2026-07-13T00:25:38 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC040_W015(6)_2025-09-23 ===
2026-07-13T00:25:38 - INFO: Camera ID: 15091912
2026-07-13T00:25:38 - INFO: Wand ID: 
2026-07-13T00:25:38 - INFO: Recorded At: 2025-09-23T12:21:17Z

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 4 las0.zip files to process
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 854 stationary frames, 135 remain

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→ 1000 Phase 2: corners:  80%|████████  | 108/135 [00:00<00:00, 121.31it/s][A
                                                                           [A[PASS2] Summary: 0 frames fixed, 5 untrusted, 135 total

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→ 1000 Phase 3: finalize:  98%|█████████▊| 132/135 [00:02<00:00, 55.12it/s][A
                                                                           [A
[TIMING] 1000 — 135 frames
  Phase 1  radial detect   (parallel)  :   2.27s  (16.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.09s  (8.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.03s
  Phase 3  finalize        (sequential):   2.45s  (18.2 ms/frame)
    decode + undistort                 :   1.38s  (10.3 ms/frame)
    corner apply + refinement          :   0.45s  (3.3 ms/frame)
    segment                            :   0.50s  (3.7 ms/frame)
    smooth                             :   0.07s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   5.84s

Processing Rigs:  25%|██▌       | 1/4 [00:10<00:32, 10.89s/it][INFO] Filtered 933 stationary frames, 153 remain

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→ 2000R Phase 2: corners:  82%|████████▏ | 126/153 [00:01<00:00, 120.84it/s][A
→ 2000R Phase 2: corners:  98%|█████████▊| 150/153 [00:01<00:00, 154.00it/s][A
                                                                            [A[PASS2] Summary: 0 frames fixed, 5 untrusted, 153 total

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→ 2000R Phase 3: finalize:  98%|█████████▊| 150/153 [00:02<00:00, 51.86it/s][A
                                                                            [A
[TIMING] 2000R — 153 frames
  Phase 1  radial detect   (parallel)  :   2.48s  (16.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.43s  (9.3 ms/frame)
  Phase 2b corner fix      (sequential):   0.03s
  Phase 3  finalize        (sequential):   2.75s  (18.0 ms/frame)
    decode + undistort                 :   1.41s  (9.2 ms/frame)
    corner apply + refinement          :   0.61s  (4.0 ms/frame)
    segment                            :   0.60s  (3.9 ms/frame)
    smooth                             :   0.08s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   6.70s

Processing Rigs:  50%|█████     | 2/4 [00:21<00:21, 10.68s/it][INFO] Filtered 727 stationary frames, 162 remain

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→ 2000L Phase 2: corners:   0%|          | 0/162 [00:00<?, ?it/s][A
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→ 2000L Phase 2: corners:  16%|█▌        | 26/162 [00:00<00:01, 74.46it/s][A
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→ 2000L Phase 2: corners:  55%|█████▍    | 89/162 [00:01<00:00, 113.61it/s][A
→ 2000L Phase 2: corners:  62%|██████▏   | 101/162 [00:01<00:00, 112.02it/s][A
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→ 2000L Phase 2: corners:  93%|█████████▎| 151/162 [00:01<00:00, 149.54it/s][A
                                                                            [A[PASS2] Summary: 0 frames fixed, 5 untrusted, 162 total

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→ 2000L Phase 3: finalize:  99%|█████████▉| 161/162 [00:02<00:00, 53.07it/s][A
                                                                            [A
[TIMING] 2000L — 162 frames
  Phase 1  radial detect   (parallel)  :   2.68s  (16.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.51s  (9.3 ms/frame)
  Phase 2b corner fix      (sequential):   0.03s
  Phase 3  finalize        (sequential):   2.95s  (18.2 ms/frame)
    decode + undistort                 :   1.51s  (9.3 ms/frame)
    corner apply + refinement          :   0.67s  (4.1 ms/frame)
    segment                            :   0.63s  (3.9 ms/frame)
    smooth                             :   0.09s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   7.18s

Processing Rigs:  75%|███████▌  | 3/4 [00:31<00:10, 10.58s/it][INFO] Filtered 884 stationary frames, 204 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 4 untrusted, 204 total

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                                                                           [A
[TIMING] 1500 — 204 frames
  Phase 1  radial detect   (parallel)  :   3.24s  (15.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.70s  (8.4 ms/frame)
  Phase 2b corner fix      (sequential):   0.04s
  Phase 3  finalize        (sequential):   3.67s  (18.0 ms/frame)
    decode + undistort                 :   1.95s  (9.5 ms/frame)
    corner apply + refinement          :   0.75s  (3.7 ms/frame)
    segment                            :   0.80s  (3.9 ms/frame)
    smooth                             :   0.11s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.66s

Processing Rigs: 100%|██████████| 4/4 [00:44<00:00, 11.32s/it]Processing Rigs: 100%|██████████| 4/4 [00:44<00:00, 11.09s/it]
[💾] Saved RigFrameDataset to /working/MDC040_W015(6)_2025-09-23/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 654 frames from 4 rigs to /working/MDC040_W015(6)_2025-09-23/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC040_W015(6)_2025-09-23/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 135
[📋] 2000R: 100 frames selected from 153
[📋] 2000L: 100 frames selected from 162
[📋] 1500: 100 frames selected from 204
[💾] Saved RigFrameDataset to /working/MDC040_W015(6)_2025-09-23/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC040_W015(6)_2025-09-23/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 60 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/60 [00:00<?, ?it/s]Computing max image from bright frames:   2%|▏         | 1/60 [00:04<04:51,  4.93s/it]Computing max image from bright frames:   5%|▌         | 3/60 [00:05<01:17,  1.35s/it]Computing max image from bright frames:   8%|▊         | 5/60 [00:05<00:39,  1.41it/s]Computing max image from bright frames:  12%|█▏        | 7/60 [00:05<00:24,  2.19it/s]Computing max image from bright frames:  15%|█▌        | 9/60 [00:05<00:16,  3.06it/s]Computing max image from bright frames:  18%|█▊        | 11/60 [00:05<00:12,  4.08it/s]Computing max image from bright frames:  20%|██        | 12/60 [00:06<00:10,  4.54it/s]Computing max image from bright frames:  22%|██▏       | 13/60 [00:06<00:09,  5.12it/s]Computing max image from bright frames:  23%|██▎       | 14/60 [00:06<00:08,  5.71it/s]Computing max image from bright frames:  27%|██▋       | 16/60 [00:06<00:06,  6.85it/s]Computing max image from bright frames:  30%|███       | 18/60 [00:06<00:05,  7.88it/s]Computing max image from bright frames:  33%|███▎      | 20/60 [00:06<00:04,  9.20it/s]Computing max image from bright frames:  37%|███▋      | 22/60 [00:07<00:04,  9.11it/s]Computing max image from bright frames:  40%|████      | 24/60 [00:07<00:03,  9.86it/s]Computing max image from bright frames:  43%|████▎     | 26/60 [00:07<00:03, 10.90it/s]Computing max image from bright frames:  47%|████▋     | 28/60 [00:07<00:02, 10.82it/s]Computing max image from bright frames:  50%|█████     | 30/60 [00:07<00:02, 11.78it/s]Computing max image from bright frames:  53%|█████▎    | 32/60 [00:07<00:02, 12.58it/s]Computing max image from bright frames:  57%|█████▋    | 34/60 [00:09<00:08,  2.90it/s]Computing max image from bright frames:  58%|█████▊    | 35/60 [00:10<00:08,  2.96it/s]Computing max image from bright frames:  62%|██████▏   | 37/60 [00:10<00:06,  3.46it/s]Computing max image from bright frames:  63%|██████▎   | 38/60 [00:10<00:05,  3.91it/s]Computing max image from bright frames:  67%|██████▋   | 40/60 [00:10<00:03,  5.32it/s]Computing max image from bright frames:  70%|███████   | 42/60 [00:10<00:02,  6.64it/s]Computing max image from bright frames:  75%|███████▌  | 45/60 [00:11<00:01,  8.06it/s]Computing max image from bright frames:  78%|███████▊  | 47/60 [00:11<00:01,  8.94it/s]Computing max image from bright frames:  83%|████████▎ | 50/60 [00:11<00:00, 10.01it/s]Computing max image from bright frames:  87%|████████▋ | 52/60 [00:11<00:00, 10.07it/s]Computing max image from bright frames:  92%|█████████▏| 55/60 [00:11<00:00, 12.71it/s]Computing max image from bright frames:  97%|█████████▋| 58/60 [00:11<00:00, 15.10it/s]Computing max image from bright frames: 100%|██████████| 60/60 [00:12<00:00, 15.45it/s]                                                                                       [✅] Casing detection complete: cx=973.0, cy=755.0

