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  1. .config/.last_opt_in_prompt.yaml +1 -0
  2. .config/.last_survey_prompt.yaml +1 -0
  3. .config/.last_update_check.json +1 -0
  4. .config/active_config +1 -0
  5. .config/config_sentinel +0 -0
  6. .config/configurations/config_default +6 -0
  7. .config/gce +1 -0
  8. .config/logs/2023.06.12/13.34.47.039331.log +596 -0
  9. .config/logs/2023.06.12/13.35.12.270546.log +5 -0
  10. .config/logs/2023.06.12/13.35.35.936702.log +169 -0
  11. .config/logs/2023.06.12/13.35.43.661450.log +5 -0
  12. .config/logs/2023.06.12/13.36.08.323143.log +8 -0
  13. .config/logs/2023.06.12/13.36.09.026206.log +8 -0
  14. .gitattributes +57 -0
  15. SHOW/.gitignore +147 -0
  16. SHOW/.gitignore copy +201 -0
  17. SHOW/.vscode/PythonImportHelper-v2-Completion.json +0 -0
  18. SHOW/.vscode/launch.json +16 -0
  19. SHOW/EDIT_insightface/storage.py +60 -0
  20. SHOW/LICENSE +88 -0
  21. SHOW/README.md +260 -0
  22. SHOW/SHOW/__init__.py +25 -0
  23. SHOW/SHOW/constants.py +12 -0
  24. SHOW/SHOW/datasets/__init__.py +4 -0
  25. SHOW/SHOW/datasets/model_func_atach.py +57 -0
  26. SHOW/SHOW/datasets/op_base.py +168 -0
  27. SHOW/SHOW/datasets/op_dataset.py +732 -0
  28. SHOW/SHOW/datasets/op_post_process.py +212 -0
  29. SHOW/SHOW/datasets/pre_dataset.py +111 -0
  30. SHOW/SHOW/datasets/pre_runner.py +108 -0
  31. SHOW/SHOW/detector/__init__.py +2 -0
  32. SHOW/SHOW/detector/face_detector.py +106 -0
  33. SHOW/SHOW/detector/fan_detector.py +62 -0
  34. SHOW/SHOW/detector/pifpaf_detector.py +53 -0
  35. SHOW/SHOW/face_iders/__init__.py +11 -0
  36. SHOW/SHOW/face_iders/arcface_ider.py +122 -0
  37. SHOW/SHOW/face_iders/base.py +38 -0
  38. SHOW/SHOW/face_iders/builder.py +10 -0
  39. SHOW/SHOW/face_iders/utils.py +23 -0
  40. SHOW/SHOW/flame/FLAME.py +283 -0
  41. SHOW/SHOW/flame/__init__.py +2 -0
  42. SHOW/SHOW/flame/lbs.py +406 -0
  43. SHOW/SHOW/image.py +119 -0
  44. SHOW/SHOW/load_assets.py +243 -0
  45. SHOW/SHOW/load_models.py +72 -0
  46. SHOW/SHOW/loggers/MyNeptuneLogger.py +66 -0
  47. SHOW/SHOW/loggers/MyTFLogger.py +31 -0
  48. SHOW/SHOW/loggers/MyTextLogger.py +29 -0
  49. SHOW/SHOW/loggers/MyWandbLogger.py +59 -0
  50. SHOW/SHOW/loggers/__init__.py +6 -0
.config/.last_opt_in_prompt.yaml ADDED
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.config/.last_survey_prompt.yaml ADDED
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+ last_prompt_time: 1686576935.2613058
.config/.last_update_check.json ADDED
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+ {"last_update_check_time": 1686576943.101163, "last_update_check_revision": 20230602161805, "notifications": [], "last_nag_times": {}}
.config/active_config ADDED
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.config/config_sentinel ADDED
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.config/configurations/config_default ADDED
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+ [component_manager]
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.config/logs/2023.06.12/13.34.47.039331.log ADDED
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+ 2023-06-12 13:35:02,536 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
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+ 2023-06-12 13:35:02,627 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-nix-20220920185015.tar.gz HTTP/1.1" 200 1837
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+ 2023-06-12 13:35:02,628 INFO ___FILE_ONLY___ ══════════════════════════════
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+ 2023-06-12 13:35:02,637 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.9 ═╣
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+ 2023-06-12 13:35:02,640 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
292
+ 2023-06-12 13:35:02,673 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bundled-python3-unix-linux-x86_64-20230428152336.tar.gz HTTP/1.1" 200 66615539
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+ 2023-06-12 13:35:06,638 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.9 ═╣
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366
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368
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369
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370
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378
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380
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384
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399
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416
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417
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420
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421
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422
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
+
445
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446
+
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448
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449
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450
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451
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452
+
453
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454
+
455
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456
+ 2023-06-12 13:35:08,506 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
457
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458
+ 2023-06-12 13:35:08,591 INFO ___FILE_ONLY___ ══════════════════════════════
459
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460
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461
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463
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464
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466
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468
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469
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470
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472
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+ 2023-06-12 13:35:08,608 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
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476
+ 2023-06-12 13:35:08,611 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
477
+ 2023-06-12 13:35:08,700 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20230526134617.tar.gz HTTP/1.1" 200 1270213
478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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495
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496
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497
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498
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499
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501
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502
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503
+ 2023-06-12 13:35:08,764 INFO ___FILE_ONLY___ ═
504
+ 2023-06-12 13:35:08,764 INFO ___FILE_ONLY___ ═
505
+ 2023-06-12 13:35:08,764 INFO ___FILE_ONLY___ ═
506
+ 2023-06-12 13:35:08,764 INFO ___FILE_ONLY___ ═
507
+ 2023-06-12 13:35:08,765 INFO ___FILE_ONLY___ ═
508
+ 2023-06-12 13:35:08,796 INFO ___FILE_ONLY___ ═══════════════
509
+ 2023-06-12 13:35:08,797 INFO ___FILE_ONLY___ ═══════════════
510
+ 2023-06-12 13:35:08,797 INFO ___FILE_ONLY___ ╝
511
+
512
+ 2023-06-12 13:35:08,806 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
513
+
514
+ 2023-06-12 13:35:08,806 INFO ___FILE_ONLY___ ╠═ Installing: anthoscli ═╣
515
+
516
+ 2023-06-12 13:35:08,806 INFO ___FILE_ONLY___ ╚
517
+ 2023-06-12 13:35:08,811 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
518
+ 2023-06-12 13:35:08,811 INFO ___FILE_ONLY___ ╝
519
+
520
+ 2023-06-12 13:35:08,813 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
521
+
522
+ 2023-06-12 13:35:08,813 INFO ___FILE_ONLY___ ╠═ Installing: anthoscli ═╣
523
+
524
+ 2023-06-12 13:35:08,813 INFO ___FILE_ONLY___ ╚
525
+ 2023-06-12 13:35:08,815 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
526
+ 2023-06-12 13:35:08,906 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-anthoscli-linux-x86_64-20230417163046.tar.gz HTTP/1.1" 200 71766688
527
+ 2023-06-12 13:35:09,438 INFO ___FILE_ONLY___ ═
528
+ 2023-06-12 13:35:09,441 INFO ___FILE_ONLY___ ═
529
+ 2023-06-12 13:35:09,444 INFO ___FILE_ONLY___ ═
530
+ 2023-06-12 13:35:09,447 INFO ___FILE_ONLY___ ═
531
+ 2023-06-12 13:35:09,450 INFO ___FILE_ONLY___ ═
532
+ 2023-06-12 13:35:09,453 INFO ___FILE_ONLY___ ═
533
+ 2023-06-12 13:35:09,456 INFO ___FILE_ONLY___ ═
534
+ 2023-06-12 13:35:09,459 INFO ___FILE_ONLY___ ═
535
+ 2023-06-12 13:35:09,462 INFO ___FILE_ONLY___ ═
536
+ 2023-06-12 13:35:09,465 INFO ___FILE_ONLY___ ═
537
+ 2023-06-12 13:35:09,468 INFO ___FILE_ONLY___ ═
538
+ 2023-06-12 13:35:09,471 INFO ___FILE_ONLY___ ═
539
+ 2023-06-12 13:35:09,473 INFO ___FILE_ONLY___ ═
540
+ 2023-06-12 13:35:09,476 INFO ___FILE_ONLY___ ═
541
+ 2023-06-12 13:35:09,479 INFO ___FILE_ONLY___ ═
542
+ 2023-06-12 13:35:09,482 INFO ___FILE_ONLY___ ═
543
+ 2023-06-12 13:35:09,485 INFO ___FILE_ONLY___ ═
544
+ 2023-06-12 13:35:09,488 INFO ___FILE_ONLY___ ═
545
+ 2023-06-12 13:35:09,491 INFO ___FILE_ONLY___ ═
546
+ 2023-06-12 13:35:09,494 INFO ___FILE_ONLY___ ═
547
+ 2023-06-12 13:35:09,496 INFO ___FILE_ONLY___ ═
548
+ 2023-06-12 13:35:09,500 INFO ___FILE_ONLY___ ═
549
+ 2023-06-12 13:35:09,502 INFO ___FILE_ONLY___ ═
550
+ 2023-06-12 13:35:09,505 INFO ___FILE_ONLY___ ═
551
+ 2023-06-12 13:35:09,508 INFO ___FILE_ONLY___ ═
552
+ 2023-06-12 13:35:09,511 INFO ___FILE_ONLY___ ═
553
+ 2023-06-12 13:35:09,515 INFO ___FILE_ONLY___ ═
554
+ 2023-06-12 13:35:09,518 INFO ___FILE_ONLY___ ═
555
+ 2023-06-12 13:35:09,521 INFO ___FILE_ONLY___ ═
556
+ 2023-06-12 13:35:09,524 INFO ___FILE_ONLY___ ═
557
+ 2023-06-12 13:35:11,558 INFO ___FILE_ONLY___ ══════════
558
+ 2023-06-12 13:35:11,563 INFO ___FILE_ONLY___ ═════════
559
+ 2023-06-12 13:35:11,587 INFO ___FILE_ONLY___ ═══════════
560
+ 2023-06-12 13:35:11,587 INFO ___FILE_ONLY___ ╝
561
+
562
+ 2023-06-12 13:35:11,610 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
563
+
564
+ 2023-06-12 13:35:11,610 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies ═╣
565
+
566
+ 2023-06-12 13:35:11,610 INFO ___FILE_ONLY___ ╚
567
+ 2023-06-12 13:35:11,613 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
568
+ 2023-06-12 13:35:11,704 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/1.1" 200 104
569
+ 2023-06-12 13:35:11,705 INFO ___FILE_ONLY___ ══════════════════════════════
570
+ 2023-06-12 13:35:11,705 INFO ___FILE_ONLY___ ══════════════════════════════
571
+ 2023-06-12 13:35:11,705 INFO ___FILE_ONLY___ ╝
572
+
573
+ 2023-06-12 13:35:11,714 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
574
+
575
+ 2023-06-12 13:35:11,714 INFO ___FILE_ONLY___ ╠═ Creating backup and activating new installation ═╣
576
+
577
+ 2023-06-12 13:35:11,714 INFO ___FILE_ONLY___ ╚
578
+ 2023-06-12 13:35:11,714 DEBUG root Attempting to move directory [/tools/google-cloud-sdk] to [/tools/google-cloud-sdk.staging/.install/.backup]
579
+ 2023-06-12 13:35:11,714 INFO ___FILE_ONLY___ ══════════════════════════════
580
+ 2023-06-12 13:35:11,714 DEBUG root Attempting to move directory [/tools/google-cloud-sdk.staging] to [/tools/google-cloud-sdk]
581
+ 2023-06-12 13:35:11,714 INFO ___FILE_ONLY___ ══════════════════════════════
582
+ 2023-06-12 13:35:11,714 INFO ___FILE_ONLY___ ╝
583
+
584
+ 2023-06-12 13:35:11,718 DEBUG root Updating notification cache...
585
+ 2023-06-12 13:35:11,719 INFO ___FILE_ONLY___
586
+
587
+ 2023-06-12 13:35:11,720 INFO ___FILE_ONLY___ Performing post processing steps...
588
+ 2023-06-12 13:35:11,721 DEBUG root Executing command: ['python3', '-S', '/tools/google-cloud-sdk/lib/gcloud.py', 'components', 'post-process']
589
+ 2023-06-12 13:35:35,241 DEBUG ___FILE_ONLY___
590
+ 2023-06-12 13:35:35,241 DEBUG ___FILE_ONLY___
591
+ 2023-06-12 13:35:35,256 INFO ___FILE_ONLY___
592
+ Update done!
593
+
594
+
595
+ 2023-06-12 13:35:35,260 DEBUG root Chosen display Format:none
596
+ 2023-06-12 13:35:35,260 INFO root Display format: "none"
.config/logs/2023.06.12/13.35.12.270546.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2023-06-12 13:35:12,271 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2023-06-12 13:35:12,273 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2023-06-12 13:35:12,275 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2023-06-12 13:35:35,133 DEBUG root Chosen display Format:none
5
+ 2023-06-12 13:35:35,133 INFO root Display format: "none"
.config/logs/2023.06.12/13.35.35.936702.log ADDED
@@ -0,0 +1,169 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-06-12 13:35:35,937 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2023-06-12 13:35:35,940 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2023-06-12 13:35:35,942 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2023-06-12 13:35:35,943 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2023-06-12 13:35:35,947 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2023-06-12 13:35:36,034 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 206160
8
+ 2023-06-12 13:35:36,055 WARNING root Component [compute] no longer exists.
9
+ 2023-06-12 13:35:36,055 WARNING root Component [preview] no longer exists.
