🟢 Starting Calibration Pipeline

=== Processing MDC101_W036_2025-12-31 ===

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

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 1303 stationary frames, 213 remain


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→ 1000 (213 images) Phase 1:  95%|█████████▍| 202/213 [00:03<00:00, 80.24it/s]

→ 1000 (213 images) Phase 1: 100%|██████████| 213/213 [00:03<00:00, 84.93it/s]

                                                                              

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→ 1000 Phase 2: corners:  26%|██▌       | 55/213 [00:00<00:01, 109.74it/s]

→ 1000 Phase 2: corners:  33%|███▎      | 70/213 [00:00<00:01, 121.53it/s]

→ 1000 Phase 2: corners:  39%|███▉      | 83/213 [00:00<00:01, 120.40it/s]

→ 1000 Phase 2: corners:  46%|████▋     | 99/213 [00:00<00:00, 129.66it/s]

→ 1000 Phase 2: corners:  54%|█████▎    | 114/213 [00:01<00:00, 133.47it/s]

→ 1000 Phase 2: corners:  61%|██████    | 129/213 [00:01<00:00, 132.04it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 144/213 [00:01<00:00, 135.25it/s]

→ 1000 Phase 2: corners:  74%|███████▍  | 158/213 [00:01<00:00, 133.45it/s]

→ 1000 Phase 2: corners:  81%|████████  | 172/213 [00:01<00:00, 133.24it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 186/213 [00:01<00:00, 134.33it/s]

                                                                           [PASS2] Frame 0000387: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000388: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000389: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000390: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000391: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000392: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000393: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000684: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000685: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000686: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000687: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001318: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001319: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001320: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001321: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000389: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000391: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000392: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000686: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001320: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 9 frames fixed, 7 untrusted, 213 total


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→ 1000 Phase 3: finalize:  91%|█████████ | 193/213 [00:03<00:00, 52.73it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 199/213 [00:03<00:00, 52.58it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 205/213 [00:04<00:00, 52.26it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 211/213 [00:04<00:00, 51.86it/s]

                                                                           
[TIMING] 1000 — 213 frames
  Phase 1  radial detect   (parallel)  :   3.36s  (15.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.65s  (7.7 ms/frame)
  Phase 2b corner fix      (sequential):   0.43s
  Phase 3  finalize        (sequential):   4.18s  (19.6 ms/frame)
    decode + undistort                 :   2.14s  (10.0 ms/frame)
    corner apply + refinement          :   1.01s  (4.8 ms/frame)
    segment                            :   0.84s  (3.9 ms/frame)
    smooth                             :   0.12s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.62s


Processing Rigs:  50%|█████     | 1/2 [00:14<00:14, 14.34s/it][INFO] Filtered 1045 stationary frames, 204 remain


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→ 500 (204 images) Phase 1:  91%|█████████ | 186/204 [00:02<00:00, 83.06it/s]

→ 500 (204 images) Phase 1:  99%|█████████▊| 201/204 [00:03<00:00, 77.60it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/204 [00:00<?, ?it/s]

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→ 500 Phase 2: corners:  21%|██        | 42/204 [00:00<00:01, 95.89it/s]

→ 500 Phase 2: corners:  26%|██▋       | 54/204 [00:00<00:01, 102.03it/s]

→ 500 Phase 2: corners:  32%|███▏      | 66/204 [00:00<00:01, 101.84it/s]

→ 500 Phase 2: corners:  39%|███▊      | 79/204 [00:00<00:01, 105.79it/s]

→ 500 Phase 2: corners:  46%|████▌     | 94/204 [00:01<00:00, 115.46it/s]

→ 500 Phase 2: corners:  54%|█████▍    | 110/204 [00:01<00:00, 125.88it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 128/204 [00:01<00:00, 138.14it/s]

→ 500 Phase 2: corners:  70%|███████   | 143/204 [00:01<00:00, 132.40it/s]

→ 500 Phase 2: corners:  77%|███████▋  | 157/204 [00:01<00:00, 129.68it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 171/204 [00:01<00:00, 126.21it/s]

→ 500 Phase 2: corners:  90%|█████████ | 184/204 [00:01<00:00, 123.14it/s]

                                                                          [PASS2] Summary: 0 frames fixed, 1 untrusted, 204 total


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→ 500 Phase 3: finalize:  80%|████████  | 164/204 [00:02<00:00, 54.63it/s]

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→ 500 Phase 3: finalize:  86%|████████▋ | 176/204 [00:03<00:00, 54.45it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 182/204 [00:03<00:00, 54.59it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 188/204 [00:03<00:00, 54.49it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 194/204 [00:03<00:00, 54.39it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 200/204 [00:03<00:00, 54.29it/s]

                                                                          
[TIMING] 500 — 204 frames
  Phase 1  radial detect   (parallel)  :   3.26s  (16.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.87s  (9.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.04s
  Phase 3  finalize        (sequential):   3.65s  (17.9 ms/frame)
    decode + undistort                 :   1.92s  (9.4 ms/frame)
    corner apply + refinement          :   0.76s  (3.7 ms/frame)
    segment                            :   0.79s  (3.9 ms/frame)
    smooth                             :   0.11s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.82s


Processing Rigs: 100%|██████████| 2/2 [00:27<00:00, 13.39s/it]
Processing Rigs: 100%|██████████| 2/2 [00:27<00:00, 13.53s/it]
[💾] Saved RigFrameDataset to /working/MDC101_W036_2025-12-31/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 417 frames from 2 rigs to /working/MDC101_W036_2025-12-31/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC101_W036_2025-12-31/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 213
[📋] 500: 100 frames selected from 204
[💾] Saved RigFrameDataset to /working/MDC101_W036_2025-12-31/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC101_W036_2025-12-31/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                      Step 2: Casing Detection                     
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-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames

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Computing max image from bright frames:  82%|████████▎ | 33/40 [01:40<00:20,  2.97s/it]
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Computing max image from bright frames:  95%|█████████▌| 38/40 [01:55<00:06,  3.06s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [01:58<00:03,  3.06s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:01<00:00,  3.05s/it]
                                                                                       
[✅] Casing detection complete: cx=993.0, cy=767.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:09,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:09,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:09,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:09,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:09,  9.13it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 77.04it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.060 | Best: {'fx': 533.333} (0.060):  27%|██▋       | 27/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.001 | Best: {'fx': 533.333} (0.060):  28%|██▊       | 28/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.004 | Best: {'fx': 533.333} (0.060):  29%|██▉       | 29/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.038 | Best: {'fx': 533.333} (0.060):  30%|███       | 30/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.037 | Best: {'fx': 533.333} (0.060):  31%|███       | 31/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.008 | Best: {'fx': 533.333} (0.060):  32%|███▏      | 32/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.012 | Best: {'fx': 533.333} (0.060):  33%|███▎      | 33/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.020 | Best: {'fx': 533.333} (0.060):  34%|███▍      | 34/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.017 | Best: {'fx': 533.333} (0.060):  35%|███▌      | 35/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.024 | Best: {'fx': 533.333} (0.060):  36%|███▌      | 36/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.021 | Best: {'fx': 533.333} (0.060):  37%|███▋      | 37/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.028 | Best: {'fx': 533.333} (0.060):  38%|███▊      | 38/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.019 | Best: {'fx': 533.333} (0.060):  39%|███▉      | 39/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.016 | Best: {'fx': 533.333} (0.060):  40%|████      | 40/100 [00:00<00:00, 90.57it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.016 | Best: {'fx': 533.333} (0.060):  41%|████      | 41/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.015 | Best: {'fx': 533.333} (0.060):  41%|████      | 41/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.014 | Best: {'fx': 533.333} (0.060):  42%|████▏     | 42/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.011 | Best: {'fx': 533.333} (0.060):  43%|████▎     | 43/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.014 | Best: {'fx': 533.333} (0.060):  44%|████▍     | 44/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.012 | Best: {'fx': 533.333} (0.060):  45%|████▌     | 45/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.013 | Best: {'fx': 533.333} (0.060):  46%|████▌     | 46/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.012 | Best: {'fx': 533.333} (0.060):  47%|████▋     | 47/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.012 | Best: {'fx': 533.333} (0.060):  48%|████▊     | 48/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.011 | Best: {'fx': 533.333} (0.060):  49%|████▉     | 49/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.011 | Best: {'fx': 533.333} (0.060):  50%|█████     | 50/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.011 | Best: {'fx': 533.333} (0.060):  51%|█████     | 51/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  52%|█████▏    | 52/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  53%|█████▎    | 53/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.011 | Best: {'fx': 533.333} (0.060):  54%|█████▍    | 54/100 [00:00<00:00, 109.18it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.011 | Best: {'fx': 533.333} (0.060):  55%|█████▌    | 55/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  55%|█████▌    | 55/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  56%|█████▌    | 56/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  57%|█████▋    | 57/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  58%|█████▊    | 58/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  59%|█████▉    | 59/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  60%|██████    | 60/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  61%|██████    | 61/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  62%|██████▏   | 62/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.010 | Best: {'fx': 533.333} (0.060):  63%|██████▎   | 63/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  64%|██████▍   | 64/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  65%|██████▌   | 65/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  66%|██████▌   | 66/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  67%|██████▋   | 67/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  68%|██████▊   | 68/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  69%|██████▉   | 69/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  70%|███████   | 70/100 [00:00<00:00, 119.21it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  71%|███████   | 71/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  71%|███████   | 71/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  72%|███████▏  | 72/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  73%|███████▎  | 73/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  74%|███████▍  | 74/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  75%|███████▌  | 75/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  76%|███████▌  | 76/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  77%|███████▋  | 77/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  78%|███████▊  | 78/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  79%|███████▉  | 79/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  80%|████████  | 80/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  81%|████████  | 81/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  82%|████████▏ | 82/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  83%|████████▎ | 83/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  84%|████████▍ | 84/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  85%|████████▌ | 85/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  86%|████████▌ | 86/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  87%|████████▋ | 87/100 [00:00<00:00, 130.64it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  88%|████████▊ | 88/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  88%|████████▊ | 88/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  89%|████████▉ | 89/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  90%|█████████ | 90/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  91%|█████████ | 91/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  92%|█████████▏| 92/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  93%|█████████▎| 93/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  94%|█████████▍| 94/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  95%|█████████▌| 95/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  96%|█████████▌| 96/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  97%|█████████▋| 97/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  98%|█████████▊| 98/100 [00:00<00:00, 141.76it/s]
Brute | Start: {'fx': 500.0} (0.006) | Current: 0.009 | Best: {'fx': 533.333} (0.060):  99%|█████████▉| 99/100 [00:00<00:00, 141.76it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   2%|▏         | 1/50 [00:00<00:05,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.010 | Best: {'fx': 509.354} (0.010):   2%|▏         | 1/50 [00:00<00:05,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.014 | Best: {'fx': 515.476} (0.014):   4%|▍         | 2/50 [00:00<00:05,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.011 | Best: {'fx': 515.476} (0.014):   6%|▌         | 3/50 [00:00<00:05,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.012 | Best: {'fx': 515.476} (0.014):   8%|▊         | 4/50 [00:00<00:05,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.016 | Best: {'fx': 516.497} (0.016):  10%|█         | 5/50 [00:00<00:05,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.012 | Best: {'fx': 516.497} (0.016):  12%|█▏        | 6/50 [00:00<00:05,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.017 | Best: {'fx': 517.517} (0.017):  14%|█▍        | 7/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.012 | Best: {'fx': 517.517} (0.017):  16%|█▌        | 8/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.012 | Best: {'fx': 517.517} (0.017):  18%|█▊        | 9/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.020 | Best: {'fx': 519.558} (0.020):  20%|██        | 10/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.022 | Best: {'fx': 520.578} (0.022):  22%|██▏       | 11/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.019 | Best: {'fx': 520.578} (0.022):  24%|██▍       | 12/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.028 | Best: {'fx': 522.619} (0.028):  26%|██▌       | 13/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.024 | Best: {'fx': 522.619} (0.028):  28%|██▊       | 14/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.031 | Best: {'fx': 523.639} (0.031):  30%|███       | 15/50 [00:00<00:04,  8.69it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.031 | Best: {'fx': 523.639} (0.031):  32%|███▏      | 16/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.033 | Best: {'fx': 524.66} (0.033):  32%|███▏      | 16/50 [00:00<00:00, 69.39it/s] 
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.036 | Best: {'fx': 525.68} (0.036):  34%|███▍      | 17/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.039 | Best: {'fx': 526.701} (0.039):  36%|███▌      | 18/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.045 | Best: {'fx': 527.721} (0.045):  38%|███▊      | 19/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.046 | Best: {'fx': 528.741} (0.046):  40%|████      | 20/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.053 | Best: {'fx': 529.762} (0.053):  42%|████▏     | 21/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.062 | Best: {'fx': 530.782} (0.062):  44%|████▍     | 22/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.061 | Best: {'fx': 530.782} (0.062):  46%|████▌     | 23/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.058 | Best: {'fx': 530.782} (0.062):  48%|████▊     | 24/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.062 | Best: {'fx': 530.782} (0.062):  50%|█████     | 25/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.037 | Best: {'fx': 530.782} (0.062):  52%|█████▏    | 26/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.039 | Best: {'fx': 530.782} (0.062):  54%|█████▍    | 27/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.053 | Best: {'fx': 530.782} (0.062):  56%|█████▌    | 28/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.050 | Best: {'fx': 530.782} (0.062):  58%|█████▊    | 29/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.045 | Best: {'fx': 530.782} (0.062):  60%|██████    | 30/50 [00:00<00:00, 69.39it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.045 | Best: {'fx': 530.782} (0.062):  62%|██████▏   | 31/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.033 | Best: {'fx': 530.782} (0.062):  62%|██████▏   | 31/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.035 | Best: {'fx': 530.782} (0.062):  64%|██████▍   | 32/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.032 | Best: {'fx': 530.782} (0.062):  66%|██████▌   | 33/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.046 | Best: {'fx': 530.782} (0.062):  68%|██████▊   | 34/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.031 | Best: {'fx': 530.782} (0.062):  70%|███████   | 35/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.030 | Best: {'fx': 530.782} (0.062):  72%|███████▏  | 36/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.029 | Best: {'fx': 530.782} (0.062):  74%|███████▍  | 37/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.025 | Best: {'fx': 530.782} (0.062):  76%|███████▌  | 38/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.027 | Best: {'fx': 530.782} (0.062):  78%|███████▊  | 39/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.028 | Best: {'fx': 530.782} (0.062):  80%|████████  | 40/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.026 | Best: {'fx': 530.782} (0.062):  82%|████████▏ | 41/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.027 | Best: {'fx': 530.782} (0.062):  84%|████████▍ | 42/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.025 | Best: {'fx': 530.782} (0.062):  86%|████████▌ | 43/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.023 | Best: {'fx': 530.782} (0.062):  88%|████████▊ | 44/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.025 | Best: {'fx': 530.782} (0.062):  90%|█████████ | 45/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.023 | Best: {'fx': 530.782} (0.062):  92%|█████████▏| 46/50 [00:00<00:00, 96.70it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.023 | Best: {'fx': 530.782} (0.062):  94%|█████████▍| 47/50 [00:00<00:00, 117.43it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.023 | Best: {'fx': 530.782} (0.062):  94%|█████████▍| 47/50 [00:00<00:00, 117.43it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.024 | Best: {'fx': 530.782} (0.062):  96%|█████████▌| 48/50 [00:00<00:00, 117.43it/s]
Brute | Start: {'fx': 533.333} (0.060) | Current: 0.022 | Best: {'fx': 530.782} (0.062):  98%|█████████▊| 49/50 [00:00<00:00, 117.43it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.7823129251701
    px: 993.0
    py: 767.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization

Initial score: 333.6805: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 333.6805, picklable_objective(x0) = 333.8379