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

  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:29,  3.36it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:29,  3.36it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:29,  3.36it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:28,  3.36it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:28,  3.36it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   5%|▌         | 5/100 [00:00<00:06, 13.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:06, 13.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 356.566} (0.000):   6%|▌         | 6/100 [00:00<00:06, 13.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 356.566} (0.000):   7%|▋         | 7/100 [00:00<00:06, 14.67it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   7%|▋         | 7/100 [00:00<00:06, 14.67it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 363.636} (0.001):   8%|▊         | 8/100 [00:00<00:06, 14.67it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:06, 14.67it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:06, 14.67it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:06, 14.67it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:05, 14.67it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  13%|█▎        | 13/100 [00:00<00:03, 23.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 23.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 398.99} (0.005):  14%|█▍        | 14/100 [00:00<00:03, 23.42it/s] Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:00<00:03, 23.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 23.45it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  16%|█▌        | 16/100 [00:00<00:03, 23.45it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 420.202} (0.006):  17%|█▋        | 17/100 [00:00<00:03, 23.45it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  18%|█▊        | 18/100 [00:00<00:03, 23.45it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.006 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:00<00:03, 23.45it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  20%|██        | 20/100 [00:00<00:03, 23.45it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:00<00:02, 29.31it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.011 | Best: {'fx': 455.556} (0.011):  21%|██        | 21/100 [00:00<00:02, 29.31it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.008 | Best: {'fx': 455.556} (0.011):  22%|██▏       | 22/100 [00:00<00:02, 29.31it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  23%|██▎       | 23/100 [00:01<00:02, 29.31it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 476.768} (0.021):  24%|██▍       | 24/100 [00:01<00:02, 29.31it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 476.768} (0.021):  25%|██▌       | 25/100 [00:01<00:02, 30.76it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 476.768} (0.021):  25%|██▌       | 25/100 [00:01<00:02, 30.76it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 483.838} (0.023):  26%|██▌       | 26/100 [00:01<00:02, 30.76it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.029 | Best: {'fx': 490.909} (0.029):  27%|██▋       | 27/100 [00:01<00:02, 30.76it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  28%|██▊       | 28/100 [00:01<00:02, 30.76it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  29%|██▉       | 29/100 [00:01<00:02, 28.50it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.034 | Best: {'fx': 505.051} (0.046):  29%|██▉       | 29/100 [00:01<00:02, 28.50it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.057 | Best: {'fx': 512.121} (0.057):  30%|███       | 30/100 [00:01<00:02, 28.50it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.063 | Best: {'fx': 519.192} (0.063):  31%|███       | 31/100 [00:01<00:02, 28.50it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.063 | Best: {'fx': 519.192} (0.063):  32%|███▏      | 32/100 [00:01<00:03, 22.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.069 | Best: {'fx': 533.333} (0.069):  32%|███▏      | 32/100 [00:01<00:03, 22.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.074 | Best: {'fx': 526.263} (0.074):  33%|███▎      | 33/100 [00:01<00:03, 22.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.067 | Best: {'fx': 526.263} (0.074):  34%|███▍      | 34/100 [00:01<00:02, 22.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.050 | Best: {'fx': 526.263} (0.074):  35%|███▌      | 35/100 [00:01<00:02, 22.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.050 | Best: {'fx': 526.263} (0.074):  36%|███▌      | 36/100 [00:01<00:02, 24.88it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.060 | Best: {'fx': 526.263} (0.074):  36%|███▌      | 36/100 [00:01<00:02, 24.88it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.045 | Best: {'fx': 526.263} (0.074):  37%|███▋      | 37/100 [00:01<00:02, 24.88it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.055 | Best: {'fx': 526.263} (0.074):  38%|███▊      | 38/100 [00:01<00:02, 24.88it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.037 | Best: {'fx': 526.263} (0.074):  39%|███▉      | 39/100 [00:01<00:02, 24.88it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.037 | Best: {'fx': 526.263} (0.074):  40%|████      | 40/100 [00:01<00:02, 26.71it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.042 | Best: {'fx': 526.263} (0.074):  40%|████      | 40/100 [00:01<00:02, 26.71it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.035 | Best: {'fx': 526.263} (0.074):  41%|████      | 41/100 [00:01<00:02, 26.71it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.039 | Best: {'fx': 526.263} (0.074):  42%|████▏     | 42/100 [00:01<00:02, 26.71it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.039 | Best: {'fx': 526.263} (0.074):  43%|████▎     | 43/100 [00:01<00:02, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.032 | Best: {'fx': 526.263} (0.074):  43%|████▎     | 43/100 [00:01<00:02, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.030 | Best: {'fx': 526.263} (0.074):  44%|████▍     | 44/100 [00:01<00:02, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.030 | Best: {'fx': 526.263} (0.074):  45%|████▌     | 45/100 [00:01<00:02, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.033 | Best: {'fx': 526.263} (0.074):  46%|████▌     | 46/100 [00:01<00:01, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 526.263} (0.074):  47%|████▋     | 47/100 [00:01<00:01, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.029 | Best: {'fx': 526.263} (0.074):  48%|████▊     | 48/100 [00:01<00:01, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 526.263} (0.074):  49%|████▉     | 49/100 [00:02<00:01, 27.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 526.263} (0.074):  50%|█████     | 50/100 [00:02<00:01, 29.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.025 | Best: {'fx': 526.263} (0.074):  50%|█████     | 50/100 [00:02<00:01, 29.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.025 | Best: {'fx': 526.263} (0.074):  51%|█████     | 51/100 [00:02<00:01, 29.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 526.263} (0.074):  52%|█████▏    | 52/100 [00:02<00:01, 29.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.074):  53%|█████▎    | 53/100 [00:02<00:01, 29.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.074):  54%|█████▍    | 54/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 526.263} (0.074):  54%|█████▍    | 54/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.074):  55%|█████▌    | 55/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.026 | Best: {'fx': 526.263} (0.074):  56%|█████▌    | 56/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.026 | Best: {'fx': 526.263} (0.074):  57%|█████▋    | 57/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.074):  58%|█████▊    | 58/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.074):  59%|█████▉    | 59/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 526.263} (0.074):  60%|██████    | 60/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 526.263} (0.074):  61%|██████    | 61/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.074):  62%|██████▏   | 62/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.074):  63%|██████▎   | 63/100 [00:02<00:01, 30.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.074):  64%|██████▍   | 64/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.074):  64%|██████▍   | 64/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 526.263} (0.074):  65%|██████▌   | 65/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 526.263} (0.074):  66%|██████▌   | 66/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 526.263} (0.074):  67%|██████▋   | 67/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.074):  68%|██████▊   | 68/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.074):  69%|██████▉   | 69/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  70%|███████   | 70/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.074):  71%|███████   | 71/100 [00:02<00:00, 43.34it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.074):  72%|███████▏  | 72/100 [00:02<00:00, 49.98it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  72%|███████▏  | 72/100 [00:02<00:00, 49.98it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  73%|███████▎  | 73/100 [00:02<00:00, 49.98it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  74%|███████▍  | 74/100 [00:02<00:00, 49.98it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  75%|███████▌  | 75/100 [00:02<00:00, 49.98it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  76%|███████▌  | 76/100 [00:02<00:00, 49.98it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  77%|███████▋  | 77/100 [00:02<00:00, 49.98it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  78%|███████▊  | 78/100 [00:02<00:00, 42.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  78%|███████▊  | 78/100 [00:02<00:00, 42.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  79%|███████▉  | 79/100 [00:02<00:00, 42.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.074):  80%|████████  | 80/100 [00:02<00:00, 42.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  81%|████████  | 81/100 [00:02<00:00, 42.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  82%|████████▏ | 82/100 [00:02<00:00, 42.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  83%|████████▎ | 83/100 [00:02<00:00, 43.37it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  83%|████████▎ | 83/100 [00:02<00:00, 43.37it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  84%|████████▍ | 84/100 [00:02<00:00, 43.37it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  85%|████████▌ | 85/100 [00:02<00:00, 43.37it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  86%|████████▌ | 86/100 [00:02<00:00, 43.37it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  87%|████████▋ | 87/100 [00:02<00:00, 43.37it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  88%|████████▊ | 88/100 [00:02<00:00, 36.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  88%|████████▊ | 88/100 [00:02<00:00, 36.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  89%|████████▉ | 89/100 [00:02<00:00, 36.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  90%|█████████ | 90/100 [00:02<00:00, 36.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  91%|█████████ | 91/100 [00:03<00:00, 36.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  92%|█████████▏| 92/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  92%|█████████▏| 92/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  93%|█████████▎| 93/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.074):  94%|█████████▍| 94/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  95%|█████████▌| 95/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  96%|█████████▌| 96/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  97%|█████████▋| 97/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  98%|█████████▊| 98/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074):  99%|█████████▉| 99/100 [00:03<00:00, 32.61it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.074): 100%|██████████| 100/100 [00:03<00:00, 36.80it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.041 | Best: {'fx': 501.263} (0.041):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.041 | Best: {'fx': 501.263} (0.041):   2%|▏         | 1/50 [00:00<00:23,  2.10it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.044 | Best: {'fx': 503.303} (0.044):   2%|▏         | 1/50 [00:00<00:23,  2.10it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.042 | Best: {'fx': 503.303} (0.044):   4%|▍         | 2/50 [00:00<00:22,  2.10it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.042 | Best: {'fx': 503.303} (0.044):   6%|▌         | 3/50 [00:00<00:07,  6.15it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.045 | Best: {'fx': 504.324} (0.045):   6%|▌         | 3/50 [00:00<00:07,  6.15it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.048 | Best: {'fx': 506.365} (0.048):   8%|▊         | 4/50 [00:00<00:07,  6.15it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.047 | Best: {'fx': 506.365} (0.048):  10%|█         | 5/50 [00:00<00:07,  6.15it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.050 | Best: {'fx': 507.385} (0.050):  12%|█▏        | 6/50 [00:00<00:07,  6.15it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.050 | Best: {'fx': 507.385} (0.050):  14%|█▍        | 7/50 [00:00<00:03, 13.97it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.051 | Best: {'fx': 508.405} (0.051):  14%|█▍        | 7/50 [00:00<00:03, 13.97it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.053 | Best: {'fx': 509.426} (0.053):  16%|█▌        | 8/50 [00:00<00:03, 13.97it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.054 | Best: {'fx': 510.446} (0.054):  18%|█▊        | 9/50 [00:00<00:02, 13.97it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.057 | Best: {'fx': 512.487} (0.057):  20%|██        | 10/50 [00:00<00:02, 13.97it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.057 | Best: {'fx': 512.487} (0.057):  22%|██▏       | 11/50 [00:00<00:02, 15.73it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.060 | Best: {'fx': 515.548} (0.060):  22%|██▏       | 11/50 [00:00<00:02, 15.73it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.061 | Best: {'fx': 516.569} (0.061):  24%|██▍       | 12/50 [00:00<00:02, 15.73it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.056 | Best: {'fx': 516.569} (0.061):  26%|██▌       | 13/50 [00:00<00:02, 15.73it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.058 | Best: {'fx': 516.569} (0.061):  28%|██▊       | 14/50 [00:00<00:02, 15.73it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.063 | Best: {'fx': 518.61} (0.063):  30%|███       | 15/50 [00:01<00:02, 15.73it/s] Brute | Start: {'fx': 526.263} (0.074) | Current: 0.063 | Best: {'fx': 518.61} (0.063):  32%|███▏      | 16/50 [00:01<00:01, 22.79it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.059 | Best: {'fx': 518.61} (0.063):  32%|███▏      | 16/50 [00:01<00:01, 22.79it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.062 | Best: {'fx': 518.61} (0.063):  34%|███▍      | 17/50 [00:01<00:01, 22.79it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.064 | Best: {'fx': 519.63} (0.064):  36%|███▌      | 18/50 [00:01<00:01, 22.79it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.070 | Best: {'fx': 523.712} (0.070):  38%|███▊      | 19/50 [00:01<00:01, 22.79it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.070 | Best: {'fx': 523.712} (0.070):  40%|████      | 20/50 [00:01<00:01, 22.60it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 523.712} (0.070):  40%|████      | 20/50 [00:01<00:01, 22.60it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 523.712} (0.070):  42%|████▏     | 21/50 [00:01<00:01, 22.60it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.066 | Best: {'fx': 523.712} (0.070):  44%|████▍     | 22/50 [00:01<00:01, 22.60it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.071 | Best: {'fx': 524.732} (0.071):  46%|████▌     | 23/50 [00:01<00:01, 22.60it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.071 | Best: {'fx': 524.732} (0.071):  48%|████▊     | 24/50 [00:01<00:01, 25.83it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.072 | Best: {'fx': 527.793} (0.072):  48%|████▊     | 24/50 [00:01<00:01, 25.83it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.069 | Best: {'fx': 527.793} (0.072):  50%|█████     | 25/50 [00:01<00:00, 25.83it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.075 | Best: {'fx': 526.773} (0.075):  52%|█████▏    | 26/50 [00:01<00:00, 25.83it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.070 | Best: {'fx': 526.773} (0.075):  54%|█████▍    | 27/50 [00:01<00:00, 25.83it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.070 | Best: {'fx': 526.773} (0.075):  56%|█████▌    | 28/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 526.773} (0.075):  56%|█████▌    | 28/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.069 | Best: {'fx': 526.773} (0.075):  58%|█████▊    | 29/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.067 | Best: {'fx': 526.773} (0.075):  60%|██████    | 30/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 526.773} (0.075):  62%|██████▏   | 31/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.072 | Best: {'fx': 526.773} (0.075):  64%|██████▍   | 32/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 526.773} (0.075):  66%|██████▌   | 33/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 526.773} (0.075):  68%|██████▊   | 34/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.066 | Best: {'fx': 526.773} (0.075):  70%|███████   | 35/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 526.773} (0.075):  72%|███████▏  | 36/50 [00:01<00:00, 27.48it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.068 | Best: {'fx': 526.773} (0.075):  74%|███████▍  | 37/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.066 | Best: {'fx': 526.773} (0.075):  74%|███████▍  | 37/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.066 | Best: {'fx': 526.773} (0.075):  76%|███████▌  | 38/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.070 | Best: {'fx': 526.773} (0.075):  78%|███████▊  | 39/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.067 | Best: {'fx': 526.773} (0.075):  80%|████████  | 40/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.064 | Best: {'fx': 526.773} (0.075):  82%|████████▏ | 41/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.060 | Best: {'fx': 526.773} (0.075):  84%|████████▍ | 42/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.063 | Best: {'fx': 526.773} (0.075):  86%|████████▌ | 43/50 [00:01<00:00, 42.30it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.063 | Best: {'fx': 526.773} (0.075):  88%|████████▊ | 44/50 [00:01<00:00, 46.38it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.059 | Best: {'fx': 526.773} (0.075):  88%|████████▊ | 44/50 [00:01<00:00, 46.38it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.062 | Best: {'fx': 526.773} (0.075):  90%|█████████ | 45/50 [00:01<00:00, 46.38it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.061 | Best: {'fx': 526.773} (0.075):  92%|█████████▏| 46/50 [00:01<00:00, 46.38it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.058 | Best: {'fx': 526.773} (0.075):  94%|█████████▍| 47/50 [00:01<00:00, 46.38it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.058 | Best: {'fx': 526.773} (0.075):  96%|█████████▌| 48/50 [00:01<00:00, 46.38it/s]Brute | Start: {'fx': 526.263} (0.074) | Current: 0.058 | Best: {'fx': 526.773} (0.075):  98%|█████████▊| 49/50 [00:01<00:00, 46.38it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 526.772830344259
    px: 973.0
    py: 755.0
    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 5 incomplete frames): 75 frames will be used for optimization
  Rigs: 1000(1000mm):18/20, 2000R(2000mm):19/20, 2000L(2000mm):19/20, 1500(1500mm):19/20
  Total: 75/80 frames used for optimization
Initial score: 157.5840: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 157.5840, picklable_objective(x0) = 158.1462
Best score: 158.1462: : 0iter [00:02, ?iter/s] Best score: 158.1462: : 1iter [00:02,  2.10s/iter]Best score: 158.1462: : 1iter [00:02,  2.10s/iter]Best score: 158.1462: : 2iter [00:02,  2.10s/iter]Best score: 158.1462: : 3iter [00:02,  1.66iter/s]Best score: 158.1462: : 3iter [00:02,  1.66iter/s]Best score: 158.1462: : 4iter [00:02,  1.66iter/s]Best score: 158.1462: : 5iter [00:02,  2.98iter/s]Best score: 158.1462: : 5iter [00:02,  2.98iter/s]Best score: 158.1462: : 6iter [00:02,  2.98iter/s]Best score: 158.1462: : 7iter [00:02,  4.38iter/s]Best score: 158.1462: : 7iter [00:02,  4.38iter/s]Best score: 158.1462: : 8iter [00:02,  4.38iter/s]Best score: 158.1462: : 9iter [00:02,  5.78iter/s]Best score: 158.1462: : 9iter [00:02,  5.78iter/s]Best score: 158.1462: : 10iter [00:02,  5.78iter/s]Best score: 158.1462: : 11iter [00:02,  7.04iter/s]Best score: 158.1462: : 11iter [00:03,  7.04iter/s]Best score: 158.1462: : 12iter [00:03,  7.04iter/s]Best score: 158.1462: : 13iter [00:03,  8.20iter/s]Best score: 158.1462: : 13iter [00:03,  8.20iter/s]Best score: 158.1462: : 14iter [00:03,  8.20iter/s]Best score: 158.1462: : 15iter [00:03,  9.17iter/s]Best score: 158.1462: : 15iter [00:03,  9.17iter/s]Best score: 158.1462: : 16iter [00:03,  9.17iter/s]Best score: 158.1462: : 17iter [00:03,  9.95iter/s]Best score: 158.1462: : 17iter [00:03,  9.95iter/s]Best score: 158.1462: : 18iter [00:03,  9.95iter/s]Best score: 158.1462: : 19iter [00:03, 10.59iter/s]Best score: 158.1462: : 19iter [00:03, 10.59iter/s]Best score: 158.1462: : 20iter [00:03, 10.59iter/s]Best score: 158.1462: : 21iter [00:03, 10.93iter/s]Best score: 158.1462: : 21iter [00:03, 10.93iter/s]Best score: 158.1462: : 22iter [00:03, 10.93iter/s]Best score: 158.1462: : 23iter [00:03, 11.31iter/s]Best score: 158.1462: : 23iter [00:03, 11.31iter/s]Best score: 158.1462: : 24iter [00:04, 11.31iter/s]Best score: 158.1462: : 25iter [00:04, 11.56iter/s]Best score: 158.1462: : 25iter [00:04, 11.56iter/s]Best score: 158.1462: : 26iter [00:04, 11.56iter/s]Best score: 158.1462: : 27iter [00:04, 11.71iter/s]Best score: 158.1462: : 27iter [00:04, 11.71iter/s]Best score: 158.1462: : 28iter [00:04, 11.71iter/s]Best score: 158.1462: : 29iter [00:04, 11.79iter/s]Best score: 158.1462: : 29iter [00:04, 11.79iter/s]Best score: 158.1462: : 30iter [00:04, 11.79iter/s]Best score: 158.1462: : 31iter [00:04, 11.72iter/s]Best score: 158.1462: : 31iter [00:04, 11.72iter/s]Best score: 158.1462: : 32iter [00:04, 11.72iter/s]Best score: 158.1462: : 33iter [00:04, 11.83iter/s]Best score: 158.1462: : 33iter [00:04, 11.83iter/s]Best score: 158.1462: : 34iter [00:04, 11.83iter/s]Best score: 158.1462: : 35iter [00:04, 11.97iter/s]Best score: 158.1462: : 35iter [00:04, 11.97iter/s]Best score: 158.1462: : 36iter [00:05, 11.97iter/s]Best score: 158.1462: : 37iter [00:05, 12.08iter/s]Best score: 158.1462: : 37iter [00:05, 12.08iter/s]Best score: 158.1462: : 38iter [00:05, 12.08iter/s]Best score: 158.1462: : 39iter [00:05, 12.14iter/s]Best score: 158.1462: : 39iter [00:05, 12.14iter/s]Best score: 158.1462: : 40iter [00:05, 12.14iter/s]Best score: 158.1462: : 41iter [00:05, 12.04iter/s]Best score: 158.1462: : 41iter [00:05, 12.04iter/s]Best score: 158.1462: : 42iter [00:05, 12.04iter/s]Best score: 158.1462: : 43iter [00:05, 12.16iter/s]Best score: 158.1462: : 43iter [00:05, 12.16iter/s]Best score: 158.1462: : 44iter [00:05, 12.16iter/s]Best score: 158.1462: : 45iter [00:05, 12.33iter/s]Best score: 158.1462: : 45iter [00:05, 12.33iter/s]Best score: 158.1462: : 46iter [00:05, 12.33iter/s]Best score: 158.1462: : 47iter [00:05, 12.36iter/s]Best score: 158.1462: : 47iter [00:05, 12.36iter/s]Best score: 158.1462: : 48iter [00:06, 12.36iter/s]Best score: 158.1462: : 49iter [00:06, 12.43iter/s]