10
+ 2023-06-12 13:35:36,056 INFO ___FILE_ONLY___
11
+
12
+ 2023-06-12 13:35:36,057 INFO ___FILE_ONLY___
13
+ Your current Google Cloud CLI version is: 434.0.0
14
+
15
+ 2023-06-12 13:35:36,057 INFO ___FILE_ONLY___ Installing components from version: 434.0.0
16
+
17
+ 2023-06-12 13:35:36,057 INFO ___FILE_ONLY___
18
+
19
+ 2023-06-12 13:35:36,057 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2023-06-12 13:35:36,058 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
21
+ 2023-06-12 13:35:36,059 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
22
+ 2023-06-12 13:35:36,060 INFO ___FILE_ONLY___ ┌──────────────────────────────────────────────┐
23
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___
24
+
25
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___ │ These components will be installed. │
26
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___
27
+
28
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___ ├───────────────────────┬────────────┬─────────┤
29
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___
30
+
31
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
32
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___
33
+
34
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___ ├───────────────────────┼────────────┼─────────┤
35
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___
36
+
37
+ 2023-06-12 13:35:36,061 INFO ___FILE_ONLY___ │
38
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___ gcloud Alpha Commands
39
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___
40
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___ │
41
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___ 2023.06.02
42
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___
43
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___ │
44
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___ < 1 MiB
45
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___
46
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___ │
47
+ 2023-06-12 13:35:36,062 INFO ___FILE_ONLY___
48
+
49
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___ │
50
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___ gcloud Beta Commands
51
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___
52
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___ │
53
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___ 2023.06.02
54
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___
55
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___ │
56
+ 2023-06-12 13:35:36,063 INFO ___FILE_ONLY___ < 1 MiB
57
+ 2023-06-12 13:35:36,064 INFO ___FILE_ONLY___
58
+ 2023-06-12 13:35:36,064 INFO ___FILE_ONLY___ │
59
+ 2023-06-12 13:35:36,064 INFO ___FILE_ONLY___
60
+
61
+ 2023-06-12 13:35:36,064 INFO ___FILE_ONLY___ └───────────────────────┴────────────┴─────────┘
62
+ 2023-06-12 13:35:36,064 INFO ___FILE_ONLY___
63
+
64
+ 2023-06-12 13:35:36,064 INFO ___FILE_ONLY___
65
+
66
+ 2023-06-12 13:35:36,066 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
67
+ 2023-06-12 13:35:36,158 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/1.1" 200 1011317
68
+ 2023-06-12 13:35:36,260 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
69
+ https://cloud.google.com/sdk/release_notes
70
+
71
+
72
+ 2023-06-12 13:35:36,263 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
73
+
74
+ 2023-06-12 13:35:36,263 INFO ___FILE_ONLY___ ╠═ Creating update staging area ═╣
75
+
76
+ 2023-06-12 13:35:36,263 INFO ___FILE_ONLY___ ╚
77
+ 2023-06-12 13:35:36,263 INFO ___FILE_ONLY___ ══════
78
+ 2023-06-12 13:35:36,862 INFO ___FILE_ONLY___ ══════
79
+ 2023-06-12 13:35:36,862 INFO ___FILE_ONLY___ ══════
80
+ 2023-06-12 13:35:37,427 INFO ___FILE_ONLY___ ═
81
+ 2023-06-12 13:35:37,523 INFO ___FILE_ONLY___ ═
82
+ 2023-06-12 13:35:37,639 INFO ___FILE_ONLY___ ═
83
+ 2023-06-12 13:35:37,739 INFO ___FILE_ONLY___ ═
84
+ 2023-06-12 13:35:37,858 INFO ___FILE_ONLY___ ═
85
+ 2023-06-12 13:35:37,933 INFO ___FILE_ONLY___ ═
86
+ 2023-06-12 13:35:38,006 INFO ___FILE_ONLY___ ═
87
+ 2023-06-12 13:35:38,105 INFO ___FILE_ONLY___ ═
88
+ 2023-06-12 13:35:38,196 INFO ___FILE_ONLY___ ═
89
+ 2023-06-12 13:35:38,276 INFO ___FILE_ONLY___ ═
90
+ 2023-06-12 13:35:38,344 INFO ___FILE_ONLY___ ═
91
+ 2023-06-12 13:35:38,474 INFO ___FILE_ONLY___ ═
92
+ 2023-06-12 13:35:38,563 INFO ___FILE_ONLY___ ═
93
+ 2023-06-12 13:35:38,616 INFO ___FILE_ONLY___ ═
94
+ 2023-06-12 13:35:38,672 INFO ___FILE_ONLY___ ═
95
+ 2023-06-12 13:35:38,733 INFO ___FILE_ONLY___ ═
96
+ 2023-06-12 13:35:38,809 INFO ___FILE_ONLY___ ═
97
+ 2023-06-12 13:35:38,862 INFO ___FILE_ONLY___ ═
98
+ 2023-06-12 13:35:38,915 INFO ___FILE_ONLY___ ═
99
+ 2023-06-12 13:35:38,969 INFO ___FILE_ONLY___ ═
100
+ 2023-06-12 13:35:39,028 INFO ___FILE_ONLY___ ═
101
+ 2023-06-12 13:35:39,082 INFO ___FILE_ONLY___ ═
102
+ 2023-06-12 13:35:39,145 INFO ___FILE_ONLY___ ═
103
+ 2023-06-12 13:35:39,201 INFO ___FILE_ONLY___ ═
104
+ 2023-06-12 13:35:39,258 INFO ___FILE_ONLY___ ═
105
+ 2023-06-12 13:35:39,307 INFO ___FILE_ONLY___ ═
106
+ 2023-06-12 13:35:39,366 INFO ___FILE_ONLY___ ═
107
+ 2023-06-12 13:35:39,480 INFO ___FILE_ONLY___ ═
108
+ 2023-06-12 13:35:39,538 INFO ___FILE_ONLY___ ═
109
+ 2023-06-12 13:35:39,606 INFO ___FILE_ONLY___ ═
110
+ 2023-06-12 13:35:39,662 INFO ___FILE_ONLY___ ═
111
+ 2023-06-12 13:35:39,704 INFO ___FILE_ONLY___ ═
112
+ 2023-06-12 13:35:39,754 INFO ___FILE_ONLY___ ═
113
+ 2023-06-12 13:35:39,820 INFO ___FILE_ONLY___ ═
114
+ 2023-06-12 13:35:39,884 INFO ___FILE_ONLY___ ═
115
+ 2023-06-12 13:35:39,945 INFO ___FILE_ONLY___ ═
116
+ 2023-06-12 13:35:39,990 INFO ___FILE_ONLY___ ═
117
+ 2023-06-12 13:35:40,153 INFO ___FILE_ONLY___ ═
118
+ 2023-06-12 13:35:40,210 INFO ___FILE_ONLY___ ═
119
+ 2023-06-12 13:35:40,281 INFO ___FILE_ONLY___ ═
120
+ 2023-06-12 13:35:40,341 INFO ___FILE_ONLY___ ═
121
+ 2023-06-12 13:35:40,402 INFO ___FILE_ONLY___ ═
122
+ 2023-06-12 13:35:40,402 INFO ___FILE_ONLY___ ╝
123
+
124
+ 2023-06-12 13:35:42,900 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
125
+
126
+ 2023-06-12 13:35:42,900 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Alpha Commands ═╣
127
+
128
+ 2023-06-12 13:35:42,900 INFO ___FILE_ONLY___ ╚
129
+ 2023-06-12 13:35:42,903 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
130
+ 2023-06-12 13:35:42,988 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-alpha-20230602161805.tar.gz HTTP/1.1" 200 800
131
+ 2023-06-12 13:35:42,989 INFO ___FILE_ONLY___ ══════════════════════════════
132
+ 2023-06-12 13:35:42,990 INFO ___FILE_ONLY___ ══════════════════════════════
133
+ 2023-06-12 13:35:42,991 INFO ___FILE_ONLY___ ╝
134
+
135
+ 2023-06-12 13:35:42,998 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
136
+
137
+ 2023-06-12 13:35:42,999 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Beta Commands ═╣
138
+
139
+ 2023-06-12 13:35:42,999 INFO ___FILE_ONLY___ ╚
140
+ 2023-06-12 13:35:43,001 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
141
+ 2023-06-12 13:35:43,086 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-beta-20230602161805.tar.gz HTTP/1.1" 200 797
142
+ 2023-06-12 13:35:43,087 INFO ___FILE_ONLY___ ══════════════════════════════
143
+ 2023-06-12 13:35:43,088 INFO ___FILE_ONLY___ ══════════════════════════════
144
+ 2023-06-12 13:35:43,088 INFO ___FILE_ONLY___ ╝
145
+
146
+ 2023-06-12 13:35:43,096 INFO ___FILE_ONLY___ ��════════════════════════════════════════════════════════════╗
147
+
148
+ 2023-06-12 13:35:43,096 INFO ___FILE_ONLY___ ╠═ Creating backup and activating new installation ═╣
149
+
150
+ 2023-06-12 13:35:43,096 INFO ___FILE_ONLY___ ╚
151
+ 2023-06-12 13:35:43,096 DEBUG root Attempting to move directory [/tools/google-cloud-sdk] to [/tools/google-cloud-sdk.staging/.install/.backup]
152
+ 2023-06-12 13:35:43,096 INFO ___FILE_ONLY___ ══════════════════════════════
153
+ 2023-06-12 13:35:43,096 DEBUG root Attempting to move directory [/tools/google-cloud-sdk.staging] to [/tools/google-cloud-sdk]
154
+ 2023-06-12 13:35:43,096 INFO ___FILE_ONLY___ ══════════════════════════════
155
+ 2023-06-12 13:35:43,096 INFO ___FILE_ONLY___ ╝
156
+
157
+ 2023-06-12 13:35:43,101 DEBUG root Updating notification cache...
158
+ 2023-06-12 13:35:43,101 INFO ___FILE_ONLY___
159
+
160
+ 2023-06-12 13:35:43,103 INFO ___FILE_ONLY___ Performing post processing steps...
161
+ 2023-06-12 13:35:43,103 DEBUG root Executing command: ['python3', '-S', '/tools/google-cloud-sdk/lib/gcloud.py', 'components', 'post-process']
162
+ 2023-06-12 13:36:07,481 DEBUG ___FILE_ONLY___
163
+ 2023-06-12 13:36:07,481 DEBUG ___FILE_ONLY___
164
+ 2023-06-12 13:36:07,643 INFO ___FILE_ONLY___
165
+ Update done!
166
+
167
+
168
+ 2023-06-12 13:36:07,647 DEBUG root Chosen display Format:none
169
+ 2023-06-12 13:36:07,647 INFO root Display format: "none"
.config/logs/2023.06.12/13.35.43.661450.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2023-06-12 13:35:43,662 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2023-06-12 13:35:43,664 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2023-06-12 13:35:43,666 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2023-06-12 13:36:07,378 DEBUG root Chosen display Format:none
5
+ 2023-06-12 13:36:07,378 INFO root Display format: "none"
.config/logs/2023.06.12/13.36.08.323143.log ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ 2023-06-12 13:36:08,325 DEBUG root Loaded Command Group: ['gcloud', 'config']
2
+ 2023-06-12 13:36:08,353 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
3
+ 2023-06-12 13:36:08,356 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "component_manager/disable_update_check", VALUE: "true"]
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+ 2023-06-12 13:36:09,054 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
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+ 2023-06-12 13:36:09,057 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "compute/gce_metadata_read_timeout_sec", VALUE: "0"]
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.gitattributes CHANGED
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+ openpose/examples/media/video.avi filter=lfs diff=lfs merge=lfs -text
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91
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SHOW/.gitignore ADDED
@@ -0,0 +1,147 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ *__pycache__*
2
+ __pycache__/
3
+ .neptune/
4
+ .ipynb_checkpoints/
5
+ .idea/
6
+ .history/
7
+ *.pyc
8
+ __pycache__
9
+ build
10
+ .idea
11
+
12
+ # Byte-compiled / optimized / DLL files
13
+ __pycache__/
14
+ *.py[cod]
15
+ *$py.class
16
+
17
+ # C extensions
18
+ *.so
19
+
20
+ # Distribution / packaging
21
+ .Python
22
+ build/
23
+ develop-eggs/
24
+ dist/
25
+ downloads/
26
+ eggs/
27
+ .eggs/
28
+ lib/
29
+ lib64/
30
+ parts/
31
+ sdist/
32
+ var/
33
+ wheels/
34
+ pip-wheel-metadata/
35
+ share/python-wheels/
36
+ *.egg-info/
37
+ .installed.cfg
38
+ *.egg
39
+ MANIFEST
40
+
41
+ # PyInstaller
42
+ # Usually these files are written by a python script from a template
43
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
44
+ *.manifest
45
+ *.spec
46
+
47
+ # Installer logs
48
+ pip-log.txt
49
+ pip-delete-this-directory.txt
50
+
51
+ # Unit test / coverage reports
52
+ htmlcov/
53
+ .tox/
54
+ .nox/
55
+ .coverage
56
+ .coverage.*
57
+ .cache
58
+ nosetests.xml
59
+ coverage.xml
60
+ *.cover
61
+ *.py,cover
62
+ .hypothesis/
63
+ .pytest_cache/
64
+
65
+ # Translations
66
+ *.mo
67
+ *.pot
68
+
69
+ # Django stuff:
70
+ *.log
71
+ local_settings.py
72
+ db.sqlite3
73
+ db.sqlite3-journal
74
+
75
+ # Flask stuff:
76
+ instance/
77
+ .webassets-cache
78
+
79
+ # Scrapy stuff:
80
+ .scrapy
81
+
82
+ # Sphinx documentation
83
+ docs/_build/
84
+
85
+ # PyBuilder
86
+ target/
87
+
88
+ # Jupyter Notebook
89
+ .ipynb_checkpoints
90
+
91
+ # IPython
92
+ profile_default/
93
+ ipython_config.py
94
+
95
+ # pyenv
96
+ .python-version
97
+
98
+ # pipenv
99
+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
100
+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
101
+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
102
+ # install all needed dependencies.
