75/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  76%|███████▌  | 76/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  77%|███████▋  | 77/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  78%|███████▊  | 78/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  79%|███████▉  | 79/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  80%|████████  | 80/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  81%|████████  | 81/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  82%|████████▏ | 82/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  83%|████████▎ | 83/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  84%|████████▍ | 84/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  85%|████████▌ | 85/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  86%|████████▌ | 86/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  87%|████████▋ | 87/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  88%|████████▊ | 88/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  89%|████████▉ | 89/100 [00:00<00:00, 122.18it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  90%|█████████ | 90/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  90%|█████████ | 90/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  91%|█████████ | 91/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  92%|█████████▏| 92/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  93%|█████████▎| 93/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  94%|█████████▍| 94/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  95%|█████████▌| 95/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  96%|█████████▌| 96/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  97%|█████████▋| 97/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  98%|█████████▊| 98/100 [00:00<00:00, 134.50it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  99%|█████████▉| 99/100 [00:00<00:00, 134.50it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   2%|▏         | 1/50 [00:00<00:05,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.017 | Best: {'fx': 510.374} (0.017):   2%|▏         | 1/50 [00:00<00:05,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.013 | Best: {'fx': 510.374} (0.017):   4%|▍         | 2/50 [00:00<00:05,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 513.435} (0.024):   6%|▌         | 3/50 [00:00<00:05,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.021 | Best: {'fx': 513.435} (0.024):   8%|▊         | 4/50 [00:00<00:05,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.022 | Best: {'fx': 513.435} (0.024):  10%|█         | 5/50 [00:00<00:05,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 514.456} (0.026):  12%|█▏        | 6/50 [00:00<00:04,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.027 | Best: {'fx': 515.476} (0.027):  14%|█▍        | 7/50 [00:00<00:04,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 517.517} (0.031):  16%|█▌        | 8/50 [00:00<00:04,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 517.517} (0.031):  18%|█▊        | 9/50 [00:00<00:04,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.033 | Best: {'fx': 518.537} (0.033):  20%|██        | 10/50 [00:00<00:04,  8.91it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.033 | Best: {'fx': 518.537} (0.033):  22%|██▏       | 11/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.035 | Best: {'fx': 519.558} (0.035):  22%|██▏       | 11/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.049 | Best: {'fx': 523.639} (0.049):  24%|██▍       | 12/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.041 | Best: {'fx': 523.639} (0.049):  26%|██▌       | 13/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.038 | Best: {'fx': 523.639} (0.049):  28%|██▊       | 14/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.044 | Best: {'fx': 523.639} (0.049):  30%|███       | 15/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.054 | Best: {'fx': 524.66} (0.054):  32%|███▏      | 16/50 [00:00<00:00, 58.40it/s] 
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.062 | Best: {'fx': 525.68} (0.062):  34%|███▍      | 17/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.069 | Best: {'fx': 526.701} (0.069):  36%|███▌      | 18/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.080 | Best: {'fx': 528.741} (0.080):  38%|███▊      | 19/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.073 | Best: {'fx': 528.741} (0.080):  40%|████      | 20/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.090 | Best: {'fx': 530.782} (0.090):  42%|████▏     | 21/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.085 | Best: {'fx': 530.782} (0.090):  44%|████▍     | 22/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.089 | Best: {'fx': 530.782} (0.090):  46%|████▌     | 23/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.088 | Best: {'fx': 530.782} (0.090):  48%|████▊     | 24/50 [00:00<00:00, 58.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.088 | Best: {'fx': 530.782} (0.090):  50%|█████     | 25/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.074 | Best: {'fx': 530.782} (0.090):  50%|█████     | 25/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.057 | Best: {'fx': 530.782} (0.090):  52%|█████▏    | 26/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.075 | Best: {'fx': 530.782} (0.090):  54%|█████▍    | 27/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.069 | Best: {'fx': 530.782} (0.090):  56%|█████▌    | 28/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.062 | Best: {'fx': 530.782} (0.090):  58%|█████▊    | 29/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.051 | Best: {'fx': 530.782} (0.090):  60%|██████    | 30/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.052 | Best: {'fx': 530.782} (0.090):  62%|██████▏   | 31/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.046 | Best: {'fx': 530.782} (0.090):  64%|██████▍   | 32/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.044 | Best: {'fx': 530.782} (0.090):  66%|██████▌   | 33/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.041 | Best: {'fx': 530.782} (0.090):  68%|██████▊   | 34/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.037 | Best: {'fx': 530.782} (0.090):  70%|███████   | 35/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 530.782} (0.090):  72%|███████▏  | 36/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.035 | Best: {'fx': 530.782} (0.090):  74%|███████▍  | 37/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.034 | Best: {'fx': 530.782} (0.090):  76%|███████▌  | 38/50 [00:00<00:00, 90.06it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.034 | Best: {'fx': 530.782} (0.090):  78%|███████▊  | 39/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.030 | Best: {'fx': 530.782} (0.090):  78%|███████▊  | 39/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 530.782} (0.090):  80%|████████  | 40/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.033 | Best: {'fx': 530.782} (0.090):  82%|████████▏ | 41/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 530.782} (0.090):  84%|████████▍ | 42/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.025 | Best: {'fx': 530.782} (0.090):  86%|████████▌ | 43/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.028 | Best: {'fx': 530.782} (0.090):  88%|████████▊ | 44/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.027 | Best: {'fx': 530.782} (0.090):  90%|█████████ | 45/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 530.782} (0.090):  92%|█████████▏| 46/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 530.782} (0.090):  94%|█████████▍| 47/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.025 | Best: {'fx': 530.782} (0.090):  96%|█████████▌| 48/50 [00:00<00:00, 107.89it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 530.782} (0.090):  98%|█████████▊| 49/50 [00:00<00:00, 107.89it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.7823129251701
    px: 1018.0
    py: 745.0
    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: 6.6333: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 6.6333, picklable_objective(x0) = 6.6333