[ITER 50] Best: 134.5647
[PARAMS] Current best parameters:
  fx: 554.3757
  fy: 554.2150
  px: 976.9236
  py: 803.2234
  k1: -0.08813801
  k2: 0.00242649
  k3: 0.04672725
  k4: -0.01219010
  p1: 0.02905672
  p2: -0.02733112
  p3: 0.01939564
  p4: 0.01996482
  s1: -0.0150
  s2: 0.0247
  s3: -0.0066
  s4: 0.0054
  tilt_x: 0.0775
  tilt_y: -0.0178
  scale: 2.9570
  rotation: 0.4203
Best score: 134.5647: : 49iter [00:06, 12.43iter/s]Best score: 134.5647: : 50iter [00:06, 12.43iter/s]Best score: 134.5647: : 51iter [00:06, 12.15iter/s]Best score: 134.5647: : 51iter [00:06, 12.15iter/s]Best score: 134.5647: : 52iter [00:06, 12.15iter/s]Best score: 134.5647: : 53iter [00:06, 12.12iter/s]Best score: 134.5647: : 53iter [00:06, 12.12iter/s]Best score: 134.5647: : 54iter [00:06, 12.12iter/s]Best score: 134.5647: : 55iter [00:06, 12.18iter/s]Best score: 134.5647: : 55iter [00:06, 12.18iter/s]Best score: 134.5647: : 56iter [00:06, 12.18iter/s]Best score: 134.5647: : 57iter [00:06, 12.22iter/s]Best score: 134.5647: : 57iter [00:06, 12.22iter/s]Best score: 134.5647: : 58iter [00:06, 12.22iter/s]Best score: 134.5647: : 59iter [00:06, 12.22iter/s]Best score: 122.2259: : 59iter [00:06, 12.22iter/s]Best score: 122.2259: : 60iter [00:07, 12.22iter/s]Best score: 122.2259: : 61iter [00:07, 12.06iter/s]Best score: 122.2259: : 61iter [00:07, 12.06iter/s]Best score: 122.2259: : 62iter [00:07, 12.06iter/s]Best score: 122.2259: : 63iter [00:07, 12.13iter/s]Best score: 122.2259: : 63iter [00:07, 12.13iter/s]Best score: 122.2259: : 64iter [00:07, 12.13iter/s]Best score: 122.2259: : 65iter [00:07, 12.18iter/s]Best score: 122.2259: : 65iter [00:07, 12.18iter/s]Best score: 122.2259: : 66iter [00:07, 12.18iter/s]Best score: 122.2259: : 67iter [00:07, 12.19iter/s]Best score: 122.2259: : 67iter [00:07, 12.19iter/s]Best score: 122.2259: : 68iter [00:07, 12.19iter/s]Best score: 122.2259: : 69iter [00:07, 12.17iter/s]Best score: 60.1256: : 69iter [00:07, 12.17iter/s] Best score: 60.1256: : 70iter [00:07, 12.17iter/s]Best score: 60.1256: : 71iter [00:07, 11.97iter/s]Best score: 60.1256: : 71iter [00:07, 11.97iter/s]Best score: 60.1256: : 72iter [00:08, 11.97iter/s]Best score: 60.1256: : 73iter [00:08, 12.02iter/s]Best score: 60.1256: : 73iter [00:08, 12.02iter/s]Best score: 60.1256: : 74iter [00:08, 12.02iter/s]Best score: 60.1256: : 75iter [00:08, 12.04iter/s]Best score: 60.1256: : 75iter [00:08, 12.04iter/s]Best score: 60.1256: : 76iter [00:08, 12.04iter/s]Best score: 60.1256: : 77iter [00:08, 12.12iter/s]Best score: 60.1256: : 77iter [00:08, 12.12iter/s]Best score: 60.1256: : 78iter [00:08, 12.12iter/s]Best score: 60.1256: : 79iter [00:08, 12.18iter/s]Best score: 49.0555: : 79iter [00:08, 12.18iter/s]Best score: 49.0555: : 80iter [00:08, 12.18iter/s]Best score: 49.0555: : 81iter [00:08, 12.02iter/s]Best score: 49.0555: : 81iter [00:08, 12.02iter/s]Best score: 49.0555: : 82iter [00:08, 12.02iter/s]Best score: 49.0555: : 83iter [00:08, 12.13iter/s]Best score: 49.0555: : 83iter [00:08, 12.13iter/s]Best score: 49.0555: : 84iter [00:09, 12.13iter/s]Best score: 49.0555: : 85iter [00:09, 12.17iter/s]Best score: 49.0555: : 85iter [00:09, 12.17iter/s]Best score: 49.0555: : 86iter [00:09, 12.17iter/s]Best score: 49.0555: : 87iter [00:09, 12.19iter/s]Best score: 49.0555: : 87iter [00:09, 12.19iter/s]Best score: 49.0555: : 88iter [00:09, 12.19iter/s]Best score: 49.0555: : 89iter [00:09, 12.22iter/s]Best score: 41.5003: : 89iter [00:09, 12.22iter/s]Best score: 41.5003: : 90iter [00:09, 12.22iter/s]Best score: 41.5003: : 91iter [00:09, 12.08iter/s]Best score: 41.5003: : 91iter [00:09, 12.08iter/s]Best score: 41.5003: : 92iter [00:09, 12.08iter/s]Best score: 41.5003: : 93iter [00:09, 12.18iter/s]Best score: 41.5003: : 93iter [00:09, 12.18iter/s]Best score: 41.5003: : 94iter [00:09, 12.18iter/s]Best score: 41.5003: : 95iter [00:09, 12.23iter/s]Best score: 41.5003: : 95iter [00:09, 12.23iter/s]Best score: 41.5003: : 96iter [00:09, 12.23iter/s]Best score: 41.5003: : 97iter [00:09, 12.25iter/s]Best score: 41.5003: : 97iter [00:10, 12.25iter/s]Best score: 41.5003: : 98iter [00:10, 12.25iter/s]Best score: 41.5003: : 99iter [00:10, 12.28iter/s]
[ITER 100] Best: 23.0653
[PARAMS] Current best parameters:
  fx: 496.9144
  fy: 500.8956
  px: 977.1658
  py: 765.5211
  k1: 0.08691627
  k2: -0.07637777
  k3: 0.04018963
  k4: -0.00858449
  p1: -0.03898756
  p2: -0.01880917
  p3: 0.01180578
  p4: 0.02332631
  s1: -0.0220
  s2: -0.0035
  s3: 0.0297
  s4: -0.0180
  tilt_x: 0.0314
  tilt_y: -0.0157
  scale: 2.8675
  rotation: 1.1088
Best score: 23.0653: : 99iter [00:10, 12.28iter/s]Best score: 23.0653: : 100iter [00:10, 12.28iter/s]Best score: 23.0653: : 101iter [00:10, 10.56iter/s]Best score: 23.0653: : 101iter [00:10, 10.56iter/s]Best score: 23.0653: : 102iter [00:10, 10.56iter/s]Best score: 23.0653: : 103iter [00:10, 11.01iter/s]Best score: 23.0653: : 103iter [00:10, 11.01iter/s]Best score: 23.0653: : 104iter [00:10, 11.01iter/s]Best score: 23.0653: : 105iter [00:10, 11.36iter/s]Best score: 23.0653: : 105iter [00:10, 11.36iter/s]Best score: 23.0653: : 106iter [00:10, 11.36iter/s]Best score: 23.0653: : 107iter [00:10, 11.63iter/s]Best score: 23.0653: : 107iter [00:10, 11.63iter/s]Best score: 23.0653: : 108iter [00:11, 11.63iter/s]Best score: 23.0653: : 109iter [00:11, 11.82iter/s]Best score: 23.0653: : 109iter [00:11, 11.82iter/s]Best score: 23.0653: : 110iter [00:11, 11.82iter/s]Best score: 23.0653: : 111iter [00:11, 11.73iter/s]Best score: 23.0653: : 111iter [00:11, 11.73iter/s]Best score: 23.0653: : 112iter [00:11, 11.73iter/s]Best score: 23.0653: : 113iter [00:11, 11.89iter/s]Best score: 23.0653: : 113iter [00:11, 11.89iter/s]Best score: 23.0653: : 114iter [00:11, 11.89iter/s]Best score: 23.0653: : 115iter [00:11, 12.00iter/s]Best score: 23.0653: : 115iter [00:11, 12.00iter/s]Best score: 23.0653: : 116iter [00:11, 12.00iter/s]Best score: 23.0653: : 117iter [00:11, 12.08iter/s]Best score: 23.0653: : 117iter [00:11, 12.08iter/s]Best score: 23.0653: : 118iter [00:11, 12.08iter/s]Best score: 23.0653: : 119iter [00:11, 12.12iter/s]Best score: 23.0653: : 119iter [00:11, 12.12iter/s]Best score: 23.0653: : 120iter [00:12, 12.12iter/s]Best score: 23.0653: : 121iter [00:12, 11.86iter/s]Best score: 23.0653: : 121iter [00:12, 11.86iter/s]Best score: 23.0653: : 122iter [00:12, 11.86iter/s]Best score: 23.0653: : 123iter [00:12, 11.95iter/s]Best score: 23.0653: : 123iter [00:12, 11.95iter/s]Best score: 23.0653: : 124iter [00:12, 11.95iter/s]Best score: 23.0653: : 125iter [00:12, 12.01iter/s]Best score: 23.0653: : 125iter [00:12, 12.01iter/s]Best score: 23.0653: : 126iter [00:12, 12.01iter/s]Best score: 23.0653: : 127iter [00:12, 12.05iter/s]Best score: 23.0653: : 127iter [00:12, 12.05iter/s]Best score: 23.0653: : 128iter [00:12, 12.05iter/s]Best score: 23.0653: : 129iter [00:12, 12.10iter/s]Best score: 23.0653: : 129iter [00:12, 12.10iter/s]Best score: 23.0653: : 130iter [00:12, 12.10iter/s]Best score: 23.0653: : 131iter [00:12, 11.97iter/s]Best score: 23.0653: : 131iter [00:12, 11.97iter/s]Best score: 23.0653: : 132iter [00:13, 11.97iter/s]Best score: 23.0653: : 133iter [00:13, 12.10iter/s]Best score: 23.0653: : 133iter [00:13, 12.10iter/s]Best score: 23.0653: : 134iter [00:13, 12.10iter/s]Best score: 23.0653: : 135iter [00:13, 12.16iter/s]Best score: 23.0653: : 135iter [00:13, 12.16iter/s]Best score: 23.0653: : 136iter [00:13, 12.16iter/s]Best score: 23.0653: : 137iter [00:13, 12.19iter/s]Best score: 23.0653: : 137iter [00:13, 12.19iter/s]Best score: 23.0653: : 138iter [00:13, 12.19iter/s]Best score: 23.0653: : 139iter [00:13, 12.22iter/s]Best score: 23.0653: : 139iter [00:13, 12.22iter/s]Best score: 23.0653: : 140iter [00:13, 12.22iter/s]Best score: 23.0653: : 141iter [00:13, 11.99iter/s]Best score: 23.0653: : 141iter [00:13, 11.99iter/s]Best score: 23.0653: : 142iter [00:13, 11.99iter/s]Best score: 23.0653: : 143iter [00:13, 12.14iter/s]Best score: 23.0653: : 143iter [00:13, 12.14iter/s]Best score: 23.0653: : 144iter [00:14, 12.14iter/s]Best score: 23.0653: : 145iter [00:14, 12.27iter/s]Best score: 23.0653: : 145iter [00:14, 12.27iter/s]Best score: 23.0653: : 146iter [00:14, 12.27iter/s]Best score: 23.0653: : 147iter [00:14, 12.36iter/s]Best score: 23.0653: : 147iter [00:14, 12.36iter/s]Best score: 23.0653: : 148iter [00:14, 12.36iter/s]Best score: 23.0653: : 149iter [00:14, 12.33iter/s]
[ITER 150] Best: 23.0653
[PARAMS] Current best parameters:
  fx: 496.9144
  fy: 500.8956
  px: 977.1658
  py: 765.5211
  k1: 0.08691627
  k2: -0.07637777
  k3: 0.04018963
  k4: -0.00858449
  p1: -0.03898756
  p2: -0.01880917
  p3: 0.01180578
  p4: 0.02332631
  s1: -0.0220
  s2: -0.0035
  s3: 0.0297
  s4: -0.0180
  tilt_x: 0.0314
  tilt_y: -0.0157
  scale: 2.8675
  rotation: 1.1088
Best score: 23.0653: : 149iter [00:14, 12.33iter/s]Best score: 23.0653: : 150iter [00:14, 12.33iter/s]Best score: 23.0653: : 151iter [00:14,  7.91iter/s]Best score: 23.0653: : 151iter [00:14,  7.91iter/s]Best score: 23.0653: : 152iter [00:14,  7.91iter/s]Best score: 23.0653: : 153iter [00:14,  8.86iter/s]Best score: 23.0653: : 153iter [00:15,  8.86iter/s]Best score: 23.0653: : 154iter [00:15,  8.86iter/s]Best score: 23.0653: : 155iter [00:15,  9.72iter/s]Best score: 23.0653: : 155iter [00:15,  9.72iter/s]Best score: 23.0653: : 156iter [00:15,  9.72iter/s]Best score: 23.0653: : 157iter [00:15, 10.36iter/s]Best score: 23.0653: : 157iter [00:15, 10.36iter/s]Best score: 23.0653: : 158iter [00:15, 10.36iter/s]Best score: 23.0653: : 159iter [00:15, 10.89iter/s]Best score: 16.4609: : 159iter [00:15, 10.89iter/s]Best score: 16.4609: : 160iter [00:15, 10.89iter/s]Best score: 16.4609: : 161iter [00:15, 11.19iter/s]Best score: 16.4609: : 161iter [00:15, 11.19iter/s]Best score: 16.4609: : 162iter [00:15, 11.19iter/s]Best score: 16.4609: : 163iter [00:15, 11.52iter/s]Best score: 16.4609: : 163iter [00:15, 11.52iter/s]Best score: 16.4609: : 164iter [00:15, 11.52iter/s]Best score: 16.4609: : 165iter [00:15, 11.73iter/s]Best score: 16.4609: : 165iter [00:16, 11.73iter/s]Best score: 16.4609: : 166iter [00:16, 11.73iter/s]Best score: 16.4609: : 167iter [00:16, 11.93iter/s]Best score: 16.4609: : 167iter [00:16, 11.93iter/s]Best score: 16.4609: : 168iter [00:16, 11.93iter/s]Best score: 16.4609: : 169iter [00:16, 12.06iter/s]Best score: 15.5708: : 169iter [00:16, 12.06iter/s]Best score: 15.5708: : 170iter [00:16, 12.06iter/s]Best score: 15.5708: : 171iter [00:16, 11.98iter/s]Best score: 15.5708: : 171iter [00:16, 11.98iter/s]Best score: 15.5708: : 172iter [00:16, 11.98iter/s]Best score: 15.5708: : 173iter [00:16, 12.11iter/s]Best score: 15.5708: : 173iter [00:16, 12.11iter/s]Best score: 15.5708: : 174iter [00:16, 12.11iter/s]Best score: 15.5708: : 175iter [00:16, 12.26iter/s]Best score: 15.5708: : 175iter [00:16, 12.26iter/s]Best score: 15.5708: : 176iter [00:16, 12.26iter/s]Best score: 15.5708: : 177iter [00:16, 12.28iter/s]Best score: 15.5708: : 177iter [00:17, 12.28iter/s]Best score: 15.5708: : 178iter [00:17, 12.28iter/s]Best score: 15.5708: : 179iter [00:17, 12.33iter/s]Best score: 7.4315: : 179iter [00:17, 12.33iter/s] Best score: 7.4315: : 180iter [00:17, 12.33iter/s]Best score: 7.4315: : 181iter [00:17, 12.20iter/s]Best score: 7.4315: : 181iter [00:17, 12.20iter/s]Best score: 7.4315: : 182iter [00:17, 12.20iter/s]Best score: 7.4315: : 183iter [00:17, 12.31iter/s]Best score: 7.4315: : 183iter [00:17, 12.31iter/s]Best score: 7.4315: : 184iter [00:17, 12.31iter/s]Best score: 7.4315: : 185iter [00:17, 12.35iter/s]Best score: 7.4315: : 185iter [00:17, 12.35iter/s]Best score: 7.4315: : 186iter [00:17, 12.35iter/s]Best score: 7.4315: : 187iter [00:17, 12.43iter/s]Best score: 7.4315: : 187iter [00:17, 12.43iter/s]Best score: 7.4315: : 188iter [00:17, 12.43iter/s]Best score: 7.4315: : 189iter [00:17, 12.48iter/s]Best score: 7.4315: : 189iter [00:17, 12.48iter/s]Best score: 7.4315: : 190iter [00:18, 12.48iter/s]Best score: 7.4315: : 191iter [00:18, 12.31iter/s]Best score: 7.4315: : 191iter [00:18, 12.31iter/s]Best score: 7.4315: : 192iter [00:18, 12.31iter/s]Best score: 7.4315: : 193iter [00:18, 12.40iter/s]Best score: 7.4315: : 193iter [00:18, 12.40iter/s]Best score: 7.4315: : 194iter [00:18, 12.40iter/s]Best score: 7.4315: : 195iter [00:18, 12.39iter/s]Best score: 7.4315: : 195iter [00:18, 12.39iter/s]Best score: 7.4315: : 196iter [00:18, 12.39iter/s]Best score: 7.4315: : 197iter [00:18, 12.42iter/s]Best score: 7.4315: : 197iter [00:18, 12.42iter/s]Best score: 7.4315: : 198iter [00:18, 12.42iter/s]Best score: 7.4315: : 199iter [00:18, 12.48iter/s]
[ITER 200] Best: 5.6473
[PARAMS] Current best parameters:
  fx: 531.2394
  fy: 532.0536
  px: 966.4411
  py: 772.0007
  k1: 0.01349772
  k2: -0.05536494
  k3: 0.05021271
  k4: -0.01383328
  p1: -0.02047448
  p2: -0.00760615
  p3: 0.01327654
  p4: 0.03351016
  s1: -0.0220
  s2: -0.0199
  s3: 0.0075
  s4: -0.0336
  tilt_x: 0.0420
  tilt_y: 0.0566
  scale: 2.9586
  rotation: -0.9311
Best score: 5.6473: : 199iter [00:18, 12.48iter/s]Best score: 5.6473: : 200iter [00:19, 12.48iter/s]Best score: 5.6473: : 201iter [00:19,  8.06iter/s]Best score: 5.6473: : 201iter [00:19,  8.06iter/s]Best score: 5.6473: : 202iter [00:19,  8.06iter/s]Best score: 5.6473: : 203iter [00:19,  9.01iter/s]Best score: 5.6473: : 203iter [00:19,  9.01iter/s]Best score: 5.6473: : 204iter [00:19,  9.01iter/s]Best score: 5.6473: : 205iter [00:19,  9.82iter/s]Best score: 5.6473: : 205iter [00:19,  9.82iter/s]Best score: 5.6473: : 206iter [00:19,  9.82iter/s]Best score: 5.6473: : 207iter [00:19, 10.49iter/s]Best score: 5.6473: : 207iter [00:19, 10.49iter/s]Best score: 5.6473: : 208iter [00:19, 10.49iter/s]Best score: 5.6473: : 209iter [00:19, 10.99iter/s]Best score: 5.4364: : 209iter [00:19, 10.99iter/s]Best score: 5.4364: : 210iter [00:19, 10.99iter/s]Best score: 5.4364: : 211iter [00:19, 11.22iter/s]Best score: 5.4364: : 211iter [00:20, 11.22iter/s]Best score: 5.4364: : 212iter [00:20, 11.22iter/s]Best score: 5.4364: : 213iter [00:20, 11.58iter/s]Best score: 5.4364: : 213iter [00:20, 11.58iter/s]Best score: 5.4364: : 214iter [00:20, 11.58iter/s]Best score: 5.4364: : 215iter [00:20, 11.87iter/s]Best score: 5.4364: : 215iter [00:20, 11.87iter/s]Best score: 5.4364: : 216iter [00:20, 11.87iter/s]Best score: 5.4364: : 217iter [00:20, 12.04iter/s]Best score: 5.4364: : 217iter [00:20, 12.04iter/s]Best score: 5.4364: : 218iter [00:20, 12.04iter/s]Best score: 5.4364: : 219iter [00:20, 12.21iter/s]Best score: 4.8846: : 219iter [00:20, 12.21iter/s]Best score: 4.8846: : 220iter [00:20, 12.21iter/s]Best score: 4.8846: : 221iter [00:20, 12.15iter/s]Best score: 4.8846: : 221iter [00:20, 12.15iter/s]Best score: 4.8846: : 222iter [00:20, 12.15iter/s]Best score: 4.8846: : 223iter [00:20, 12.33iter/s]Best score: 4.8846: : 223iter [00:21, 12.33iter/s]Best score: 4.8846: : 224iter [00:21, 12.33iter/s]Best score: 4.8846: : 225iter [00:21, 12.45iter/s]Best score: 4.8846: : 225iter [00:21, 12.45iter/s]Best score: 4.8846: : 226iter [00:21, 12.45iter/s]Best score: 4.8846: : 227iter [00:21, 12.41iter/s]Best score: 4.8846: : 227iter [00:21, 12.41iter/s]Best score: 4.8846: : 228iter [00:21, 12.41iter/s]Best score: 4.8846: : 229iter [00:21, 12.40iter/s]Best score: 3.9828: : 229iter [00:21, 12.40iter/s]Best score: 3.9828: : 230iter [00:21, 12.40iter/s]Best score: 3.9828: : 231iter [00:21, 12.19iter/s]Best score: 3.9828: : 231iter [00:21, 12.19iter/s]Best score: 3.9828: : 232iter [00:21, 12.19iter/s]Best score: 3.9828: : 233iter [00:21, 12.29iter/s]Best score: 3.9828: : 233iter [00:21, 12.29iter/s]Best score: 3.9828: : 234iter [00:21, 12.29iter/s]Best score: 3.9828: : 235iter [00:21, 12.35iter/s]Best score: 3.9828: : 235iter [00:21, 12.35iter/s]Best score: 3.9828: : 236iter [00:22, 12.35iter/s]Best score: 3.9828: : 237iter [00:22, 12.44iter/s]Best score: 3.9828: : 237iter [00:22, 12.44iter/s]Best score: 3.9828: : 238iter [00:22, 12.44iter/s]Best score: 3.9828: : 239iter [00:22, 12.51iter/s]Best score: 3.1974: : 239iter [00:22, 12.51iter/s]Best score: 3.1974: : 240iter [00:22, 12.51iter/s]Best score: 3.1974: : 241iter [00:22, 12.34iter/s]Best score: 3.1974: : 241iter [00:22, 12.34iter/s]Best score: 3.1974: : 242iter [00:22, 12.34iter/s]Best score: 3.1974: : 243iter [00:22, 12.39iter/s]Best score: 3.1974: : 243iter [00:22, 12.39iter/s]Best score: 3.1974: : 244iter [00:22, 12.39iter/s]Best score: 3.1974: : 245iter [00:22, 12.40iter/s]Best score: 3.1974: : 245iter [00:22, 12.40iter/s]Best score: 3.1974: : 246iter [00:22, 12.40iter/s]Best score: 3.1974: : 247iter [00:22, 12.43iter/s]Best score: 3.1974: : 247iter [00:22, 12.43iter/s]Best score: 3.1974: : 248iter [00:23, 12.43iter/s]Best score: 3.1974: : 249iter [00:23, 12.45iter/s]
[ITER 250] Best: 2.6874Best score: 2.6874: : 249iter [00:23, 12.45iter/s]
[PARAMS] Current best parameters:
  fx: 542.8029
  fy: 541.6482
  px: 972.5971
  py: 768.8727
  k1: 0.02261847
  k2: -0.07331210
  k3: 0.05110774
  k4: -0.01149340
  p1: -0.01872011
  p2: -0.01059059
  p3: 0.01608760
  p4: 0.02581541
  s1: -0.0148
  s2: -0.0192
  s3: -0.0104
  s4: -0.0203
  tilt_x: 0.0355
  tilt_y: 0.0226
  scale: 2.9206
  rotation: -0.4488
Best score: 2.6874: : 250iter [00:23, 12.45iter/s]Best score: 2.6874: : 251iter [00:23,  8.08iter/s]Best score: 2.6874: : 251iter [00:23,  8.08iter/s]Best score: 2.6874: : 252iter [00:23,  8.08iter/s]Best score: 2.6874: : 253iter [00:23,  9.06iter/s]Best score: 2.6874: : 253iter [00:23,  9.06iter/s]Best score: 2.6874: : 254iter [00:23,  9.06iter/s]Best score: 2.6874: : 255iter [00:23,  9.84iter/s]Best score: 2.6874: : 255iter [00:23,  9.84iter/s]Best score: 2.6874: : 256iter [00:23,  9.84iter/s]Best score: 2.6874: : 257iter [00:23, 10.53iter/s]Best score: 2.6874: : 257iter [00:24, 10.53iter/s]Best score: 2.6874: : 258iter [00:24, 10.53iter/s]Best score: 2.6874: : 259iter [00:24, 11.05iter/s]Best score: 1.5646: : 259iter [00:24, 11.05iter/s]Best score: 1.5646: : 260iter [00:24, 11.05iter/s]Best score: 1.5646: : 261iter [00:24, 11.28iter/s]Best score: 1.5646: : 261iter [00:24, 11.28iter/s]Best score: 1.5646: : 262iter [00:24, 11.28iter/s]Best score: 1.5646: : 263iter [00:24, 11.61iter/s]Best score: 1.5646: : 263iter [00:24, 11.61iter/s]Best score: 1.5646: : 264iter [00:24, 11.61iter/s]Best score: 1.5646: : 265iter [00:24, 11.91iter/s]Best score: 1.5646: : 265iter [00:24, 11.91iter/s]Best score: 1.5646: : 266iter [00:24, 11.91iter/s]Best score: 1.5646: : 267iter [00:24, 12.12iter/s]Best score: 1.5646: : 267iter [00:24, 12.12iter/s]Best score: 1.5646: : 268iter [00:24, 12.12iter/s]Best score: 1.5646: : 269iter [00:24, 12.32iter/s]Best score: 1.0538: : 269iter [00:25, 12.32iter/s]Best score: 1.0538: : 270iter [00:25, 12.32iter/s]Best score: 1.0538: : 271iter [00:25, 12.16iter/s]Best score: 1.0538: : 271iter [00:25, 12.16iter/s]Best score: 1.0538: : 272iter [00:25, 12.16iter/s]Best score: 1.0538: : 273iter [00:25, 12.24iter/s]Best score: 1.0538: : 273iter [00:25, 12.24iter/s]Best score: 1.0538: : 274iter [00:25, 12.24iter/s]Best score: 1.0538: : 275iter [00:25, 12.30iter/s]Best score: 1.0538: : 275iter [00:25, 12.30iter/s]Best score: 1.0538: : 276iter [00:25, 12.30iter/s]Best score: 1.0538: : 277iter [00:25, 12.37iter/s]Best score: 1.0538: : 277iter [00:25, 12.37iter/s]Best score: 1.0538: : 278iter [00:25, 12.37iter/s]Best score: 1.0538: : 279iter [00:25, 12.39iter/s]Best score: 1.0538: : 279iter [00:25, 12.39iter/s]Best score: 1.0538: : 280iter [00:25, 12.39iter/s]Best score: 1.0538: : 281iter [00:25, 12.26iter/s]Best score: 1.0538: : 281iter [00:25, 12.26iter/s]Best score: 1.0538: : 282iter [00:26, 12.26iter/s]Best score: 1.0538: : 283iter [00:26, 12.39iter/s]Best score: 1.0538: : 283iter [00:26, 12.39iter/s]Best score: 1.0538: : 284iter [00:26, 12.39iter/s]Best score: 1.0538: : 285iter [00:26, 12.45iter/s]Best score: 1.0538: : 285iter [00:26, 12.45iter/s]Best score: 1.0538: : 286iter [00:26, 12.45iter/s]Best score: 1.0538: : 287iter [00:26, 12.50iter/s]Best score: 1.0538: : 287iter [00:26, 12.50iter/s]Best score: 1.0538: : 288iter [00:26, 12.50iter/s]Best score: 1.0538: : 289iter [00:26, 12.50iter/s]Best score: 1.0538: : 289iter [00:26, 12.50iter/s]Best score: 1.0538: : 290iter [00:26, 12.50iter/s]Best score: 1.0538: : 291iter [00:26, 12.40iter/s]Best score: 1.0538: : 291iter [00:26, 12.40iter/s]Best score: 1.0538: : 292iter [00:26, 12.40iter/s]Best score: 1.0538: : 293iter [00:26, 12.43iter/s]Best score: 1.0538: : 293iter [00:26, 12.43iter/s]Best score: 1.0538: : 294iter [00:27, 12.43iter/s]Best score: 1.0538: : 295iter [00:27, 12.46iter/s]Best score: 1.0538: : 295iter [00:27, 12.46iter/s]Best score: 1.0538: : 296iter [00:27, 12.46iter/s]Best score: 1.0538: : 297iter [00:27, 12.48iter/s]Best score: 1.0538: : 297iter [00:27, 12.48iter/s]Best score: 1.0538: : 298iter [00:27, 12.48iter/s]Best score: 1.0538: : 299iter [00:27, 12.48iter/s]
