2026-05-06T00:51:31 - INFO: Slick 0.0.8a10

🟢 Starting Calibration Pipeline

=== Processing MDC019_W001(6)_2025-06-27 ===
2026-05-06T00:51:31 - INFO: Camera ID: 11481089
2026-05-06T00:51:31 - INFO: Wand ID: 
2026-05-06T00:51:31 - INFO: Recorded At: 2025-06-27T12:04:26Z

-------------------------------------------------------------------
                        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 1120 stationary frames, 284 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 1 untrusted, 284 total

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                                                                           [A
[TIMING] 1500 — 284 frames
  Phase 1  radial detect   (parallel)  :   4.32s  (15.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.12s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   0.06s
  Phase 3  finalize        (sequential):   5.29s  (18.6 ms/frame)
    decode + undistort                 :   2.83s  (10.0 ms/frame)
    corner apply + refinement          :   1.12s  (3.9 ms/frame)
    segment                            :   1.10s  (3.9 ms/frame)
    smooth                             :   0.16s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  11.79s

Processing Rigs:  25%|██▌       | 1/4 [00:17<00:53, 17.73s/it][INFO] Filtered 953 stationary frames, 188 remain

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                                                                           [A[PASS2] Frame 0000287: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000288: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000289: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000290: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001149: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001150: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001151: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001152: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000289: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001151: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 4 frames fixed, 5 untrusted, 188 total

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                                                                           [A
[TIMING] 1000 — 188 frames
  Phase 1  radial detect   (parallel)  :   3.01s  (16.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.89s  (10.0 ms/frame)
  Phase 2b corner fix      (sequential):   0.24s
  Phase 3  finalize        (sequential):   3.22s  (17.1 ms/frame)
    decode + undistort                 :   1.74s  (9.2 ms/frame)
    corner apply + refinement          :   0.61s  (3.3 ms/frame)
    segment                            :   0.71s  (3.8 ms/frame)
    smooth                             :   0.10s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.35s

Processing Rigs:  50%|█████     | 2/4 [00:29<00:28, 14.29s/it][INFO] Filtered 948 stationary frames, 211 remain

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                                                                            [A[PASS2] Summary: 0 frames fixed, 1 untrusted, 211 total

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                                                                            [A
[TIMING] 2000R — 211 frames
  Phase 1  radial detect   (parallel)  :   3.39s  (16.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.86s  (8.8 ms/frame)
  Phase 2b corner fix      (sequential):   0.04s
  Phase 3  finalize        (sequential):   3.79s  (18.0 ms/frame)
    decode + undistort                 :   1.96s  (9.3 ms/frame)
    corner apply + refinement          :   0.83s  (3.9 ms/frame)
    segment                            :   0.82s  (3.9 ms/frame)
    smooth                             :   0.12s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.09s

Processing Rigs:  75%|███████▌  | 3/4 [00:42<00:13, 13.49s/it][INFO] Filtered 1123 stationary frames, 210 remain

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                                                                            [A[PASS2] Frame 0000836: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 0 untrusted, 210 total

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                                                                            [A
[TIMING] 2000L — 210 frames
  Phase 1  radial detect   (parallel)  :   3.57s  (17.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.03s  (9.7 ms/frame)
  Phase 2b corner fix      (sequential):   0.09s
  Phase 3  finalize        (sequential):   3.79s  (18.1 ms/frame)
    decode + undistort                 :   1.96s  (9.3 ms/frame)
    corner apply + refinement          :   0.83s  (4.0 ms/frame)
    segment                            :   0.82s  (3.9 ms/frame)
    smooth                             :   0.11s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.50s

Processing Rigs: 100%|██████████| 4/4 [00:55<00:00, 13.46s/it]Processing Rigs: 100%|██████████| 4/4 [00:55<00:00, 13.89s/it]
[💾] Saved RigFrameDataset to /working/MDC019_W001(6)_2025-06-27/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 893 frames from 4 rigs to /working/MDC019_W001(6)_2025-06-27/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC019_W001(6)_2025-06-27/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 284
[📋] 1000: 100 frames selected from 188
[📋] 2000R: 100 frames selected from 211
[📋] 2000L: 100 frames selected from 210
[💾] Saved RigFrameDataset to /working/MDC019_W001(6)_2025-06-27/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC019_W001(6)_2025-06-27/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
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from bright frames:  42%|████▎     | 34/80 [01:49<02:28,  3.23s/it]Computing max image from bright frames:  44%|████▍     | 35/80 [01:52<02:24,  3.21s/it]Computing max image from bright frames:  45%|████▌     | 36/80 [01:55<02:20,  3.20s/it]Computing max image from bright frames:  46%|████▋     | 37/80 [01:58<02:17,  3.20s/it]Computing max image from bright frames:  48%|████▊     | 38/80 [02:01<02:14,  3.20s/it]Computing max image from bright frames:  49%|████▉     | 39/80 [02:05<02:10,  3.19s/it]Computing max image from bright frames:  50%|█████     | 40/80 [02:06<01:47,  2.69s/it]Computing max image from bright frames:  51%|█████▏    | 41/80 [02:09<01:50,  2.83s/it]Computing max image from bright frames:  52%|█████▎    | 42/80 [02:12<01:51,  2.94s/it]Computing max image from bright frames:  54%|█████▍    | 43/80 [02:16<01:51,  3.01s/it]Computing max image from bright frames:  55%|█████▌    | 44/80 [02:19<01:49,  3.05s/it]Computing max image from bright frames:  56%|█████▋    | 45/80 [02:22<01:47,  3.08s/it]Computing max image from bright frames:  57%|█████▊    | 46/80 [02:25<01:46,  3.13s/it]Computing max image from bright frames:  59%|█████▉    | 47/80 [02:28<01:43,  3.14s/it]Computing max image from bright frames:  60%|██████    | 48/80 [02:32<01:41,  3.17s/it]Computing max image from bright frames:  61%|██████▏   | 49/80 [02:35<01:39,  3.20s/it]Computing max image from bright frames:  62%|██████▎   | 50/80 [02:38<01:36,  3.21s/it]Computing max image from bright frames:  64%|██████▍   | 51/80 [02:41<01:33,  3.22s/it]Computing max image from bright frames:  65%|██████▌   | 52/80 [02:45<01:31,  3.25s/it]Computing max image from bright frames:  66%|██████▋   | 53/80 [02:48<01:27,  3.25s/it]Computing max image from bright frames:  68%|██████▊   | 54/80 [02:51<01:23,  3.23s/it]Computing max image from bright frames:  69%|██████▉   | 55/80 [02:54<01:20,  3.22s/it]Computing max image from bright frames:  70%|███████   | 56/80 [02:57<01:16,  3.20s/it]Computing max image from bright frames:  71%|███████▏  | 57/80 [03:01<01:13,  3.19s/it]Computing max image from bright frames:  72%|███████▎  | 58/80 [03:04<01:10,  3.19s/it]Computing max image from bright frames:  74%|███████▍  | 59/80 [03:07<01:06,  3.19s/it]Computing max image from bright frames:  75%|███████▌  | 60/80 [03:09<00:54,  2.70s/it]Computing max image from bright frames:  76%|███████▋  | 61/80 [03:12<00:54,  2.85s/it]Computing max image from bright frames:  78%|███████▊  | 62/80 [03:15<00:53,  2.95s/it]Computing max image from bright frames:  79%|███████▉  | 63/80 [03:18<00:51,  3.03s/it]Computing max image from bright frames:  80%|████████  | 64/80 [03:21<00:49,  3.07s/it]Computing max image from bright frames:  81%|████████▏ | 65/80 [03:25<00:46,  3.11s/it]Computing max image from bright frames:  82%|████████▎ | 66/80 [03:28<00:43,  3.14s/it]Computing max image from bright frames:  84%|████████▍ | 67/80 [03:31<00:40,  3.15s/it]Computing max image from bright frames:  85%|████████▌ | 68/80 [03:34<00:38,  3.17s/it]Computing max image from bright frames:  86%|████████▋ | 69/80 [03:37<00:35,  3.20s/it]Computing max image from bright frames:  88%|████████▊ | 70/80 [03:41<00:32,  3.22s/it]Computing max image from bright frames:  89%|████████▉ | 71/80 [03:44<00:29,  3.23s/it]Computing max image from bright frames:  90%|█████████ | 72/80 [03:47<00:25,  3.25s/it]Computing max image from bright frames:  91%|█████████▏| 73/80 [03:50<00:22,  3.25s/it]Computing max image from bright frames:  92%|█████████▎| 74/80 [03:54<00:19,  3.26s/it]Computing max image from bright frames:  94%|█████████▍| 75/80 [03:57<00:16,  3.25s/it]Computing max image from bright frames:  95%|█████████▌| 76/80 [04:00<00:12,  3.23s/it]Computing max image from bright frames:  96%|█████████▋| 77/80 [04:03<00:09,  3.21s/it]Computing max image from bright frames:  98%|█████████▊| 78/80 [04:06<00:06,  3.20s/it]Computing max image from bright frames:  99%|█████████▉| 79/80 [04:10<00:03,  3.20s/it]Computing max image from bright frames: 100%|██████████| 80/80 [04:11<00:00,  2.73s/it]                                                                                       [✅] Casing detection complete: cx=992.0, cy=748.0