Best score: 6.6333: : 0iter [00:01, ?iter/s] 
Best score: 6.6333: : 1iter [00:01,  1.98s/iter]
Best score: 6.6333: : 1iter [00:02,  1.98s/iter]
Best score: 6.6333: : 2iter [00:02,  1.98s/iter]
Best score: 6.6333: : 3iter [00:02,  1.98s/iter]
Best score: 6.6333: : 4iter [00:02,  1.98s/iter]
Best score: 6.6333: : 5iter [00:02,  3.15iter/s]
Best score: 6.6333: : 5iter [00:02,  3.15iter/s]
Best score: 6.6333: : 6iter [00:02,  3.15iter/s]
Best score: 6.6333: : 7iter [00:02,  3.15iter/s]
Best score: 6.6333: : 8iter [00:02,  3.15iter/s]
Best score: 6.6333: : 9iter [00:02,  6.36iter/s]
Best score: 6.6333: : 9iter [00:02,  6.36iter/s]
Best score: 6.6333: : 10iter [00:02,  6.36iter/s]
Best score: 6.6333: : 11iter [00:02,  6.36iter/s]
Best score: 6.6333: : 12iter [00:02,  6.36iter/s]
Best score: 6.6333: : 13iter [00:02, 10.02iter/s]
Best score: 6.6333: : 13iter [00:02, 10.02iter/s]
Best score: 6.6333: : 14iter [00:02, 10.02iter/s]
Best score: 6.6333: : 15iter [00:02, 10.02iter/s]
Best score: 6.6333: : 16iter [00:02, 10.02iter/s]
Best score: 6.6333: : 17iter [00:02, 14.02iter/s]
Best score: 6.6333: : 17iter [00:02, 14.02iter/s]
Best score: 6.6333: : 18iter [00:02, 14.02iter/s]
Best score: 6.6333: : 19iter [00:02, 14.02iter/s]
Best score: 6.6333: : 20iter [00:02, 14.02iter/s]
Best score: 6.6333: : 21iter [00:02, 18.09iter/s]
Best score: 6.6333: : 21iter [00:02, 18.09iter/s]
Best score: 6.6333: : 22iter [00:02, 18.09iter/s]
Best score: 6.6333: : 23iter [00:02, 18.09iter/s]
Best score: 6.6333: : 24iter [00:02, 18.09iter/s]
Best score: 6.6333: : 25iter [00:02, 22.10iter/s]
Best score: 6.6333: : 25iter [00:02, 22.10iter/s]
Best score: 6.6333: : 26iter [00:02, 22.10iter/s]
Best score: 6.6333: : 27iter [00:02, 22.10iter/s]
Best score: 6.6333: : 28iter [00:02, 22.10iter/s]
Best score: 6.6333: : 29iter [00:02, 25.71iter/s]
Best score: 6.6333: : 29iter [00:02, 25.71iter/s]
Best score: 6.6333: : 30iter [00:02, 25.71iter/s]
Best score: 6.6333: : 31iter [00:02, 25.71iter/s]
Best score: 6.6333: : 32iter [00:02, 25.71iter/s]
Best score: 6.6333: : 33iter [00:02, 28.62iter/s]
Best score: 6.6333: : 33iter [00:02, 28.62iter/s]
Best score: 6.6333: : 34iter [00:02, 28.62iter/s]
Best score: 6.6333: : 35iter [00:02, 28.62iter/s]
Best score: 6.6333: : 36iter [00:02, 28.62iter/s]
Best score: 6.6333: : 37iter [00:02, 31.15iter/s]
Best score: 6.6333: : 37iter [00:02, 31.15iter/s]
Best score: 6.6333: : 38iter [00:02, 31.15iter/s]
Best score: 6.6333: : 39iter [00:02, 31.15iter/s]
Best score: 6.6333: : 40iter [00:03, 31.15iter/s]
Best score: 6.6333: : 41iter [00:03, 33.01iter/s]
Best score: 6.6333: : 41iter [00:03, 33.01iter/s]
Best score: 6.6333: : 42iter [00:03, 33.01iter/s]
Best score: 6.6333: : 43iter [00:03, 33.01iter/s]
Best score: 6.6333: : 44iter [00:03, 33.01iter/s]
Best score: 6.6333: : 45iter [00:03, 34.59iter/s]
Best score: 6.6333: : 45iter [00:03, 34.59iter/s]
Best score: 6.6333: : 46iter [00:03, 34.59iter/s]
Best score: 6.6333: : 47iter [00:03, 34.59iter/s]
Best score: 6.6333: : 48iter [00:03, 34.59iter/s]
Best score: 6.6333: : 49iter [00:03, 35.96iter/s]
[ITER 50] Best: 6.6333
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 745.0000
  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: 6.6333: : 49iter [00:03, 35.96iter/s]
Best score: 6.6333: : 50iter [00:03, 35.96iter/s]
Best score: 6.6333: : 51iter [00:03, 35.96iter/s]
Best score: 6.6333: : 52iter [00:03, 35.96iter/s]
Best score: 6.6333: : 53iter [00:03, 36.61iter/s]
Best score: 6.6333: : 53iter [00:03, 36.61iter/s]
Best score: 6.6333: : 54iter [00:03, 36.61iter/s]
Best score: 6.6333: : 55iter [00:03, 36.61iter/s]
Best score: 6.6333: : 56iter [00:03, 36.61iter/s]
Best score: 6.6333: : 57iter [00:03, 37.31iter/s]
Best score: 6.6333: : 57iter [00:03, 37.31iter/s]
Best score: 6.6333: : 58iter [00:03, 37.31iter/s]
Best score: 6.6333: : 59iter [00:03, 37.31iter/s]
Best score: 6.6333: : 60iter [00:03, 37.31iter/s]
Best score: 6.6333: : 61iter [00:03, 37.66iter/s]
Best score: 6.6333: : 61iter [00:03, 37.66iter/s]
Best score: 6.6333: : 62iter [00:03, 37.66iter/s]
Best score: 6.6333: : 63iter [00:03, 37.66iter/s]
Best score: 6.6333: : 64iter [00:03, 37.66iter/s]
Best score: 6.6333: : 65iter [00:03, 38.13iter/s]
Best score: 6.6333: : 65iter [00:03, 38.13iter/s]
Best score: 6.6333: : 66iter [00:03, 38.13iter/s]
Best score: 6.6333: : 67iter [00:03, 38.13iter/s]
Best score: 6.6333: : 68iter [00:03, 38.13iter/s]
Best score: 6.6333: : 69iter [00:03, 38.43iter/s]
Best score: 6.6333: : 69iter [00:03, 38.43iter/s]
Best score: 6.6333: : 70iter [00:03, 38.43iter/s]
Best score: 6.6333: : 71iter [00:03, 38.43iter/s]
Best score: 6.6333: : 72iter [00:03, 38.43iter/s]
Best score: 6.6333: : 73iter [00:03, 38.43iter/s]
Best score: 6.6333: : 73iter [00:03, 38.43iter/s]
Best score: 6.6333: : 74iter [00:03, 38.43iter/s]
Best score: 6.6333: : 75iter [00:03, 38.43iter/s]
Best score: 6.6333: : 76iter [00:03, 38.43iter/s]
Best score: 6.6333: : 77iter [00:03, 38.34iter/s]
Best score: 6.6333: : 77iter [00:03, 38.34iter/s]
Best score: 6.6333: : 78iter [00:03, 38.34iter/s]
Best score: 6.6333: : 79iter [00:04, 38.34iter/s]
Best score: 6.6333: : 80iter [00:04, 38.34iter/s]
Best score: 6.6333: : 81iter [00:04, 38.30iter/s]
Best score: 6.6333: : 81iter [00:04, 38.30iter/s]
Best score: 6.6333: : 82iter [00:04, 38.30iter/s]
Best score: 6.6333: : 83iter [00:04, 38.30iter/s]
Best score: 6.6333: : 84iter [00:04, 38.30iter/s]
Best score: 6.6333: : 85iter [00:04, 38.62iter/s]
Best score: 6.6333: : 85iter [00:04, 38.62iter/s]
Best score: 6.6333: : 86iter [00:04, 38.62iter/s]
Best score: 6.6333: : 87iter [00:04, 38.62iter/s]
Best score: 6.6333: : 88iter [00:04, 38.62iter/s]
Best score: 6.6333: : 89iter [00:04, 38.40iter/s]
Best score: 6.6333: : 89iter [00:04, 38.40iter/s]
Best score: 6.6333: : 90iter [00:04, 38.40iter/s]
Best score: 6.6333: : 91iter [00:04, 38.40iter/s]
Best score: 6.6333: : 92iter [00:04, 38.40iter/s]
Best score: 6.6333: : 93iter [00:04, 38.39iter/s]
Best score: 6.6333: : 93iter [00:04, 38.39iter/s]
Best score: 6.6333: : 94iter [00:04, 38.39iter/s]
Best score: 6.6333: : 95iter [00:04, 38.39iter/s]
Best score: 6.6333: : 96iter [00:04, 38.39iter/s]
Best score: 6.6333: : 97iter [00:04, 38.75iter/s]
Best score: 6.6333: : 97iter [00:04, 38.75iter/s]
Best score: 6.6333: : 98iter [00:04, 38.75iter/s]
[ITER 100] Best: 6.6333
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 745.0000
  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: 6.6333: : 99iter [00:04, 38.75iter/s]
Best score: 6.6333: : 100iter [00:04, 38.75iter/s]
Best score: 6.6333: : 101iter [00:04, 36.03iter/s]
Best score: 6.6333: : 101iter [00:04, 36.03iter/s]
Best score: 6.6333: : 102iter [00:04, 36.03iter/s]
Best score: 6.6333: : 103iter [00:04, 36.03iter/s]
Best score: 6.6333: : 104iter [00:04, 36.03iter/s]
Best score: 6.6333: : 105iter [00:04, 36.79iter/s]
Best score: 6.6333: : 105iter [00:04, 36.79iter/s]
Best score: 6.6333: : 106iter [00:04, 36.79iter/s]
Best score: 6.6333: : 107iter [00:04, 36.79iter/s]
Best score: 6.6333: : 108iter [00:04, 36.79iter/s]
Best score: 6.6333: : 109iter [00:04, 37.46iter/s]
Best score: 6.6333: : 109iter [00:04, 37.46iter/s]
Best score: 6.6333: : 110iter [00:04, 37.46iter/s]
Best score: 6.6333: : 111iter [00:04, 37.46iter/s]
Best score: 6.6333: : 112iter [00:04, 37.46iter/s]
Best score: 6.6333: : 113iter [00:04, 37.63iter/s]
Best score: 6.6333: : 113iter [00:04, 37.63iter/s]
Best score: 6.6333: : 114iter [00:04, 37.63iter/s]
Best score: 6.6333: : 115iter [00:04, 37.63iter/s]
Best score: 6.6333: : 116iter [00:05, 37.63iter/s]
Best score: 6.6333: : 117iter [00:05, 37.77iter/s]
Best score: 6.6333: : 117iter [00:05, 37.77iter/s]
Best score: 6.6333: : 118iter [00:05, 37.77iter/s]
Best score: 6.6333: : 119iter [00:05, 37.77iter/s]
Best score: 6.6333: : 120iter [00:05, 37.77iter/s]
Best score: 6.6333: : 121iter [00:05, 37.78iter/s]
Best score: 6.6333: : 121iter [00:05, 37.78iter/s]
Best score: 6.6333: : 122iter [00:05, 37.78iter/s]
Best score: 6.6333: : 123iter [00:05, 37.78iter/s]
Best score: 6.6333: : 124iter [00:05, 37.78iter/s]
Best score: 6.6333: : 125iter [00:05, 38.04iter/s]
Best score: 6.6333: : 125iter [00:05, 38.04iter/s]
Best score: 6.6333: : 126iter [00:05, 38.04iter/s]
Best score: 6.6333: : 127iter [00:05, 38.04iter/s]
Best score: 6.6333: : 128iter [00:05, 38.04iter/s]
Best score: 6.6333: : 129iter [00:05, 38.35iter/s]
Best score: 6.6333: : 129iter [00:05, 38.35iter/s]
Best score: 6.6333: : 130iter [00:05, 38.35iter/s]
Best score: 6.6333: : 131iter [00:05, 38.35iter/s]
Best score: 6.6333: : 132iter [00:05, 38.35iter/s]
Best score: 6.6333: : 133iter [00:05, 38.35iter/s]
Best score: 6.6333: : 133iter [00:05, 38.35iter/s]
Best score: 6.6333: : 134iter [00:05, 38.35iter/s]
Best score: 6.6333: : 135iter [00:05, 38.35iter/s]
Best score: 6.6333: : 136iter [00:05, 38.35iter/s]
Best score: 6.6333: : 137iter [00:05, 38.54iter/s]
Best score: 6.6333: : 137iter [00:05, 38.54iter/s]
Best score: 6.6333: : 138iter [00:05, 38.54iter/s]
Best score: 6.6333: : 139iter [00:05, 38.54iter/s]
Best score: 6.6333: : 140iter [00:05, 38.54iter/s]
Best score: 6.6333: : 141iter [00:05, 38.48iter/s]
Best score: 6.6333: : 141iter [00:05, 38.48iter/s]
Best score: 6.6333: : 142iter [00:05, 38.48iter/s]
Best score: 6.6333: : 143iter [00:05, 38.48iter/s]
Best score: 6.6333: : 144iter [00:05, 38.48iter/s]
Best score: 6.6333: : 145iter [00:05, 38.65iter/s]
Best score: 6.6333: : 145iter [00:05, 38.65iter/s]
Best score: 6.6333: : 146iter [00:05, 38.65iter/s]
Best score: 6.6333: : 147iter [00:05, 38.65iter/s]
Best score: 6.6333: : 148iter [00:05, 38.65iter/s]
Best score: 6.6333: : 149iter [00:05, 38.59iter/s]
[ITER 150] Best: 6.6333
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 745.0000
  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: 6.6333: : 149iter [00:05, 38.59iter/s]
Best score: 6.6333: : 150iter [00:05, 38.59iter/s]
Best score: 6.6333: : 151iter [00:06, 38.59iter/s]
Best score: 6.6333: : 152iter [00:06, 38.59iter/s]
Best score: 6.6333: : 153iter [00:06, 30.89iter/s]
Best score: 6.6333: : 153iter [00:06, 30.89iter/s]
Best score: 6.6333: : 154iter [00:06, 30.89iter/s]
Best score: 6.6333: : 155iter [00:06, 30.89iter/s]
Best score: 6.6333: : 156iter [00:06, 30.89iter/s]
Best score: 6.6333: : 157iter [00:06, 32.94iter/s]
Best score: 6.6333: : 157iter [00:06, 32.94iter/s]
Best score: 6.6333: : 158iter [00:06, 32.94iter/s]
Best score: 6.6333: : 159iter [00:06, 32.94iter/s]
Best score: 6.6333: : 160iter [00:06, 32.94iter/s]
Best score: 6.6333: : 161iter [00:06, 34.34iter/s]
Best score: 6.6333: : 161iter [00:06, 34.34iter/s]
Best score: 6.6333: : 162iter [00:06, 34.34iter/s]
Best score: 6.6333: : 163iter [00:06, 34.34iter/s]
Best score: 6.6333: : 164iter [00:06, 34.34iter/s]
Best score: 6.6333: : 165iter [00:06, 35.68iter/s]
Best score: 6.6333: : 165iter [00:06, 35.68iter/s]
Best score: 6.6333: : 166iter [00:06, 35.68iter/s]
Best score: 6.6333: : 167iter [00:06, 35.68iter/s]
Best score: 6.6333: : 168iter [00:06, 35.68iter/s]
Best score: 6.6333: : 169iter [00:06, 36.63iter/s]
Best score: 6.6333: : 169iter [00:06, 36.63iter/s]
Best score: 6.6333: : 170iter [00:06, 36.63iter/s]
Best score: 6.6333: : 171iter [00:06, 36.63iter/s]
Best score: 6.6333: : 172iter [00:06, 36.63iter/s]
Best score: 6.6333: : 173iter [00:06, 37.09iter/s]
Best score: 6.6333: : 173iter [00:06, 37.09iter/s]
Best score: 6.6333: : 174iter [00:06, 37.09iter/s]
Best score: 6.6333: : 175iter [00:06, 37.09iter/s]
Best score: 6.6333: : 176iter [00:06, 37.09iter/s]
Best score: 6.6333: : 177iter [00:06, 37.71iter/s]
Best score: 6.6333: : 177iter [00:06, 37.71iter/s]
Best score: 6.6333: : 178iter [00:06, 37.71iter/s]
Best score: 6.6333: : 179iter [00:06, 37.71iter/s]
Best score: 6.6333: : 180iter [00:06, 37.71iter/s]
Best score: 6.6333: : 181iter [00:06, 37.92iter/s]
Best score: 6.6333: : 181iter [00:06, 37.92iter/s]
Best score: 6.6333: : 182iter [00:06, 37.92iter/s]
Best score: 6.6333: : 183iter [00:06, 37.92iter/s]
Best score: 6.6333: : 184iter [00:06, 37.92iter/s]
Best score: 6.6333: : 185iter [00:06, 38.32iter/s]
Best score: 6.6333: : 185iter [00:06, 38.32iter/s]
Best score: 6.6333: : 186iter [00:06, 38.32iter/s]
Best score: 6.6333: : 187iter [00:06, 38.32iter/s]
Best score: 6.6333: : 188iter [00:06, 38.32iter/s]
Best score: 6.6333: : 189iter [00:06, 38.61iter/s]
Best score: 6.6333: : 189iter [00:06, 38.61iter/s]
Best score: 6.6333: : 190iter [00:07, 38.61iter/s]
Best score: 6.6333: : 191iter [00:07, 38.61iter/s]
Best score: 6.6333: : 192iter [00:07, 38.61iter/s]
Best score: 6.6333: : 193iter [00:07, 38.40iter/s]
Best score: 6.6333: : 193iter [00:07, 38.40iter/s]
Best score: 6.6333: : 194iter [00:07, 38.40iter/s]
Best score: 6.6333: : 195iter [00:07, 38.40iter/s]
Best score: 6.6333: : 196iter [00:07, 38.40iter/s]
Best score: 6.6333: : 197iter [00:07, 38.65iter/s]
Best score: 6.6333: : 197iter [00:07, 38.65iter/s]
Best score: 6.6333: : 198iter [00:07, 38.65iter/s]
[ITER 200] Best: 6.6333
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 745.0000
  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: 6.6333: : 199iter [00:07, 38.65iter/s]
Best score: 6.6333: : 200iter [00:07, 38.65iter/s]
Best score: 6.6333: : 201iter [00:07, 31.18iter/s]
Best score: 6.6333: : 201iter [00:07, 31.18iter/s]
Best score: 6.6333: : 202iter [00:07, 31.18iter/s]
Best score: 6.6333: : 203iter [00:07, 31.18iter/s]
Best score: 6.6333: : 204iter [00:07, 31.18iter/s]
Best score: 6.6333: : 205iter [00:07, 33.33iter/s]
Best score: 6.6333: : 205iter [00:07, 33.33iter/s]
Best score: 6.6333: : 206iter [00:07, 33.33iter/s]
Best score: 6.6333: : 207iter [00:07, 33.33iter/s]
Best score: 6.6333: : 208iter [00:07, 33.33iter/s]
Best score: 6.6333: : 209iter [00:07, 34.98iter/s]
Best score: 6.6333: : 209iter [00:07, 34.98iter/s]
Best score: 6.6333: : 210iter [00:07, 34.98iter/s]
Best score: 6.6333: : 211iter [00:07, 34.98iter/s]
Best score: 6.6333: : 212iter [00:07, 34.98iter/s]
Best score: 6.6333: : 213iter [00:07, 35.88iter/s]
Best score: 6.6333: : 213iter [00:07, 35.88iter/s]
Best score: 6.6333: : 214iter [00:07, 35.88iter/s]
Best score: 6.6333: : 215iter [00:07, 35.88iter/s]
Best score: 6.6333: : 216iter [00:07, 35.88iter/s]
Best score: 6.6333: : 217iter [00:07, 36.98iter/s]
Best score: 6.6333: : 217iter [00:07, 36.98iter/s]
Best score: 6.6333: : 218iter [00:07, 36.98iter/s]
Best score: 6.6333: : 219iter [00:07, 36.98iter/s]
Best score: 6.6333: : 220iter [00:07, 36.98iter/s]
Best score: 6.6333: : 221iter [00:07, 37.62iter/s]
Best score: 6.6333: : 221iter [00:07, 37.62iter/s]
Best score: 6.6333: : 222iter [00:07, 37.62iter/s]
Best score: 6.6333: : 223iter [00:07, 37.62iter/s]
Best score: 6.6333: : 224iter [00:07, 37.62iter/s]
Best score: 6.6333: : 225iter [00:07, 37.62iter/s]
Best score: 6.6333: : 226iter [00:07, 38.45iter/s]
Best score: 6.6333: : 226iter [00:08, 38.45iter/s]
Best score: 6.6333: : 227iter [00:08, 38.45iter/s]
Best score: 6.6333: : 228iter [00:08, 38.45iter/s]
Best score: 6.6333: : 229iter [00:08, 38.45iter/s]
Best score: 6.6333: : 230iter [00:08, 38.70iter/s]
Best score: 6.6333: : 230iter [00:08, 38.70iter/s]
Best score: 6.6333: : 231iter [00:08, 38.70iter/s]
Best score: 6.6333: : 232iter [00:08, 38.70iter/s]
Best score: 6.6333: : 233iter [00:08, 38.70iter/s]
Best score: 6.6333: : 234iter [00:08, 38.70iter/s]
Best score: 6.6333: : 235iter [00:08, 39.29iter/s]
Best score: 6.6333: : 235iter [00:08, 39.29iter/s]
Best score: 6.6333: : 236iter [00:08, 39.29iter/s]
Best score: 6.6333: : 237iter [00:08, 39.29iter/s]
Best score: 6.6333: : 238iter [00:08, 39.29iter/s]
Best score: 6.6333: : 239iter [00:08, 39.29iter/s]
Best score: 6.6333: : 240iter [00:08, 39.33iter/s]
Best score: 6.6333: : 240iter [00:08, 39.33iter/s]
Best score: 6.6333: : 241iter [00:08, 39.33iter/s]
Best score: 6.6333: : 242iter [00:08, 39.33iter/s]
Best score: 6.6333: : 243iter [00:08, 39.33iter/s]
Best score: 6.6333: : 244iter [00:08, 39.33iter/s]
Best score: 6.6333: : 245iter [00:08, 39.59iter/s]
Best score: 6.6333: : 245iter [00:08, 39.59iter/s]
Best score: 6.6333: : 246iter [00:08, 39.59iter/s]
Best score: 6.6333: : 247iter [00:08, 39.59iter/s]
Best score: 6.6333: : 248iter [00:08, 39.59iter/s]
Best score: 6.6333: : 249iter [00:08, 39.70iter/s]
[ITER 250] Best: 6.6333
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 745.0000
  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: 6.6333: : 249iter [00:08, 39.70iter/s]
Best score: 6.6333: : 250iter [00:08, 39.70iter/s]
Best score: 6.6333: : 251iter [00:08, 39.70iter/s]
Best score: 6.6333: : 252iter [00:08, 39.70iter/s]
Best score: 6.6333: : 253iter [00:08, 32.64iter/s]
Best score: 6.6333: : 253iter [00:08, 32.64iter/s]
Best score: 6.6333: : 254iter [00:08, 32.64iter/s]
Best score: 6.6333: : 255iter [00:08, 32.64iter/s]
Best score: 6.6333: : 256iter [00:08, 32.64iter/s]
Best score: 6.6333: : 257iter [00:08, 34.33iter/s]
Best score: 6.6333: : 257iter [00:08, 34.33iter/s]
Best score: 6.6333: : 258iter [00:08, 34.33iter/s]
Best score: 3.2895: : 259iter [00:08, 34.33iter/s]
Best score: 3.2895: : 260iter [00:08, 34.33iter/s]
Best score: 3.2895: : 261iter [00:08, 35.49iter/s]
Best score: 3.2895: : 261iter [00:08, 35.49iter/s]
Best score: 3.2895: : 262iter [00:08, 35.49iter/s]
Best score: 3.2895: : 263iter [00:09, 35.49iter/s]
Best score: 3.2895: : 264iter [00:09, 35.49iter/s]
Best score: 3.2895: : 265iter [00:09, 36.69iter/s]
Best score: 3.2895: : 265iter [00:09, 36.69iter/s]
Best score: 3.2895: : 266iter [00:09, 36.69iter/s]
Best score: 3.2895: : 267iter [00:09, 36.69iter/s]
Best score: 3.2895: : 268iter [00:09, 36.69iter/s]
Best score: 3.2895: : 269iter [00:09, 37.56iter/s]
Best score: 3.2895: : 269iter [00:09, 37.56iter/s]
Best score: 3.2895: : 270iter [00:09, 37.56iter/s]
Best score: 3.2895: : 271iter [00:09, 37.56iter/s]
Best score: 3.2895: : 272iter [00:09, 37.56iter/s]
Best score: 3.2895: : 273iter [00:09, 38.04iter/s]
Best score: 3.2895: : 273iter [00:09, 38.04iter/s]
Best score: 3.2895: : 274iter [00:09, 38.04iter/s]
Best score: 3.2895: : 275iter [00:09, 38.04iter/s]
Best score: 3.2895: : 276iter [00:09, 38.04iter/s]
Best score: 3.2895: : 277iter [00:09, 38.57iter/s]
Best score: 3.2895: : 277iter [00:09, 38.57iter/s]
Best score: 3.2895: : 278iter [00:09, 38.57iter/s]
Best score: 3.2895: : 279iter [00:09, 38.57iter/s]
Best score: 3.2895: : 280iter [00:09, 38.57iter/s]
Best score: 3.2895: : 281iter [00:09, 38.58iter/s]
Best score: 3.2895: : 281iter [00:09, 38.58iter/s]
Best score: 3.2895: : 282iter [00:09, 38.58iter/s]
Best score: 3.2895: : 283iter [00:09, 38.58iter/s]
Best score: 3.2895: : 284iter [00:09, 38.58iter/s]
Best score: 3.2895: : 285iter [00:09, 38.98iter/s]
Best score: 3.2895: : 285iter [00:09, 38.98iter/s]
Best score: 3.2895: : 286iter [00:09, 38.98iter/s]
Best score: 3.2895: : 287iter [00:09, 38.98iter/s]
Best score: 3.2895: : 288iter [00:09, 38.98iter/s]
Best score: 3.2895: : 289iter [00:09, 38.98iter/s]
Best score: 3.2895: : 290iter [00:09, 39.14iter/s]
Best score: 3.2895: : 290iter [00:09, 39.14iter/s]
Best score: 3.2895: : 291iter [00:09, 39.14iter/s]
Best score: 3.2895: : 292iter [00:09, 39.14iter/s]
Best score: 3.2895: : 293iter [00:09, 39.14iter/s]
Best score: 3.2895: : 294iter [00:09, 39.05iter/s]
Best score: 3.2895: : 294iter [00:09, 39.05iter/s]
Best score: 3.2895: : 295iter [00:09, 39.05iter/s]
Best score: 3.2895: : 296iter [00:09, 39.05iter/s]
Best score: 3.2895: : 297iter [00:09, 39.05iter/s]
Best score: 3.2895: : 298iter [00:09, 39.29iter/s]
Best score: 3.2895: : 298iter [00:09, 39.29iter/s]
[ITER 300] Best: 2.0490
[PARAMS] Current best parameters:
  fx: 533.2367
  fy: 532.7194
  px: 1017.9370
  py: 747.6670
  k1: 0.00373218
  k2: -0.01053222
  k3: 0.00395743
  k4: -0.00020463
  p1: 0.00534929
  p2: 0.00047068
  p3: -0.00039170
  p4: -0.00351236
  s1: 0.0067
  s2: -0.0009
  s3: 0.0031
  s4: 0.0005
  tilt_x: 0.0110
  tilt_y: -0.0006
  scale: 3.0356
  rotation: 0.1306