Best score: 333.8379: : 0iter [00:01, ?iter/s] 
Best score: 333.8379: : 1iter [00:01,  1.96s/iter]
Best score: 333.8379: : 1iter [00:01,  1.96s/iter]
Best score: 333.8379: : 2iter [00:02,  1.96s/iter]
Best score: 333.8379: : 3iter [00:02,  1.96s/iter]
Best score: 333.8379: : 4iter [00:02,  1.96s/iter]
Best score: 333.8379: : 5iter [00:02,  3.19iter/s]
Best score: 333.8379: : 5iter [00:02,  3.19iter/s]
Best score: 333.8379: : 6iter [00:02,  3.19iter/s]
Best score: 333.8379: : 7iter [00:02,  3.19iter/s]
Best score: 333.8379: : 8iter [00:02,  3.19iter/s]
Best score: 333.8379: : 9iter [00:02,  6.43iter/s]
Best score: 333.8379: : 9iter [00:02,  6.43iter/s]
Best score: 333.8379: : 10iter [00:02,  6.43iter/s]
Best score: 333.8379: : 11iter [00:02,  6.43iter/s]
Best score: 333.8379: : 12iter [00:02,  6.43iter/s]
Best score: 333.8379: : 13iter [00:02, 10.15iter/s]
Best score: 333.8379: : 13iter [00:02, 10.15iter/s]
Best score: 333.8379: : 14iter [00:02, 10.15iter/s]
Best score: 333.8379: : 15iter [00:02, 10.15iter/s]
Best score: 333.8379: : 16iter [00:02, 10.15iter/s]
Best score: 333.8379: : 17iter [00:02, 14.23iter/s]
Best score: 333.8379: : 17iter [00:02, 14.23iter/s]
Best score: 333.8379: : 18iter [00:02, 14.23iter/s]
Best score: 333.8379: : 19iter [00:02, 14.23iter/s]
Best score: 333.8379: : 20iter [00:02, 14.23iter/s]
Best score: 333.8379: : 21iter [00:02, 18.24iter/s]
Best score: 333.8379: : 21iter [00:02, 18.24iter/s]
Best score: 333.8379: : 22iter [00:02, 18.24iter/s]
Best score: 333.8379: : 23iter [00:02, 18.24iter/s]
Best score: 333.8379: : 24iter [00:02, 18.24iter/s]
Best score: 333.8379: : 25iter [00:02, 22.24iter/s]
Best score: 333.8379: : 25iter [00:02, 22.24iter/s]
Best score: 333.8379: : 26iter [00:02, 22.24iter/s]
Best score: 333.8379: : 27iter [00:02, 22.24iter/s]
Best score: 333.8379: : 28iter [00:02, 22.24iter/s]
Best score: 333.8379: : 29iter [00:02, 25.87iter/s]
Best score: 333.8379: : 29iter [00:02, 25.87iter/s]
Best score: 333.8379: : 30iter [00:02, 25.87iter/s]
Best score: 333.8379: : 31iter [00:02, 25.87iter/s]
Best score: 333.8379: : 32iter [00:02, 25.87iter/s]
Best score: 333.8379: : 33iter [00:02, 28.88iter/s]
Best score: 333.8379: : 33iter [00:02, 28.88iter/s]
Best score: 333.8379: : 34iter [00:02, 28.88iter/s]
Best score: 333.8379: : 35iter [00:02, 28.88iter/s]
Best score: 333.8379: : 36iter [00:02, 28.88iter/s]
Best score: 333.8379: : 37iter [00:02, 31.45iter/s]
Best score: 333.8379: : 37iter [00:02, 31.45iter/s]
Best score: 333.8379: : 38iter [00:02, 31.45iter/s]
Best score: 333.8379: : 39iter [00:02, 31.45iter/s]
Best score: 333.8379: : 40iter [00:02, 31.45iter/s]
Best score: 333.8379: : 41iter [00:02, 33.23iter/s]
Best score: 333.8379: : 41iter [00:03, 33.23iter/s]
Best score: 333.8379: : 42iter [00:03, 33.23iter/s]
Best score: 333.8379: : 43iter [00:03, 33.23iter/s]
Best score: 333.8379: : 44iter [00:03, 33.23iter/s]
Best score: 333.8379: : 45iter [00:03, 34.88iter/s]
Best score: 333.8379: : 45iter [00:03, 34.88iter/s]
Best score: 333.8379: : 46iter [00:03, 34.88iter/s]
Best score: 333.8379: : 47iter [00:03, 34.88iter/s]
Best score: 333.8379: : 48iter [00:03, 34.88iter/s]
Best score: 333.8379: : 49iter [00:03, 36.11iter/s]
[ITER 50] Best: 333.8379
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 993.0000
  py: 767.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 333.8379: : 49iter [00:03, 36.11iter/s]
Best score: 333.8379: : 50iter [00:03, 36.11iter/s]
Best score: 333.8379: : 51iter [00:03, 36.11iter/s]
Best score: 333.8379: : 52iter [00:03, 36.11iter/s]
Best score: 333.8379: : 53iter [00:03, 36.86iter/s]
Best score: 333.8379: : 53iter [00:03, 36.86iter/s]
Best score: 333.8379: : 54iter [00:03, 36.86iter/s]
Best score: 333.8379: : 55iter [00:03, 36.86iter/s]
Best score: 333.8379: : 56iter [00:03, 36.86iter/s]
Best score: 333.8379: : 57iter [00:03, 37.61iter/s]
Best score: 333.8379: : 57iter [00:03, 37.61iter/s]
Best score: 333.8379: : 58iter [00:03, 37.61iter/s]
Best score: 333.8379: : 59iter [00:03, 37.61iter/s]
Best score: 333.8379: : 60iter [00:03, 37.61iter/s]
Best score: 333.8379: : 61iter [00:03, 37.90iter/s]
Best score: 333.8379: : 61iter [00:03, 37.90iter/s]
Best score: 333.8379: : 62iter [00:03, 37.90iter/s]
Best score: 333.8379: : 63iter [00:03, 37.90iter/s]
Best score: 333.8379: : 64iter [00:03, 37.90iter/s]
Best score: 333.8379: : 65iter [00:03, 38.43iter/s]
Best score: 333.8379: : 65iter [00:03, 38.43iter/s]
Best score: 333.8379: : 66iter [00:03, 38.43iter/s]
Best score: 333.8379: : 67iter [00:03, 38.43iter/s]
Best score: 333.8379: : 68iter [00:03, 38.43iter/s]
Best score: 333.8379: : 69iter [00:03, 38.73iter/s]
Best score: 333.8379: : 69iter [00:03, 38.73iter/s]
Best score: 333.8379: : 70iter [00:03, 38.73iter/s]
Best score: 333.8379: : 71iter [00:03, 38.73iter/s]
Best score: 333.8379: : 72iter [00:03, 38.73iter/s]
Best score: 333.8379: : 73iter [00:03, 38.66iter/s]
Best score: 333.8379: : 73iter [00:03, 38.66iter/s]
Best score: 333.8379: : 74iter [00:03, 38.66iter/s]
Best score: 333.8379: : 75iter [00:03, 38.66iter/s]
Best score: 333.8379: : 76iter [00:03, 38.66iter/s]
Best score: 333.8379: : 77iter [00:03, 38.94iter/s]
Best score: 333.8379: : 77iter [00:03, 38.94iter/s]
Best score: 333.8379: : 78iter [00:03, 38.94iter/s]
Best score: 333.8379: : 79iter [00:03, 38.94iter/s]
Best score: 333.8379: : 80iter [00:04, 38.94iter/s]
Best score: 333.8379: : 81iter [00:04, 38.87iter/s]
Best score: 333.8379: : 81iter [00:04, 38.87iter/s]
Best score: 333.8379: : 82iter [00:04, 38.87iter/s]
Best score: 333.8379: : 83iter [00:04, 38.87iter/s]
Best score: 333.8379: : 84iter [00:04, 38.87iter/s]
Best score: 333.8379: : 85iter [00:04, 39.09iter/s]
Best score: 333.8379: : 85iter [00:04, 39.09iter/s]
Best score: 333.8379: : 86iter [00:04, 39.09iter/s]
Best score: 333.8379: : 87iter [00:04, 39.09iter/s]
Best score: 333.8379: : 88iter [00:04, 39.09iter/s]
Best score: 333.8379: : 89iter [00:04, 39.32iter/s]
Best score: 333.8379: : 89iter [00:04, 39.32iter/s]
Best score: 333.8379: : 90iter [00:04, 39.32iter/s]
Best score: 333.8379: : 91iter [00:04, 39.32iter/s]
Best score: 333.8379: : 92iter [00:04, 39.32iter/s]
Best score: 333.8379: : 93iter [00:04, 39.08iter/s]
Best score: 333.8379: : 93iter [00:04, 39.08iter/s]
Best score: 333.8379: : 94iter [00:04, 39.08iter/s]
Best score: 333.8379: : 95iter [00:04, 39.08iter/s]
Best score: 333.8379: : 96iter [00:04, 39.08iter/s]
Best score: 333.8379: : 97iter [00:04, 39.11iter/s]
Best score: 333.8379: : 97iter [00:04, 39.11iter/s]
Best score: 333.8379: : 98iter [00:04, 39.11iter/s]
[ITER 100] Best: 333.8379
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 993.0000
  py: 767.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 333.8379: : 99iter [00:04, 39.11iter/s]
Best score: 333.8379: : 100iter [00:04, 39.11iter/s]
Best score: 333.8379: : 101iter [00:04, 36.35iter/s]
Best score: 333.8379: : 101iter [00:04, 36.35iter/s]
Best score: 333.8379: : 102iter [00:04, 36.35iter/s]
Best score: 333.8379: : 103iter [00:04, 36.35iter/s]
Best score: 333.8379: : 104iter [00:04, 36.35iter/s]
Best score: 333.8379: : 105iter [00:04, 37.22iter/s]
Best score: 333.8379: : 105iter [00:04, 37.22iter/s]
Best score: 333.8379: : 106iter [00:04, 37.22iter/s]
Best score: 333.8379: : 107iter [00:04, 37.22iter/s]
Best score: 333.8379: : 108iter [00:04, 37.22iter/s]
Best score: 333.8379: : 109iter [00:04, 37.83iter/s]
Best score: 333.8379: : 109iter [00:04, 37.83iter/s]
Best score: 333.8379: : 110iter [00:04, 37.83iter/s]
Best score: 333.8379: : 111iter [00:04, 37.83iter/s]
Best score: 333.8379: : 112iter [00:04, 37.83iter/s]
Best score: 333.8379: : 113iter [00:04, 38.02iter/s]
Best score: 333.8379: : 113iter [00:04, 38.02iter/s]
Best score: 333.8379: : 114iter [00:04, 38.02iter/s]
Best score: 333.8379: : 115iter [00:04, 38.02iter/s]
Best score: 333.8379: : 116iter [00:04, 38.02iter/s]
Best score: 333.8379: : 117iter [00:04, 38.54iter/s]
Best score: 333.8379: : 117iter [00:04, 38.54iter/s]
Best score: 333.8379: : 118iter [00:05, 38.54iter/s]
Best score: 333.8379: : 119iter [00:05, 38.54iter/s]
Best score: 333.8379: : 120iter [00:05, 38.54iter/s]
Best score: 333.8379: : 121iter [00:05, 38.68iter/s]
Best score: 333.8379: : 121iter [00:05, 38.68iter/s]
Best score: 333.8379: : 122iter [00:05, 38.68iter/s]
Best score: 333.8379: : 123iter [00:05, 38.68iter/s]
Best score: 333.8379: : 124iter [00:05, 38.68iter/s]
Best score: 333.8379: : 125iter [00:05, 38.82iter/s]
Best score: 333.8379: : 125iter [00:05, 38.82iter/s]
Best score: 333.8379: : 126iter [00:05, 38.82iter/s]
Best score: 333.8379: : 127iter [00:05, 38.82iter/s]
Best score: 333.8379: : 128iter [00:05, 38.82iter/s]
Best score: 333.8379: : 129iter [00:05, 39.10iter/s]
Best score: 333.8379: : 129iter [00:05, 39.10iter/s]
Best score: 333.8379: : 130iter [00:05, 39.10iter/s]
Best score: 333.8379: : 131iter [00:05, 39.10iter/s]
Best score: 333.8379: : 132iter [00:05, 39.10iter/s]
Best score: 333.8379: : 133iter [00:05, 38.97iter/s]
Best score: 333.8379: : 133iter [00:05, 38.97iter/s]
Best score: 333.8379: : 134iter [00:05, 38.97iter/s]
Best score: 333.8379: : 135iter [00:05, 38.97iter/s]
Best score: 333.8379: : 136iter [00:05, 38.97iter/s]
Best score: 333.8379: : 137iter [00:05, 39.14iter/s]
Best score: 333.8379: : 137iter [00:05, 39.14iter/s]
Best score: 333.8379: : 138iter [00:05, 39.14iter/s]
Best score: 333.8379: : 139iter [00:05, 39.14iter/s]
Best score: 333.8379: : 140iter [00:05, 39.14iter/s]
Best score: 333.8379: : 141iter [00:05, 39.01iter/s]
Best score: 333.8379: : 141iter [00:05, 39.01iter/s]
Best score: 333.8379: : 142iter [00:05, 39.01iter/s]
Best score: 333.8379: : 143iter [00:05, 39.01iter/s]
Best score: 333.8379: : 144iter [00:05, 39.01iter/s]
Best score: 333.8379: : 145iter [00:05, 39.07iter/s]
Best score: 333.8379: : 145iter [00:05, 39.07iter/s]
Best score: 333.8379: : 146iter [00:05, 39.07iter/s]
Best score: 333.8379: : 147iter [00:05, 39.07iter/s]
Best score: 333.8379: : 148iter [00:05, 39.07iter/s]
Best score: 333.8379: : 149iter [00:05, 39.10iter/s]
[ITER 150] Best: 333.8379
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 993.0000
  py: 767.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 333.8379: : 149iter [00:05, 39.10iter/s]
Best score: 333.8379: : 150iter [00:05, 39.10iter/s]
Best score: 333.8379: : 151iter [00:05, 39.10iter/s]
Best score: 333.8379: : 152iter [00:05, 39.10iter/s]
Best score: 333.8379: : 153iter [00:05, 31.85iter/s]
Best score: 333.8379: : 153iter [00:05, 31.85iter/s]
Best score: 333.8379: : 154iter [00:06, 31.85iter/s]
Best score: 333.8379: : 155iter [00:06, 31.85iter/s]
Best score: 333.8379: : 156iter [00:06, 31.85iter/s]
Best score: 333.8379: : 157iter [00:06, 33.76iter/s]
Best score: 333.8379: : 157iter [00:06, 33.76iter/s]
Best score: 333.8379: : 158iter [00:06, 33.76iter/s]
Best score: 182.5531: : 159iter [00:06, 33.76iter/s]
Best score: 182.5531: : 160iter [00:06, 33.76iter/s]
Best score: 182.5531: : 161iter [00:06, 35.08iter/s]
Best score: 182.5531: : 161iter [00:06, 35.08iter/s]
Best score: 182.5531: : 162iter [00:06, 35.08iter/s]
Best score: 182.5531: : 163iter [00:06, 35.08iter/s]
Best score: 182.5531: : 164iter [00:06, 35.08iter/s]
Best score: 182.5531: : 165iter [00:06, 36.42iter/s]
Best score: 182.5531: : 165iter [00:06, 36.42iter/s]
Best score: 182.5531: : 166iter [00:06, 36.42iter/s]
Best score: 182.5531: : 167iter [00:06, 36.42iter/s]
Best score: 182.5531: : 168iter [00:06, 36.42iter/s]
Best score: 182.5531: : 169iter [00:06, 37.22iter/s]
Best score: 182.5531: : 169iter [00:06, 37.22iter/s]
Best score: 182.5531: : 170iter [00:06, 37.22iter/s]
Best score: 182.5531: : 171iter [00:06, 37.22iter/s]
Best score: 182.5531: : 172iter [00:06, 37.22iter/s]
Best score: 182.5531: : 173iter [00:06, 37.57iter/s]
Best score: 182.5531: : 173iter [00:06, 37.57iter/s]
Best score: 182.5531: : 174iter [00:06, 37.57iter/s]
Best score: 182.5531: : 175iter [00:06, 37.57iter/s]
Best score: 182.5531: : 176iter [00:06, 37.57iter/s]
Best score: 182.5531: : 177iter [00:06, 38.11iter/s]
Best score: 182.5531: : 177iter [00:06, 38.11iter/s]
Best score: 182.5531: : 178iter [00:06, 38.11iter/s]
Best score: 167.5271: : 179iter [00:06, 38.11iter/s]
Best score: 167.5271: : 180iter [00:06, 38.11iter/s]
Best score: 167.5271: : 181iter [00:06, 38.30iter/s]
Best score: 167.5271: : 181iter [00:06, 38.30iter/s]
Best score: 167.5271: : 182iter [00:06, 38.30iter/s]
Best score: 167.5271: : 183iter [00:06, 38.30iter/s]
Best score: 167.5271: : 184iter [00:06, 38.30iter/s]
Best score: 167.5271: : 185iter [00:06, 38.58iter/s]
Best score: 167.5271: : 185iter [00:06, 38.58iter/s]
Best score: 167.5271: : 186iter [00:06, 38.58iter/s]
Best score: 167.5271: : 187iter [00:06, 38.58iter/s]
Best score: 167.5271: : 188iter [00:06, 38.58iter/s]
Best score: 167.5271: : 189iter [00:06, 38.82iter/s]
Best score: 167.5271: : 189iter [00:06, 38.82iter/s]
Best score: 167.5271: : 190iter [00:06, 38.82iter/s]
Best score: 167.5271: : 191iter [00:06, 38.82iter/s]
Best score: 167.5271: : 192iter [00:06, 38.82iter/s]
Best score: 167.5271: : 193iter [00:06, 38.72iter/s]
Best score: 167.5271: : 193iter [00:07, 38.72iter/s]
Best score: 167.5271: : 194iter [00:07, 38.72iter/s]
Best score: 167.5271: : 195iter [00:07, 38.72iter/s]
Best score: 167.5271: : 196iter [00:07, 38.72iter/s]
Best score: 167.5271: : 197iter [00:07, 38.97iter/s]
Best score: 167.5271: : 197iter [00:07, 38.97iter/s]
Best score: 167.5271: : 198iter [00:07, 38.97iter/s]
[ITER 200] Best: 167.5271
[PARAMS] Current best parameters:
  fx: 504.1904
  fy: 504.6522
  px: 998.0040
  py: 780.7238
  k1: -0.00898738
  k2: 0.02145044
  k3: 0.00949206
  k4: -0.00567001
  p1: 0.00154996
  p2: 0.02505590
  p3: -0.01881542
  p4: -0.02355107
  s1: -0.0186
  s2: 0.0071
  s3: -0.0033
  s4: 0.0103
  tilt_x: 0.0422
  tilt_y: -0.0491
  scale: 2.9474
  rotation: -1.0240