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

  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:30,  3.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:30,  3.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:29,  3.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:29,  3.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:09, 10.19it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:09, 10.19it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   5%|▌         | 5/100 [00:00<00:09, 10.19it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   6%|▌         | 6/100 [00:00<00:07, 12.12it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   6%|▌         | 6/100 [00:00<00:07, 12.12it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 356.566} (0.000):   7%|▋         | 7/100 [00:00<00:07, 12.12it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.000 | Best: {'fx': 356.566} (0.000):   8%|▊         | 8/100 [00:00<00:07, 12.12it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:07, 12.12it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  10%|█         | 10/100 [00:00<00:07, 12.12it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:04, 21.49it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.002 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:04, 21.49it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:04, 21.49it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 21.49it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  14%|█▍        | 14/100 [00:00<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  14%|█▍        | 14/100 [00:00<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:00<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  17%|█▋        | 17/100 [00:00<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  18%|█▊        | 18/100 [00:00<00:03, 23.62it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  18%|█▊        | 18/100 [00:00<00:03, 23.62it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:01<00:03, 23.62it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.009 | Best: {'fx': 441.414} (0.009):  20%|██        | 20/100 [00:01<00:03, 23.62it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:01<00:03, 23.62it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.012 | Best: {'fx': 455.556} (0.012):  22%|██▏       | 22/100 [00:01<00:03, 23.62it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.012 | Best: {'fx': 455.556} (0.012):  23%|██▎       | 23/100 [00:01<00:02, 29.01it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  23%|██▎       | 23/100 [00:01<00:02, 29.01it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:02, 29.01it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.024 | Best: {'fx': 483.838} (0.024):  25%|██▌       | 25/100 [00:01<00:02, 29.01it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 483.838} (0.024):  26%|██▌       | 26/100 [00:01<00:02, 29.01it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 483.838} (0.024):  27%|██▋       | 27/100 [00:01<00:02, 29.17it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.037 | Best: {'fx': 497.98} (0.037):  27%|██▋       | 27/100 [00:01<00:02, 29.17it/s] Brute | Start: {'fx': 500.0} (0.039) | Current: 0.029 | Best: {'fx': 497.98} (0.037):  28%|██▊       | 28/100 [00:01<00:02, 29.17it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.047 | Best: {'fx': 505.051} (0.047):  29%|██▉       | 29/100 [00:01<00:02, 29.17it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.056 | Best: {'fx': 512.121} (0.056):  30%|███       | 30/100 [00:01<00:02, 29.17it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.056 | Best: {'fx': 512.121} (0.056):  31%|███       | 31/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.063 | Best: {'fx': 519.192} (0.063):  31%|███       | 31/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.072 | Best: {'fx': 526.263} (0.072):  32%|███▏      | 32/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.075 | Best: {'fx': 540.404} (0.075):  33%|███▎      | 33/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.075 | Best: {'fx': 540.404} (0.075):  34%|███▍      | 34/100 [00:01<00:03, 19.45it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.080 | Best: {'fx': 533.333} (0.080):  34%|███▍      | 34/100 [00:01<00:03, 19.45it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.060 | Best: {'fx': 533.333} (0.080):  35%|███▌      | 35/100 [00:01<00:03, 19.45it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.068 | Best: {'fx': 533.333} (0.080):  36%|███▌      | 36/100 [00:01<00:03, 19.45it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.049 | Best: {'fx': 533.333} (0.080):  37%|███▋      | 37/100 [00:01<00:03, 19.45it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.049 | Best: {'fx': 533.333} (0.080):  38%|███▊      | 38/100 [00:01<00:02, 21.91it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.053 | Best: {'fx': 533.333} (0.080):  38%|███▊      | 38/100 [00:01<00:02, 21.91it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.045 | Best: {'fx': 533.333} (0.080):  39%|███▉      | 39/100 [00:01<00:02, 21.91it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.032 | Best: {'fx': 533.333} (0.080):  40%|████      | 40/100 [00:02<00:02, 21.91it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.032 | Best: {'fx': 533.333} (0.080):  41%|████      | 41/100 [00:02<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.037 | Best: {'fx': 533.333} (0.080):  41%|████      | 41/100 [00:02<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.031 | Best: {'fx': 533.333} (0.080):  42%|████▏     | 42/100 [00:02<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.030 | Best: {'fx': 533.333} (0.080):  43%|████▎     | 43/100 [00:02<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.041 | Best: {'fx': 533.333} (0.080):  44%|████▍     | 44/100 [00:02<00:02, 18.92it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.039 | Best: {'fx': 533.333} (0.080):  45%|████▌     | 45/100 [00:02<00:02, 18.92it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.035 | Best: {'fx': 533.333} (0.080):  46%|████▌     | 46/100 [00:02<00:02, 18.92it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.035 | Best: {'fx': 533.333} (0.080):  47%|████▋     | 47/100 [00:02<00:02, 25.23it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.028 | Best: {'fx': 533.333} (0.080):  47%|████▋     | 47/100 [00:02<00:02, 25.23it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.029 | Best: {'fx': 533.333} (0.080):  48%|████▊     | 48/100 [00:02<00:02, 25.23it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.033 | Best: {'fx': 533.333} (0.080):  49%|████▉     | 49/100 [00:02<00:02, 25.23it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.027 | Best: {'fx': 533.333} (0.080):  50%|█████     | 50/100 [00:02<00:01, 25.23it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.028 | Best: {'fx': 533.333} (0.080):  51%|█████     | 51/100 [00:02<00:01, 25.23it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.028 | Best: {'fx': 533.333} (0.080):  52%|█████▏    | 52/100 [00:02<00:01, 27.90it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.025 | Best: {'fx': 533.333} (0.080):  52%|█████▏    | 52/100 [00:02<00:01, 27.90it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.023 | Best: {'fx': 533.333} (0.080):  53%|█████▎    | 53/100 [00:02<00:01, 27.90it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.027 | Best: {'fx': 533.333} (0.080):  54%|█████▍    | 54/100 [00:02<00:01, 27.90it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.026 | Best: {'fx': 533.333} (0.080):  55%|█████▌    | 55/100 [00:02<00:01, 27.90it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.025 | Best: {'fx': 533.333} (0.080):  56%|█████▌    | 56/100 [00:02<00:01, 27.90it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.025 | Best: {'fx': 533.333} (0.080):  57%|█████▋    | 57/100 [00:02<00:01, 27.90it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.025 | Best: {'fx': 533.333} (0.080):  58%|█████▊    | 58/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 533.333} (0.080):  58%|█████▊    | 58/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.024 | Best: {'fx': 533.333} (0.080):  59%|█████▉    | 59/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.023 | Best: {'fx': 533.333} (0.080):  60%|██████    | 60/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.023 | Best: {'fx': 533.333} (0.080):  61%|██████    | 61/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 533.333} (0.080):  62%|██████▏   | 62/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.023 | Best: {'fx': 533.333} (0.080):  63%|██████▎   | 63/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 533.333} (0.080):  64%|██████▍   | 64/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 533.333} (0.080):  65%|██████▌   | 65/100 [00:02<00:01, 30.37it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 533.333} (0.080):  66%|██████▌   | 66/100 [00:02<00:00, 40.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.022 | Best: {'fx': 533.333} (0.080):  66%|██████▌   | 66/100 [00:02<00:00, 40.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.024 | Best: {'fx': 533.333} (0.080):  67%|██████▋   | 67/100 [00:02<00:00, 40.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.024 | Best: {'fx': 533.333} (0.080):  68%|██████▊   | 68/100 [00:02<00:00, 40.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  69%|██████▉   | 69/100 [00:02<00:00, 40.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  70%|███████   | 70/100 [00:02<00:00, 40.29it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  71%|███████   | 71/100 [00:02<00:00, 39.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  71%|███████   | 71/100 [00:02<00:00, 39.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  72%|███████▏  | 72/100 [00:02<00:00, 39.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  73%|███████▎  | 73/100 [00:02<00:00, 39.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  74%|███████▍  | 74/100 [00:02<00:00, 39.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  75%|███████▌  | 75/100 [00:02<00:00, 39.11it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  76%|███████▌  | 76/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  76%|███████▌  | 76/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  77%|███████▋  | 77/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  78%|███████▊  | 78/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  79%|███████▉  | 79/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  80%|████████  | 80/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  81%|████████  | 81/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  82%|████████▏ | 82/100 [00:02<00:00, 39.33it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  83%|████████▎ | 83/100 [00:02<00:00, 46.60it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  83%|████████▎ | 83/100 [00:03<00:00, 46.60it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  84%|████████▍ | 84/100 [00:03<00:00, 46.60it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  85%|████████▌ | 85/100 [00:03<00:00, 46.60it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  86%|████████▌ | 86/100 [00:03<00:00, 46.60it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.020 | Best: {'fx': 533.333} (0.080):  87%|████████▋ | 87/100 [00:03<00:00, 46.60it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  88%|████████▊ | 88/100 [00:03<00:00, 46.60it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  89%|████████▉ | 89/100 [00:03<00:00, 36.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  89%|████████▉ | 89/100 [00:03<00:00, 36.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  90%|█████████ | 90/100 [00:03<00:00, 36.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  91%|█████████ | 91/100 [00:03<00:00, 36.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  92%|█████████▏| 92/100 [00:03<00:00, 36.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  93%|█████████▎| 93/100 [00:03<00:00, 36.22it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  94%|█████████▍| 94/100 [00:03<00:00, 33.79it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  94%|█████████▍| 94/100 [00:03<00:00, 33.79it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  95%|█████████▌| 95/100 [00:03<00:00, 33.79it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  96%|█████████▌| 96/100 [00:03<00:00, 33.79it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  97%|█████████▋| 97/100 [00:03<00:00, 33.79it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  98%|█████████▊| 98/100 [00:03<00:00, 33.79it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  99%|█████████▉| 99/100 [00:03<00:00, 36.54it/s]Brute | Start: {'fx': 500.0} (0.039) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  99%|█████████▉| 99/100 [00:03<00:00, 36.54it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.051 | Best: {'fx': 508.333} (0.051):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.051 | Best: {'fx': 508.333} (0.051):   2%|▏         | 1/50 [00:00<00:26,  1.87it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.053 | Best: {'fx': 509.354} (0.053):   2%|▏         | 1/50 [00:00<00:26,  1.87it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.054 | Best: {'fx': 510.374} (0.054):   4%|▍         | 2/50 [00:00<00:25,  1.87it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.055 | Best: {'fx': 511.395} (0.055):   6%|▌         | 3/50 [00:00<00:25,  1.87it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.056 | Best: {'fx': 512.415} (0.056):   8%|▊         | 4/50 [00:00<00:24,  1.87it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.059 | Best: {'fx': 514.456} (0.059):  10%|█         | 5/50 [00:00<00:24,  1.87it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.059 | Best: {'fx': 514.456} (0.059):  12%|█▏        | 6/50 [00:00<00:04,  9.86it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.059 | Best: {'fx': 515.476} (0.059):  12%|█▏        | 6/50 [00:00<00:04,  9.86it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.061 | Best: {'fx': 517.517} (0.061):  14%|█▍        | 7/50 [00:00<00:04,  9.86it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.060 | Best: {'fx': 517.517} (0.061):  16%|█▌        | 8/50 [00:00<00:04,  9.86it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.060 | Best: {'fx': 517.517} (0.061):  18%|█▊        | 9/50 [00:00<00:03, 13.11it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.058 | Best: {'fx': 517.517} (0.061):  18%|█▊        | 9/50 [00:00<00:03, 13.11it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.062 | Best: {'fx': 518.537} (0.062):  20%|██        | 10/50 [00:00<00:03, 13.11it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.066 | Best: {'fx': 521.599} (0.066):  22%|██▏       | 11/50 [00:00<00:02, 13.11it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.064 | Best: {'fx': 521.599} (0.066):  24%|██▍       | 12/50 [00:00<00:02, 13.11it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.064 | Best: {'fx': 521.599} (0.066):  26%|██▌       | 13/50 [00:01<00:02, 13.11it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.064 | Best: {'fx': 521.599} (0.066):  28%|██▊       | 14/50 [00:01<00:02, 17.95it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.067 | Best: {'fx': 522.619} (0.067):  28%|██▊       | 14/50 [00:01<00:02, 17.95it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.068 | Best: {'fx': 523.639} (0.068):  30%|███       | 15/50 [00:01<00:01, 17.95it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.072 | Best: {'fx': 526.701} (0.072):  32%|███▏      | 16/50 [00:01<00:01, 17.95it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.072 | Best: {'fx': 526.701} (0.072):  34%|███▍      | 17/50 [00:01<00:01, 18.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.074 | Best: {'fx': 527.721} (0.074):  34%|███▍      | 17/50 [00:01<00:01, 18.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.071 | Best: {'fx': 527.721} (0.074):  36%|███▌      | 18/50 [00:01<00:01, 18.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.070 | Best: {'fx': 527.721} (0.074):  38%|███▊      | 19/50 [00:01<00:01, 18.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.070 | Best: {'fx': 527.721} (0.074):  40%|████      | 20/50 [00:01<00:01, 19.97it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.075 | Best: {'fx': 528.741} (0.075):  40%|████      | 20/50 [00:01<00:01, 19.97it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.078 | Best: {'fx': 530.782} (0.078):  42%|████▏     | 21/50 [00:01<00:01, 19.97it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.077 | Best: {'fx': 530.782} (0.078):  44%|████▍     | 22/50 [00:01<00:01, 19.97it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.077 | Best: {'fx': 530.782} (0.078):  46%|████▌     | 23/50 [00:01<00:01, 19.90it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.079 | Best: {'fx': 531.803} (0.079):  46%|████▌     | 23/50 [00:01<00:01, 19.90it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.078 | Best: {'fx': 531.803} (0.079):  48%|████▊     | 24/50 [00:01<00:01, 19.90it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.079 | Best: {'fx': 532.823} (0.079):  50%|█████     | 25/50 [00:01<00:01, 19.90it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.075 | Best: {'fx': 532.823} (0.079):  52%|█████▏    | 26/50 [00:01<00:01, 19.90it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.075 | Best: {'fx': 532.823} (0.079):  54%|█████▍    | 27/50 [00:01<00:01, 19.90it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.077 | Best: {'fx': 532.823} (0.079):  56%|█████▌    | 28/50 [00:01<00:01, 19.90it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.077 | Best: {'fx': 532.823} (0.079):  58%|█████▊    | 29/50 [00:01<00:00, 27.88it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.076 | Best: {'fx': 532.823} (0.079):  58%|█████▊    | 29/50 [00:01<00:00, 27.88it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.072 | Best: {'fx': 532.823} (0.079):  60%|██████    | 30/50 [00:01<00:00, 27.88it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.076 | Best: {'fx': 532.823} (0.079):  62%|██████▏   | 31/50 [00:01<00:00, 27.88it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.068 | Best: {'fx': 532.823} (0.079):  64%|██████▍   | 32/50 [00:01<00:00, 27.88it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.068 | Best: {'fx': 532.823} (0.079):  66%|██████▌   | 33/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.071 | Best: {'fx': 532.823} (0.079):  66%|██████▌   | 33/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.075 | Best: {'fx': 532.823} (0.079):  68%|██████▊   | 34/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.070 | Best: {'fx': 532.823} (0.079):  70%|███████   | 35/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.073 | Best: {'fx': 532.823} (0.079):  72%|███████▏  | 36/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.075 | Best: {'fx': 532.823} (0.079):  74%|███████▍  | 37/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.067 | Best: {'fx': 532.823} (0.079):  76%|███████▌  | 38/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.076 | Best: {'fx': 532.823} (0.079):  78%|███████▊  | 39/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.066 | Best: {'fx': 532.823} (0.079):  80%|████████  | 40/50 [00:01<00:00, 30.01it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.066 | Best: {'fx': 532.823} (0.079):  82%|████████▏ | 41/50 [00:01<00:00, 40.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.064 | Best: {'fx': 532.823} (0.079):  82%|████████▏ | 41/50 [00:01<00:00, 40.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.064 | Best: {'fx': 532.823} (0.079):  84%|████████▍ | 42/50 [00:01<00:00, 40.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.063 | Best: {'fx': 532.823} (0.079):  86%|████████▌ | 43/50 [00:01<00:00, 40.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.059 | Best: {'fx': 532.823} (0.079):  88%|████████▊ | 44/50 [00:01<00:00, 40.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.062 | Best: {'fx': 532.823} (0.079):  90%|█████████ | 45/50 [00:01<00:00, 40.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.058 | Best: {'fx': 532.823} (0.079):  92%|█████████▏| 46/50 [00:01<00:00, 40.71it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.058 | Best: {'fx': 532.823} (0.079):  94%|█████████▍| 47/50 [00:01<00:00, 42.34it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.061 | Best: {'fx': 532.823} (0.079):  94%|█████████▍| 47/50 [00:01<00:00, 42.34it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.057 | Best: {'fx': 532.823} (0.079):  96%|█████████▌| 48/50 [00:01<00:00, 42.34it/s]Brute | Start: {'fx': 533.333} (0.080) | Current: 0.056 | Best: {'fx': 532.823} (0.079):  98%|█████████▊| 49/50 [00:01<00:00, 42.34it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 533.3333333333334