Best score: 2.0490: : 299iter [00:09, 39.29iter/s]
Best score: 2.0490: : 300iter [00:10, 39.29iter/s]
Best score: 2.0490: : 301iter [00:10, 39.29iter/s]
Best score: 2.0490: : 302iter [00:10, 31.89iter/s]
Best score: 2.0490: : 302iter [00:10, 31.89iter/s]
Best score: 2.0490: : 303iter [00:10, 31.89iter/s]
Best score: 2.0490: : 304iter [00:10, 31.89iter/s]
Best score: 2.0490: : 305iter [00:10, 31.89iter/s]
Best score: 2.0490: : 306iter [00:10, 33.89iter/s]
Best score: 2.0490: : 306iter [00:10, 33.89iter/s]
Best score: 2.0490: : 307iter [00:10, 33.89iter/s]
Best score: 2.0490: : 308iter [00:10, 33.89iter/s]
Best score: 2.0490: : 309iter [00:10, 33.89iter/s]
Best score: 2.0490: : 310iter [00:10, 35.29iter/s]
Best score: 2.0490: : 310iter [00:10, 35.29iter/s]
Best score: 2.0490: : 311iter [00:10, 35.29iter/s]
Best score: 2.0490: : 312iter [00:10, 35.29iter/s]
Best score: 2.0490: : 313iter [00:10, 35.29iter/s]
Best score: 2.0490: : 314iter [00:10, 36.51iter/s]
Best score: 2.0490: : 314iter [00:10, 36.51iter/s]
Best score: 2.0490: : 315iter [00:10, 36.51iter/s]
Best score: 2.0490: : 316iter [00:10, 36.51iter/s]
Best score: 2.0490: : 317iter [00:10, 36.51iter/s]
Best score: 2.0490: : 318iter [00:10, 37.39iter/s]
Best score: 2.0490: : 318iter [00:10, 37.39iter/s]
Best score: 2.0490: : 319iter [00:10, 37.39iter/s]
Best score: 2.0490: : 320iter [00:10, 37.39iter/s]
Best score: 2.0490: : 321iter [00:10, 37.39iter/s]
Best score: 2.0490: : 322iter [00:10, 37.74iter/s]
Best score: 2.0490: : 322iter [00:10, 37.74iter/s]
Best score: 2.0490: : 323iter [00:10, 37.74iter/s]
Best score: 2.0490: : 324iter [00:10, 37.74iter/s]
Best score: 2.0490: : 325iter [00:10, 37.74iter/s]
Best score: 2.0490: : 326iter [00:10, 38.34iter/s]
Best score: 2.0490: : 326iter [00:10, 38.34iter/s]
Best score: 2.0490: : 327iter [00:10, 38.34iter/s]
Best score: 2.0490: : 328iter [00:10, 38.34iter/s]
Best score: 1.2467: : 329iter [00:10, 38.34iter/s]
Best score: 1.2467: : 330iter [00:10, 38.40iter/s]
Best score: 1.2467: : 330iter [00:10, 38.40iter/s]
Best score: 1.2467: : 331iter [00:10, 38.40iter/s]
Best score: 1.2467: : 332iter [00:10, 38.40iter/s]
Best score: 1.2467: : 333iter [00:10, 38.40iter/s]
Best score: 1.2467: : 334iter [00:10, 38.76iter/s]
Best score: 1.2467: : 334iter [00:10, 38.76iter/s]
Best score: 1.2467: : 335iter [00:10, 38.76iter/s]
Best score: 1.2467: : 336iter [00:10, 38.76iter/s]
Best score: 1.2467: : 337iter [00:10, 38.76iter/s]
Best score: 1.2467: : 338iter [00:11, 38.76iter/s]
Best score: 1.2467: : 339iter [00:11, 39.18iter/s]
Best score: 1.2467: : 339iter [00:11, 39.18iter/s]
Best score: 1.2467: : 340iter [00:11, 39.18iter/s]
Best score: 1.2467: : 341iter [00:11, 39.18iter/s]
Best score: 1.2467: : 342iter [00:11, 39.18iter/s]
Best score: 1.2467: : 343iter [00:11, 39.18iter/s]
Best score: 1.2467: : 343iter [00:11, 39.18iter/s]
Best score: 1.2467: : 344iter [00:11, 39.18iter/s]
Best score: 1.2467: : 345iter [00:11, 39.18iter/s]
Best score: 1.2467: : 346iter [00:11, 39.18iter/s]
Best score: 1.2467: : 347iter [00:11, 39.29iter/s]
Best score: 1.2467: : 347iter [00:11, 39.29iter/s]
Best score: 1.2467: : 348iter [00:11, 39.29iter/s]
[ITER 350] Best: 1.0441
[PARAMS] Current best parameters:
  fx: 546.0590
  fy: 545.4473
  px: 1015.7632
  py: 743.3936
  k1: -0.00532635
  k2: -0.03194561
  k3: 0.03191698
  k4: -0.00936147
  p1: 0.00473667
  p2: -0.01011234
  p3: 0.00134517
  p4: 0.00577449
  s1: 0.0102
  s2: -0.0077
  s3: -0.0043
  s4: -0.0076
  tilt_x: -0.0104
  tilt_y: -0.0107
  scale: 2.9967
  rotation: 0.1133

Best score: 1.0441: : 349iter [00:11, 39.29iter/s]
Best score: 1.0441: : 350iter [00:11, 39.29iter/s]
Best score: 1.0441: : 351iter [00:11, 31.72iter/s]
Best score: 1.0441: : 351iter [00:11, 31.72iter/s]
Best score: 1.0441: : 352iter [00:11, 31.72iter/s]
Best score: 1.0441: : 353iter [00:11, 31.72iter/s]
Best score: 1.0441: : 354iter [00:11, 31.72iter/s]
Best score: 1.0441: : 355iter [00:11, 31.72iter/s]
Best score: 1.0441: : 356iter [00:11, 34.18iter/s]
Best score: 1.0441: : 356iter [00:11, 34.18iter/s]
Best score: 1.0441: : 357iter [00:11, 34.18iter/s]
Best score: 1.0441: : 358iter [00:11, 34.18iter/s]
Best score: 0.6757: : 359iter [00:11, 34.18iter/s]
Best score: 0.6757: : 360iter [00:11, 35.29iter/s]
Best score: 0.6757: : 360iter [00:11, 35.29iter/s]
Best score: 0.6757: : 361iter [00:11, 35.29iter/s]
Best score: 0.6757: : 362iter [00:11, 35.29iter/s]
Best score: 0.6757: : 363iter [00:11, 35.29iter/s]
Best score: 0.6757: : 364iter [00:11, 36.40iter/s]
Best score: 0.6757: : 364iter [00:11, 36.40iter/s]
Best score: 0.6757: : 365iter [00:11, 36.40iter/s]
Best score: 0.6757: : 366iter [00:11, 36.40iter/s]
Best score: 0.6757: : 367iter [00:11, 36.40iter/s]
Best score: 0.6757: : 368iter [00:11, 37.30iter/s]
Best score: 0.6757: : 368iter [00:11, 37.30iter/s]
Best score: 0.6757: : 369iter [00:11, 37.30iter/s]
Best score: 0.6757: : 370iter [00:11, 37.30iter/s]
Best score: 0.6757: : 371iter [00:11, 37.30iter/s]
Best score: 0.6757: : 372iter [00:11, 37.73iter/s]
Best score: 0.6757: : 372iter [00:11, 37.73iter/s]
Best score: 0.6757: : 373iter [00:11, 37.73iter/s]
Best score: 0.6757: : 374iter [00:12, 37.73iter/s]
Best score: 0.6757: : 375iter [00:12, 37.73iter/s]
Best score: 0.6757: : 376iter [00:12, 37.98iter/s]
Best score: 0.6757: : 376iter [00:12, 37.98iter/s]
Best score: 0.6757: : 377iter [00:12, 37.98iter/s]
Best score: 0.6757: : 378iter [00:12, 37.98iter/s]
Best score: 0.6757: : 379iter [00:12, 37.98iter/s]
Best score: 0.6757: : 380iter [00:12, 38.31iter/s]
Best score: 0.6757: : 380iter [00:12, 38.31iter/s]
Best score: 0.6757: : 381iter [00:12, 38.31iter/s]
Best score: 0.6757: : 382iter [00:12, 38.31iter/s]
Best score: 0.6757: : 383iter [00:12, 38.31iter/s]
Best score: 0.6757: : 384iter [00:12, 38.31iter/s]
Best score: 0.6757: : 385iter [00:12, 39.04iter/s]
Best score: 0.6757: : 385iter [00:12, 39.04iter/s]
Best score: 0.6757: : 386iter [00:12, 39.04iter/s]
Best score: 0.6757: : 387iter [00:12, 39.04iter/s]
Best score: 0.6757: : 388iter [00:12, 39.04iter/s]
Best score: 0.6757: : 389iter [00:12, 39.21iter/s]
Best score: 0.6265: : 389iter [00:12, 39.21iter/s]
Best score: 0.6265: : 390iter [00:12, 39.21iter/s]
Best score: 0.6265: : 391iter [00:12, 39.21iter/s]
Best score: 0.6265: : 392iter [00:12, 39.21iter/s]
Best score: 0.6265: : 393iter [00:12, 39.00iter/s]
Best score: 0.6265: : 393iter [00:12, 39.00iter/s]
Best score: 0.6265: : 394iter [00:12, 39.00iter/s]
Best score: 0.6265: : 395iter [00:12, 39.00iter/s]
Best score: 0.6265: : 396iter [00:12, 39.00iter/s]
Best score: 0.6265: : 397iter [00:12, 39.08iter/s]
Best score: 0.6265: : 397iter [00:12, 39.08iter/s]
Best score: 0.6265: : 398iter [00:12, 39.08iter/s]
[ITER 400] Best: 0.6265
[PARAMS] Current best parameters:
  fx: 538.0517
  fy: 537.1954
  px: 1014.9232
  py: 745.9267
  k1: 0.01124539
  k2: -0.03153396
  k3: 0.02164037
  k4: -0.00542423
  p1: 0.01422623
  p2: -0.01024459
  p3: -0.00386944
  p4: 0.00183098
  s1: 0.0090
  s2: -0.0003
  s3: -0.0044
  s4: -0.0044
  tilt_x: -0.0169
  tilt_y: -0.0086
  scale: 2.9933
  rotation: 0.0044

Best score: 0.6265: : 399iter [00:12, 39.08iter/s]
Best score: 0.6265: : 400iter [00:12, 39.08iter/s]
Best score: 0.6265: : 401iter [00:12, 26.36iter/s]
Best score: 0.6265: : 401iter [00:12, 26.36iter/s]
Best score: 0.6265: : 402iter [00:12, 26.36iter/s]
Best score: 0.6265: : 403iter [00:12, 26.36iter/s]
Best score: 0.6265: : 404iter [00:12, 26.36iter/s]
Best score: 0.6265: : 405iter [00:12, 29.27iter/s]
Best score: 0.6265: : 405iter [00:12, 29.27iter/s]
Best score: 0.6265: : 406iter [00:12, 29.27iter/s]
Best score: 0.6265: : 407iter [00:13, 29.27iter/s]
Best score: 0.6265: : 408iter [00:13, 29.27iter/s]
Best score: 0.6265: : 409iter [00:13, 29.27iter/s]
Best score: 0.6265: : 410iter [00:13, 32.16iter/s]
Best score: 0.6265: : 410iter [00:13, 32.16iter/s]
Best score: 0.6265: : 411iter [00:13, 32.16iter/s]
Best score: 0.6265: : 412iter [00:13, 32.16iter/s]
Best score: 0.6265: : 413iter [00:13, 32.16iter/s]
Best score: 0.6265: : 414iter [00:13, 32.16iter/s]
Best score: 0.6265: : 415iter [00:13, 34.40iter/s]
Best score: 0.6265: : 415iter [00:13, 34.40iter/s]
Best score: 0.6265: : 416iter [00:13, 34.40iter/s]
Best score: 0.6265: : 417iter [00:13, 34.40iter/s]
Best score: 0.6265: : 418iter [00:13, 34.40iter/s]
Best score: 0.6265: : 419iter [00:13, 34.40iter/s]
Best score: 0.6265: : 420iter [00:13, 35.82iter/s]
Best score: 0.6265: : 420iter [00:13, 35.82iter/s]
Best score: 0.6265: : 421iter [00:13, 35.82iter/s]
Best score: 0.6265: : 422iter [00:13, 35.82iter/s]
Best score: 0.6265: : 423iter [00:13, 35.82iter/s]
Best score: 0.6265: : 424iter [00:13, 36.83iter/s]
Best score: 0.6265: : 424iter [00:13, 36.83iter/s]
Best score: 0.6265: : 425iter [00:13, 36.83iter/s]
Best score: 0.6265: : 426iter [00:13, 36.83iter/s]
Best score: 0.6265: : 427iter [00:13, 36.83iter/s]
Best score: 0.6265: : 428iter [00:13, 36.83iter/s]
Best score: 0.6265: : 429iter [00:13, 37.83iter/s]
Best score: 0.6265: : 429iter [00:13, 37.83iter/s]
Best score: 0.6265: : 430iter [00:13, 37.83iter/s]
Best score: 0.6265: : 431iter [00:13, 37.83iter/s]
Best score: 0.6265: : 432iter [00:13, 37.83iter/s]
Best score: 0.6265: : 433iter [00:13, 38.14iter/s]
Best score: 0.6265: : 433iter [00:13, 38.14iter/s]
Best score: 0.6265: : 434iter [00:13, 38.14iter/s]
Best score: 0.6265: : 435iter [00:13, 38.14iter/s]
Best score: 0.6265: : 436iter [00:13, 38.14iter/s]
Best score: 0.6265: : 437iter [00:13, 38.14iter/s]
Best score: 0.6265: : 438iter [00:13, 38.74iter/s]
Best score: 0.6265: : 438iter [00:13, 38.74iter/s]
Best score: 0.6265: : 439iter [00:13, 38.74iter/s]
Best score: 0.6265: : 440iter [00:13, 38.74iter/s]
Best score: 0.6265: : 441iter [00:13, 38.74iter/s]
Best score: 0.6265: : 442iter [00:13, 38.89iter/s]
Best score: 0.6265: : 442iter [00:13, 38.89iter/s]
Best score: 0.6265: : 443iter [00:13, 38.89iter/s]
Best score: 0.6265: : 444iter [00:13, 38.89iter/s]
Best score: 0.6265: : 445iter [00:13, 38.89iter/s]
Best score: 0.6265: : 446iter [00:13, 38.89iter/s]
Best score: 0.6265: : 447iter [00:13, 39.30iter/s]
Best score: 0.6265: : 447iter [00:14, 39.30iter/s]
Best score: 0.6265: : 448iter [00:14, 39.30iter/s]
[ITER 450] Best: 0.5046
[PARAMS] Current best parameters:
  fx: 535.7613
  fy: 534.9473
  px: 1015.7728
  py: 745.0384
  k1: 0.01040264
  k2: -0.02996859
  k3: 0.02323684
  k4: -0.00632637
  p1: 0.01633721
  p2: -0.01098370
  p3: -0.00563549
  p4: 0.00119860
  s1: 0.0132
  s2: -0.0009
  s3: -0.0104
  s4: -0.0005
  tilt_x: -0.0257
  tilt_y: -0.0157
  scale: 2.9951
  rotation: 0.0213