Best score: 167.5271: : 199iter [00:07, 38.97iter/s]
Best score: 167.5271: : 200iter [00:07, 38.97iter/s]
Best score: 167.5271: : 201iter [00:07, 31.64iter/s]
Best score: 167.5271: : 201iter [00:07, 31.64iter/s]
Best score: 167.5271: : 202iter [00:07, 31.64iter/s]
Best score: 167.5271: : 203iter [00:07, 31.64iter/s]
Best score: 167.5271: : 204iter [00:07, 31.64iter/s]
Best score: 167.5271: : 205iter [00:07, 31.64iter/s]
Best score: 167.5271: : 206iter [00:07, 34.17iter/s]
Best score: 167.5271: : 206iter [00:07, 34.17iter/s]
Best score: 167.5271: : 207iter [00:07, 34.17iter/s]
Best score: 167.5271: : 208iter [00:07, 34.17iter/s]
Best score: 119.6858: : 209iter [00:07, 34.17iter/s]
Best score: 119.6858: : 210iter [00:07, 35.38iter/s]
Best score: 119.6858: : 210iter [00:07, 35.38iter/s]
Best score: 119.6858: : 211iter [00:07, 35.38iter/s]
Best score: 119.6858: : 212iter [00:07, 35.38iter/s]
Best score: 119.6858: : 213iter [00:07, 35.38iter/s]
Best score: 119.6858: : 214iter [00:07, 36.51iter/s]
Best score: 119.6858: : 214iter [00:07, 36.51iter/s]
Best score: 119.6858: : 215iter [00:07, 36.51iter/s]
Best score: 119.6858: : 216iter [00:07, 36.51iter/s]
Best score: 119.6858: : 217iter [00:07, 36.51iter/s]
Best score: 119.6858: : 218iter [00:07, 36.51iter/s]
Best score: 119.6858: : 219iter [00:07, 37.61iter/s]
Best score: 81.7555: : 219iter [00:07, 37.61iter/s] 
Best score: 81.7555: : 220iter [00:07, 37.61iter/s]
Best score: 81.7555: : 221iter [00:07, 37.61iter/s]
Best score: 81.7555: : 222iter [00:07, 37.61iter/s]
Best score: 81.7555: : 223iter [00:07, 37.94iter/s]
Best score: 81.7555: : 223iter [00:07, 37.94iter/s]
Best score: 81.7555: : 224iter [00:07, 37.94iter/s]
Best score: 81.7555: : 225iter [00:07, 37.94iter/s]
Best score: 81.7555: : 226iter [00:07, 37.94iter/s]
Best score: 81.7555: : 227iter [00:07, 37.94iter/s]
Best score: 81.7555: : 228iter [00:07, 38.68iter/s]
Best score: 81.7555: : 228iter [00:07, 38.68iter/s]
Best score: 75.3274: : 229iter [00:07, 38.68iter/s]
Best score: 75.3274: : 230iter [00:08, 38.68iter/s]
Best score: 75.3274: : 231iter [00:08, 38.68iter/s]
Best score: 75.3274: : 232iter [00:08, 38.83iter/s]
Best score: 75.3274: : 232iter [00:08, 38.83iter/s]
Best score: 75.3274: : 233iter [00:08, 38.83iter/s]
Best score: 75.3274: : 234iter [00:08, 38.83iter/s]
Best score: 75.3274: : 235iter [00:08, 38.83iter/s]
Best score: 75.3274: : 236iter [00:08, 38.83iter/s]
Best score: 75.3274: : 237iter [00:08, 39.38iter/s]
Best score: 75.3274: : 237iter [00:08, 39.38iter/s]
Best score: 75.3274: : 238iter [00:08, 39.38iter/s]
Best score: 75.3274: : 239iter [00:08, 39.38iter/s]
Best score: 75.3274: : 240iter [00:08, 39.38iter/s]
Best score: 75.3274: : 241iter [00:08, 39.28iter/s]
Best score: 75.3274: : 241iter [00:08, 39.28iter/s]
Best score: 75.3274: : 242iter [00:08, 39.28iter/s]
Best score: 75.3274: : 243iter [00:08, 39.28iter/s]
Best score: 75.3274: : 244iter [00:08, 39.28iter/s]
Best score: 75.3274: : 245iter [00:08, 39.31iter/s]
Best score: 75.3274: : 245iter [00:08, 39.31iter/s]
Best score: 75.3274: : 246iter [00:08, 39.31iter/s]
Best score: 75.3274: : 247iter [00:08, 39.31iter/s]
Best score: 75.3274: : 248iter [00:08, 39.31iter/s]
[ITER 250] Best: 42.0721
[PARAMS] Current best parameters:
  fx: 519.8215
  fy: 518.3156
  px: 1000.7982
  py: 778.2941
  k1: -0.06891015
  k2: 0.03284669
  k3: 0.03098261
  k4: -0.01498664
  p1: 0.00205535
  p2: 0.03870558
  p3: -0.03914864
  p4: 0.00070645
  s1: -0.0479
  s2: 0.0144
  s3: 0.0011
  s4: 0.0241
  tilt_x: -0.0050
  tilt_y: -0.0464
  scale: 3.1486
  rotation: 0.6454

Best score: 42.0721: : 249iter [00:08, 39.31iter/s]
Best score: 42.0721: : 250iter [00:08, 39.40iter/s]
Best score: 42.0721: : 250iter [00:08, 39.40iter/s]
Best score: 42.0721: : 251iter [00:08, 39.40iter/s]
Best score: 42.0721: : 252iter [00:08, 39.40iter/s]
Best score: 42.0721: : 253iter [00:08, 39.40iter/s]
Best score: 42.0721: : 254iter [00:08, 32.56iter/s]
Best score: 42.0721: : 254iter [00:08, 32.56iter/s]
Best score: 42.0721: : 255iter [00:08, 32.56iter/s]
Best score: 42.0721: : 256iter [00:08, 32.56iter/s]
Best score: 42.0721: : 257iter [00:08, 32.56iter/s]
Best score: 42.0721: : 258iter [00:08, 32.56iter/s]
Best score: 42.0721: : 259iter [00:08, 34.76iter/s]
Best score: 42.0721: : 259iter [00:08, 34.76iter/s]
Best score: 42.0721: : 260iter [00:08, 34.76iter/s]
Best score: 42.0721: : 261iter [00:08, 34.76iter/s]
Best score: 42.0721: : 262iter [00:08, 34.76iter/s]
Best score: 42.0721: : 263iter [00:08, 35.98iter/s]
Best score: 42.0721: : 263iter [00:08, 35.98iter/s]
Best score: 42.0721: : 264iter [00:08, 35.98iter/s]
Best score: 42.0721: : 265iter [00:08, 35.98iter/s]
Best score: 42.0721: : 266iter [00:08, 35.98iter/s]
Best score: 42.0721: : 267iter [00:09, 35.98iter/s]
Best score: 42.0721: : 268iter [00:09, 37.40iter/s]
Best score: 42.0721: : 268iter [00:09, 37.40iter/s]
Best score: 24.6127: : 269iter [00:09, 37.40iter/s]
Best score: 24.6127: : 270iter [00:09, 37.40iter/s]
Best score: 24.6127: : 271iter [00:09, 37.40iter/s]
Best score: 24.6127: : 272iter [00:09, 37.85iter/s]
Best score: 24.6127: : 272iter [00:09, 37.85iter/s]
Best score: 24.6127: : 273iter [00:09, 37.85iter/s]
Best score: 24.6127: : 274iter [00:09, 37.85iter/s]
Best score: 24.6127: : 275iter [00:09, 37.85iter/s]
Best score: 24.6127: : 276iter [00:09, 37.85iter/s]
Best score: 24.6127: : 277iter [00:09, 38.62iter/s]
Best score: 24.6127: : 277iter [00:09, 38.62iter/s]
Best score: 24.6127: : 278iter [00:09, 38.62iter/s]
Best score: 14.2277: : 279iter [00:09, 38.62iter/s]
Best score: 14.2277: : 280iter [00:09, 38.62iter/s]
Best score: 14.2277: : 281iter [00:09, 38.86iter/s]
Best score: 14.2277: : 281iter [00:09, 38.86iter/s]
Best score: 14.2277: : 282iter [00:09, 38.86iter/s]
Best score: 14.2277: : 283iter [00:09, 38.86iter/s]
Best score: 14.2277: : 284iter [00:09, 38.86iter/s]
Best score: 14.2277: : 285iter [00:09, 39.08iter/s]
Best score: 14.2277: : 285iter [00:09, 39.08iter/s]
Best score: 14.2277: : 286iter [00:09, 39.08iter/s]
Best score: 14.2277: : 287iter [00:09, 39.08iter/s]
Best score: 14.2277: : 288iter [00:09, 39.08iter/s]
Best score: 14.2277: : 289iter [00:09, 39.28iter/s]
Best score: 9.9503: : 289iter [00:09, 39.28iter/s] 
Best score: 9.9503: : 290iter [00:09, 39.28iter/s]
Best score: 9.9503: : 291iter [00:09, 39.28iter/s]
Best score: 9.9503: : 292iter [00:09, 39.28iter/s]
Best score: 9.9503: : 293iter [00:09, 39.01iter/s]
Best score: 9.9503: : 293iter [00:09, 39.01iter/s]
Best score: 9.9503: : 294iter [00:09, 39.01iter/s]
Best score: 9.9503: : 295iter [00:09, 39.01iter/s]
Best score: 9.9503: : 296iter [00:09, 39.01iter/s]
Best score: 9.9503: : 297iter [00:09, 39.01iter/s]
Best score: 9.9503: : 298iter [00:09, 39.36iter/s]
Best score: 9.9503: : 298iter [00:09, 39.36iter/s]
[ITER 300] Best: 9.9503
[PARAMS] Current best parameters:
  fx: 509.9247
  fy: 514.1967
  px: 1003.1215
  py: 791.3057
  k1: -0.09861721
  k2: 0.19158111
  k3: -0.10495078
  k4: 0.01809218
  p1: 0.00415819
  p2: 0.03553206
  p3: 0.01082392
  p4: 0.00168240
  s1: -0.0286
  s2: -0.0087
  s3: 0.0070
  s4: -0.0030
  tilt_x: 0.0536
  tilt_y: -0.0523
  scale: 3.0067
  rotation: -0.4504

Best score: 9.9503: : 299iter [00:09, 39.36iter/s]
Best score: 9.9503: : 300iter [00:09, 39.36iter/s]
Best score: 9.9503: : 301iter [00:09, 39.36iter/s]
Best score: 9.9503: : 302iter [00:09, 32.29iter/s]
Best score: 9.9503: : 302iter [00:09, 32.29iter/s]
Best score: 9.9503: : 303iter [00:10, 32.29iter/s]
Best score: 9.9503: : 304iter [00:10, 32.29iter/s]
Best score: 9.9503: : 305iter [00:10, 32.29iter/s]
Best score: 9.9503: : 306iter [00:10, 32.29iter/s]
Best score: 9.9503: : 307iter [00:10, 34.55iter/s]
Best score: 9.9503: : 307iter [00:10, 34.55iter/s]
Best score: 9.9503: : 308iter [00:10, 34.55iter/s]
Best score: 9.9503: : 309iter [00:10, 34.55iter/s]
Best score: 9.9503: : 310iter [00:10, 34.55iter/s]
Best score: 9.9503: : 311iter [00:10, 35.69iter/s]
Best score: 9.9503: : 311iter [00:10, 35.69iter/s]
Best score: 9.9503: : 312iter [00:10, 35.69iter/s]
Best score: 9.9503: : 313iter [00:10, 35.69iter/s]
Best score: 9.9503: : 314iter [00:10, 35.69iter/s]
Best score: 9.9503: : 315iter [00:10, 36.80iter/s]
Best score: 9.9503: : 315iter [00:10, 36.80iter/s]
Best score: 9.9503: : 316iter [00:10, 36.80iter/s]
Best score: 9.9503: : 317iter [00:10, 36.80iter/s]
Best score: 9.9503: : 318iter [00:10, 36.80iter/s]
Best score: 6.9193: : 319iter [00:10, 36.80iter/s]
Best score: 6.9193: : 320iter [00:10, 37.64iter/s]
Best score: 6.9193: : 320iter [00:10, 37.64iter/s]
Best score: 6.9193: : 321iter [00:10, 37.64iter/s]
Best score: 6.9193: : 322iter [00:10, 37.64iter/s]
Best score: 6.9193: : 323iter [00:10, 37.64iter/s]
Best score: 6.9193: : 324iter [00:10, 38.27iter/s]
Best score: 6.9193: : 324iter [00:10, 38.27iter/s]
Best score: 6.9193: : 325iter [00:10, 38.27iter/s]
Best score: 6.9193: : 326iter [00:10, 38.27iter/s]
Best score: 6.9193: : 327iter [00:10, 38.27iter/s]
Best score: 6.9193: : 328iter [00:10, 38.62iter/s]
Best score: 6.9193: : 328iter [00:10, 38.62iter/s]
Best score: 6.0507: : 329iter [00:10, 38.62iter/s]
Best score: 6.0507: : 330iter [00:10, 38.62iter/s]
Best score: 6.0507: : 331iter [00:10, 38.62iter/s]
Best score: 6.0507: : 332iter [00:10, 38.72iter/s]
Best score: 6.0507: : 332iter [00:10, 38.72iter/s]
Best score: 6.0507: : 333iter [00:10, 38.72iter/s]
Best score: 6.0507: : 334iter [00:10, 38.72iter/s]
Best score: 6.0507: : 335iter [00:10, 38.72iter/s]
Best score: 6.0507: : 336iter [00:10, 38.72iter/s]
Best score: 6.0507: : 337iter [00:10, 39.27iter/s]
Best score: 6.0507: : 337iter [00:10, 39.27iter/s]
Best score: 6.0507: : 338iter [00:10, 39.27iter/s]
Best score: 3.9167: : 339iter [00:10, 39.27iter/s]
Best score: 3.9167: : 340iter [00:10, 39.27iter/s]
Best score: 3.9167: : 341iter [00:10, 39.14iter/s]
Best score: 3.9167: : 341iter [00:10, 39.14iter/s]
Best score: 3.9167: : 342iter [00:10, 39.14iter/s]
Best score: 3.9167: : 343iter [00:11, 39.14iter/s]
Best score: 3.9167: : 344iter [00:11, 39.14iter/s]
Best score: 3.9167: : 345iter [00:11, 39.14iter/s]
Best score: 3.9167: : 346iter [00:11, 39.48iter/s]
Best score: 3.9167: : 346iter [00:11, 39.48iter/s]
Best score: 3.9167: : 347iter [00:11, 39.48iter/s]
Best score: 3.9167: : 348iter [00:11, 39.48iter/s]
[ITER 350] Best: 3.9167
[PARAMS] Current best parameters:
  fx: 507.0493
  fy: 509.0384
  px: 1003.5688
  py: 788.6029
  k1: -0.08489884
  k2: 0.17155608
  k3: -0.09658500
  k4: 0.01801043
  p1: 0.00371775
  p2: 0.03403909
  p3: 0.00263180
  p4: 0.00177580
  s1: -0.0175
  s2: -0.0063
  s3: 0.0139
  s4: -0.0027
  tilt_x: 0.0390
  tilt_y: -0.0496
  scale: 3.0369
  rotation: -0.2233