    px: 992.0
    py: 748.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 80 frames will be used for optimization
  Rigs: 1500(1500mm):20/20, 1000(1000mm):20/20, 2000R(2000mm):20/20, 2000L(2000mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 68.0296: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 68.0296, picklable_objective(x0) = 68.0808
Best score: 68.0808: : 0iter [00:01, ?iter/s] Best score: 68.0808: : 1iter [00:01,  1.99s/iter]Best score: 68.0808: : 1iter [00:02,  1.99s/iter]Best score: 68.0808: : 2iter [00:02,  1.99s/iter]Best score: 68.0808: : 3iter [00:02,  1.72iter/s]Best score: 68.0808: : 3iter [00:02,  1.72iter/s]Best score: 68.0808: : 4iter [00:02,  1.72iter/s]Best score: 68.0808: : 5iter [00:02,  3.07iter/s]Best score: 68.0808: : 5iter [00:02,  3.07iter/s]Best score: 68.0808: : 6iter [00:02,  3.07iter/s]Best score: 68.0808: : 7iter [00:02,  4.47iter/s]Best score: 68.0808: : 7iter [00:02,  4.47iter/s]Best score: 68.0808: : 8iter [00:02,  4.47iter/s]Best score: 68.0808: : 9iter [00:02,  5.82iter/s]Best score: 68.0808: : 9iter [00:02,  5.82iter/s]Best score: 68.0808: : 10iter [00:02,  5.82iter/s]Best score: 68.0808: : 11iter [00:02,  6.98iter/s]Best score: 68.0808: : 11iter [00:02,  6.98iter/s]Best score: 68.0808: : 12iter [00:03,  6.98iter/s]Best score: 68.0808: : 13iter [00:03,  8.06iter/s]Best score: 68.0808: : 13iter [00:03,  8.06iter/s]Best score: 68.0808: : 14iter [00:03,  8.06iter/s]Best score: 68.0808: : 15iter [00:03,  8.94iter/s]Best score: 68.0808: : 15iter [00:03,  8.94iter/s]Best score: 68.0808: : 16iter [00:03,  8.94iter/s]Best score: 68.0808: : 17iter [00:03,  9.62iter/s]Best score: 68.0808: : 17iter [00:03,  9.62iter/s]Best score: 68.0808: : 18iter [00:03,  9.62iter/s]Best score: 68.0808: : 19iter [00:03, 10.20iter/s]Best score: 68.0808: : 19iter [00:03, 10.20iter/s]Best score: 68.0808: : 20iter [00:03, 10.20iter/s]Best score: 68.0808: : 21iter [00:03, 10.51iter/s]Best score: 68.0808: : 21iter [00:03, 10.51iter/s]Best score: 68.0808: : 22iter [00:03, 10.51iter/s]Best score: 68.0808: : 23iter [00:03, 10.89iter/s]Best score: 68.0808: : 23iter [00:03, 10.89iter/s]Best score: 68.0808: : 24iter [00:04, 10.89iter/s]Best score: 68.0808: : 25iter [00:04, 11.19iter/s]Best score: 68.0808: : 25iter [00:04, 11.19iter/s]Best score: 68.0808: : 26iter [00:04, 11.19iter/s]Best score: 68.0808: : 27iter [00:04, 11.34iter/s]Best score: 68.0808: : 27iter [00:04, 11.34iter/s]Best score: 68.0808: : 28iter [00:04, 11.34iter/s]Best score: 68.0808: : 29iter [00:04, 11.42iter/s]Best score: 68.0808: : 29iter [00:04, 11.42iter/s]Best score: 68.0808: : 30iter [00:04, 11.42iter/s]Best score: 68.0808: : 31iter [00:04, 11.36iter/s]Best score: 68.0808: : 31iter [00:04, 11.36iter/s]Best score: 68.0808: : 32iter [00:04, 11.36iter/s]Best score: 68.0808: : 33iter [00:04, 11.46iter/s]Best score: 68.0808: : 33iter [00:04, 11.46iter/s]Best score: 68.0808: : 34iter [00:04, 11.46iter/s]Best score: 68.0808: : 35iter [00:04, 11.53iter/s]Best score: 68.0808: : 35iter [00:05, 11.53iter/s]Best score: 68.0808: : 36iter [00:05, 11.53iter/s]Best score: 68.0808: : 37iter [00:05, 11.62iter/s]Best score: 68.0808: : 37iter [00:05, 11.62iter/s]Best score: 68.0808: : 38iter [00:05, 11.62iter/s]Best score: 68.0808: : 39iter [00:05, 11.62iter/s]Best score: 68.0808: : 39iter [00:05, 11.62iter/s]Best score: 68.0808: : 40iter [00:05, 11.62iter/s]Best score: 68.0808: : 41iter [00:05, 11.53iter/s]Best score: 68.0808: : 41iter [00:05, 11.53iter/s]Best score: 68.0808: : 42iter [00:05, 11.53iter/s]Best score: 68.0808: : 43iter [00:05, 11.56iter/s]Best score: 68.0808: : 43iter [00:05, 11.56iter/s]Best score: 68.0808: : 44iter [00:05, 11.56iter/s]Best score: 68.0808: : 45iter [00:05, 11.63iter/s]Best score: 68.0808: : 45iter [00:05, 11.63iter/s]Best score: 68.0808: : 46iter [00:05, 11.63iter/s]Best score: 68.0808: : 47iter [00:05, 11.70iter/s]Best score: 68.0808: : 47iter [00:06, 11.70iter/s]Best score: 68.0808: : 48iter [00:06, 11.70iter/s]Best score: 68.0808: : 49iter [00:06, 11.78iter/s]
[ITER 50] Best: 68.0808
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  px: 992.0000
  py: 748.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 68.0808: : 49iter [00:06, 11.78iter/s]Best score: 68.0808: : 50iter [00:06, 11.78iter/s]Best score: 68.0808: : 51iter [00:06, 11.63iter/s]Best score: 68.0808: : 51iter [00:06, 11.63iter/s]Best score: 68.0808: : 52iter [00:06, 11.63iter/s]Best score: 68.0808: : 53iter [00:06, 11.70iter/s]Best score: 68.0808: : 53iter [00:06, 11.70iter/s]Best score: 68.0808: : 54iter [00:06, 11.70iter/s]Best score: 68.0808: : 55iter [00:06, 11.75iter/s]Best score: 68.0808: : 55iter [00:06, 11.75iter/s]Best score: 68.0808: : 56iter [00:06, 11.75iter/s]Best score: 68.0808: : 57iter [00:06, 11.79iter/s]Best score: 68.0808: : 57iter [00:06, 11.79iter/s]Best score: 68.0808: : 58iter [00:06, 11.79iter/s]Best score: 68.0808: : 59iter [00:06, 11.85iter/s]Best score: 33.9830: : 59iter [00:07, 11.85iter/s]Best score: 33.9830: : 60iter [00:07, 11.85iter/s]Best score: 33.9830: : 61iter [00:07, 11.63iter/s]Best score: 33.9830: : 61iter [00:07, 11.63iter/s]Best score: 33.9830: : 62iter [00:07, 11.63iter/s]Best score: 33.9830: : 63iter [00:07, 11.72iter/s]Best score: 33.9830: : 63iter [00:07, 11.72iter/s]Best score: 33.9830: : 64iter [00:07, 11.72iter/s]Best score: 33.9830: : 65iter [00:07, 11.77iter/s]Best score: 33.9830: : 65iter [00:07, 11.77iter/s]Best score: 33.9830: : 66iter [00:07, 11.77iter/s]Best score: 33.9830: : 67iter [00:07, 11.85iter/s]Best score: 33.9830: : 67iter [00:07, 11.85iter/s]Best score: 33.9830: : 68iter [00:07, 11.85iter/s]Best score: 33.9830: : 69iter [00:07, 11.89iter/s]Best score: 20.7951: : 69iter [00:07, 11.89iter/s]Best score: 20.7951: : 70iter [00:07, 11.89iter/s]Best score: 20.7951: : 71iter [00:07, 11.71iter/s]Best score: 20.7951: : 71iter [00:08, 11.71iter/s]Best score: 20.7951: : 72iter [00:08, 11.71iter/s]Best score: 20.7951: : 73iter [00:08, 11.80iter/s]Best score: 20.7951: : 73iter [00:08, 11.80iter/s]Best score: 20.7951: : 74iter [00:08, 11.80iter/s]Best score: 20.7951: : 75iter [00:08, 11.82iter/s]Best score: 20.7951: : 75iter [00:08, 11.82iter/s]Best score: 20.7951: : 76iter [00:08, 11.82iter/s]Best score: 20.7951: : 77iter [00:08, 11.79iter/s]Best score: 20.7951: : 77iter [00:08, 11.79iter/s]Best score: 20.7951: : 78iter [00:08, 11.79iter/s]Best score: 20.7951: : 79iter [00:08, 11.78iter/s]Best score: 20.7951: : 79iter [00:08, 11.78iter/s]Best score: 20.7951: : 80iter [00:08, 11.78iter/s]Best score: 20.7951: : 81iter [00:08, 11.67iter/s]Best score: 20.7951: : 81iter [00:08, 11.67iter/s]Best score: 20.7951: : 82iter [00:09, 11.67iter/s]Best score: 20.7951: : 83iter [00:09, 11.79iter/s]Best score: 20.7951: : 83iter [00:09, 11.79iter/s]Best score: 20.7951: : 84iter [00:09, 11.79iter/s]Best score: 20.7951: : 85iter [00:09, 11.85iter/s]Best score: 20.7951: : 85iter [00:09, 11.85iter/s]Best score: 20.7951: : 86iter [00:09, 11.85iter/s]Best score: 20.7951: : 87iter [00:09, 11.86iter/s]Best score: 20.7951: : 87iter [00:09, 11.86iter/s]Best score: 20.7951: : 88iter [00:09, 11.86iter/s]Best score: 20.7951: : 89iter [00:09, 11.86iter/s]Best score: 12.8219: : 89iter [00:09, 11.86iter/s]Best score: 12.8219: : 90iter [00:09, 11.86iter/s]Best score: 12.8219: : 91iter [00:09, 11.68iter/s]Best score: 12.8219: : 91iter [00:09, 11.68iter/s]Best score: 12.8219: : 92iter [00:09, 11.68iter/s]Best score: 12.8219: : 93iter [00:09, 11.76iter/s]Best score: 12.8219: : 93iter [00:09, 11.76iter/s]Best score: 12.8219: : 94iter [00:10, 11.76iter/s]Best score: 12.8219: : 95iter [00:10, 11.79iter/s]Best score: 12.8219: : 95iter [00:10, 11.79iter/s]Best score: 12.8219: : 96iter [00:10, 11.79iter/s]Best score: 12.8219: : 97iter [00:10, 11.78iter/s]Best score: 12.8219: : 97iter [00:10, 11.78iter/s]Best score: 12.8219: : 98iter [00:10, 11.78iter/s]Best score: 12.8219: : 99iter [00:10, 11.78iter/s]
[ITER 100] Best: 8.4888
[PARAMS] Current best parameters:
  fx: 544.0063
  fy: 545.4233
  px: 992.0342
  py: 763.6704
  k1: -0.00029120
  k2: 0.01792189
  k3: -0.02665122
  k4: 0.00703825
  p1: 0.00251279
  p2: 0.00070331
  p3: 0.02005479
  p4: -0.03175035
  s1: -0.0181
  s2: 0.0129
  s3: 0.0015
  s4: -0.0019
  tilt_x: 0.0237
  tilt_y: 0.0377
  scale: 2.9418
  rotation: -0.3537
Best score: 8.4888: : 99iter [00:10, 11.78iter/s] Best score: 8.4888: : 100iter [00:10, 11.78iter/s]Best score: 8.4888: : 101iter [00:10, 10.06iter/s]Best score: 8.4888: : 101iter [00:10, 10.06iter/s]Best score: 8.4888: : 102iter [00:10, 10.06iter/s]Best score: 8.4888: : 103iter [00:10, 10.52iter/s]Best score: 8.4888: : 103iter [00:10, 10.52iter/s]Best score: 8.4888: : 104iter [00:10, 10.52iter/s]Best score: 8.4888: : 105iter [00:10, 10.85iter/s]Best score: 8.4888: : 105iter [00:11, 10.85iter/s]Best score: 8.4888: : 106iter [00:11, 10.85iter/s]Best score: 8.4888: : 107iter [00:11, 11.07iter/s]Best score: 8.4888: : 107iter [00:11, 11.07iter/s]Best score: 8.4888: : 108iter [00:11, 11.07iter/s]Best score: 8.4888: : 109iter [00:11, 11.25iter/s]Best score: 8.4888: : 109iter [00:11, 11.25iter/s]Best score: 8.4888: : 110iter [00:11, 11.25iter/s]Best score: 8.4888: : 111iter [00:11, 11.19iter/s]Best score: 8.4888: : 111iter [00:11, 11.19iter/s]Best score: 8.4888: : 112iter [00:11, 11.19iter/s]Best score: 8.4888: : 113iter [00:11, 11.32iter/s]Best score: 8.4888: : 113iter [00:11, 11.32iter/s]Best score: 8.4888: : 114iter [00:11, 11.32iter/s]Best score: 8.4888: : 115iter [00:11, 11.43iter/s]Best score: 8.4888: : 115iter [00:11, 11.43iter/s]Best score: 8.4888: : 116iter [00:12, 11.43iter/s]Best score: 8.4888: : 117iter [00:12, 11.48iter/s]Best score: 8.4888: : 117iter [00:12, 11.48iter/s]Best score: 8.4888: : 118iter [00:12, 11.48iter/s]Best score: 8.4888: : 119iter [00:12, 11.50iter/s]Best score: 8.4888: : 119iter [00:12, 11.50iter/s]Best score: 8.4888: : 120iter [00:12, 11.50iter/s]Best score: 8.4888: : 121iter [00:12, 11.43iter/s]Best score: 8.4888: : 121iter [00:12, 11.43iter/s]Best score: 8.4888: : 122iter [00:12, 11.43iter/s]Best score: 8.4888: : 123iter [00:12, 11.48iter/s]Best score: 8.4888: : 123iter [00:12, 11.48iter/s]Best score: 8.4888: : 124iter [00:12, 11.48iter/s]Best score: 8.4888: : 125iter [00:12, 11.54iter/s]Best score: 8.4888: : 125iter [00:12, 11.54iter/s]Best score: 8.4888: : 126iter [00:12, 11.54iter/s]Best score: 8.4888: : 127iter [00:12, 11.60iter/s]Best score: 8.4888: : 127iter [00:12, 11.60iter/s]Best score: 8.4888: : 128iter [00:13, 11.60iter/s]Best score: 8.4888: : 129iter [00:13, 11.65iter/s]Best score: 8.4888: : 129iter [00:13, 11.65iter/s]Best score: 8.4888: : 130iter [00:13, 11.65iter/s]Best score: 8.4888: : 131iter [00:13, 11.50iter/s]Best score: 8.4888: : 131iter [00:13, 11.50iter/s]Best score: 8.4888: : 132iter [00:13, 11.50iter/s]Best score: 8.4888: : 133iter [00:13, 11.58iter/s]Best score: 8.4888: : 133iter [00:13, 11.58iter/s]Best score: 8.4888: : 134iter [00:13, 11.58iter/s]Best score: 8.4888: : 135iter [00:13, 11.64iter/s]Best score: 8.4888: : 135iter [00:13, 11.64iter/s]Best score: 8.4888: : 136iter [00:13, 11.64iter/s]Best score: 8.4888: : 137iter [00:13, 11.68iter/s]Best score: 8.4888: : 137iter [00:13, 11.68iter/s]Best score: 8.4888: : 138iter [00:13, 11.68iter/s]Best score: 8.4888: : 139iter [00:13, 11.74iter/s]Best score: 8.4888: : 139iter [00:13, 11.74iter/s]Best score: 8.4888: : 140iter [00:14, 11.74iter/s]Best score: 8.4888: : 141iter [00:14, 11.60iter/s]Best score: 8.4888: : 141iter [00:14, 11.60iter/s]Best score: 8.4888: : 142iter [00:14, 11.60iter/s]Best score: 8.4888: : 143iter [00:14, 11.64iter/s]Best score: 8.4888: : 143iter [00:14, 11.64iter/s]Best score: 8.4888: : 144iter [00:14, 11.64iter/s]Best score: 8.4888: : 145iter [00:14, 11.67iter/s]Best score: 8.4888: : 145iter [00:14, 11.67iter/s]Best score: 8.4888: : 146iter [00:14, 11.67iter/s]Best score: 8.4888: : 147iter [00:14, 11.66iter/s]Best score: 8.4888: : 147iter [00:14, 11.66iter/s]Best score: 8.4888: : 148iter [00:14, 11.66iter/s]Best score: 8.4888: : 149iter [00:14, 11.69iter/s]
[ITER 150] Best: 8.4888
[PARAMS] Current best parameters:
  fx: 544.0063
  fy: 545.4233
  px: 992.0342
  py: 763.6704
  k1: -0.00029120
  k2: 0.01792189
  k3: -0.02665122
  k4: 0.00703825
  p1: 0.00251279
  p2: 0.00070331
  p3: 0.02005479
  p4: -0.03175035
  s1: -0.0181
  s2: 0.0129
  s3: 0.0015
  s4: -0.0019
  tilt_x: 0.0237
  tilt_y: 0.0377
  scale: 2.9418
  rotation: -0.3537
Best score: 8.4888: : 149iter [00:14, 11.69iter/s]Best score: 8.4888: : 150iter [00:15, 11.69iter/s]Best score: 8.4888: : 151iter [00:15,  7.55iter/s]Best score: 8.4888: : 151iter [00:15,  7.55iter/s]Best score: 8.4888: : 152iter [00:15,  7.55iter/s]Best score: 8.4888: : 153iter [00:15,  8.49iter/s]Best score: 8.4888: : 153iter [00:15,  8.49iter/s]Best score: 8.4888: : 154iter [00:15,  8.49iter/s]Best score: 8.4888: : 155iter [00:15,  9.25iter/s]Best score: 8.4888: : 155iter [00:15,  9.25iter/s]Best score: 8.4888: : 156iter [00:15,  9.25iter/s]Best score: 8.4888: : 157iter [00:15,  9.91iter/s]Best score: 8.4888: : 157iter [00:15,  9.91iter/s]Best score: 8.4888: : 158iter [00:15,  9.91iter/s]Best score: 8.4888: : 159iter [00:15, 10.42iter/s]Best score: 8.4888: : 159iter [00:16, 10.42iter/s]Best score: 8.4888: : 160iter [00:16, 10.42iter/s]Best score: 8.4888: : 161iter [00:16, 10.64iter/s]Best score: 8.4888: : 161iter [00:16, 10.64iter/s]Best score: 8.4888: : 162iter [00:16, 10.64iter/s]Best score: 8.4888: : 163iter [00:16, 11.01iter/s]Best score: 8.4888: : 163iter [00:16, 11.01iter/s]Best score: 8.4888: : 164iter [00:16, 11.01iter/s]Best score: 8.4888: : 165iter [00:16, 11.29iter/s]Best score: 8.4888: : 165iter [00:16, 11.29iter/s]Best score: 8.4888: : 166iter [00:16, 11.29iter/s]Best score: 8.4888: : 167iter [00:16, 11.50iter/s]Best score: 8.4888: : 167iter [00:16, 11.50iter/s]Best score: 8.4888: : 168iter [00:16, 11.50iter/s]Best score: 8.4888: : 169iter [00:16, 11.59iter/s]Best score: 8.4888: : 169iter [00:16, 11.59iter/s]Best score: 8.4888: : 170iter [00:16, 11.59iter/s]Best score: 8.4888: : 171iter [00:16, 11.50iter/s]Best score: 8.4888: : 171iter [00:17, 11.50iter/s]Best score: 8.4888: : 172iter [00:17, 11.50iter/s]Best score: 8.4888: : 173iter [00:17, 11.63iter/s]Best score: 8.4888: : 173iter [00:17, 11.63iter/s]Best score: 8.4888: : 174iter [00:17, 11.63iter/s]Best score: 8.4888: : 175iter [00:17, 11.74iter/s]Best score: 8.4888: : 175iter [00:17, 11.74iter/s]Best score: 8.4888: : 176iter [00:17, 11.74iter/s]Best score: 8.4888: : 177iter [00:17, 11.85iter/s]Best score: 8.4888: : 177iter [00:17, 11.85iter/s]Best score: 8.4888: : 178iter [00:17, 11.85iter/s]Best score: 8.4888: : 179iter [00:17, 11.92iter/s]Best score: 8.4888: : 179iter [00:17, 11.92iter/s]Best score: 8.4888: : 180iter [00:17, 11.92iter/s]Best score: 8.4888: : 181iter [00:17, 11.71iter/s]Best score: 8.4888: : 181iter [00:17, 11.71iter/s]Best score: 8.4888: : 182iter [00:17, 11.71iter/s]Best score: 8.4888: : 183iter [00:17, 11.77iter/s]Best score: 8.4888: : 183iter [00:18, 11.77iter/s]Best score: 8.4888: : 184iter [00:18, 11.77iter/s]Best score: 8.4888: : 185iter [00:18, 11.81iter/s]Best score: 8.4888: : 185iter [00:18, 11.81iter/s]Best score: 8.4888: : 186iter [00:18, 11.81iter/s]Best score: 8.4888: : 187iter [00:18, 11.86iter/s]Best score: 8.4888: : 187iter [00:18, 11.86iter/s]Best score: 8.4888: : 188iter [00:18, 11.86iter/s]Best score: 8.4888: : 189iter [00:18, 11.94iter/s]Best score: 8.4888: : 189iter [00:18, 11.94iter/s]Best score: 8.4888: : 190iter [00:18, 11.94iter/s]Best score: 8.4888: : 191iter [00:18, 11.77iter/s]Best score: 8.4888: : 191iter [00:18, 11.77iter/s]Best score: 8.4888: : 192iter [00:18, 11.77iter/s]Best score: 8.4888: : 193iter [00:18, 11.87iter/s]Best score: 8.4888: : 193iter [00:18, 11.87iter/s]Best score: 8.4888: : 194iter [00:18, 11.87iter/s]Best score: 8.4888: : 195iter [00:18, 11.91iter/s]Best score: 8.4888: : 195iter [00:19, 11.91iter/s]Best score: 8.4888: : 196iter [00:19, 11.91iter/s]Best score: 8.4888: : 197iter [00:19, 11.90iter/s]Best score: 8.4888: : 197iter [00:19, 11.90iter/s]Best score: 8.4888: : 198iter [00:19, 11.90iter/s]Best score: 8.4888: : 199iter [00:19, 11.84iter/s]
[ITER 200] Best: 8.4888
[PARAMS] Current best parameters:
  fx: 544.0063
  fy: 545.4233
  px: 992.0342
  py: 763.6704
  k1: -0.00029120
  k2: 0.01792189
  k3: -0.02665122
  k4: 0.00703825
  p1: 0.00251279
  p2: 0.00070331
  p3: 0.02005479
  p4: -0.03175035
  s1: -0.0181
  s2: 0.0129
  s3: 0.0015