103
+ #Pipfile.lock
104
+
105
+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
106
+ __pypackages__/
107
+
108
+ # Celery stuff
109
+ celerybeat-schedule
110
+ celerybeat.pid
111
+
112
+ # SageMath parsed files
113
+ *.sage.py
114
+
115
+ # Environments
116
+ .env
117
+ .venv
118
+ env/
119
+ venv/
120
+ ENV/
121
+ env.bak/
122
+ venv.bak/
123
+
124
+ # Spyder project settings
125
+ .spyderproject
126
+ .spyproject
127
+
128
+ # Rope project settings
129
+ .ropeproject
130
+
131
+ # mkdocs documentation
132
+ /site
133
+
134
+ # mypy
135
+ .mypy_cache/
136
+ .dmypy.json
137
+ dmypy.json
138
+
139
+ # Pyre type checker
140
+ .pyre/
141
+ transformations/nerf
142
+ models/*
143
+
144
+ modules/MICA/demo
145
+
146
+ .vscode/
147
+ .vscode/PythonImportHelper-v2-Completion.json
SHOW/.gitignore copy ADDED
@@ -0,0 +1,201 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ body_templates
2
+ cfg_files_templates
3
+ *__pycache__*
4
+ __pycache__/
5
+ out/
6
+ videos/
7
+ results/
8
+ wandb/
9
+ draft/
10
+ Gestures/
11
+ Gestures_extras/
12
+ output/
13
+ .neptune/
14
+
15
+ .ipynb_checkpoints/
16
+ .idea/
17
+ .history/
18
+ crop_video_out/
19
+ pixie-env
20
+ eval-script
21
+ results
22
+ data/*
23
+
24
+ Testsamples/*/results
25
+ logs/
26
+ *.pyc
27
+ __pycache__
28
+ build
29
+
30
+
31
+ *.jpg
32
+ *.png
33
+ *.webm
34
+ *.avi
35
+ *.html
36
+ *.pyc
37
+ *.o
38
+ *.so
39
+ *.obj
40
+ *.npy
41
+ *.npz
42
+ *.pkl
43
+ *.zip
44
+ *.h5
45
+ *.json
46
+
47
+ test_results
48
+ input
49
+ images
50
+ FFHQ
51
+ FFHQ_seg
52
+
53
+ configs/logs
54
+ configs/jobs
55
+ slogs
56
+
57
+ .idea
58
+
59
+ # Byte-compiled / optimized / DLL files
60
+ __pycache__/
61
+ *.py[cod]
62
+ *$py.class
63
+
64
+ # C extensions
65
+ *.so
66
+
67
+ # Distribution / packaging
68
+ .Python
69
+ build/
70
+ develop-eggs/
71
+ dist/
72
+ downloads/
73
+ eggs/
74
+ .eggs/
75
+ lib/
76
+ lib64/
77
+ parts/
78
+ sdist/
79
+ var/
80
+ wheels/
81
+ pip-wheel-metadata/
82
+ share/python-wheels/
83
+ *.egg-info/
84
+ .installed.cfg
85
+ *.egg
86
+ MANIFEST
87
+
88
+ # PyInstaller
89
+ # Usually these files are written by a python script from a template
90
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
91
+ *.manifest
92
+ *.spec
93
+
94
+ # Installer logs
95
+ pip-log.txt
96
+ pip-delete-this-directory.txt
97
+
98
+ # Unit test / coverage reports
99
+ htmlcov/
100
+ .tox/
101
+ .nox/
102
+ .coverage
103
+ .coverage.*
104
+ .cache
105
+ nosetests.xml
106
+ coverage.xml
107
+ *.cover
108
+ *.py,cover
109
+ .hypothesis/
110
+ .pytest_cache/
111
+
112
+ # Translations
113
+ *.mo
114
+ *.pot
115
+
116
+ # Django stuff:
117
+ *.log
118
+ local_settings.py
119
+ db.sqlite3
120
+ db.sqlite3-journal
121
+
122
+ # Flask stuff:
123
+ instance/
124
+ .webassets-cache
125
+
126
+ # Scrapy stuff:
127
+ .scrapy
128
+
129
+ # Sphinx documentation
130
+ docs/_build/
131
+
132
+ # PyBuilder
133
+ target/
134
+
135
+ # Jupyter Notebook
136
+ .ipynb_checkpoints
137
+
138
+ # IPython
139
+ profile_default/
140
+ ipython_config.py
141
+
142
+ # pyenv
143
+ .python-version
144
+
145
+ # pipenv
146
+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
147
+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
148
+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
149
+ # install all needed dependencies.
150
+ #Pipfile.lock
151
+
152
+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
153
+ __pypackages__/
154
+
155
+ # Celery stuff
156
+ celerybeat-schedule
157
+ celerybeat.pid
158
+
159
+ # SageMath parsed files
160
+ *.sage.py
161
+
162
+ # Environments
163
+ .env
164
+ .venv
165
+ env/
166
+ venv/
167
+ ENV/
168
+ env.bak/
169
+ venv.bak/
170
+
171
+ # Spyder project settings
172
+ .spyderproject
173
+ .spyproject
174
+
175
+ # Rope project settings
176
+ .ropeproject
177
+
178
+ # mkdocs documentation
179
+ /site
180
+
181
+ # mypy
182
+ .mypy_cache/
183
+ .dmypy.json
184
+ dmypy.json
185
+
186
+ # Pyre type checker
187
+ .pyre/
188
+ transformations/nerf
189
+ models/*
190
+
191
+ test/demo_video
192
+
193
+ !test/demo_video/half.mp4
194
+
195
+ !models/*.py
196
+ !data/*.png
197
+ !data/id_pic
198
+ !test/*.py
199
+
200
+ !doc/*.jpg
201
+ !doc/*.png
SHOW/.vscode/PythonImportHelper-v2-Completion.json ADDED
The diff for this file is too large to render. See raw diff
 
SHOW/.vscode/launch.json ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ // Use IntelliSense to learn about possible attributes.
3
+ // Hover to view descriptions of existing attributes.
4
+ // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
5
+ "version": "0.2.0",
6
+ "configurations": [
7
+ {
8
+ "name": "Python: Current File",
9
+ "type": "python",
10
+ "request": "launch",
11
+ "program": "${file}",
12
+ "console": "integratedTerminal",
13
+ "justMyCode": true
14
+ }
15
+ ]
16
+ }
SHOW/EDIT_insightface/storage.py ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os
3
+ import os.path as osp
4
+ import zipfile
5
+ from .download import download_file
6
+
7
+ #BASE_REPO_URL='http://storage.insightface.ai/files'
8
+ #BASE_REPO_URL='http://insightface.cn-sh2.ufileos.com'
9
+ BASE_REPO_URL='http://d1gsb2o3ihr2l5.cloudfront.net'
10
+
11
+ def download(sub_dir, name, force=False, root='~/.insightface'):
12
+ _root = os.path.expanduser(root)
13
+ dir_path = os.path.join(_root, sub_dir, name)
14
+ if osp.exists(dir_path) and not force:
15
+ return dir_path
16
+ print('download_path:', dir_path)
17
+ zip_file_path = os.path.join(_root, sub_dir, name + '.zip')
18
+
19
+ print("zip_file_path", zip_file_path)
20
+
21
+ model_url = "%s/%s/%s.zip"%(BASE_REPO_URL, sub_dir, name)
22
+ #model_url = "%s/%s.zip"%(BASE_REPO_URL, name)
23
+
24
+ if not os.path.exists(zip_file_path):
25
+ download_file(model_url,
26
+ path=zip_file_path,
27
+ overwrite=True)
28
+ if not os.path.exists(dir_path):
29
+ os.makedirs(dir_path)
30
+ with zipfile.ZipFile(zip_file_path) as zf:
31
+ zf.extractall(dir_path)
32
+ #os.remove(zip_file_path)
33
+ return dir_path
34
+
35
+ def ensure_available(sub_dir, name, root='~/.insightface'):
36
+ return download(sub_dir, name, force=False, root=root)
37
+
38
+ def download_onnx(sub_dir, model_file, force=False, root='~/.insightface', download_zip=False):
39
+ _root = os.path.expanduser(root)
40
+ model_root = osp.join(_root, sub_dir)
41
+ new_model_file = osp.join(model_root, model_file)
42
+ if osp.exists(new_model_file) and not force:
43
+ return new_model_file
44
+ if not osp.exists(model_root):
45
+ os.makedirs(model_root)
46
+ print('download_path:', new_model_file)
47
+ if not download_zip:
48
+ model_url = "%s/%s/%s"%(BASE_REPO_URL, sub_dir, model_file)
49
+ download_file(model_url,
50
+ path=new_model_file,
51
+ overwrite=True)
52
+ else:
53
+ model_url = "%s/%s/%s.zip"%(BASE_REPO_URL, sub_dir, model_file)
54
+ zip_file_path = new_model_file+".zip"
55
+ download_file(model_url,
56
+ path=zip_file_path,
57
+ overwrite=True)
58
+ with zipfile.ZipFile(zip_file_path) as zf:
59
+ zf.extractall(model_root)
60
+ return new_model_file
SHOW/LICENSE ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ License
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+
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+ Software Copyright License for non-commercial scientific research purposes
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+ Please read carefully the following terms and conditions and any accompanying documentation before you download
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+ and/or use the DECA model, data and software, (the "Model & Software"), including 3D meshes, software, and scripts.
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+ By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use
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+ of this github repository), you acknowledge that you have read these terms and conditions, understand them, and
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+ agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use
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+ the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights
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+ under this License
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+ Ownership / Licensees
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+ The Model & Software and the associated materials has been developed at the
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+ Max Planck Institute for Intelligent Systems (hereinafter "MPI").
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+
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+ Any copyright or patent right is owned by and proprietary material of the
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+ Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (hereinafter “MPG”; MPI and MPG hereinafter
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+ License Grant
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+ • To install the Model & Software on computers owned, leased or otherwise controlled by you and/or your organization.
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+ • To use the Model & Software for the sole purpose of performing peaceful non-commercial scientific research,
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+ non-commercial education, or non-commercial artistic projects.
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+
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+ Any other use, in particular any use for commercial, pornographic, military, or surveillance purposes is prohibited.
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+ This includes, without limitation, incorporation in a commercial product, use in a commercial service,
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+ or production of other artefacts for commercial purposes.
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+ The Model & Software may not be used to create fake, libelous, misleading, or defamatory content of any kind, excluding
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+ analyses in peer-reviewed scientific research.
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+
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+ The Model & Software may not be reproduced, modified and/or made available in any form to any third party
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+ without Max-Planck’s prior written permission.
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+ The Model & Software may not be used for pornographic purposes or to generate pornographic material whether
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+ No Distribution
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+ The Model & Software and the license herein granted shall not be copied, shared, distributed, re-sold, offered
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+ purposes only.
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+ Disclaimer of Representations and Warranties
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+ You expressly acknowledge and agree that the Model & Software results from basic research, is provided “AS IS”,
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+ may contain errors, and that any use of the Model & Software is at your sole risk.
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+ makes no representations or warranties (i) regarding the merchantability or fitness for a particular purpose of
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+ intellectual property rights of a third party, and (iii) that the use of the Model & Software will not cause any
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+
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+ Limitation of Liability
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+ Because this Model & Software License Agreement qualifies as a donation, according to Section 521 of the German
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+ Civil Code (Bürgerliches Gesetzbuch – BGB) Licensor as a donor is liable for intent and gross negligence only.
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+ If the Licensor fraudulently conceals a legal or material defect, they are obliged to compensate the Licensee
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+
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+ Licensor shall be liable for loss of data only up to the amount of typical recovery costs which would have
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+ arisen had proper and regular data backup measures been taken. For the avoidance of doubt Licensor shall be
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+ liable in accordance with the German Product Liability Act in the event of product liability. The foregoing
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+ applies also to Licensor’s legal representatives or assistants in performance. Any further liability shall
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+ be excluded. Patent claims generated through the usage of the Model & Software cannot be directed towards the copyright holders.
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+ The Model & Software is provided in the state of development the licensor defines. If modified or extended by
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+ No Maintenance Services
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+ Licensor nevertheless reserves the right to update, modify, or discontinue the Model & Software at any time.
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+ Defects of the Model & Software must be notified in writing to the Licensor with a comprehensible description
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+ of the error symptoms. The notification of the defect should enable the reproduction of the error.
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+ The Licensee is encouraged to communicate any use, results, modification or publication.
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+ Publications using the Model & Software
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+ You acknowledge that the Model & Software is a valuable scientific resource and agree to appropriately reference
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+ the following paper in any publication making use of the Model & Software.
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+ Commercial licensing opportunities
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+ For commercial uses of the Model & Software, please send email to [email protected]
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+ This Agreement shall be governed by the laws of the Federal Republic of Germany except for the UN Sales Convention.
SHOW/README.md ADDED
@@ -0,0 +1,260 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # SHOW: Synchronous HOlistic body in the Wild
2
+ <b>[CVPR2023] Generating Holistic 3D Human Motion from Speech</b>
3
+
4
+ [[Project Page](https://talkshow.is.tue.mpg.de)] [[Arxiv](https://export.arxiv.org/abs/2212.04420)] [[Colab](https://colab.research.google.com/drive/1ZGuRX-m_2xEZ2JGGpvyePTLDBPKFd41I?usp=sharing)]
5
+
6
+ <p align="center">
7
+ <img src="doc/images/overview.png">
8
+ </p>
9
+
10
+ This repository provides the official implementation of SHOW(Synchronous HOlistic body in the Wild). Given rgb images or videos only, SHOW can reconstruct holistic whole body mesh results. Please refer to the [arXiv paper](https://export.arxiv.org/abs/2212.04420) for more details.
11
+
12
+
13
+ **What you can use**:
14
+
15
+ * **easy and efficient:** adapts SMPLify-X to the videos of
16
+ talking persons with several good practices.
17
+ * **state-of-art:** Compared to other methods, ours produces more accurate and stable results with details.
18
+
19
+ <p align="center">
20
+ <img src="doc/show.gif">
21
+ </p>
22
+
23
+ ## Getting Started
24
+
25
+ Take a quick tour on colab: [[Colab]](https://colab.research.google.com/drive/1ZGuRX-m_2xEZ2JGGpvyePTLDBPKFd41I?usp=sharing).
26
+
27
+ <!-- Alternatively, you can directly run the [ipynb file](SHOW_demo.ipynb) in the Jupyter environment. -->
28
+
29
+ ### Installation
30
+
31
+ To install SHOW, please execute `pip install git+https://github.com/yhw-yhw/SHOW.git` or
32
+
33
+ ```bash
34
+ git clone https://github.com/yhw-yhw/SHOW.git
35
+ cd SHOW && pip install -v -e .
36
+ ```
37
+
38
+ ### Preliminary
39
+
40
+ - [environment] Using virtual environment by runing
41
+
42
+ ```bash
43
+ conda create -n env_SHOW python=3.9
44
+ eval "$(conda shell.bash hook)"
45
+ conda activate env_SHOW
46
+ ```
47
+
48
+ install pytorch using `pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116` or
49
+
50
+ ```bash
51
+ conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
52
+ ```
53
+
54
+ You can run
55
+
56
+ ```bash
57
+ cd SHOW
58
+ cd modules/MICA && pip install -r requirements.txt
59
+ cd ../PIXIE && pip install -r requirements.txt
60
+ cd ../PyMAF && pip install -r requirements.txt
61
+ cd ../DECA && pip install -r requirements.txt
62
+ cd ../.. && pip install -r requirements.txt
63
+ ```
64
+
65
+ Note that Pytorch3D may require manuall installation (see instructions [here](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md)).
66
+
67
+ - [models] download packed model files, and put it in the same level directory as SHOW
68
+
69
+ ```bash
70
+ wget https://www.dropbox.com/s/gqdcu51ilo44k3i/models.zip?dl=0 -O models.zip
71
+ wget https://www.dropbox.com/s/r14bl9mhvngohla/data.zip?dl=0 -O data.zip
72
+ unzip data.zip
73
+ 7za x models.zip
74
+ ```
75
+
76
+ - [OpenPose]: follow the code from [OpenPose Colab notebook](https://colab.research.google.com/github/tugstugi/dl-colab-notebooks/blob/master/notebooks/OpenPose.ipynb, and change OpenPose bin path `openpose_root_path` and `openpose_bin_path` in `configs\configs\machine_cfg.py`.