Best score: 3.9167: : 349iter [00:11, 39.48iter/s]
Best score: 3.9167: : 350iter [00:11, 39.21iter/s]
Best score: 3.9167: : 350iter [00:11, 39.21iter/s]
Best score: 3.9167: : 351iter [00:11, 39.21iter/s]
Best score: 3.9167: : 352iter [00:11, 39.21iter/s]
Best score: 3.9167: : 353iter [00:11, 39.21iter/s]
Best score: 3.9167: : 354iter [00:11, 32.32iter/s]
Best score: 3.9167: : 354iter [00:11, 32.32iter/s]
Best score: 3.9167: : 355iter [00:11, 32.32iter/s]
Best score: 3.9167: : 356iter [00:11, 32.32iter/s]
Best score: 3.9167: : 357iter [00:11, 32.32iter/s]
Best score: 3.9167: : 358iter [00:11, 32.32iter/s]
Best score: 3.9167: : 359iter [00:11, 34.64iter/s]
Best score: 2.9183: : 359iter [00:11, 34.64iter/s]
Best score: 2.9183: : 360iter [00:11, 34.64iter/s]
Best score: 2.9183: : 361iter [00:11, 34.64iter/s]
Best score: 2.9183: : 362iter [00:11, 34.64iter/s]
Best score: 2.9183: : 363iter [00:11, 35.84iter/s]
Best score: 2.9183: : 363iter [00:11, 35.84iter/s]
Best score: 2.9183: : 364iter [00:11, 35.84iter/s]
Best score: 2.9183: : 365iter [00:11, 35.84iter/s]
Best score: 2.9183: : 366iter [00:11, 35.84iter/s]
Best score: 2.9183: : 367iter [00:11, 35.84iter/s]
Best score: 2.9183: : 368iter [00:11, 37.11iter/s]
Best score: 2.9183: : 368iter [00:11, 37.11iter/s]
Best score: 2.3816: : 369iter [00:11, 37.11iter/s]
Best score: 2.3816: : 370iter [00:11, 37.11iter/s]
Best score: 2.3816: : 371iter [00:11, 37.11iter/s]
Best score: 2.3816: : 372iter [00:11, 37.54iter/s]
Best score: 2.3816: : 372iter [00:11, 37.54iter/s]
Best score: 2.3816: : 373iter [00:11, 37.54iter/s]
Best score: 2.3816: : 374iter [00:11, 37.54iter/s]
Best score: 2.3816: : 375iter [00:11, 37.54iter/s]
Best score: 2.3816: : 376iter [00:11, 37.54iter/s]
Best score: 2.3816: : 377iter [00:11, 38.40iter/s]
Best score: 2.3816: : 377iter [00:11, 38.40iter/s]
Best score: 2.3816: : 378iter [00:11, 38.40iter/s]
Best score: 1.6782: : 379iter [00:12, 38.40iter/s]
Best score: 1.6782: : 380iter [00:12, 38.40iter/s]
Best score: 1.6782: : 381iter [00:12, 38.62iter/s]
Best score: 1.6782: : 381iter [00:12, 38.62iter/s]
Best score: 1.6782: : 382iter [00:12, 38.62iter/s]
Best score: 1.6782: : 383iter [00:12, 38.62iter/s]
Best score: 1.6782: : 384iter [00:12, 38.62iter/s]
Best score: 1.6782: : 385iter [00:12, 38.62iter/s]
Best score: 1.6782: : 386iter [00:12, 39.21iter/s]
Best score: 1.6782: : 386iter [00:12, 39.21iter/s]
Best score: 1.6782: : 387iter [00:12, 39.21iter/s]
Best score: 1.6782: : 388iter [00:12, 39.21iter/s]
Best score: 1.3966: : 389iter [00:12, 39.21iter/s]
Best score: 1.3966: : 390iter [00:12, 39.28iter/s]
Best score: 1.3966: : 390iter [00:12, 39.28iter/s]
Best score: 1.3966: : 391iter [00:12, 39.28iter/s]
Best score: 1.3966: : 392iter [00:12, 39.28iter/s]
Best score: 1.3966: : 393iter [00:12, 39.28iter/s]
Best score: 1.3966: : 394iter [00:12, 31.54iter/s]
Best score: 1.3966: : 394iter [00:12, 31.54iter/s]
Best score: 1.3966: : 395iter [00:12, 31.54iter/s]
Best score: 1.3966: : 396iter [00:12, 31.54iter/s]
Best score: 1.3966: : 397iter [00:12, 31.54iter/s]
Best score: 1.3966: : 398iter [00:12, 31.54iter/s]
Best score: 1.3966: : 399iter [00:12, 34.06iter/s]
[ITER 400] Best: 1.3717
[PARAMS] Current best parameters:
  fx: 506.4402
  fy: 507.5711
  px: 1001.6479
  py: 786.5668
  k1: -0.07986000
  k2: 0.14896855
  k3: -0.07853125
  k4: 0.01394032
  p1: 0.00807650
  p2: 0.03101959
  p3: -0.00622521
  p4: -0.00106973
  s1: -0.0181
  s2: 0.0002
  s3: 0.0147
  s4: 0.0005
  tilt_x: 0.0245
  tilt_y: -0.0496
  scale: 3.0870
  rotation: -0.1303

Best score: 1.3717: : 399iter [00:12, 34.06iter/s]
Best score: 1.3717: : 400iter [00:12, 34.06iter/s]
Best score: 1.3717: : 401iter [00:12, 34.06iter/s]
Best score: 1.3717: : 402iter [00:12, 34.06iter/s]
Best score: 1.3717: : 403iter [00:12, 29.73iter/s]
Best score: 1.3717: : 403iter [00:12, 29.73iter/s]
Best score: 1.3717: : 404iter [00:12, 29.73iter/s]
Best score: 1.3717: : 405iter [00:12, 29.73iter/s]
Best score: 1.3717: : 406iter [00:12, 29.73iter/s]
Best score: 1.3717: : 407iter [00:12, 29.73iter/s]
Best score: 1.3717: : 408iter [00:12, 32.63iter/s]
Best score: 1.3717: : 408iter [00:12, 32.63iter/s]
Best score: 1.1301: : 409iter [00:12, 32.63iter/s]
Best score: 1.1301: : 410iter [00:12, 32.63iter/s]
Best score: 1.1301: : 411iter [00:12, 32.63iter/s]
Best score: 1.1301: : 412iter [00:12, 34.26iter/s]
Best score: 1.1301: : 412iter [00:13, 34.26iter/s]
Best score: 1.1301: : 413iter [00:13, 34.26iter/s]
Best score: 1.1301: : 414iter [00:13, 34.26iter/s]
Best score: 1.1301: : 415iter [00:13, 34.26iter/s]
Best score: 1.1301: : 416iter [00:13, 35.50iter/s]
Best score: 1.1301: : 416iter [00:13, 35.50iter/s]
Best score: 1.1301: : 417iter [00:13, 35.50iter/s]
Best score: 1.1301: : 418iter [00:13, 35.50iter/s]
Best score: 1.1301: : 419iter [00:13, 35.50iter/s]
Best score: 1.1301: : 420iter [00:13, 36.53iter/s]
Best score: 1.1301: : 420iter [00:13, 36.53iter/s]
Best score: 1.1301: : 421iter [00:13, 36.53iter/s]
Best score: 1.1301: : 422iter [00:13, 36.53iter/s]
Best score: 1.1301: : 423iter [00:13, 36.53iter/s]
Best score: 1.1301: : 424iter [00:13, 37.43iter/s]
Best score: 1.1301: : 424iter [00:13, 37.43iter/s]
Best score: 1.1301: : 425iter [00:13, 37.43iter/s]
Best score: 1.1301: : 426iter [00:13, 37.43iter/s]
Best score: 1.1301: : 427iter [00:13, 37.43iter/s]
Best score: 1.1301: : 428iter [00:13, 37.43iter/s]
Best score: 1.1301: : 429iter [00:13, 38.45iter/s]
Best score: 1.0551: : 429iter [00:13, 38.45iter/s]
Best score: 1.0551: : 430iter [00:13, 38.45iter/s]
Best score: 1.0551: : 431iter [00:13, 38.45iter/s]
Best score: 1.0551: : 432iter [00:13, 38.45iter/s]
Best score: 1.0551: : 433iter [00:13, 38.74iter/s]
Best score: 1.0551: : 433iter [00:13, 38.74iter/s]
Best score: 1.0551: : 434iter [00:13, 38.74iter/s]
Best score: 1.0551: : 435iter [00:13, 38.74iter/s]
Best score: 1.0551: : 436iter [00:13, 38.74iter/s]
Best score: 1.0551: : 437iter [00:13, 38.74iter/s]
Best score: 1.0551: : 438iter [00:13, 39.35iter/s]
Best score: 1.0551: : 438iter [00:13, 39.35iter/s]
Best score: 1.0436: : 439iter [00:13, 39.35iter/s]
Best score: 1.0436: : 440iter [00:13, 39.35iter/s]
Best score: 1.0436: : 441iter [00:13, 39.35iter/s]
Best score: 1.0436: : 442iter [00:13, 39.39iter/s]
Best score: 1.0436: : 442iter [00:13, 39.39iter/s]
Best score: 1.0436: : 443iter [00:13, 39.39iter/s]
Best score: 1.0436: : 444iter [00:13, 39.39iter/s]
Best score: 1.0436: : 445iter [00:13, 39.39iter/s]
Best score: 1.0436: : 446iter [00:13, 39.39iter/s]
Best score: 1.0436: : 447iter [00:13, 39.70iter/s]
Best score: 1.0436: : 447iter [00:13, 39.70iter/s]
Best score: 1.0436: : 448iter [00:13, 39.70iter/s]
[ITER 450] Best: 0.9701
[PARAMS] Current best parameters:
  fx: 498.7412
  fy: 499.7566
  px: 999.4637
  py: 783.7361
  k1: -0.05056501
  k2: 0.12545275
  k3: -0.06827073
  k4: 0.01199524
  p1: 0.00207057
  p2: 0.02833522
  p3: -0.00778111
  p4: 0.00020160
  s1: -0.0170
  s2: -0.0002
  s3: 0.0251
  s4: -0.0028
  tilt_x: 0.0211
  tilt_y: -0.0448
  scale: 3.0432
  rotation: -0.1531

Best score: 0.9701: : 449iter [00:13, 39.70iter/s]
Best score: 0.9701: : 450iter [00:14, 39.70iter/s]
Best score: 0.9701: : 451iter [00:14, 32.61iter/s]
Best score: 0.9701: : 451iter [00:14, 32.61iter/s]
Best score: 0.9701: : 452iter [00:14, 32.61iter/s]
Best score: 0.9701: : 453iter [00:14, 32.61iter/s]
Best score: 0.9701: : 454iter [00:14, 32.61iter/s]
Best score: 0.9701: : 455iter [00:14, 32.61iter/s]
Best score: 0.9701: : 456iter [00:14, 34.79iter/s]
Best score: 0.9701: : 456iter [00:14, 34.79iter/s]
Best score: 0.9701: : 457iter [00:14, 34.79iter/s]
Best score: 0.9701: : 458iter [00:14, 34.79iter/s]
Best score: 0.8227: : 459iter [00:14, 34.79iter/s]
Best score: 0.8227: : 460iter [00:14, 35.91iter/s]
Best score: 0.8227: : 460iter [00:14, 35.91iter/s]
Best score: 0.8227: : 461iter [00:14, 35.91iter/s]
Best score: 0.8227: : 462iter [00:14, 35.91iter/s]
Best score: 0.8227: : 463iter [00:14, 35.91iter/s]
Best score: 0.8227: : 464iter [00:14, 35.91iter/s]
Best score: 0.8227: : 465iter [00:14, 37.30iter/s]
Best score: 0.8227: : 465iter [00:14, 37.30iter/s]
Best score: 0.8227: : 466iter [00:14, 37.30iter/s]
Best score: 0.8227: : 467iter [00:14, 37.30iter/s]
Best score: 0.8227: : 468iter [00:14, 37.30iter/s]
Best score: 0.8227: : 469iter [00:14, 37.96iter/s]
Best score: 0.6779: : 469iter [00:14, 37.96iter/s]
Best score: 0.6779: : 470iter [00:14, 37.96iter/s]
Best score: 0.6779: : 471iter [00:14, 37.96iter/s]
Best score: 0.6779: : 472iter [00:14, 37.96iter/s]
Best score: 0.6779: : 473iter [00:14, 38.28iter/s]
Best score: 0.6779: : 473iter [00:14, 38.28iter/s]
Best score: 0.6779: : 474iter [00:14, 38.28iter/s]
Best score: 0.6779: : 475iter [00:14, 38.28iter/s]
Best score: 0.6779: : 476iter [00:14, 38.28iter/s]
Best score: 0.6779: : 477iter [00:14, 38.28iter/s]
Best score: 0.6779: : 478iter [00:14, 39.02iter/s]
Best score: 0.6779: : 478iter [00:14, 39.02iter/s]
Best score: 0.6617: : 479iter [00:14, 39.02iter/s]
Best score: 0.6617: : 480iter [00:14, 39.02iter/s]
Best score: 0.6617: : 481iter [00:14, 39.02iter/s]
Best score: 0.6617: : 482iter [00:14, 38.97iter/s]
Best score: 0.6617: : 482iter [00:14, 38.97iter/s]
Best score: 0.6617: : 483iter [00:14, 38.97iter/s]
Best score: 0.6617: : 484iter [00:14, 38.97iter/s]
Best score: 0.6617: : 485iter [00:14, 38.97iter/s]
Best score: 0.6617: : 486iter [00:14, 39.22iter/s]
Best score: 0.6617: : 486iter [00:14, 39.22iter/s]
Best score: 0.6617: : 487iter [00:14, 39.22iter/s]
Best score: 0.6617: : 488iter [00:14, 39.22iter/s]
Best score: 0.6617: : 489iter [00:15, 39.22iter/s]
Best score: 0.6617: : 490iter [00:15, 39.10iter/s]
Best score: 0.6617: : 490iter [00:15, 39.10iter/s]
Best score: 0.6617: : 491iter [00:15, 39.10iter/s]
Best score: 0.6617: : 492iter [00:15, 39.10iter/s]
Best score: 0.6617: : 493iter [00:15, 39.10iter/s]
Best score: 0.6617: : 494iter [00:15, 39.10iter/s]
Best score: 0.6617: : 495iter [00:15, 39.64iter/s]
Best score: 0.6617: : 495iter [00:15, 39.64iter/s]
Best score: 0.6617: : 496iter [00:15, 39.64iter/s]
Best score: 0.6617: : 497iter [00:15, 39.64iter/s]
Best score: 0.6617: : 498iter [00:15, 39.64iter/s]
[ITER 500] Best: 0.6099
[PARAMS] Current best parameters:
  fx: 503.3493
  fy: 503.8510
  px: 997.8369
  py: 780.7367
  k1: -0.05864470
  k2: 0.13005604
  k3: -0.07018817
  k4: 0.01234950
  p1: 0.00467974
  p2: 0.02733575
  p3: -0.01204046
  p4: -0.00141538
  s1: -0.0101
  s2: 0.0003
  s3: 0.0236
  s4: 0.0001
  tilt_x: 0.0004
  tilt_y: -0.0390
  scale: 3.0449
  rotation: -0.0922

Best score: 0.6099: : 499iter [00:15, 39.64iter/s]
Best score: 0.6099: : 500iter [00:15, 39.73iter/s]
Best score: 0.6099: : 500iter [00:15, 39.73iter/s]
Best score: 0.6099: : 501iter [00:15, 39.73iter/s]
Best score: 0.6099: : 502iter [00:15, 39.73iter/s]
Best score: 0.6099: : 503iter [00:15, 39.73iter/s]
Best score: 0.6099: : 504iter [00:15, 32.89iter/s]
Best score: 0.6099: : 504iter [00:15, 32.89iter/s]
Best score: 0.6099: : 505iter [00:15, 32.89iter/s]
Best score: 0.6099: : 506iter [00:15, 32.89iter/s]
Best score: 0.6099: : 507iter [00:15, 32.89iter/s]
Best score: 0.6099: : 508iter [00:15, 32.89iter/s]
Best score: 0.6099: : 509iter [00:15, 34.99iter/s]
Best score: 0.6099: : 509iter [00:15, 34.99iter/s]
Best score: 0.6099: : 510iter [00:15, 34.99iter/s]
Best score: 0.6099: : 511iter [00:15, 34.99iter/s]
Best score: 0.6099: : 512iter [00:15, 34.99iter/s]
Best score: 0.6099: : 513iter [00:15, 35.95iter/s]
Best score: 0.6099: : 513iter [00:15, 35.95iter/s]
Best score: 0.6099: : 514iter [00:15, 35.95iter/s]
Best score: 0.6099: : 515iter [00:15, 35.95iter/s]
Best score: 0.6099: : 516iter [00:15, 35.95iter/s]
Best score: 0.6099: : 517iter [00:15, 35.95iter/s]
Best score: 0.6099: : 518iter [00:15, 37.27iter/s]
Best score: 0.6099: : 518iter [00:15, 37.27iter/s]
Best score: 0.5645: : 519iter [00:15, 37.27iter/s]
Best score: 0.5645: : 520iter [00:15, 37.27iter/s]
Best score: 0.5645: : 521iter [00:15, 37.27iter/s]
Best score: 0.5645: : 522iter [00:15, 37.94iter/s]
Best score: 0.5645: : 522iter [00:15, 37.94iter/s]
Best score: 0.5645: : 523iter [00:15, 37.94iter/s]
Best score: 0.5645: : 524iter [00:15, 37.94iter/s]
Best score: 0.5645: : 525iter [00:15, 37.94iter/s]
Best score: 0.5645: : 526iter [00:16, 37.94iter/s]
Best score: 0.5645: : 527iter [00:16, 38.58iter/s]
Best score: 0.5645: : 527iter [00:16, 38.58iter/s]
Best score: 0.5645: : 528iter [00:16, 38.58iter/s]
Best score: 0.5411: : 529iter [00:16, 38.58iter/s]
Best score: 0.5411: : 530iter [00:16, 38.58iter/s]
Best score: 0.5411: : 531iter [00:16, 38.59iter/s]
Best score: 0.5411: : 531iter [00:16, 38.59iter/s]
Best score: 0.5411: : 532iter [00:16, 38.59iter/s]
Best score: 0.5411: : 533iter [00:16, 38.59iter/s]
Best score: 0.5411: : 534iter [00:16, 38.59iter/s]
Best score: 0.5411: : 535iter [00:16, 38.59iter/s]
Best score: 0.5411: : 536iter [00:16, 39.15iter/s]
Best score: 0.5411: : 536iter [00:16, 39.15iter/s]
Best score: 0.5411: : 537iter [00:16, 39.15iter/s]
Best score: 0.5411: : 538iter [00:16, 39.15iter/s]
Best score: 0.5291: : 539iter [00:16, 39.15iter/s]
Best score: 0.5291: : 540iter [00:16, 39.17iter/s]
Best score: 0.5291: : 540iter [00:16, 39.17iter/s]
Best score: 0.5291: : 541iter [00:16, 39.17iter/s]
Best score: 0.5291: : 542iter [00:16, 39.17iter/s]
Best score: 0.5291: : 543iter [00:16, 39.17iter/s]
Best score: 0.5291: : 544iter [00:16, 39.17iter/s]
Best score: 0.5291: : 545iter [00:16, 39.55iter/s]
Best score: 0.5291: : 545iter [00:16, 39.55iter/s]
Best score: 0.5291: : 546iter [00:16, 39.55iter/s]
Best score: 0.5291: : 547iter [00:16, 39.55iter/s]
Best score: 0.5291: : 548iter [00:16, 39.55iter/s]
[ITER 550] Best: 0.4839
[PARAMS] Current best parameters:
  fx: 499.6223
  fy: 500.0326
  px: 994.8427
  py: 777.8152
  k1: -0.04187932
  k2: 0.10542246
  k3: -0.05416376
  k4: 0.00879738
  p1: 0.00382076
  p2: 0.02533751
  p3: -0.01644052
  p4: 0.00031685
  s1: -0.0048
  s2: -0.0014
  s3: 0.0230
  s4: -0.0007
  tilt_x: -0.0107
  tilt_y: -0.0280
  scale: 3.0427
  rotation: -0.0940