  s4: -0.0019
  tilt_x: 0.0237
  tilt_y: 0.0377
  scale: 2.9418
  rotation: -0.3537
Best score: 8.4888: : 199iter [00:19, 11.84iter/s]Best score: 8.4888: : 200iter [00:19, 11.84iter/s]Best score: 8.4888: : 201iter [00:19,  7.65iter/s]Best score: 8.4888: : 201iter [00:19,  7.65iter/s]Best score: 8.4888: : 202iter [00:19,  7.65iter/s]Best score: 8.4888: : 203iter [00:19,  8.58iter/s]Best score: 8.4888: : 203iter [00:20,  8.58iter/s]Best score: 8.4888: : 204iter [00:20,  8.58iter/s]Best score: 8.4888: : 205iter [00:20,  9.36iter/s]Best score: 8.4888: : 205iter [00:20,  9.36iter/s]Best score: 8.4888: : 206iter [00:20,  9.36iter/s]Best score: 8.4888: : 207iter [00:20, 10.02iter/s]Best score: 8.4888: : 207iter [00:20, 10.02iter/s]Best score: 8.4888: : 208iter [00:20, 10.02iter/s]Best score: 8.4888: : 209iter [00:20, 10.47iter/s]Best score: 7.0385: : 209iter [00:20, 10.47iter/s]Best score: 7.0385: : 210iter [00:20, 10.47iter/s]Best score: 7.0385: : 211iter [00:20, 10.73iter/s]Best score: 7.0385: : 211iter [00:20, 10.73iter/s]Best score: 7.0385: : 212iter [00:20, 10.73iter/s]Best score: 7.0385: : 213iter [00:20, 11.07iter/s]Best score: 7.0385: : 213iter [00:20, 11.07iter/s]Best score: 7.0385: : 214iter [00:20, 11.07iter/s]Best score: 7.0385: : 215iter [00:20, 11.33iter/s]Best score: 7.0385: : 215iter [00:21, 11.33iter/s]Best score: 7.0385: : 216iter [00:21, 11.33iter/s]Best score: 7.0385: : 217iter [00:21, 11.54iter/s]Best score: 7.0385: : 217iter [00:21, 11.54iter/s]Best score: 7.0385: : 218iter [00:21, 11.54iter/s]Best score: 7.0385: : 219iter [00:21, 11.71iter/s]Best score: 6.2627: : 219iter [00:21, 11.71iter/s]Best score: 6.2627: : 220iter [00:21, 11.71iter/s]Best score: 6.2627: : 221iter [00:21, 11.59iter/s]Best score: 6.2627: : 221iter [00:21, 11.59iter/s]Best score: 6.2627: : 222iter [00:21, 11.59iter/s]Best score: 6.2627: : 223iter [00:21, 11.70iter/s]Best score: 6.2627: : 223iter [00:21, 11.70iter/s]Best score: 6.2627: : 224iter [00:21, 11.70iter/s]Best score: 6.2627: : 225iter [00:21, 11.81iter/s]Best score: 6.2627: : 225iter [00:21, 11.81iter/s]Best score: 6.2627: : 226iter [00:21, 11.81iter/s]Best score: 6.2627: : 227iter [00:21, 11.83iter/s]Best score: 6.2627: : 227iter [00:22, 11.83iter/s]Best score: 6.2627: : 228iter [00:22, 11.83iter/s]Best score: 6.2627: : 229iter [00:22, 11.90iter/s]Best score: 6.2627: : 229iter [00:22, 11.90iter/s]Best score: 6.2627: : 230iter [00:22, 11.90iter/s]Best score: 6.2627: : 231iter [00:22, 11.67iter/s]Best score: 6.2627: : 231iter [00:22, 11.67iter/s]Best score: 6.2627: : 232iter [00:22, 11.67iter/s]Best score: 6.2627: : 233iter [00:22, 11.77iter/s]Best score: 6.2627: : 233iter [00:22, 11.77iter/s]Best score: 6.2627: : 234iter [00:22, 11.77iter/s]Best score: 6.2627: : 235iter [00:22, 11.84iter/s]Best score: 6.2627: : 235iter [00:22, 11.84iter/s]Best score: 6.2627: : 236iter [00:22, 11.84iter/s]Best score: 6.2627: : 237iter [00:22, 11.88iter/s]Best score: 6.2627: : 237iter [00:22, 11.88iter/s]Best score: 6.2627: : 238iter [00:22, 11.88iter/s]Best score: 6.2627: : 239iter [00:22, 11.89iter/s]Best score: 5.4289: : 239iter [00:23, 11.89iter/s]Best score: 5.4289: : 240iter [00:23, 11.89iter/s]Best score: 5.4289: : 241iter [00:23, 11.71iter/s]Best score: 5.4289: : 241iter [00:23, 11.71iter/s]Best score: 5.4289: : 242iter [00:23, 11.71iter/s]Best score: 5.4289: : 243iter [00:23, 11.81iter/s]Best score: 5.4289: : 243iter [00:23, 11.81iter/s]Best score: 5.4289: : 244iter [00:23, 11.81iter/s]Best score: 5.4289: : 245iter [00:23, 11.83iter/s]Best score: 5.4289: : 245iter [00:23, 11.83iter/s]Best score: 5.4289: : 246iter [00:23, 11.83iter/s]Best score: 5.4289: : 247iter [00:23, 11.82iter/s]Best score: 5.4289: : 247iter [00:23, 11.82iter/s]Best score: 5.4289: : 248iter [00:23, 11.82iter/s]Best score: 5.4289: : 249iter [00:23, 11.78iter/s]
[ITER 250] Best: 5.4289
[PARAMS] Current best parameters:
  fx: 520.7935
  fy: 522.9115
  px: 995.5973
  py: 770.0410
  k1: 0.06565740
  k2: -0.04847729
  k3: -0.00156025
  k4: 0.00612865
  p1: 0.01793720
  p2: -0.00080595
  p3: 0.04161778
  p4: -0.01895364
  s1: -0.0431
  s2: 0.0256
  s3: -0.0143
  s4: -0.0074
  tilt_x: 0.0182
  tilt_y: 0.0305
  scale: 2.9344
  rotation: -0.2325
Best score: 5.4289: : 249iter [00:23, 11.78iter/s]Best score: 5.4289: : 250iter [00:24, 11.78iter/s]Best score: 5.4289: : 251iter [00:24,  7.63iter/s]Best score: 5.4289: : 251iter [00:24,  7.63iter/s]Best score: 5.4289: : 252iter [00:24,  7.63iter/s]Best score: 5.4289: : 253iter [00:24,  8.53iter/s]Best score: 5.4289: : 253iter [00:24,  8.53iter/s]Best score: 5.4289: : 254iter [00:24,  8.53iter/s]Best score: 5.4289: : 255iter [00:24,  9.32iter/s]Best score: 5.4289: : 255iter [00:24,  9.32iter/s]Best score: 5.4289: : 256iter [00:24,  9.32iter/s]Best score: 5.4289: : 257iter [00:24,  9.99iter/s]Best score: 5.4289: : 257iter [00:24,  9.99iter/s]Best score: 5.4289: : 258iter [00:24,  9.99iter/s]Best score: 5.4289: : 259iter [00:24, 10.55iter/s]Best score: 5.1481: : 259iter [00:25, 10.55iter/s]Best score: 5.1481: : 260iter [00:25, 10.55iter/s]Best score: 5.1481: : 261iter [00:25, 10.79iter/s]Best score: 5.1481: : 261iter [00:25, 10.79iter/s]Best score: 5.1481: : 262iter [00:25, 10.79iter/s]Best score: 5.1481: : 263iter [00:25, 11.14iter/s]Best score: 5.1481: : 263iter [00:25, 11.14iter/s]Best score: 5.1481: : 264iter [00:25, 11.14iter/s]Best score: 5.1481: : 265iter [00:25, 11.41iter/s]Best score: 5.1481: : 265iter [00:25, 11.41iter/s]Best score: 5.1481: : 266iter [00:25, 11.41iter/s]Best score: 5.1481: : 267iter [00:25, 11.61iter/s]Best score: 5.1481: : 267iter [00:25, 11.61iter/s]Best score: 5.1481: : 268iter [00:25, 11.61iter/s]Best score: 5.1481: : 269iter [00:25, 11.65iter/s]Best score: 4.2637: : 269iter [00:26, 11.65iter/s]Best score: 4.2637: : 270iter [00:26, 11.65iter/s]Best score: 4.2637: : 271iter [00:26,  9.21iter/s]Best score: 4.2637: : 271iter [00:26,  9.21iter/s]Best score: 4.2637: : 272iter [00:26,  9.21iter/s]Best score: 4.2637: : 273iter [00:26,  9.92iter/s]Best score: 4.2637: : 273iter [00:26,  9.92iter/s]Best score: 4.2637: : 274iter [00:26,  9.92iter/s]Best score: 4.2637: : 275iter [00:26, 10.49iter/s]Best score: 4.2637: : 275iter [00:26, 10.49iter/s]Best score: 4.2637: : 276iter [00:26, 10.49iter/s]Best score: 4.2637: : 277iter [00:26, 10.86iter/s]Best score: 4.2637: : 277iter [00:26, 10.86iter/s]Best score: 4.2637: : 278iter [00:26, 10.86iter/s]Best score: 4.2637: : 279iter [00:26, 11.16iter/s]Best score: 1.5695: : 279iter [00:26, 11.16iter/s]Best score: 1.5695: : 280iter [00:26, 11.16iter/s]Best score: 1.5695: : 281iter [00:26, 11.22iter/s]Best score: 1.5695: : 281iter [00:27, 11.22iter/s]Best score: 1.5695: : 282iter [00:27, 11.22iter/s]Best score: 1.5695: : 283iter [00:27, 11.47iter/s]Best score: 1.5695: : 283iter [00:27, 11.47iter/s]Best score: 1.5695: : 284iter [00:27, 11.47iter/s]Best score: 1.5695: : 285iter [00:27, 11.64iter/s]Best score: 1.5695: : 285iter [00:27, 11.64iter/s]Best score: 1.5695: : 286iter [00:27, 11.64iter/s]Best score: 1.5695: : 287iter [00:27, 11.74iter/s]Best score: 1.5695: : 287iter [00:27, 11.74iter/s]Best score: 1.5695: : 288iter [00:27, 11.74iter/s]Best score: 1.5695: : 289iter [00:27, 11.83iter/s]Best score: 1.5695: : 289iter [00:27, 11.83iter/s]Best score: 1.5695: : 290iter [00:27, 11.83iter/s]Best score: 1.5695: : 291iter [00:27, 11.70iter/s]Best score: 1.5695: : 291iter [00:27, 11.70iter/s]Best score: 1.5695: : 292iter [00:27, 11.70iter/s]Best score: 1.5695: : 293iter [00:27, 11.78iter/s]Best score: 1.5695: : 293iter [00:28, 11.78iter/s]Best score: 1.5695: : 294iter [00:28, 11.78iter/s]Best score: 1.5695: : 295iter [00:28, 11.83iter/s]Best score: 1.5695: : 295iter [00:28, 11.83iter/s]Best score: 1.5695: : 296iter [00:28, 11.83iter/s]Best score: 1.5695: : 297iter [00:28, 11.88iter/s]Best score: 1.5695: : 297iter [00:28, 11.88iter/s]Best score: 1.5695: : 298iter [00:28, 11.88iter/s]Best score: 1.5695: : 299iter [00:28, 11.93iter/s]
[ITER 300] Best: 1.4408
[PARAMS] Current best parameters:
  fx: 567.5437
  fy: 567.8964
  px: 988.6355
  py: 766.3777
  k1: -0.02946395
  k2: 0.02867566
  k3: -0.04032288
  k4: 0.01325941
  p1: -0.00592718
  p2: -0.02911396
  p3: 0.02638685
  p4: -0.01179563
  s1: -0.0214
  s2: 0.0201
  s3: -0.0008
  s4: 0.0095
  tilt_x: 0.0454
  tilt_y: 0.0020
  scale: 2.9631
  rotation: -0.0242
Best score: 1.4408: : 299iter [00:28, 11.93iter/s]Best score: 1.4408: : 300iter [00:28, 11.93iter/s]Best score: 1.4408: : 301iter [00:28,  7.65iter/s]Best score: 1.4408: : 301iter [00:29,  7.65iter/s]Best score: 1.4408: : 302iter [00:29,  7.65iter/s]Best score: 1.4408: : 303iter [00:29,  8.55iter/s]Best score: 1.4408: : 303iter [00:29,  8.55iter/s]Best score: 1.4408: : 304iter [00:29,  8.55iter/s]Best score: 1.4408: : 305iter [00:29,  9.33iter/s]Best score: 1.4408: : 305iter [00:29,  9.33iter/s]Best score: 1.4408: : 306iter [00:29,  9.33iter/s]Best score: 1.4408: : 307iter [00:29,  9.97iter/s]Best score: 1.4408: : 307iter [00:29,  9.97iter/s]Best score: 1.4408: : 308iter [00:29,  9.97iter/s]Best score: 1.4408: : 309iter [00:29, 10.51iter/s]Best score: 1.2042: : 309iter [00:29, 10.51iter/s]Best score: 1.2042: : 310iter [00:29, 10.51iter/s]Best score: 1.2042: : 311iter [00:29, 10.75iter/s]Best score: 1.2042: : 311iter [00:29, 10.75iter/s]Best score: 1.2042: : 312iter [00:29, 10.75iter/s]Best score: 1.2042: : 313iter [00:29, 11.12iter/s]Best score: 1.2042: : 313iter [00:30, 11.12iter/s]Best score: 1.2042: : 314iter [00:30, 11.12iter/s]Best score: 1.2042: : 315iter [00:30, 11.37iter/s]Best score: 1.2042: : 315iter [00:30, 11.37iter/s]Best score: 1.2042: : 316iter [00:30, 11.37iter/s]Best score: 1.2042: : 317iter [00:30, 11.56iter/s]Best score: 1.2042: : 317iter [00:30, 11.56iter/s]Best score: 1.2042: : 318iter [00:30, 11.56iter/s]Best score: 1.2042: : 319iter [00:30, 11.70iter/s]Best score: 1.2042: : 319iter [00:30, 11.70iter/s]Best score: 1.2042: : 320iter [00:30, 11.70iter/s]Best score: 1.2042: : 321iter [00:30, 11.61iter/s]Best score: 1.2042: : 321iter [00:30, 11.61iter/s]Best score: 1.2042: : 322iter [00:30, 11.61iter/s]Best score: 1.2042: : 323iter [00:30, 11.67iter/s]Best score: 1.2042: : 323iter [00:30, 11.67iter/s]Best score: 1.2042: : 324iter [00:31, 11.67iter/s]Best score: 1.2042: : 325iter [00:31, 11.72iter/s]Best score: 1.2042: : 325iter [00:31, 11.72iter/s]Best score: 1.2042: : 326iter [00:31, 11.72iter/s]Best score: 1.2042: : 327iter [00:31, 11.77iter/s]Best score: 1.2042: : 327iter [00:31, 11.77iter/s]Best score: 1.2042: : 328iter [00:31, 11.77iter/s]Best score: 1.2042: : 329iter [00:31, 11.81iter/s]Best score: 1.1428: : 329iter [00:31, 11.81iter/s]Best score: 1.1428: : 330iter [00:31, 11.81iter/s]Best score: 1.1428: : 331iter [00:31, 11.69iter/s]Best score: 1.1428: : 331iter [00:31, 11.69iter/s]Best score: 1.1428: : 332iter [00:31, 11.69iter/s]Best score: 1.1428: : 333iter [00:31, 11.77iter/s]Best score: 1.1428: : 333iter [00:31, 11.77iter/s]Best score: 1.1428: : 334iter [00:31, 11.77iter/s]Best score: 1.1428: : 335iter [00:31, 11.80iter/s]Best score: 1.1428: : 335iter [00:31, 11.80iter/s]Best score: 1.1428: : 336iter [00:32, 11.80iter/s]Best score: 1.1428: : 337iter [00:32, 11.78iter/s]Best score: 1.1428: : 337iter [00:32, 11.78iter/s]Best score: 1.1428: : 338iter [00:32, 11.78iter/s]Best score: 1.1428: : 339iter [00:32, 11.78iter/s]Best score: 0.8784: : 339iter [00:32, 11.78iter/s]Best score: 0.8784: : 340iter [00:32, 11.78iter/s]Best score: 0.8784: : 341iter [00:32, 11.63iter/s]Best score: 0.8784: : 341iter [00:32, 11.63iter/s]Best score: 0.8784: : 342iter [00:32, 11.63iter/s]Best score: 0.8784: : 343iter [00:32, 11.71iter/s]Best score: 0.8784: : 343iter [00:32, 11.71iter/s]Best score: 0.8784: : 344iter [00:32, 11.71iter/s]Best score: 0.8784: : 345iter [00:32, 11.82iter/s]Best score: 0.8784: : 345iter [00:32, 11.82iter/s]Best score: 0.8784: : 346iter [00:32, 11.82iter/s]Best score: 0.8784: : 347iter [00:32, 11.86iter/s]Best score: 0.8784: : 347iter [00:32, 11.86iter/s]Best score: 0.8784: : 348iter [00:33, 11.86iter/s]Best score: 0.8784: : 349iter [00:33, 11.90iter/s]
[ITER 350] Best: 0.8784
[PARAMS] Current best parameters:
  fx: 558.2494
  fy: 559.3224
  px: 993.6605
  py: 769.6080
  k1: -0.05899148
  k2: 0.05008876
  k3: -0.02917629
  k4: 0.00620418
  p1: -0.00648332
  p2: -0.01923660
  p3: 0.03061742
  p4: -0.01171154
  s1: -0.0222
  s2: 0.0125
  s3: -0.0065
  s4: 0.0173
  tilt_x: 0.0597
  tilt_y: -0.0022
  scale: 3.0511
  rotation: -0.0945
Best score: 0.8784: : 349iter [00:33, 11.90iter/s]Best score: 0.8784: : 350iter [00:33, 11.90iter/s]Best score: 0.8784: : 351iter [00:33,  7.62iter/s]Best score: 0.8784: : 351iter [00:33,  7.62iter/s]Best score: 0.8784: : 352iter [00:33,  7.62iter/s]Best score: 0.8784: : 353iter [00:33,  8.57iter/s]Best score: 0.8784: : 353iter [00:33,  8.57iter/s]Best score: 0.8784: : 354iter [00:33,  8.57iter/s]Best score: 0.8784: : 355iter [00:33,  9.39iter/s]Best score: 0.8784: : 355iter [00:33,  9.39iter/s]Best score: 0.8784: : 356iter [00:34,  9.39iter/s]Best score: 0.8784: : 357iter [00:34, 10.02iter/s]Best score: 0.8784: : 357iter [00:34, 10.02iter/s]Best score: 0.8784: : 358iter [00:34, 10.02iter/s]Best score: 0.8784: : 359iter [00:34, 10.56iter/s]Best score: 0.6684: : 359iter [00:34, 10.56iter/s]Best score: 0.6684: : 360iter [00:34, 10.56iter/s]Best score: 0.6684: : 361iter [00:34, 10.80iter/s]Best score: 0.6684: : 361iter [00:34, 10.80iter/s]Best score: 0.6684: : 362iter [00:34, 10.80iter/s]Best score: 0.6684: : 363iter [00:34, 11.17iter/s]Best score: 0.6684: : 363iter [00:34, 11.17iter/s]Best score: 0.6684: : 364iter [00:34, 11.17iter/s]Best score: 0.6684: : 365iter [00:34, 11.34iter/s]Best score: 0.6684: : 365iter [00:34, 11.34iter/s]Best score: 0.6684: : 366iter [00:34, 11.34iter/s]Best score: 0.6684: : 367iter [00:34, 11.55iter/s]Best score: 0.6684: : 367iter [00:34, 11.55iter/s]Best score: 0.6684: : 368iter [00:35, 11.55iter/s]Best score: 0.6684: : 369iter [00:35, 11.71iter/s]Best score: 0.6684: : 369iter [00:35, 11.71iter/s]Best score: 0.6684: : 370iter [00:35, 11.71iter/s]Best score: 0.6684: : 371iter [00:35, 11.62iter/s]Best score: 0.6684: : 371iter [00:35, 11.62iter/s]Best score: 0.6684: : 372iter [00:35, 11.62iter/s]Best score: 0.6684: : 373iter [00:35, 11.72iter/s]Best score: 0.6684: : 373iter [00:35, 11.72iter/s]Best score: 0.6684: : 374iter [00:35, 11.72iter/s]Best score: 0.6684: : 375iter [00:35, 11.78iter/s]Best score: 0.6684: : 375iter [00:35, 11.78iter/s]Best score: 0.6684: : 376iter [00:35, 11.78iter/s]Best score: 0.6684: : 377iter [00:35, 11.85iter/s]Best score: 0.6684: : 377iter [00:35, 11.85iter/s]Best score: 0.6684: : 378iter [00:35, 11.85iter/s]Best score: 0.6684: : 379iter [00:35, 11.88iter/s]Best score: 0.6162: : 379iter [00:35, 11.88iter/s]Best score: 0.6162: : 380iter [00:36, 11.88iter/s]Best score: 0.6162: : 381iter [00:36, 11.72iter/s]Best score: 0.6162: : 381iter [00:36, 11.72iter/s]Best score: 0.6162: : 382iter [00:36, 11.72iter/s]Best score: 0.6162: : 383iter [00:36, 11.83iter/s]Best score: 0.6162: : 383iter [00:36, 11.83iter/s]Best score: 0.6162: : 384iter [00:36, 11.83iter/s]Best score: 0.6162: : 385iter [00:36, 11.79iter/s]Best score: 0.6162: : 385iter [00:36, 11.79iter/s]Best score: 0.6162: : 386iter [00:36, 11.79iter/s]Best score: 0.6162: : 387iter [00:36, 11.84iter/s]Best score: 0.6162: : 387iter [00:36, 11.84iter/s]Best score: 0.6162: : 388iter [00:36, 11.84iter/s]Best score: 0.6162: : 389iter [00:36, 11.90iter/s]Best score: 0.5543: : 389iter [00:36, 11.90iter/s]Best score: 0.5543: : 390iter [00:36, 11.90iter/s]Best score: 0.5543: : 391iter [00:36, 11.76iter/s]Best score: 0.5543: : 391iter [00:36, 11.76iter/s]Best score: 0.5543: : 392iter [00:37, 11.76iter/s]Best score: 0.5543: : 393iter [00:37, 11.85iter/s]Best score: 0.5543: : 393iter [00:37, 11.85iter/s]Best score: 0.5543: : 394iter [00:37, 11.85iter/s]Best score: 0.5543: : 395iter [00:37, 11.93iter/s]Best score: 0.5543: : 395iter [00:37, 11.93iter/s]Best score: 0.5543: : 396iter [00:37, 11.93iter/s]Best score: 0.5543: : 397iter [00:37, 11.97iter/s]Best score: 0.5543: : 397iter [00:37, 11.97iter/s]Best score: 0.5543: : 398iter [00:37, 11.97iter/s]Best score: 0.5543: : 399iter [00:37, 12.00iter/s]
[ITER 400] Best: 0.5380
[PARAMS] Current best parameters:
  fx: 551.9647
  fy: 552.8034
  px: 997.3571
  py: 771.2578
  k1: -0.02051538
  k2: 0.00463423
  k3: -0.00653250
  k4: 0.00228708
  p1: 0.00253656
  p2: -0.00973319
  p3: 0.03283716
  p4: -0.01734021
  s1: -0.0343
  s2: 0.0200
  s3: -0.0072
  s4: 0.0152
  tilt_x: 0.0483
  tilt_y: -0.0050
  scale: 3.0031
  rotation: -0.0735
Best score: 0.5380: : 399iter [00:37, 12.00iter/s]Best score: 0.5380: : 400iter [00:38, 12.00iter/s]Best score: 0.5380: : 401iter [00:38,  7.70iter/s]Best score: 0.5380: : 401iter [00:38,  7.70iter/s]Best score: 0.5380: : 402iter [00:38,  7.70iter/s]Best score: 0.5380: : 403iter [00:38,  8.64iter/s]Best score: 0.5380: : 403iter [00:38,  8.64iter/s]Best score: 0.5380: : 404iter [00:38,  8.64iter/s]Best score: 0.5380: : 405iter [00:38,  9.45iter/s]Best score: 0.5380: : 405iter [00:38,  9.45iter/s]Best score: 0.5380: : 406iter [00:38,  9.45iter/s]Best score: 0.5380: : 407iter [00:38, 10.07iter/s]Best score: 0.5380: : 407iter [00:38, 10.07iter/s]Best score: 0.5380: : 408iter [00:38, 10.07iter/s]Best score: 0.5380: : 409iter [00:38, 10.56iter/s]Best score: 0.4857: : 409iter [00:38, 10.56iter/s]Best score: 0.4857: : 410iter [00:38, 10.56iter/s]Best score: 0.4857: : 411iter [00:38, 10.79iter/s]Best score: 0.4857: : 411iter [00:38, 10.79iter/s]Best score: 0.4857: : 412iter [00:39, 10.79iter/s]Best score: 0.4857: : 413iter [00:39, 11.13iter/s]Best score: 0.4857: : 413iter [00:39, 11.13iter/s]Best score: 0.4857: : 414iter [00:39, 11.13iter/s]Best score: 0.4857: : 415iter [00:39, 11.34iter/s]Best score: 0.4857: : 415iter [00:39, 11.34iter/s]Best score: 0.4857: : 416iter [00:39, 11.34iter/s]Best score: 0.4857: : 417iter [00:39, 11.48iter/s]Best score: 0.4857: : 417iter [00:39, 11.48iter/s]Best score: 0.4857: : 418iter [00:39, 11.48iter/s]Best score: 0.4857: : 419iter [00:39, 11.57iter/s]Best score: 0.4661: : 419iter [00:39, 11.57iter/s]Best score: 0.4661: : 420iter [00:39, 11.57iter/s]Best score: 0.4661: : 421iter [00:39, 11.48iter/s]Best score: 0.4661: : 421iter [00:39, 11.48iter/s]Best score: 0.4661: : 422iter [00:39, 11.48iter/s]Best score: 0.4661: : 423iter [00:39, 11.64iter/s]Best score: 0.4661: : 423iter [00:39, 11.64iter/s]Best score: 0.4661: : 424iter [00:40, 11.64iter/s]Best score: 0.4661: : 425iter [00:40, 11.72iter/s]Best score: 0.4661: : 425iter [00:40, 11.72iter/s]Best score: 0.4661: : 426iter [00:40, 11.72iter/s]Best score: 0.4661: : 427iter [00:40, 11.78iter/s]Best score: 0.4661: : 427iter [00:40, 11.78iter/s]Best score: 0.4661: : 428iter [00:40, 11.78iter/s]Best score: 0.4661: : 429iter [00:40, 11.79iter/s]Best score: 0.4136: : 429iter [00:40, 11.79iter/s]Best score: 0.4136: : 430iter [00:40, 11.79iter/s]Best score: 0.4136: : 431iter [00:40, 11.66iter/s]Best score: 0.4136: : 431iter [00:40, 11.66iter/s]Best score: 0.4136: : 432iter [00:40, 11.66iter/s]Best score: 0.4136: : 433iter [00:40, 11.75iter/s]Best score: 0.4136: : 433iter [00:40, 11.75iter/s]Best score: 0.4136: : 434iter [00:40, 11.75iter/s]Best score: 0.4136: : 435iter [00:40, 11.83iter/s]Best score: 0.4136: : 435iter [00:40, 11.83iter/s]Best score: 0.4136: : 436iter [00:41, 11.83iter/s]Best score: 0.4136: : 437iter [00:41, 11.88iter/s]Best score: 0.4136: : 437iter [00:41, 11.88iter/s]Best score: 0.4136: : 438iter [00:41, 11.88iter/s]Best score: 0.4136: : 439iter [00:41, 11.88iter/s]Best score: 0.4052: : 439iter [00:41, 11.88iter/s]Best score: 0.4052: : 440iter [00:41, 11.88iter/s]Best score: 0.4052: : 441iter [00:41, 11.70iter/s]Best score: 0.4052: : 441iter [00:41, 11.70iter/s]Best score: 0.4052: : 442iter [00:41, 11.70iter/s]Best score: 0.4052: : 443iter [00:41, 11.74iter/s]Best score: 0.4052: : 443iter [00:41, 11.74iter/s]Best score: 0.4052: : 444iter [00:41, 11.74iter/s]Best score: 0.4052: : 445iter [00:41, 11.74iter/s]Best score: 0.4052: : 445iter [00:41, 11.74iter/s]Best score: 0.4052: : 446iter [00:41, 11.74iter/s]Best score: 0.4052: : 447iter [00:41, 11.77iter/s]Best score: 0.4052: : 447iter [00:42, 11.77iter/s]Best score: 0.4052: : 448iter [00:42, 11.77iter/s]Best score: 0.4052: : 449iter [00:42, 11.85iter/s]