[ITER 300] Best: 0.9718
[PARAMS] Current best parameters:
  fx: 525.7827
  fy: 525.2990
  px: 965.3157
  py: 767.7850
  k1: 0.04999971
  k2: -0.07107843
  k3: 0.04459303
  k4: -0.01095680
  p1: -0.01693213
  p2: -0.01051725
  p3: 0.02043097
  p4: 0.01935040
  s1: -0.0185
  s2: -0.0210
  s3: 0.0036
  s4: -0.0204
  tilt_x: 0.0097
  tilt_y: 0.0608
  scale: 2.8900
  rotation: -0.2714
Best score: 0.9718: : 299iter [00:27, 12.48iter/s]Best score: 0.9718: : 300iter [00:27, 12.48iter/s]Best score: 0.9718: : 301iter [00:27,  8.03iter/s]Best score: 0.9718: : 301iter [00:27,  8.03iter/s]Best score: 0.9718: : 302iter [00:27,  8.03iter/s]Best score: 0.9718: : 303iter [00:27,  9.02iter/s]Best score: 0.9718: : 303iter [00:28,  9.02iter/s]Best score: 0.9718: : 304iter [00:28,  9.02iter/s]Best score: 0.9718: : 305iter [00:28,  9.85iter/s]Best score: 0.9718: : 305iter [00:28,  9.85iter/s]Best score: 0.9718: : 306iter [00:28,  9.85iter/s]Best score: 0.9718: : 307iter [00:28, 10.55iter/s]Best score: 0.9718: : 307iter [00:28, 10.55iter/s]Best score: 0.9718: : 308iter [00:28, 10.55iter/s]Best score: 0.9718: : 309iter [00:28, 11.11iter/s]Best score: 0.7485: : 309iter [00:28, 11.11iter/s]Best score: 0.7485: : 310iter [00:28, 11.11iter/s]Best score: 0.7485: : 311iter [00:28, 11.35iter/s]Best score: 0.7485: : 311iter [00:28, 11.35iter/s]Best score: 0.7485: : 312iter [00:28, 11.35iter/s]Best score: 0.7485: : 313iter [00:28, 11.69iter/s]Best score: 0.7485: : 313iter [00:28, 11.69iter/s]Best score: 0.7485: : 314iter [00:28, 11.69iter/s]Best score: 0.7485: : 315iter [00:28, 11.91iter/s]Best score: 0.7485: : 315iter [00:28, 11.91iter/s]Best score: 0.7485: : 316iter [00:29, 11.91iter/s]Best score: 0.7485: : 317iter [00:29, 12.10iter/s]Best score: 0.7485: : 317iter [00:29, 12.10iter/s]Best score: 0.7485: : 318iter [00:29, 12.10iter/s]Best score: 0.7485: : 319iter [00:29, 12.21iter/s]Best score: 0.7485: : 319iter [00:29, 12.21iter/s]Best score: 0.7485: : 320iter [00:29, 12.21iter/s]Best score: 0.7485: : 321iter [00:29, 12.13iter/s]Best score: 0.7485: : 321iter [00:29, 12.13iter/s]Best score: 0.7485: : 322iter [00:29, 12.13iter/s]Best score: 0.7485: : 323iter [00:29, 12.32iter/s]Best score: 0.7485: : 323iter [00:29, 12.32iter/s]Best score: 0.7485: : 324iter [00:29, 12.32iter/s]Best score: 0.7485: : 325iter [00:29, 12.38iter/s]Best score: 0.7485: : 325iter [00:29, 12.38iter/s]Best score: 0.7485: : 326iter [00:29, 12.38iter/s]Best score: 0.7485: : 327iter [00:29, 12.43iter/s]Best score: 0.7485: : 327iter [00:29, 12.43iter/s]Best score: 0.7485: : 328iter [00:30, 12.43iter/s]Best score: 0.7485: : 329iter [00:30, 12.46iter/s]Best score: 0.6222: : 329iter [00:30, 12.46iter/s]Best score: 0.6222: : 330iter [00:30, 12.46iter/s]Best score: 0.6222: : 331iter [00:30, 12.33iter/s]Best score: 0.6222: : 331iter [00:30, 12.33iter/s]Best score: 0.6222: : 332iter [00:30, 12.33iter/s]Best score: 0.6222: : 333iter [00:30, 12.40iter/s]Best score: 0.6222: : 333iter [00:30, 12.40iter/s]Best score: 0.6222: : 334iter [00:30, 12.40iter/s]Best score: 0.6222: : 335iter [00:30, 12.43iter/s]Best score: 0.6222: : 335iter [00:30, 12.43iter/s]Best score: 0.6222: : 336iter [00:30, 12.43iter/s]Best score: 0.6222: : 337iter [00:30, 12.46iter/s]Best score: 0.6222: : 337iter [00:30, 12.46iter/s]Best score: 0.6222: : 338iter [00:30, 12.46iter/s]Best score: 0.6222: : 339iter [00:30, 12.49iter/s]Best score: 0.5569: : 339iter [00:30, 12.49iter/s]Best score: 0.5569: : 340iter [00:31, 12.49iter/s]Best score: 0.5569: : 341iter [00:31, 12.34iter/s]Best score: 0.5569: : 341iter [00:31, 12.34iter/s]Best score: 0.5569: : 342iter [00:31, 12.34iter/s]Best score: 0.5569: : 343iter [00:31, 12.42iter/s]Best score: 0.5569: : 343iter [00:31, 12.42iter/s]Best score: 0.5569: : 344iter [00:31, 12.42iter/s]Best score: 0.5569: : 345iter [00:31, 12.44iter/s]Best score: 0.5569: : 345iter [00:31, 12.44iter/s]Best score: 0.5569: : 346iter [00:31, 12.44iter/s]Best score: 0.5569: : 347iter [00:31, 12.44iter/s]Best score: 0.5569: : 347iter [00:31, 12.44iter/s]Best score: 0.5569: : 348iter [00:31, 12.44iter/s]Best score: 0.5569: : 349iter [00:31, 12.40iter/s]
[ITER 350] Best: 0.5161
[PARAMS] Current best parameters:
  fx: 538.1149
  fy: 538.3440
  px: 972.6060
  py: 767.9163
  k1: 0.04797458
  k2: -0.08659732
  k3: 0.05098555
  k4: -0.01115576
  p1: -0.01939442
  p2: -0.01422977
  p3: 0.01770035
  p4: 0.02108024
  s1: -0.0147
  s2: -0.0169
  s3: -0.0008
  s4: -0.0181
  tilt_x: 0.0185
  tilt_y: 0.0208
  scale: 2.8739
  rotation: -0.4115
Best score: 0.5161: : 349iter [00:31, 12.40iter/s]Best score: 0.5161: : 350iter [00:32, 12.40iter/s]Best score: 0.5161: : 351iter [00:32,  7.96iter/s]Best score: 0.5161: : 351iter [00:32,  7.96iter/s]Best score: 0.5161: : 352iter [00:32,  7.96iter/s]Best score: 0.5161: : 353iter [00:32,  8.94iter/s]Best score: 0.5161: : 353iter [00:32,  8.94iter/s]Best score: 0.5161: : 354iter [00:32,  8.94iter/s]Best score: 0.5161: : 355iter [00:32,  9.81iter/s]Best score: 0.5161: : 355iter [00:32,  9.81iter/s]Best score: 0.5161: : 356iter [00:32,  9.81iter/s]Best score: 0.5161: : 357iter [00:32, 10.50iter/s]Best score: 0.5161: : 357iter [00:32, 10.50iter/s]Best score: 0.5161: : 358iter [00:32, 10.50iter/s]Best score: 0.5161: : 359iter [00:32, 11.04iter/s]Best score: 0.5161: : 359iter [00:32, 11.04iter/s]Best score: 0.5161: : 360iter [00:32, 11.04iter/s]Best score: 0.5161: : 361iter [00:32, 11.23iter/s]Best score: 0.5161: : 361iter [00:32, 11.23iter/s]Best score: 0.5161: : 362iter [00:33, 11.23iter/s]Best score: 0.5161: : 363iter [00:33, 11.63iter/s]Best score: 0.5161: : 363iter [00:33, 11.63iter/s]Best score: 0.5161: : 364iter [00:33, 11.63iter/s]Best score: 0.5161: : 365iter [00:33, 11.86iter/s]Best score: 0.5161: : 365iter [00:33, 11.86iter/s]Best score: 0.5161: : 366iter [00:33, 11.86iter/s]Best score: 0.5161: : 367iter [00:33, 12.01iter/s]Best score: 0.5161: : 367iter [00:33, 12.01iter/s]Best score: 0.5161: : 368iter [00:33, 12.01iter/s]Best score: 0.5161: : 369iter [00:33, 12.15iter/s]Best score: 0.4876: : 369iter [00:33, 12.15iter/s]Best score: 0.4876: : 370iter [00:33, 12.15iter/s]Best score: 0.4876: : 371iter [00:33, 12.10iter/s]Best score: 0.4876: : 371iter [00:33, 12.10iter/s]Best score: 0.4876: : 372iter [00:33, 12.10iter/s]Best score: 0.4876: : 373iter [00:33, 12.18iter/s]Best score: 0.4876: : 373iter [00:33, 12.18iter/s]Best score: 0.4876: : 374iter [00:34, 12.18iter/s]Best score: 0.4876: : 375iter [00:34, 12.27iter/s]Best score: 0.4876: : 375iter [00:34, 12.27iter/s]Best score: 0.4876: : 376iter [00:34, 12.27iter/s]Best score: 0.4876: : 377iter [00:34, 12.34iter/s]Best score: 0.4876: : 377iter [00:34, 12.34iter/s]Best score: 0.4876: : 378iter [00:34, 12.34iter/s]Best score: 0.4876: : 379iter [00:34, 12.42iter/s]Best score: 0.4309: : 379iter [00:34, 12.42iter/s]Best score: 0.4309: : 380iter [00:34, 12.42iter/s]Best score: 0.4309: : 381iter [00:34, 12.27iter/s]Best score: 0.4309: : 381iter [00:34, 12.27iter/s]Best score: 0.4309: : 382iter [00:34, 12.27iter/s]Best score: 0.4309: : 383iter [00:34, 12.37iter/s]Best score: 0.4309: : 383iter [00:34, 12.37iter/s]Best score: 0.4309: : 384iter [00:34, 12.37iter/s]Best score: 0.4309: : 385iter [00:34, 12.42iter/s]Best score: 0.4309: : 385iter [00:34, 12.42iter/s]Best score: 0.4309: : 386iter [00:35, 12.42iter/s]Best score: 0.4309: : 387iter [00:35, 12.44iter/s]Best score: 0.4309: : 387iter [00:35, 12.44iter/s]Best score: 0.4309: : 388iter [00:35, 12.44iter/s]Best score: 0.4309: : 389iter [00:35, 12.45iter/s]Best score: 0.3951: : 389iter [00:35, 12.45iter/s]Best score: 0.3951: : 390iter [00:35, 12.45iter/s]Best score: 0.3951: : 391iter [00:35, 12.29iter/s]Best score: 0.3951: : 391iter [00:35, 12.29iter/s]Best score: 0.3951: : 392iter [00:35, 12.29iter/s]Best score: 0.3951: : 393iter [00:35, 12.40iter/s]Best score: 0.3951: : 393iter [00:35, 12.40iter/s]Best score: 0.3951: : 394iter [00:35, 12.40iter/s]Best score: 0.3951: : 395iter [00:35, 12.41iter/s]Best score: 0.3951: : 395iter [00:35, 12.41iter/s]Best score: 0.3951: : 396iter [00:35, 12.41iter/s]Best score: 0.3951: : 397iter [00:35, 12.49iter/s]Best score: 0.3951: : 397iter [00:35, 12.49iter/s]Best score: 0.3951: : 398iter [00:35, 12.49iter/s]Best score: 0.3951: : 399iter [00:35, 12.49iter/s]
[ITER 400] Best: 0.3570
[PARAMS] Current best parameters:
  fx: 538.8647
  fy: 538.6883
  px: 972.6019
  py: 766.9241
  k1: 0.04493390
  k2: -0.09025964
  k3: 0.05652596
  k4: -0.01263741
  p1: -0.01771487
  p2: -0.01300659
  p3: 0.02471439
  p4: 0.00801250
  s1: -0.0156
  s2: -0.0098
  s3: -0.0027
  s4: -0.0090
  tilt_x: 0.0086
  tilt_y: 0.0229
  scale: 2.8815
  rotation: -0.4845
Best score: 0.3570: : 399iter [00:36, 12.49iter/s]Best score: 0.3570: : 400iter [00:36, 12.49iter/s]Best score: 0.3570: : 401iter [00:36,  8.03iter/s]Best score: 0.3570: : 401iter [00:36,  8.03iter/s]Best score: 0.3570: : 402iter [00:36,  8.03iter/s]Best score: 0.3570: : 403iter [00:36,  8.99iter/s]Best score: 0.3570: : 403iter [00:36,  8.99iter/s]Best score: 0.3570: : 404iter [00:36,  8.99iter/s]Best score: 0.3570: : 405iter [00:36,  9.80iter/s]Best score: 0.3570: : 405iter [00:36,  9.80iter/s]Best score: 0.3570: : 406iter [00:36,  9.80iter/s]Best score: 0.3570: : 407iter [00:36, 10.47iter/s]Best score: 0.3570: : 407iter [00:36, 10.47iter/s]Best score: 0.3570: : 408iter [00:37, 10.47iter/s]Best score: 0.3570: : 409iter [00:37, 11.05iter/s]Best score: 0.3570: : 409iter [00:37, 11.05iter/s]Best score: 0.3570: : 410iter [00:37, 11.05iter/s]Best score: 0.3570: : 411iter [00:37, 11.29iter/s]Best score: 0.3570: : 411iter [00:37, 11.29iter/s]Best score: 0.3570: : 412iter [00:37, 11.29iter/s]Best score: 0.3570: : 413iter [00:37, 11.62iter/s]Best score: 0.3570: : 413iter [00:37, 11.62iter/s]Best score: 0.3570: : 414iter [00:37, 11.62iter/s]Best score: 0.3570: : 415iter [00:37, 11.85iter/s]Best score: 0.3570: : 415iter [00:37, 11.85iter/s]Best score: 0.3570: : 416iter [00:37, 11.85iter/s]Best score: 0.3570: : 417iter [00:37, 12.03iter/s]Best score: 0.3570: : 417iter [00:37, 12.03iter/s]Best score: 0.3570: : 418iter [00:37, 12.03iter/s]Best score: 0.3570: : 419iter [00:37, 12.14iter/s]Best score: 0.3570: : 419iter [00:37, 12.14iter/s]Best score: 0.3570: : 420iter [00:38, 12.14iter/s]Best score: 0.3570: : 421iter [00:38, 11.99iter/s]Best score: 0.3570: : 421iter [00:38, 11.99iter/s]Best score: 0.3570: : 422iter [00:38, 11.99iter/s]Best score: 0.3570: : 423iter [00:38, 12.11iter/s]Best score: 0.3570: : 423iter [00:38, 12.11iter/s]Best score: 0.3570: : 424iter [00:38, 12.11iter/s]Best score: 0.3570: : 425iter [00:38, 12.21iter/s]Best score: 0.3570: : 425iter [00:38, 12.21iter/s]Best score: 0.3570: : 426iter [00:38, 12.21iter/s]Best score: 0.3570: : 427iter [00:38, 12.20iter/s]Best score: 0.3570: : 427iter [00:38, 12.20iter/s]Best score: 0.3570: : 428iter [00:38, 12.20iter/s]Best score: 0.3570: : 429iter [00:38, 12.23iter/s]Best score: 0.3452: : 429iter [00:38, 12.23iter/s]Best score: 0.3452: : 430iter [00:38, 12.23iter/s]Best score: 0.3452: : 431iter [00:38, 12.07iter/s]Best score: 0.3452: : 431iter [00:38, 12.07iter/s]Best score: 0.3452: : 432iter [00:39, 12.07iter/s]Best score: 0.3452: : 433iter [00:39, 12.18iter/s]Best score: 0.3452: : 433iter [00:39, 12.18iter/s]Best score: 0.3452: : 434iter [00:39, 12.18iter/s]Best score: 0.3452: : 435iter [00:39, 12.21iter/s]Best score: 0.3452: : 435iter [00:39, 12.21iter/s]Best score: 0.3452: : 436iter [00:39, 12.21iter/s]Best score: 0.3452: : 437iter [00:39, 12.29iter/s]Best score: 0.3452: : 437iter [00:39, 12.29iter/s]Best score: 0.3452: : 438iter [00:39, 12.29iter/s]Best score: 0.3452: : 439iter [00:39, 12.30iter/s]Best score: 0.3441: : 439iter [00:39, 12.30iter/s]Best score: 0.3441: : 440iter [00:39, 12.30iter/s]Best score: 0.3441: : 441iter [00:39, 12.14iter/s]Best score: 0.3441: : 441iter [00:39, 12.14iter/s]Best score: 0.3441: : 442iter [00:39, 12.14iter/s]Best score: 0.3441: : 443iter [00:39, 12.19iter/s]Best score: 0.3441: : 443iter [00:39, 12.19iter/s]Best score: 0.3441: : 444iter [00:40, 12.19iter/s]Best score: 0.3441: : 445iter [00:40, 12.25iter/s]Best score: 0.3441: : 445iter [00:40, 12.25iter/s]Best score: 0.3441: : 446iter [00:40, 12.25iter/s]Best score: 0.3441: : 447iter [00:40, 12.27iter/s]Best score: 0.3441: : 447iter [00:40, 12.27iter/s]Best score: 0.3441: : 448iter [00:40, 12.27iter/s]Best score: 0.3441: : 449iter [00:40, 12.31iter/s]
[ITER 450] Best: 0.3296
[PARAMS] Current best parameters:
  fx: 536.1193
  fy: 536.2269
  px: 970.9272
  py: 763.3155
  k1: 0.04980203
  k2: -0.09160430
  k3: 0.05552351
  k4: -0.01207938
  p1: -0.02078652
  p2: -0.01736030
  p3: 0.02501617
  p4: 0.01089492
  s1: -0.0185
  s2: -0.0091
  s3: -0.0038
  s4: -0.0118
  tilt_x: -0.0008
  tilt_y: 0.0212
  scale: 2.8818
  rotation: -0.4816
Best score: 0.3296: : 449iter [00:40, 12.31iter/s]Best score: 0.3296: : 450iter [00:40, 12.31iter/s]Best score: 0.3296: : 451iter [00:40,  8.00iter/s]Best score: 0.3296: : 451iter [00:40,  8.00iter/s]Best score: 0.3296: : 452iter [00:40,  8.00iter/s]Best score: 0.3296: : 453iter [00:40,  8.97iter/s]Best score: 0.3296: : 453iter [00:41,  8.97iter/s]Best score: 0.3296: : 454iter [00:41,  8.97iter/s]Best score: 0.3296: : 455iter [00:41,  9.79iter/s]Best score: 0.3296: : 455iter [00:41,  9.79iter/s]Best score: 0.3296: : 456iter [00:41,  9.79iter/s]Best score: 0.3296: : 457iter [00:41, 10.48iter/s]Best score: 0.3296: : 457iter [00:41, 10.48iter/s]Best score: 0.3296: : 458iter [00:41, 10.48iter/s]Best score: 0.3296: : 459iter [00:41, 11.01iter/s]Best score: 0.3128: : 459iter [00:41, 11.01iter/s]Best score: 0.3128: : 460iter [00:41, 11.01iter/s]Best score: 0.3128: : 461iter [00:41, 11.23iter/s]Best score: 0.3128: : 461iter [00:41, 11.23iter/s]Best score: 0.3128: : 462iter [00:41, 11.23iter/s]Best score: 0.3128: : 463iter [00:41, 11.56iter/s]Best score: 0.3128: : 463iter [00:41, 11.56iter/s]Best score: 0.3128: : 464iter [00:41, 11.56iter/s]Best score: 0.3128: : 465iter [00:41, 11.82iter/s]Best score: 0.3128: : 465iter [00:41, 11.82iter/s]Best score: 0.3128: : 466iter [00:42, 11.82iter/s]Best score: 0.3128: : 467iter [00:42, 11.98iter/s]Best score: 0.3128: : 467iter [00:42, 11.98iter/s]Best score: 0.3128: : 468iter [00:42, 11.98iter/s]Best score: 0.3128: : 469iter [00:42, 12.10iter/s]Best score: 0.3128: : 469iter [00:42, 12.10iter/s]Best score: 0.3128: : 470iter [00:42, 12.10iter/s]Best score: 0.3128: : 471iter [00:42, 11.96iter/s]Best score: 0.3128: : 471iter [00:42, 11.96iter/s]Best score: 0.3128: : 472iter [00:42, 11.96iter/s]Best score: 0.3128: : 473iter [00:42, 12.06iter/s]Best score: 0.3128: : 473iter [00:42, 12.06iter/s]Best score: 0.3128: : 474iter [00:42, 12.06iter/s]Best score: 0.3128: : 475iter [00:42,  9.84iter/s]Best score: 0.3128: : 475iter [00:42,  9.84iter/s]Best score: 0.3128: : 476iter [00:43,  9.84iter/s]Best score: 0.3128: : 477iter [00:43, 10.53iter/s]Best score: 0.3128: : 477iter [00:43, 10.53iter/s]Best score: 0.3128: : 478iter [00:43, 10.53iter/s]Best score: 0.3128: : 479iter [00:43, 11.08iter/s]Best score: 0.2944: : 479iter [00:43, 11.08iter/s]Best score: 0.2944: : 480iter [00:43, 11.08iter/s]Best score: 0.2944: : 481iter [00:43, 11.31iter/s]Best score: 0.2944: : 481iter [00:43, 11.31iter/s]Best score: 0.2944: : 482iter [00:43, 11.31iter/s]Best score: 0.2944: : 483iter [00:43, 11.64iter/s]Best score: 0.2944: : 483iter [00:43, 11.64iter/s]Best score: 0.2944: : 484iter [00:43, 11.64iter/s]Best score: 0.2944: : 485iter [00:43, 11.75iter/s]Best score: 0.2944: : 485iter [00:43, 11.75iter/s]Best score: 0.2944: : 486iter [00:43, 11.75iter/s]Best score: 0.2944: : 487iter [00:43, 11.97iter/s]Best score: 0.2944: : 487iter [00:43, 11.97iter/s]Best score: 0.2944: : 488iter [00:44, 11.97iter/s]Best score: 0.2944: : 489iter [00:44, 12.07iter/s]Best score: 0.2944: : 489iter [00:44, 12.07iter/s]Best score: 0.2944: : 490iter [00:44, 12.07iter/s]Best score: 0.2944: : 491iter [00:44, 11.95iter/s]Best score: 0.2944: : 491iter [00:44, 11.95iter/s]Best score: 0.2944: : 492iter [00:44, 11.95iter/s]Best score: 0.2944: : 493iter [00:44, 12.16iter/s]Best score: 0.2944: : 493iter [00:44, 12.16iter/s]Best score: 0.2944: : 494iter [00:44, 12.16iter/s]Best score: 0.2944: : 495iter [00:44, 12.30iter/s]Best score: 0.2944: : 495iter [00:44, 12.30iter/s]Best score: 0.2944: : 496iter [00:44, 12.30iter/s]Best score: 0.2944: : 497iter [00:44, 12.33iter/s]Best score: 0.2944: : 497iter [00:44, 12.33iter/s]Best score: 0.2944: : 498iter [00:44, 12.33iter/s]Best score: 0.2944: : 499iter [00:44, 12.36iter/s]
[ITER 500] Best: 0.2877
[PARAMS] Current best parameters:
  fx: 537.2164
  fy: 537.2402
  px: 973.3194
  py: 764.8472
  k1: 0.04476783
  k2: -0.08966717
  k3: 0.05731089
  k4: -0.01303255
  p1: -0.02063095
  p2: -0.01698091
  p3: 0.02733345
  p4: 0.01115831
  s1: -0.0161
  s2: -0.0112
  s3: -0.0053
  s4: -0.0115
  tilt_x: 0.0060
  tilt_y: 0.0119
  scale: 2.8865
  rotation: -0.4820
Best score: 0.2877: : 499iter [00:44, 12.36iter/s]Best score: 0.2877: : 500iter [00:45, 12.36iter/s]Best score: 0.2877: : 501iter [00:45,  8.02iter/s]Best score: 0.2877: : 501iter [00:45,  8.02iter/s]Best score: 0.2877: : 502iter [00:45,  8.02iter/s]Best score: 0.2877: : 503iter [00:45,  9.01iter/s]Best score: 0.2877: : 503iter [00:45,  9.01iter/s]Best score: 0.2877: : 504iter [00:45,  9.01iter/s]Best score: 0.2877: : 505iter [00:45,  9.83iter/s]Best score: 0.2877: : 505iter [00:45,  9.83iter/s]Best score: 0.2877: : 506iter [00:45,  9.83iter/s]Best score: 0.2877: : 507iter [00:45, 10.50iter/s]Best score: 0.2877: : 507iter [00:45, 10.50iter/s]Best score: 0.2877: : 508iter [00:45, 10.50iter/s]Best score: 0.2877: : 509iter [00:45, 11.00iter/s]Best score: 0.2877: : 509iter [00:45, 11.00iter/s]Best score: 0.2877: : 510iter [00:46, 11.00iter/s]Best score: 0.2877: : 511iter [00:46, 11.22iter/s]Best score: 0.2877: : 511iter [00:46, 11.22iter/s]Best score: 0.2877: : 512iter [00:46, 11.22iter/s]Best score: 0.2877: : 513iter [00:46, 11.54iter/s]Best score: 0.2877: : 513iter [00:46, 11.54iter/s]Best score: 0.2877: : 514iter [00:46, 11.54iter/s]Best score: 0.2877: : 515iter [00:46, 11.79iter/s]Best score: 0.2877: : 515iter [00:46, 11.79iter/s]Best score: 0.2877: : 516iter [00:46, 11.79iter/s]Best score: 0.2877: : 517iter [00:46, 11.96iter/s]Best score: 0.2877: : 517iter [00:46, 11.96iter/s]Best score: 0.2877: : 518iter [00:46, 11.96iter/s]Best score: 0.2877: : 519iter [00:46, 12.10iter/s]Best score: 0.2864: : 519iter [00:46, 12.10iter/s]Best score: 0.2864: : 520iter [00:46, 12.10iter/s]Best score: 0.2864: : 521iter [00:46, 11.96iter/s]Best score: 0.2864: : 521iter [00:46, 11.96iter/s]Best score: 0.2864: : 522iter [00:47, 11.96iter/s]Best score: 0.2864: : 523iter [00:47, 12.07iter/s]Best score: 0.2864: : 523iter [00:47, 12.07iter/s]Best score: 0.2864: : 524iter [00:47, 12.07iter/s]Best score: 0.2864: : 525iter [00:47, 12.21iter/s]Best score: 0.2864: : 525iter [00:47, 12.21iter/s]Best score: 0.2864: : 526iter [00:47, 12.21iter/s]Best score: 0.2864: : 527iter [00:47, 12.32iter/s]Best score: 0.2864: : 527iter [00:47, 12.32iter/s]Best score: 0.2864: : 528iter [00:47, 12.32iter/s]Best score: 0.2864: : 529iter [00:47, 12.39iter/s]Best score: 0.2861: : 529iter [00:47, 12.39iter/s]Best score: 0.2861: : 530iter [00:47, 12.39iter/s]Best score: 0.2861: : 531iter [00:47, 12.22iter/s]Best score: 0.2861: : 531iter [00:47, 12.22iter/s]Best score: 0.2861: : 532iter [00:47, 12.22iter/s]Best score: 0.2861: : 533iter [00:47, 12.32iter/s]Best score: 0.2861: : 533iter [00:47, 12.32iter/s]Best score: 0.2861: : 534iter [00:48, 12.32iter/s]Best score: 0.2861: : 535iter [00:48, 12.35iter/s]Best score: 0.2861: : 535iter [00:48, 12.35iter/s]Best score: 0.2861: : 536iter [00:48, 12.35iter/s]Best score: 0.2861: : 537iter [00:48, 12.35iter/s]Best score: 0.2861: : 537iter [00:48, 12.35iter/s]Best score: 0.2861: : 538iter [00:48, 12.35iter/s]Best score: 0.2861: : 539iter [00:48, 12.38iter/s]Best score: 0.2844: : 539iter [00:48, 12.38iter/s]Best score: 0.2844: : 540iter [00:48, 12.38iter/s]Best score: 0.2844: : 541iter [00:48, 12.23iter/s]Best score: 0.2844: : 541iter [00:48, 12.23iter/s]Best score: 0.2844: : 542iter [00:48, 12.23iter/s]Best score: 0.2844: : 543iter [00:48, 12.35iter/s]Best score: 0.2844: : 543iter [00:48, 12.35iter/s]Best score: 0.2844: : 544iter [00:48, 12.35iter/s]Best score: 0.2844: : 545iter [00:48, 12.41iter/s]Best score: 0.2844: : 545iter [00:48, 12.41iter/s]Best score: 0.2844: : 546iter [00:48, 12.41iter/s]Best score: 0.2844: : 547iter [00:48, 12.42iter/s]Best score: 0.2844: : 547iter [00:49, 12.42iter/s]Best score: 0.2844: : 548iter [00:49, 12.42iter/s]Best score: 0.2844: : 549iter [00:49, 12.47iter/s]
[ITER 550] Best: 0.2824
[PARAMS] Current best parameters:
  fx: 537.8898
  fy: 537.9286
  px: 974.7865
  py: 764.3112
  k1: 0.04145171
  k2: -0.08747735
  k3: 0.05742433
  k4: -0.01332395
  p1: -0.02178185
  p2: -0.01962174
  p3: 0.02774948
  p4: 0.01184678
  s1: -0.0132
  s2: -0.0111
  s3: -0.0058
  s4: -0.0114
  tilt_x: 0.0060
  tilt_y: -0.0005
  scale: 2.8893
  rotation: -0.4581