Best score: 0.4839: : 549iter [00:16, 39.55iter/s]
Best score: 0.4839: : 550iter [00:16, 39.48iter/s]
Best score: 0.4839: : 550iter [00:16, 39.48iter/s]
Best score: 0.4839: : 551iter [00:16, 39.48iter/s]
Best score: 0.4839: : 552iter [00:16, 39.48iter/s]
Best score: 0.4839: : 553iter [00:16, 39.48iter/s]
Best score: 0.4839: : 554iter [00:16, 32.74iter/s]
Best score: 0.4839: : 554iter [00:16, 32.74iter/s]
Best score: 0.4839: : 555iter [00:16, 32.74iter/s]
Best score: 0.4839: : 556iter [00:16, 32.74iter/s]
Best score: 0.4839: : 557iter [00:16, 32.74iter/s]
Best score: 0.4839: : 558iter [00:16, 32.74iter/s]
Best score: 0.4839: : 559iter [00:16, 34.81iter/s]
Best score: 0.4839: : 559iter [00:16, 34.81iter/s]
Best score: 0.4839: : 560iter [00:16, 34.81iter/s]
Best score: 0.4839: : 561iter [00:16, 34.81iter/s]
Best score: 0.4839: : 562iter [00:17, 34.81iter/s]
Best score: 0.4839: : 563iter [00:17, 35.94iter/s]
Best score: 0.4839: : 563iter [00:17, 35.94iter/s]
Best score: 0.4839: : 564iter [00:17, 35.94iter/s]
Best score: 0.4839: : 565iter [00:17, 35.94iter/s]
Best score: 0.4839: : 566iter [00:17, 35.94iter/s]
Best score: 0.4839: : 567iter [00:17, 35.94iter/s]
Best score: 0.4839: : 568iter [00:17, 37.31iter/s]
Best score: 0.4839: : 568iter [00:17, 37.31iter/s]
Best score: 0.4770: : 569iter [00:17, 37.31iter/s]
Best score: 0.4770: : 570iter [00:17, 37.31iter/s]
Best score: 0.4770: : 571iter [00:17, 37.31iter/s]
Best score: 0.4770: : 572iter [00:17, 37.75iter/s]
Best score: 0.4770: : 572iter [00:17, 37.75iter/s]
Best score: 0.4770: : 573iter [00:17, 37.75iter/s]
Best score: 0.4770: : 574iter [00:17, 37.75iter/s]
Best score: 0.4770: : 575iter [00:17, 37.75iter/s]
Best score: 0.4770: : 576iter [00:17, 37.75iter/s]
Best score: 0.4770: : 577iter [00:17, 38.54iter/s]
Best score: 0.4770: : 577iter [00:17, 38.54iter/s]
Best score: 0.4770: : 578iter [00:17, 38.54iter/s]
Best score: 0.4250: : 579iter [00:17, 38.54iter/s]
Best score: 0.4250: : 580iter [00:17, 38.54iter/s]
Best score: 0.4250: : 581iter [00:17, 38.72iter/s]
Best score: 0.4250: : 581iter [00:17, 38.72iter/s]
Best score: 0.4250: : 582iter [00:17, 38.72iter/s]
Best score: 0.4250: : 583iter [00:17, 38.72iter/s]
Best score: 0.4250: : 584iter [00:17, 38.72iter/s]
Best score: 0.4250: : 585iter [00:17, 38.72iter/s]
Best score: 0.4250: : 586iter [00:17, 39.25iter/s]
Best score: 0.4250: : 586iter [00:17, 39.25iter/s]
Best score: 0.4250: : 587iter [00:17, 39.25iter/s]
Best score: 0.4250: : 588iter [00:17, 39.25iter/s]
Best score: 0.4058: : 589iter [00:17, 39.25iter/s]
Best score: 0.4058: : 590iter [00:17, 39.34iter/s]
Best score: 0.4058: : 590iter [00:17, 39.34iter/s]
Best score: 0.4058: : 591iter [00:17, 39.34iter/s]
Best score: 0.4058: : 592iter [00:17, 39.34iter/s]
Best score: 0.4058: : 593iter [00:17, 39.34iter/s]
Best score: 0.4058: : 594iter [00:17, 39.34iter/s]
Best score: 0.4058: : 595iter [00:17, 39.72iter/s]
Best score: 0.4058: : 595iter [00:17, 39.72iter/s]
Best score: 0.4058: : 596iter [00:17, 39.72iter/s]
Best score: 0.4058: : 597iter [00:17, 39.72iter/s]
Best score: 0.4058: : 598iter [00:17, 39.72iter/s]
[ITER 600] Best: 0.3962
[PARAMS] Current best parameters:
  fx: 494.5469
  fy: 494.6505
  px: 993.0493
  py: 773.0273
  k1: -0.01943057
  k2: 0.07776159
  k3: -0.03702535
  k4: 0.00498245
  p1: 0.00313147
  p2: 0.02186191
  p3: -0.02069739
  p4: 0.00048264
  s1: -0.0021
  s2: 0.0004
  s3: 0.0239
  s4: -0.0010
  tilt_x: -0.0305
  tilt_y: -0.0265
  scale: 3.0233
  rotation: -0.0673

Best score: 0.3962: : 599iter [00:17, 39.72iter/s]
Best score: 0.3962: : 600iter [00:17, 39.62iter/s]
Best score: 0.3962: : 600iter [00:18, 39.62iter/s]
Best score: 0.3962: : 601iter [00:18, 39.62iter/s]
Best score: 0.3962: : 602iter [00:18, 39.62iter/s]
Best score: 0.3962: : 603iter [00:18, 39.62iter/s]
Best score: 0.3962: : 604iter [00:18, 32.95iter/s]
Best score: 0.3962: : 604iter [00:18, 32.95iter/s]
Best score: 0.3962: : 605iter [00:18, 32.95iter/s]
Best score: 0.3962: : 606iter [00:18, 32.95iter/s]
Best score: 0.3962: : 607iter [00:18, 32.95iter/s]
Best score: 0.3962: : 608iter [00:18, 32.95iter/s]
Best score: 0.3962: : 609iter [00:18, 35.14iter/s]
Best score: 0.3885: : 609iter [00:18, 35.14iter/s]
Best score: 0.3885: : 610iter [00:18, 35.14iter/s]
Best score: 0.3885: : 611iter [00:18, 35.14iter/s]
Best score: 0.3885: : 612iter [00:18, 35.14iter/s]
Best score: 0.3885: : 613iter [00:18, 36.13iter/s]
Best score: 0.3885: : 613iter [00:18, 36.13iter/s]
Best score: 0.3885: : 614iter [00:18, 36.13iter/s]
Best score: 0.3885: : 615iter [00:18, 36.13iter/s]
Best score: 0.3885: : 616iter [00:18, 36.13iter/s]
Best score: 0.3885: : 617iter [00:18, 36.13iter/s]
Best score: 0.3885: : 618iter [00:18, 37.44iter/s]
Best score: 0.3885: : 618iter [00:18, 37.44iter/s]
Best score: 0.3885: : 619iter [00:18, 37.44iter/s]
Best score: 0.3885: : 620iter [00:18, 37.44iter/s]
Best score: 0.3885: : 621iter [00:18, 37.44iter/s]
Best score: 0.3885: : 622iter [00:18, 38.03iter/s]
Best score: 0.3885: : 622iter [00:18, 38.03iter/s]
Best score: 0.3885: : 623iter [00:18, 38.03iter/s]
Best score: 0.3885: : 624iter [00:18, 38.03iter/s]
Best score: 0.3885: : 625iter [00:18, 38.03iter/s]
Best score: 0.3885: : 626iter [00:18, 38.03iter/s]
Best score: 0.3885: : 627iter [00:18, 38.84iter/s]
Best score: 0.3885: : 627iter [00:18, 38.84iter/s]
Best score: 0.3885: : 628iter [00:18, 38.84iter/s]
Best score: 0.3584: : 629iter [00:18, 38.84iter/s]
Best score: 0.3584: : 630iter [00:18, 38.84iter/s]
Best score: 0.3584: : 631iter [00:18, 38.77iter/s]
Best score: 0.3584: : 631iter [00:18, 38.77iter/s]
Best score: 0.3584: : 632iter [00:18, 38.77iter/s]
Best score: 0.3584: : 633iter [00:18, 38.77iter/s]
Best score: 0.3584: : 634iter [00:18, 38.77iter/s]
Best score: 0.3584: : 635iter [00:18, 38.77iter/s]
Best score: 0.3584: : 636iter [00:18, 39.27iter/s]
Best score: 0.3584: : 636iter [00:18, 39.27iter/s]
Best score: 0.3584: : 637iter [00:18, 39.27iter/s]
Best score: 0.3584: : 638iter [00:18, 39.27iter/s]
Best score: 0.3424: : 639iter [00:19, 39.27iter/s]
Best score: 0.3424: : 640iter [00:19, 39.23iter/s]
Best score: 0.3424: : 640iter [00:19, 39.23iter/s]
Best score: 0.3424: : 641iter [00:19, 39.23iter/s]
Best score: 0.3424: : 642iter [00:19, 39.23iter/s]
Best score: 0.3424: : 643iter [00:19, 39.23iter/s]
Best score: 0.3424: : 644iter [00:19, 39.42iter/s]
Best score: 0.3424: : 644iter [00:19, 39.42iter/s]
Best score: 0.3424: : 645iter [00:19, 39.42iter/s]
Best score: 0.3424: : 646iter [00:19, 39.42iter/s]
Best score: 0.3424: : 647iter [00:19, 39.42iter/s]
Best score: 0.3424: : 648iter [00:19, 39.42iter/s]
Best score: 0.3424: : 649iter [00:19, 39.85iter/s]
[ITER 650] Best: 0.3133
[PARAMS] Current best parameters:
  fx: 492.3348
  fy: 492.5258
  px: 990.5318
  py: 769.3865
  k1: -0.00669333
  k2: 0.05753878
  k3: -0.02388567
  k4: 0.00211176
  p1: 0.00249915
  p2: 0.01985167
  p3: -0.02315844
  p4: 0.00083350
  s1: 0.0023
  s2: -0.0008
  s3: 0.0245
  s4: -0.0012
  tilt_x: -0.0460
  tilt_y: -0.0185
  scale: 3.0253
  rotation: -0.0595

Best score: 0.3133: : 649iter [00:19, 39.85iter/s]
Best score: 0.3133: : 650iter [00:19, 39.85iter/s]
Best score: 0.3133: : 651iter [00:19, 39.85iter/s]
Best score: 0.3133: : 652iter [00:19, 39.85iter/s]
Best score: 0.3133: : 653iter [00:19, 32.71iter/s]
Best score: 0.3133: : 653iter [00:19, 32.71iter/s]
Best score: 0.3133: : 654iter [00:19, 32.71iter/s]
Best score: 0.3133: : 655iter [00:19, 32.71iter/s]
Best score: 0.3133: : 656iter [00:19, 32.71iter/s]
Best score: 0.3133: : 657iter [00:19, 32.71iter/s]
Best score: 0.3133: : 658iter [00:19, 34.95iter/s]
Best score: 0.3133: : 658iter [00:19, 34.95iter/s]
Best score: 0.3133: : 659iter [00:19, 34.95iter/s]
Best score: 0.3133: : 660iter [00:19, 34.95iter/s]
Best score: 0.3133: : 661iter [00:19, 34.95iter/s]
Best score: 0.3133: : 662iter [00:19, 36.02iter/s]
Best score: 0.3133: : 662iter [00:19, 36.02iter/s]
Best score: 0.3133: : 663iter [00:19, 36.02iter/s]
Best score: 0.3133: : 664iter [00:19, 36.02iter/s]
Best score: 0.3133: : 665iter [00:19, 36.02iter/s]
Best score: 0.3133: : 666iter [00:19, 36.02iter/s]
Best score: 0.3133: : 667iter [00:19, 37.28iter/s]
Best score: 0.3133: : 667iter [00:19, 37.28iter/s]
Best score: 0.3133: : 668iter [00:19, 37.28iter/s]
Best score: 0.3133: : 669iter [00:19, 37.28iter/s]
Best score: 0.3133: : 670iter [00:19, 37.28iter/s]
Best score: 0.3133: : 671iter [00:19, 37.78iter/s]
Best score: 0.3133: : 671iter [00:19, 37.78iter/s]
Best score: 0.3133: : 672iter [00:19, 37.78iter/s]
Best score: 0.3133: : 673iter [00:19, 37.78iter/s]
Best score: 0.3133: : 674iter [00:19, 37.78iter/s]
Best score: 0.3133: : 675iter [00:19, 37.78iter/s]
Best score: 0.3133: : 676iter [00:19, 38.58iter/s]
Best score: 0.3133: : 676iter [00:20, 38.58iter/s]
Best score: 0.3133: : 677iter [00:20, 38.58iter/s]
Best score: 0.3133: : 678iter [00:20, 38.58iter/s]
Best score: 0.2951: : 679iter [00:20, 38.58iter/s]
Best score: 0.2951: : 680iter [00:20, 38.75iter/s]
Best score: 0.2951: : 680iter [00:20, 38.75iter/s]
Best score: 0.2951: : 681iter [00:20, 38.75iter/s]
Best score: 0.2951: : 682iter [00:20, 38.75iter/s]
Best score: 0.2951: : 683iter [00:20, 38.75iter/s]
Best score: 0.2951: : 684iter [00:20, 38.75iter/s]
Best score: 0.2951: : 685iter [00:20, 39.35iter/s]
Best score: 0.2951: : 685iter [00:20, 39.35iter/s]
Best score: 0.2951: : 686iter [00:20, 39.35iter/s]
Best score: 0.2951: : 687iter [00:20, 39.35iter/s]
Best score: 0.2951: : 688iter [00:20, 39.35iter/s]
Best score: 0.2951: : 689iter [00:20, 39.35iter/s]
Best score: 0.2951: : 690iter [00:20, 39.51iter/s]
Best score: 0.2951: : 690iter [00:20, 39.51iter/s]
Best score: 0.2951: : 691iter [00:20, 39.51iter/s]
Best score: 0.2951: : 692iter [00:20, 39.51iter/s]
Best score: 0.2951: : 693iter [00:20, 39.51iter/s]
Best score: 0.2951: : 694iter [00:20, 39.51iter/s]
Best score: 0.2951: : 695iter [00:20, 39.93iter/s]
Best score: 0.2951: : 695iter [00:20, 39.93iter/s]
Best score: 0.2951: : 696iter [00:20, 39.93iter/s]
Best score: 0.2951: : 697iter [00:20, 39.93iter/s]
Best score: 0.2951: : 698iter [00:20, 39.93iter/s]
[ITER 700] Best: 0.2936
[PARAMS] Current best parameters:
  fx: 490.7418
  fy: 490.9195
  px: 989.7517
  py: 765.8890
  k1: 0.00448977
  k2: 0.05054358
  k3: -0.02359899
  k4: 0.00258331
  p1: -0.00100129
  p2: 0.01763769
  p3: -0.02424562
  p4: 0.00205891
  s1: 0.0068
  s2: -0.0031
  s3: 0.0268
  s4: -0.0026
  tilt_x: -0.0552
  tilt_y: -0.0194
  scale: 3.0074
  rotation: -0.0483