[ITER 450] Best: 0.4000
[PARAMS] Current best parameters:
  fx: 547.6431
  fy: 547.5855
  px: 997.7377
  py: 764.1881
  k1: 0.00472702
  k2: -0.03883749
  k3: 0.02747912
  k4: -0.00702318
  p1: 0.00139165
  p2: -0.00256288
  p3: 0.02690810
  p4: -0.02595018
  s1: -0.0276
  s2: 0.0239
  s3: -0.0132
  s4: 0.0273
  tilt_x: 0.0173
  tilt_y: 0.0121
  scale: 2.9857
  rotation: -0.1059
Best score: 0.4000: : 449iter [00:42, 11.85iter/s]Best score: 0.4000: : 450iter [00:42, 11.85iter/s]Best score: 0.4000: : 451iter [00:42,  7.66iter/s]Best score: 0.4000: : 451iter [00:42,  7.66iter/s]Best score: 0.4000: : 452iter [00:42,  7.66iter/s]Best score: 0.4000: : 453iter [00:42,  8.60iter/s]Best score: 0.4000: : 453iter [00:42,  8.60iter/s]Best score: 0.4000: : 454iter [00:42,  8.60iter/s]Best score: 0.4000: : 455iter [00:42,  9.40iter/s]Best score: 0.4000: : 455iter [00:42,  9.40iter/s]Best score: 0.4000: : 456iter [00:43,  9.40iter/s]Best score: 0.4000: : 457iter [00:43, 10.06iter/s]Best score: 0.4000: : 457iter [00:43, 10.06iter/s]Best score: 0.4000: : 458iter [00:43, 10.06iter/s]Best score: 0.4000: : 459iter [00:43, 10.57iter/s]Best score: 0.3888: : 459iter [00:43, 10.57iter/s]Best score: 0.3888: : 460iter [00:43, 10.57iter/s]Best score: 0.3888: : 461iter [00:43, 10.80iter/s]Best score: 0.3888: : 461iter [00:43, 10.80iter/s]Best score: 0.3888: : 462iter [00:43, 10.80iter/s]Best score: 0.3888: : 463iter [00:43, 11.15iter/s]Best score: 0.3888: : 463iter [00:43, 11.15iter/s]Best score: 0.3888: : 464iter [00:43, 11.15iter/s]Best score: 0.3888: : 465iter [00:43, 11.31iter/s]Best score: 0.3888: : 465iter [00:43, 11.31iter/s]Best score: 0.3888: : 466iter [00:43, 11.31iter/s]Best score: 0.3888: : 467iter [00:43, 11.45iter/s]Best score: 0.3888: : 467iter [00:44, 11.45iter/s]Best score: 0.3888: : 468iter [00:44, 11.45iter/s]Best score: 0.3888: : 469iter [00:44, 11.54iter/s]Best score: 0.3888: : 469iter [00:44, 11.54iter/s]Best score: 0.3888: : 470iter [00:44, 11.54iter/s]Best score: 0.3888: : 471iter [00:44, 11.41iter/s]Best score: 0.3888: : 471iter [00:44, 11.41iter/s]Best score: 0.3888: : 472iter [00:44, 11.41iter/s]Best score: 0.3888: : 473iter [00:44, 11.57iter/s]Best score: 0.3888: : 473iter [00:44, 11.57iter/s]Best score: 0.3888: : 474iter [00:44, 11.57iter/s]Best score: 0.3888: : 475iter [00:44, 11.66iter/s]Best score: 0.3888: : 475iter [00:44, 11.66iter/s]Best score: 0.3888: : 476iter [00:44, 11.66iter/s]Best score: 0.3888: : 477iter [00:44, 11.75iter/s]Best score: 0.3888: : 477iter [00:44, 11.75iter/s]Best score: 0.3888: : 478iter [00:44, 11.75iter/s]Best score: 0.3888: : 479iter [00:44, 11.79iter/s]Best score: 0.3578: : 479iter [00:45, 11.79iter/s]Best score: 0.3578: : 480iter [00:45, 11.79iter/s]Best score: 0.3578: : 481iter [00:45, 11.64iter/s]Best score: 0.3578: : 481iter [00:45, 11.64iter/s]Best score: 0.3578: : 482iter [00:45, 11.64iter/s]Best score: 0.3578: : 483iter [00:45, 11.72iter/s]Best score: 0.3578: : 483iter [00:45, 11.72iter/s]Best score: 0.3578: : 484iter [00:45, 11.72iter/s]Best score: 0.3578: : 485iter [00:45, 11.78iter/s]Best score: 0.3578: : 485iter [00:45, 11.78iter/s]Best score: 0.3578: : 486iter [00:45, 11.78iter/s]Best score: 0.3578: : 487iter [00:45, 11.84iter/s]Best score: 0.3578: : 487iter [00:45, 11.84iter/s]Best score: 0.3578: : 488iter [00:45, 11.84iter/s]Best score: 0.3578: : 489iter [00:45, 11.86iter/s]Best score: 0.3578: : 489iter [00:45, 11.86iter/s]Best score: 0.3578: : 490iter [00:45, 11.86iter/s]Best score: 0.3578: : 491iter [00:45, 11.71iter/s]Best score: 0.3578: : 491iter [00:46, 11.71iter/s]Best score: 0.3578: : 492iter [00:46, 11.71iter/s]Best score: 0.3578: : 493iter [00:46, 11.78iter/s]Best score: 0.3578: : 493iter [00:46, 11.78iter/s]Best score: 0.3578: : 494iter [00:46, 11.78iter/s]Best score: 0.3578: : 495iter [00:46, 11.82iter/s]Best score: 0.3578: : 495iter [00:46, 11.82iter/s]Best score: 0.3578: : 496iter [00:46, 11.82iter/s]Best score: 0.3578: : 497iter [00:46, 11.88iter/s]Best score: 0.3578: : 497iter [00:46, 11.88iter/s]Best score: 0.3578: : 498iter [00:46, 11.88iter/s]Best score: 0.3578: : 499iter [00:46, 11.90iter/s]
[ITER 500] Best: 0.3578
[PARAMS] Current best parameters:
  fx: 556.5090
  fy: 556.3251
  px: 996.4633
  py: 765.7389
  k1: -0.01124020
  k2: -0.02648139
  k3: 0.01919430
  k4: -0.00464343
  p1: -0.00745423
  p2: -0.00813715
  p3: 0.03352789
  p4: -0.01928571
  s1: -0.0331
  s2: 0.0204
  s3: -0.0072
  s4: 0.0190
  tilt_x: 0.0414
  tilt_y: 0.0066
  scale: 2.9917
  rotation: -0.0388
Best score: 0.3578: : 499iter [00:46, 11.90iter/s]Best score: 0.3578: : 500iter [00:47, 11.90iter/s]Best score: 0.3578: : 501iter [00:47,  7.64iter/s]Best score: 0.3578: : 501iter [00:47,  7.64iter/s]Best score: 0.3578: : 502iter [00:47,  7.64iter/s]Best score: 0.3578: : 503iter [00:47,  8.57iter/s]Best score: 0.3578: : 503iter [00:47,  8.57iter/s]Best score: 0.3578: : 504iter [00:47,  8.57iter/s]Best score: 0.3578: : 505iter [00:47,  9.36iter/s]Best score: 0.3578: : 505iter [00:47,  9.36iter/s]Best score: 0.3578: : 506iter [00:47,  9.36iter/s]Best score: 0.3578: : 507iter [00:47, 10.01iter/s]Best score: 0.3578: : 507iter [00:47, 10.01iter/s]Best score: 0.3578: : 508iter [00:47, 10.01iter/s]Best score: 0.3578: : 509iter [00:47, 10.51iter/s]Best score: 0.3578: : 509iter [00:47, 10.51iter/s]Best score: 0.3578: : 510iter [00:47, 10.51iter/s]Best score: 0.3578: : 511iter [00:47, 10.75iter/s]Best score: 0.3578: : 511iter [00:48, 10.75iter/s]Best score: 0.3578: : 512iter [00:48, 10.75iter/s]Best score: 0.3578: : 513iter [00:48, 11.09iter/s]Best score: 0.3578: : 513iter [00:48, 11.09iter/s]Best score: 0.3578: : 514iter [00:48, 11.09iter/s]Best score: 0.3578: : 515iter [00:48, 11.26iter/s]Best score: 0.3578: : 515iter [00:48, 11.26iter/s]Best score: 0.3578: : 516iter [00:48, 11.26iter/s]Best score: 0.3578: : 517iter [00:48, 11.46iter/s]Best score: 0.3578: : 517iter [00:48, 11.46iter/s]Best score: 0.3578: : 518iter [00:48, 11.46iter/s]Best score: 0.3578: : 519iter [00:48, 11.59iter/s]Best score: 0.3518: : 519iter [00:48, 11.59iter/s]Best score: 0.3518: : 520iter [00:48, 11.59iter/s]Best score: 0.3518: : 521iter [00:48, 11.53iter/s]Best score: 0.3518: : 521iter [00:48, 11.53iter/s]Best score: 0.3518: : 522iter [00:48, 11.53iter/s]Best score: 0.3518: : 523iter [00:48, 11.68iter/s]Best score: 0.3518: : 523iter [00:49, 11.68iter/s]Best score: 0.3518: : 524iter [00:49, 11.68iter/s]Best score: 0.3518: : 525iter [00:49, 11.76iter/s]Best score: 0.3518: : 525iter [00:49, 11.76iter/s]Best score: 0.3518: : 526iter [00:49, 11.76iter/s]Best score: 0.3518: : 527iter [00:49, 11.85iter/s]Best score: 0.3518: : 527iter [00:49, 11.85iter/s]Best score: 0.3518: : 528iter [00:49, 11.85iter/s]Best score: 0.3518: : 529iter [00:49, 11.89iter/s]Best score: 0.3465: : 529iter [00:49, 11.89iter/s]Best score: 0.3465: : 530iter [00:49, 11.89iter/s]Best score: 0.3465: : 531iter [00:49, 11.65iter/s]Best score: 0.3465: : 531iter [00:49, 11.65iter/s]Best score: 0.3465: : 532iter [00:49, 11.65iter/s]Best score: 0.3465: : 533iter [00:49, 11.69iter/s]Best score: 0.3465: : 533iter [00:49, 11.69iter/s]Best score: 0.3465: : 534iter [00:49, 11.69iter/s]Best score: 0.3465: : 535iter [00:49, 11.73iter/s]Best score: 0.3465: : 535iter [00:50, 11.73iter/s]Best score: 0.3465: : 536iter [00:50, 11.73iter/s]Best score: 0.3465: : 537iter [00:50, 11.76iter/s]Best score: 0.3465: : 537iter [00:50, 11.76iter/s]Best score: 0.3465: : 538iter [00:50, 11.76iter/s]Best score: 0.3465: : 539iter [00:50, 11.78iter/s]Best score: 0.3303: : 539iter [00:50, 11.78iter/s]Best score: 0.3303: : 540iter [00:50, 11.78iter/s]Best score: 0.3303: : 541iter [00:50, 11.66iter/s]Best score: 0.3303: : 541iter [00:50, 11.66iter/s]Best score: 0.3303: : 542iter [00:50, 11.66iter/s]Best score: 0.3303: : 543iter [00:50, 11.74iter/s]Best score: 0.3303: : 543iter [00:50, 11.74iter/s]Best score: 0.3303: : 544iter [00:50, 11.74iter/s]Best score: 0.3303: : 545iter [00:50, 11.81iter/s]Best score: 0.3303: : 545iter [00:50, 11.81iter/s]Best score: 0.3303: : 546iter [00:51, 11.81iter/s]Best score: 0.3303: : 547iter [00:51, 11.88iter/s]Best score: 0.3303: : 547iter [00:51, 11.88iter/s]Best score: 0.3303: : 548iter [00:51, 11.88iter/s]Best score: 0.3303: : 549iter [00:51, 11.90iter/s]
[ITER 550] Best: 0.3303
[PARAMS] Current best parameters:
  fx: 558.8519
  fy: 558.5657
  px: 993.5514
  py: 760.0321
  k1: -0.00082040
  k2: -0.05110176
  k3: 0.03650423
  k4: -0.00881948
  p1: -0.00678958
  p2: -0.00986198
  p3: 0.02383203
  p4: -0.01900378
  s1: -0.0268
  s2: 0.0214
  s3: -0.0052
  s4: 0.0204
  tilt_x: 0.0107
  tilt_y: 0.0158
  scale: 2.9818
  rotation: -0.0382
Best score: 0.3303: : 549iter [00:51, 11.90iter/s]Best score: 0.3303: : 550iter [00:51, 11.90iter/s]Best score: 0.3303: : 551iter [00:51,  7.66iter/s]Best score: 0.3303: : 551iter [00:51,  7.66iter/s]Best score: 0.3303: : 552iter [00:51,  7.66iter/s]Best score: 0.3303: : 553iter [00:51,  8.60iter/s]Best score: 0.3303: : 553iter [00:51,  8.60iter/s]Best score: 0.3303: : 554iter [00:51,  8.60iter/s]Best score: 0.3303: : 555iter [00:51,  9.34iter/s]Best score: 0.3303: : 555iter [00:52,  9.34iter/s]Best score: 0.3303: : 556iter [00:52,  9.34iter/s]Best score: 0.3303: : 557iter [00:52, 10.01iter/s]Best score: 0.3303: : 557iter [00:52, 10.01iter/s]Best score: 0.3303: : 558iter [00:52, 10.01iter/s]Best score: 0.3303: : 559iter [00:52, 10.55iter/s]Best score: 0.3280: : 559iter [00:52, 10.55iter/s]Best score: 0.3280: : 560iter [00:52, 10.55iter/s]Best score: 0.3280: : 561iter [00:52, 10.77iter/s]Best score: 0.3280: : 561iter [00:52, 10.77iter/s]Best score: 0.3280: : 562iter [00:52, 10.77iter/s]Best score: 0.3280: : 563iter [00:52, 11.13iter/s]Best score: 0.3280: : 563iter [00:52, 11.13iter/s]Best score: 0.3280: : 564iter [00:52, 11.13iter/s]Best score: 0.3280: : 565iter [00:52, 11.39iter/s]Best score: 0.3280: : 565iter [00:52, 11.39iter/s]Best score: 0.3280: : 566iter [00:52, 11.39iter/s]Best score: 0.3280: : 567iter [00:52, 11.55iter/s]Best score: 0.3280: : 567iter [00:53, 11.55iter/s]Best score: 0.3280: : 568iter [00:53, 11.55iter/s]Best score: 0.3280: : 569iter [00:53, 11.67iter/s]Best score: 0.3181: : 569iter [00:53, 11.67iter/s]Best score: 0.3181: : 570iter [00:53, 11.67iter/s]Best score: 0.3181: : 571iter [00:53, 11.59iter/s]Best score: 0.3181: : 571iter [00:53, 11.59iter/s]Best score: 0.3181: : 572iter [00:53, 11.59iter/s]Best score: 0.3181: : 573iter [00:53, 11.66iter/s]Best score: 0.3181: : 573iter [00:53, 11.66iter/s]Best score: 0.3181: : 574iter [00:53, 11.66iter/s]Best score: 0.3181: : 575iter [00:53, 11.73iter/s]Best score: 0.3181: : 575iter [00:53, 11.73iter/s]Best score: 0.3181: : 576iter [00:53, 11.73iter/s]Best score: 0.3181: : 577iter [00:53, 11.79iter/s]Best score: 0.3181: : 577iter [00:53, 11.79iter/s]Best score: 0.3181: : 578iter [00:54, 11.79iter/s]Best score: 0.3181: : 579iter [00:54, 11.86iter/s]Best score: 0.3181: : 579iter [00:54, 11.86iter/s]Best score: 0.3181: : 580iter [00:54, 11.86iter/s]Best score: 0.3181: : 581iter [00:54, 11.69iter/s]Best score: 0.3181: : 581iter [00:54, 11.69iter/s]Best score: 0.3181: : 582iter [00:54, 11.69iter/s]Best score: 0.3181: : 583iter [00:54, 11.78iter/s]Best score: 0.3181: : 583iter [00:54, 11.78iter/s]Best score: 0.3181: : 584iter [00:54, 11.78iter/s]Best score: 0.3181: : 585iter [00:54, 11.88iter/s]Best score: 0.3181: : 585iter [00:54, 11.88iter/s]Best score: 0.3181: : 586iter [00:54, 11.88iter/s]Best score: 0.3181: : 587iter [00:54, 11.93iter/s]Best score: 0.3181: : 587iter [00:54, 11.93iter/s]Best score: 0.3181: : 588iter [00:54, 11.93iter/s]Best score: 0.3181: : 589iter [00:54, 11.96iter/s]Best score: 0.3181: : 589iter [00:54, 11.96iter/s]Best score: 0.3181: : 590iter [00:55, 11.96iter/s]Best score: 0.3181: : 591iter [00:55, 11.81iter/s]Best score: 0.3181: : 591iter [00:55, 11.81iter/s]Best score: 0.3181: : 592iter [00:55, 11.81iter/s]Best score: 0.3181: : 593iter [00:55, 11.87iter/s]Best score: 0.3181: : 593iter [00:55, 11.87iter/s]Best score: 0.3181: : 594iter [00:55, 11.87iter/s]Best score: 0.3181: : 595iter [00:55, 11.94iter/s]Best score: 0.3181: : 595iter [00:55, 11.94iter/s]Best score: 0.3181: : 596iter [00:55, 11.94iter/s]Best score: 0.3181: : 597iter [00:55, 11.97iter/s]Best score: 0.3181: : 597iter [00:55, 11.97iter/s]Best score: 0.3181: : 598iter [00:55, 11.97iter/s]Best score: 0.3181: : 599iter [00:55, 12.00iter/s]
[ITER 600] Best: 0.3135
[PARAMS] Current best parameters:
  fx: 549.4488
  fy: 549.2788
  px: 998.5718
  py: 761.7635
  k1: 0.01377344
  k2: -0.05899810
  k3: 0.03996804
  k4: -0.00948453
  p1: -0.00326546
  p2: -0.00484403
  p3: 0.02404814
  p4: -0.02373651
  s1: -0.0259
  s2: 0.0244
  s3: -0.0073
  s4: 0.0243
  tilt_x: 0.0134
  tilt_y: -0.0001
  scale: 2.9813
  rotation: -0.0759
Best score: 0.3135: : 599iter [00:55, 12.00iter/s]Best score: 0.3135: : 600iter [00:56, 12.00iter/s]Best score: 0.3135: : 601iter [00:56,  7.70iter/s]Best score: 0.3135: : 601iter [00:56,  7.70iter/s]Best score: 0.3135: : 602iter [00:56,  7.70iter/s]Best score: 0.3135: : 603iter [00:56,  8.60iter/s]Best score: 0.3135: : 603iter [00:56,  8.60iter/s]Best score: 0.3135: : 604iter [00:56,  8.60iter/s]Best score: 0.3135: : 605iter [00:56,  9.42iter/s]Best score: 0.3135: : 605iter [00:56,  9.42iter/s]Best score: 0.3135: : 606iter [00:56,  9.42iter/s]Best score: 0.3135: : 607iter [00:56, 10.09iter/s]Best score: 0.3135: : 607iter [00:56, 10.09iter/s]Best score: 0.3135: : 608iter [00:56, 10.09iter/s]Best score: 0.3135: : 609iter [00:56, 10.58iter/s]Best score: 0.3073: : 609iter [00:56, 10.58iter/s]Best score: 0.3073: : 610iter [00:57, 10.58iter/s]Best score: 0.3073: : 611iter [00:57, 10.79iter/s]Best score: 0.3073: : 611iter [00:57, 10.79iter/s]Best score: 0.3073: : 612iter [00:57, 10.79iter/s]Best score: 0.3073: : 613iter [00:57, 11.13iter/s]Best score: 0.3073: : 613iter [00:57, 11.13iter/s]Best score: 0.3073: : 614iter [00:57, 11.13iter/s]Best score: 0.3073: : 615iter [00:57, 11.38iter/s]Best score: 0.3073: : 615iter [00:57, 11.38iter/s]Best score: 0.3073: : 616iter [00:57, 11.38iter/s]Best score: 0.3073: : 617iter [00:57, 11.57iter/s]Best score: 0.3073: : 617iter [00:57, 11.57iter/s]Best score: 0.3073: : 618iter [00:57, 11.57iter/s]Best score: 0.3073: : 619iter [00:57, 11.67iter/s]Best score: 0.2871: : 619iter [00:57, 11.67iter/s]Best score: 0.2871: : 620iter [00:57, 11.67iter/s]Best score: 0.2871: : 621iter [00:57, 11.58iter/s]Best score: 0.2871: : 621iter [00:57, 11.58iter/s]Best score: 0.2871: : 622iter [00:58, 11.58iter/s]Best score: 0.2871: : 623iter [00:58, 11.71iter/s]Best score: 0.2871: : 623iter [00:58, 11.71iter/s]Best score: 0.2871: : 624iter [00:58, 11.71iter/s]Best score: 0.2871: : 625iter [00:58, 11.80iter/s]Best score: 0.2871: : 625iter [00:58, 11.80iter/s]Best score: 0.2871: : 626iter [00:58, 11.80iter/s]Best score: 0.2871: : 627iter [00:58, 11.84iter/s]Best score: 0.2871: : 627iter [00:58, 11.84iter/s]Best score: 0.2871: : 628iter [00:58, 11.84iter/s]Best score: 0.2871: : 629iter [00:58, 11.89iter/s]Best score: 0.2838: : 629iter [00:58, 11.89iter/s]Best score: 0.2838: : 630iter [00:58, 11.89iter/s]Best score: 0.2838: : 631iter [00:58, 11.70iter/s]Best score: 0.2838: : 631iter [00:58, 11.70iter/s]Best score: 0.2838: : 632iter [00:58, 11.70iter/s]Best score: 0.2838: : 633iter [00:58, 11.80iter/s]Best score: 0.2838: : 633iter [00:58, 11.80iter/s]Best score: 0.2838: : 634iter [00:59, 11.80iter/s]Best score: 0.2838: : 635iter [00:59, 11.86iter/s]Best score: 0.2838: : 635iter [00:59, 11.86iter/s]Best score: 0.2838: : 636iter [00:59, 11.86iter/s]Best score: 0.2838: : 637iter [00:59, 11.87iter/s]Best score: 0.2838: : 637iter [00:59, 11.87iter/s]Best score: 0.2838: : 638iter [00:59, 11.87iter/s]Best score: 0.2838: : 639iter [00:59, 11.80iter/s]Best score: 0.2712: : 639iter [00:59, 11.80iter/s]Best score: 0.2712: : 640iter [00:59, 11.80iter/s]Best score: 0.2712: : 641iter [00:59, 11.63iter/s]Best score: 0.2712: : 641iter [00:59, 11.63iter/s]Best score: 0.2712: : 642iter [00:59, 11.63iter/s]Best score: 0.2712: : 643iter [00:59, 11.75iter/s]Best score: 0.2712: : 643iter [00:59, 11.75iter/s]Best score: 0.2712: : 644iter [00:59, 11.75iter/s]Best score: 0.2712: : 645iter [00:59, 11.82iter/s]Best score: 0.2712: : 645iter [00:59, 11.82iter/s]Best score: 0.2712: : 646iter [01:00, 11.82iter/s]Best score: 0.2712: : 647iter [01:00, 11.87iter/s]Best score: 0.2712: : 647iter [01:00, 11.87iter/s]Best score: 0.2712: : 648iter [01:00, 11.87iter/s]Best score: 0.2712: : 649iter [01:00, 11.91iter/s]
[ITER 650] Best: 0.2663
[PARAMS] Current best parameters:
  fx: 541.9559
  fy: 541.9415
  px: 999.3020
  py: 752.4420
  k1: 0.03069856
  k2: -0.07652692
  k3: 0.04987103
  k4: -0.01130227
  p1: -0.00253183
  p2: 0.00307735
  p3: 0.02824694
  p4: -0.02176792
  s1: -0.0382
  s2: 0.0233
  s3: -0.0218
  s4: 0.0219
  tilt_x: -0.0338
  tilt_y: 0.0128
  scale: 2.9831
  rotation: -0.0817