Best score: 0.5046: : 449iter [00:14, 39.30iter/s]
Best score: 0.5046: : 450iter [00:14, 39.30iter/s]
Best score: 0.5046: : 451iter [00:14, 32.35iter/s]
Best score: 0.5046: : 451iter [00:14, 32.35iter/s]
Best score: 0.5046: : 452iter [00:14, 32.35iter/s]
Best score: 0.5046: : 453iter [00:14, 32.35iter/s]
Best score: 0.5046: : 454iter [00:14, 32.35iter/s]
Best score: 0.5046: : 455iter [00:14, 34.17iter/s]
Best score: 0.5046: : 455iter [00:14, 34.17iter/s]
Best score: 0.5046: : 456iter [00:14, 34.17iter/s]
Best score: 0.5046: : 457iter [00:14, 34.17iter/s]
Best score: 0.5046: : 458iter [00:14, 34.17iter/s]
Best score: 0.5046: : 459iter [00:14, 35.63iter/s]
Best score: 0.4757: : 459iter [00:14, 35.63iter/s]
Best score: 0.4757: : 460iter [00:14, 35.63iter/s]
Best score: 0.4757: : 461iter [00:14, 35.63iter/s]
Best score: 0.4757: : 462iter [00:14, 35.63iter/s]
Best score: 0.4757: : 463iter [00:14, 36.42iter/s]
Best score: 0.4757: : 463iter [00:14, 36.42iter/s]
Best score: 0.4757: : 464iter [00:14, 36.42iter/s]
Best score: 0.4757: : 465iter [00:14, 36.42iter/s]
Best score: 0.4757: : 466iter [00:14, 36.42iter/s]
Best score: 0.4757: : 467iter [00:14, 37.40iter/s]
Best score: 0.4757: : 467iter [00:14, 37.40iter/s]
Best score: 0.4757: : 468iter [00:14, 37.40iter/s]
Best score: 0.4137: : 469iter [00:14, 37.40iter/s]
Best score: 0.4137: : 470iter [00:14, 37.40iter/s]
Best score: 0.4137: : 471iter [00:14, 37.92iter/s]
Best score: 0.4137: : 471iter [00:14, 37.92iter/s]
Best score: 0.4137: : 472iter [00:14, 37.92iter/s]
Best score: 0.4137: : 473iter [00:14, 37.92iter/s]
Best score: 0.4137: : 474iter [00:14, 37.92iter/s]
Best score: 0.4137: : 475iter [00:14, 37.92iter/s]
Best score: 0.4137: : 476iter [00:14, 38.75iter/s]
Best score: 0.4137: : 476iter [00:14, 38.75iter/s]
Best score: 0.4137: : 477iter [00:14, 38.75iter/s]
Best score: 0.4137: : 478iter [00:14, 38.75iter/s]
Best score: 0.4137: : 479iter [00:14, 38.75iter/s]
Best score: 0.4137: : 480iter [00:14, 38.77iter/s]
Best score: 0.4137: : 480iter [00:14, 38.77iter/s]
Best score: 0.4137: : 481iter [00:14, 38.77iter/s]
Best score: 0.4137: : 482iter [00:14, 38.77iter/s]
Best score: 0.4137: : 483iter [00:15, 38.77iter/s]
Best score: 0.4137: : 484iter [00:15, 39.08iter/s]
Best score: 0.4137: : 484iter [00:15, 39.08iter/s]
Best score: 0.4137: : 485iter [00:15, 39.08iter/s]
Best score: 0.4137: : 486iter [00:15, 39.08iter/s]
Best score: 0.4137: : 487iter [00:15, 39.08iter/s]
Best score: 0.4137: : 488iter [00:15, 39.08iter/s]
Best score: 0.4137: : 489iter [00:15, 39.38iter/s]
Best score: 0.4059: : 489iter [00:15, 39.38iter/s]
Best score: 0.4059: : 490iter [00:15, 39.38iter/s]
Best score: 0.4059: : 491iter [00:15, 39.38iter/s]
Best score: 0.4059: : 492iter [00:15, 39.38iter/s]
Best score: 0.4059: : 493iter [00:15, 39.17iter/s]
Best score: 0.4059: : 493iter [00:15, 39.17iter/s]
Best score: 0.4059: : 494iter [00:15, 39.17iter/s]
Best score: 0.4059: : 495iter [00:15, 39.17iter/s]
Best score: 0.4059: : 496iter [00:15, 39.17iter/s]
Best score: 0.4059: : 497iter [00:15, 39.39iter/s]
Best score: 0.4059: : 497iter [00:15, 39.39iter/s]
Best score: 0.4059: : 498iter [00:15, 39.39iter/s]
[ITER 500] Best: 0.3578
[PARAMS] Current best parameters:
  fx: 538.5738
  fy: 538.1508
  px: 1015.8061
  py: 743.7287
  k1: 0.01006463
  k2: -0.02037325
  k3: 0.01073081
  k4: -0.00263778
  p1: 0.01321800
  p2: -0.01139442
  p3: -0.00115091
  p4: 0.00056356
  s1: 0.0113
  s2: -0.0007
  s3: -0.0118
  s4: -0.0010
  tilt_x: -0.0260
  tilt_y: -0.0156
  scale: 2.9758
  rotation: 0.0364

Best score: 0.3578: : 499iter [00:15, 39.39iter/s]
Best score: 0.3578: : 500iter [00:15, 39.39iter/s]
Best score: 0.3578: : 501iter [00:15, 31.90iter/s]
Best score: 0.3578: : 501iter [00:15, 31.90iter/s]
Best score: 0.3578: : 502iter [00:15, 31.90iter/s]
Best score: 0.3578: : 503iter [00:15, 31.90iter/s]
Best score: 0.3578: : 504iter [00:15, 31.90iter/s]
Best score: 0.3578: : 505iter [00:15, 33.74iter/s]
Best score: 0.3578: : 505iter [00:15, 33.74iter/s]
Best score: 0.3578: : 506iter [00:15, 33.74iter/s]
Best score: 0.3578: : 507iter [00:15, 33.74iter/s]
Best score: 0.3578: : 508iter [00:15, 33.74iter/s]
Best score: 0.3578: : 509iter [00:15, 35.28iter/s]
Best score: 0.3578: : 509iter [00:15, 35.28iter/s]
Best score: 0.3578: : 510iter [00:15, 35.28iter/s]
Best score: 0.3578: : 511iter [00:15, 35.28iter/s]
Best score: 0.3578: : 512iter [00:15, 35.28iter/s]
Best score: 0.3578: : 513iter [00:15, 36.41iter/s]
Best score: 0.3578: : 513iter [00:15, 36.41iter/s]
Best score: 0.3578: : 514iter [00:15, 36.41iter/s]
Best score: 0.3578: : 515iter [00:15, 36.41iter/s]
Best score: 0.3578: : 516iter [00:15, 36.41iter/s]
Best score: 0.3578: : 517iter [00:15, 37.33iter/s]
Best score: 0.3578: : 517iter [00:15, 37.33iter/s]
Best score: 0.3578: : 518iter [00:15, 37.33iter/s]
Best score: 0.3504: : 519iter [00:15, 37.33iter/s]
Best score: 0.3504: : 520iter [00:16, 37.33iter/s]
Best score: 0.3504: : 521iter [00:16, 37.78iter/s]
Best score: 0.3504: : 521iter [00:16, 37.78iter/s]
Best score: 0.3504: : 522iter [00:16, 37.78iter/s]
Best score: 0.3504: : 523iter [00:16, 37.78iter/s]
Best score: 0.3504: : 524iter [00:16, 37.78iter/s]
Best score: 0.3504: : 525iter [00:16, 37.78iter/s]
Best score: 0.3504: : 526iter [00:16, 38.61iter/s]
Best score: 0.3504: : 526iter [00:16, 38.61iter/s]
Best score: 0.3504: : 527iter [00:16, 38.61iter/s]
Best score: 0.3504: : 528iter [00:16, 38.61iter/s]
Best score: 0.3504: : 529iter [00:16, 38.61iter/s]
Best score: 0.3504: : 530iter [00:16, 38.68iter/s]
Best score: 0.3504: : 530iter [00:16, 38.68iter/s]
Best score: 0.3504: : 531iter [00:16, 38.68iter/s]
Best score: 0.3504: : 532iter [00:16, 38.68iter/s]
Best score: 0.3504: : 533iter [00:16, 38.68iter/s]
Best score: 0.3504: : 534iter [00:16, 39.00iter/s]
Best score: 0.3504: : 534iter [00:16, 39.00iter/s]
Best score: 0.3504: : 535iter [00:16, 39.00iter/s]
Best score: 0.3504: : 536iter [00:16, 39.00iter/s]
Best score: 0.3504: : 537iter [00:16, 39.00iter/s]
Best score: 0.3504: : 538iter [00:16, 39.27iter/s]
Best score: 0.3504: : 538iter [00:16, 39.27iter/s]
Best score: 0.3285: : 539iter [00:16, 39.27iter/s]
Best score: 0.3285: : 540iter [00:16, 39.27iter/s]
Best score: 0.3285: : 541iter [00:16, 39.27iter/s]
Best score: 0.3285: : 542iter [00:16, 39.08iter/s]
Best score: 0.3285: : 542iter [00:16, 39.08iter/s]
Best score: 0.3285: : 543iter [00:16, 39.08iter/s]
Best score: 0.3285: : 544iter [00:16, 39.08iter/s]
Best score: 0.3285: : 545iter [00:16, 39.08iter/s]
Best score: 0.3285: : 546iter [00:16, 39.08iter/s]
Best score: 0.3285: : 547iter [00:16, 39.49iter/s]
Best score: 0.3285: : 547iter [00:16, 39.49iter/s]
Best score: 0.3285: : 548iter [00:16, 39.49iter/s]
[ITER 550] Best: 0.3179
[PARAMS] Current best parameters:
  fx: 534.4719
  fy: 534.1571
  px: 1013.1569
  py: 744.6692
  k1: 0.01712522
  k2: -0.02268730
  k3: 0.01082995
  k4: -0.00254509
  p1: 0.01283080
  p2: -0.01197709
  p3: -0.00551885
  p4: 0.00161798
  s1: 0.0115
  s2: -0.0009
  s3: -0.0082
  s4: -0.0009
  tilt_x: -0.0205
  tilt_y: -0.0042
  scale: 2.9686
  rotation: 0.0436

Best score: 0.3179: : 549iter [00:16, 39.49iter/s]
Best score: 0.3179: : 550iter [00:16, 39.49iter/s]
Best score: 0.3179: : 551iter [00:16, 32.28iter/s]
Best score: 0.3179: : 551iter [00:16, 32.28iter/s]
Best score: 0.3179: : 552iter [00:16, 32.28iter/s]
Best score: 0.3179: : 553iter [00:16, 32.28iter/s]
Best score: 0.3179: : 554iter [00:16, 32.28iter/s]
Best score: 0.3179: : 555iter [00:16, 32.28iter/s]
Best score: 0.3179: : 556iter [00:16, 34.52iter/s]
Best score: 0.3179: : 556iter [00:17, 34.52iter/s]
Best score: 0.3179: : 557iter [00:17, 34.52iter/s]
Best score: 0.3179: : 558iter [00:17, 34.52iter/s]
Best score: 0.3179: : 559iter [00:17, 34.52iter/s]
Best score: 0.3179: : 560iter [00:17, 35.63iter/s]
Best score: 0.3179: : 560iter [00:17, 35.63iter/s]
Best score: 0.3179: : 561iter [00:17, 35.63iter/s]
Best score: 0.3179: : 562iter [00:17, 35.63iter/s]
Best score: 0.3179: : 563iter [00:17, 35.63iter/s]
Best score: 0.3179: : 564iter [00:17, 36.74iter/s]
Best score: 0.3179: : 564iter [00:17, 36.74iter/s]
Best score: 0.3179: : 565iter [00:17, 36.74iter/s]
Best score: 0.3179: : 566iter [00:17, 36.74iter/s]
Best score: 0.3179: : 567iter [00:17, 36.74iter/s]
Best score: 0.3179: : 568iter [00:17, 36.74iter/s]
Best score: 0.3179: : 569iter [00:17, 37.88iter/s]
Best score: 0.3165: : 569iter [00:17, 37.88iter/s]
Best score: 0.3165: : 570iter [00:17, 37.88iter/s]
Best score: 0.3165: : 571iter [00:17, 37.88iter/s]
Best score: 0.3165: : 572iter [00:17, 37.88iter/s]
Best score: 0.3165: : 573iter [00:17, 38.02iter/s]
Best score: 0.3165: : 573iter [00:17, 38.02iter/s]
Best score: 0.3165: : 574iter [00:17, 38.02iter/s]
Best score: 0.3165: : 575iter [00:17, 38.02iter/s]
Best score: 0.3165: : 576iter [00:17, 38.02iter/s]
Best score: 0.3165: : 577iter [00:17, 38.50iter/s]
Best score: 0.3165: : 577iter [00:17, 38.50iter/s]
Best score: 0.3165: : 578iter [00:17, 38.50iter/s]
Best score: 0.3060: : 579iter [00:17, 38.50iter/s]
Best score: 0.3060: : 580iter [00:17, 38.50iter/s]
Best score: 0.3060: : 581iter [00:17, 38.45iter/s]
Best score: 0.3060: : 581iter [00:17, 38.45iter/s]
Best score: 0.3060: : 582iter [00:17, 38.45iter/s]
Best score: 0.3060: : 583iter [00:17, 38.45iter/s]
Best score: 0.3060: : 584iter [00:17, 38.45iter/s]
Best score: 0.3060: : 585iter [00:17, 38.71iter/s]
Best score: 0.3060: : 585iter [00:17, 38.71iter/s]
Best score: 0.3060: : 586iter [00:17, 38.71iter/s]
Best score: 0.3060: : 587iter [00:17, 38.71iter/s]
Best score: 0.3060: : 588iter [00:17, 38.71iter/s]
Best score: 0.2907: : 589iter [00:17, 38.71iter/s]
Best score: 0.2907: : 590iter [00:17, 38.85iter/s]
Best score: 0.2907: : 590iter [00:17, 38.85iter/s]
Best score: 0.2907: : 591iter [00:17, 38.85iter/s]
Best score: 0.2907: : 592iter [00:17, 38.85iter/s]
Best score: 0.2907: : 593iter [00:17, 38.85iter/s]
Best score: 0.2907: : 594iter [00:17, 39.03iter/s]
Best score: 0.2907: : 594iter [00:17, 39.03iter/s]
Best score: 0.2907: : 595iter [00:18, 39.03iter/s]
Best score: 0.2907: : 596iter [00:18, 39.03iter/s]
Best score: 0.2907: : 597iter [00:18, 39.03iter/s]
Best score: 0.2907: : 598iter [00:18, 39.20iter/s]
Best score: 0.2907: : 598iter [00:18, 39.20iter/s]
[ITER 600] Best: 0.2784
[PARAMS] Current best parameters:
  fx: 537.5271
  fy: 537.4780
  px: 1012.7415
  py: 741.3136
  k1: 0.00868951
  k2: -0.01379566
  k3: 0.00545734
  k4: -0.00134412
  p1: 0.00951742
  p2: -0.01426776
  p3: -0.00171925
  p4: 0.00088315
  s1: 0.0106
  s2: -0.0004
  s3: -0.0099
  s4: -0.0008
  tilt_x: -0.0312
  tilt_y: -0.0059
  scale: 2.9730
  rotation: 0.0614