Best score: 0.2936: : 699iter [00:20, 39.93iter/s]
Best score: 0.2936: : 700iter [00:20, 39.83iter/s]
Best score: 0.2936: : 700iter [00:20, 39.83iter/s]
Best score: 0.2936: : 701iter [00:20, 39.83iter/s]
Best score: 0.2936: : 702iter [00:20, 39.83iter/s]
Best score: 0.2936: : 703iter [00:20, 39.83iter/s]
Best score: 0.2936: : 704iter [00:20, 28.18iter/s]
Best score: 0.2936: : 704iter [00:20, 28.18iter/s]
Best score: 0.2936: : 705iter [00:20, 28.18iter/s]
Best score: 0.2936: : 706iter [00:20, 28.18iter/s]
Best score: 0.2936: : 707iter [00:20, 28.18iter/s]
Best score: 0.2936: : 708iter [00:20, 30.58iter/s]
Best score: 0.2936: : 708iter [00:20, 30.58iter/s]
Best score: 0.2936: : 709iter [00:21, 30.58iter/s]
Best score: 0.2936: : 710iter [00:21, 30.58iter/s]
Best score: 0.2936: : 711iter [00:21, 30.58iter/s]
Best score: 0.2936: : 712iter [00:21, 32.64iter/s]
Best score: 0.2936: : 712iter [00:21, 32.64iter/s]
Best score: 0.2936: : 713iter [00:21, 32.64iter/s]
Best score: 0.2936: : 714iter [00:21, 32.64iter/s]
Best score: 0.2936: : 715iter [00:21, 32.64iter/s]
Best score: 0.2936: : 716iter [00:21, 32.64iter/s]
Best score: 0.2936: : 717iter [00:21, 34.92iter/s]
Best score: 0.2936: : 717iter [00:21, 34.92iter/s]
Best score: 0.2936: : 718iter [00:21, 34.92iter/s]
Best score: 0.2840: : 719iter [00:21, 34.92iter/s]
Best score: 0.2840: : 720iter [00:21, 34.92iter/s]
Best score: 0.2840: : 721iter [00:21, 35.90iter/s]
Best score: 0.2840: : 721iter [00:21, 35.90iter/s]
Best score: 0.2840: : 722iter [00:21, 35.90iter/s]
Best score: 0.2840: : 723iter [00:21, 35.90iter/s]
Best score: 0.2840: : 724iter [00:21, 35.90iter/s]
Best score: 0.2840: : 725iter [00:21, 35.90iter/s]
Best score: 0.2840: : 726iter [00:21, 37.16iter/s]
Best score: 0.2840: : 726iter [00:21, 37.16iter/s]
Best score: 0.2840: : 727iter [00:21, 37.16iter/s]
Best score: 0.2840: : 728iter [00:21, 37.16iter/s]
Best score: 0.2819: : 729iter [00:21, 37.16iter/s]
Best score: 0.2819: : 730iter [00:21, 37.68iter/s]
Best score: 0.2819: : 730iter [00:21, 37.68iter/s]
Best score: 0.2819: : 731iter [00:21, 37.68iter/s]
Best score: 0.2819: : 732iter [00:21, 37.68iter/s]
Best score: 0.2819: : 733iter [00:21, 37.68iter/s]
Best score: 0.2819: : 734iter [00:21, 37.68iter/s]
Best score: 0.2819: : 735iter [00:21, 38.50iter/s]
Best score: 0.2819: : 735iter [00:21, 38.50iter/s]
Best score: 0.2819: : 736iter [00:21, 38.50iter/s]
Best score: 0.2819: : 737iter [00:21, 38.50iter/s]
Best score: 0.2819: : 738iter [00:21, 38.50iter/s]
Best score: 0.2772: : 739iter [00:21, 38.50iter/s]
Best score: 0.2772: : 740iter [00:21, 38.77iter/s]
Best score: 0.2772: : 740iter [00:21, 38.77iter/s]
Best score: 0.2772: : 741iter [00:21, 38.77iter/s]
Best score: 0.2772: : 742iter [00:21, 38.77iter/s]
Best score: 0.2772: : 743iter [00:21, 38.77iter/s]
Best score: 0.2772: : 744iter [00:21, 38.77iter/s]
Best score: 0.2772: : 745iter [00:21, 39.34iter/s]
Best score: 0.2772: : 745iter [00:21, 39.34iter/s]
Best score: 0.2772: : 746iter [00:21, 39.34iter/s]
Best score: 0.2772: : 747iter [00:21, 39.34iter/s]
Best score: 0.2772: : 748iter [00:21, 39.34iter/s]
[ITER 750] Best: 0.2772
[PARAMS] Current best parameters:
  fx: 490.9751
  fy: 491.0716
  px: 987.8290
  py: 766.2950
  k1: 0.00705819
  k2: 0.04275551
  k3: -0.01786103
  k4: 0.00131773
  p1: 0.00094164
  p2: 0.01508436
  p3: -0.02710140
  p4: 0.00261563
  s1: 0.0079
  s2: -0.0024
  s3: 0.0272
  s4: -0.0031
  tilt_x: -0.0566
  tilt_y: -0.0163
  scale: 3.0126
  rotation: -0.0625

Best score: 0.2772: : 749iter [00:22, 39.34iter/s]
Best score: 0.2772: : 750iter [00:22, 39.40iter/s]
Best score: 0.2772: : 750iter [00:22, 39.40iter/s]
Best score: 0.2772: : 751iter [00:22, 39.40iter/s]
Best score: 0.2772: : 752iter [00:22, 39.40iter/s]
Best score: 0.2772: : 753iter [00:22, 39.40iter/s]
Best score: 0.2772: : 754iter [00:22, 33.04iter/s]
Best score: 0.2772: : 754iter [00:22, 33.04iter/s]
Best score: 0.2772: : 755iter [00:22, 33.04iter/s]
Best score: 0.2772: : 756iter [00:22, 33.04iter/s]
Best score: 0.2772: : 757iter [00:22, 33.04iter/s]
Best score: 0.2772: : 758iter [00:22, 33.04iter/s]
Best score: 0.2772: : 759iter [00:22, 35.11iter/s]
Best score: 0.2756: : 759iter [00:22, 35.11iter/s]
Best score: 0.2756: : 760iter [00:22, 35.11iter/s]
Best score: 0.2756: : 761iter [00:22, 35.11iter/s]
Best score: 0.2756: : 762iter [00:22, 35.11iter/s]
Best score: 0.2756: : 763iter [00:22, 36.11iter/s]
Best score: 0.2756: : 763iter [00:22, 36.11iter/s]
Best score: 0.2756: : 764iter [00:22, 36.11iter/s]
Best score: 0.2756: : 765iter [00:22, 36.11iter/s]
Best score: 0.2756: : 766iter [00:22, 36.11iter/s]
Best score: 0.2756: : 767iter [00:22, 36.11iter/s]
Best score: 0.2756: : 768iter [00:22, 37.46iter/s]
Best score: 0.2756: : 768iter [00:22, 37.46iter/s]
Best score: 0.2614: : 769iter [00:22, 37.46iter/s]
Best score: 0.2614: : 770iter [00:22, 37.46iter/s]
Best score: 0.2614: : 771iter [00:22, 37.46iter/s]
Best score: 0.2614: : 772iter [00:22, 38.08iter/s]
Best score: 0.2614: : 772iter [00:22, 38.08iter/s]
Best score: 0.2614: : 773iter [00:22, 38.08iter/s]
Best score: 0.2614: : 774iter [00:22, 38.08iter/s]
Best score: 0.2614: : 775iter [00:22, 38.08iter/s]
Best score: 0.2614: : 776iter [00:22, 38.08iter/s]
Best score: 0.2614: : 777iter [00:22, 38.72iter/s]
Best score: 0.2614: : 777iter [00:22, 38.72iter/s]
Best score: 0.2614: : 778iter [00:22, 38.72iter/s]
Best score: 0.2614: : 779iter [00:22, 38.72iter/s]
Best score: 0.2614: : 780iter [00:22, 38.72iter/s]
Best score: 0.2614: : 781iter [00:22, 38.88iter/s]
Best score: 0.2614: : 781iter [00:22, 38.88iter/s]
Best score: 0.2614: : 782iter [00:22, 38.88iter/s]
Best score: 0.2614: : 783iter [00:22, 38.88iter/s]
Best score: 0.2614: : 784iter [00:22, 38.88iter/s]
Best score: 0.2614: : 785iter [00:22, 38.88iter/s]
Best score: 0.2614: : 786iter [00:22, 39.44iter/s]
Best score: 0.2614: : 786iter [00:23, 39.44iter/s]
Best score: 0.2614: : 787iter [00:23, 39.44iter/s]
Best score: 0.2614: : 788iter [00:23, 39.44iter/s]
Best score: 0.2583: : 789iter [00:23, 39.44iter/s]
Best score: 0.2583: : 790iter [00:23, 39.49iter/s]
Best score: 0.2583: : 790iter [00:23, 39.49iter/s]
Best score: 0.2583: : 791iter [00:23, 39.49iter/s]
Best score: 0.2583: : 792iter [00:23, 39.49iter/s]
Best score: 0.2583: : 793iter [00:23, 39.49iter/s]
Best score: 0.2583: : 794iter [00:23, 39.49iter/s]
Best score: 0.2583: : 795iter [00:23, 39.71iter/s]
Best score: 0.2583: : 795iter [00:23, 39.71iter/s]
Best score: 0.2583: : 796iter [00:23, 39.71iter/s]
Best score: 0.2583: : 797iter [00:23, 39.71iter/s]
Best score: 0.2583: : 798iter [00:23, 39.71iter/s]
[ITER 800] Best: 0.2583
[PARAMS] Current best parameters:
  fx: 490.7229
  fy: 490.9426
  px: 987.4539
  py: 764.2893
  k1: 0.01492972
  k2: 0.03314515
  k3: -0.01369782
  k4: 0.00071009
  p1: 0.00003303
  p2: 0.01441353
  p3: -0.02602559
  p4: 0.00255804
  s1: 0.0087
  s2: -0.0028
  s3: 0.0257
  s4: -0.0030
  tilt_x: -0.0640
  tilt_y: -0.0153
  scale: 3.0055
  rotation: -0.0559

Best score: 0.2583: : 799iter [00:23, 39.71iter/s]
Best score: 0.2583: : 800iter [00:23, 39.72iter/s]
Best score: 0.2583: : 800iter [00:23, 39.72iter/s]
Best score: 0.2583: : 801iter [00:23, 39.72iter/s]
Best score: 0.2583: : 802iter [00:23, 39.72iter/s]
Best score: 0.2583: : 803iter [00:23, 39.72iter/s]
Best score: 0.2583: : 804iter [00:23, 32.89iter/s]
Best score: 0.2583: : 804iter [00:23, 32.89iter/s]
Best score: 0.2583: : 805iter [00:23, 32.89iter/s]
Best score: 0.2583: : 806iter [00:23, 32.89iter/s]
Best score: 0.2583: : 807iter [00:23, 32.89iter/s]
Best score: 0.2583: : 808iter [00:23, 32.89iter/s]
Best score: 0.2583: : 809iter [00:23, 35.01iter/s]
Best score: 0.2583: : 809iter [00:23, 35.01iter/s]
Best score: 0.2583: : 810iter [00:23, 35.01iter/s]
Best score: 0.2583: : 811iter [00:23, 35.01iter/s]
Best score: 0.2583: : 812iter [00:23, 35.01iter/s]
Best score: 0.2583: : 813iter [00:23, 36.14iter/s]
Best score: 0.2583: : 813iter [00:23, 36.14iter/s]
Best score: 0.2583: : 814iter [00:23, 36.14iter/s]
Best score: 0.2583: : 815iter [00:23, 36.14iter/s]
Best score: 0.2583: : 816iter [00:23, 36.14iter/s]
Best score: 0.2583: : 817iter [00:23, 36.14iter/s]
Best score: 0.2583: : 818iter [00:23, 37.50iter/s]
Best score: 0.2583: : 818iter [00:23, 37.50iter/s]
Best score: 0.2583: : 819iter [00:23, 37.50iter/s]
Best score: 0.2583: : 820iter [00:23, 37.50iter/s]
Best score: 0.2583: : 821iter [00:23, 37.50iter/s]
Best score: 0.2583: : 822iter [00:23, 37.98iter/s]
Best score: 0.2583: : 822iter [00:23, 37.98iter/s]
Best score: 0.2583: : 823iter [00:24, 37.98iter/s]
Best score: 0.2583: : 824iter [00:24, 37.98iter/s]
Best score: 0.2583: : 825iter [00:24, 37.98iter/s]
Best score: 0.2583: : 826iter [00:24, 37.98iter/s]
Best score: 0.2583: : 827iter [00:24, 38.71iter/s]
Best score: 0.2583: : 827iter [00:24, 38.71iter/s]
Best score: 0.2583: : 828iter [00:24, 38.71iter/s]
Best score: 0.2583: : 829iter [00:24, 38.71iter/s]
Best score: 0.2583: : 830iter [00:24, 38.71iter/s]
Best score: 0.2583: : 831iter [00:24, 38.79iter/s]
Best score: 0.2583: : 831iter [00:24, 38.79iter/s]
Best score: 0.2583: : 832iter [00:24, 38.79iter/s]
Best score: 0.2583: : 833iter [00:24, 38.79iter/s]
Best score: 0.2583: : 834iter [00:24, 38.79iter/s]
Best score: 0.2583: : 835iter [00:24, 39.08iter/s]
Best score: 0.2583: : 835iter [00:24, 39.08iter/s]
Best score: 0.2583: : 836iter [00:24, 39.08iter/s]
Best score: 0.2583: : 837iter [00:24, 39.08iter/s]
Best score: 0.2583: : 838iter [00:24, 39.08iter/s]
Best score: 0.2583: : 839iter [00:24, 39.08iter/s]
Best score: 0.2583: : 840iter [00:24, 39.20iter/s]
Best score: 0.2583: : 840iter [00:24, 39.20iter/s]
Best score: 0.2583: : 841iter [00:24, 39.20iter/s]
Best score: 0.2583: : 842iter [00:24, 39.20iter/s]
Best score: 0.2583: : 843iter [00:24, 39.20iter/s]
Best score: 0.2583: : 844iter [00:24, 39.20iter/s]
Best score: 0.2583: : 845iter [00:24, 39.61iter/s]
Best score: 0.2583: : 845iter [00:24, 39.61iter/s]
Best score: 0.2583: : 846iter [00:24, 39.61iter/s]
Best score: 0.2583: : 847iter [00:24, 39.61iter/s]
Best score: 0.2583: : 848iter [00:24, 39.61iter/s]
[ITER 850] Best: 0.2583
[PARAMS] Current best parameters:
  fx: 491.4020
  fy: 491.6422
  px: 987.2463
  py: 763.9281
  k1: 0.01448734
  k2: 0.03296107
  k3: -0.01382221
  k4: 0.00079434
  p1: 0.00037997
  p2: 0.01445369
  p3: -0.02626354
  p4: 0.00225371
  s1: 0.0094
  s2: -0.0028
  s3: 0.0258
  s4: -0.0029
  tilt_x: -0.0665
  tilt_y: -0.0142
  scale: 3.0063
  rotation: -0.0533

Best score: 0.2583: : 849iter [00:24, 39.61iter/s]
Best score: 0.2583: : 850iter [00:24, 39.60iter/s]
Best score: 0.2583: : 850iter [00:24, 39.60iter/s]
Best score: 0.2583: : 851iter [00:24, 39.60iter/s]
Best score: 0.2583: : 852iter [00:24, 39.60iter/s]
Best score: 0.2583: : 853iter [00:24, 39.60iter/s]
Best score: 0.2583: : 854iter [00:24, 33.01iter/s]
Best score: 0.2583: : 854iter [00:24, 33.01iter/s]
Best score: 0.2583: : 855iter [00:24, 33.01iter/s]
Best score: 0.2583: : 856iter [00:24, 33.01iter/s]
Best score: 0.2583: : 857iter [00:24, 33.01iter/s]
Best score: 0.2583: : 858iter [00:24, 33.01iter/s]
Best score: 0.2583: : 859iter [00:24, 35.05iter/s]
Best score: 0.2583: : 859iter [00:24, 35.05iter/s]
Best score: 0.2583: : 860iter [00:25, 35.05iter/s]
Best score: 0.2583: : 861iter [00:25, 35.05iter/s]
Best score: 0.2583: : 862iter [00:25, 35.05iter/s]
Best score: 0.2583: : 863iter [00:25, 36.10iter/s]
Best score: 0.2583: : 863iter [00:25, 36.10iter/s]
Best score: 0.2583: : 864iter [00:25, 36.10iter/s]
Best score: 0.2583: : 865iter [00:25, 36.10iter/s]
Best score: 0.2583: : 866iter [00:25, 36.10iter/s]
Best score: 0.2583: : 867iter [00:25, 36.10iter/s]
Best score: 0.2583: : 868iter [00:25, 37.42iter/s]
Best score: 0.2583: : 868iter [00:25, 37.42iter/s]
Best score: 0.2572: : 869iter [00:25, 37.42iter/s]
Best score: 0.2572: : 870iter [00:25, 37.42iter/s]
Best score: 0.2572: : 871iter [00:25, 37.42iter/s]
Best score: 0.2572: : 872iter [00:25, 37.89iter/s]
Best score: 0.2572: : 872iter [00:25, 37.89iter/s]
Best score: 0.2572: : 873iter [00:25, 37.89iter/s]
Best score: 0.2572: : 874iter [00:25, 37.89iter/s]
Best score: 0.2572: : 875iter [00:25, 37.89iter/s]
Best score: 0.2572: : 876iter [00:25, 37.89iter/s]
Best score: 0.2572: : 877iter [00:25, 38.58iter/s]
Best score: 0.2572: : 877iter [00:25, 38.58iter/s]
Best score: 0.2572: : 878iter [00:25, 38.58iter/s]
Best score: 0.2563: : 879iter [00:25, 38.58iter/s]
Best score: 0.2563: : 880iter [00:25, 38.58iter/s]
Best score: 0.2563: : 881iter [00:25, 38.68iter/s]
Best score: 0.2563: : 881iter [00:25, 38.68iter/s]
Best score: 0.2563: : 882iter [00:25, 38.68iter/s]
Best score: 0.2563: : 883iter [00:25, 38.68iter/s]
Best score: 0.2563: : 884iter [00:25, 38.68iter/s]
Best score: 0.2563: : 885iter [00:25, 38.68iter/s]
Best score: 0.2563: : 886iter [00:25, 39.24iter/s]
Best score: 0.2563: : 886iter [00:25, 39.24iter/s]
Best score: 0.2563: : 887iter [00:25, 39.24iter/s]
Best score: 0.2563: : 888iter [00:25, 39.24iter/s]
Best score: 0.2563: : 889iter [00:25, 39.24iter/s]
Best score: 0.2563: : 890iter [00:25, 39.22iter/s]
Best score: 0.2563: : 890iter [00:25, 39.22iter/s]
Best score: 0.2563: : 891iter [00:25, 39.22iter/s]
Best score: 0.2563: : 892iter [00:25, 39.22iter/s]
Best score: 0.2563: : 893iter [00:25, 39.22iter/s]
Best score: 0.2563: : 894iter [00:25, 39.22iter/s]
Best score: 0.2563: : 895iter [00:25, 39.66iter/s]
Best score: 0.2563: : 895iter [00:25, 39.66iter/s]
Best score: 0.2563: : 896iter [00:25, 39.66iter/s]
Best score: 0.2563: : 897iter [00:25, 39.66iter/s]
Best score: 0.2563: : 898iter [00:25, 39.66iter/s]
[ITER 900] Best: 0.2558
[PARAMS] Current best parameters:
  fx: 491.1311
  fy: 491.3716
  px: 987.0456
  py: 764.2803
  k1: 0.01583997
  k2: 0.03158991
  k3: -0.01307022
  k4: 0.00061739
  p1: 0.00025949
  p2: 0.01444451
  p3: -0.02576856
  p4: 0.00223563
  s1: 0.0086
  s2: -0.0028
  s3: 0.0251
  s4: -0.0027
  tilt_x: -0.0644
  tilt_y: -0.0135
  scale: 3.0043
  rotation: -0.0564