Best score: 0.2824: : 549iter [00:49, 12.47iter/s]Best score: 0.2824: : 550iter [00:49, 12.47iter/s]Best score: 0.2824: : 551iter [00:49,  8.05iter/s]Best score: 0.2824: : 551iter [00:49,  8.05iter/s]Best score: 0.2824: : 552iter [00:49,  8.05iter/s]Best score: 0.2824: : 553iter [00:49,  9.00iter/s]Best score: 0.2824: : 553iter [00:49,  9.00iter/s]Best score: 0.2824: : 554iter [00:49,  9.00iter/s]Best score: 0.2824: : 555iter [00:49,  9.79iter/s]Best score: 0.2824: : 555iter [00:50,  9.79iter/s]Best score: 0.2824: : 556iter [00:50,  9.79iter/s]Best score: 0.2824: : 557iter [00:50, 10.43iter/s]Best score: 0.2824: : 557iter [00:50, 10.43iter/s]Best score: 0.2824: : 558iter [00:50, 10.43iter/s]Best score: 0.2824: : 559iter [00:50, 10.98iter/s]Best score: 0.2824: : 559iter [00:50, 10.98iter/s]Best score: 0.2824: : 560iter [00:50, 10.98iter/s]Best score: 0.2824: : 561iter [00:50, 11.19iter/s]Best score: 0.2824: : 561iter [00:50, 11.19iter/s]Best score: 0.2824: : 562iter [00:50, 11.19iter/s]Best score: 0.2824: : 563iter [00:50, 11.54iter/s]Best score: 0.2824: : 563iter [00:50, 11.54iter/s]Best score: 0.2824: : 564iter [00:50, 11.54iter/s]Best score: 0.2824: : 565iter [00:50, 11.77iter/s]Best score: 0.2824: : 565iter [00:50, 11.77iter/s]Best score: 0.2824: : 566iter [00:50, 11.77iter/s]Best score: 0.2824: : 567iter [00:50, 11.99iter/s]Best score: 0.2824: : 567iter [00:50, 11.99iter/s]Best score: 0.2824: : 568iter [00:51, 11.99iter/s]Best score: 0.2824: : 569iter [00:51, 12.18iter/s]Best score: 0.2805: : 569iter [00:51, 12.18iter/s]Best score: 0.2805: : 570iter [00:51, 12.18iter/s]Best score: 0.2805: : 571iter [00:51, 12.06iter/s]Best score: 0.2805: : 571iter [00:51, 12.06iter/s]Best score: 0.2805: : 572iter [00:51, 12.06iter/s]Best score: 0.2805: : 573iter [00:51, 12.20iter/s]Best score: 0.2805: : 573iter [00:51, 12.20iter/s]Best score: 0.2805: : 574iter [00:51, 12.20iter/s]Best score: 0.2805: : 575iter [00:51, 12.25iter/s]Best score: 0.2805: : 575iter [00:51, 12.25iter/s]Best score: 0.2805: : 576iter [00:51, 12.25iter/s]Best score: 0.2805: : 577iter [00:51, 12.33iter/s]Best score: 0.2805: : 577iter [00:51, 12.33iter/s]Best score: 0.2805: : 578iter [00:51, 12.33iter/s]Best score: 0.2805: : 579iter [00:51, 12.43iter/s]Best score: 0.2777: : 579iter [00:51, 12.43iter/s]Best score: 0.2777: : 580iter [00:52, 12.43iter/s]Best score: 0.2777: : 581iter [00:52, 12.26iter/s]Best score: 0.2777: : 581iter [00:52, 12.26iter/s]Best score: 0.2777: : 582iter [00:52, 12.26iter/s]Best score: 0.2777: : 583iter [00:52, 12.38iter/s]Best score: 0.2777: : 583iter [00:52, 12.38iter/s]Best score: 0.2777: : 584iter [00:52, 12.38iter/s]Best score: 0.2777: : 585iter [00:52, 12.40iter/s]Best score: 0.2777: : 585iter [00:52, 12.40iter/s]Best score: 0.2777: : 586iter [00:52, 12.40iter/s]Best score: 0.2777: : 587iter [00:52, 12.39iter/s]Best score: 0.2777: : 587iter [00:52, 12.39iter/s]Best score: 0.2777: : 588iter [00:52, 12.39iter/s]Best score: 0.2777: : 589iter [00:52, 12.48iter/s]Best score: 0.2761: : 589iter [00:52, 12.48iter/s]Best score: 0.2761: : 590iter [00:52, 12.48iter/s]Best score: 0.2761: : 591iter [00:52, 12.30iter/s]Best score: 0.2761: : 591iter [00:52, 12.30iter/s]Best score: 0.2761: : 592iter [00:53, 12.30iter/s]Best score: 0.2761: : 593iter [00:53, 12.39iter/s]Best score: 0.2761: : 593iter [00:53, 12.39iter/s]Best score: 0.2761: : 594iter [00:53, 12.39iter/s]Best score: 0.2761: : 595iter [00:53, 12.49iter/s]Best score: 0.2761: : 595iter [00:53, 12.49iter/s]Best score: 0.2761: : 596iter [00:53, 12.49iter/s]Best score: 0.2761: : 597iter [00:53, 12.47iter/s]Best score: 0.2761: : 597iter [00:53, 12.47iter/s]Best score: 0.2761: : 598iter [00:53, 12.47iter/s]Best score: 0.2761: : 599iter [00:53, 12.46iter/s]
[ITER 600] Best: 0.2761
[PARAMS] Current best parameters:
  fx: 543.9203
  fy: 543.8368
  px: 976.7322
  py: 763.9591
  k1: 0.03432915
  k2: -0.08691613
  k3: 0.05859264
  k4: -0.01381536
  p1: -0.02156459
  p2: -0.01839745
  p3: 0.02697150
  p4: 0.01026902
  s1: -0.0112
  s2: -0.0101
  s3: -0.0049
  s4: -0.0107
  tilt_x: 0.0037
  tilt_y: -0.0068
  scale: 2.8876
  rotation: -0.4498
Best score: 0.2761: : 599iter [00:53, 12.46iter/s]Best score: 0.2761: : 600iter [00:53, 12.46iter/s]Best score: 0.2761: : 601iter [00:53,  7.86iter/s]Best score: 0.2761: : 601iter [00:54,  7.86iter/s]Best score: 0.2761: : 602iter [00:54,  7.86iter/s]Best score: 0.2761: : 603iter [00:54,  8.83iter/s]Best score: 0.2761: : 603iter [00:54,  8.83iter/s]Best score: 0.2761: : 604iter [00:54,  8.83iter/s]Best score: 0.2761: : 605iter [00:54,  9.68iter/s]Best score: 0.2761: : 605iter [00:54,  9.68iter/s]Best score: 0.2761: : 606iter [00:54,  9.68iter/s]Best score: 0.2761: : 607iter [00:54, 10.39iter/s]Best score: 0.2761: : 607iter [00:54, 10.39iter/s]Best score: 0.2761: : 608iter [00:54, 10.39iter/s]Best score: 0.2761: : 609iter [00:54, 10.95iter/s]Best score: 0.2759: : 609iter [00:54, 10.95iter/s]Best score: 0.2759: : 610iter [00:54, 10.95iter/s]Best score: 0.2759: : 611iter [00:54, 11.21iter/s]Best score: 0.2759: : 611iter [00:54, 11.21iter/s]Best score: 0.2759: : 612iter [00:54, 11.21iter/s]Best score: 0.2759: : 613iter [00:54, 11.58iter/s]Best score: 0.2759: : 613iter [00:55, 11.58iter/s]Best score: 0.2759: : 614iter [00:55, 11.58iter/s]Best score: 0.2759: : 615iter [00:55, 11.85iter/s]Best score: 0.2759: : 615iter [00:55, 11.85iter/s]Best score: 0.2759: : 616iter [00:55, 11.85iter/s]Best score: 0.2759: : 617iter [00:55, 12.09iter/s]Best score: 0.2759: : 617iter [00:55, 12.09iter/s]Best score: 0.2759: : 618iter [00:55, 12.09iter/s]Best score: 0.2759: : 619iter [00:55, 12.23iter/s]Best score: 0.2747: : 619iter [00:55, 12.23iter/s]Best score: 0.2747: : 620iter [00:55, 12.23iter/s]Best score: 0.2747: : 621iter [00:55, 12.09iter/s]Best score: 0.2747: : 621iter [00:55, 12.09iter/s]Best score: 0.2747: : 622iter [00:55, 12.09iter/s]Best score: 0.2747: : 623iter [00:55, 12.11iter/s]Best score: 0.2747: : 623iter [00:55, 12.11iter/s]Best score: 0.2747: : 624iter [00:55, 12.11iter/s]Best score: 0.2747: : 625iter [00:55, 12.24iter/s]Best score: 0.2747: : 625iter [00:55, 12.24iter/s]Best score: 0.2747: : 626iter [00:56, 12.24iter/s]Best score: 0.2747: : 627iter [00:56, 12.27iter/s]Best score: 0.2747: : 627iter [00:56, 12.27iter/s]Best score: 0.2747: : 628iter [00:56, 12.27iter/s]Best score: 0.2747: : 629iter [00:56, 12.32iter/s]Best score: 0.2731: : 629iter [00:56, 12.32iter/s]Best score: 0.2731: : 630iter [00:56, 12.32iter/s]Best score: 0.2731: : 631iter [00:56, 12.14iter/s]Best score: 0.2731: : 631iter [00:56, 12.14iter/s]Best score: 0.2731: : 632iter [00:56, 12.14iter/s]Best score: 0.2731: : 633iter [00:56, 12.24iter/s]Best score: 0.2731: : 633iter [00:56, 12.24iter/s]Best score: 0.2731: : 634iter [00:56, 12.24iter/s]Best score: 0.2731: : 635iter [00:56, 12.33iter/s]Best score: 0.2731: : 635iter [00:56, 12.33iter/s]Best score: 0.2731: : 636iter [00:56, 12.33iter/s]Best score: 0.2731: : 637iter [00:56, 12.41iter/s]Best score: 0.2731: : 637iter [00:56, 12.41iter/s]Best score: 0.2731: : 638iter [00:57, 12.41iter/s]Best score: 0.2731: : 639iter [00:57, 12.43iter/s]Best score: 0.2731: : 639iter [00:57, 12.43iter/s]Best score: 0.2731: : 640iter [00:57, 12.43iter/s]Best score: 0.2731: : 641iter [00:57, 12.26iter/s]Best score: 0.2731: : 641iter [00:57, 12.26iter/s]Best score: 0.2731: : 642iter [00:57, 12.26iter/s]Best score: 0.2731: : 643iter [00:57, 12.36iter/s]Best score: 0.2731: : 643iter [00:57, 12.36iter/s]Best score: 0.2731: : 644iter [00:57, 12.36iter/s]Best score: 0.2731: : 645iter [00:57, 12.37iter/s]Best score: 0.2731: : 645iter [00:57, 12.37iter/s]Best score: 0.2731: : 646iter [00:57, 12.37iter/s]Best score: 0.2731: : 647iter [00:57, 12.32iter/s]Best score: 0.2731: : 647iter [00:57, 12.32iter/s]Best score: 0.2731: : 648iter [00:57, 12.32iter/s]Best score: 0.2731: : 649iter [00:57, 12.33iter/s]
[ITER 650] Best: 0.2719
[PARAMS] Current best parameters:
  fx: 539.8139
  fy: 539.7859
  px: 975.6708
  py: 762.5896
  k1: 0.03837996
  k2: -0.08581559
  k3: 0.05701971
  k4: -0.01337571
  p1: -0.02282410
  p2: -0.02096145
  p3: 0.02920059
  p4: 0.01185706
  s1: -0.0122
  s2: -0.0113
  s3: -0.0067
  s4: -0.0128
  tilt_x: -0.0001
  tilt_y: -0.0073
  scale: 2.8881
  rotation: -0.4517
Best score: 0.2719: : 649iter [00:57, 12.33iter/s]Best score: 0.2719: : 650iter [00:58, 12.33iter/s]Best score: 0.2719: : 651iter [00:58,  7.92iter/s]Best score: 0.2719: : 651iter [00:58,  7.92iter/s]Best score: 0.2719: : 652iter [00:58,  7.92iter/s]Best score: 0.2719: : 653iter [00:58,  8.84iter/s]Best score: 0.2719: : 653iter [00:58,  8.84iter/s]Best score: 0.2719: : 654iter [00:58,  8.84iter/s]Best score: 0.2719: : 655iter [00:58,  9.64iter/s]Best score: 0.2719: : 655iter [00:58,  9.64iter/s]Best score: 0.2719: : 656iter [00:58,  9.64iter/s]Best score: 0.2719: : 657iter [00:58, 10.32iter/s]Best score: 0.2719: : 657iter [00:58, 10.32iter/s]Best score: 0.2719: : 658iter [00:58, 10.32iter/s]Best score: 0.2719: : 659iter [00:58, 10.88iter/s]Best score: 0.2719: : 659iter [00:59, 10.88iter/s]Best score: 0.2719: : 660iter [00:59, 10.88iter/s]Best score: 0.2719: : 661iter [00:59, 11.14iter/s]Best score: 0.2719: : 661iter [00:59, 11.14iter/s]Best score: 0.2719: : 662iter [00:59, 11.14iter/s]Best score: 0.2719: : 663iter [00:59, 11.48iter/s]Best score: 0.2719: : 663iter [00:59, 11.48iter/s]Best score: 0.2719: : 664iter [00:59, 11.48iter/s]Best score: 0.2719: : 665iter [00:59, 11.70iter/s]Best score: 0.2719: : 665iter [00:59, 11.70iter/s]Best score: 0.2719: : 666iter [00:59, 11.70iter/s]Best score: 0.2719: : 667iter [00:59, 11.76iter/s]Best score: 0.2719: : 667iter [00:59, 11.76iter/s]Best score: 0.2719: : 668iter [00:59, 11.76iter/s]Best score: 0.2719: : 669iter [00:59, 11.94iter/s]Best score: 0.2709: : 669iter [00:59, 11.94iter/s]Best score: 0.2709: : 670iter [00:59, 11.94iter/s]Best score: 0.2709: : 671iter [00:59, 11.91iter/s]Best score: 0.2709: : 671iter [01:00, 11.91iter/s]Best score: 0.2709: : 672iter [01:00, 11.91iter/s]Best score: 0.2709: : 673iter [01:00, 12.11iter/s]Best score: 0.2709: : 673iter [01:00, 12.11iter/s]Best score: 0.2709: : 674iter [01:00, 12.11iter/s]Best score: 0.2709: : 675iter [01:00, 12.21iter/s]Best score: 0.2709: : 675iter [01:00, 12.21iter/s]Best score: 0.2709: : 676iter [01:00, 12.21iter/s]Best score: 0.2709: : 677iter [01:00, 12.33iter/s]Best score: 0.2709: : 677iter [01:00, 12.33iter/s]Best score: 0.2709: : 678iter [01:00, 12.33iter/s]Best score: 0.2709: : 679iter [01:00, 12.36iter/s]Best score: 0.2709: : 679iter [01:00, 12.36iter/s]Best score: 0.2709: : 680iter [01:00, 12.36iter/s]Best score: 0.2709: : 681iter [01:00, 12.07iter/s]Best score: 0.2709: : 681iter [01:00, 12.07iter/s]Best score: 0.2709: : 682iter [01:00, 12.07iter/s]Best score: 0.2709: : 683iter [01:00, 12.14iter/s]Best score: 0.2709: : 683iter [01:01, 12.14iter/s]Best score: 0.2709: : 684iter [01:01, 12.14iter/s]Best score: 0.2709: : 685iter [01:01, 12.20iter/s]Best score: 0.2709: : 685iter [01:01, 12.20iter/s]Best score: 0.2709: : 686iter [01:01, 12.20iter/s]Best score: 0.2709: : 687iter [01:01, 12.33iter/s]Best score: 0.2709: : 687iter [01:01, 12.33iter/s]Best score: 0.2709: : 688iter [01:01, 12.33iter/s]Best score: 0.2709: : 689iter [01:01, 12.42iter/s]Best score: 0.2698: : 689iter [01:01, 12.42iter/s]Best score: 0.2698: : 690iter [01:01, 12.42iter/s]Best score: 0.2698: : 691iter [01:01, 12.21iter/s]Best score: 0.2698: : 691iter [01:01, 12.21iter/s]Best score: 0.2698: : 692iter [01:01, 12.21iter/s]Best score: 0.2698: : 693iter [01:01, 12.27iter/s]Best score: 0.2698: : 693iter [01:01, 12.27iter/s]Best score: 0.2698: : 694iter [01:01, 12.27iter/s]Best score: 0.2698: : 695iter [01:01, 12.31iter/s]Best score: 0.2698: : 695iter [01:01, 12.31iter/s]Best score: 0.2698: : 696iter [01:02, 12.31iter/s]Best score: 0.2698: : 697iter [01:02, 12.35iter/s]Best score: 0.2698: : 697iter [01:02, 12.35iter/s]Best score: 0.2698: : 698iter [01:02, 12.35iter/s]Best score: 0.2698: : 699iter [01:02, 12.47iter/s]
[ITER 700] Best: 0.2694
[PARAMS] Current best parameters:
  fx: 535.3426
  fy: 535.2718
  px: 975.0603
  py: 762.8709
  k1: 0.03960423
  k2: -0.07927648
  k3: 0.05205590
  k4: -0.01218539
  p1: -0.02422591
  p2: -0.02217554
  p3: 0.03044555
  p4: 0.01339986
  s1: -0.0122
  s2: -0.0134
  s3: -0.0071
  s4: -0.0137
  tilt_x: 0.0044
  tilt_y: -0.0071
  scale: 2.8922
  rotation: -0.4605
Best score: 0.2694: : 699iter [01:02, 12.47iter/s]Best score: 0.2694: : 700iter [01:02, 12.47iter/s]Best score: 0.2694: : 701iter [01:02,  8.04iter/s]Best score: 0.2694: : 701iter [01:02,  8.04iter/s]Best score: 0.2694: : 702iter [01:02,  8.04iter/s]Best score: 0.2694: : 703iter [01:02,  8.99iter/s]Best score: 0.2694: : 703iter [01:02,  8.99iter/s]Best score: 0.2694: : 704iter [01:02,  8.99iter/s]Best score: 0.2694: : 705iter [01:02,  9.79iter/s]Best score: 0.2694: : 705iter [01:03,  9.79iter/s]Best score: 0.2694: : 706iter [01:03,  9.79iter/s]Best score: 0.2694: : 707iter [01:03, 10.51iter/s]Best score: 0.2694: : 707iter [01:03, 10.51iter/s]Best score: 0.2694: : 708iter [01:03, 10.51iter/s]Best score: 0.2694: : 709iter [01:03, 11.06iter/s]Best score: 0.2683: : 709iter [01:03, 11.06iter/s]Best score: 0.2683: : 710iter [01:03, 11.06iter/s]Best score: 0.2683: : 711iter [01:03, 11.23iter/s]Best score: 0.2683: : 711iter [01:03, 11.23iter/s]Best score: 0.2683: : 712iter [01:03, 11.23iter/s]Best score: 0.2683: : 713iter [01:03, 11.54iter/s]Best score: 0.2683: : 713iter [01:03, 11.54iter/s]Best score: 0.2683: : 714iter [01:03, 11.54iter/s]Best score: 0.2683: : 715iter [01:03, 11.83iter/s]Best score: 0.2683: : 715iter [01:03, 11.83iter/s]Best score: 0.2683: : 716iter [01:03, 11.83iter/s]Best score: 0.2683: : 717iter [01:03, 11.97iter/s]Best score: 0.2683: : 717iter [01:04, 11.97iter/s]Best score: 0.2683: : 718iter [01:04, 11.97iter/s]Best score: 0.2683: : 719iter [01:04, 12.05iter/s]Best score: 0.2682: : 719iter [01:04, 12.05iter/s]Best score: 0.2682: : 720iter [01:04, 12.05iter/s]Best score: 0.2682: : 721iter [01:04, 11.96iter/s]Best score: 0.2682: : 721iter [01:04, 11.96iter/s]Best score: 0.2682: : 722iter [01:04, 11.96iter/s]Best score: 0.2682: : 723iter [01:04, 12.09iter/s]Best score: 0.2682: : 723iter [01:04, 12.09iter/s]Best score: 0.2682: : 724iter [01:04, 12.09iter/s]Best score: 0.2682: : 725iter [01:04, 12.27iter/s]Best score: 0.2682: : 725iter [01:04, 12.27iter/s]Best score: 0.2682: : 726iter [01:04, 12.27iter/s]Best score: 0.2682: : 727iter [01:04, 12.38iter/s]Best score: 0.2682: : 727iter [01:04, 12.38iter/s]Best score: 0.2682: : 728iter [01:04, 12.38iter/s]Best score: 0.2682: : 729iter [01:04, 12.42iter/s]Best score: 0.2674: : 729iter [01:05, 12.42iter/s]Best score: 0.2674: : 730iter [01:05, 12.42iter/s]Best score: 0.2674: : 731iter [01:05, 12.24iter/s]Best score: 0.2674: : 731iter [01:05, 12.24iter/s]Best score: 0.2674: : 732iter [01:05, 12.24iter/s]Best score: 0.2674: : 733iter [01:05, 12.31iter/s]Best score: 0.2674: : 733iter [01:05, 12.31iter/s]Best score: 0.2674: : 734iter [01:05, 12.31iter/s]Best score: 0.2674: : 735iter [01:05, 12.33iter/s]Best score: 0.2674: : 735iter [01:05, 12.33iter/s]Best score: 0.2674: : 736iter [01:05, 12.33iter/s]Best score: 0.2674: : 737iter [01:05, 12.34iter/s]Best score: 0.2674: : 737iter [01:05, 12.34iter/s]Best score: 0.2674: : 738iter [01:05, 12.34iter/s]Best score: 0.2674: : 739iter [01:05, 12.43iter/s]Best score: 0.2665: : 739iter [01:05, 12.43iter/s]Best score: 0.2665: : 740iter [01:05, 12.43iter/s]Best score: 0.2665: : 741iter [01:05, 12.27iter/s]Best score: 0.2665: : 741iter [01:05, 12.27iter/s]Best score: 0.2665: : 742iter [01:06, 12.27iter/s]Best score: 0.2665: : 743iter [01:06, 12.38iter/s]Best score: 0.2665: : 743iter [01:06, 12.38iter/s]Best score: 0.2665: : 744iter [01:06, 12.38iter/s]Best score: 0.2665: : 745iter [01:06, 12.37iter/s]Best score: 0.2665: : 745iter [01:06, 12.37iter/s]Best score: 0.2665: : 746iter [01:06, 12.37iter/s]Best score: 0.2665: : 747iter [01:06, 12.37iter/s]Best score: 0.2665: : 747iter [01:06, 12.37iter/s]Best score: 0.2665: : 748iter [01:06, 12.37iter/s]Best score: 0.2665: : 749iter [01:06, 12.35iter/s]Best score: 0.2662: : 749iter [01:06, 12.35iter/s]
[ITER 750] Best: 0.2662
[PARAMS] Current best parameters:
  fx: 537.0786
  fy: 536.9630
  px: 974.8157
  py: 762.5831
  k1: 0.03325072
  k2: -0.07081341
  k3: 0.04682169
  k4: -0.01107215
  p1: -0.02591083
  p2: -0.02371194
  p3: 0.03042101
  p4: 0.01514922
  s1: -0.0118
  s2: -0.0145
  s3: -0.0046
  s4: -0.0162
  tilt_x: 0.0062
  tilt_y: -0.0089
  scale: 2.8939
  rotation: -0.4576
Best score: 0.2662: : 750iter [01:07, 12.35iter/s]Best score: 0.2662: : 751iter [01:07,  8.02iter/s]Best score: 0.2662: : 751iter [01:07,  8.02iter/s]Best score: 0.2662: : 752iter [01:07,  8.02iter/s]Best score: 0.2662: : 753iter [01:07,  8.99iter/s]Best score: 0.2662: : 753iter [01:07,  8.99iter/s]Best score: 0.2662: : 754iter [01:07,  8.99iter/s]Best score: 0.2662: : 755iter [01:07,  9.81iter/s]Best score: 0.2662: : 755iter [01:07,  9.81iter/s]Best score: 0.2662: : 756iter [01:07,  9.81iter/s]Best score: 0.2662: : 757iter [01:07, 10.46iter/s]Best score: 0.2662: : 757iter [01:07, 10.46iter/s]Best score: 0.2662: : 758iter [01:07, 10.46iter/s]Best score: 0.2662: : 759iter [01:07, 11.01iter/s]Best score: 0.2646: : 759iter [01:07, 11.01iter/s]Best score: 0.2646: : 760iter [01:07, 11.01iter/s]Best score: 0.2646: : 761iter [01:07, 11.20iter/s]Best score: 0.2646: : 761iter [01:07, 11.20iter/s]Best score: 0.2646: : 762iter [01:07, 11.20iter/s]Best score: 0.2646: : 763iter [01:07, 11.54iter/s]Best score: 0.2646: : 763iter [01:08, 11.54iter/s]Best score: 0.2646: : 764iter [01:08, 11.54iter/s]Best score: 0.2646: : 765iter [01:08, 11.77iter/s]Best score: 0.2646: : 765iter [01:08, 11.77iter/s]Best score: 0.2646: : 766iter [01:08, 11.77iter/s]Best score: 0.2646: : 767iter [01:08, 11.97iter/s]Best score: 0.2646: : 767iter [01:08, 11.97iter/s]Best score: 0.2646: : 768iter [01:08, 11.97iter/s]Best score: 0.2646: : 769iter [01:08, 12.12iter/s]Best score: 0.2645: : 769iter [01:08, 12.12iter/s]Best score: 0.2645: : 770iter [01:08, 12.12iter/s]Best score: 0.2645: : 771iter [01:08, 12.00iter/s]Best score: 0.2645: : 771iter [01:08, 12.00iter/s]Best score: 0.2645: : 772iter [01:08, 12.00iter/s]Best score: 0.2645: : 773iter [01:08, 12.12iter/s]Best score: 0.2645: : 773iter [01:08, 12.12iter/s]Best score: 0.2645: : 774iter [01:08, 12.12iter/s]Best score: 0.2645: : 775iter [01:08, 12.18iter/s]Best score: 0.2645: : 775iter [01:09, 12.18iter/s]Best score: 0.2645: : 776iter [01:09, 12.18iter/s]Best score: 0.2645: : 777iter [01:09, 12.26iter/s]Best score: 0.2645: : 777iter [01:09, 12.26iter/s]Best score: 0.2645: : 778iter [01:09, 12.26iter/s]Best score: 0.2645: : 779iter [01:09, 12.32iter/s]Best score: 0.2642: : 779iter [01:09, 12.32iter/s]Best score: 0.2642: : 780iter [01:09, 12.32iter/s]Best score: 0.2642: : 781iter [01:09, 12.17iter/s]Best score: 0.2642: : 781iter [01:09, 12.17iter/s]Best score: 0.2642: : 782iter [01:09, 12.17iter/s]Best score: 0.2642: : 783iter [01:09, 12.23iter/s]Best score: 0.2642: : 783iter [01:09, 12.23iter/s]Best score: 0.2642: : 784iter [01:09, 12.23iter/s]Best score: 0.2642: : 785iter [01:09, 12.31iter/s]Best score: 0.2642: : 785iter [01:09, 12.31iter/s]Best score: 0.2642: : 786iter [01:09, 12.31iter/s]Best score: 0.2642: : 787iter [01:09, 12.34iter/s]Best score: 0.2642: : 787iter [01:10, 12.34iter/s]Best score: 0.2642: : 788iter [01:10, 12.34iter/s]Best score: 0.2642: : 789iter [01:10, 12.39iter/s]Best score: 0.2626: : 789iter [01:10, 12.39iter/s]Best score: 0.2626: : 790iter [01:10, 12.39iter/s]Best score: 0.2626: : 791iter [01:10, 12.19iter/s]Best score: 0.2626: : 791iter [01:10, 12.19iter/s]Best score: 0.2626: : 792iter [01:10, 12.19iter/s]Best score: 0.2626: : 793iter [01:10, 12.23iter/s]Best score: 0.2626: : 793iter [01:10, 12.23iter/s]Best score: 0.2626: : 794iter [01:10, 12.23iter/s]Best score: 0.2626: : 795iter [01:10, 12.33iter/s]Best score: 0.2626: : 795iter [01:10, 12.33iter/s]Best score: 0.2626: : 796iter [01:10, 12.33iter/s]Best score: 0.2626: : 797iter [01:10, 12.43iter/s]Best score: 0.2626: : 797iter [01:10, 12.43iter/s]Best score: 0.2626: : 798iter [01:10, 12.43iter/s]Best score: 0.2626: : 799iter [01:10, 12.45iter/s]
[ITER 800] Best: 0.2626
[PARAMS] Current best parameters:
  fx: 536.1194
  fy: 536.1254
  px: 975.4545
  py: 764.1737
  k1: 0.03110659
  k2: -0.06427822
  k3: 0.04193311
  k4: -0.00986896
  p1: -0.02552728
  p2: -0.02280163
  p3: 0.03039263
  p4: 0.01575664
  s1: -0.0114
  s2: -0.0157
  s3: -0.0039
  s4: -0.0166
  tilt_x: 0.0130
  tilt_y: -0.0103
  scale: 2.8963
  rotation: -0.4596
Best score: 0.2626: : 799iter [01:10, 12.45iter/s]Best score: 0.2626: : 800iter [01:11, 12.45iter/s]Best score: 0.2626: : 801iter [01:11,  8.05iter/s]Best score: 0.2626: : 801iter [01:11,  8.05iter/s]Best score: 0.2626: : 802iter [01:11,  8.05iter/s]Best score: 0.2626: : 803iter [01:11,  9.02iter/s]Best score: 0.2626: : 803iter [01:11,  9.02iter/s]Best score: 0.2626: : 804iter [01:11,  9.02iter/s]Best score: 0.2626: : 805iter [01:11,  9.88iter/s]Best score: 0.2626: : 805iter [01:11,  9.88iter/s]Best score: 0.2626: : 806iter [01:11,  9.88iter/s]Best score: 0.2626: : 807iter [01:11, 10.60iter/s]Best score: 0.2626: : 807iter [01:11, 10.60iter/s]Best score: 0.2626: : 808iter [01:11, 10.60iter/s]Best score: 0.2626: : 809iter [01:11, 11.16iter/s]Best score: 0.2626: : 809iter [01:12, 11.16iter/s]Best score: 0.2626: : 810iter [01:12, 11.16iter/s]Best score: 0.2626: : 811iter [01:12, 11.39iter/s]Best score: 0.2626: : 811iter [01:12, 11.39iter/s]Best score: 0.2626: : 812iter [01:12, 11.39iter/s]Best score: 0.2626: : 813iter [01:12, 11.70iter/s]Best score: 0.2626: : 813iter [01:12, 11.70iter/s]Best score: 0.2626: : 814iter [01:12, 11.70iter/s]Best score: 0.2626: : 815iter [01:12, 11.91iter/s]Best score: 0.2626: : 815iter [01:12, 11.91iter/s]Best score: 0.2626: : 816iter [01:12, 11.91iter/s]Best score: 0.2626: : 817iter [01:12,  9.97iter/s]Best score: 0.2626: : 817iter [01:12,  9.97iter/s]Best score: 0.2626: : 818iter [01:12,  9.97iter/s]Best score: 0.2626: : 819iter [01:12, 10.59iter/s]Best score: 0.2619: : 819iter [01:12, 10.59iter/s]Best score: 0.2619: : 820iter [01:13, 10.59iter/s]Best score: 0.2619: : 821iter [01:13, 10.92iter/s]Best score: 0.2619: : 821iter [01:13, 10.92iter/s]Best score: 0.2619: : 822iter [01:13, 10.92iter/s]Best score: 0.2619: : 823iter [01:13, 11.38iter/s]Best score: 0.2619: : 823iter [01:13, 11.38iter/s]Best score: 0.2619: : 824iter [01:13, 11.38iter/s]Best score: 0.2619: : 825iter [01:13, 11.70iter/s]Best score: 0.2619: : 825iter [01:13, 11.70iter/s]Best score: 0.2619: : 826iter [01:13, 11.70iter/s]Best score: 0.2619: : 827iter [01:13, 11.91iter/s]Best score: 0.2619: : 827iter [01:13, 11.91iter/s]Best score: 0.2619: : 828iter [01:13, 11.91iter/s]Best score: 0.2619: : 829iter [01:13, 11.96iter/s]Best score: 0.2619: : 829iter [01:13, 11.96iter/s]Best score: 0.2619: : 830iter [01:13, 11.96iter/s]Best score: 0.2619: : 831iter [01:13, 11.94iter/s]Best score: 0.2619: : 831iter [01:13, 11.94iter/s]Best score: 0.2619: : 832iter [01:14, 11.94iter/s]Best score: 0.2619: : 833iter [01:14, 11.94iter/s]Best score: 0.2619: : 833iter [01:14, 11.94iter/s]Best score: 0.2619: : 834iter [01:14, 11.94iter/s]Best score: 0.2619: : 835iter [01:14, 12.07iter/s]Best score: 0.2619: : 835iter [01:14, 12.07iter/s]Best score: 0.2619: : 836iter [01:14, 12.07iter/s]Best score: 0.2619: : 837iter [01:14, 12.18iter/s]Best score: 0.2619: : 837iter [01:14, 12.18iter/s]Best score: 0.2619: : 838iter [01:14, 12.18iter/s]Best score: 0.2619: : 839iter [01:14, 12.25iter/s]Best score: 0.2615: : 839iter [01:14, 12.25iter/s]Best score: 0.2615: : 840iter [01:14, 12.25iter/s]Best score: 0.2615: : 841iter [01:14, 12.15iter/s]Best score: 0.2615: : 841iter [01:14, 12.15iter/s]Best score: 0.2615: : 842iter [01:14, 12.15iter/s]Best score: 0.2615: : 843iter [01:14, 12.24iter/s]Best score: 0.2615: : 843iter [01:14, 12.24iter/s]Best score: 0.2615: : 844iter [01:15, 12.24iter/s]Best score: 0.2615: : 845iter [01:15, 12.29iter/s]Best score: 0.2615: : 845iter [01:15, 12.29iter/s]Best score: 0.2615: : 846iter [01:15, 12.29iter/s]Best score: 0.2615: : 847iter [01:15, 12.33iter/s]Best score: 0.2615: : 847iter [01:15, 12.33iter/s]Best score: 0.2615: : 848iter [01:15, 12.33iter/s]Best score: 0.2615: : 849iter [01:15, 12.32iter/s]