77
+
78
+ - [MMPose]: Make sure to install mmcv-full, and set env variable `mmpose_root`
79
+
80
+ ```bash
81
+ pip install openmim
82
+ mim install mmcv-full
83
+ git clone https://github.com/open-mmlab/mmdetection
84
+ cd /content/mmdetection && python setup.py install
85
+ git clone https://github.com/open-mmlab/mmpose
86
+ export mmpose_root = $mmpose_root$
87
+ ```
88
+
89
+ - models for `inisghtface`:
90
+ 1) [antelopev2](https://keeper.mpdl.mpg.de/f/2d58b7fed5a74cb5be83/?dl=1)
91
+ 2) [buffalo_l](https://keeper.mpdl.mpg.de/f/8faabd353cfc457fa5c5/?dl=1)
92
+
93
+ use the following command as reference
94
+
95
+ ```bash
96
+ mkdir -p ~/.insightface/models
97
+ cd ~/.insightface/models
98
+ wget https://keeper.mpdl.mpg.de/f/2d58b7fed5a74cb5be83/?dl=1 -O antelopev2.zip
99
+ wget https://keeper.mpdl.mpg.de/f/8faabd353cfc457fa5c5/?dl=1 -O buffalo_l.zip
100
+ mkdir -p antelopev2 && cd antelopev2 && unzip -o ../antelopev2.zip
101
+ mkdir -p buffalo_l && cd buffalo_l && unzip -o ../buffalo_l.zip
102
+ ```
103
+
104
+
105
+ - [others] `pip uninstall -y xtcocotools && pip install xtcocotools --no-binary xtcocotools`
106
+
107
+ ### Run
108
+
109
+ Run SHOW on demo video
110
+
111
+ ```bash
112
+ python main.py --speaker_name -1 --all_top_dir ./test/demo_video/half.mp4
113
+ ```
114
+
115
+ It takes 15-20 minutes for 5s 30 FPS video on Colab Tesla T4.
116
+
117
+ The final results are stored in `./test/demo_video/ours_exp`. All the smplx variables can be found in `./test/demo_video/ours_exp/final_all.pkl`, and the visualization can be viewed in `./test/demo_video/ours_exp/final_vis.mp4`.
118
+
119
+ ## Datasets
120
+
121
+ ### Download Dataset Videos
122
+
123
+ Download all videos from youtube, please refer to (https://github.com/amirbar/speech2gesture), or using the following script: ```download_youtube.py```, remember to install `yt-dlp`. After downloading all videos, you can using `SHOW_intervals_subject4.csv` for video interval cropping.
124
+
125
+ ### Visualize Dataset
126
+
127
+ After running SHOW, we will get processed data in a pkl file. Here we can visualize pkl from our provided dataset.
128
+
129
+ ```bash
130
+ python render_pkl_release.py \
131
+ --pkl_file_path test/demo_pkl/all.pkl \
132
+ --out_images_path test/demo_pkl/ours_images \
133
+ --output_video_path test/demo_pkl/ours.mp4 \
134
+ --smplx_model_path ../models/smplx/SMPLX_NEUTRAL_2020_org.npz
135
+ ```
136
+
137
+ ### Download Links
138
+
139
+ The data reconstructed by SHOW is released, you can download it
140
+
141
+ - [[Dropbox]](https://www.dropbox.com/sh/f1gu531w5s2sbqd/AAA2I7oLolEkcXnWI6tnwUpAa?dl=0)
142
+
143
+ <!-- Larger datasets will be released later -->
144
+
145
+ ### Dataset Description
146
+
147
+ - speaker=oliver/chemistry/conan/seth
148
+ - The maximum length of video clip is 10s with 30 fps
149
+ - Format of files in the compressed package:
150
+ - `{speaker}_wav_tar.tar.gz`:
151
+ - The path format of each file is: `speaker/video_fn/seq_fn.wav`
152
+ - Audio obtained from the original video at 22k sampling rate
153
+ - `{speaker}_pkl_tar.tar.gz`:
154
+ - The path format of each file is: `speaker/video_fn/seq_fn.pkl`
155
+ - Data contained in the pkl file:
156
+
157
+ ```bash
158
+ width，height: the video width and height
159
+ center: the center point of the video
160
+ batch_size: the sequence length
161
+ camera_transl: the displacement of the camera
162
+ focal_length: the pixel focal length of a camera
163
+ body_pose_axis: (bs, 21, 3)
164
+ expression: (bs, 100)
165
+ jaw_pose: (bs,3)
166
+ betas: (300)
167
+ global_orient: (bs,3)
168
+ transl: (bs,3)
169
+ left_hand_pose: (bs,12)
170
+ right_hand_pose: (bs,12)
171
+ leye_pose: (bs,3)
172
+ reye_pose: (bs,3)
173
+ pose_embedding: (bs,32)
174
+ ```
175
+
176
+ - Set the config of smplx model as follows:
177
+
178
+ ```python
179
+ smplx_cfg=dict(
180
+ model_path='path_to_smplx_model'
181
+ model_type= 'smplx',
182
+ gender= 'neutral',
183
+ use_face_contour= True,
184
+ use_pca= True,
185
+ flat_hand_mean= False,
186
+ use_hands= True,
187
+ use_face= True,
188
+ num_pca_comps= 12,
189
+ num_betas= 300,
190
+ num_expression_coeffs= 100,
191
+ )
192
+ ```
193
+
194
+ - In practice, global orient and transl parameters should be fixed as the first frame and the lower part of the body pose should be fixed as sitting or standing position: [code](post_process.py)
195
+
196
+ ### SMPLX expression dim convert tool
197
+
198
+ [code](cvt_exp_dim_tool.py)
199
+
200
+ usage
201
+ ```
202
+ python cvt_exp_dim_tool.py \
203
+ --target-exp-dim 50 \
204
+ --pkl-path ./rich.npy \
205
+ --model-path ../models/smplx/SMPLX_MALE_shape2019_exp2020.npz
206
+ ```
207
+
208
+ ## Citation
209
+
210
+ If you use this project in your research please cite this paper:
211
+ ```
212
+ @inproceedings{yi2022generating,
213
+ title={Generating Holistic 3D Human Motion from Speech},
214
+ author={Yi, Hongwei and Liang, Hualin and Liu, Yifei and Cao, Qiong and Wen, Yandong and Bolkart, Timo and Tao, Dacheng and Black, Michael J},
215
+ booktitle={CVPR},
216
+ year={2023}
217
+ }
218
+ ```
219
+
220
+ ## Issues
221
+
222
+ - If following error is encountered
223
+ ```
224
+ RuntimeError: Subtraction, the `-` operator, with a bool tensor is not supported. If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.
225
+ ```
226
+
227
+ Open torchgeometry/core/conversions.py file and replace line 304-306 with
228
+
229
+ ```
230
+ mask_c1 = mask_d2 * (~ mask_d0_d1)
231
+ mask_c2 = (~ mask_d2) * mask_d0_nd1
232
+ mask_c3 = (~ mask_d2) * (~ mask_d0_nd1)
233
+ ```
234
+
235
+ ## License
236
+
237
+ This code and model are available for non-commercial scientific research purposes as defined in the [LICENSE](LICENSE) file.
238
+ By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE).
239
+
240
+ ## Acknowledgements
241
+
242
+ For functions or scripts that are based on external sources, we acknowledge the origin individually in each file. We specifically thanks [WOJCIECH ZIELONKA](https://zielon.github.io/) and [Justus Thies](https://justusthies.github.io/) for sharing their face tracking codebase.
243
+
244
+ Here are some great resources we benefit:
245
+
246
+ - [SMPLify-X](https://smpl-x.is.tue.mpg.de/)
247
+ - [DECA](https://github.com/YadiraF/DECA) for face expression initialization
248
+ - [PIXIE](https://pixie.is.tue.mpg.de/) and [PyMAF-X](https://github.com/HongwenZhang/PyMAF-X) for SMPL-X parameters initialization
249
+ - [DeepLab](https://github.com/leimao/DeepLab-V3) for person segmentation
250
+ - [MICA](https://github.com/Zielon/MICA) and [https://github.com/HavenFeng/photometric_optimization] for face tracking
251
+ - [MICA_Tracker](https://github.com/Zielon/metrical-tracker)
252
+ - [Pytorch3D](https://pytorch3d.org/) for rendering
253
+ - [FAN](https://github.com/1adrianb/2D-and-3D-face-alignment) for landmark detection
254
+ - [arcface-pytorch](https://github.com/ronghuaiyang/arcface-pytorch)
255
+
256
+ ## Contact
257
+
258
+ For questions, please contact [email protected] or [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).
259
+
260
+ For commercial licensing, please contact [email protected]
SHOW/SHOW/__init__.py ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import matplotlib
3
+ import platform
4
+
5
+ if platform.system() == 'Windows':
6
+ matplotlib.use('TkAgg')
7
+
8
+ if platform.system() == "Linux":
9
+ os.environ['PYOPENGL_PLATFORM'] = 'egl'
10
+
11
+ from .utils import *
12
+ from .face_iders import *
13
+ from .loggers import *
14
+ from .image import *
15
+ from .detector import *
16
+
17
+ from mmcv.runner import OPTIMIZERS
18
+ from mmcv.runner.builder import RUNNERS
19
+
20
+
21
+ def build_optim(cfg):
22
+ return OPTIMIZERS.build(cfg)
23
+
24
+ def build_runner(cfg):
25
+ return RUNNERS.build(cfg)
SHOW/SHOW/constants.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ sitting_pose = [
2
+ 0.0, 0.0, 0.0, -1.1826512813568115, 0.23866955935955048, 0.15146760642528534, -1.2604516744613647,
3
+ -0.3160211145877838, -0.1603458970785141, 0.0, 0.0, 0.0, 1.1654603481292725, 0.0, 0.0,
4
+ 1.2521806955337524, 0.041598282754421234, -0.06312154978513718, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
5
+ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
6
+ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
7
+ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
8
+ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
9
+ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
10
+ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
11
+ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
12
+ 0.0, 0.0, 0.0]
SHOW/SHOW/datasets/__init__.py ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ from .op_base import *
2
+ from .op_dataset import *
3
+ from .op_post_process import *
4
+ from .pre_dataset import *
SHOW/SHOW/datasets/model_func_atach.py ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import smplx
3
+ from smplx.body_models import SMPLXLayer
4
+ import numpy as np
5
+
6
+
7
+ # (PIXIE_init_hand-hand_mean)@inv_hand_comp=hand_pca_delta
8
+ # hand_pca_delta@hand_comp+hand_mean=PIXIE_init_hand
9
+ # hand_pca_full@hand_comp=PIXIE_init_hand
10
+
11
+
12
+ def hand_pca_to_axis(self, lhand_pca, rhand_pca):
13
+ # device=self.left_hand_mean.device
14
+ lhand_axis = torch.einsum('bi,ij->bj', [lhand_pca, self.left_hand_components])
15
+ rhand_axis = torch.einsum('bi,ij->bj', [rhand_pca, self.right_hand_components])
16
+
17
+ if not self.flat_hand_mean:
18
+ lhand_axis=lhand_axis+self.left_hand_mean
19
+ rhand_axis=rhand_axis+self.right_hand_mean
20
+
21
+ return lhand_axis,rhand_axis
22
+
23
+
24
+ def hand_axis_to_pca(self, lhand_axis, rhand_axis):
25
+ device=self.left_hand_mean.device
26
+
27
+ if isinstance(lhand_axis, np.ndarray):
28
+ lhand_axis = torch.from_numpy(lhand_axis)
29
+ if isinstance(rhand_axis, np.ndarray):
30
+ rhand_axis = torch.from_numpy(rhand_axis)
31
+
32
+ lhand_axis = lhand_axis.reshape(-1, 45).to(device)
33
+ rhand_axis = rhand_axis.reshape(-1, 45).to(device)
34
+
35
+ if not self.flat_hand_mean:
36
+ lhand_axis=lhand_axis-self.left_hand_mean
37
+ rhand_axis=rhand_axis-self.right_hand_mean
38
+
39
+ lhand_pca = torch.einsum('bi,ij->bj', [lhand_axis, self.l_comp])
40
+ rhand_pca = torch.einsum('bi,ij->bj', [rhand_axis, self.r_comp])
41
+
42
+ # return lhand_pca, rhand_pca
43
+ return lhand_pca.to('cpu'), rhand_pca.to('cpu')
44
+
45
+
46
+ def atach_model_func(model):
47
+ if not hasattr(model, 'hand_axis_to_pca'):
48
+ setattr(model, 'hand_axis_to_pca',hand_axis_to_pca)
49
+
50
+ if not hasattr(model, 'hand_pca_to_axis'):
51
+ setattr(model, 'hand_pca_to_axis',hand_pca_to_axis)
52
+
53
+ if not hasattr(model, 'l_comp'):
54
+ l_comp = torch.linalg.pinv(model.left_hand_components)
55
+ r_comp = torch.linalg.pinv(model.right_hand_components)
56
+ setattr(model, 'l_comp', l_comp)
57
+ setattr(model, 'r_comp', r_comp)
SHOW/SHOW/datasets/op_base.py ADDED
@@ -0,0 +1,168 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from scipy.io import savemat, loadmat
3
+ from collections import namedtuple
4
+ from collections import defaultdict
5
+ import numpy as np
6
+ import json
7
+ import torch
8
+ import glob
9
+ import cv2
10
+ import os.path as osp
11
+ import os
12
+ from pathlib import Path
13
+ from typing import Union
14
+ from functools import reduce, partial
15
+ from easydict import EasyDict
16
+ from loguru import logger
17
+
18
+ return_item_tuple = namedtuple(
19
+ 'return_item_tuple',
20
+ ['keypoints_2d', 'gender_gt']
21
+ )
22
+ return_item_tuple.__new__.__defaults__ = (None,)*len(return_item_tuple._fields)
23
+
24
+
25
+ class op_base(object):
26
+
27
+ def get_joint_weights(self) -> torch.Tensor:
28