Best score: 0.2558: : 899iter [00:25, 39.66iter/s]
Best score: 0.2558: : 900iter [00:25, 39.66iter/s]
Best score: 0.2558: : 900iter [00:26, 39.66iter/s]
Best score: 0.2558: : 901iter [00:26, 39.66iter/s]
Best score: 0.2558: : 902iter [00:26, 39.66iter/s]
Best score: 0.2558: : 903iter [00:26, 39.66iter/s]
Best score: 0.2558: : 904iter [00:26, 32.88iter/s]
Best score: 0.2558: : 904iter [00:26, 32.88iter/s]
Best score: 0.2558: : 905iter [00:26, 32.88iter/s]
Best score: 0.2558: : 906iter [00:26, 32.88iter/s]
Best score: 0.2558: : 907iter [00:26, 32.88iter/s]
Best score: 0.2558: : 908iter [00:26, 34.35iter/s]
Best score: 0.2558: : 908iter [00:26, 34.35iter/s]
Best score: 0.2551: : 909iter [00:26, 34.35iter/s]
Best score: 0.2551: : 910iter [00:26, 34.35iter/s]
Best score: 0.2551: : 911iter [00:26, 34.35iter/s]
Best score: 0.2551: : 912iter [00:26, 35.39iter/s]
Best score: 0.2551: : 912iter [00:26, 35.39iter/s]
Best score: 0.2551: : 913iter [00:26, 35.39iter/s]
Best score: 0.2551: : 914iter [00:26, 35.39iter/s]
Best score: 0.2551: : 915iter [00:26, 35.39iter/s]
Best score: 0.2551: : 916iter [00:26, 35.39iter/s]
Best score: 0.2551: : 917iter [00:26, 36.89iter/s]
Best score: 0.2551: : 917iter [00:26, 36.89iter/s]
Best score: 0.2551: : 918iter [00:26, 36.89iter/s]
Best score: 0.2551: : 919iter [00:26, 36.89iter/s]
Best score: 0.2551: : 920iter [00:26, 36.89iter/s]
Best score: 0.2551: : 921iter [00:26, 37.51iter/s]
Best score: 0.2551: : 921iter [00:26, 37.51iter/s]
Best score: 0.2551: : 922iter [00:26, 37.51iter/s]
Best score: 0.2551: : 923iter [00:26, 37.51iter/s]
Best score: 0.2551: : 924iter [00:26, 37.51iter/s]
Best score: 0.2551: : 925iter [00:26, 37.51iter/s]
Best score: 0.2551: : 926iter [00:26, 38.44iter/s]
Best score: 0.2551: : 926iter [00:26, 38.44iter/s]
Best score: 0.2551: : 927iter [00:26, 38.44iter/s]
Best score: 0.2551: : 928iter [00:26, 38.44iter/s]
Best score: 0.2539: : 929iter [00:26, 38.44iter/s]
Best score: 0.2539: : 930iter [00:26, 38.71iter/s]
Best score: 0.2539: : 930iter [00:26, 38.71iter/s]
Best score: 0.2539: : 931iter [00:26, 38.71iter/s]
Best score: 0.2539: : 932iter [00:26, 38.71iter/s]
Best score: 0.2539: : 933iter [00:26, 38.71iter/s]
Best score: 0.2539: : 934iter [00:26, 38.71iter/s]
Best score: 0.2539: : 935iter [00:26, 39.21iter/s]
Best score: 0.2539: : 935iter [00:26, 39.21iter/s]
Best score: 0.2539: : 936iter [00:27, 39.21iter/s]
Best score: 0.2539: : 937iter [00:27, 39.21iter/s]
Best score: 0.2539: : 938iter [00:27, 39.21iter/s]
Best score: 0.2527: : 939iter [00:27, 39.21iter/s]
Best score: 0.2527: : 940iter [00:27, 39.45iter/s]
Best score: 0.2527: : 940iter [00:27, 39.45iter/s]
Best score: 0.2527: : 941iter [00:27, 39.45iter/s]
Best score: 0.2527: : 942iter [00:27, 39.45iter/s]
Best score: 0.2527: : 943iter [00:27, 39.45iter/s]
Best score: 0.2527: : 944iter [00:27, 39.45iter/s]
Best score: 0.2527: : 945iter [00:27, 39.66iter/s]
Best score: 0.2527: : 945iter [00:27, 39.66iter/s]
Best score: 0.2527: : 946iter [00:27, 39.66iter/s]
Best score: 0.2527: : 947iter [00:27, 39.66iter/s]
Best score: 0.2527: : 948iter [00:27, 39.66iter/s]
[ITER 950] Best: 0.2526
[PARAMS] Current best parameters:
  fx: 490.0880
  fy: 490.3429
  px: 987.9116
  py: 763.9973
  k1: 0.01679347
  k2: 0.03291839
  k3: -0.01429489
  k4: 0.00090712
  p1: -0.00032000
  p2: 0.01493257
  p3: -0.02351337
  p4: 0.00176139
  s1: 0.0063
  s2: -0.0022
  s3: 0.0234
  s4: -0.0022
  tilt_x: -0.0646
  tilt_y: -0.0163
  scale: 3.0029
  rotation: -0.0545

Best score: 0.2526: : 949iter [00:27, 39.66iter/s]
Best score: 0.2526: : 950iter [00:27, 39.57iter/s]
Best score: 0.2526: : 950iter [00:27, 39.57iter/s]
Best score: 0.2526: : 951iter [00:27, 39.57iter/s]
Best score: 0.2526: : 952iter [00:27, 39.57iter/s]
Best score: 0.2526: : 953iter [00:27, 39.57iter/s]
Best score: 0.2526: : 954iter [00:27, 32.97iter/s]
Best score: 0.2526: : 954iter [00:27, 32.97iter/s]
Best score: 0.2526: : 955iter [00:27, 32.97iter/s]
Best score: 0.2526: : 956iter [00:27, 32.97iter/s]
Best score: 0.2526: : 957iter [00:27, 32.97iter/s]
Best score: 0.2526: : 958iter [00:27, 32.97iter/s]
Best score: 0.2526: : 959iter [00:27, 34.95iter/s]
Best score: 0.2524: : 959iter [00:27, 34.95iter/s]
Best score: 0.2524: : 960iter [00:27, 34.95iter/s]
Best score: 0.2524: : 961iter [00:27, 34.95iter/s]
Best score: 0.2524: : 962iter [00:27, 34.95iter/s]
Best score: 0.2524: : 963iter [00:27, 35.96iter/s]
Best score: 0.2524: : 963iter [00:27, 35.96iter/s]
Best score: 0.2524: : 964iter [00:27, 35.96iter/s]
Best score: 0.2524: : 965iter [00:27, 35.96iter/s]
Best score: 0.2524: : 966iter [00:27, 35.96iter/s]
Best score: 0.2524: : 967iter [00:27, 35.96iter/s]
Best score: 0.2524: : 968iter [00:27, 37.23iter/s]
Best score: 0.2524: : 968iter [00:27, 37.23iter/s]
Best score: 0.2523: : 969iter [00:27, 37.23iter/s]
Best score: 0.2523: : 970iter [00:27, 37.23iter/s]
Best score: 0.2523: : 971iter [00:27, 37.23iter/s]
Best score: 0.2523: : 972iter [00:27, 37.77iter/s]
Best score: 0.2523: : 972iter [00:27, 37.77iter/s]
Best score: 0.2523: : 973iter [00:28, 37.77iter/s]
Best score: 0.2523: : 974iter [00:28, 37.77iter/s]
Best score: 0.2523: : 975iter [00:28, 37.77iter/s]
Best score: 0.2523: : 976iter [00:28, 37.77iter/s]
Best score: 0.2523: : 977iter [00:28, 38.56iter/s]
Best score: 0.2523: : 977iter [00:28, 38.56iter/s]
Best score: 0.2523: : 978iter [00:28, 38.56iter/s]
Best score: 0.2523: : 979iter [00:28, 38.56iter/s]
Best score: 0.2523: : 980iter [00:28, 38.56iter/s]
Best score: 0.2523: : 981iter [00:28, 38.82iter/s]
Best score: 0.2523: : 981iter [00:28, 38.82iter/s]
Best score: 0.2523: : 982iter [00:28, 38.82iter/s]
Best score: 0.2523: : 983iter [00:28, 38.82iter/s]
Best score: 0.2523: : 984iter [00:28, 38.82iter/s]
Best score: 0.2523: : 985iter [00:28, 38.82iter/s]
Best score: 0.2523: : 986iter [00:28, 39.32iter/s]
Best score: 0.2523: : 986iter [00:28, 39.32iter/s]
Best score: 0.2523: : 987iter [00:28, 39.32iter/s]
Best score: 0.2523: : 988iter [00:28, 39.32iter/s]
Best score: 0.2510: : 989iter [00:28, 39.32iter/s]
Best score: 0.2510: : 990iter [00:28, 39.16iter/s]
Best score: 0.2510: : 990iter [00:28, 39.16iter/s]
Best score: 0.2510: : 991iter [00:28, 39.16iter/s]
Best score: 0.2510: : 992iter [00:28, 39.16iter/s]
Best score: 0.2510: : 993iter [00:28, 39.16iter/s]
Best score: 0.2510: : 994iter [00:28, 39.16iter/s]
Best score: 0.2510: : 995iter [00:28, 39.65iter/s]
Best score: 0.2510: : 995iter [00:28, 39.65iter/s]
Best score: 0.2510: : 996iter [00:28, 39.65iter/s]
Best score: 0.2510: : 997iter [00:28, 39.65iter/s]
Best score: 0.2510: : 998iter [00:28, 39.65iter/s]
[ITER 1000] Best: 0.2503
[PARAMS] Current best parameters:
  fx: 488.4753
  fy: 488.7908
  px: 987.9253
  py: 763.7202
  k1: 0.02016318
  k2: 0.03174365
  k3: -0.01433106
  k4: 0.00100161
  p1: -0.00034421
  p2: 0.01495616
  p3: -0.02249746
  p4: 0.00176191
  s1: 0.0053
  s2: -0.0022
  s3: 0.0221
  s4: -0.0021
  tilt_x: -0.0655
  tilt_y: -0.0161
  scale: 2.9998
  rotation: -0.0516

Best score: 0.2503: : 999iter [00:28, 39.65iter/s]
Best score: 0.2503: : 1000iter [00:28, 39.57iter/s]
Best score: 0.2503: : 1000iter [00:28, 39.57iter/s]
Best score: 0.2503: : 1001iter [00:28, 39.57iter/s]
Best score: 0.2503: : 1002iter [00:28, 39.57iter/s]
Best score: 0.2503: : 1003iter [00:28, 39.57iter/s]
Best score: 0.2503: : 1004iter [00:28, 32.99iter/s]
Best score: 0.2503: : 1004iter [00:28, 32.99iter/s]
Best score: 0.2503: : 1005iter [00:28, 32.99iter/s]
Best score: 0.2503: : 1006iter [00:28, 32.99iter/s]
Best score: 0.2503: : 1007iter [00:28, 32.99iter/s]
Best score: 0.2503: : 1008iter [00:28, 32.99iter/s]
Best score: 0.2503: : 1009iter [00:28, 35.10iter/s]
Best score: 0.2500: : 1009iter [00:28, 35.10iter/s]
Best score: 0.2500: : 1010iter [00:29, 35.10iter/s]
Best score: 0.2500: : 1011iter [00:29, 35.10iter/s]
Best score: 0.2500: : 1012iter [00:29, 35.10iter/s]
Best score: 0.2500: : 1013iter [00:29, 36.10iter/s]
Best score: 0.2500: : 1013iter [00:29, 36.10iter/s]
Best score: 0.2500: : 1014iter [00:29, 36.10iter/s]
Best score: 0.2500: : 1015iter [00:29, 36.10iter/s]
Best score: 0.2500: : 1016iter [00:29, 36.10iter/s]
Best score: 0.2500: : 1017iter [00:29, 36.10iter/s]
Best score: 0.2500: : 1018iter [00:29, 31.04iter/s]
Best score: 0.2500: : 1018iter [00:29, 31.04iter/s]
Best score: 0.2493: : 1019iter [00:29, 31.04iter/s]
Best score: 0.2493: : 1020iter [00:29, 31.04iter/s]
Best score: 0.2493: : 1021iter [00:29, 31.04iter/s]
Best score: 0.2493: : 1022iter [00:29, 32.85iter/s]
Best score: 0.2493: : 1022iter [00:29, 32.85iter/s]
Best score: 0.2493: : 1023iter [00:29, 32.85iter/s]
Best score: 0.2493: : 1024iter [00:29, 32.85iter/s]
Best score: 0.2493: : 1025iter [00:29, 32.85iter/s]
Best score: 0.2493: : 1026iter [00:29, 32.85iter/s]
Best score: 0.2493: : 1027iter [00:29, 34.99iter/s]
Best score: 0.2493: : 1027iter [00:29, 34.99iter/s]
Best score: 0.2493: : 1028iter [00:29, 34.99iter/s]
Best score: 0.2486: : 1029iter [00:29, 34.99iter/s]
Best score: 0.2486: : 1030iter [00:29, 34.99iter/s]
Best score: 0.2486: : 1031iter [00:29, 36.04iter/s]
Best score: 0.2486: : 1031iter [00:29, 36.04iter/s]
Best score: 0.2486: : 1032iter [00:29, 36.04iter/s]
Best score: 0.2486: : 1033iter [00:29, 36.04iter/s]
Best score: 0.2486: : 1034iter [00:29, 36.04iter/s]
Best score: 0.2486: : 1035iter [00:29, 36.04iter/s]
Best score: 0.2486: : 1036iter [00:29, 37.25iter/s]
Best score: 0.2486: : 1036iter [00:29, 37.25iter/s]
Best score: 0.2486: : 1037iter [00:29, 37.25iter/s]
Best score: 0.2486: : 1038iter [00:29, 37.25iter/s]
Best score: 0.2486: : 1039iter [00:29, 37.25iter/s]
Best score: 0.2486: : 1040iter [00:29, 37.84iter/s]
Best score: 0.2486: : 1040iter [00:29, 37.84iter/s]
Best score: 0.2486: : 1041iter [00:29, 37.84iter/s]
Best score: 0.2486: : 1042iter [00:29, 37.84iter/s]
Best score: 0.2486: : 1043iter [00:29, 37.84iter/s]
Best score: 0.2486: : 1044iter [00:29, 37.84iter/s]
Best score: 0.2486: : 1045iter [00:29, 38.56iter/s]
Best score: 0.2486: : 1045iter [00:29, 38.56iter/s]
Best score: 0.2486: : 1046iter [00:30, 38.56iter/s]
Best score: 0.2486: : 1047iter [00:30, 38.56iter/s]
Best score: 0.2486: : 1048iter [00:30, 38.56iter/s]
[ITER 1050] Best: 0.2485
[PARAMS] Current best parameters:
  fx: 487.0733
  fy: 487.4787
  px: 988.1251
  py: 762.8631
  k1: 0.02251493
  k2: 0.03135466
  k3: -0.01455929
  k4: 0.00109091
  p1: -0.00026470
  p2: 0.01555554
  p3: -0.02087802
  p4: 0.00150834
  s1: 0.0034
  s2: -0.0020
  s3: 0.0198
  s4: -0.0020
  tilt_x: -0.0692
  tilt_y: -0.0157
  scale: 2.9986
  rotation: -0.0519