Best score: 0.2784: : 599iter [00:18, 39.20iter/s]
Best score: 0.2784: : 600iter [00:18, 39.20iter/s]
Best score: 0.2784: : 601iter [00:18, 39.20iter/s]
Best score: 0.2784: : 602iter [00:18, 31.82iter/s]
Best score: 0.2784: : 602iter [00:18, 31.82iter/s]
Best score: 0.2784: : 603iter [00:18, 31.82iter/s]
Best score: 0.2784: : 604iter [00:18, 31.82iter/s]
Best score: 0.2784: : 605iter [00:18, 31.82iter/s]
Best score: 0.2784: : 606iter [00:18, 31.82iter/s]
Best score: 0.2784: : 607iter [00:18, 34.20iter/s]
Best score: 0.2784: : 607iter [00:18, 34.20iter/s]
Best score: 0.2784: : 608iter [00:18, 34.20iter/s]
Best score: 0.2781: : 609iter [00:18, 34.20iter/s]
Best score: 0.2781: : 610iter [00:18, 34.20iter/s]
Best score: 0.2781: : 611iter [00:18, 35.41iter/s]
Best score: 0.2781: : 611iter [00:18, 35.41iter/s]
Best score: 0.2781: : 612iter [00:18, 35.41iter/s]
Best score: 0.2781: : 613iter [00:18, 35.41iter/s]
Best score: 0.2781: : 614iter [00:18, 35.41iter/s]
Best score: 0.2781: : 615iter [00:18, 36.56iter/s]
Best score: 0.2781: : 615iter [00:18, 36.56iter/s]
Best score: 0.2781: : 616iter [00:18, 36.56iter/s]
Best score: 0.2781: : 617iter [00:18, 36.56iter/s]
Best score: 0.2781: : 618iter [00:18, 36.56iter/s]
Best score: 0.2781: : 619iter [00:18, 37.43iter/s]
Best score: 0.2776: : 619iter [00:18, 37.43iter/s]
Best score: 0.2776: : 620iter [00:18, 37.43iter/s]
Best score: 0.2776: : 621iter [00:18, 37.43iter/s]
Best score: 0.2776: : 622iter [00:18, 37.43iter/s]
Best score: 0.2776: : 623iter [00:18, 37.79iter/s]
Best score: 0.2776: : 623iter [00:18, 37.79iter/s]
Best score: 0.2776: : 624iter [00:18, 37.79iter/s]
Best score: 0.2776: : 625iter [00:18, 37.79iter/s]
Best score: 0.2776: : 626iter [00:18, 37.79iter/s]
Best score: 0.2776: : 627iter [00:18, 38.30iter/s]
Best score: 0.2776: : 627iter [00:18, 38.30iter/s]
Best score: 0.2776: : 628iter [00:18, 38.30iter/s]
Best score: 0.2772: : 629iter [00:18, 38.30iter/s]
Best score: 0.2772: : 630iter [00:18, 38.30iter/s]
Best score: 0.2772: : 631iter [00:18, 38.46iter/s]
Best score: 0.2772: : 631iter [00:18, 38.46iter/s]
Best score: 0.2772: : 632iter [00:19, 38.46iter/s]
Best score: 0.2772: : 633iter [00:19, 38.46iter/s]
Best score: 0.2772: : 634iter [00:19, 38.46iter/s]
Best score: 0.2772: : 635iter [00:19, 38.87iter/s]
Best score: 0.2772: : 635iter [00:19, 38.87iter/s]
Best score: 0.2772: : 636iter [00:19, 38.87iter/s]
Best score: 0.2772: : 637iter [00:19, 38.87iter/s]
Best score: 0.2772: : 638iter [00:19, 38.87iter/s]
Best score: 0.2772: : 639iter [00:19, 39.09iter/s]
Best score: 0.2715: : 639iter [00:19, 39.09iter/s]
Best score: 0.2715: : 640iter [00:19, 39.09iter/s]
Best score: 0.2715: : 641iter [00:19, 39.09iter/s]
Best score: 0.2715: : 642iter [00:19, 39.09iter/s]
Best score: 0.2715: : 643iter [00:19, 38.87iter/s]
Best score: 0.2715: : 643iter [00:19, 38.87iter/s]
Best score: 0.2715: : 644iter [00:19, 38.87iter/s]
Best score: 0.2715: : 645iter [00:19, 38.87iter/s]
Best score: 0.2715: : 646iter [00:19, 38.87iter/s]
Best score: 0.2715: : 647iter [00:19, 38.87iter/s]
Best score: 0.2715: : 648iter [00:19, 39.32iter/s]
Best score: 0.2715: : 648iter [00:19, 39.32iter/s]
[ITER 650] Best: 0.2693
[PARAMS] Current best parameters:
  fx: 538.2327
  fy: 538.2674
  px: 1012.4337
  py: 739.3801
  k1: 0.00598445
  k2: -0.01296172
  k3: 0.00595924
  k4: -0.00155637
  p1: 0.00809395
  p2: -0.01553040
  p3: -0.00200312
  p4: 0.00131605
  s1: 0.0122
  s2: -0.0010
  s3: -0.0115
  s4: -0.0005
  tilt_x: -0.0378
  tilt_y: -0.0070
  scale: 2.9805
  rotation: 0.0733

Best score: 0.2693: : 649iter [00:19, 39.32iter/s]
Best score: 0.2693: : 650iter [00:19, 39.32iter/s]
Best score: 0.2693: : 651iter [00:19, 39.32iter/s]
Best score: 0.2693: : 652iter [00:19, 32.06iter/s]
Best score: 0.2693: : 652iter [00:19, 32.06iter/s]
Best score: 0.2693: : 653iter [00:19, 32.06iter/s]
Best score: 0.2693: : 654iter [00:19, 32.06iter/s]
Best score: 0.2693: : 655iter [00:19, 32.06iter/s]
Best score: 0.2693: : 656iter [00:19, 33.97iter/s]
Best score: 0.2693: : 656iter [00:19, 33.97iter/s]
Best score: 0.2693: : 657iter [00:19, 33.97iter/s]
Best score: 0.2693: : 658iter [00:19, 33.97iter/s]
Best score: 0.2693: : 659iter [00:19, 33.97iter/s]
Best score: 0.2693: : 660iter [00:19, 35.28iter/s]
Best score: 0.2693: : 660iter [00:19, 35.28iter/s]
Best score: 0.2693: : 661iter [00:19, 35.28iter/s]
Best score: 0.2693: : 662iter [00:19, 35.28iter/s]
Best score: 0.2693: : 663iter [00:19, 35.28iter/s]
Best score: 0.2693: : 664iter [00:19, 36.45iter/s]
Best score: 0.2693: : 664iter [00:19, 36.45iter/s]
Best score: 0.2693: : 665iter [00:19, 36.45iter/s]
Best score: 0.2693: : 666iter [00:19, 36.45iter/s]
Best score: 0.2693: : 667iter [00:19, 36.45iter/s]
Best score: 0.2693: : 668iter [00:19, 37.32iter/s]
Best score: 0.2693: : 668iter [00:20, 37.32iter/s]
Best score: 0.2673: : 669iter [00:20, 37.32iter/s]
Best score: 0.2673: : 670iter [00:20, 37.32iter/s]
Best score: 0.2673: : 671iter [00:20, 37.32iter/s]
Best score: 0.2673: : 672iter [00:20, 37.71iter/s]
Best score: 0.2673: : 672iter [00:20, 37.71iter/s]
Best score: 0.2673: : 673iter [00:20, 37.71iter/s]
Best score: 0.2673: : 674iter [00:20, 37.71iter/s]
Best score: 0.2673: : 675iter [00:20, 37.71iter/s]
Best score: 0.2673: : 676iter [00:20, 37.71iter/s]
Best score: 0.2673: : 677iter [00:20, 38.53iter/s]
Best score: 0.2673: : 677iter [00:20, 38.53iter/s]
Best score: 0.2673: : 678iter [00:20, 38.53iter/s]
Best score: 0.2673: : 679iter [00:20, 38.53iter/s]
Best score: 0.2673: : 680iter [00:20, 38.53iter/s]
Best score: 0.2673: : 681iter [00:20, 38.79iter/s]
Best score: 0.2673: : 681iter [00:20, 38.79iter/s]
Best score: 0.2673: : 682iter [00:20, 38.79iter/s]
Best score: 0.2673: : 683iter [00:20, 38.79iter/s]
Best score: 0.2673: : 684iter [00:20, 38.79iter/s]
Best score: 0.2673: : 685iter [00:20, 39.12iter/s]
Best score: 0.2673: : 685iter [00:20, 39.12iter/s]
Best score: 0.2673: : 686iter [00:20, 39.12iter/s]
Best score: 0.2673: : 687iter [00:20, 39.12iter/s]
Best score: 0.2673: : 688iter [00:20, 39.12iter/s]
Best score: 0.2628: : 689iter [00:20, 39.12iter/s]
Best score: 0.2628: : 690iter [00:20, 39.34iter/s]
Best score: 0.2628: : 690iter [00:20, 39.34iter/s]
Best score: 0.2628: : 691iter [00:20, 39.34iter/s]
Best score: 0.2628: : 692iter [00:20, 39.34iter/s]
Best score: 0.2628: : 693iter [00:20, 39.34iter/s]
Best score: 0.2628: : 694iter [00:20, 39.37iter/s]
Best score: 0.2628: : 694iter [00:20, 39.37iter/s]
Best score: 0.2628: : 695iter [00:20, 39.37iter/s]
Best score: 0.2628: : 696iter [00:20, 39.37iter/s]
Best score: 0.2628: : 697iter [00:20, 39.37iter/s]
Best score: 0.2628: : 698iter [00:20, 39.36iter/s]
Best score: 0.2628: : 698iter [00:20, 39.36iter/s]
[ITER 700] Best: 0.2581
[PARAMS] Current best parameters:
  fx: 535.9216
  fy: 535.8129
  px: 1014.2072
  py: 739.1447
  k1: 0.00551124
  k2: -0.00820972
  k3: 0.00293994
  k4: -0.00092932
  p1: 0.01023857
  p2: -0.01299376
  p3: 0.00027248
  p4: -0.00067071
  s1: 0.0087
  s2: 0.0010
  s3: -0.0144
  s4: 0.0006
  tilt_x: -0.0434
  tilt_y: -0.0112
  scale: 2.9858
  rotation: 0.0716

Best score: 0.2581: : 699iter [00:20, 39.36iter/s]
Best score: 0.2581: : 700iter [00:20, 39.36iter/s]
Best score: 0.2581: : 701iter [00:20, 39.36iter/s]
Best score: 0.2581: : 702iter [00:20, 31.88iter/s]
Best score: 0.2581: : 702iter [00:20, 31.88iter/s]
Best score: 0.2581: : 703iter [00:20, 31.88iter/s]
Best score: 0.2581: : 704iter [00:21, 31.88iter/s]
Best score: 0.2581: : 705iter [00:21, 31.88iter/s]
Best score: 0.2581: : 706iter [00:21, 33.79iter/s]
Best score: 0.2581: : 706iter [00:21, 33.79iter/s]
Best score: 0.2581: : 707iter [00:21, 33.79iter/s]
Best score: 0.2581: : 708iter [00:21, 33.79iter/s]
Best score: 0.2581: : 709iter [00:21, 33.79iter/s]
Best score: 0.2581: : 710iter [00:21, 35.09iter/s]
Best score: 0.2581: : 710iter [00:21, 35.09iter/s]
Best score: 0.2581: : 711iter [00:21, 35.09iter/s]
Best score: 0.2581: : 712iter [00:21, 35.09iter/s]
Best score: 0.2581: : 713iter [00:21, 35.09iter/s]
Best score: 0.2581: : 714iter [00:21, 36.29iter/s]
Best score: 0.2581: : 714iter [00:21, 36.29iter/s]
Best score: 0.2581: : 715iter [00:21, 36.29iter/s]
Best score: 0.2581: : 716iter [00:21, 36.29iter/s]
Best score: 0.2581: : 717iter [00:21, 36.29iter/s]
Best score: 0.2581: : 718iter [00:21, 30.12iter/s]
Best score: 0.2581: : 718iter [00:21, 30.12iter/s]
Best score: 0.2560: : 719iter [00:21, 30.12iter/s]
Best score: 0.2560: : 720iter [00:21, 30.12iter/s]
Best score: 0.2560: : 721iter [00:21, 30.12iter/s]
Best score: 0.2560: : 722iter [00:21, 32.10iter/s]
Best score: 0.2560: : 722iter [00:21, 32.10iter/s]
Best score: 0.2560: : 723iter [00:21, 32.10iter/s]
Best score: 0.2560: : 724iter [00:21, 32.10iter/s]
Best score: 0.2560: : 725iter [00:21, 32.10iter/s]
Best score: 0.2560: : 726iter [00:21, 34.11iter/s]
Best score: 0.2560: : 726iter [00:21, 34.11iter/s]
Best score: 0.2560: : 727iter [00:21, 34.11iter/s]
Best score: 0.2560: : 728iter [00:21, 34.11iter/s]
Best score: 0.2560: : 729iter [00:21, 34.11iter/s]
Best score: 0.2560: : 730iter [00:21, 35.42iter/s]
Best score: 0.2560: : 730iter [00:21, 35.42iter/s]
Best score: 0.2560: : 731iter [00:21, 35.42iter/s]
Best score: 0.2560: : 732iter [00:21, 35.42iter/s]
Best score: 0.2560: : 733iter [00:21, 35.42iter/s]
Best score: 0.2560: : 734iter [00:21, 36.66iter/s]
Best score: 0.2560: : 734iter [00:21, 36.66iter/s]
Best score: 0.2560: : 735iter [00:21, 36.66iter/s]
Best score: 0.2560: : 736iter [00:21, 36.66iter/s]
Best score: 0.2560: : 737iter [00:21, 36.66iter/s]
Best score: 0.2560: : 738iter [00:21, 37.49iter/s]
Best score: 0.2560: : 738iter [00:21, 37.49iter/s]
Best score: 0.2560: : 739iter [00:21, 37.49iter/s]
Best score: 0.2560: : 740iter [00:22, 37.49iter/s]
Best score: 0.2560: : 741iter [00:22, 37.49iter/s]
Best score: 0.2560: : 742iter [00:22, 37.82iter/s]
Best score: 0.2560: : 742iter [00:22, 37.82iter/s]
Best score: 0.2560: : 743iter [00:22, 37.82iter/s]
Best score: 0.2560: : 744iter [00:22, 37.82iter/s]
Best score: 0.2560: : 745iter [00:22, 37.82iter/s]
Best score: 0.2560: : 746iter [00:22, 38.43iter/s]
Best score: 0.2560: : 746iter [00:22, 38.43iter/s]
Best score: 0.2560: : 747iter [00:22, 38.43iter/s]
Best score: 0.2560: : 748iter [00:22, 38.43iter/s]
[ITER 750] Best: 0.2560
[PARAMS] Current best parameters:
  fx: 536.3807
  fy: 536.3114
  px: 1013.4140
  py: 738.7616
  k1: 0.00463644
  k2: -0.00628858
  k3: 0.00117059
  k4: -0.00044165
  p1: 0.00905268
  p2: -0.01389728
  p3: 0.00115609
  p4: -0.00026148
  s1: 0.0081
  s2: 0.0005
  s3: -0.0151
  s4: 0.0009
  tilt_x: -0.0433
  tilt_y: -0.0090
  scale: 2.9834
  rotation: 0.0693