Best score: 0.2663: : 649iter [01:00, 11.91iter/s]Best score: 0.2663: : 650iter [01:00, 11.91iter/s]Best score: 0.2663: : 651iter [01:00,  7.66iter/s]Best score: 0.2663: : 651iter [01:00,  7.66iter/s]Best score: 0.2663: : 652iter [01:00,  7.66iter/s]Best score: 0.2663: : 653iter [01:00,  8.60iter/s]Best score: 0.2663: : 653iter [01:00,  8.60iter/s]Best score: 0.2663: : 654iter [01:01,  8.60iter/s]Best score: 0.2663: : 655iter [01:01,  9.42iter/s]Best score: 0.2663: : 655iter [01:01,  9.42iter/s]Best score: 0.2663: : 656iter [01:01,  9.42iter/s]Best score: 0.2663: : 657iter [01:01, 10.09iter/s]Best score: 0.2663: : 657iter [01:01, 10.09iter/s]Best score: 0.2663: : 658iter [01:01, 10.09iter/s]Best score: 0.2663: : 659iter [01:01, 10.62iter/s]Best score: 0.2486: : 659iter [01:01, 10.62iter/s]Best score: 0.2486: : 660iter [01:01, 10.62iter/s]Best score: 0.2486: : 661iter [01:01, 10.86iter/s]Best score: 0.2486: : 661iter [01:01, 10.86iter/s]Best score: 0.2486: : 662iter [01:01, 10.86iter/s]Best score: 0.2486: : 663iter [01:01, 11.20iter/s]Best score: 0.2486: : 663iter [01:01, 11.20iter/s]Best score: 0.2486: : 664iter [01:01, 11.20iter/s]Best score: 0.2486: : 665iter [01:01, 11.44iter/s]Best score: 0.2486: : 665iter [01:01, 11.44iter/s]Best score: 0.2486: : 666iter [01:02, 11.44iter/s]Best score: 0.2486: : 667iter [01:02, 11.60iter/s]Best score: 0.2486: : 667iter [01:02, 11.60iter/s]Best score: 0.2486: : 668iter [01:02, 11.60iter/s]Best score: 0.2486: : 669iter [01:02, 11.70iter/s]Best score: 0.2466: : 669iter [01:02, 11.70iter/s]Best score: 0.2466: : 670iter [01:02, 11.70iter/s]Best score: 0.2466: : 671iter [01:02, 11.57iter/s]Best score: 0.2466: : 671iter [01:02, 11.57iter/s]Best score: 0.2466: : 672iter [01:02, 11.57iter/s]Best score: 0.2466: : 673iter [01:02, 11.73iter/s]Best score: 0.2466: : 673iter [01:02, 11.73iter/s]Best score: 0.2466: : 674iter [01:02, 11.73iter/s]Best score: 0.2466: : 675iter [01:02, 11.78iter/s]Best score: 0.2466: : 675iter [01:02, 11.78iter/s]Best score: 0.2466: : 676iter [01:02, 11.78iter/s]Best score: 0.2466: : 677iter [01:02, 11.85iter/s]Best score: 0.2466: : 677iter [01:02, 11.85iter/s]Best score: 0.2466: : 678iter [01:03, 11.85iter/s]Best score: 0.2466: : 679iter [01:03, 11.90iter/s]Best score: 0.2420: : 679iter [01:03, 11.90iter/s]Best score: 0.2420: : 680iter [01:03, 11.90iter/s]Best score: 0.2420: : 681iter [01:03, 11.68iter/s]Best score: 0.2420: : 681iter [01:03, 11.68iter/s]Best score: 0.2420: : 682iter [01:03, 11.68iter/s]Best score: 0.2420: : 683iter [01:03, 11.78iter/s]Best score: 0.2420: : 683iter [01:03, 11.78iter/s]Best score: 0.2420: : 684iter [01:03, 11.78iter/s]Best score: 0.2420: : 685iter [01:03, 11.84iter/s]Best score: 0.2420: : 685iter [01:03, 11.84iter/s]Best score: 0.2420: : 686iter [01:03, 11.84iter/s]Best score: 0.2420: : 687iter [01:03, 11.89iter/s]Best score: 0.2420: : 687iter [01:03, 11.89iter/s]Best score: 0.2420: : 688iter [01:03, 11.89iter/s]Best score: 0.2420: : 689iter [01:03, 11.93iter/s]Best score: 0.2413: : 689iter [01:03, 11.93iter/s]Best score: 0.2413: : 690iter [01:04, 11.93iter/s]Best score: 0.2413: : 691iter [01:04, 11.78iter/s]Best score: 0.2413: : 691iter [01:04, 11.78iter/s]Best score: 0.2413: : 692iter [01:04, 11.78iter/s]Best score: 0.2413: : 693iter [01:04, 11.87iter/s]Best score: 0.2413: : 693iter [01:04, 11.87iter/s]Best score: 0.2413: : 694iter [01:04, 11.87iter/s]Best score: 0.2413: : 695iter [01:04, 11.93iter/s]Best score: 0.2413: : 695iter [01:04, 11.93iter/s]Best score: 0.2413: : 696iter [01:04, 11.93iter/s]Best score: 0.2413: : 697iter [01:04, 11.97iter/s]Best score: 0.2413: : 697iter [01:04, 11.97iter/s]Best score: 0.2413: : 698iter [01:04, 11.97iter/s]Best score: 0.2413: : 699iter [01:04, 11.99iter/s]
[ITER 700] Best: 0.2378
[PARAMS] Current best parameters:
  fx: 536.1084
  fy: 536.4291
  px: 1001.8422
  py: 750.7855
  k1: 0.03730067
  k2: -0.07857738
  k3: 0.05182374
  k4: -0.01204506
  p1: 0.00192380
  p2: 0.00579507
  p3: 0.03677449
  p4: -0.02621661
  s1: -0.0457
  s2: 0.0275
  s3: -0.0376
  s4: 0.0267
  tilt_x: -0.0492
  tilt_y: 0.0062
  scale: 2.9828
  rotation: -0.0779
Best score: 0.2378: : 699iter [01:04, 11.99iter/s]Best score: 0.2378: : 700iter [01:05, 11.99iter/s]Best score: 0.2378: : 701iter [01:05,  7.67iter/s]Best score: 0.2378: : 701iter [01:05,  7.67iter/s]Best score: 0.2378: : 702iter [01:05,  7.67iter/s]Best score: 0.2378: : 703iter [01:05,  8.61iter/s]Best score: 0.2378: : 703iter [01:05,  8.61iter/s]Best score: 0.2378: : 704iter [01:05,  8.61iter/s]Best score: 0.2378: : 705iter [01:05,  9.43iter/s]Best score: 0.2378: : 705iter [01:05,  9.43iter/s]Best score: 0.2378: : 706iter [01:05,  9.43iter/s]Best score: 0.2378: : 707iter [01:05, 10.07iter/s]Best score: 0.2378: : 707iter [01:05, 10.07iter/s]Best score: 0.2378: : 708iter [01:05, 10.07iter/s]Best score: 0.2378: : 709iter [01:05, 10.61iter/s]Best score: 0.2378: : 709iter [01:05, 10.61iter/s]Best score: 0.2378: : 710iter [01:06, 10.61iter/s]Best score: 0.2378: : 711iter [01:06, 10.84iter/s]Best score: 0.2378: : 711iter [01:06, 10.84iter/s]Best score: 0.2378: : 712iter [01:06, 10.84iter/s]Best score: 0.2378: : 713iter [01:06, 11.19iter/s]Best score: 0.2378: : 713iter [01:06, 11.19iter/s]Best score: 0.2378: : 714iter [01:06, 11.19iter/s]Best score: 0.2378: : 715iter [01:06, 11.44iter/s]Best score: 0.2378: : 715iter [01:06, 11.44iter/s]Best score: 0.2378: : 716iter [01:06, 11.44iter/s]Best score: 0.2378: : 717iter [01:06, 11.62iter/s]Best score: 0.2378: : 717iter [01:06, 11.62iter/s]Best score: 0.2378: : 718iter [01:06, 11.62iter/s]Best score: 0.2378: : 719iter [01:06, 11.75iter/s]Best score: 0.2362: : 719iter [01:06, 11.75iter/s]Best score: 0.2362: : 720iter [01:06, 11.75iter/s]Best score: 0.2362: : 721iter [01:06, 11.65iter/s]Best score: 0.2362: : 721iter [01:06, 11.65iter/s]Best score: 0.2362: : 722iter [01:07, 11.65iter/s]Best score: 0.2362: : 723iter [01:07, 11.78iter/s]Best score: 0.2362: : 723iter [01:07, 11.78iter/s]Best score: 0.2362: : 724iter [01:07, 11.78iter/s]Best score: 0.2362: : 725iter [01:07, 11.85iter/s]Best score: 0.2362: : 725iter [01:07, 11.85iter/s]Best score: 0.2362: : 726iter [01:07, 11.85iter/s]Best score: 0.2362: : 727iter [01:07,  9.82iter/s]Best score: 0.2362: : 727iter [01:07,  9.82iter/s]Best score: 0.2362: : 728iter [01:07,  9.82iter/s]Best score: 0.2362: : 729iter [01:07, 10.40iter/s]Best score: 0.2335: : 729iter [01:07, 10.40iter/s]Best score: 0.2335: : 730iter [01:07, 10.40iter/s]Best score: 0.2335: : 731iter [01:07, 10.67iter/s]Best score: 0.2335: : 731iter [01:07, 10.67iter/s]Best score: 0.2335: : 732iter [01:08, 10.67iter/s]Best score: 0.2335: : 733iter [01:08, 11.01iter/s]Best score: 0.2335: : 733iter [01:08, 11.01iter/s]Best score: 0.2335: : 734iter [01:08, 11.01iter/s]Best score: 0.2335: : 735iter [01:08, 11.30iter/s]Best score: 0.2335: : 735iter [01:08, 11.30iter/s]Best score: 0.2335: : 736iter [01:08, 11.30iter/s]Best score: 0.2335: : 737iter [01:08, 11.53iter/s]Best score: 0.2335: : 737iter [01:08, 11.53iter/s]Best score: 0.2335: : 738iter [01:08, 11.53iter/s]Best score: 0.2335: : 739iter [01:08, 11.69iter/s]Best score: 0.2299: : 739iter [01:08, 11.69iter/s]Best score: 0.2299: : 740iter [01:08, 11.69iter/s]Best score: 0.2299: : 741iter [01:08, 11.61iter/s]Best score: 0.2299: : 741iter [01:08, 11.61iter/s]Best score: 0.2299: : 742iter [01:08, 11.61iter/s]Best score: 0.2299: : 743iter [01:08, 11.74iter/s]Best score: 0.2299: : 743iter [01:08, 11.74iter/s]Best score: 0.2299: : 744iter [01:09, 11.74iter/s]Best score: 0.2299: : 745iter [01:09, 11.87iter/s]Best score: 0.2299: : 745iter [01:09, 11.87iter/s]Best score: 0.2299: : 746iter [01:09, 11.87iter/s]Best score: 0.2299: : 747iter [01:09, 11.92iter/s]Best score: 0.2299: : 747iter [01:09, 11.92iter/s]Best score: 0.2299: : 748iter [01:09, 11.92iter/s]Best score: 0.2299: : 749iter [01:09, 11.96iter/s]
[ITER 750] Best: 0.2263
[PARAMS] Current best parameters:
  fx: 540.2204
  fy: 540.5380
  px: 1001.4759
  py: 752.3358
  k1: 0.01740066
  k2: -0.04830520
  k3: 0.03163018
  k4: -0.00738956
  p1: 0.00707244
  p2: 0.00701576
  p3: 0.02904828
  p4: -0.02863710
  s1: -0.0368
  s2: 0.0272
  s3: -0.0332
  s4: 0.0289
  tilt_x: -0.0529
  tilt_y: 0.0106
  scale: 2.9916
  rotation: -0.0609
Best score: 0.2263: : 749iter [01:09, 11.96iter/s]Best score: 0.2263: : 750iter [01:09, 11.96iter/s]Best score: 0.2263: : 751iter [01:09,  7.69iter/s]Best score: 0.2263: : 751iter [01:09,  7.69iter/s]Best score: 0.2263: : 752iter [01:09,  7.69iter/s]Best score: 0.2263: : 753iter [01:09,  8.61iter/s]Best score: 0.2263: : 753iter [01:10,  8.61iter/s]Best score: 0.2263: : 754iter [01:10,  8.61iter/s]Best score: 0.2263: : 755iter [01:10,  9.43iter/s]Best score: 0.2263: : 755iter [01:10,  9.43iter/s]Best score: 0.2263: : 756iter [01:10,  9.43iter/s]Best score: 0.2263: : 757iter [01:10, 10.03iter/s]Best score: 0.2263: : 757iter [01:10, 10.03iter/s]Best score: 0.2263: : 758iter [01:10, 10.03iter/s]Best score: 0.2263: : 759iter [01:10, 10.52iter/s]Best score: 0.2263: : 759iter [01:10, 10.52iter/s]Best score: 0.2263: : 760iter [01:10, 10.52iter/s]Best score: 0.2263: : 761iter [01:10, 10.77iter/s]Best score: 0.2263: : 761iter [01:10, 10.77iter/s]Best score: 0.2263: : 762iter [01:10, 10.77iter/s]Best score: 0.2263: : 763iter [01:10, 11.13iter/s]Best score: 0.2263: : 763iter [01:10, 11.13iter/s]Best score: 0.2263: : 764iter [01:10, 11.13iter/s]Best score: 0.2263: : 765iter [01:10, 11.41iter/s]Best score: 0.2263: : 765iter [01:11, 11.41iter/s]Best score: 0.2263: : 766iter [01:11, 11.41iter/s]Best score: 0.2263: : 767iter [01:11, 11.61iter/s]Best score: 0.2263: : 767iter [01:11, 11.61iter/s]Best score: 0.2263: : 768iter [01:11, 11.61iter/s]Best score: 0.2263: : 769iter [01:11, 11.74iter/s]Best score: 0.2205: : 769iter [01:11, 11.74iter/s]Best score: 0.2205: : 770iter [01:11, 11.74iter/s]Best score: 0.2205: : 771iter [01:11, 11.64iter/s]Best score: 0.2205: : 771iter [01:11, 11.64iter/s]Best score: 0.2205: : 772iter [01:11, 11.64iter/s]Best score: 0.2205: : 773iter [01:11, 11.76iter/s]Best score: 0.2205: : 773iter [01:11, 11.76iter/s]Best score: 0.2205: : 774iter [01:11, 11.76iter/s]Best score: 0.2205: : 775iter [01:11, 11.85iter/s]Best score: 0.2205: : 775iter [01:11, 11.85iter/s]Best score: 0.2205: : 776iter [01:11, 11.85iter/s]Best score: 0.2205: : 777iter [01:11, 11.89iter/s]Best score: 0.2205: : 777iter [01:12, 11.89iter/s]Best score: 0.2205: : 778iter [01:12, 11.89iter/s]Best score: 0.2205: : 779iter [01:12, 11.92iter/s]Best score: 0.2205: : 779iter [01:12, 11.92iter/s]Best score: 0.2205: : 780iter [01:12, 11.92iter/s]Best score: 0.2205: : 781iter [01:12, 11.76iter/s]Best score: 0.2205: : 781iter [01:12, 11.76iter/s]Best score: 0.2205: : 782iter [01:12, 11.76iter/s]Best score: 0.2205: : 783iter [01:12, 11.86iter/s]Best score: 0.2205: : 783iter [01:12, 11.86iter/s]Best score: 0.2205: : 784iter [01:12, 11.86iter/s]Best score: 0.2205: : 785iter [01:12, 11.89iter/s]Best score: 0.2205: : 785iter [01:12, 11.89iter/s]Best score: 0.2205: : 786iter [01:12, 11.89iter/s]Best score: 0.2205: : 787iter [01:12, 11.95iter/s]Best score: 0.2205: : 787iter [01:12, 11.95iter/s]Best score: 0.2205: : 788iter [01:12, 11.95iter/s]Best score: 0.2205: : 789iter [01:12, 12.00iter/s]Best score: 0.2199: : 789iter [01:13, 12.00iter/s]Best score: 0.2199: : 790iter [01:13, 12.00iter/s]Best score: 0.2199: : 791iter [01:13, 11.81iter/s]Best score: 0.2199: : 791iter [01:13, 11.81iter/s]Best score: 0.2199: : 792iter [01:13, 11.81iter/s]Best score: 0.2199: : 793iter [01:13, 11.91iter/s]Best score: 0.2199: : 793iter [01:13, 11.91iter/s]Best score: 0.2199: : 794iter [01:13, 11.91iter/s]Best score: 0.2199: : 795iter [01:13, 11.93iter/s]Best score: 0.2199: : 795iter [01:13, 11.93iter/s]Best score: 0.2199: : 796iter [01:13, 11.93iter/s]Best score: 0.2199: : 797iter [01:13, 11.97iter/s]Best score: 0.2199: : 797iter [01:13, 11.97iter/s]Best score: 0.2199: : 798iter [01:13, 11.97iter/s]Best score: 0.2199: : 799iter [01:13, 12.01iter/s]
[ITER 800] Best: 0.2199
[PARAMS] Current best parameters:
  fx: 538.4541
  fy: 538.7431
  px: 1002.1026
  py: 752.1740
  k1: 0.02416272
  k2: -0.06061261
  k3: 0.04128595
  k4: -0.00990537
  p1: 0.00452802
  p2: 0.00583680
  p3: 0.02866585
  p4: -0.02610425
  s1: -0.0363
  s2: 0.0266
  s3: -0.0294
  s4: 0.0261
  tilt_x: -0.0487
  tilt_y: 0.0052
  scale: 2.9919
  rotation: -0.0743
Best score: 0.2199: : 799iter [01:13, 12.01iter/s]Best score: 0.2199: : 800iter [01:14, 12.01iter/s]Best score: 0.2199: : 801iter [01:14,  7.68iter/s]Best score: 0.2199: : 801iter [01:14,  7.68iter/s]Best score: 0.2199: : 802iter [01:14,  7.68iter/s]Best score: 0.2199: : 803iter [01:14,  8.63iter/s]Best score: 0.2199: : 803iter [01:14,  8.63iter/s]Best score: 0.2199: : 804iter [01:14,  8.63iter/s]Best score: 0.2199: : 805iter [01:14,  9.44iter/s]Best score: 0.2199: : 805iter [01:14,  9.44iter/s]Best score: 0.2199: : 806iter [01:14,  9.44iter/s]Best score: 0.2199: : 807iter [01:14, 10.11iter/s]Best score: 0.2199: : 807iter [01:14, 10.11iter/s]Best score: 0.2199: : 808iter [01:14, 10.11iter/s]Best score: 0.2199: : 809iter [01:14, 10.64iter/s]Best score: 0.2188: : 809iter [01:15, 10.64iter/s]Best score: 0.2188: : 810iter [01:15, 10.64iter/s]Best score: 0.2188: : 811iter [01:15, 10.88iter/s]Best score: 0.2188: : 811iter [01:15, 10.88iter/s]Best score: 0.2188: : 812iter [01:15, 10.88iter/s]Best score: 0.2188: : 813iter [01:15, 11.15iter/s]Best score: 0.2188: : 813iter [01:15, 11.15iter/s]Best score: 0.2188: : 814iter [01:15, 11.15iter/s]Best score: 0.2188: : 815iter [01:15, 11.37iter/s]Best score: 0.2188: : 815iter [01:15, 11.37iter/s]Best score: 0.2188: : 816iter [01:15, 11.37iter/s]Best score: 0.2188: : 817iter [01:15, 11.56iter/s]Best score: 0.2188: : 817iter [01:15, 11.56iter/s]Best score: 0.2188: : 818iter [01:15, 11.56iter/s]Best score: 0.2188: : 819iter [01:15, 11.71iter/s]Best score: 0.2180: : 819iter [01:15, 11.71iter/s]Best score: 0.2180: : 820iter [01:15, 11.71iter/s]Best score: 0.2180: : 821iter [01:15, 11.61iter/s]Best score: 0.2180: : 821iter [01:16, 11.61iter/s]Best score: 0.2180: : 822iter [01:16, 11.61iter/s]Best score: 0.2180: : 823iter [01:16, 11.76iter/s]Best score: 0.2180: : 823iter [01:16, 11.76iter/s]Best score: 0.2180: : 824iter [01:16, 11.76iter/s]Best score: 0.2180: : 825iter [01:16, 11.86iter/s]Best score: 0.2180: : 825iter [01:16, 11.86iter/s]Best score: 0.2180: : 826iter [01:16, 11.86iter/s]Best score: 0.2180: : 827iter [01:16, 11.94iter/s]Best score: 0.2180: : 827iter [01:16, 11.94iter/s]Best score: 0.2180: : 828iter [01:16, 11.94iter/s]Best score: 0.2180: : 829iter [01:16, 11.99iter/s]Best score: 0.2180: : 829iter [01:16, 11.99iter/s]Best score: 0.2180: : 830iter [01:16, 11.99iter/s]Best score: 0.2180: : 831iter [01:16, 11.82iter/s]Best score: 0.2180: : 831iter [01:16, 11.82iter/s]Best score: 0.2180: : 832iter [01:16, 11.82iter/s]Best score: 0.2180: : 833iter [01:16, 11.91iter/s]Best score: 0.2180: : 833iter [01:17, 11.91iter/s]Best score: 0.2180: : 834iter [01:17, 11.91iter/s]Best score: 0.2180: : 835iter [01:17, 11.97iter/s]Best score: 0.2180: : 835iter [01:17, 11.97iter/s]Best score: 0.2180: : 836iter [01:17, 11.97iter/s]Best score: 0.2180: : 837iter [01:17, 12.02iter/s]Best score: 0.2180: : 837iter [01:17, 12.02iter/s]Best score: 0.2180: : 838iter [01:17, 12.02iter/s]Best score: 0.2180: : 839iter [01:17, 12.04iter/s]Best score: 0.2180: : 839iter [01:17, 12.04iter/s]Best score: 0.2180: : 840iter [01:17, 12.04iter/s]Best score: 0.2180: : 841iter [01:17, 11.83iter/s]Best score: 0.2180: : 841iter [01:17, 11.83iter/s]Best score: 0.2180: : 842iter [01:17, 11.83iter/s]Best score: 0.2180: : 843iter [01:17, 11.90iter/s]Best score: 0.2180: : 843iter [01:17, 11.90iter/s]Best score: 0.2180: : 844iter [01:17, 11.90iter/s]Best score: 0.2180: : 845iter [01:17, 11.96iter/s]Best score: 0.2180: : 845iter [01:18, 11.96iter/s]Best score: 0.2180: : 846iter [01:18, 11.96iter/s]Best score: 0.2180: : 847iter [01:18, 12.01iter/s]Best score: 0.2180: : 847iter [01:18, 12.01iter/s]Best score: 0.2180: : 848iter [01:18, 12.01iter/s]Best score: 0.2180: : 849iter [01:18, 12.05iter/s]
[ITER 850] Best: 0.2171
[PARAMS] Current best parameters:
  fx: 539.2059
  fy: 539.4841
  px: 1001.3843
  py: 752.0628
  k1: 0.02103154
  k2: -0.05538583
  k3: 0.03765513
  k4: -0.00908034
  p1: 0.00532309
  p2: 0.00490411
  p3: 0.02927030
  p4: -0.02666622
  s1: -0.0359
  s2: 0.0265
  s3: -0.0308
  s4: 0.0263
  tilt_x: -0.0511
  tilt_y: 0.0069
  scale: 2.9925
  rotation: -0.0726
Best score: 0.2171: : 849iter [01:18, 12.05iter/s]Best score: 0.2171: : 850iter [01:18, 12.05iter/s]Best score: 0.2171: : 851iter [01:18,  7.60iter/s]Best score: 0.2171: : 851iter [01:18,  7.60iter/s]Best score: 0.2171: : 852iter [01:18,  7.60iter/s]Best score: 0.2171: : 853iter [01:18,  8.55iter/s]Best score: 0.2171: : 853iter [01:19,  8.55iter/s]Best score: 0.2171: : 854iter [01:19,  8.55iter/s]Best score: 0.2171: : 855iter [01:19,  9.38iter/s]Best score: 0.2171: : 855iter [01:19,  9.38iter/s]Best score: 0.2171: : 856iter [01:19,  9.38iter/s]Best score: 0.2171: : 857iter [01:19, 10.07iter/s]Best score: 0.2171: : 857iter [01:19, 10.07iter/s]Best score: 0.2171: : 858iter [01:19, 10.07iter/s]Best score: 0.2171: : 859iter [01:19, 10.61iter/s]Best score: 0.2169: : 859iter [01:19, 10.61iter/s]Best score: 0.2169: : 860iter [01:19, 10.61iter/s]Best score: 0.2169: : 861iter [01:19, 10.83iter/s]Best score: 0.2169: : 861iter [01:19, 10.83iter/s]Best score: 0.2169: : 862iter [01:19, 10.83iter/s]Best score: 0.2169: : 863iter [01:19, 11.18iter/s]Best score: 0.2169: : 863iter [01:19, 11.18iter/s]Best score: 0.2169: : 864iter [01:19, 11.18iter/s]Best score: 0.2169: : 865iter [01:19, 11.44iter/s]Best score: 0.2169: : 865iter [01:20, 11.44iter/s]Best score: 0.2169: : 866iter [01:20, 11.44iter/s]Best score: 0.2169: : 867iter [01:20, 11.63iter/s]Best score: 0.2169: : 867iter [01:20, 11.63iter/s]Best score: 0.2169: : 868iter [01:20, 11.63iter/s]Best score: 0.2169: : 869iter [01:20, 11.75iter/s]Best score: 0.2148: : 869iter [01:20, 11.75iter/s]Best score: 0.2148: : 870iter [01:20, 11.75iter/s]Best score: 0.2148: : 871iter [01:20, 11.66iter/s]Best score: 0.2148: : 871iter [01:20, 11.66iter/s]Best score: 0.2148: : 872iter [01:20, 11.66iter/s]Best score: 0.2148: : 873iter [01:20, 11.77iter/s]Best score: 0.2148: : 873iter [01:20, 11.77iter/s]Best score: 0.2148: : 874iter [01:20, 11.77iter/s]Best score: 0.2148: : 875iter [01:20, 11.86iter/s]Best score: 0.2148: : 875iter [01:20, 11.86iter/s]Best score: 0.2148: : 876iter [01:20, 11.86iter/s]Best score: 0.2148: : 877iter [01:20, 11.95iter/s]Best score: 0.2148: : 877iter [01:21, 11.95iter/s]Best score: 0.2148: : 878iter [01:21, 11.95iter/s]Best score: 0.2148: : 879iter [01:21, 12.01iter/s]Best score: 0.2144: : 879iter [01:21, 12.01iter/s]Best score: 0.2144: : 880iter [01:21, 12.01iter/s]Best score: 0.2144: : 881iter [01:21, 11.85iter/s]Best score: 0.2144: : 881iter [01:21, 11.85iter/s]Best score: 0.2144: : 882iter [01:21, 11.85iter/s]Best score: 0.2144: : 883iter [01:21, 11.93iter/s]Best score: 0.2144: : 883iter [01:21, 11.93iter/s]Best score: 0.2144: : 884iter [01:21, 11.93iter/s]Best score: 0.2144: : 885iter [01:21, 12.00iter/s]Best score: 0.2144: : 885iter [01:21, 12.00iter/s]Best score: 0.2144: : 886iter [01:21, 12.00iter/s]Best score: 0.2144: : 887iter [01:21, 12.00iter/s]Best score: 0.2144: : 887iter [01:21, 12.00iter/s]Best score: 0.2144: : 888iter [01:21, 12.00iter/s]Best score: 0.2144: : 889iter [01:21, 12.03iter/s]Best score: 0.2144: : 889iter [01:22, 12.03iter/s]Best score: 0.2144: : 890iter [01:22, 12.03iter/s]Best score: 0.2144: : 891iter [01:22, 11.83iter/s]Best score: 0.2144: : 891iter [01:22, 11.83iter/s]Best score: 0.2144: : 892iter [01:22, 11.83iter/s]Best score: 0.2144: : 893iter [01:22, 11.92iter/s]Best score: 0.2144: : 893iter [01:22, 11.92iter/s]Best score: 0.2144: : 894iter [01:22, 11.92iter/s]Best score: 0.2144: : 895iter [01:22, 11.98iter/s]Best score: 0.2144: : 895iter [01:22, 11.98iter/s]Best score: 0.2144: : 896iter [01:22, 11.98iter/s]Best score: 0.2144: : 897iter [01:22, 12.02iter/s]Best score: 0.2144: : 897iter [01:22, 12.02iter/s]Best score: 0.2144: : 898iter [01:22, 12.02iter/s]Best score: 0.2144: : 899iter [01:22, 12.05iter/s]