Best score: 0.2485: : 1049iter [00:30, 38.56iter/s]
Best score: 0.2485: : 1050iter [00:30, 38.86iter/s]
Best score: 0.2485: : 1050iter [00:30, 38.86iter/s]
Best score: 0.2485: : 1051iter [00:30, 38.86iter/s]
Best score: 0.2485: : 1052iter [00:30, 38.86iter/s]
Best score: 0.2485: : 1053iter [00:30, 38.86iter/s]
Best score: 0.2485: : 1054iter [00:30, 32.69iter/s]
Best score: 0.2485: : 1054iter [00:30, 32.69iter/s]
Best score: 0.2485: : 1055iter [00:30, 32.69iter/s]
Best score: 0.2485: : 1056iter [00:30, 32.69iter/s]
Best score: 0.2485: : 1057iter [00:30, 32.69iter/s]
Best score: 0.2485: : 1058iter [00:30, 32.69iter/s]
Best score: 0.2485: : 1059iter [00:30, 34.87iter/s]
Best score: 0.2485: : 1059iter [00:30, 34.87iter/s]
Best score: 0.2485: : 1060iter [00:30, 34.87iter/s]
Best score: 0.2485: : 1061iter [00:30, 34.87iter/s]
Best score: 0.2485: : 1062iter [00:30, 34.87iter/s]
Best score: 0.2485: : 1063iter [00:30, 36.01iter/s]
Best score: 0.2485: : 1063iter [00:30, 36.01iter/s]
Best score: 0.2485: : 1064iter [00:30, 36.01iter/s]
Best score: 0.2485: : 1065iter [00:30, 36.01iter/s]
Best score: 0.2485: : 1066iter [00:30, 36.01iter/s]
Best score: 0.2485: : 1067iter [00:30, 36.01iter/s]
Best score: 0.2485: : 1068iter [00:30, 37.20iter/s]
Best score: 0.2485: : 1068iter [00:30, 37.20iter/s]
Best score: 0.2485: : 1069iter [00:30, 37.20iter/s]
Best score: 0.2485: : 1070iter [00:30, 37.20iter/s]
Best score: 0.2485: : 1071iter [00:30, 37.20iter/s]
Best score: 0.2485: : 1072iter [00:30, 37.61iter/s]
Best score: 0.2485: : 1072iter [00:30, 37.61iter/s]
Best score: 0.2485: : 1073iter [00:30, 37.61iter/s]
Best score: 0.2485: : 1074iter [00:30, 37.61iter/s]
Best score: 0.2485: : 1075iter [00:30, 37.61iter/s]
Best score: 0.2485: : 1076iter [00:30, 37.61iter/s]
Best score: 0.2485: : 1077iter [00:30, 38.40iter/s]
Best score: 0.2485: : 1077iter [00:30, 38.40iter/s]
Best score: 0.2485: : 1078iter [00:30, 38.40iter/s]
Best score: 0.2485: : 1079iter [00:30, 38.40iter/s]
Best score: 0.2485: : 1080iter [00:30, 38.40iter/s]
Best score: 0.2485: : 1081iter [00:30, 38.65iter/s]
Best score: 0.2485: : 1081iter [00:30, 38.65iter/s]
Best score: 0.2485: : 1082iter [00:30, 38.65iter/s]
Best score: 0.2485: : 1083iter [00:31, 38.65iter/s]
Best score: 0.2485: : 1084iter [00:31, 38.65iter/s]
Best score: 0.2485: : 1085iter [00:31, 38.65iter/s]
Best score: 0.2485: : 1086iter [00:31, 39.27iter/s]
Best score: 0.2485: : 1086iter [00:31, 39.27iter/s]
Best score: 0.2485: : 1087iter [00:31, 39.27iter/s]
Best score: 0.2485: : 1088iter [00:31, 39.27iter/s]
Best score: 0.2484: : 1089iter [00:31, 39.27iter/s]
Best score: 0.2484: : 1090iter [00:31, 39.30iter/s]
Best score: 0.2484: : 1090iter [00:31, 39.30iter/s]
Best score: 0.2484: : 1091iter [00:31, 39.30iter/s]
Best score: 0.2484: : 1092iter [00:31, 39.30iter/s]
Best score: 0.2484: : 1093iter [00:31, 39.30iter/s]
Best score: 0.2484: : 1094iter [00:31, 39.49iter/s]
Best score: 0.2484: : 1094iter [00:31, 39.49iter/s]
Best score: 0.2484: : 1095iter [00:31, 39.49iter/s]
Best score: 0.2484: : 1096iter [00:31, 39.49iter/s]
Best score: 0.2484: : 1097iter [00:31, 39.49iter/s]
Best score: 0.2484: : 1098iter [00:31, 39.49iter/s]
Best score: 0.2484: : 1099iter [00:31, 39.75iter/s]
[ITER 1100] Best: 0.2484
[PARAMS] Current best parameters:
  fx: 487.3565
  fy: 487.7496
  px: 988.0078
  py: 763.1518
  k1: 0.02130770
  k2: 0.03214308
  k3: -0.01467084
  k4: 0.00106268
  p1: -0.00008608
  p2: 0.01550133
  p3: -0.02143943
  p4: 0.00163941
  s1: 0.0039
  s2: -0.0021
  s3: 0.0206
  s4: -0.0022
  tilt_x: -0.0684
  tilt_y: -0.0153
  scale: 2.9995
  rotation: -0.0535

Best score: 0.2484: : 1099iter [00:31, 39.75iter/s]
Best score: 0.2484: : 1100iter [00:31, 34.93iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.248390 -> 0.248231

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,793 → 25,793 within 3500mm ring
  Ring polish improved score: 0.248231 -> 0.247836
  Success: True
  Tangential distortion: p1=-0.000089, p2=0.015501, p3=-0.021442, p4=0.001639
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2478
[✅] Parameters copied to: /working/MDC101_W036_2025-12-31/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 487.35649493434045
    fy: 487.74960269660767
    px: 988.0078424738588
    py: 763.151780673629
    k1: 0.021306187764020632
    k2: 0.03214198152107785
    k3: -0.014669972348701881
    k4: 0.0010634132837809905
    p1: -8.931008590451633e-05
    p2: 0.01550100410831329
    p3: -0.02144205187023652
    p4: 0.0016388428584453584
    scale: 2.9994754473828684
    rotation: -0.053523578579912405
    mmpp: 1.25958662740914
    nmpp: 1259586.62740914

[🔄] Converting 417 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/213 [00:00<?, ?frame/s]

   🔄 1000:   8%|▊         | 18/213 [00:00<00:01, 172.22frame/s]

   🔄 1000:  17%|█▋        | 36/213 [00:00<00:01, 174.68frame/s]

   🔄 1000:  25%|██▌       | 54/213 [00:00<00:00, 175.84frame/s]

   🔄 1000:  34%|███▍      | 72/213 [00:00<00:00, 175.99frame/s]

   🔄 1000:  42%|████▏     | 90/213 [00:00<00:00, 175.06frame/s]

   🔄 1000:  51%|█████     | 108/213 [00:00<00:00, 174.51frame/s]

   🔄 1000:  59%|█████▉    | 126/213 [00:00<00:00, 175.00frame/s]

   🔄 1000:  68%|██████▊   | 144/213 [00:00<00:00, 175.27frame/s]

   🔄 1000:  76%|███████▌  | 162/213 [00:00<00:00, 175.34frame/s]

   🔄 1000:  85%|████████▍ | 180/213 [00:01<00:00, 175.73frame/s]

   🔄 1000:  93%|█████████▎| 198/213 [00:01<00:00, 176.07frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.20s/rig]

   🔄 500:   0%|          | 0/204 [00:00<?, ?frame/s]

   🔄 500:   9%|▉         | 18/204 [00:00<00:01, 179.76frame/s]

   🔄 500:  18%|█▊        | 36/204 [00:00<00:00, 179.26frame/s]

   🔄 500:  26%|██▋       | 54/204 [00:00<00:00, 178.75frame/s]

   🔄 500:  35%|███▌      | 72/204 [00:00<00:00, 178.09frame/s]

   🔄 500:  44%|████▍     | 90/204 [00:00<00:00, 178.15frame/s]

   🔄 500:  53%|█████▎    | 109/204 [00:00<00:00, 180.00frame/s]

   🔄 500:  63%|██████▎   | 129/204 [00:00<00:00, 184.18frame/s]

   🔄 500:  73%|███████▎  | 148/204 [00:00<00:00, 181.57frame/s]

   🔄 500:  82%|████████▏ | 167/204 [00:00<00:00, 180.65frame/s]

   🔄 500:  91%|█████████ | 186/204 [00:01<00:00, 179.78frame/s]

   🔄 500: 100%|██████████| 204/204 [00:01<00:00, 178.17frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.09s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.11s/rig]
[✅] Saved PTDs: /working/MDC101_W036_2025-12-31/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 172.47frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 172.80frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 172.15frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 173.02frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 172.95frame/s]

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

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 175.13frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 175.33frame/s]

   🔄 500:  55%|█████▌    | 55/100 [00:00<00:00, 177.93frame/s]

   🔄 500:  74%|███████▍  | 74/100 [00:00<00:00, 180.11frame/s]

   🔄 500:  93%|█████████▎| 93/100 [00:00<00:00, 179.31frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.00rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.02s/rig]
[✅] Saved PTDs: /working/MDC101_W036_2025-12-31/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 98533/3080448 [00:00<00:03, 985309.80px/s]
📤 Writing LUT:   7%|▋         | 206629/3080448 [00:00<00:02, 1041570.35px/s]
📤 Writing LUT:  10%|█         | 312317/3080448 [00:00<00:02, 1048558.04px/s]
📤 Writing LUT:  14%|█▎        | 417173/3080448 [00:00<00:02, 1030577.84px/s]
📤 Writing LUT:  17%|█▋        | 520277/3080448 [00:00<00:02, 999724.48px/s] 
📤 Writing LUT:  20%|██        | 620406/3080448 [00:00<00:02, 962868.74px/s]
📤 Writing LUT:  23%|██▎       | 716959/3080448 [00:00<00:02, 945033.61px/s]
📤 Writing LUT:  26%|██▋       | 811636/3080448 [00:00<00:02, 927139.69px/s]
📤 Writing LUT:  29%|██▉       | 904467/3080448 [00:00<00:02, 921665.23px/s]
📤 Writing LUT:  32%|███▏      | 996698/3080448 [00:01<00:02, 921788.21px/s]
📤 Writing LUT:  35%|███▌      | 1088922/3080448 [00:01<00:02, 915536.78px/s]
📤 Writing LUT:  38%|███▊      | 1181377/3080448 [00:01<00:02, 918217.01px/s]
📤 Writing LUT:  41%|████▏     | 1273223/3080448 [00:01<00:01, 917634.55px/s]
📤 Writing LUT:  44%|████▍     | 1365849/3080448 [00:01<00:01, 920209.47px/s]
📤 Writing LUT:  47%|████▋     | 1457884/3080448 [00:01<00:01, 918698.69px/s]
📤 Writing LUT:  50%|█████     | 1549763/3080448 [00:01<00:01, 916457.42px/s]
📤 Writing LUT:  53%|█████▎    | 1641415/3080448 [00:01<00:01, 915594.61px/s]
📤 Writing LUT:  56%|█████▋    | 1732979/3080448 [00:01<00:01, 911705.82px/s]
📤 Writing LUT:  59%|█████▉    | 1825050/3080448 [00:01<00:01, 914391.90px/s]
📤 Writing LUT:  62%|██████▏   | 1916495/3080448 [00:02<00:01, 912847.88px/s]
📤 Writing LUT:  65%|██████▌   | 2009422/3080448 [00:02<00:01, 917753.69px/s]
📤 Writing LUT:  68%|██████▊   | 2102502/3080448 [00:02<00:01, 921656.53px/s]
📤 Writing LUT:  71%|███████▏  | 2195727/3080448 [00:02<00:00, 924826.13px/s]
📤 Writing LUT:  74%|███████▍  | 2288214/3080448 [00:02<00:00, 924689.61px/s]
📤 Writing LUT:  77%|███████▋  | 2381043/3080448 [00:02<00:00, 925768.19px/s]
📤 Writing LUT:  80%|████████  | 2473622/3080448 [00:02<00:00, 925438.32px/s]
📤 Writing LUT:  83%|████████▎ | 2566168/3080448 [00:02<00:00, 920251.81px/s]
📤 Writing LUT:  86%|████████▋ | 2659305/3080448 [00:02<00:00, 923571.96px/s]
📤 Writing LUT:  89%|████████▉ | 2751669/3080448 [00:02<00:00, 922140.81px/s]
📤 Writing LUT:  92%|█████████▏| 2843888/3080448 [00:03<00:00, 921571.72px/s]
📤 Writing LUT:  95%|█████████▌| 2936049/3080448 [00:03<00:00, 920599.77px/s]
📤 Writing LUT:  98%|█████████▊| 3030090/3080448 [00:03<00:00, 926526.55px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 932631.02px/s]
[✅] LUT written to: /working/MDC101_W036_2025-12-31/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC101_W036_2025-12-31' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC101_W036_2025-12-31/calibration/MDC101_W036_2025-12-31_unknown.lut

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

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

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

   🔥 1000:   0%|          | 0/213 [00:00<?, ?frame/s]

   🔥 1000:   4%|▍         | 9/213 [00:00<00:02, 81.96frame/s]

   🔥 1000:   9%|▉         | 19/213 [00:00<00:02, 87.19frame/s]

   🔥 1000:  13%|█▎        | 28/213 [00:00<00:02, 88.31frame/s]

   🔥 1000:  17%|█▋        | 37/213 [00:00<00:02, 86.96frame/s]

   🔥 1000:  22%|██▏       | 46/213 [00:00<00:01, 86.28frame/s]

   🔥 1000:  26%|██▌       | 55/213 [00:00<00:01, 85.60frame/s]

   🔥 1000:  30%|███       | 64/213 [00:00<00:01, 85.44frame/s]

   🔥 1000:  35%|███▍      | 74/213 [00:00<00:01, 87.38frame/s]

   🔥 1000:  39%|███▉      | 84/213 [00:00<00:01, 88.42frame/s]

   🔥 1000:  44%|████▎     | 93/213 [00:01<00:01, 88.37frame/s]

   🔥 1000:  48%|████▊     | 102/213 [00:01<00:01, 87.39frame/s]

   🔥 1000:  52%|█████▏    | 111/213 [00:01<00:01, 87.81frame/s]

   🔥 1000:  57%|█████▋    | 121/213 [00:01<00:01, 88.82frame/s]

   🔥 1000:  61%|██████    | 130/213 [00:01<00:00, 88.52frame/s]

   🔥 1000:  65%|██████▌   | 139/213 [00:01<00:00, 88.06frame/s]

   🔥 1000:  69%|██████▉   | 148/213 [00:01<00:00, 88.38frame/s]

   🔥 1000:  74%|███████▎  | 157/213 [00:01<00:00, 87.84frame/s]

   🔥 1000:  78%|███████▊  | 166/213 [00:01<00:00, 87.42frame/s]

   🔥 1000:  82%|████████▏ | 175/213 [00:02<00:00, 86.42frame/s]

   🔥 1000:  86%|████████▋ | 184/213 [00:02<00:00, 85.51frame/s]

   🔥 1000:  91%|█████████ | 193/213 [00:02<00:00, 86.56frame/s]

   🔥 1000:  95%|█████████▌| 203/213 [00:02<00:00, 88.57frame/s]

   🔥 1000: 100%|██████████| 213/213 [00:02<00:00, 89.39frame/s]

                                                                [✅] 1000: /working/MDC101_W036_2025-12-31/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC101_W036_2025-12-31/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.36s/rig]

   🔥 500:   0%|          | 0/204 [00:00<?, ?frame/s]

   🔥 500:   4%|▍         | 9/204 [00:00<00:02, 82.37frame/s]

   🔥 500:   9%|▉         | 18/204 [00:00<00:02, 83.64frame/s]

   🔥 500:  13%|█▎        | 27/204 [00:00<00:02, 84.86frame/s]

   🔥 500:  18%|█▊        | 37/204 [00:00<00:01, 87.11frame/s]

   🔥 500:  23%|██▎       | 47/204 [00:00<00:01, 88.28frame/s]

   🔥 500:  27%|██▋       | 56/204 [00:00<00:01, 88.14frame/s]

   🔥 500:  32%|███▏      | 65/204 [00:00<00:01, 87.98frame/s]

   🔥 500:  36%|███▋      | 74/204 [00:00<00:01, 85.88frame/s]

   🔥 500:  41%|████      | 83/204 [00:00<00:01, 84.67frame/s]

   🔥 500:  45%|████▌     | 92/204 [00:01<00:01, 84.26frame/s]

   🔥 500:  50%|████▉     | 101/204 [00:01<00:01, 84.78frame/s]

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   🔥 500:  63%|██████▎   | 128/204 [00:01<00:00, 84.45frame/s]

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   🔥 500:  89%|████████▉ | 182/204 [00:02<00:00, 83.14frame/s]

   🔥 500:  94%|█████████▎| 191/204 [00:02<00:00, 83.32frame/s]

   🔥 500:  98%|█████████▊| 200/204 [00:02<00:00, 82.36frame/s]

                                                               [✅] 500: /working/MDC101_W036_2025-12-31/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC101_W036_2025-12-31/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:08<00:00,  4.33s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:08<00:00,  4.49s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC101_W036_2025-12-31/calibration/debug/5_heatmap
[🏁] Finished processing MDC101_W036_2025-12-31


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