[ITER 850] Best: 0.2612
[PARAMS] Current best parameters:
  fx: 535.0300
  fy: 535.0883
  px: 975.9609
  py: 764.5438
  k1: 0.02989082
  k2: -0.05922372
  k3: 0.03824474
  k4: -0.00901104
  p1: -0.02488563
  p2: -0.02342192
  p3: 0.03031647
  p4: 0.01759659
  s1: -0.0108
  s2: -0.0170
  s3: -0.0038
  s4: -0.0186
  tilt_x: 0.0136
  tilt_y: -0.0138
  scale: 2.8976
  rotation: -0.4596
Best score: 0.2612: : 849iter [01:15, 12.32iter/s]Best score: 0.2612: : 850iter [01:15, 12.32iter/s]Best score: 0.2612: : 851iter [01:15,  7.94iter/s]Best score: 0.2612: : 851iter [01:15,  7.94iter/s]Best score: 0.2612: : 852iter [01:15,  7.94iter/s]Best score: 0.2612: : 853iter [01:15,  8.91iter/s]Best score: 0.2612: : 853iter [01:16,  8.91iter/s]Best score: 0.2612: : 854iter [01:16,  8.91iter/s]Best score: 0.2612: : 855iter [01:16,  9.76iter/s]Best score: 0.2612: : 855iter [01:16,  9.76iter/s]Best score: 0.2612: : 856iter [01:16,  9.76iter/s]Best score: 0.2612: : 857iter [01:16, 10.49iter/s]Best score: 0.2612: : 857iter [01:16, 10.49iter/s]Best score: 0.2612: : 858iter [01:16, 10.49iter/s]Best score: 0.2612: : 859iter [01:16, 11.03iter/s]Best score: 0.2606: : 859iter [01:16, 11.03iter/s]Best score: 0.2606: : 860iter [01:16, 11.03iter/s]Best score: 0.2606: : 861iter [01:16, 11.27iter/s]Best score: 0.2606: : 861iter [01:16, 11.27iter/s]Best score: 0.2606: : 862iter [01:16, 11.27iter/s]Best score: 0.2606: : 863iter [01:16, 11.61iter/s]Best score: 0.2606: : 863iter [01:16, 11.61iter/s]Best score: 0.2606: : 864iter [01:16, 11.61iter/s]Best score: 0.2606: : 865iter [01:16, 11.80iter/s]Best score: 0.2606: : 865iter [01:17, 11.80iter/s]Best score: 0.2606: : 866iter [01:17, 11.80iter/s]Best score: 0.2606: : 867iter [01:17, 12.02iter/s]Best score: 0.2606: : 867iter [01:17, 12.02iter/s]Best score: 0.2606: : 868iter [01:17, 12.02iter/s]Best score: 0.2606: : 869iter [01:17, 12.23iter/s]Best score: 0.2603: : 869iter [01:17, 12.23iter/s]Best score: 0.2603: : 870iter [01:17, 12.23iter/s]Best score: 0.2603: : 871iter [01:17, 12.07iter/s]Best score: 0.2603: : 871iter [01:17, 12.07iter/s]Best score: 0.2603: : 872iter [01:17, 12.07iter/s]Best score: 0.2603: : 873iter [01:17, 12.13iter/s]Best score: 0.2603: : 873iter [01:17, 12.13iter/s]Best score: 0.2603: : 874iter [01:17, 12.13iter/s]Best score: 0.2603: : 875iter [01:17, 12.22iter/s]Best score: 0.2603: : 875iter [01:17, 12.22iter/s]Best score: 0.2603: : 876iter [01:17, 12.22iter/s]Best score: 0.2603: : 877iter [01:17, 12.26iter/s]Best score: 0.2603: : 877iter [01:17, 12.26iter/s]Best score: 0.2603: : 878iter [01:18, 12.26iter/s]Best score: 0.2603: : 879iter [01:18, 12.37iter/s]Best score: 0.2593: : 879iter [01:18, 12.37iter/s]Best score: 0.2593: : 880iter [01:18, 12.37iter/s]Best score: 0.2593: : 881iter [01:18, 12.24iter/s]Best score: 0.2593: : 881iter [01:18, 12.24iter/s]Best score: 0.2593: : 882iter [01:18, 12.24iter/s]Best score: 0.2593: : 883iter [01:18, 12.29iter/s]Best score: 0.2593: : 883iter [01:18, 12.29iter/s]Best score: 0.2593: : 884iter [01:18, 12.29iter/s]Best score: 0.2593: : 885iter [01:18, 12.34iter/s]Best score: 0.2593: : 885iter [01:18, 12.34iter/s]Best score: 0.2593: : 886iter [01:18, 12.34iter/s]Best score: 0.2593: : 887iter [01:18, 12.37iter/s]Best score: 0.2593: : 887iter [01:18, 12.37iter/s]Best score: 0.2593: : 888iter [01:18, 12.37iter/s]Best score: 0.2593: : 889iter [01:18, 12.45iter/s]Best score: 0.2593: : 889iter [01:18, 12.45iter/s]Best score: 0.2593: : 890iter [01:19, 12.45iter/s]Best score: 0.2593: : 891iter [01:19, 12.12iter/s]Best score: 0.2593: : 891iter [01:19, 12.12iter/s]Best score: 0.2593: : 892iter [01:19, 12.12iter/s]Best score: 0.2593: : 893iter [01:19, 12.22iter/s]Best score: 0.2593: : 893iter [01:19, 12.22iter/s]Best score: 0.2593: : 894iter [01:19, 12.22iter/s]Best score: 0.2593: : 895iter [01:19, 12.22iter/s]Best score: 0.2593: : 895iter [01:19, 12.22iter/s]Best score: 0.2593: : 896iter [01:19, 12.22iter/s]Best score: 0.2593: : 897iter [01:19, 12.30iter/s]Best score: 0.2593: : 897iter [01:19, 12.30iter/s]Best score: 0.2593: : 898iter [01:19, 12.30iter/s]Best score: 0.2593: : 899iter [01:19, 12.29iter/s]
[ITER 900] Best: 0.2593
[PARAMS] Current best parameters:
  fx: 532.7055
  fy: 532.6812
  px: 976.8664
  py: 764.7519
  k1: 0.02795104
  k2: -0.05133259
  k3: 0.03281777
  k4: -0.00775044
  p1: -0.02622052
  p2: -0.02479756
  p3: 0.03088941
  p4: 0.01815982
  s1: -0.0090
  s2: -0.0179
  s3: -0.0027
  s4: -0.0191
  tilt_x: 0.0173
  tilt_y: -0.0211
  scale: 2.9027
  rotation: -0.4715
Best score: 0.2593: : 899iter [01:19, 12.29iter/s]Best score: 0.2593: : 900iter [01:20, 12.29iter/s]Best score: 0.2593: : 901iter [01:20,  7.97iter/s]Best score: 0.2593: : 901iter [01:20,  7.97iter/s]Best score: 0.2593: : 902iter [01:20,  7.97iter/s]Best score: 0.2593: : 903iter [01:20,  8.93iter/s]Best score: 0.2593: : 903iter [01:20,  8.93iter/s]Best score: 0.2593: : 904iter [01:20,  8.93iter/s]Best score: 0.2593: : 905iter [01:20,  9.74iter/s]Best score: 0.2593: : 905iter [01:20,  9.74iter/s]Best score: 0.2593: : 906iter [01:20,  9.74iter/s]Best score: 0.2593: : 907iter [01:20, 10.39iter/s]Best score: 0.2593: : 907iter [01:20, 10.39iter/s]Best score: 0.2593: : 908iter [01:20, 10.39iter/s]Best score: 0.2593: : 909iter [01:20, 10.90iter/s]Best score: 0.2592: : 909iter [01:20, 10.90iter/s]Best score: 0.2592: : 910iter [01:20, 10.90iter/s]Best score: 0.2592: : 911iter [01:20, 11.17iter/s]Best score: 0.2592: : 911iter [01:21, 11.17iter/s]Best score: 0.2592: : 912iter [01:21, 11.17iter/s]Best score: 0.2592: : 913iter [01:21, 11.56iter/s]Best score: 0.2592: : 913iter [01:21, 11.56iter/s]Best score: 0.2592: : 914iter [01:21, 11.56iter/s]Best score: 0.2592: : 915iter [01:21, 11.69iter/s]Best score: 0.2592: : 915iter [01:21, 11.69iter/s]Best score: 0.2592: : 916iter [01:21, 11.69iter/s]Best score: 0.2592: : 917iter [01:21, 11.89iter/s]Best score: 0.2592: : 917iter [01:21, 11.89iter/s]Best score: 0.2592: : 918iter [01:21, 11.89iter/s]Best score: 0.2592: : 919iter [01:21, 11.95iter/s]Best score: 0.2584: : 919iter [01:21, 11.95iter/s]Best score: 0.2584: : 920iter [01:21, 11.95iter/s]Best score: 0.2584: : 921iter [01:21, 11.76iter/s]Best score: 0.2584: : 921iter [01:21, 11.76iter/s]Best score: 0.2584: : 922iter [01:21, 11.76iter/s]Best score: 0.2584: : 923iter [01:21, 11.93iter/s]Best score: 0.2584: : 923iter [01:22, 11.93iter/s]Best score: 0.2584: : 924iter [01:22, 11.93iter/s]Best score: 0.2584: : 925iter [01:22, 12.03iter/s]Best score: 0.2584: : 925iter [01:22, 12.03iter/s]Best score: 0.2584: : 926iter [01:22, 12.03iter/s]Best score: 0.2584: : 927iter [01:22, 12.12iter/s]Best score: 0.2584: : 927iter [01:22, 12.12iter/s]Best score: 0.2584: : 928iter [01:22, 12.12iter/s]Best score: 0.2584: : 929iter [01:22, 12.15iter/s]Best score: 0.2584: : 929iter [01:22, 12.15iter/s]Best score: 0.2584: : 930iter [01:22, 12.15iter/s]Best score: 0.2584: : 931iter [01:22, 12.01iter/s]Best score: 0.2584: : 931iter [01:22, 12.01iter/s]Best score: 0.2584: : 932iter [01:22, 12.01iter/s]Best score: 0.2584: : 933iter [01:22, 12.08iter/s]Best score: 0.2584: : 933iter [01:22, 12.08iter/s]Best score: 0.2584: : 934iter [01:22, 12.08iter/s]Best score: 0.2584: : 935iter [01:22, 12.13iter/s]Best score: 0.2584: : 935iter [01:23, 12.13iter/s]Best score: 0.2584: : 936iter [01:23, 12.13iter/s]Best score: 0.2584: : 937iter [01:23, 12.16iter/s]Best score: 0.2584: : 937iter [01:23, 12.16iter/s]Best score: 0.2584: : 938iter [01:23, 12.16iter/s]Best score: 0.2584: : 939iter [01:23, 12.19iter/s]Best score: 0.2583: : 939iter [01:23, 12.19iter/s]Best score: 0.2583: : 940iter [01:23, 12.19iter/s]Best score: 0.2583: : 941iter [01:23, 12.01iter/s]Best score: 0.2583: : 941iter [01:23, 12.01iter/s]Best score: 0.2583: : 942iter [01:23, 12.01iter/s]Best score: 0.2583: : 943iter [01:23, 12.16iter/s]Best score: 0.2583: : 943iter [01:23, 12.16iter/s]Best score: 0.2583: : 944iter [01:23, 12.16iter/s]Best score: 0.2583: : 945iter [01:23, 12.21iter/s]Best score: 0.2583: : 945iter [01:23, 12.21iter/s]Best score: 0.2583: : 946iter [01:23, 12.21iter/s]Best score: 0.2583: : 947iter [01:23, 12.22iter/s]Best score: 0.2583: : 947iter [01:24, 12.22iter/s]Best score: 0.2583: : 948iter [01:24, 12.22iter/s]Best score: 0.2583: : 949iter [01:24, 12.25iter/s]
[ITER 950] Best: 0.2577
[PARAMS] Current best parameters:
  fx: 531.1059
  fy: 531.0901
  px: 976.1724
  py: 764.6823
  k1: 0.02715631
  k2: -0.04655678
  k3: 0.02932754
  k4: -0.00690778
  p1: -0.02676003
  p2: -0.02543425
  p3: 0.02945324
  p4: 0.01817763
  s1: -0.0075
  s2: -0.0177
  s3: -0.0009
  s4: -0.0190
  tilt_x: 0.0180
  tilt_y: -0.0191
  scale: 2.9043
  rotation: -0.4699
Best score: 0.2577: : 949iter [01:24, 12.25iter/s]Best score: 0.2577: : 950iter [01:24, 12.25iter/s]Best score: 0.2577: : 951iter [01:24,  7.95iter/s]Best score: 0.2577: : 951iter [01:24,  7.95iter/s]Best score: 0.2577: : 952iter [01:24,  7.95iter/s]Best score: 0.2577: : 953iter [01:24,  8.89iter/s]Best score: 0.2577: : 953iter [01:24,  8.89iter/s]Best score: 0.2577: : 954iter [01:24,  8.89iter/s]Best score: 0.2577: : 955iter [01:24,  9.75iter/s]Best score: 0.2577: : 955iter [01:24,  9.75iter/s]Best score: 0.2577: : 956iter [01:25,  9.75iter/s]Best score: 0.2577: : 957iter [01:25, 10.48iter/s]Best score: 0.2577: : 957iter [01:25, 10.48iter/s]Best score: 0.2577: : 958iter [01:25, 10.48iter/s]Best score: 0.2577: : 959iter [01:25, 11.04iter/s]Best score: 0.2577: : 959iter [01:25, 11.04iter/s]Best score: 0.2577: : 960iter [01:25, 11.04iter/s]Best score: 0.2577: : 961iter [01:25, 11.27iter/s]Best score: 0.2577: : 961iter [01:25, 11.27iter/s]Best score: 0.2577: : 962iter [01:25, 11.27iter/s]Best score: 0.2577: : 963iter [01:25, 11.65iter/s]Best score: 0.2577: : 963iter [01:25, 11.65iter/s]Best score: 0.2577: : 964iter [01:25, 11.65iter/s]Best score: 0.2577: : 965iter [01:25, 11.91iter/s]Best score: 0.2577: : 965iter [01:25, 11.91iter/s]Best score: 0.2577: : 966iter [01:25, 11.91iter/s]Best score: 0.2577: : 967iter [01:25, 12.08iter/s]Best score: 0.2577: : 967iter [01:25, 12.08iter/s]Best score: 0.2577: : 968iter [01:26, 12.08iter/s]Best score: 0.2577: : 969iter [01:26, 12.20iter/s]Best score: 0.2571: : 969iter [01:26, 12.20iter/s]Best score: 0.2571: : 970iter [01:26, 12.20iter/s]Best score: 0.2571: : 971iter [01:26, 12.06iter/s]Best score: 0.2571: : 971iter [01:26, 12.06iter/s]Best score: 0.2571: : 972iter [01:26, 12.06iter/s]Best score: 0.2571: : 973iter [01:26, 12.17iter/s]Best score: 0.2571: : 973iter [01:26, 12.17iter/s]Best score: 0.2571: : 974iter [01:26, 12.17iter/s]Best score: 0.2571: : 975iter [01:26, 12.23iter/s]Best score: 0.2571: : 975iter [01:26, 12.23iter/s]Best score: 0.2571: : 976iter [01:26, 12.23iter/s]Best score: 0.2571: : 977iter [01:26, 12.26iter/s]Best score: 0.2571: : 977iter [01:26, 12.26iter/s]Best score: 0.2571: : 978iter [01:26, 12.26iter/s]Best score: 0.2571: : 979iter [01:26, 12.30iter/s]Best score: 0.2569: : 979iter [01:26, 12.30iter/s]Best score: 0.2569: : 980iter [01:26, 12.30iter/s]Best score: 0.2569: : 981iter [01:26, 12.12iter/s]Best score: 0.2569: : 981iter [01:27, 12.12iter/s]Best score: 0.2569: : 982iter [01:27, 12.12iter/s]Best score: 0.2569: : 983iter [01:27, 12.20iter/s]Best score: 0.2569: : 983iter [01:27, 12.20iter/s]Best score: 0.2569: : 984iter [01:27, 12.20iter/s]Best score: 0.2569: : 985iter [01:27, 12.26iter/s]Best score: 0.2569: : 985iter [01:27, 12.26iter/s]Best score: 0.2569: : 986iter [01:27, 12.26iter/s]Best score: 0.2569: : 987iter [01:27, 12.29iter/s]Best score: 0.2569: : 987iter [01:27, 12.29iter/s]Best score: 0.2569: : 988iter [01:27, 12.29iter/s]Best score: 0.2569: : 989iter [01:27, 12.32iter/s]Best score: 0.2569: : 989iter [01:27, 12.32iter/s]Best score: 0.2569: : 990iter [01:27, 12.32iter/s]Best score: 0.2569: : 991iter [01:27, 12.17iter/s]Best score: 0.2569: : 991iter [01:27, 12.17iter/s]Best score: 0.2569: : 992iter [01:27, 12.17iter/s]Best score: 0.2569: : 993iter [01:27, 12.27iter/s]Best score: 0.2569: : 993iter [01:28, 12.27iter/s]Best score: 0.2569: : 994iter [01:28, 12.27iter/s]Best score: 0.2569: : 995iter [01:28, 12.40iter/s]Best score: 0.2569: : 995iter [01:28, 12.40iter/s]Best score: 0.2569: : 996iter [01:28, 12.40iter/s]Best score: 0.2569: : 997iter [01:28, 12.42iter/s]Best score: 0.2569: : 997iter [01:28, 12.42iter/s]Best score: 0.2569: : 998iter [01:28, 12.42iter/s]Best score: 0.2569: : 999iter [01:28, 12.44iter/s]
[ITER 1000] Best: 0.2567
[PARAMS] Current best parameters:
  fx: 530.2435
  fy: 530.2113
  px: 976.1635
  py: 764.6211
  k1: 0.02593457
  k2: -0.04283562
  k3: 0.02678439
  k4: -0.00630275
  p1: -0.02693166
  p2: -0.02603615
  p3: 0.02797292
  p4: 0.01824189
  s1: -0.0053
  s2: -0.0180
  s3: 0.0011
  s4: -0.0194
  tilt_x: 0.0181
  tilt_y: -0.0203
  scale: 2.9066
  rotation: -0.4675
Best score: 0.2567: : 999iter [01:28, 12.44iter/s]Best score: 0.2567: : 1000iter [01:28, 12.44iter/s]Best score: 0.2567: : 1001iter [01:28,  8.02iter/s]Best score: 0.2567: : 1001iter [01:28,  8.02iter/s]Best score: 0.2567: : 1002iter [01:29,  8.02iter/s]Best score: 0.2567: : 1003iter [01:29,  9.02iter/s]Best score: 0.2567: : 1003iter [01:29,  9.02iter/s]Best score: 0.2567: : 1004iter [01:29,  9.02iter/s]Best score: 0.2567: : 1005iter [01:29,  9.86iter/s]Best score: 0.2567: : 1005iter [01:29,  9.86iter/s]Best score: 0.2567: : 1006iter [01:29,  9.86iter/s]Best score: 0.2567: : 1007iter [01:29, 10.51iter/s]Best score: 0.2567: : 1007iter [01:29, 10.51iter/s]Best score: 0.2567: : 1008iter [01:29, 10.51iter/s]Best score: 0.2567: : 1009iter [01:29, 10.99iter/s]Best score: 0.2553: : 1009iter [01:29, 10.99iter/s]Best score: 0.2553: : 1010iter [01:29, 10.99iter/s]Best score: 0.2553: : 1011iter [01:29, 11.20iter/s]Best score: 0.2553: : 1011iter [01:29, 11.20iter/s]Best score: 0.2553: : 1012iter [01:29, 11.20iter/s]Best score: 0.2553: : 1013iter [01:29, 11.55iter/s]Best score: 0.2553: : 1013iter [01:29, 11.55iter/s]Best score: 0.2553: : 1014iter [01:30, 11.55iter/s]Best score: 0.2553: : 1015iter [01:30, 11.84iter/s]Best score: 0.2553: : 1015iter [01:30, 11.84iter/s]Best score: 0.2553: : 1016iter [01:30, 11.84iter/s]Best score: 0.2553: : 1017iter [01:30, 12.04iter/s]Best score: 0.2553: : 1017iter [01:30, 12.04iter/s]Best score: 0.2553: : 1018iter [01:30, 12.04iter/s]Best score: 0.2553: : 1019iter [01:30, 12.09iter/s]Best score: 0.2540: : 1019iter [01:30, 12.09iter/s]Best score: 0.2540: : 1020iter [01:30, 12.09iter/s]Best score: 0.2540: : 1021iter [01:30, 11.97iter/s]Best score: 0.2540: : 1021iter [01:30, 11.97iter/s]Best score: 0.2540: : 1022iter [01:30, 11.97iter/s]Best score: 0.2540: : 1023iter [01:30, 12.11iter/s]Best score: 0.2540: : 1023iter [01:30, 12.11iter/s]Best score: 0.2540: : 1024iter [01:30, 12.11iter/s]Best score: 0.2540: : 1025iter [01:30, 12.21iter/s]Best score: 0.2540: : 1025iter [01:30, 12.21iter/s]Best score: 0.2540: : 1026iter [01:31, 12.21iter/s]Best score: 0.2540: : 1027iter [01:31, 12.31iter/s]Best score: 0.2540: : 1027iter [01:31, 12.31iter/s]Best score: 0.2540: : 1028iter [01:31, 12.31iter/s]Best score: 0.2540: : 1029iter [01:31, 12.36iter/s]Best score: 0.2539: : 1029iter [01:31, 12.36iter/s]Best score: 0.2539: : 1030iter [01:31, 12.36iter/s]Best score: 0.2539: : 1031iter [01:31, 12.21iter/s]Best score: 0.2539: : 1031iter [01:31, 12.21iter/s]Best score: 0.2539: : 1032iter [01:31, 12.21iter/s]Best score: 0.2539: : 1033iter [01:31, 12.33iter/s]Best score: 0.2539: : 1033iter [01:31, 12.33iter/s]Best score: 0.2539: : 1034iter [01:31, 12.33iter/s]Best score: 0.2539: : 1035iter [01:31, 12.42iter/s]Best score: 0.2539: : 1035iter [01:31, 12.42iter/s]Best score: 0.2539: : 1036iter [01:31, 12.42iter/s]Best score: 0.2539: : 1037iter [01:31, 12.53iter/s]Best score: 0.2539: : 1037iter [01:31, 12.53iter/s]Best score: 0.2539: : 1038iter [01:31, 12.53iter/s]Best score: 0.2539: : 1039iter [01:31, 12.55iter/s]Best score: 0.2533: : 1039iter [01:32, 12.55iter/s]Best score: 0.2533: : 1040iter [01:32, 12.55iter/s]Best score: 0.2533: : 1041iter [01:32, 12.38iter/s]Best score: 0.2533: : 1041iter [01:32, 12.38iter/s]Best score: 0.2533: : 1042iter [01:32, 12.38iter/s]Best score: 0.2533: : 1043iter [01:32, 12.42iter/s]Best score: 0.2533: : 1043iter [01:32, 12.42iter/s]Best score: 0.2533: : 1044iter [01:32, 12.42iter/s]Best score: 0.2533: : 1045iter [01:32, 12.40iter/s]Best score: 0.2533: : 1045iter [01:32, 12.40iter/s]Best score: 0.2533: : 1046iter [01:32, 12.40iter/s]Best score: 0.2533: : 1047iter [01:32, 12.33iter/s]Best score: 0.2533: : 1047iter [01:32, 12.33iter/s]Best score: 0.2533: : 1048iter [01:32, 12.33iter/s]Best score: 0.2533: : 1049iter [01:32, 12.35iter/s]
[ITER 1050] Best: 0.2533
[PARAMS] Current best parameters:
  fx: 530.5778
  fy: 530.5954
  px: 976.1751
  py: 764.4583
  k1: 0.02500630
  k2: -0.04124446
  k3: 0.02546701
  k4: -0.00594668
  p1: -0.02512238
  p2: -0.02422019
  p3: 0.02087726
  p4: 0.01927958
  s1: 0.0001
  s2: -0.0192
  s3: 0.0063
  s4: -0.0207
  tilt_x: 0.0139
  tilt_y: -0.0168
  scale: 2.9059
  rotation: -0.4628
Best score: 0.2533: : 1049iter [01:32, 12.35iter/s]Best score: 0.2533: : 1050iter [01:33, 12.35iter/s]Best score: 0.2533: : 1051iter [01:33,  7.98iter/s]Best score: 0.2533: : 1051iter [01:33,  7.98iter/s]Best score: 0.2533: : 1052iter [01:33,  7.98iter/s]Best score: 0.2533: : 1053iter [01:33,  8.93iter/s]Best score: 0.2533: : 1053iter [01:33,  8.93iter/s]Best score: 0.2533: : 1054iter [01:33,  8.93iter/s]Best score: 0.2533: : 1055iter [01:33,  9.75iter/s]Best score: 0.2533: : 1055iter [01:33,  9.75iter/s]Best score: 0.2533: : 1056iter [01:33,  9.75iter/s]Best score: 0.2533: : 1057iter [01:33, 10.42iter/s]Best score: 0.2533: : 1057iter [01:33, 10.42iter/s]Best score: 0.2533: : 1058iter [01:33, 10.42iter/s]Best score: 0.2533: : 1059iter [01:33, 10.94iter/s]Best score: 0.2533: : 1059iter [01:33, 10.94iter/s]Best score: 0.2533: : 1060iter [01:34, 10.94iter/s]Best score: 0.2533: : 1061iter [01:34, 11.20iter/s]Best score: 0.2533: : 1061iter [01:34, 11.20iter/s]Best score: 0.2533: : 1062iter [01:34, 11.20iter/s]Best score: 0.2533: : 1063iter [01:34, 11.53iter/s]Best score: 0.2533: : 1063iter [01:34, 11.53iter/s]Best score: 0.2533: : 1064iter [01:34, 11.53iter/s]Best score: 0.2533: : 1065iter [01:34, 11.78iter/s]Best score: 0.2533: : 1065iter [01:34, 11.78iter/s]Best score: 0.2533: : 1066iter [01:34, 11.78iter/s]Best score: 0.2533: : 1067iter [01:34, 12.04iter/s]Best score: 0.2533: : 1067iter [01:34, 12.04iter/s]Best score: 0.2533: : 1068iter [01:34, 12.04iter/s]Best score: 0.2533: : 1069iter [01:34, 12.21iter/s]Best score: 0.2530: : 1069iter [01:34, 12.21iter/s]Best score: 0.2530: : 1070iter [01:34, 12.21iter/s]Best score: 0.2530: : 1071iter [01:34, 12.11iter/s]Best score: 0.2530: : 1071iter [01:34, 12.11iter/s]Best score: 0.2530: : 1072iter [01:35, 12.11iter/s]Best score: 0.2530: : 1073iter [01:35, 12.21iter/s]Best score: 0.2530: : 1073iter [01:35, 12.21iter/s]Best score: 0.2530: : 1074iter [01:35, 12.21iter/s]Best score: 0.2530: : 1075iter [01:35, 12.35iter/s]Best score: 0.2530: : 1075iter [01:35, 12.35iter/s]Best score: 0.2530: : 1076iter [01:35, 12.35iter/s]Best score: 0.2530: : 1077iter [01:35, 12.40iter/s]Best score: 0.2530: : 1077iter [01:35, 12.40iter/s]Best score: 0.2530: : 1078iter [01:35, 12.40iter/s]Best score: 0.2530: : 1079iter [01:35, 12.43iter/s]Best score: 0.2527: : 1079iter [01:35, 12.43iter/s]Best score: 0.2527: : 1080iter [01:35, 12.43iter/s]Best score: 0.2527: : 1081iter [01:35, 12.27iter/s]Best score: 0.2527: : 1081iter [01:35, 12.27iter/s]Best score: 0.2527: : 1082iter [01:35, 12.27iter/s]Best score: 0.2527: : 1083iter [01:35, 12.33iter/s]Best score: 0.2527: : 1083iter [01:35, 12.33iter/s]Best score: 0.2527: : 1084iter [01:35, 12.33iter/s]Best score: 0.2527: : 1085iter [01:35, 12.36iter/s]Best score: 0.2527: : 1085iter [01:36, 12.36iter/s]Best score: 0.2527: : 1086iter [01:36, 12.36iter/s]Best score: 0.2527: : 1087iter [01:36, 12.43iter/s]Best score: 0.2527: : 1087iter [01:36, 12.43iter/s]Best score: 0.2527: : 1088iter [01:36, 12.43iter/s]Best score: 0.2527: : 1089iter [01:36, 12.43iter/s]Best score: 0.2527: : 1089iter [01:36, 12.43iter/s]Best score: 0.2527: : 1090iter [01:36, 12.43iter/s]Best score: 0.2527: : 1091iter [01:36, 12.18iter/s]Best score: 0.2527: : 1091iter [01:36, 12.18iter/s]Best score: 0.2527: : 1092iter [01:36, 12.18iter/s]Best score: 0.2527: : 1093iter [01:36, 12.22iter/s]Best score: 0.2527: : 1093iter [01:36, 12.22iter/s]Best score: 0.2527: : 1094iter [01:36, 12.22iter/s]Best score: 0.2527: : 1095iter [01:36, 12.27iter/s]Best score: 0.2527: : 1095iter [01:36, 12.27iter/s]Best score: 0.2527: : 1096iter [01:36, 12.27iter/s]Best score: 0.2527: : 1097iter [01:36, 12.30iter/s]Best score: 0.2527: : 1097iter [01:37, 12.30iter/s]Best score: 0.2527: : 1098iter [01:37, 12.30iter/s]Best score: 0.2527: : 1099iter [01:37, 12.36iter/s]
[ITER 1100] Best: 0.2502
[PARAMS] Current best parameters:
  fx: 531.6551
  fy: 531.5867
  px: 976.6018
  py: 763.0437
  k1: 0.01998386
  k2: -0.03612928
  k3: 0.02337213
  k4: -0.00567314
  p1: -0.02576068
  p2: -0.02483697
  p3: 0.01808325
  p4: 0.02141510
  s1: 0.0037
  s2: -0.0212
  s3: 0.0084
  s4: -0.0227
  tilt_x: 0.0086
  tilt_y: -0.0201
  scale: 2.9091
  rotation: -0.4606
Best score: 0.2502: : 1099iter [01:37, 12.36iter/s]Best score: 0.2502: : 1100iter [01:37, 11.28iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.250204 -> 0.249018