[ITER 900] Best: 0.2141
[PARAMS] Current best parameters:
  fx: 541.9799
  fy: 542.3933
  px: 1002.1370
  py: 751.1940
  k1: 0.00931545
  k2: -0.04112285
  k3: 0.02958493
  k4: -0.00742547
  p1: 0.00488472
  p2: 0.00738571
  p3: 0.02480912
  p4: -0.02625587
  s1: -0.0334
  s2: 0.0261
  s3: -0.0267
  s4: 0.0262
  tilt_x: -0.0546
  tilt_y: 0.0081
  scale: 2.9993
  rotation: -0.0686
Best score: 0.2141: : 899iter [01:22, 12.05iter/s]Best score: 0.2141: : 900iter [01:23, 12.05iter/s]Best score: 0.2141: : 901iter [01:23,  7.65iter/s]Best score: 0.2141: : 901iter [01:23,  7.65iter/s]Best score: 0.2141: : 902iter [01:23,  7.65iter/s]Best score: 0.2141: : 903iter [01:23,  8.55iter/s]Best score: 0.2141: : 903iter [01:23,  8.55iter/s]Best score: 0.2141: : 904iter [01:23,  8.55iter/s]Best score: 0.2141: : 905iter [01:23,  9.35iter/s]Best score: 0.2141: : 905iter [01:23,  9.35iter/s]Best score: 0.2141: : 906iter [01:23,  9.35iter/s]Best score: 0.2141: : 907iter [01:23, 10.04iter/s]Best score: 0.2141: : 907iter [01:23, 10.04iter/s]Best score: 0.2141: : 908iter [01:23, 10.04iter/s]Best score: 0.2141: : 909iter [01:23, 10.55iter/s]Best score: 0.2139: : 909iter [01:24, 10.55iter/s]Best score: 0.2139: : 910iter [01:24, 10.55iter/s]Best score: 0.2139: : 911iter [01:24, 10.78iter/s]Best score: 0.2139: : 911iter [01:24, 10.78iter/s]Best score: 0.2139: : 912iter [01:24, 10.78iter/s]Best score: 0.2139: : 913iter [01:24, 11.15iter/s]Best score: 0.2139: : 913iter [01:24, 11.15iter/s]Best score: 0.2139: : 914iter [01:24, 11.15iter/s]Best score: 0.2139: : 915iter [01:24, 11.43iter/s]Best score: 0.2139: : 915iter [01:24, 11.43iter/s]Best score: 0.2139: : 916iter [01:24, 11.43iter/s]Best score: 0.2139: : 917iter [01:24, 11.63iter/s]Best score: 0.2139: : 917iter [01:24, 11.63iter/s]Best score: 0.2139: : 918iter [01:24, 11.63iter/s]Best score: 0.2139: : 919iter [01:24, 11.78iter/s]Best score: 0.2107: : 919iter [01:24, 11.78iter/s]Best score: 0.2107: : 920iter [01:24, 11.78iter/s]Best score: 0.2107: : 921iter [01:24, 11.68iter/s]Best score: 0.2107: : 921iter [01:25, 11.68iter/s]Best score: 0.2107: : 922iter [01:25, 11.68iter/s]Best score: 0.2107: : 923iter [01:25, 11.81iter/s]Best score: 0.2107: : 923iter [01:25, 11.81iter/s]Best score: 0.2107: : 924iter [01:25, 11.81iter/s]Best score: 0.2107: : 925iter [01:25, 11.90iter/s]Best score: 0.2107: : 925iter [01:25, 11.90iter/s]Best score: 0.2107: : 926iter [01:25, 11.90iter/s]Best score: 0.2107: : 927iter [01:25, 11.97iter/s]Best score: 0.2107: : 927iter [01:25, 11.97iter/s]Best score: 0.2107: : 928iter [01:25, 11.97iter/s]Best score: 0.2107: : 929iter [01:25, 12.00iter/s]Best score: 0.2096: : 929iter [01:25, 12.00iter/s]Best score: 0.2096: : 930iter [01:25, 12.00iter/s]Best score: 0.2096: : 931iter [01:25, 11.84iter/s]Best score: 0.2096: : 931iter [01:25, 11.84iter/s]Best score: 0.2096: : 932iter [01:25, 11.84iter/s]Best score: 0.2096: : 933iter [01:25, 11.84iter/s]Best score: 0.2096: : 933iter [01:26, 11.84iter/s]Best score: 0.2096: : 934iter [01:26, 11.84iter/s]Best score: 0.2096: : 935iter [01:26, 11.92iter/s]Best score: 0.2096: : 935iter [01:26, 11.92iter/s]Best score: 0.2096: : 936iter [01:26, 11.92iter/s]Best score: 0.2096: : 937iter [01:26, 12.00iter/s]Best score: 0.2096: : 937iter [01:26, 12.00iter/s]Best score: 0.2096: : 938iter [01:26, 12.00iter/s]Best score: 0.2096: : 939iter [01:26, 12.02iter/s]Best score: 0.2067: : 939iter [01:26, 12.02iter/s]Best score: 0.2067: : 940iter [01:26, 12.02iter/s]Best score: 0.2067: : 941iter [01:26, 11.84iter/s]Best score: 0.2067: : 941iter [01:26, 11.84iter/s]Best score: 0.2067: : 942iter [01:26, 11.84iter/s]Best score: 0.2067: : 943iter [01:26, 11.91iter/s]Best score: 0.2067: : 943iter [01:26, 11.91iter/s]Best score: 0.2067: : 944iter [01:26, 11.91iter/s]Best score: 0.2067: : 945iter [01:26, 11.98iter/s]Best score: 0.2067: : 945iter [01:27, 11.98iter/s]Best score: 0.2067: : 946iter [01:27, 11.98iter/s]Best score: 0.2067: : 947iter [01:27, 12.03iter/s]Best score: 0.2067: : 947iter [01:27, 12.03iter/s]Best score: 0.2067: : 948iter [01:27, 12.03iter/s]Best score: 0.2067: : 949iter [01:27, 12.06iter/s]
[ITER 950] Best: 0.2067
[PARAMS] Current best parameters:
  fx: 549.2553
  fy: 549.6411
  px: 1001.4422
  py: 749.5463
  k1: -0.01399136
  k2: -0.01248804
  k3: 0.01153162
  k4: -0.00331452
  p1: 0.00235946
  p2: 0.00460349
  p3: 0.02250533
  p4: -0.02553262
  s1: -0.0296
  s2: 0.0258
  s3: -0.0235
  s4: 0.0255
  tilt_x: -0.0583
  tilt_y: 0.0053
  scale: 3.0094
  rotation: -0.0675
Best score: 0.2067: : 949iter [01:27, 12.06iter/s]Best score: 0.2067: : 950iter [01:27, 12.06iter/s]Best score: 0.2067: : 951iter [01:27,  7.72iter/s]Best score: 0.2067: : 951iter [01:27,  7.72iter/s]Best score: 0.2067: : 952iter [01:27,  7.72iter/s]Best score: 0.2067: : 953iter [01:27,  8.66iter/s]Best score: 0.2067: : 953iter [01:28,  8.66iter/s]Best score: 0.2067: : 954iter [01:28,  8.66iter/s]Best score: 0.2067: : 955iter [01:28,  9.47iter/s]Best score: 0.2067: : 955iter [01:28,  9.47iter/s]Best score: 0.2067: : 956iter [01:28,  9.47iter/s]Best score: 0.2067: : 957iter [01:28, 10.14iter/s]Best score: 0.2067: : 957iter [01:28, 10.14iter/s]Best score: 0.2067: : 958iter [01:28, 10.14iter/s]Best score: 0.2067: : 959iter [01:28, 10.67iter/s]Best score: 0.2067: : 959iter [01:28, 10.67iter/s]Best score: 0.2067: : 960iter [01:28, 10.67iter/s]Best score: 0.2067: : 961iter [01:28, 10.90iter/s]Best score: 0.2067: : 961iter [01:28, 10.90iter/s]Best score: 0.2067: : 962iter [01:28, 10.90iter/s]Best score: 0.2067: : 963iter [01:28, 11.21iter/s]Best score: 0.2067: : 963iter [01:28, 11.21iter/s]Best score: 0.2067: : 964iter [01:28, 11.21iter/s]Best score: 0.2067: : 965iter [01:28, 11.46iter/s]Best score: 0.2067: : 965iter [01:29, 11.46iter/s]Best score: 0.2067: : 966iter [01:29, 11.46iter/s]Best score: 0.2067: : 967iter [01:29, 11.52iter/s]Best score: 0.2067: : 967iter [01:29, 11.52iter/s]Best score: 0.2067: : 968iter [01:29, 11.52iter/s]Best score: 0.2067: : 969iter [01:29, 11.69iter/s]Best score: 0.2067: : 969iter [01:29, 11.69iter/s]Best score: 0.2067: : 970iter [01:29, 11.69iter/s]Best score: 0.2067: : 971iter [01:29, 11.63iter/s]Best score: 0.2067: : 971iter [01:29, 11.63iter/s]Best score: 0.2067: : 972iter [01:29, 11.63iter/s]Best score: 0.2067: : 973iter [01:29, 11.77iter/s]Best score: 0.2067: : 973iter [01:29, 11.77iter/s]Best score: 0.2067: : 974iter [01:29, 11.77iter/s]Best score: 0.2067: : 975iter [01:29, 11.87iter/s]Best score: 0.2067: : 975iter [01:29, 11.87iter/s]Best score: 0.2067: : 976iter [01:29, 11.87iter/s]Best score: 0.2067: : 977iter [01:29, 11.94iter/s]Best score: 0.2067: : 977iter [01:30, 11.94iter/s]Best score: 0.2067: : 978iter [01:30, 11.94iter/s]Best score: 0.2067: : 979iter [01:30, 12.00iter/s]Best score: 0.2067: : 979iter [01:30, 12.00iter/s]Best score: 0.2067: : 980iter [01:30, 12.00iter/s]Best score: 0.2067: : 981iter [01:30, 11.81iter/s]Best score: 0.2067: : 981iter [01:30, 11.81iter/s]Best score: 0.2067: : 982iter [01:30, 11.81iter/s]Best score: 0.2067: : 983iter [01:30, 11.90iter/s]Best score: 0.2067: : 983iter [01:30, 11.90iter/s]Best score: 0.2067: : 984iter [01:30, 11.90iter/s]Best score: 0.2067: : 985iter [01:30, 11.97iter/s]Best score: 0.2067: : 985iter [01:30, 11.97iter/s]Best score: 0.2067: : 986iter [01:30, 11.97iter/s]Best score: 0.2067: : 987iter [01:30, 11.97iter/s]Best score: 0.2067: : 987iter [01:30, 11.97iter/s]Best score: 0.2067: : 988iter [01:30, 11.97iter/s]Best score: 0.2067: : 989iter [01:30, 12.01iter/s]Best score: 0.2067: : 989iter [01:31, 12.01iter/s]Best score: 0.2067: : 990iter [01:31, 12.01iter/s]Best score: 0.2067: : 991iter [01:31, 11.85iter/s]Best score: 0.2067: : 991iter [01:31, 11.85iter/s]Best score: 0.2067: : 992iter [01:31, 11.85iter/s]Best score: 0.2067: : 993iter [01:31, 11.94iter/s]Best score: 0.2067: : 993iter [01:31, 11.94iter/s]Best score: 0.2067: : 994iter [01:31, 11.94iter/s]Best score: 0.2067: : 995iter [01:31, 11.99iter/s]Best score: 0.2067: : 995iter [01:31, 11.99iter/s]Best score: 0.2067: : 996iter [01:31, 11.99iter/s]Best score: 0.2067: : 997iter [01:31, 11.98iter/s]Best score: 0.2067: : 997iter [01:31, 11.98iter/s]Best score: 0.2067: : 998iter [01:31, 11.98iter/s]Best score: 0.2067: : 999iter [01:31, 11.99iter/s]
[ITER 1000] Best: 0.2067
[PARAMS] Current best parameters:
  fx: 549.2553
  fy: 549.6411
  px: 1001.4422
  py: 749.5463
  k1: -0.01399136
  k2: -0.01248804
  k3: 0.01153162
  k4: -0.00331452
  p1: 0.00235946
  p2: 0.00460349
  p3: 0.02250533
  p4: -0.02553262
  s1: -0.0296
  s2: 0.0258
  s3: -0.0235
  s4: 0.0255
  tilt_x: -0.0583
  tilt_y: 0.0053
  scale: 3.0094
  rotation: -0.0675
Best score: 0.2067: : 999iter [01:31, 11.99iter/s]Best score: 0.2067: : 1000iter [01:32, 11.99iter/s]Best score: 0.2067: : 1001iter [01:32,  7.65iter/s]Best score: 0.2067: : 1001iter [01:32,  7.65iter/s]Best score: 0.2067: : 1002iter [01:32,  7.65iter/s]Best score: 0.2067: : 1003iter [01:32,  8.57iter/s]Best score: 0.2067: : 1003iter [01:32,  8.57iter/s]Best score: 0.2067: : 1004iter [01:32,  8.57iter/s]Best score: 0.2067: : 1005iter [01:32,  9.38iter/s]Best score: 0.2067: : 1005iter [01:32,  9.38iter/s]Best score: 0.2067: : 1006iter [01:32,  9.38iter/s]Best score: 0.2067: : 1007iter [01:32, 10.04iter/s]Best score: 0.2067: : 1007iter [01:32, 10.04iter/s]Best score: 0.2067: : 1008iter [01:32, 10.04iter/s]Best score: 0.2067: : 1009iter [01:32, 10.51iter/s]Best score: 0.2061: : 1009iter [01:33, 10.51iter/s]Best score: 0.2061: : 1010iter [01:33, 10.51iter/s]Best score: 0.2061: : 1011iter [01:33, 10.77iter/s]Best score: 0.2061: : 1011iter [01:33, 10.77iter/s]Best score: 0.2061: : 1012iter [01:33, 10.77iter/s]Best score: 0.2061: : 1013iter [01:33, 11.07iter/s]Best score: 0.2061: : 1013iter [01:33, 11.07iter/s]Best score: 0.2061: : 1014iter [01:33, 11.07iter/s]Best score: 0.2061: : 1015iter [01:33, 11.32iter/s]Best score: 0.2061: : 1015iter [01:33, 11.32iter/s]Best score: 0.2061: : 1016iter [01:33, 11.32iter/s]Best score: 0.2061: : 1017iter [01:33, 11.54iter/s]Best score: 0.2061: : 1017iter [01:33, 11.54iter/s]Best score: 0.2061: : 1018iter [01:33, 11.54iter/s]Best score: 0.2061: : 1019iter [01:33, 11.71iter/s]Best score: 0.2054: : 1019iter [01:33, 11.71iter/s]Best score: 0.2054: : 1020iter [01:33, 11.71iter/s]Best score: 0.2054: : 1021iter [01:33, 11.63iter/s]Best score: 0.2054: : 1021iter [01:34, 11.63iter/s]Best score: 0.2054: : 1022iter [01:34, 11.63iter/s]Best score: 0.2054: : 1023iter [01:34, 11.74iter/s]Best score: 0.2054: : 1023iter [01:34, 11.74iter/s]Best score: 0.2054: : 1024iter [01:34, 11.74iter/s]Best score: 0.2054: : 1025iter [01:34, 11.85iter/s]Best score: 0.2054: : 1025iter [01:34, 11.85iter/s]Best score: 0.2054: : 1026iter [01:34, 11.85iter/s]Best score: 0.2054: : 1027iter [01:34, 11.89iter/s]Best score: 0.2054: : 1027iter [01:34, 11.89iter/s]Best score: 0.2054: : 1028iter [01:34, 11.89iter/s]Best score: 0.2054: : 1029iter [01:34, 11.95iter/s]Best score: 0.2050: : 1029iter [01:34, 11.95iter/s]Best score: 0.2050: : 1030iter [01:34, 11.95iter/s]Best score: 0.2050: : 1031iter [01:34, 11.80iter/s]Best score: 0.2050: : 1031iter [01:34, 11.80iter/s]Best score: 0.2050: : 1032iter [01:34, 11.80iter/s]Best score: 0.2050: : 1033iter [01:34, 11.89iter/s]Best score: 0.2050: : 1033iter [01:35, 11.89iter/s]Best score: 0.2050: : 1034iter [01:35, 11.89iter/s]Best score: 0.2050: : 1035iter [01:35, 11.96iter/s]Best score: 0.2050: : 1035iter [01:35, 11.96iter/s]Best score: 0.2050: : 1036iter [01:35, 11.96iter/s]Best score: 0.2050: : 1037iter [01:35, 11.91iter/s]Best score: 0.2050: : 1037iter [01:35, 11.91iter/s]Best score: 0.2050: : 1038iter [01:35, 11.91iter/s]Best score: 0.2050: : 1039iter [01:35, 11.91iter/s]Best score: 0.2047: : 1039iter [01:35, 11.91iter/s]Best score: 0.2047: : 1040iter [01:35, 11.91iter/s]Best score: 0.2047: : 1041iter [01:35, 11.71iter/s]Best score: 0.2047: : 1041iter [01:35, 11.71iter/s]Best score: 0.2047: : 1042iter [01:35, 11.71iter/s]Best score: 0.2047: : 1043iter [01:35, 11.83iter/s]Best score: 0.2047: : 1043iter [01:35, 11.83iter/s]Best score: 0.2047: : 1044iter [01:35, 11.83iter/s]Best score: 0.2047: : 1045iter [01:35, 11.85iter/s]Best score: 0.2047: : 1045iter [01:36, 11.85iter/s]Best score: 0.2047: : 1046iter [01:36, 11.85iter/s]Best score: 0.2047: : 1047iter [01:36, 11.89iter/s]Best score: 0.2047: : 1047iter [01:36, 11.89iter/s]Best score: 0.2047: : 1048iter [01:36, 11.89iter/s]Best score: 0.2047: : 1049iter [01:36, 11.96iter/s]
[ITER 1050] Best: 0.2036
[PARAMS] Current best parameters:
  fx: 548.4619
  fy: 548.8540
  px: 1001.4192
  py: 749.0735
  k1: -0.01043077
  k2: -0.01711202
  k3: 0.01435579
  k4: -0.00395777
  p1: 0.00167428
  p2: 0.00304453
  p3: 0.02319049
  p4: -0.02481886
  s1: -0.0292
  s2: 0.0254
  s3: -0.0249
  s4: 0.0253
  tilt_x: -0.0590
  tilt_y: 0.0025
  scale: 3.0076
  rotation: -0.0646
Best score: 0.2036: : 1049iter [01:36, 11.96iter/s]Best score: 0.2036: : 1050iter [01:36, 11.96iter/s]Best score: 0.2036: : 1051iter [01:36,  7.71iter/s]Best score: 0.2036: : 1051iter [01:36,  7.71iter/s]Best score: 0.2036: : 1052iter [01:36,  7.71iter/s]Best score: 0.2036: : 1053iter [01:36,  8.66iter/s]Best score: 0.2036: : 1053iter [01:37,  8.66iter/s]Best score: 0.2036: : 1054iter [01:37,  8.66iter/s]Best score: 0.2036: : 1055iter [01:37,  9.47iter/s]Best score: 0.2036: : 1055iter [01:37,  9.47iter/s]Best score: 0.2036: : 1056iter [01:37,  9.47iter/s]Best score: 0.2036: : 1057iter [01:37, 10.11iter/s]Best score: 0.2036: : 1057iter [01:37, 10.11iter/s]Best score: 0.2036: : 1058iter [01:37, 10.11iter/s]Best score: 0.2036: : 1059iter [01:37, 10.65iter/s]Best score: 0.2036: : 1059iter [01:37, 10.65iter/s]Best score: 0.2036: : 1060iter [01:37, 10.65iter/s]Best score: 0.2036: : 1061iter [01:37, 10.89iter/s]Best score: 0.2036: : 1061iter [01:37, 10.89iter/s]Best score: 0.2036: : 1062iter [01:37, 10.89iter/s]Best score: 0.2036: : 1063iter [01:37, 11.24iter/s]Best score: 0.2036: : 1063iter [01:37, 11.24iter/s]Best score: 0.2036: : 1064iter [01:37, 11.24iter/s]Best score: 0.2036: : 1065iter [01:37, 11.51iter/s]Best score: 0.2036: : 1065iter [01:38, 11.51iter/s]Best score: 0.2036: : 1066iter [01:38, 11.51iter/s]Best score: 0.2036: : 1067iter [01:38, 11.65iter/s]Best score: 0.2036: : 1067iter [01:38, 11.65iter/s]Best score: 0.2036: : 1068iter [01:38, 11.65iter/s]Best score: 0.2036: : 1069iter [01:38, 11.79iter/s]Best score: 0.2036: : 1069iter [01:38, 11.79iter/s]Best score: 0.2036: : 1070iter [01:38, 11.79iter/s]Best score: 0.2036: : 1071iter [01:38, 11.68iter/s]Best score: 0.2036: : 1071iter [01:38, 11.68iter/s]Best score: 0.2036: : 1072iter [01:38, 11.68iter/s]Best score: 0.2036: : 1073iter [01:38, 11.79iter/s]Best score: 0.2036: : 1073iter [01:38, 11.79iter/s]Best score: 0.2036: : 1074iter [01:38, 11.79iter/s]Best score: 0.2036: : 1075iter [01:38, 11.83iter/s]Best score: 0.2036: : 1075iter [01:38, 11.83iter/s]Best score: 0.2036: : 1076iter [01:38, 11.83iter/s]Best score: 0.2036: : 1077iter [01:38, 11.92iter/s]Best score: 0.2036: : 1077iter [01:39, 11.92iter/s]Best score: 0.2036: : 1078iter [01:39, 11.92iter/s]Best score: 0.2036: : 1079iter [01:39, 11.98iter/s]Best score: 0.2036: : 1079iter [01:39, 11.98iter/s]Best score: 0.2036: : 1080iter [01:39, 11.98iter/s]Best score: 0.2036: : 1081iter [01:39, 11.73iter/s]Best score: 0.2036: : 1081iter [01:39, 11.73iter/s]Best score: 0.2036: : 1082iter [01:39, 11.73iter/s]Best score: 0.2036: : 1083iter [01:39, 11.67iter/s]Best score: 0.2036: : 1083iter [01:39, 11.67iter/s]Best score: 0.2036: : 1084iter [01:39, 11.67iter/s]Best score: 0.2036: : 1085iter [01:39, 11.79iter/s]Best score: 0.2036: : 1085iter [01:39, 11.79iter/s]Best score: 0.2036: : 1086iter [01:39, 11.79iter/s]Best score: 0.2036: : 1087iter [01:39, 11.91iter/s]Best score: 0.2036: : 1087iter [01:39, 11.91iter/s]Best score: 0.2036: : 1088iter [01:39, 11.91iter/s]Best score: 0.2036: : 1089iter [01:39, 11.98iter/s]Best score: 0.2036: : 1089iter [01:40, 11.98iter/s]Best score: 0.2036: : 1090iter [01:40, 11.98iter/s]Best score: 0.2036: : 1091iter [01:40, 11.78iter/s]Best score: 0.2036: : 1091iter [01:40, 11.78iter/s]Best score: 0.2036: : 1092iter [01:40, 11.78iter/s]Best score: 0.2036: : 1093iter [01:40, 11.81iter/s]Best score: 0.2036: : 1093iter [01:40, 11.81iter/s]Best score: 0.2036: : 1094iter [01:40, 11.81iter/s]Best score: 0.2036: : 1095iter [01:40, 11.86iter/s]Best score: 0.2036: : 1095iter [01:40, 11.86iter/s]Best score: 0.2036: : 1096iter [01:40, 11.86iter/s]Best score: 0.2036: : 1097iter [01:40, 11.88iter/s]Best score: 0.2036: : 1097iter [01:40, 11.88iter/s]Best score: 0.2036: : 1098iter [01:40, 11.88iter/s]Best score: 0.2036: : 1099iter [01:40, 11.94iter/s]
[ITER 1100] Best: 0.2031
[PARAMS] Current best parameters:
  fx: 548.5335
  fy: 548.9003
  px: 1001.2709
  py: 749.0585
  k1: -0.01093074
  k2: -0.01594588
  k3: 0.01339275
  k4: -0.00370571
  p1: 0.00104732
  p2: 0.00257565
  p3: 0.02330223
  p4: -0.02420224
  s1: -0.0291
  s2: 0.0249
  s3: -0.0243
  s4: 0.0247
  tilt_x: -0.0578
  tilt_y: 0.0023
  scale: 3.0074
  rotation: -0.0642
Best score: 0.2031: : 1099iter [01:40, 11.94iter/s]Best score: 0.2031: : 1100iter [01:41, 10.87iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.203132 -> 0.202528