+ # @return optim_weights: [1,135,1]
29
+ self.optim_weights = torch.ones(
30
+ self.num_joints +
31
+ 2 * self.use_hands +
32
+ 51 * self.use_face +
33
+ 17 * self.use_face_contour,
34
+ dtype=self.dtype
35
+ ).to(self.device)
36
+
37
+ self.optim_weights = self.optim_weights[None, ..., None]
38
+
39
+ return self.optim_weights
40
+
41
+ def get_smplx_to_o3d_R(self,) -> np.ndarray:
42
+ R = np.array([[-1, 0, 0], [0, -1, 0], [0, 0, 1]])
43
+ return R
44
+
45
+ def get_smplx_to_pyrender_R(self,) -> np.ndarray:
46
+ R = np.array([[1, 0, 0], [0, -1, 0], [0, 0, -1]])
47
+ return R
48
+
49
+ def get_smplx_to_pyrender_T(self,) -> np.ndarray:
50
+ cam_transl = self.pp.cam_transl
51
+ if isinstance(cam_transl, torch.Tensor):
52
+ cam_transl = cam_transl.cpu().detach().numpy()
53
+ cam_t = cam_transl[0].copy()
54
+ cam_t[0] *= -1
55
+ return cam_t
56
+
57
+ def get_smplx_to_o3d_T(self,) -> np.ndarray:
58
+ # assert(self.pp.cam_transl is not None)
59
+ return self.cvt_pixie_cam_to_o3d(self.pp.cam_transl)
60
+
61
+ def cvt_pixie_cam_to_o3d(self, cam_transl) -> np.ndarray:
62
+ if isinstance(cam_transl, torch.Tensor):
63
+ cam_transl = cam_transl.cpu().detach().numpy()
64
+ cam_t = cam_transl[0].copy()
65
+ cam_t[0] *= -1
66
+ cam_t[1] *= -1
67
+ return cam_t
68
+
69
+ def get_smplx_to_pyrender_K(self, cam_transl) -> np.ndarray:
70
+ if isinstance(cam_transl, torch.Tensor):
71
+ T = cam_transl.detach().cpu().numpy()
72
+ T[1] *= -1
73
+ K = np.eye(4)
74
+ K[:3, 3] = T
75
+ return K
76
+
77
+ def get_smplx_to_pyrender_K2(self,) -> np.ndarray:
78
+ from smplx.lbs import transform_mat
79
+ R = self.get_smplx_to_pyrender_R()
80
+ T = self.get_smplx_to_pyrender_T()
81
+ K = transform_mat(torch.from_numpy(R).unsqueeze(0),
82
+ torch.from_numpy(T).unsqueeze(0).unsqueeze(dim=-1)
83
+ ).numpy()[0]
84
+ return K
85
+
86
+ @classmethod
87
+ def smpl_to_openpose(
88
+ cls,
89
+ use_face=True,
90
+ use_hands=True,
91
+ use_face_contour=True,
92
+ ) -> np.ndarray:
93
+ # smplx-->openpose_body_25
94
+ body_mapping = np.array([55, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5,
95
+ 8, 1, 4, 7, 56, 57, 58, 59, 60, 61, 62,
96
+ 63, 64, 65], dtype=np.int32) # 25
97
+ mapping = [body_mapping]
98
+
99
+ if use_hands:
100
+ lhand_mapping = np.array([20, 37, 38, 39, 66, 25, 26, 27,
101
+ 67, 28, 29, 30, 68, 34, 35, 36, 69,
102
+ 31, 32, 33, 70], dtype=np.int32)
103
+ rhand_mapping = np.array([21, 52, 53, 54, 71, 40, 41, 42, 72,
104
+ 43, 44, 45, 73, 49, 50, 51, 74, 46,
105
+ 47, 48, 75], dtype=np.int32)
106
+ mapping += [lhand_mapping, rhand_mapping]
107
+
108
+ if use_face:
109
+ # end_idx = 127 + 17 * use_face_contour
110
+ face_mapping = np.arange(76, 127 + 17 * use_face_contour,
111
+ dtype=np.int32)
112
+ mapping += [face_mapping]
113
+
114
+ return np.concatenate(mapping)
115
+
116
+ @classmethod
117
+ def read_keypoints(
118
+ cls,
119
+ keypoint_fn=None,
120
+ use_hands=True,
121
+ use_face=True,
122
+ use_face_contour=True
123
+ ):
124
+
125
+ with open(keypoint_fn) as f:
126
+ data = json.load(f)
127
+
128
+ # body_size_list = []
129
+ keypoints = []
130
+ gender_gt = []
131
+
132
+ for _, person_data in enumerate(data['people']):
133
+ body_keypoints = np.array(
134
+ person_data['pose_keypoints_2d'], dtype=np.float32)
135
+ body_keypoints = body_keypoints.reshape([-1, 3])
136
+
137
+ if use_hands:
138
+ left_hand_keyp = np.array(
139
+ person_data['hand_left_keypoints_2d'],
140
+ dtype=np.float32).reshape([-1, 3])
141
+ right_hand_keyp = np.array(
142
+ person_data['hand_right_keypoints_2d'],
143
+ dtype=np.float32).reshape([-1, 3])
144
+ body_keypoints = np.concatenate(
145
+ [body_keypoints, left_hand_keyp, right_hand_keyp], axis=0)
146
+
147
+ if use_face:
148
+ face_keypoints = np.array(
149
+ person_data['face_keypoints_2d'],
150
+ dtype=np.float32).reshape([-1, 3])[17: 17 + 51, :]
151
+ contour_keyps = np.array(
152
+ [], dtype=body_keypoints.dtype).reshape(0, 3)
153
+ if use_face_contour:
154
+ contour_keyps = np.array(
155
+ person_data['face_keypoints_2d'],
156
+ dtype=np.float32).reshape([-1, 3])[:17, :]
157
+ body_keypoints = np.concatenate(
158
+ [body_keypoints, face_keypoints, contour_keyps], axis=0)
159
+ keypoints.append(body_keypoints)
160
+
161
+ if 'gender_gt' in person_data:
162
+ gender_gt.append(person_data['gender_gt'])
163
+
164
+ # keypoints: [B,135,3]
165
+ return return_item_tuple(
166
+ keypoints,
167
+ gender_gt
168
+ )
SHOW/SHOW/datasets/op_dataset.py ADDED
@@ -0,0 +1,732 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from collections import defaultdict
3
+ import numpy as np
4
+ import json
5
+ import torch
6
+ import glob
7
+ import cv2
8
+ import os.path as osp
9
+ import os
10
+ import sys
11
+ from pathlib import Path
12
+ from .op_post_process import op_post_process
13
+ from .pre_dataset import *
14
+ from ..image import *
15
+ from typing import Union
16
+ from functools import reduce, partial
17
+ from easydict import EasyDict
18
+ from loguru import logger
19
+ from .op_base import op_base
20
+ from ..utils import glob_exts_in_path
21
+ from ..utils import default_timers
22
+ from ..utils import is_empty_dir,ext_files_num_in_dir,img_files_num_in_dir,run_openpose
23
+ from ..face_iders import match_faces
24
+ from .pre_runner import run_pymafx
25
+ from tqdm import tqdm
26
+
27
+
28
+ from modules.PIXIE.demos.api_multi_pixie import api_multi_body
29
+ from modules.DECA.demos.api_multi_deca import api_multi_deca
30
+ from SHOW.detector.face_detector import FaceDetector
31
+ from SHOW.video_filter.deeplab_seg import deeplab_seg
32
+ from SHOW.utils.video import images_to_video
33
+ import joblib
34
+ import SHOW
35
+
36
+
37
+ class op_dataset(op_base):
38
+ NUM_BODY_JOINTS = 25
39
+ NUM_HAND_JOINTS = 20
40
+
41
+ def __init__(
42
+ self,
43
+
44
+ dtype=torch.float32,
45
+ device='cpu',
46
+ batch_size=-1,
47
+ config=None,
48
+ face_ider=None,
49
+ person_face_emb: np.ndarray = None,
50
+ ):
51
+ self.config = config
52
+ self.use_hands = config.use_hands
53
+ self.use_face = config.use_face
54
+ self.use_face_contour = config.use_face_contour
55
+
56
+ self.person_face_emb = person_face_emb
57
+
58
+
59
+ self.batch_size = batch_size
60
+ self.device = device
61
+ self.dtype = dtype
62
+
63
+ self.all_processed_item = []
64
+ self.corr_center_pymaf = None
65
+ self.correspond_center = None
66
+ self.correspond_bbox = None
67
+ self.face_ider = face_ider
68
+ self.cnt = 0
69
+
70
+ assert(osp.exists(self.config.img_folder))
71
+
72
+ self.get_all_dict=None
73
+ self.focal = np.array(5000)
74
+ self.match_thres = 0.08 # 30/720=0.04
75
+ self.num_joints = (self.NUM_BODY_JOINTS + 2 * self.NUM_HAND_JOINTS * self.use_hands)
76
+ self.int_id_to_item = defaultdict(dict)
77
+ # self.all_item_list = self.int_id_to_item = {
78
+ # 0: {
79
+ # 'img': 'path/img'
80
+ # }
81
+ # 1: {
82
+ # 'img': 'path/img'
83
+ # 'fan': 'path/fan'
84
+ # }
85
+ # }
86
+ self.all_processed_item = []
87
+ # [
88
+ # {
89
+ # 'img': np.array,
90
+ # 'pixie': {
91
+ # 'face_kpt': np.array
92
+ # },
93
+ # }
94
+ # {
95
+ # 'img': np.array,
96
+ # 'pixie': {
97
+ # 'face_kpt': np.array
98
+ # },
99
+ # 'fan': None, # match failed
100
+ # }
101
+ # ]
102
+ self.find_item_template_list={}
103
+ ref_len = img_files_num_in_dir(self.config.img_folder)
104
+
105
+
106
+ def match_by_bbox(ret_list, get_bbox_center_func):
107
+ if (len(ret_list) < 1 or ret_list is None):
108
+ logger.error(f'length of ret_list < 1')
109
+ return None
110
+
111
+ if self.correspond_center is not None:
112
+ dist1 = dist2 = np.inf
113
+ for ret in ret_list:
114
+ bbox, cur_center = get_bbox_center_func(ret)
115
+ dist1 = np.abs(cur_center[0]-self.correspond_center[0])/self.width
116
+ dist2 = np.abs(cur_center[1]-self.correspond_center[1])/self.height
117
+ # 根据xy方向的偏移的比率判断
118
+ if dist1 < self.match_thres and dist2 < self.match_thres:
119
+ if bbox is not None:
120
+ self.correspond_bbox = bbox
121
+ self.correspond_center = cur_center
122
+ return ret
123
+ else:
124
+ logger.info(f'corresponding center is None')
125
+ ret = ret_list[0]
126
+ bbox, cur_center = get_bbox_center_func(ret)
127
+ self.correspond_bbox = bbox
128
+ self.correspond_center = cur_center
129
+ return ret
130
+
131
+ # self.correspond_center is not None and match failed
132
+ if len(ret_list)==1:
133
+ return ret_list[0]
134
+ return None
135
+
136
+ # images -----------------------------------
137
+ if True:
138
+ def empty_func(*args, **kwargs):
139
+ pass
140
+
141
+ find_item_template_img={
142
+ 'name': 'img',
143
+ 'exts': ['png', 'jpg'],
144
+ 'dir': self.config.img_folder,
145
+ 'judge_prepare_data_func': empty_func,
146
+ 'run_prepare_data_func': empty_func,
147
+ 'post_prepare_data_func': empty_func,
148
+ 'match_item_func': empty_func,
149
+ 'read_item_func': empty_func,
150
+ }
151
+ self.find_item_template_list['img']=find_item_template_img
152
+
153
+ # FAN -----------------------------------
154
+ if True:
155
+ def judge_prepare_data_func_fan():
156
+ return ext_files_num_in_dir(
157
+ self.config.fan_npy_folder,
158
+ exts=['*.npy', '*.npy.empty']
159
+ ) < ref_len
160
+
161
+ def run_prepare_data_func_fan():
162
+ fan = SHOW.detector.fan_detector.FAN_Detector()
163
+ fan.predict(self.config.img_folder,
164
+ self.config.fan_npy_folder,
165
+ save_vis=self.config.saveVis,
166
+ fan_vis_dir=self.config.fan_npy_folder_vis)
167
+ del fan
168
+
169
+ def post_prepare_data_func_fan():
170
+ if (self.config.saveVis and
171
+ not Path(self.config.fan_npy_folder_v).exists() and
172
+ not is_empty_dir(self.config.fan_npy_folder_vis)
173
+ ):
174
+ logger.info(f'converting {self.config.fan_npy_folder_vis} to {self.config.fan_npy_folder_v}')
175
+ images_to_video(
176
+ input_folder=self.config.fan_npy_folder_vis,
177
+ output_path=self.config.fan_npy_folder_v,
178
+ img_format = None,
179
+ fps=30,
180
+ )
181
+
182
+ def match_item_func_fan(fan_ret_list):
183
+ def get_bbox_center_func(input_ret):
184
+ face_kpt = input_ret
185
+ bbox = lmk2d_to_bbox(face_kpt, self.height, self.width)
186
+ xmin, ymin, xmax, ymax = [int(i) for i in bbox]
187
+ cur_center = [int((xmin+xmax)/2), int((ymin+ymax)/2)]
188
+ return bbox, cur_center
189
+ return match_by_bbox(
190
+ fan_ret_list,
191
+ get_bbox_center_func)
192
+
193
+ def read_item_func_fan(lmk_path):
194
+ return np.load(lmk_path, allow_pickle=True)
195
+
196
+ def post_read_func_fan(template):
197
+ # fan_valid_flag = [0 if i is None else 1 for i in template['files_list']]
198
+ fan_valid_flag = [0 if i.get('fan') is None else 1 for i in self.all_processed_item]
199
+ self.get_all_dict['fan_valid']=np.array(fan_valid_flag)
200
+
201
+ fan_fill = [np.zeros((68,2)) if i.get('fan') is None else i['fan'] for i in self.all_processed_item]
202
+ self.get_all_dict['fan'] = np.array(fan_fill)
203
+
204
+ find_item_template_fan={
205
+ 'name': 'fan',
206
+ 'exts': ['npy'],
207
+ 'files_list': [],
208
+ 'dir': self.config.fan_npy_folder,
209
+ 'judge_prepare_data_func': judge_prepare_data_func_fan,
210
+ 'run_prepare_data_func': run_prepare_data_func_fan,
211
+ 'post_prepare_data_func': post_prepare_data_func_fan,
212
+ 'match_item_func': match_item_func_fan,
213
+ 'read_item_func': read_item_func_fan,
214
+ 'post_read_func': post_read_func_fan,
215
+ }
216
+ self.find_item_template_list['fan']=find_item_template_fan
217
+
218
+ # openpose -----------------------------------
219
+ if True:
220
+ def judge_prepare_data_func_op():
221
+ return is_empty_dir(self.config.keyp_folder)
222
+
223
+ def run_prepare_data_func_op():
224
+ with default_timers['run_openpose']:
225
+ run_openpose(
226
+ low_res=self.config.low_res,
227
+ openpose_root_path=self.config.openpose_root_path,
228
+ openpose_bin_path=self.config.openpose_bin_path,