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 93,975 → 86,451 within 3500mm ring
  Ring polish did not improve score: 0.249018
  Success: True
  Tangential distortion: p1=-0.025624, p2=-0.024669, p3=0.018017, p4=0.021560
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2490
[✅] Parameters copied to: /working/MDC040_W015(6)_2025-09-23/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 531.655122376699
    fy: 531.5867205062162
    px: 976.6018237683527
    py: 763.0437134402875
    k1: 0.01991018569098663
    k2: -0.03612673837742267
    k3: 0.023328126262167022
    k4: -0.005633637858363081
    p1: -0.02562378778507242
    p2: -0.024668794267198157
    p3: 0.018017431175349742
    p4: 0.021560417102748194
    scale: 2.9096426285267216
    rotation: -0.46060083245315286
    mmpp: 3.3684934350134426

[🔄] Converting 654 frames to PTD, creating debug images for 400 calibration frames...
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  r4 = r2**2
/app/slick/utils/distortion_utils.py:171: RuntimeWarning: invalid value encountered in add
  y_p = M[1, 0] * xn + M[1, 1] * yn + M[1, 2]
/app/slick/utils/distortion_utils.py:174: RuntimeWarning: invalid value encountered in divide
  corrected[..., 0] = x_p / w_p

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                                                                  [A📁 Rigs:  75%|███████▌  | 3/4 [00:04<00:01,  1.53s/rig]
   🔄 1500:   0%|          | 0/204 [00:00<?, ?frame/s][A
   🔄 1500:  11%|█         | 22/204 [00:00<00:00, 211.26frame/s][A
   🔄 1500:  22%|██▏       | 44/204 [00:00<00:00, 189.85frame/s][A
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   🔄 1500:  69%|██████▊   | 140/204 [00:00<00:00, 179.70frame/s][A
   🔄 1500:  77%|███████▋  | 158/204 [00:00<00:00, 178.05frame/s][A
   🔄 1500:  86%|████████▋ | 176/204 [00:00<00:00, 177.46frame/s][A
   🔄 1500:  95%|█████████▌| 194/204 [00:01<00:00, 177.14frame/s][A
                                                                 [A📁 Rigs: 100%|██████████| 4/4 [00:06<00:00,  1.72s/rig]📁 Rigs: 100%|██████████| 4/4 [00:06<00:00,  1.63s/rig]
[✅] Saved PTDs: /working/MDC040_W015(6)_2025-09-23/calibration/debug/4_undistorted/output/ptd
📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]
   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 1000:  23%|██▎       | 23/100 [00:00<00:00, 220.22frame/s][A
   🔄 1000:  46%|████▌     | 46/100 [00:00<00:00, 195.69frame/s][A
   🔄 1000:  66%|██████▌   | 66/100 [00:00<00:00, 188.34frame/s][A
   🔄 1000:  85%|████████▌ | 85/100 [00:00<00:00, 184.79frame/s][A
                                                                [A📁 Rigs:  25%|██▌       | 1/4 [00:00<00:02,  1.02rig/s]
   🔄 2000R:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 2000R:  21%|██        | 21/100 [00:00<00:00, 201.26frame/s][A
   🔄 2000R:  42%|████▏     | 42/100 [00:00<00:00, 186.19frame/s][A
   🔄 2000R:  61%|██████    | 61/100 [00:00<00:00, 180.72frame/s][A
   🔄 2000R:  80%|████████  | 80/100 [00:00<00:00, 179.03frame/s][A
   🔄 2000R:  98%|█████████▊| 98/100 [00:00<00:00, 176.72frame/s][A
                                                                 [A📁 Rigs:  50%|█████     | 2/4 [00:02<00:02,  1.01s/rig]
   🔄 2000L:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 2000L:  20%|██        | 20/100 [00:00<00:00, 197.50frame/s][A
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   🔄 2000L:  59%|█████▉    | 59/100 [00:00<00:00, 178.31frame/s][A
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   🔄 2000L:  95%|█████████▌| 95/100 [00:00<00:00, 177.27frame/s][A
                                                                 [A📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:00,  1.04rig/s]
   🔄 1500:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 1500:  21%|██        | 21/100 [00:00<00:00, 203.85frame/s][A
   🔄 1500:  42%|████▏     | 42/100 [00:00<00:00, 187.24frame/s][A
   🔄 1500:  61%|██████    | 61/100 [00:00<00:00, 184.48frame/s][A
   🔄 1500:  80%|████████  | 80/100 [00:00<00:00, 181.84frame/s][A
   🔄 1500:  99%|█████████▉| 99/100 [00:00<00:00, 180.09frame/s][A
                                                                [A📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.00s/rig]📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.01rig/s]
[✅] Saved PTDs: /working/MDC040_W015(6)_2025-09-23/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:   2%|▏         | 75817/3080448 [00:00<00:03, 758159.88px/s]📤 Writing LUT:   5%|▌         | 158145/3080448 [00:00<00:04, 671325.18px/s]📤 Writing LUT:   8%|▊         | 239754/3080448 [00:00<00:03, 731346.75px/s]📤 Writing LUT:  10%|█         | 322430/3080448 [00:00<00:03, 766837.89px/s]📤 Writing LUT:  13%|█▎        | 405478/3080448 [00:00<00:03, 788920.32px/s]📤 Writing LUT:  16%|█▌        | 491558/3080448 [00:00<00:03, 812741.43px/s]📤 Writing LUT:  19%|█▊        | 574258/3080448 [00:00<00:03, 817322.10px/s]📤 Writing LUT:  21%|██▏       | 659484/3080448 [00:00<00:02, 828311.30px/s]📤 Writing LUT:  24%|██▍       | 745252/3080448 [00:00<00:02, 837416.45px/s]📤 Writing LUT:  27%|██▋       | 829167/3080448 [00:01<00:02, 831311.29px/s]📤 Writing LUT:  30%|██▉       | 914114/3080448 [00:01<00:02, 836816.19px/s]📤 Writing LUT:  32%|███▏      | 997890/3080448 [00:01<00:02, 835013.31px/s]📤 Writing LUT:  35%|███▌      | 1081457/3080448 [00:01<00:02, 832857.95px/s]📤 Writing LUT:  38%|███▊      | 1165142/3080448 [00:01<00:02, 834055.69px/s]📤 Writing LUT:  41%|████      | 1248580/3080448 [00:01<00:02, 833709.46px/s]📤 Writing LUT:  43%|████▎     | 1332423/3080448 [00:01<00:02, 835124.42px/s]📤 Writing LUT:  46%|████▌     | 1416038/3080448 [00:01<00:01, 835429.31px/s]📤 Writing LUT:  49%|████▊     | 1499593/3080448 [00:01<00:01, 834301.02px/s]📤 Writing LUT:  51%|█████▏    | 1583103/3080448 [00:01<00:01, 834537.72px/s]📤 Writing LUT:  54%|█████▍    | 1666620/3080448 [00:02<00:01, 834724.52px/s]📤 Writing LUT:  57%|█████▋    | 1751109/3080448 [00:02<00:01, 837772.24px/s]📤 Writing LUT:  60%|█████▉    | 1834890/3080448 [00:02<00:01, 837190.00px/s]📤 Writing LUT:  62%|██████▏   | 1918612/3080448 [00:02<00:01, 835982.74px/s]📤 Writing LUT:  65%|██████▍   | 2002213/3080448 [00:02<00:01, 832840.02px/s]📤 Writing LUT:  68%|██████▊   | 2085501/3080448 [00:02<00:01, 831041.27px/s]📤 Writing LUT:  70%|███████   | 2168741/3080448 [00:02<00:01, 831443.89px/s]📤 Writing LUT:  73%|███████▎  | 2253562/3080448 [00:02<00:00, 836457.61px/s]📤 Writing LUT:  76%|███████▌  | 2339633/3080448 [00:02<00:00, 843716.39px/s]📤 Writing LUT:  79%|███████▊  | 2424153/3080448 [00:02<00:00, 844159.27px/s]📤 Writing LUT:  81%|████████▏ | 2508630/3080448 [00:03<00:00, 844338.33px/s]📤 Writing LUT:  84%|████████▍ | 2593066/3080448 [00:03<00:00, 843865.56px/s]📤 Writing LUT:  87%|████████▋ | 2678143/3080448 [00:03<00:00, 845934.08px/s]📤 Writing LUT:  90%|████████▉ | 2762810/3080448 [00:03<00:00, 846151.94px/s]📤 Writing LUT:  92%|█████████▏| 2847917/3080448 [00:03<00:00, 847624.53px/s]📤 Writing LUT:  95%|█████████▌| 2934631/3080448 [00:03<00:00, 853476.38px/s]📤 Writing LUT:  98%|█████████▊| 3021363/3080448 [00:03<00:00, 857625.75px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 830774.60px/s]
[✅] LUT written to: /working/MDC040_W015(6)_2025-09-23/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC040_W015(6)_2025-09-23' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
📁 Rigs (heatmap):   0%|          | 0/4 [00:00<?, ?rig/s]
   🔥 1000:   0%|          | 0/135 [00:00<?, ?frame/s][A[✅] LUT copied to: /working/MDC040_W015(6)_2025-09-23/calibration/MDC040_W015(6)_2025-09-23_unknown.lut