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 100,588 → 92,296 within 3500mm ring
  Ring polish did not improve score: 0.202528
  Success: True
  Tangential distortion: p1=0.000782, p2=0.002362, p3=0.023716, p4=-0.023611
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2025
[✅] Parameters copied to: /working/MDC019_W001(6)_2025-06-27/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 548.5334979504265
    fy: 548.9003732075063
    px: 1001.2708635898193
    py: 749.0584572536254
    k1: -0.010960521596571971
    k2: -0.01577504512933749
    k3: 0.013270834807605108
    k4: -0.0036836660881274202
    p1: 0.000782077891897175
    p2: 0.0023623332318073137
    p3: 0.02371624736794063
    p4: -0.023610780211534566
    scale: 3.006843578708339
    rotation: -0.0642221986660278
    mmpp: 3.44685837359034
    nmpp: 3446858.3735903404

[🔄] Converting 893 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDC019_W001(6)_2025-06-27/calibration/debug/4_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC019_W001(6)_2025-06-27/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.
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[✅] LUT written to: /working/MDC019_W001(6)_2025-06-27/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC019_W001(6)_2025-06-27' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC019_W001(6)_2025-06-27/calibration/MDC019_W001(6)_2025-06-27_unknown.lut

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

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)
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                                                                [A[✅] 1500: /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/1500_heatmap_fitted.png
📁 Rigs (heatmap):  25%|██▌       | 1/4 [00:04<00:14,  4.72s/rig]
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                                                                [A[✅] 1000: /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap):  50%|█████     | 2/4 [00:07<00:07,  3.55s/rig]
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                                                                 [A[✅] 2000R: /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:11<00:03,  3.68s/rig]
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                                                                 [A[✅] 2000L: /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:15<00:00,  3.73s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:15<00:00,  3.78s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC019_W001(6)_2025-06-27/calibration/debug/5_heatmap
[🏁] Finished processing MDC019_W001(6)_2025-06-27


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