229
+ img_dir=self.config.img_folder,
230
+ out_dir=self.config.keyp_folder,
231
+ img_out=self.config.keyp_folder_vis if self.config.saveVis else None,
232
+ )
233
+
234
+ def post_prepare_data_func_op():
235
+ if (self.config.saveVis and
236
+ not Path(self.config.keyp_folder_v).exists() and
237
+ not is_empty_dir(self.config.keyp_folder_vis)
238
+ ):
239
+ logger.info(f'converting {self.config.keyp_folder_vis} to {self.config.keyp_folder_v}')
240
+ images_to_video(
241
+ input_folder=self.config.keyp_folder_vis,
242
+ output_path=self.config.keyp_folder_v,
243
+ img_format = None,
244
+ fps=30,
245
+ )
246
+
247
+ def match_item_func_op(keypoints):
248
+ def get_bbox_center_func(keypoint):
249
+ body_j = keypoint[0:25, :]
250
+ head_j = body_j[0]
251
+ x, y, _ = head_j
252
+ cur_center = [x, y]
253
+ return None, cur_center
254
+ return match_by_bbox(
255
+ keypoints,
256
+ get_bbox_center_func)
257
+
258
+ def read_item_func_op(file_path):
259
+ keypoints, _ = self.read_keypoints(keypoint_fn=file_path)
260
+ return keypoints
261
+
262
+ find_item_template_op={
263
+ 'name': 'op',
264
+ 'exts': ['json'],
265
+ 'dir':self.config.keyp_folder,
266
+ 'judge_prepare_data_func': judge_prepare_data_func_op,
267
+ 'run_prepare_data_func': run_prepare_data_func_op,
268
+ 'post_prepare_data_func': post_prepare_data_func_op,
269
+ 'match_item_func': match_item_func_op,
270
+ 'read_item_func': read_item_func_op,
271
+ }
272
+ self.find_item_template_list['op']=find_item_template_op
273
+
274
+ # deca -----------------------------------
275
+ if True:
276
+ def judge_prepare_data_func_deca():
277
+ return ext_files_num_in_dir(
278
+ self.config.deca_mat_folder,
279
+ exts=['*.pkl', '*.pkl.empty']
280
+ ) < ref_len
281
+
282
+ def run_prepare_data_func_deca():
283
+ with default_timers['api_multi_deca']:
284
+ logger.info(f'running api_multi_deca')
285
+ api_multi_deca(
286
+ inputpath=self.config.img_folder,
287
+ savefolder=self.config.deca_mat_folder,
288
+ visfolder=self.config.deca_mat_folder_vis,
289
+ saveVis=self.config.saveVis,
290
+ face_detector=self.config.face_detector
291
+ )
292
+
293
+ def post_prepare_data_func_deca():
294
+ if (self.config.saveVis and
295
+ not Path(self.config.deca_mat_folder_v).exists() and
296
+ not is_empty_dir(self.config.deca_mat_folder_vis)
297
+ ):
298
+ logger.info(f'converting {self.config.deca_mat_folder_vis} to {self.config.deca_mat_folder_v}')
299
+ images_to_video(
300
+ input_folder=self.config.deca_mat_folder_vis,
301
+ output_path=self.config.deca_mat_folder_v,
302
+ img_format = None,
303
+ fps=30,
304
+ )
305
+
306
+ def match_item_func_deca(deca_ret_list):
307
+ def get_bbox_center_func(input_ret):
308
+ bbox = input_ret['face_bbox']
309
+ xmin, ymin, xmax, ymax = [int(i) for i in bbox]
310
+ cur_center = [int((xmin+xmax)/2), int((ymin+ymax)/2)]
311
+ return bbox, cur_center
312
+ return match_by_bbox(
313
+ deca_ret_list,
314
+ get_bbox_center_func)
315
+
316
+ def read_item_func_deca(deca):
317
+ return read_deca(deca)
318
+
319
+ find_item_template_deca={
320
+ 'name': 'deca',
321
+ 'exts': ['pkl'],
322
+ 'dir':self.config.deca_mat_folder,
323
+ 'judge_prepare_data_func': judge_prepare_data_func_deca,
324
+ 'run_prepare_data_func': run_prepare_data_func_deca,
325
+ 'post_prepare_data_func': post_prepare_data_func_deca,
326
+ 'match_item_func': match_item_func_deca,
327
+ 'read_item_func': read_item_func_deca,
328
+ }
329
+ self.find_item_template_list['deca']=find_item_template_deca
330
+
331
+ # pixie -----------------------------------
332
+ if True:
333
+ def judge_prepare_data_func_pixie():
334
+ return ext_files_num_in_dir(
335
+ self.config.pixie_mat_folder,
336
+ exts=['*.pkl', '*.pkl.empty']) < ref_len
337
+
338
+ def run_prepare_data_func_pixie():
339
+ with default_timers['api_multi_body']:
340
+ logger.info(r'running pixie')
341
+ api_multi_body(
342
+ imgfolder=self.config.img_folder,
343
+ savefolder=self.config.pixie_mat_folder,
344
+ visfolder=self.config.pixie_mat_folder_vis,
345
+ saveVis=self.config.saveVis,
346
+ rasterizer_type=self.config.rasterizer_type
347
+ )
348
+
349
+ def post_prepare_data_func_pixie():
350
+ if (self.config.saveVis and
351
+ not Path(self.config.pixie_mat_folder_v).exists() and
352
+ not is_empty_dir(self.config.pixie_mat_folder_vis)
353
+ ):
354
+ logger.info(f'converting {self.config.pixie_mat_folder_vis} to {self.config.pixie_mat_folder_v}')
355
+ images_to_video(
356
+ input_folder=self.config.pixie_mat_folder_vis,
357
+ output_path=self.config.pixie_mat_folder_v,
358
+ img_format = None,
359
+ fps=30,
360
+ )
361
+
362
+ def match_item_func_pixie(pixie_ret_list):
363
+ def get_bbox_center_func(input_ret):
364
+ bbox = input_ret['face_bbox']
365
+ xmin, ymin, xmax, ymax = [int(i) for i in bbox]
366
+ cur_center = [int((xmin+xmax)/2), int((ymin+ymax)/2)]
367
+ return bbox, cur_center
368
+ return match_by_bbox(
369
+ pixie_ret_list,
370
+ get_bbox_center_func)
371
+
372
+ def read_item_func_pixie(pixie):
373
+ return read_pixie(
374
+ pixie, self.height, self.width,
375
+ cvt_hand_func=self.config.cvt_hand_func)
376
+
377
+ find_item_template_pixie={
378
+ 'name': 'pixie',
379
+ 'exts': ['pkl'],
380
+ 'dir':self.config.pixie_mat_folder,
381
+ 'judge_prepare_data_func': judge_prepare_data_func_pixie,
382
+ 'run_prepare_data_func': run_prepare_data_func_pixie,
383
+ 'post_prepare_data_func': post_prepare_data_func_pixie,
384
+ 'match_item_func': match_item_func_pixie,
385
+ 'read_item_func': read_item_func_pixie,
386
+ }
387
+ self.find_item_template_list['pixie']=find_item_template_pixie
388
+
389
+ # mp -----------------------------------
390
+ if True:
391
+ def judge_prepare_data_func_mp():
392
+ return self.config.use_mp_loss and (
393
+ ext_files_num_in_dir(self.config.mp_npz_folder,
394
+ exts=['*.npz', '*.npz.empty']) < ref_len
395
+ )
396
+
397
+ def run_prepare_data_func_mp():
398
+ if self.config.use_mp_loss:
399
+ with default_timers['face_detector']:
400
+ logger.info(f'running face detection')
401
+ if self.__dict__.get('face_detector',None) is None:
402
+ self.face_detector = FaceDetector()
403
+
404
+ self.face_detector.predict_batch(
405
+ img_folder=self.config.img_folder,
406
+ savefolder=self.config.mp_npz_folder,
407
+ visfolder=self.config.mp_npz_folder_vis,
408
+ saveVis=self.config.saveVis
409
+ )
410
+
411
+ def post_prepare_data_func_mp():
412
+ if (self.config.saveVis and
413
+ not Path(self.config.mp_npz_folder_v).exists() and
414
+ not is_empty_dir(self.config.mp_npz_folder_vis)
415
+ ):
416
+ logger.info(f'converting {self.config.mp_npz_folder_vis} to {self.config.mp_npz_folder_v}')
417
+ images_to_video(
418
+ input_folder=self.config.mp_npz_folder_vis,
419
+ output_path=self.config.mp_npz_folder_v,
420
+ img_format = None,
421
+ fps=30,
422
+ )
423
+
424
+ def match_item_func_mp(mp_ret_list):
425
+ def get_bbox_center_func(input_ret):
426
+ face_kpt = input_ret['lmk2d']
427
+ bbox = lmk2d_to_bbox(face_kpt, self.height, self.width)
428
+ xmin, ymin, xmax, ymax = [int(i) for i in bbox]
429
+ cur_center = [int((xmin+xmax)/2), int((ymin+ymax)/2)]
430
+ return bbox, cur_center
431
+ return match_by_bbox(
432
+ mp_ret_list,
433
+ get_bbox_center_func)
434
+
435
+ def read_item_func_mp(mp):
436
+ return read_mp(mp, self.height, self.width)
437
+
438
+ find_item_template_mp={
439
+ 'name': 'mp',
440
+ 'exts': ['npz'],
441
+ 'dir':self.config.mp_npz_folder,
442
+ 'judge_prepare_data_func': judge_prepare_data_func_mp,
443
+ 'run_prepare_data_func': run_prepare_data_func_mp,
444
+ 'post_prepare_data_func': post_prepare_data_func_mp,
445
+ 'match_item_func': match_item_func_mp,
446
+ 'read_item_func': read_item_func_mp,
447
+ }
448
+ self.find_item_template_list['mp']=find_item_template_mp
449
+
450
+ # seg -----------------------------------
451
+ if True:
452
+ def judge_prepare_data_func_seg():
453
+ return self.config.use_silhouette_loss and (
454
+ ext_files_num_in_dir(self.config.seg_img_folder,
455
+ exts=['*.jpg', '*.png']) < ref_len
456
+ )
457
+
458
+ def run_prepare_data_func_seg():
459
+ if self.config.use_silhouette_loss:
460
+ with default_timers['deeplab_seg']:
461
+ logger.info(f'running deeplab segmentation')
462
+ if not hasattr(self,'deeplab_seg'):
463
+ self.deeplab_seg=deeplab_seg()
464
+
465
+ self.deeplab_seg.predict_batch(
466
+ img_folder=self.config.img_folder,
467
+ savefolder=self.config.seg_img_folder,
468
+ saveVis=True
469
+ )
470
+
471
+ def post_prepare_data_func_seg():
472
+ if (self.config.saveVis and
473
+ is_empty_dir(self.config.seg_img_folder_vis)
474
+ ):
475
+ if not hasattr(self,'deeplab_seg'):
476
+ self.deeplab_seg=deeplab_seg()
477
+ logger.info(f'running deeplab segmentation visualization')
478
+ self.deeplab_seg.predict_batch(
479
+ img_folder=self.config.img_folder,
480
+ savefolder=self.config.seg_img_folder_vis,
481
+ saveVis=True,
482
+ save_mode='mask',
483
+ )
484
+
485
+ if (self.config.saveVis and
486
+ not Path(self.config.seg_img_folder_v).exists() and
487
+ not is_empty_dir(self.config.seg_img_folder)
488
+ ):
489
+ logger.info(f'convert {self.config.seg_img_folder} to video')
490
+ images_to_video(
491
+ input_folder=self.config.seg_img_folder_vis,
492
+ output_path=self.config.seg_img_folder_v,
493
+ img_format = None,
494
+ fps=30,
495
+ )
496
+
497
+ def match_item_func_seg(seg):
498
+ return seg
499
+
500
+ def read_item_func_seg(seg):
501
+ return cv2.imread(seg,cv2.IMREAD_GRAYSCALE)/255
502
+
503
+ find_item_template_seg={
504
+ 'name': 'seg',
505
+ 'exts': ['jpg'],
506
+ 'dir':self.config.seg_img_folder,
507
+ 'judge_prepare_data_func': judge_prepare_data_func_seg,
508
+ 'run_prepare_data_func': run_prepare_data_func_seg,
509
+ 'post_prepare_data_func': post_prepare_data_func_seg,
510
+ 'match_item_func': match_item_func_seg,
511
+ 'read_item_func': read_item_func_seg,
512
+ }
513
+ self.find_item_template_list['seg']=find_item_template_seg
514
+
515
+ # pymaf -----------------------------------
516
+ if True:
517
+ def judge_prepare_data_func_pymaf():
518
+ return self.config.use_pymaf_hand
519
+
520
+ def run_prepare_data_func_pymaf():
521
+ if self.config.use_pymaf_hand:
522
+ if (not Path(self.config.pymaf_pkl_path).exists()):
523
+ logger.info('Running pymaf-x')
524
+ run_pymafx(
525
+ image_folder=self.config.img_folder,
526
+ output_folder=self.config.pymaf_pkl_folder,
527
+ no_render=not self.config.saveVis,
528
+ pymaf_code_dir=os.path.join(
529
+ os.path.dirname(os.path.abspath(__file__)),
530
+ '../../modules/PyMAF')
531
+ )
532
+
533
+ def post_prepare_data_func_pymaf():
534
+ if (
535
+ self.config.saveVis and
536
+ not Path(self.config.pymaf_pkl_folder_v).exists() and
537
+ not is_empty_dir(self.config.pymaf_folder_vis)
538
+ ):
539
+ images_to_video(
540
+ input_folder=self.config.pymaf_folder_vis,
541
+ output_path=self.config.pymaf_pkl_folder_v,
542
+ img_format = None,
543
+ fps=30,
544
+ )
545
+
546
+ def match_item_func_pymaf():
547
+ pass
548
+
549
+ def read_item_func_pymaf():
550
+ pass
551
+
552
+ def post_read_func_pymaf(template):
553
+
554
+ if (
555
+ self.config.use_pymaf_hand and
556
+ Path(self.config.pymaf_pkl_path).exists()
557
+ ):
558
+ logger.info(f'load pymaf file %s' % self.config.pymaf_pkl_path)
559
+ pymaf_out_data=joblib.load(self.config.pymaf_pkl_path)
560
+ smplx_params=pymaf_out_data['smplx_params']
561
+ joints2d=pymaf_out_data['joints2d']
562
+ nose_j_list=[i[0] for i in joints2d]
563
+
564
+ self.nose_j_list=nose_j_list
565
+ self.smplx_params=smplx_params
566
+
567
+ matched_smplx_params=[]
568
+ for idx,nose_j in enumerate(self.nose_j_list):
569
+ dist1 = np.abs(nose_j[0]-self.corr_center_pymaf[0])/self.width
570
+ dist2 = np.abs(nose_j[1]-self.corr_center_pymaf[1])/self.height