Best score: 0.2560: : 749iter [00:22, 38.43iter/s]
Best score: 0.2560: : 750iter [00:22, 38.53iter/s]
Best score: 0.2560: : 750iter [00:22, 38.53iter/s]
Best score: 0.2560: : 751iter [00:22, 38.53iter/s]
Best score: 0.2560: : 752iter [00:22, 38.53iter/s]
Best score: 0.2560: : 753iter [00:22, 38.53iter/s]
Best score: 0.2560: : 754iter [00:22, 31.39iter/s]
Best score: 0.2560: : 754iter [00:22, 31.39iter/s]
Best score: 0.2560: : 755iter [00:22, 31.39iter/s]
Best score: 0.2560: : 756iter [00:22, 31.39iter/s]
Best score: 0.2560: : 757iter [00:22, 31.39iter/s]
Best score: 0.2560: : 758iter [00:22, 33.52iter/s]
Best score: 0.2560: : 758iter [00:22, 33.52iter/s]
Best score: 0.2537: : 759iter [00:22, 33.52iter/s]
Best score: 0.2537: : 760iter [00:22, 33.52iter/s]
Best score: 0.2537: : 761iter [00:22, 33.52iter/s]
Best score: 0.2537: : 762iter [00:22, 34.84iter/s]
Best score: 0.2537: : 762iter [00:22, 34.84iter/s]
Best score: 0.2537: : 763iter [00:22, 34.84iter/s]
Best score: 0.2537: : 764iter [00:22, 34.84iter/s]
Best score: 0.2537: : 765iter [00:22, 34.84iter/s]
Best score: 0.2537: : 766iter [00:22, 36.21iter/s]
Best score: 0.2537: : 766iter [00:22, 36.21iter/s]
Best score: 0.2537: : 767iter [00:22, 36.21iter/s]
Best score: 0.2537: : 768iter [00:22, 36.21iter/s]
Best score: 0.2536: : 769iter [00:22, 36.21iter/s]
Best score: 0.2536: : 770iter [00:22, 37.06iter/s]
Best score: 0.2536: : 770iter [00:22, 37.06iter/s]
Best score: 0.2536: : 771iter [00:22, 37.06iter/s]
Best score: 0.2536: : 772iter [00:22, 37.06iter/s]
Best score: 0.2536: : 773iter [00:22, 37.06iter/s]
Best score: 0.2536: : 774iter [00:22, 37.75iter/s]
Best score: 0.2536: : 774iter [00:22, 37.75iter/s]
Best score: 0.2536: : 775iter [00:22, 37.75iter/s]
Best score: 0.2536: : 776iter [00:23, 37.75iter/s]
Best score: 0.2536: : 777iter [00:23, 37.75iter/s]
Best score: 0.2536: : 778iter [00:23, 38.30iter/s]
Best score: 0.2536: : 778iter [00:23, 38.30iter/s]
Best score: 0.2536: : 779iter [00:23, 38.30iter/s]
Best score: 0.2536: : 780iter [00:23, 38.30iter/s]
Best score: 0.2536: : 781iter [00:23, 38.30iter/s]
Best score: 0.2536: : 782iter [00:23, 38.34iter/s]
Best score: 0.2536: : 782iter [00:23, 38.34iter/s]
Best score: 0.2536: : 783iter [00:23, 38.34iter/s]
Best score: 0.2536: : 784iter [00:23, 38.34iter/s]
Best score: 0.2536: : 785iter [00:23, 38.34iter/s]
Best score: 0.2536: : 786iter [00:23, 38.71iter/s]
Best score: 0.2536: : 786iter [00:23, 38.71iter/s]
Best score: 0.2536: : 787iter [00:23, 38.71iter/s]
Best score: 0.2536: : 788iter [00:23, 38.71iter/s]
Best score: 0.2534: : 789iter [00:23, 38.71iter/s]
Best score: 0.2534: : 790iter [00:23, 38.66iter/s]
Best score: 0.2534: : 790iter [00:23, 38.66iter/s]
Best score: 0.2534: : 791iter [00:23, 38.66iter/s]
Best score: 0.2534: : 792iter [00:23, 38.66iter/s]
Best score: 0.2534: : 793iter [00:23, 38.66iter/s]
Best score: 0.2534: : 794iter [00:23, 39.01iter/s]
Best score: 0.2534: : 794iter [00:23, 39.01iter/s]
Best score: 0.2534: : 795iter [00:23, 39.01iter/s]
Best score: 0.2534: : 796iter [00:23, 39.01iter/s]
Best score: 0.2534: : 797iter [00:23, 39.01iter/s]
Best score: 0.2534: : 798iter [00:23, 39.01iter/s]
Best score: 0.2534: : 799iter [00:23, 39.39iter/s]
[ITER 800] Best: 0.2531
[PARAMS] Current best parameters:
  fx: 536.7871
  fy: 536.7088
  px: 1013.9380
  py: 738.8819
  k1: 0.00433037
  k2: -0.00764663
  k3: 0.00253502
  k4: -0.00080301
  p1: 0.00969274
  p2: -0.01345783
  p3: 0.00068986
  p4: -0.00052657
  s1: 0.0085
  s2: 0.0008
  s3: -0.0146
  s4: 0.0007
  tilt_x: -0.0439
  tilt_y: -0.0106
  scale: 2.9859
  rotation: 0.0682

Best score: 0.2531: : 799iter [00:23, 39.39iter/s]
Best score: 0.2531: : 800iter [00:23, 39.39iter/s]
Best score: 0.2531: : 801iter [00:23, 39.39iter/s]
Best score: 0.2531: : 802iter [00:23, 39.39iter/s]
Best score: 0.2531: : 803iter [00:23, 32.11iter/s]
Best score: 0.2531: : 803iter [00:23, 32.11iter/s]
Best score: 0.2531: : 804iter [00:23, 32.11iter/s]
Best score: 0.2531: : 805iter [00:23, 32.11iter/s]
Best score: 0.2531: : 806iter [00:23, 32.11iter/s]
Best score: 0.2531: : 807iter [00:23, 33.96iter/s]
Best score: 0.2531: : 807iter [00:23, 33.96iter/s]
Best score: 0.2531: : 808iter [00:23, 33.96iter/s]
Best score: 0.2531: : 809iter [00:23, 33.96iter/s]
Best score: 0.2531: : 810iter [00:23, 33.96iter/s]
Best score: 0.2531: : 811iter [00:23, 35.27iter/s]
Best score: 0.2531: : 811iter [00:23, 35.27iter/s]
Best score: 0.2531: : 812iter [00:24, 35.27iter/s]
Best score: 0.2531: : 813iter [00:24, 35.27iter/s]
Best score: 0.2531: : 814iter [00:24, 35.27iter/s]
Best score: 0.2531: : 815iter [00:24, 35.27iter/s]
Best score: 0.2531: : 816iter [00:24, 36.92iter/s]
Best score: 0.2531: : 816iter [00:24, 36.92iter/s]
Best score: 0.2531: : 817iter [00:24, 36.92iter/s]
Best score: 0.2531: : 818iter [00:24, 36.92iter/s]
Best score: 0.2531: : 819iter [00:24, 36.92iter/s]
Best score: 0.2531: : 820iter [00:24, 37.48iter/s]
Best score: 0.2531: : 820iter [00:24, 37.48iter/s]
Best score: 0.2531: : 821iter [00:24, 37.48iter/s]
Best score: 0.2531: : 822iter [00:24, 37.48iter/s]
Best score: 0.2531: : 823iter [00:24, 37.48iter/s]
Best score: 0.2531: : 824iter [00:24, 38.02iter/s]
Best score: 0.2531: : 824iter [00:24, 38.02iter/s]
Best score: 0.2531: : 825iter [00:24, 38.02iter/s]
Best score: 0.2531: : 826iter [00:24, 38.02iter/s]
Best score: 0.2531: : 827iter [00:24, 38.02iter/s]
Best score: 0.2531: : 828iter [00:24, 38.54iter/s]
Best score: 0.2531: : 828iter [00:24, 38.54iter/s]
Best score: 0.2531: : 829iter [00:24, 38.54iter/s]
Best score: 0.2531: : 830iter [00:24, 38.54iter/s]
Best score: 0.2531: : 831iter [00:24, 38.54iter/s]
Best score: 0.2531: : 832iter [00:24, 38.59iter/s]
Best score: 0.2531: : 832iter [00:24, 38.59iter/s]
Best score: 0.2531: : 833iter [00:24, 38.59iter/s]
Best score: 0.2531: : 834iter [00:24, 38.59iter/s]
Best score: 0.2531: : 835iter [00:24, 38.59iter/s]
Best score: 0.2531: : 836iter [00:24, 38.86iter/s]
Best score: 0.2531: : 836iter [00:24, 38.86iter/s]
Best score: 0.2531: : 837iter [00:24, 38.86iter/s]
Best score: 0.2531: : 838iter [00:24, 38.86iter/s]
Best score: 0.2531: : 839iter [00:24, 38.86iter/s]
Best score: 0.2531: : 840iter [00:24, 38.87iter/s]
Best score: 0.2531: : 840iter [00:24, 38.87iter/s]
Best score: 0.2531: : 841iter [00:24, 38.87iter/s]
Best score: 0.2531: : 842iter [00:24, 38.87iter/s]
Best score: 0.2531: : 843iter [00:24, 38.87iter/s]
Best score: 0.2531: : 844iter [00:24, 38.78iter/s]
Best score: 0.2531: : 844iter [00:24, 38.78iter/s]
Best score: 0.2531: : 845iter [00:24, 38.78iter/s]
Best score: 0.2531: : 846iter [00:24, 38.78iter/s]
Best score: 0.2531: : 847iter [00:24, 38.78iter/s]
Best score: 0.2531: : 848iter [00:24, 38.94iter/s]
Best score: 0.2531: : 848iter [00:24, 38.94iter/s]
[ITER 850] Best: 0.2530
[PARAMS] Current best parameters:
  fx: 535.9369
  fy: 535.8682
  px: 1013.8838
  py: 738.8075
  k1: 0.00525866
  k2: -0.00715561
  k3: 0.00188520
  k4: -0.00062437
  p1: 0.00982874
  p2: -0.01350594
  p3: 0.00073003
  p4: -0.00058519
  s1: 0.0086
  s2: 0.0008
  s3: -0.0147
  s4: 0.0007
  tilt_x: -0.0445
  tilt_y: -0.0105
  scale: 2.9855
  rotation: 0.0651

Best score: 0.2530: : 849iter [00:24, 38.94iter/s]
Best score: 0.2530: : 850iter [00:25, 38.94iter/s]
Best score: 0.2530: : 851iter [00:25, 38.94iter/s]
Best score: 0.2530: : 852iter [00:25, 31.48iter/s]
Best score: 0.2530: : 852iter [00:25, 31.48iter/s]
Best score: 0.2530: : 853iter [00:25, 31.48iter/s]
Best score: 0.2530: : 854iter [00:25, 31.48iter/s]
Best score: 0.2530: : 855iter [00:25, 31.48iter/s]
Best score: 0.2530: : 856iter [00:25, 33.56iter/s]
Best score: 0.2530: : 856iter [00:25, 33.56iter/s]
Best score: 0.2530: : 857iter [00:25, 33.56iter/s]
Best score: 0.2530: : 858iter [00:25, 33.56iter/s]
Best score: 0.2530: : 859iter [00:25, 33.56iter/s]
Best score: 0.2530: : 860iter [00:25, 34.92iter/s]
Best score: 0.2530: : 860iter [00:25, 34.92iter/s]
Best score: 0.2530: : 861iter [00:25, 34.92iter/s]
Best score: 0.2530: : 862iter [00:25, 34.92iter/s]
Best score: 0.2530: : 863iter [00:25, 34.92iter/s]
Best score: 0.2530: : 864iter [00:25, 34.92iter/s]
Best score: 0.2530: : 865iter [00:25, 36.56iter/s]
Best score: 0.2530: : 865iter [00:25, 36.56iter/s]
Best score: 0.2530: : 866iter [00:25, 36.56iter/s]
Best score: 0.2530: : 867iter [00:25, 36.56iter/s]
Best score: 0.2530: : 868iter [00:25, 36.56iter/s]
Best score: 0.2530: : 869iter [00:25, 37.46iter/s]
Best score: 0.2524: : 869iter [00:25, 37.46iter/s]
Best score: 0.2524: : 870iter [00:25, 37.46iter/s]
Best score: 0.2524: : 871iter [00:25, 37.46iter/s]
Best score: 0.2524: : 872iter [00:25, 37.46iter/s]
Best score: 0.2524: : 873iter [00:25, 37.70iter/s]
Best score: 0.2524: : 873iter [00:25, 37.70iter/s]
Best score: 0.2524: : 874iter [00:25, 37.70iter/s]
Best score: 0.2524: : 875iter [00:25, 37.70iter/s]
Best score: 0.2524: : 876iter [00:25, 37.70iter/s]
Best score: 0.2524: : 877iter [00:25, 38.29iter/s]
Best score: 0.2524: : 877iter [00:25, 38.29iter/s]
Best score: 0.2524: : 878iter [00:25, 38.29iter/s]
Best score: 0.2524: : 879iter [00:25, 38.29iter/s]
Best score: 0.2524: : 880iter [00:25, 38.29iter/s]
Best score: 0.2524: : 881iter [00:25, 38.42iter/s]
Best score: 0.2524: : 881iter [00:25, 38.42iter/s]
Best score: 0.2524: : 882iter [00:25, 38.42iter/s]
Best score: 0.2524: : 883iter [00:25, 38.42iter/s]
Best score: 0.2524: : 884iter [00:25, 38.42iter/s]
Best score: 0.2524: : 885iter [00:25, 38.75iter/s]
Best score: 0.2524: : 885iter [00:25, 38.75iter/s]
Best score: 0.2524: : 886iter [00:25, 38.75iter/s]
Best score: 0.2524: : 887iter [00:25, 38.75iter/s]
Best score: 0.2524: : 888iter [00:26, 38.75iter/s]
Best score: 0.2524: : 889iter [00:26, 39.01iter/s]
Best score: 0.2522: : 889iter [00:26, 39.01iter/s]
Best score: 0.2522: : 890iter [00:26, 39.01iter/s]
Best score: 0.2522: : 891iter [00:26, 39.01iter/s]
Best score: 0.2522: : 892iter [00:26, 39.01iter/s]
Best score: 0.2522: : 893iter [00:26, 38.94iter/s]
Best score: 0.2522: : 893iter [00:26, 38.94iter/s]
Best score: 0.2522: : 894iter [00:26, 38.94iter/s]
Best score: 0.2522: : 895iter [00:26, 38.94iter/s]
Best score: 0.2522: : 896iter [00:26, 38.94iter/s]
Best score: 0.2522: : 897iter [00:26, 38.41iter/s]
Best score: 0.2522: : 897iter [00:26, 38.41iter/s]
Best score: 0.2522: : 898iter [00:26, 38.41iter/s]
[ITER 900] Best: 0.2521
[PARAMS] Current best parameters:
  fx: 535.9100
  fy: 535.8643
  px: 1013.7515
  py: 738.5447
  k1: 0.00483830
  k2: -0.00580459
  k3: 0.00076333
  k4: -0.00033884
  p1: 0.00961783
  p2: -0.01370859
  p3: 0.00088555
  p4: -0.00059118
  s1: 0.0087
  s2: 0.0008
  s3: -0.0152
  s4: 0.0009
  tilt_x: -0.0454
  tilt_y: -0.0102
  scale: 2.9851
  rotation: 0.0678