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

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)

   🔥 1000:   7%|▋         | 9/135 [00:00<00:01, 89.70frame/s][A
   🔥 1000:  13%|█▎        | 18/135 [00:00<00:01, 86.37frame/s][A
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   🔥 1000:  50%|█████     | 68/135 [00:00<00:00, 90.14frame/s][A
   🔥 1000:  58%|█████▊    | 78/135 [00:00<00:00, 87.41frame/s][A
   🔥 1000:  64%|██████▍   | 87/135 [00:00<00:00, 85.09frame/s][A
   🔥 1000:  71%|███████   | 96/135 [00:01<00:00, 84.22frame/s][A
   🔥 1000:  78%|███████▊  | 105/135 [00:01<00:00, 83.73frame/s][A
   🔥 1000:  84%|████████▍ | 114/135 [00:01<00:00, 84.84frame/s][A
   🔥 1000:  91%|█████████ | 123/135 [00:01<00:00, 85.22frame/s][A
   🔥 1000:  98%|█████████▊| 132/135 [00:01<00:00, 84.91frame/s][A
                                                                [A[✅] 1000: /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap):  25%|██▌       | 1/4 [00:02<00:07,  2.34s/rig]
   🔥 2000R:   0%|          | 0/153 [00:00<?, ?frame/s][A
   🔥 2000R:   7%|▋         | 10/153 [00:00<00:01, 99.74frame/s][A
   🔥 2000R:  13%|█▎        | 20/153 [00:00<00:01, 84.53frame/s][A
   🔥 2000R:  19%|█▉        | 29/153 [00:00<00:01, 82.15frame/s][A
   🔥 2000R:  25%|██▍       | 38/153 [00:00<00:01, 81.92frame/s][A
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   🔥 2000R:  37%|███▋      | 57/153 [00:00<00:01, 86.72frame/s][A
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   🔥 2000R:  57%|█████▋    | 87/153 [00:00<00:00, 91.02frame/s][A
   🔥 2000R:  63%|██████▎   | 97/153 [00:01<00:00, 91.17frame/s][A
   🔥 2000R:  70%|██████▉   | 107/153 [00:01<00:00, 90.73frame/s][A
   🔥 2000R:  76%|███████▋  | 117/153 [00:01<00:00, 90.61frame/s][A
   🔥 2000R:  83%|████████▎ | 127/153 [00:01<00:00, 88.66frame/s][A
   🔥 2000R:  89%|████████▉ | 136/153 [00:01<00:00, 86.16frame/s][A
   🔥 2000R:  95%|█████████▍| 145/153 [00:01<00:00, 83.83frame/s][A
                                                                 [A[✅] 2000R: /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
📁 Rigs (heatmap):  50%|█████     | 2/4 [00:05<00:05,  2.97s/rig]
   🔥 2000L:   0%|          | 0/162 [00:00<?, ?frame/s][A
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   🔥 2000L:  96%|█████████▌| 155/162 [00:01<00:00, 82.28frame/s][A
                                                                 [A[✅] 2000L: /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:09<00:03,  3.18s/rig]
   🔥 1500:   0%|          | 0/204 [00:00<?, ?frame/s][A
   🔥 1500:   5%|▌         | 11/204 [00:00<00:01, 101.45frame/s][A
   🔥 1500:  11%|█         | 22/204 [00:00<00:01, 94.12frame/s] [A
   🔥 1500:  16%|█▌        | 32/204 [00:00<00:01, 91.13frame/s][A
   🔥 1500:  21%|██        | 42/204 [00:00<00:01, 84.89frame/s][A
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   🔥 1500:  37%|███▋      | 76/204 [00:00<00:01, 78.18frame/s][A
   🔥 1500:  41%|████      | 84/204 [00:01<00:01, 78.22frame/s][A
   🔥 1500:  45%|████▌     | 92/204 [00:01<00:01, 78.05frame/s][A
   🔥 1500:  50%|████▉     | 101/204 [00:01<00:01, 78.90frame/s][A
   🔥 1500:  54%|█████▍    | 111/204 [00:01<00:01, 82.85frame/s][A
   🔥 1500:  59%|█████▉    | 120/204 [00:01<00:01, 83.13frame/s][A
   🔥 1500:  63%|██████▎   | 129/204 [00:01<00:00, 81.80frame/s][A
   🔥 1500:  68%|██████▊   | 138/204 [00:01<00:00, 81.08frame/s][A
   🔥 1500:  72%|███████▏  | 147/204 [00:01<00:00, 80.32frame/s][A
   🔥 1500:  76%|███████▋  | 156/204 [00:01<00:00, 79.97frame/s][A
   🔥 1500:  81%|████████  | 165/204 [00:02<00:00, 81.28frame/s][A
   🔥 1500:  86%|████████▌ | 175/204 [00:02<00:00, 84.62frame/s][A
   🔥 1500:  90%|█████████ | 184/204 [00:02<00:00, 84.71frame/s][A
   🔥 1500:  95%|█████████▌| 194/204 [00:02<00:00, 86.73frame/s][A
   🔥 1500: 100%|██████████| 204/204 [00:02<00:00, 89.06frame/s][A
                                                                [A[✅] 1500: /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap/1500_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:13<00:00,  3.47s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:13<00:00,  3.27s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC040_W015(6)_2025-09-23/calibration/debug/5_heatmap
[🏁] Finished processing MDC040_W015(6)_2025-09-23


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