571
+ if dist1 < self.match_thres and dist2 < self.match_thres:
572
+ self.corr_center_pymaf=nose_j
573
+ matched_smplx_params.append(self.smplx_params[idx])
574
+
575
+ lhand_list=[e['left_hand_pose'] for e in matched_smplx_params]#(1,15,3,3)
576
+ rhand_list=[e['right_hand_pose'] for e in matched_smplx_params]
577
+ lhand_rot=torch.cat(lhand_list,dim=0)
578
+ rhand_rot=torch.cat(rhand_list,dim=0)
579
+
580
+ from pytorch3d.transforms import matrix_to_axis_angle
581
+ lhand_axis=matrix_to_axis_angle(lhand_rot)
582
+ rhand_axis=matrix_to_axis_angle(rhand_rot)
583
+
584
+ cvt_hand_func=self.config.cvt_hand_func
585
+ lhand_pca, rhand_pca=cvt_hand_func(lhand_axis,rhand_axis)
586
+
587
+ self.pymaf_lhand_pca=lhand_pca
588
+ self.pymaf_rhand_pca=rhand_pca
589
+
590
+ if 1:
591
+ logger.info(f'matched pymaf_lhand_pca shape {self.pymaf_lhand_pca.shape}')
592
+ self.pymaf_lhand_pca=self.pymaf_lhand_pca[:self.batch_size,:]
593
+ self.pymaf_rhand_pca=self.pymaf_rhand_pca[:self.batch_size,:]
594
+
595
+ if self.pymaf_lhand_pca.shape[0]==self.batch_size:
596
+ logger.warning(f'replaced r&l hand with pymaf')
597
+ self.get_all_dict['init_data']['lhand']=self.pymaf_lhand_pca
598
+ self.get_all_dict['init_data']['rhand']=self.pymaf_rhand_pca
599
+
600
+
601
+ find_item_template_pymaf={
602
+ 'name': 'pymaf',
603
+ 'exts': ['xxx'],
604
+ 'dir':self.config.pymaf_pkl_folder,
605
+ 'judge_prepare_data_func': judge_prepare_data_func_pymaf,
606
+ 'run_prepare_data_func': run_prepare_data_func_pymaf,
607
+ 'post_prepare_data_func': post_prepare_data_func_pymaf,
608
+ 'match_item_func': match_item_func_pymaf,
609
+ 'read_item_func': read_item_func_pymaf,
610
+ 'post_read_func': post_read_func_pymaf,
611
+ }
612
+ self.find_item_template_list['pymaf']=find_item_template_pymaf
613
+
614
+
615
+ def initialize(self):
616
+ for template in self.find_item_template_list.values():
617
+
618
+ if template['judge_prepare_data_func']():
619
+ template['run_prepare_data_func']()
620
+ template['post_prepare_data_func']()
621
+ template['files_list'] = glob_exts_in_path(
622
+ template['dir'], img_ext = template['exts'])
623
+
624
+ for file_path in template['files_list']:
625
+ fn_id, _ = osp.splitext(osp.split(file_path)[1])
626
+ fn_id = int(fn_id.split('_')[0].split('.')[0])
627
+ self.int_id_to_item[fn_id][template['name']] = file_path
628
+
629
+ self.all_item_list = [[k, v] for k, v in self.int_id_to_item.items() if v.get('img') is not None]
630
+ self.all_item_list = sorted(self.all_item_list, key=lambda x: x[0])
631
+ self.all_item_list = [i[1] for i in self.all_item_list]
632
+
633
+ assert(Path(self.all_item_list[0]['img']).exists())
634
+ self.height, self.width, _ = cv2.imread(self.all_item_list[0]['img']).shape
635
+
636
+ if self.batch_size == -1:
637
+ self.batch_size = len(self.all_item_list)
638
+ else:
639
+ self.all_item_list = self.all_item_list[:self.batch_size]
640
+
641
+ if self.batch_size>300:
642
+ self.batch_size=300
643
+
644
+ def __len__(self):
645
+ return self.batch_size
646
+
647
+ def __iter__(self):
648
+ return self
649
+
650
+ def __next__(self):
651
+ if self.cnt >= self.__len__():
652
+ raise StopIteration
653
+
654
+ assert(self.cnt >= 0)
655
+ self.cnt += 1
656
+
657
+ return self.__getitem__(self.cnt-1)
658
+
659
+ def __getitem__(self, idx):
660
+
661
+ img_path = self.all_item_list[idx]['img']
662
+
663
+ # get_bbox_by_emb_and_deca
664
+ assert(Path(img_path).exists())
665
+ img = cv2.imread(img_path).astype(np.float32)
666
+ assert(img is not None)
667
+ img_fn, self.img_ext = osp.splitext(osp.split(img_path)[1])
668
+ self.height, self.width, _ = img.shape
669
+ # bgr,hw3,uint8
670
+
671
+ if self.person_face_emb is not None:
672
+ if self.correspond_center is None:
673
+ self.correspond_center, self.correspond_bbox = match_faces(
674
+ img, self.face_ider,
675
+ self.person_face_emb)
676
+ if self.corr_center_pymaf is None:
677
+ self.corr_center_pymaf=self.correspond_center
678
+ if self.correspond_center is None:
679
+ logger.warning("correspond_center return None")
680
+
681
+ img = img[:, :, ::-1] / 255.0
682
+ img = img.transpose(2, 0, 1)
683
+ # c,h,w; rgb 0-1
684
+
685
+ correspond_info = {}
686
+ for item_name,item_path in self.all_item_list[idx].items():
687
+ item_dict = self.find_item_template_list[item_name]
688
+ item_con = item_dict['read_item_func'](item_path)
689
+ correspond_info[item_name] = item_dict['match_item_func'](item_con)
690
+
691
+ correspond_info.update(
692
+ img=img,
693
+ img_fn=img_fn,
694
+ img_path=img_path)
695
+ return EasyDict(correspond_info)
696
+
697
+
698
+ def get_all(self):
699
+
700
+ for idx in tqdm(list(range(self.batch_size)),desc='reading raw files'):
701
+ assert(type(idx) == int)
702
+ self.all_processed_item.append(
703
+ self.__getitem__(idx)
704
+ )
705
+
706
+ self.pp = op_post_process(self.all_processed_item,
707
+ device=self.device,
708
+ dtype=self.dtype)
709
+ self.get_all_dict = self.pp.run()
710
+
711
+ for template in self.find_item_template_list.values():
712
+ post_read_func=template.get('post_read_func')
713
+ if post_read_func:
714
+ post_read_func(template)
715
+
716
+ return self.get_all_dict
717
+
718
+ def get_modify_jt_weight(self) -> torch.Tensor:
719
+ # @return optim_weights: [1,135,1]
720
+ self.get_joint_weights()
721
+
722
+ self.optim_weights[:, 2, :] *= self.config.op_shoulder_conf_weight # shoulder
723
+ self.optim_weights[:, 5, :] *= self.config.op_shoulder_conf_weight # shoulder
724
+ self.optim_weights[:, 8, :] *= self.config.op_root_conf_weight # root
725
+
726
+ if 0:
727
+ self.optim_weights[:, :25] = 1 # body
728
+ self.optim_weights[:, 25:67] = 2 # hand
729
+ self.optim_weights[:, 67:] = 0 # face
730
+
731
+ # print(self.optim_weights)
732
+ return self.optim_weights
SHOW/SHOW/datasets/op_post_process.py ADDED
@@ -0,0 +1,212 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from scipy.io import savemat, loadmat
2
+ from collections import namedtuple
3
+ from collections import defaultdict
4
+ import numpy as np
5
+ import json
6
+ import torch
7
+ import glob
8
+ import cv2
9
+ import os.path as osp
10
+ import os
11
+ from pathlib import Path
12
+ from .pre_dataset import *
13
+ from typing import Union
14
+ from functools import reduce, partial
15
+ from ..utils import cvt_dict_to_tensor
16
+ from loguru import logger
17
+
18
+
19
+ class op_post_process(object):
20
+ def __init__(self, all_processed_item, device, dtype):
21
+ self.all_processed_item = all_processed_item
22
+ self.device = device
23
+ self.dtype = dtype
24
+
25
+ def run(self):
26
+ self.parse_batch()
27
+ self.merge_list_to_tensor()
28
+ return self.parse_data
29
+
30
+ def check_valid(self):
31
+ self.valid_dict = defaultdict(list)
32
+
33
+ for out in self.all_processed_item:
34
+ for key in ['pixie', 'mp', 'op', 'deca']:
35
+ self.valid_dict[key].append(
36
+ 1 if out.get(key, None) is not None else 0)
37
+
38
+ for key, val in self.valid_dict.items():
39
+ logger.info(f'{key}:{val}')
40
+
41
+ def merge_list_to_tensor(self,):
42
+
43
+ self.exp = np.stack(self.exp_list) # (bs, 50)
44
+ self.pose = np.stack(self.pose_list) # (bs, 21, 3)
45
+ self.jaw = np.stack(self.jaw_list) # (batch_size, 3)
46
+
47
+ self.global_orient = np.stack(
48
+ self.global_orient_list) # (batch_size, 3)
49
+ self.cam_transl = np.stack(self.cam_transl_list) # (batch_size, 3)
50
+ self.rhand = np.concatenate(
51
+ self.rhand_list, axis=0) # (batch_size, 12)
52
+ self.lhand = np.concatenate(
53
+ self.lhand_list, axis=0) # (batch_size, 12)
54
+
55
+ for idx in range(len(self.all_processed_item)):
56
+ if self.all_processed_item[idx].get('pixie',None) is not None:
57
+ self.betas = self.all_processed_item[idx]['pixie']['shape'] # (200,)
58
+ break
59
+
60
+ if not hasattr(self, 'betas'):
61
+ self.betas = np.zeros(200)
62
+
63
+ self.op_kpts = np.stack(self.op_kpts_list, axis=0) # (bs, 135, 3)
64
+ self.mp_kpts = np.stack(self.mp_kpts_list, axis=0) # (bs, 478, 2)
65
+ self.deca_kpts = np.concatenate(
66
+ self.deca_kpts_list, axis=0) # (bs, 68, 2)
67
+
68
+ self.op_valid_flag = np.array(self.valid_dict['op']) # (bs)
69
+ self.mp_valid_flag = np.array(self.valid_dict['mp']) # (bs)
70
+ self.deca_valid_flag = np.array(self.valid_dict['deca']) # (bs)
71
+ # op_valid_flag=torch.tensor(op_valid_flag,device=self.device).long()
72
+
73
+ batch_size = self.exp.shape[0]
74
+ self.seg_stack=np.stack(self.seg_list)
75
+
76
+ self.exp = np.concatenate([(self.exp), np.zeros(
77
+ (batch_size, 50))], axis=-1) # torch.Size([bs, 100])
78
+ self.betas = np.concatenate([(self.betas), np.zeros(100)])[
79
+ None, ...] # torch.Size([1, 300])
80
+
81
+ self.mica_head_transl = np.zeros((1, 3))
82
+ self.leye_pose = np.zeros((batch_size, 3))
83
+ self.reye_pose = np.zeros((batch_size, 3))
84
+ self.transl = np.zeros((batch_size, 3))
85
+
86
+ ret_dict=dict(
87
+ init_data=dict(
88
+ betas=self.betas,
89
+ exp=self.exp,
90
+ jaw=self.jaw,
91
+ rhand=self.rhand,
92
+ lhand=self.lhand,
93
+ pose=self.pose,
94
+ global_orient=self.global_orient,
95
+ cam_transl=self.cam_transl,
96
+ mica_head_transl=self.mica_head_transl,
97
+ leye_pose=self.leye_pose,
98
+ reye_pose=self.reye_pose,
99
+ transl=self.transl
100
+ ),
101
+ gt_data=dict(
102
+ op_kpts=self.op_kpts,
103
+ mp_kpts=self.mp_kpts,
104
+ deca_kpts=self.deca_kpts,
105
+ op_valid_flag=self.op_valid_flag,
106
+ mp_valid_flag=self.mp_valid_flag,
107
+ deca_valid_flag=self.deca_valid_flag,
108
+ seg_stack=self.seg_stack
109
+ )
110
+ )
111
+ self.parse_data = cvt_dict_to_tensor(ret_dict,self.device,self.dtype)
112
+
113
+ #expression (bs, 50)
114
+ #body_pose_axis (bs, 21, 3)
115
+ #jaw_pose (bs,3)
116
+ #global_orient (bs,3)
117
+ #transl (bs,3)
118
+ #left_hand_pose (bs,12)
119
+ #right_hand_pose (bs,12)
120
+ #betas (200)
121
+
122
+ # jaw: (B,3)
123
+ # body: (B,21,3)
124
+ # hands: 2*(B,15,3)
125
+ return self.parse_data
126
+
127
+ def parse_batch(self):
128
+
129
+ self.global_orient_list = []
130
+ self.cam_transl_list = []
131
+ self.jaw_list = []
132
+ self.exp_list = []
133
+ self.lhand_list = []
134
+ self.rhand_list = []
135
+ self.pose_list = []
136
+ self.mp_kpts_list = []
137
+ self.op_kpts_list = []
138
+ self.deca_kpts_list = []
139
+ self.seg_list = []
140
+ last_nonempty=dict(
141
+ pixie=None,
142
+ deca=None,
143
+ )
144
+ for i in range(len(self.all_processed_item)):
145
+ item_pos=len(self.all_processed_item)-1-i
146
+ item=self.all_processed_item[item_pos]
147
+ for key in last_nonempty.keys():
148
+ out_val=item.get(key,None)
149
+ if out_val is not None:
150
+ last_nonempty[key]=out_val
151
+ if out_val is None:
152
+ self.all_processed_item[item_pos][key]=last_nonempty[key]
153
+
154
+ for i in range(len(self.all_processed_item)):
155
+ item_pos=i
156
+ item=self.all_processed_item[item_pos]
157
+ for key in last_nonempty.keys():
158
+ out_val=item.get(key,None)
159
+ if out_val is not None:
160
+ last_nonempty[key]=out_val
161
+ if out_val is None:
162
+ self.all_processed_item[item_pos][key]=last_nonempty[key]
163
+
164
+ logger.info(f'after filling init datas: ')
165
+ self.check_valid()
166
+
167
+ for out in self.all_processed_item:
168
+ #########################
169
+ out_pixie = out.get('pixie', None)
170
+ if out_pixie is None:
171
+ out_pixie = {
172
+ 'body_pose_63': np.zeros((21, 3)),
173
+ 'left_hand_pose': np.zeros((1, 12)),
174
+ 'right_hand_pose': np.zeros((1, 12)),
175
+ 'global_orient': np.zeros((1, 3)),
176
+ 'transl': np.zeros(3),
177
+ }
178
+ self.pose_list.append(out_pixie['body_pose_63'])
179
+ self.lhand_list.append(out_pixie['left_hand_pose'])
180
+ self.rhand_list.append(out_pixie['right_hand_pose'])
181
+ self.cam_transl_list.append(out_pixie['transl'])
182