Best score: 0.2521: : 899iter [00:26, 38.41iter/s]
Best score: 0.2521: : 900iter [00:26, 38.41iter/s]
Best score: 0.2521: : 901iter [00:26, 31.41iter/s]
Best score: 0.2521: : 901iter [00:26, 31.41iter/s]
Best score: 0.2521: : 902iter [00:26, 31.41iter/s]
Best score: 0.2521: : 903iter [00:26, 31.41iter/s]
Best score: 0.2521: : 904iter [00:26, 31.41iter/s]
Best score: 0.2521: : 905iter [00:26, 33.56iter/s]
Best score: 0.2521: : 905iter [00:26, 33.56iter/s]
Best score: 0.2521: : 906iter [00:26, 33.56iter/s]
Best score: 0.2521: : 907iter [00:26, 33.56iter/s]
Best score: 0.2521: : 908iter [00:26, 33.56iter/s]
Best score: 0.2521: : 909iter [00:26, 35.25iter/s]
Best score: 0.2521: : 909iter [00:26, 35.25iter/s]
Best score: 0.2521: : 910iter [00:26, 35.25iter/s]
Best score: 0.2521: : 911iter [00:26, 35.25iter/s]
Best score: 0.2521: : 912iter [00:26, 35.25iter/s]
Best score: 0.2521: : 913iter [00:26, 36.17iter/s]
Best score: 0.2521: : 913iter [00:26, 36.17iter/s]
Best score: 0.2521: : 914iter [00:26, 36.17iter/s]
Best score: 0.2521: : 915iter [00:26, 36.17iter/s]
Best score: 0.2521: : 916iter [00:26, 36.17iter/s]
Best score: 0.2521: : 917iter [00:26, 37.10iter/s]
Best score: 0.2521: : 917iter [00:26, 37.10iter/s]
Best score: 0.2521: : 918iter [00:26, 37.10iter/s]
Best score: 0.2521: : 919iter [00:26, 37.10iter/s]
Best score: 0.2521: : 920iter [00:26, 37.10iter/s]
Best score: 0.2521: : 921iter [00:26, 37.57iter/s]
Best score: 0.2521: : 921iter [00:26, 37.57iter/s]
Best score: 0.2521: : 922iter [00:26, 37.57iter/s]
Best score: 0.2521: : 923iter [00:26, 37.57iter/s]
Best score: 0.2521: : 924iter [00:27, 37.57iter/s]
Best score: 0.2521: : 925iter [00:27, 37.57iter/s]
Best score: 0.2521: : 926iter [00:27, 38.48iter/s]
Best score: 0.2521: : 926iter [00:27, 38.48iter/s]
Best score: 0.2521: : 927iter [00:27, 38.48iter/s]
Best score: 0.2521: : 928iter [00:27, 38.48iter/s]
Best score: 0.2520: : 929iter [00:27, 38.48iter/s]
Best score: 0.2520: : 930iter [00:27, 38.60iter/s]
Best score: 0.2520: : 930iter [00:27, 38.60iter/s]
Best score: 0.2520: : 931iter [00:27, 38.60iter/s]
Best score: 0.2520: : 932iter [00:27, 38.60iter/s]
Best score: 0.2520: : 933iter [00:27, 38.60iter/s]
Best score: 0.2520: : 934iter [00:27, 38.84iter/s]
Best score: 0.2520: : 934iter [00:27, 38.84iter/s]
Best score: 0.2520: : 935iter [00:27, 38.84iter/s]
Best score: 0.2520: : 936iter [00:27, 38.84iter/s]
Best score: 0.2520: : 937iter [00:27, 38.84iter/s]
Best score: 0.2520: : 938iter [00:27, 39.13iter/s]
Best score: 0.2520: : 938iter [00:27, 39.13iter/s]
Best score: 0.2518: : 939iter [00:27, 39.13iter/s]
Best score: 0.2518: : 940iter [00:27, 39.13iter/s]
Best score: 0.2518: : 941iter [00:27, 39.13iter/s]
Best score: 0.2518: : 942iter [00:27, 39.11iter/s]
Best score: 0.2518: : 942iter [00:27, 39.11iter/s]
Best score: 0.2518: : 943iter [00:27, 39.11iter/s]
Best score: 0.2518: : 944iter [00:27, 39.11iter/s]
Best score: 0.2518: : 945iter [00:27, 39.11iter/s]
Best score: 0.2518: : 946iter [00:27, 39.33iter/s]
Best score: 0.2518: : 946iter [00:27, 39.33iter/s]
Best score: 0.2518: : 947iter [00:27, 39.33iter/s]
Best score: 0.2518: : 948iter [00:27, 39.33iter/s]
[ITER 950] Best: 0.2518
[PARAMS] Current best parameters:
  fx: 535.0151
  fy: 535.0003
  px: 1013.7771
  py: 738.3084
  k1: 0.00676955
  k2: -0.00734501
  k3: 0.00149939
  k4: -0.00046921
  p1: 0.00980855
  p2: -0.01387971
  p3: 0.00001896
  p4: -0.00034889
  s1: 0.0100
  s2: 0.0004
  s3: -0.0150
  s4: 0.0008
  tilt_x: -0.0469
  tilt_y: -0.0106
  scale: 2.9848
  rotation: 0.0737

Best score: 0.2518: : 949iter [00:27, 39.33iter/s]
Best score: 0.2518: : 950iter [00:27, 39.11iter/s]
Best score: 0.2518: : 950iter [00:27, 39.11iter/s]
Best score: 0.2518: : 951iter [00:27, 39.11iter/s]
Best score: 0.2518: : 952iter [00:27, 39.11iter/s]
Best score: 0.2518: : 953iter [00:27, 39.11iter/s]
Best score: 0.2518: : 954iter [00:27, 31.72iter/s]
Best score: 0.2518: : 954iter [00:27, 31.72iter/s]
Best score: 0.2518: : 955iter [00:27, 31.72iter/s]
Best score: 0.2518: : 956iter [00:27, 31.72iter/s]
Best score: 0.2518: : 957iter [00:27, 31.72iter/s]
Best score: 0.2518: : 958iter [00:27, 33.65iter/s]
Best score: 0.2518: : 958iter [00:27, 33.65iter/s]
Best score: 0.2518: : 959iter [00:27, 33.65iter/s]
Best score: 0.2518: : 960iter [00:28, 33.65iter/s]
Best score: 0.2518: : 961iter [00:28, 33.65iter/s]
Best score: 0.2518: : 962iter [00:28, 35.03iter/s]
Best score: 0.2518: : 962iter [00:28, 35.03iter/s]
Best score: 0.2518: : 963iter [00:28, 35.03iter/s]
Best score: 0.2518: : 964iter [00:28, 35.03iter/s]
Best score: 0.2518: : 965iter [00:28, 35.03iter/s]
Best score: 0.2518: : 966iter [00:28, 36.29iter/s]
Best score: 0.2518: : 966iter [00:28, 36.29iter/s]
Best score: 0.2518: : 967iter [00:28, 36.29iter/s]
Best score: 0.2518: : 968iter [00:28, 36.29iter/s]
Best score: 0.2518: : 969iter [00:28, 36.29iter/s]
Best score: 0.2518: : 970iter [00:28, 37.04iter/s]
Best score: 0.2518: : 970iter [00:28, 37.04iter/s]
Best score: 0.2518: : 971iter [00:28, 37.04iter/s]
Best score: 0.2518: : 972iter [00:28, 37.04iter/s]
Best score: 0.2518: : 973iter [00:28, 37.04iter/s]
Best score: 0.2518: : 974iter [00:28, 37.84iter/s]
Best score: 0.2518: : 974iter [00:28, 37.84iter/s]
Best score: 0.2518: : 975iter [00:28, 37.84iter/s]
Best score: 0.2518: : 976iter [00:28, 37.84iter/s]
Best score: 0.2518: : 977iter [00:28, 37.84iter/s]
Best score: 0.2518: : 978iter [00:28, 37.84iter/s]
Best score: 0.2518: : 979iter [00:28, 38.58iter/s]
Best score: 0.2518: : 979iter [00:28, 38.58iter/s]
Best score: 0.2518: : 980iter [00:28, 38.58iter/s]
Best score: 0.2518: : 981iter [00:28, 38.58iter/s]
Best score: 0.2518: : 982iter [00:28, 38.58iter/s]
Best score: 0.2518: : 983iter [00:28, 38.61iter/s]
Best score: 0.2518: : 983iter [00:28, 38.61iter/s]
Best score: 0.2518: : 984iter [00:28, 38.61iter/s]
Best score: 0.2518: : 985iter [00:28, 38.61iter/s]
Best score: 0.2518: : 986iter [00:28, 38.61iter/s]
Best score: 0.2518: : 987iter [00:28, 38.61iter/s]
Best score: 0.2518: : 988iter [00:28, 39.17iter/s]
Best score: 0.2518: : 988iter [00:28, 39.17iter/s]
Best score: 0.2518: : 989iter [00:28, 39.17iter/s]
Best score: 0.2518: : 990iter [00:28, 39.17iter/s]
Best score: 0.2518: : 991iter [00:28, 39.17iter/s]
Best score: 0.2518: : 992iter [00:28, 38.99iter/s]
Best score: 0.2518: : 992iter [00:28, 38.99iter/s]
Best score: 0.2518: : 993iter [00:28, 38.99iter/s]
Best score: 0.2518: : 994iter [00:28, 38.99iter/s]
Best score: 0.2518: : 995iter [00:28, 38.99iter/s]
Best score: 0.2518: : 996iter [00:28, 39.20iter/s]
Best score: 0.2518: : 996iter [00:28, 39.20iter/s]
Best score: 0.2518: : 997iter [00:28, 39.20iter/s]
Best score: 0.2518: : 998iter [00:28, 39.20iter/s]
[ITER 1000] Best: 0.2510
[PARAMS] Current best parameters:
  fx: 534.0006
  fy: 533.9482
  px: 1013.8367
  py: 737.8661
  k1: 0.00681897
  k2: -0.00651968
  k3: 0.00129629
  k4: -0.00048114
  p1: 0.01055434
  p2: -0.01331642
  p3: 0.00070456
  p4: -0.00062675
  s1: 0.0087
  s2: 0.0007
  s3: -0.0166
  s4: 0.0010
  tilt_x: -0.0506
  tilt_y: -0.0100
  scale: 2.9886
  rotation: 0.0709

Best score: 0.2510: : 999iter [00:29, 39.20iter/s]
Best score: 0.2510: : 1000iter [00:29, 39.02iter/s]
Best score: 0.2510: : 1000iter [00:29, 39.02iter/s]
Best score: 0.2510: : 1001iter [00:29, 39.02iter/s]
Best score: 0.2510: : 1002iter [00:29, 39.02iter/s]
Best score: 0.2510: : 1003iter [00:29, 39.02iter/s]
Best score: 0.2510: : 1004iter [00:29, 31.85iter/s]
Best score: 0.2510: : 1004iter [00:29, 31.85iter/s]
Best score: 0.2510: : 1005iter [00:29, 31.85iter/s]
Best score: 0.2510: : 1006iter [00:29, 31.85iter/s]
Best score: 0.2510: : 1007iter [00:29, 31.85iter/s]
Best score: 0.2510: : 1008iter [00:29, 33.82iter/s]
Best score: 0.2510: : 1008iter [00:29, 33.82iter/s]
Best score: 0.2509: : 1009iter [00:29, 33.82iter/s]
Best score: 0.2509: : 1010iter [00:29, 33.82iter/s]
Best score: 0.2509: : 1011iter [00:29, 33.82iter/s]
Best score: 0.2509: : 1012iter [00:29, 35.04iter/s]
Best score: 0.2509: : 1012iter [00:29, 35.04iter/s]
Best score: 0.2509: : 1013iter [00:29, 35.04iter/s]
Best score: 0.2509: : 1014iter [00:29, 35.04iter/s]
Best score: 0.2509: : 1015iter [00:29, 35.04iter/s]
Best score: 0.2509: : 1016iter [00:29, 36.36iter/s]
Best score: 0.2509: : 1016iter [00:29, 36.36iter/s]
Best score: 0.2509: : 1017iter [00:29, 36.36iter/s]
Best score: 0.2509: : 1018iter [00:29, 36.36iter/s]
Best score: 0.2509: : 1019iter [00:29, 36.36iter/s]
Best score: 0.2509: : 1020iter [00:29, 36.90iter/s]
Best score: 0.2509: : 1020iter [00:29, 36.90iter/s]
Best score: 0.2509: : 1021iter [00:29, 36.90iter/s]
Best score: 0.2509: : 1022iter [00:29, 36.90iter/s]
Best score: 0.2509: : 1023iter [00:29, 36.90iter/s]
Best score: 0.2509: : 1024iter [00:29, 37.67iter/s]
Best score: 0.2509: : 1024iter [00:29, 37.67iter/s]
Best score: 0.2509: : 1025iter [00:29, 37.67iter/s]
Best score: 0.2509: : 1026iter [00:29, 37.67iter/s]
Best score: 0.2509: : 1027iter [00:29, 37.67iter/s]
Best score: 0.2509: : 1028iter [00:29, 38.28iter/s]
Best score: 0.2509: : 1028iter [00:29, 38.28iter/s]
Best score: 0.2509: : 1029iter [00:29, 38.28iter/s]
Best score: 0.2509: : 1030iter [00:29, 38.28iter/s]
Best score: 0.2509: : 1031iter [00:29, 38.28iter/s]
Best score: 0.2509: : 1032iter [00:29, 31.17iter/s]
Best score: 0.2509: : 1032iter [00:30, 31.17iter/s]
Best score: 0.2509: : 1033iter [00:30, 31.17iter/s]
Best score: 0.2509: : 1034iter [00:30, 31.17iter/s]
Best score: 0.2509: : 1035iter [00:30, 31.17iter/s]
Best score: 0.2509: : 1036iter [00:30, 33.27iter/s]
Best score: 0.2509: : 1036iter [00:30, 33.27iter/s]
Best score: 0.2509: : 1037iter [00:30, 33.27iter/s]
Best score: 0.2509: : 1038iter [00:30, 33.27iter/s]
Best score: 0.2508: : 1039iter [00:30, 33.27iter/s]
Best score: 0.2508: : 1040iter [00:30, 34.63iter/s]
Best score: 0.2508: : 1040iter [00:30, 34.63iter/s]
Best score: 0.2508: : 1041iter [00:30, 34.63iter/s]
Best score: 0.2508: : 1042iter [00:30, 34.63iter/s]
Best score: 0.2508: : 1043iter [00:30, 34.63iter/s]
Best score: 0.2508: : 1044iter [00:30, 36.00iter/s]
Best score: 0.2508: : 1044iter [00:30, 36.00iter/s]
Best score: 0.2508: : 1045iter [00:30, 36.00iter/s]
Best score: 0.2508: : 1046iter [00:30, 36.00iter/s]
Best score: 0.2508: : 1047iter [00:30, 36.00iter/s]
Best score: 0.2508: : 1048iter [00:30, 36.00iter/s]
Best score: 0.2508: : 1049iter [00:30, 37.31iter/s]
[ITER 1050] Best: 0.2508
[PARAMS] Current best parameters:
  fx: 533.7630
  fy: 533.7011
  px: 1013.8134
  py: 737.8868
  k1: 0.00683600
  k2: -0.00553237
  k3: 0.00043822
  k4: -0.00026982
  p1: 0.01053937
  p2: -0.01331427
  p3: 0.00082792
  p4: -0.00062131
  s1: 0.0084
  s2: 0.0008
  s3: -0.0171
  s4: 0.0012
  tilt_x: -0.0504
  tilt_y: -0.0099
  scale: 2.9877
  rotation: 0.0702

Best score: 0.2508: : 1049iter [00:30, 37.31iter/s]
Best score: 0.2508: : 1050iter [00:30, 37.31iter/s]
Best score: 0.2508: : 1051iter [00:30, 37.31iter/s]
Best score: 0.2508: : 1052iter [00:30, 37.31iter/s]
Best score: 0.2508: : 1053iter [00:30, 31.11iter/s]
Best score: 0.2508: : 1053iter [00:30, 31.11iter/s]
Best score: 0.2508: : 1054iter [00:30, 31.11iter/s]
Best score: 0.2508: : 1055iter [00:30, 31.11iter/s]
Best score: 0.2508: : 1056iter [00:30, 31.11iter/s]
Best score: 0.2508: : 1057iter [00:30, 33.20iter/s]
Best score: 0.2508: : 1057iter [00:30, 33.20iter/s]
Best score: 0.2508: : 1058iter [00:30, 33.20iter/s]
Best score: 0.2508: : 1059iter [00:30, 33.20iter/s]
Best score: 0.2508: : 1060iter [00:30, 33.20iter/s]
Best score: 0.2508: : 1061iter [00:30, 34.66iter/s]
Best score: 0.2508: : 1061iter [00:30, 34.66iter/s]
Best score: 0.2508: : 1062iter [00:30, 34.66iter/s]
Best score: 0.2508: : 1063iter [00:30, 34.66iter/s]
Best score: 0.2508: : 1064iter [00:30, 34.66iter/s]
Best score: 0.2508: : 1065iter [00:30, 34.66iter/s]
Best score: 0.2508: : 1066iter [00:30, 36.35iter/s]
Best score: 0.2508: : 1066iter [00:30, 36.35iter/s]
Best score: 0.2508: : 1067iter [00:30, 36.35iter/s]
Best score: 0.2508: : 1068iter [00:30, 36.35iter/s]
Best score: 0.2507: : 1069iter [00:31, 36.35iter/s]
Best score: 0.2507: : 1070iter [00:31, 37.07iter/s]
Best score: 0.2507: : 1070iter [00:31, 37.07iter/s]
Best score: 0.2507: : 1071iter [00:31, 37.07iter/s]
Best score: 0.2507: : 1072iter [00:31, 37.07iter/s]
Best score: 0.2507: : 1073iter [00:31, 37.07iter/s]
Best score: 0.2507: : 1074iter [00:31, 37.79iter/s]
Best score: 0.2507: : 1074iter [00:31, 37.79iter/s]
Best score: 0.2507: : 1075iter [00:31, 37.79iter/s]
Best score: 0.2507: : 1076iter [00:31, 37.79iter/s]
Best score: 0.2507: : 1077iter [00:31, 37.79iter/s]
Best score: 0.2507: : 1078iter [00:31, 38.24iter/s]
Best score: 0.2507: : 1078iter [00:31, 38.24iter/s]
Best score: 0.2507: : 1079iter [00:31, 38.24iter/s]
Best score: 0.2507: : 1080iter [00:31, 38.24iter/s]
Best score: 0.2507: : 1081iter [00:31, 38.24iter/s]
Best score: 0.2507: : 1082iter [00:31, 38.28iter/s]
Best score: 0.2507: : 1082iter [00:31, 38.28iter/s]
Best score: 0.2507: : 1083iter [00:31, 38.28iter/s]
Best score: 0.2507: : 1084iter [00:31, 38.28iter/s]
Best score: 0.2507: : 1085iter [00:31, 38.28iter/s]
Best score: 0.2507: : 1086iter [00:31, 38.71iter/s]
Best score: 0.2507: : 1086iter [00:31, 38.71iter/s]
Best score: 0.2507: : 1087iter [00:31, 38.71iter/s]
Best score: 0.2507: : 1088iter [00:31, 38.71iter/s]
Best score: 0.2507: : 1089iter [00:31, 38.71iter/s]
Best score: 0.2507: : 1090iter [00:31, 38.63iter/s]
Best score: 0.2507: : 1090iter [00:31, 38.63iter/s]
Best score: 0.2507: : 1091iter [00:31, 38.63iter/s]
Best score: 0.2507: : 1092iter [00:31, 38.63iter/s]
Best score: 0.2507: : 1093iter [00:31, 38.63iter/s]
Best score: 0.2507: : 1094iter [00:31, 38.63iter/s]
Best score: 0.2507: : 1095iter [00:31, 39.15iter/s]
Best score: 0.2507: : 1095iter [00:31, 39.15iter/s]
Best score: 0.2507: : 1096iter [00:31, 39.15iter/s]
Best score: 0.2507: : 1097iter [00:31, 39.15iter/s]
Best score: 0.2507: : 1098iter [00:31, 39.15iter/s]
[ITER 1100] Best: 0.2507
[PARAMS] Current best parameters:
  fx: 533.5492
  fy: 533.4960
  px: 1013.8043
  py: 737.8816
  k1: 0.00746207
  k2: -0.00622745
  k3: 0.00082591
  k4: -0.00034695
  p1: 0.01059464
  p2: -0.01332635
  p3: 0.00073291
  p4: -0.00061903
  s1: 0.0087
  s2: 0.0007
  s3: -0.0169
  s4: 0.0011
  tilt_x: -0.0505
  tilt_y: -0.0098
  scale: 2.9876
  rotation: 0.0702

Best score: 0.2507: : 1099iter [00:31, 39.15iter/s]
Best score: 0.2507: : 1100iter [00:31, 39.17iter/s]
Best score: 0.2507: : 1100iter [00:31, 34.51iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.250682 -> 0.250631

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,956 → 25,956 within 3500mm ring
  Ring polish did not improve score: 0.250631
  Success: True
  Tangential distortion: p1=0.010594, p2=-0.013326, p3=0.000733, p4=-0.000618
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2506
[✅] Parameters copied to: /working/MDC044_W027_2025-05-29/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 533.549243327199
    fy: 533.4960104276461
    px: 1013.8042931632506
    py: 737.8815854417443
    k1: 0.007464236946397935
    k2: -0.006225240450811235
    k3: 0.0008265420292599031
    k4: -0.000348253732549806
    p1: 0.010594075642349479
    p2: -0.013325727645219837
    p3: 0.0007334356324617164
    p4: -0.0006180783738278404
    scale: 2.9875890295578404
    rotation: 0.07024145819220212
    mmpp: 1.2734234147232821

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

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

   🔄 1000:   0%|          | 0/224 [00:00<?, ?frame/s]

   🔄 1000:   8%|▊         | 18/224 [00:00<00:01, 170.16frame/s]

   🔄 1000:  16%|█▌        | 36/224 [00:00<00:01, 172.00frame/s]

   🔄 1000:  24%|██▍       | 54/224 [00:00<00:00, 173.21frame/s]

   🔄 1000:  32%|███▏      | 72/224 [00:00<00:00, 173.56frame/s]

   🔄 1000:  40%|████      | 90/224 [00:00<00:00, 172.96frame/s]

   🔄 1000:  48%|████▊     | 108/224 [00:00<00:00, 172.03frame/s]

   🔄 1000:  56%|█████▋    | 126/224 [00:00<00:00, 171.51frame/s]

   🔄 1000:  64%|██████▍   | 144/224 [00:00<00:00, 171.19frame/s]

   🔄 1000:  72%|███████▏  | 162/224 [00:00<00:00, 172.04frame/s]

   🔄 1000:  80%|████████  | 180/224 [00:01<00:00, 173.23frame/s]

   🔄 1000:  88%|████████▊ | 198/224 [00:01<00:00, 173.69frame/s]

   🔄 1000:  96%|█████████▋| 216/224 [00:01<00:00, 173.70frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.33s/rig]

   🔄 500:   0%|          | 0/199 [00:00<?, ?frame/s]

   🔄 500:   9%|▉         | 18/199 [00:00<00:01, 173.41frame/s]

   🔄 500:  18%|█▊        | 36/199 [00:00<00:00, 174.08frame/s]

   🔄 500:  27%|██▋       | 54/199 [00:00<00:00, 174.52frame/s]

   🔄 500:  36%|███▌      | 72/199 [00:00<00:00, 173.79frame/s]

   🔄 500:  45%|████▌     | 90/199 [00:00<00:00, 174.77frame/s]

   🔄 500:  54%|█████▍    | 108/199 [00:00<00:00, 176.43frame/s]

   🔄 500:  63%|██████▎   | 126/199 [00:00<00:00, 176.48frame/s]

   🔄 500:  72%|███████▏  | 144/199 [00:00<00:00, 175.85frame/s]

   🔄 500:  81%|████████▏ | 162/199 [00:00<00:00, 175.80frame/s]

   🔄 500:  90%|█████████ | 180/199 [00:01<00:00, 175.18frame/s]

   🔄 500:  99%|█████████▉| 198/199 [00:01<00:00, 174.03frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.15s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.17s/rig]
[✅] Saved PTDs: /working/MDC044_W027_2025-05-29/calibration/debug/4_undistorted/output/ptd

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

   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 173.90frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 173.17frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 170.02frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 169.27frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 170.84frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.11s/rig]

   🔄 500:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 171.13frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 172.60frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 174.16frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 173.86frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 173.73frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.01s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.03s/rig]
[✅] Saved PTDs: /working/MDC044_W027_2025-05-29/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%|▎         | 85059/3080448 [00:00<00:03, 850580.67px/s]
📤 Writing LUT:   6%|▌         | 181406/3080448 [00:00<00:03, 916981.23px/s]
📤 Writing LUT:   9%|▉         | 273325/3080448 [00:00<00:03, 917988.20px/s]
📤 Writing LUT:  12%|█▏        | 365124/3080448 [00:00<00:03, 903447.98px/s]
📤 Writing LUT:  15%|█▍        | 455504/3080448 [00:00<00:02, 890766.13px/s]
📤 Writing LUT:  18%|█▊        | 544622/3080448 [00:00<00:02, 885617.25px/s]
📤 Writing LUT:  21%|██        | 633209/3080448 [00:00<00:02, 876638.70px/s]
📤 Writing LUT:  23%|██▎       | 721526/3080448 [00:00<00:02, 878680.59px/s]
📤 Writing LUT:  26%|██▋       | 810693/3080448 [00:00<00:02, 882690.15px/s]
📤 Writing LUT:  29%|██▉       | 901079/3080448 [00:01<00:02, 889169.61px/s]
📤 Writing LUT:  32%|███▏      | 990548/3080448 [00:01<00:02, 890847.78px/s]
📤 Writing LUT:  35%|███▌      | 1079646/3080448 [00:01<00:02, 887816.00px/s]
📤 Writing LUT:  38%|███▊      | 1168438/3080448 [00:01<00:02, 883610.41px/s]
📤 Writing LUT:  41%|████      | 1256809/3080448 [00:01<00:02, 876073.57px/s]
📤 Writing LUT:  44%|████▎     | 1346473/3080448 [00:01<00:01, 882202.16px/s]
📤 Writing LUT:  47%|████▋     | 1434904/3080448 [00:01<00:01, 882830.79px/s]
📤 Writing LUT:  49%|████▉     | 1523201/3080448 [00:01<00:01, 874930.28px/s]
📤 Writing LUT:  52%|█████▏    | 1610716/3080448 [00:01<00:01, 871890.42px/s]
📤 Writing LUT:  55%|█████▌    | 1697920/3080448 [00:01<00:01, 869676.35px/s]
📤 Writing LUT:  58%|█████▊    | 1787193/3080448 [00:02<00:01, 876535.75px/s]
📤 Writing LUT:  61%|██████    | 1874859/3080448 [00:02<00:01, 871084.54px/s]
📤 Writing LUT:  64%|██████▎   | 1961981/3080448 [00:02<00:01, 868924.45px/s]
📤 Writing LUT:  67%|██████▋   | 2048883/3080448 [00:02<00:01, 866287.24px/s]
📤 Writing LUT:  69%|██████▉   | 2135518/3080448 [00:02<00:01, 861644.75px/s]
📤 Writing LUT:  72%|███████▏  | 2225437/3080448 [00:02<00:00, 872806.49px/s]
📤 Writing LUT:  75%|███████▌  | 2315005/3080448 [00:02<00:00, 879624.41px/s]
📤 Writing LUT:  78%|███████▊  | 2402983/3080448 [00:02<00:00, 874468.38px/s]
📤 Writing LUT:  81%|████████  | 2490445/3080448 [00:02<00:00, 873082.91px/s]
📤 Writing LUT:  84%|████████▎ | 2577764/3080448 [00:02<00:00, 871296.74px/s]
📤 Writing LUT:  87%|████████▋ | 2665575/3080448 [00:03<00:00, 873327.84px/s]
📤 Writing LUT:  89%|████████▉ | 2756644/3080448 [00:03<00:00, 884487.26px/s]
📤 Writing LUT:  92%|█████████▏| 2847502/3080448 [00:03<00:00, 891693.39px/s]
📤 Writing LUT:  95%|█████████▌| 2936680/3080448 [00:03<00:00, 890355.04px/s]
📤 Writing LUT:  98%|█████████▊| 3028003/3080448 [00:03<00:00, 897198.88px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 881971.18px/s]
[✅] LUT written to: /working/MDC044_W027_2025-05-29/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC044_W027_2025-05-29' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC044_W027_2025-05-29/calibration/MDC044_W027_2025-05-29_unknown.lut

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

[ℹ️] Detected device type: MD (rigs: [500, 1000]mm)

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

   🔥 1000:   0%|          | 0/224 [00:00<?, ?frame/s]

   🔥 1000:   4%|▎         | 8/224 [00:00<00:02, 78.39frame/s]

   🔥 1000:   8%|▊         | 17/224 [00:00<00:02, 82.42frame/s]

   🔥 1000:  12%|█▏        | 26/224 [00:00<00:02, 84.79frame/s]

   🔥 1000:  16%|█▌        | 35/224 [00:00<00:02, 86.25frame/s]

   🔥 1000:  20%|█▉        | 44/224 [00:00<00:02, 86.14frame/s]

   🔥 1000:  24%|██▎       | 53/224 [00:00<00:02, 84.41frame/s]

   🔥 1000:  28%|██▊       | 62/224 [00:00<00:01, 83.54frame/s]

   🔥 1000:  32%|███▏      | 71/224 [00:00<00:01, 83.33frame/s]

   🔥 1000:  36%|███▌      | 80/224 [00:00<00:01, 82.83frame/s]

   🔥 1000:  40%|███▉      | 89/224 [00:01<00:01, 82.93frame/s]

   🔥 1000:  44%|████▍     | 98/224 [00:01<00:01, 83.36frame/s]

   🔥 1000:  48%|████▊     | 107/224 [00:01<00:01, 83.90frame/s]

   🔥 1000:  52%|█████▏    | 116/224 [00:01<00:01, 84.23frame/s]

   🔥 1000:  56%|█████▌    | 125/224 [00:01<00:01, 85.02frame/s]

   🔥 1000:  60%|██████    | 135/224 [00:01<00:01, 86.68frame/s]

   🔥 1000:  64%|██████▍   | 144/224 [00:01<00:00, 85.12frame/s]

   🔥 1000:  68%|██████▊   | 153/224 [00:01<00:00, 84.39frame/s]

   🔥 1000:  72%|███████▏  | 162/224 [00:01<00:00, 84.27frame/s]

   🔥 1000:  76%|███████▋  | 171/224 [00:02<00:00, 83.78frame/s]

   🔥 1000:  80%|████████  | 180/224 [00:02<00:00, 83.96frame/s]

   🔥 1000:  84%|████████▍ | 189/224 [00:02<00:00, 85.05frame/s]

   🔥 1000:  88%|████████▊ | 198/224 [00:02<00:00, 86.30frame/s]

   🔥 1000:  92%|█████████▏| 207/224 [00:02<00:00, 86.31frame/s]

   🔥 1000:  96%|█████████▋| 216/224 [00:02<00:00, 86.08frame/s]

                                                                [✅] 1000: /working/MDC044_W027_2025-05-29/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC044_W027_2025-05-29/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.66s/rig]

   🔥 500:   0%|          | 0/199 [00:00<?, ?frame/s]

   🔥 500:   4%|▍         | 8/199 [00:00<00:02, 74.63frame/s]

   🔥 500:   8%|▊         | 16/199 [00:00<00:02, 76.90frame/s]

   🔥 500:  12%|█▏        | 24/199 [00:00<00:02, 77.19frame/s]

   🔥 500:  16%|█▌        | 32/199 [00:00<00:02, 77.49frame/s]

   🔥 500:  21%|██        | 41/199 [00:00<00:02, 78.90frame/s]

   🔥 500:  25%|██▌       | 50/199 [00:00<00:01, 79.45frame/s]

   🔥 500:  29%|██▉       | 58/199 [00:00<00:01, 79.17frame/s]

   🔥 500:  33%|███▎      | 66/199 [00:00<00:01, 78.65frame/s]

   🔥 500:  37%|███▋      | 74/199 [00:00<00:01, 78.61frame/s]

   🔥 500:  41%|████      | 82/199 [00:01<00:01, 78.68frame/s]

   🔥 500:  46%|████▌     | 91/199 [00:01<00:01, 79.48frame/s]

   🔥 500:  50%|████▉     | 99/199 [00:01<00:01, 79.57frame/s]

   🔥 500:  54%|█████▍    | 108/199 [00:01<00:01, 79.69frame/s]

   🔥 500:  58%|█████▊    | 116/199 [00:01<00:01, 79.50frame/s]

   🔥 500:  62%|██████▏   | 124/199 [00:01<00:00, 78.49frame/s]

   🔥 500:  66%|██████▋   | 132/199 [00:01<00:00, 77.75frame/s]

   🔥 500:  70%|███████   | 140/199 [00:01<00:00, 77.73frame/s]

   🔥 500:  74%|███████▍  | 148/199 [00:01<00:00, 77.76frame/s]

   🔥 500:  78%|███████▊  | 156/199 [00:01<00:00, 77.70frame/s]

   🔥 500:  82%|████████▏ | 164/199 [00:02<00:00, 78.06frame/s]

   🔥 500:  87%|████████▋ | 173/199 [00:02<00:00, 78.90frame/s]

   🔥 500:  91%|█████████ | 181/199 [00:02<00:00, 78.53frame/s]

   🔥 500:  95%|█████████▍| 189/199 [00:02<00:00, 78.01frame/s]

   🔥 500:  99%|█████████▉| 197/199 [00:02<00:00, 77.92frame/s]

                                                               [✅] 500: /working/MDC044_W027_2025-05-29/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC044_W027_2025-05-29/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.49s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.67s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC044_W027_2025-05-29/calibration/debug/5_heatmap
[🏁] Finished processing MDC044_W027_2025-05-29


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