+ self.global_orient_list.append(out_pixie['global_orient'])
183
+
184
+ #########################
185
+ out_deca = out.get('deca', None)
186
+ if out_deca is None:
187
+ out_deca = {
188
+ 'exp': np.zeros(50),
189
+ 'jaw': np.zeros(3),
190
+ 'lmk2d': np.zeros((1, 68, 2)),
191
+ }
192
+ self.exp_list.append(out_deca['exp'])
193
+ self.jaw_list.append(out_deca['jaw'])
194
+ self.deca_kpts_list.append(out_deca['lmk2d'])
195
+
196
+ #########################
197
+ out_mp = out.get('mp', None)
198
+ if out_mp is None:
199
+ out_mp = {
200
+ 'lmk2d': np.zeros((478, 2))
201
+ }
202
+ self.mp_kpts_list.append(out_mp['lmk2d'])
203
+
204
+ #########################
205
+ out_op = out.get('op', None)
206
+ if out_op is None:
207
+ out_op = np.zeros((135, 3))
208
+ self.op_kpts_list.append(out_op)
209
+
210
+ #########################
211
+ out_seg = out.get('seg', None)
212
+ self.seg_list.append(out_seg)
SHOW/SHOW/datasets/pre_dataset.py ADDED
@@ -0,0 +1,111 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from scipy.io import savemat, loadmat
2
+ from collections import namedtuple
3
+ import numpy as np
4
+ import json
5
+ import torch
6
+ import glob
7
+ import cv2
8
+ import os.path as osp
9
+ import os
10
+ import pickle
11
+ from ..image import lmk2d_to_bbox
12
+ import mmcv
13
+
14
+ deca_exp_to_smplx_X = np.load(
15
+ osp.join(os.path.dirname(__file__),
16
+ '../../../data/flame2020to2019_exp_trafo.npy')
17
+ )
18
+
19
+
20
+ def deca_exp_to_smplx(e_deca):
21
+ e_deca = np.concatenate([e_deca, np.zeros(50)])
22
+ e_smplx = deca_exp_to_smplx_X.dot(e_deca)
23
+ e_smplx = e_smplx[:50]
24
+ return e_smplx
25
+
26
+
27
+ def read_mp(mp_npz_file, height, width):
28
+ try:
29
+ mp_npz = np.load(mp_npz_file, allow_pickle=True)
30
+ except:
31
+ import traceback
32
+ traceback.print_exc()
33
+ return None
34
+
35
+ mp_npz = list(mp_npz.values())
36
+ return_list = []
37
+ for ret in mp_npz:
38
+ # (478,2)
39
+ return_list.append({
40
+ 'lmk2d': ret[0],
41
+ })
42
+
43
+ return return_list
44
+
45
+
46
+ def read_pixie(pixie_mat_file, height, width, cvt_hand_func=None):
47
+ pixie_ret_list = mmcv.load(pixie_mat_file)
48
+
49
+ assert cvt_hand_func, 'cvt_hand_func must set'
50
+
51
+ return_list = []
52
+ for ret in pixie_ret_list:
53
+ for key, val in ret.items():
54
+ if isinstance(val, np.ndarray) and val.shape[0] == 1:
55
+ ret[key] = ret[key][0]
56
+
57
+ face_bbox = lmk2d_to_bbox(ret['face_kpt'], height, width)
58
+
59
+ if 1:
60
+ lhand, rhand = cvt_hand_func(
61
+ ret['left_hand_pose'],
62
+ ret['right_hand_pose'],
63
+ )
64
+ ret['left_hand_pose'] = lhand
65
+ ret['right_hand_pose'] = rhand
66
+
67
+ ret['face_bbox'] = face_bbox
68
+
69
+ return_list.append(ret)
70
+
71
+ return return_list
72
+
73
+
74
+ def read_deca(deca_mat_file):
75
+ assert(osp.exists(deca_mat_file))
76
+
77
+ deca_ret_list = mmcv.load(deca_mat_file)
78
+
79
+ assert(deca_ret_list != [])
80
+ return_list = []
81
+
82
+ for ret in deca_ret_list:
83
+ for key, val in ret.items():
84
+ if isinstance(val, np.ndarray) and val.shape[0] == 1:
85
+ ret[key] = ret[key][0]
86
+
87
+ deca_lmk = torch.tensor(ret['landmarks2d'])
88
+ org2deca_tform = torch.tensor(ret['tform'])
89
+
90
+ # deca_lmk=deca_lmk[:, :, :2]
91
+ deca_lmk = deca_lmk[None, ...][:, :, :2]
92
+ deca_lmk[:, :, 0] = (deca_lmk[:, :, 0] + 1) * 0.5 * 224
93
+ deca_lmk[:, :, 1] = (deca_lmk[:, :, 1] + 1) * 0.5 * 224
94
+
95
+ tform_T = torch.inverse(org2deca_tform[None, ...]).transpose(1, 2)
96
+ bs, n_points, _ = deca_lmk.shape
97
+ tmp_one = torch.ones(
98
+ [bs, n_points, 1], device=deca_lmk.device, dtype=deca_lmk.dtype)
99
+ deca_lmk = torch.cat([deca_lmk, tmp_one], dim=-1)
100
+ org_deca_lmk = torch.bmm(deca_lmk, tform_T)[:, :, :2]
101
+
102
+ smplx_exp = deca_exp_to_smplx(ret['expression_params'])
103
+
104
+ return_list.append({
105
+ 'face_bbox': ret['bbox'], # array([774., 177., 969., 372.])
106
+ 'lmk2d': org_deca_lmk, # torch.Size([1, 68, 2])
107
+ 'exp': smplx_exp, # shape:(50,)
108
+ 'jaw': ret['pose_params'][3:],
109
+ })
110
+
111
+ return return_list
SHOW/SHOW/datasets/pre_runner.py ADDED
@@ -0,0 +1,108 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+ from loguru import logger
4
+ import subprocess
5
+ import torch
6
+
7
+ class path_enter(object):
8
+ def __init__(self,target_path=None):
9
+ self.origin_path=None
10
+ self.target_path=target_path
11
+
12
+ def __enter__(self):
13
+ if sys.path[0]!=self.target_path:
14
+ sys.path.insert(
15
+ 0,self.target_path
16
+ )
17
+
18
+ if self.target_path:
19
+ self.origin_path=os.getcwd()
20
+ os.chdir(self.target_path)
21
+ logger.info(f'entered: {self.target_path}; origin_path: {self.origin_path}')
22
+
23
+ def __exit__(self, exc_type, exc_value, trace):
24
+ if self.origin_path:
25
+ os.chdir(self.origin_path)
26
+ logger.info(f'exit to origin_path: {self.origin_path}')
27
+
28
+
29
+ def run_smplifyx_org(
30
+ image_folder,
31
+ output_folder,
32
+ smplifyx_code_dir,
33
+ log_cmds=True,
34
+ **kwargs,
35
+ ):
36
+ with path_enter(smplifyx_code_dir):
37
+ data_folder=os.path.dirname(image_folder)
38
+ cmds=[
39
+ 'python smplifyx/main.py --config cfg_files/fit_smplx.yaml',
40
+ '--data_folder', data_folder,
41
+ '--output_folder',output_folder,
42
+ '--img_folder','image',
43
+ '--keyp_folder','op',
44
+ '--model_folder ../../../models/smplx_model',
45
+ '--vposer_ckpt ../../../models/vposer_v1_0',
46
+ '--visualize="True"',
47
+ # '--visualize="False"',
48
+ ]
49
+ cmds=[str(i) for i in cmds]
50
+ cmds=' '.join(cmds)
51
+ if log_cmds:
52
+ logger.info(f'log_cmds: {cmds}')
53
+ subprocess.run(cmds,shell=True)
54
+ logger.info(f'done')
55
+
56
+
57
+ def run_pymafx(
58
+ image_folder,
59
+ output_folder,
60
+ pymaf_code_dir,
61
+ log_cmds=True,
62
+ no_render=True,
63
+ ):
64
+ with path_enter(pymaf_code_dir):
65
+ cmds=[
66
+ 'python apps/demo_smplx.py',
67
+ # 'python -m apps.demo_smplx',
68
+ '--image_folder',f'"{image_folder}"',
69
+ '--output_folder',f'"{output_folder}"',
70
+ '--detection_threshold 0.3',
71
+ ]
72
+
73
+ if no_render:
74
+ cmds+=['--no_render']
75
+
76
+ cmds+=[
77
+ '--pretrained_model data/pretrained_model/PyMAF-X_model_checkpoint.pt',
78
+ '--misc TRAIN.BHF_MODE full_body MODEL.EVAL_MODE True MODEL.PyMAF.HAND_VIS_TH 0.1'
79
+ ]
80
+
81
+ cmds=[str(i) for i in cmds]
82
+ cmds=' '.join(cmds)
83
+ if log_cmds:
84
+ logger.info(f'log_cmds: {cmds}')
85
+ subprocess.run(cmds,shell=True)
86
+ logger.info(f'run_pymafx done')
87
+
88
+
89
+ def run_psfr(
90
+ image_folder,
91
+ image_sup_folder,
92
+ log_cmds=True,
93
+ ):
94
+ psfr_code_dir=os.path.join(os.path.dirname(__file__),'../../modules/PSFRGAN')
95
+ with path_enter(psfr_code_dir):
96
+ cmds=[
97
+ 'python test_enhance_dir_unalign.py',
98
+ '--src_dir',f'"{image_folder}"',
99
+ '--results_dir',f'"{image_sup_folder}"',
100
+ ]
101
+ cmds=[str(i) for i in cmds]
102
+ cmds=' '.join(cmds)
103
+ if log_cmds:
104
+ logger.info(f'log_cmds: {cmds}')
105
+ subprocess.run(cmds,shell=True)
106
+ logger.info(f'done')
107
+
108
+
SHOW/SHOW/detector/__init__.py ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ from .face_detector import *
2
+ from .fan_detector import *
SHOW/SHOW/detector/face_detector.py ADDED
@@ -0,0 +1,106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import mediapipe as mp
2
+ import numpy as np
3
+ import PIL
4
+ import cv2
5
+ from glob import glob
6
+ import os
7
+ from pathlib import Path
8
+ import tqdm
9
+ from ..utils.paths import glob_exts_in_path
10
+ mp_drawing = mp.solutions.drawing_utils
11
+ mp_drawing_styles = mp.solutions.drawing_styles
12
+ mp_face_mesh = mp.solutions.face_mesh
13
+
14
+
15
+ class FaceDetector:
16
+ def __init__(self, type='google', device='cpu'):
17
+ self.type = type
18
+ self.detector = mp_face_mesh.FaceMesh(
19
+ static_image_mode=False,
20
+ max_num_faces=3,
21
+ refine_landmarks=True,
22
+ min_detection_confidence=0.5,
23
+ min_tracking_confidence=0.5)
24
+
25
+
26
+ def dense_multi_face(self, image):
27
+ # assert(self.type == 'google','FaceDetector => Wrong type for dense detection!')
28
+ results = self.detector.process(image)
29
+
30
+ if results.multi_face_landmarks is None:
31
+ return None,None
32
+
33
+ lmks_list=[]
34
+ for i in results.multi_face_landmarks:
35
+ lmks = i.landmark
36
+ lmks = np.array(list(map(lambda l: np.array([l.x, l.y]), lmks)))
37
+ lmks[:, 0] = lmks[:, 0] * image.shape[1]
38
+ lmks[:, 1] = lmks[:, 1] * image.shape[0]
39
+ lmks_list.append(lmks)
40
+
41
+ # lmks_list: (num,478,2)
42
+ return np.array(lmks_list),results
43
+
44
+
45
+ def predict_batch(self,
46
+ img_folder,
47
+ savefolder,
48
+ visfolder,
49
+ saveVis=True):
50
+ Path(savefolder).mkdir(exist_ok=True,parents=True)
51
+ Path(visfolder).mkdir(exist_ok=True,parents=True)
52
+
53
+ self.imagepath_list = glob_exts_in_path(img_folder,img_ext=['png', 'jpg','jpeg'])
54
+
55
+ for imagepath in tqdm.tqdm(self.imagepath_list):
56
+ imagename=Path(imagepath).stem
57
+ out_npz_name=os.path.join(savefolder,imagename+'_dense.npz')
58
+ out_img_name=os.path.join(visfolder,imagename+'.jpg')
59
+ self.predict(
60
+ imagepath,
61
+ out_npz_name=out_npz_name,
62
+ out_img_name=out_img_name,
63
+ saveVis=saveVis
64
+ )
65
+
66
+
67
+ def predict(self,full_file_name,out_npz_name,out_img_name,saveVis):
68
+ pil_im = PIL.Image.open(full_file_name).convert('RGB')
69
+ image = np.array(pil_im)
70
+
71
+ lmks_list,results=self.dense_multi_face(image)
72
+ if lmks_list is not None:
73
+ np.savez(out_npz_name, lmks_list)
74
+ if saveVis:
75
+ image=self.draw(image,results)
76
+ cv2.imwrite(out_img_name, image)
77
+ else:
78
+ open(out_npz_name+'.empty', 'a').close()
79
+
80
+
81
+ def draw(self,image,results):
82
+ image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
83
+ image.flags.writeable = True
84
+ for face_landmarks in results.multi_face_landmarks:
85
+ mp_drawing.draw_landmarks(
86
+ image=image,
87
+ landmark_list=face_landmarks,
88
+ connections=mp_face_mesh.FACEMESH_TESSELATION,
89
+ landmark_drawing_spec=None,
90
+ connection_drawing_spec=mp_drawing_styles
91
+ .get_default_face_mesh_tesselation_style())
92
+ mp_drawing.draw_landmarks(
93
+ image=image,
94
+ landmark_list=face_landmarks,
95
+ connections=mp_face_mesh.FACEMESH_CONTOURS,
96
+ landmark_drawing_spec=None,
97
+ connection_drawing_spec=mp_drawing_styles
98
+ .get_default_face_mesh_contours_style())
99
+ mp_drawing.draw_landmarks(
100
+ image=image,
101
+ landmark_list=face_landmarks,
102
+ connections=mp_face_mesh.FACEMESH_IRISES,
103
+ landmark_drawing_spec=None,
104
+ connection_drawing_spec=mp_drawing_styles
105
+ .get_default_face_mesh_iris_connections_style())
106
+ return image
SHOW/SHOW/detector/fan_detector.py ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pathlib import Path
2
+ from tqdm import tqdm
3
+ import os
4
+ import numpy as np
5
+ import SHOW
6
+ import cv2
7
+ import torch
8
+ import torchvision.transforms.functional as F
9
+ from PIL import Image
10
+
11
+
12
+ class FAN_Detector(object):
13
+ def __init__(self,device='cuda'):
14
+ if self.__dict__.get('face_detector',None) is None:
15
+ import face_alignment
16
+ self.face_detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device=device)
17
+
18
+ def predict(self,
19
+ img_folder,fan_npy_folder,
20
+ save_vis,fan_vis_dir):
21
+ if save_vis:
22
+ os.makedirs(fan_vis_dir,exist_ok=True)
23
+
24
+ Path(fan_npy_folder).mkdir(exist_ok=True, parents=True)
25
+ imagepath_list = SHOW.glob_exts_in_path(
26
+ img_folder,
27
+ img_ext=['png', 'jpg', 'jpeg'])
28
+
29
+ for imagepath in tqdm(imagepath_list):
30
+ imagename = Path(imagepath).stem
31
+ lmk_path = os.path.join(fan_npy_folder, imagename + '.npy')
32
+
33
+ if not os.path.exists(lmk_path):
34
+
35
+ # cv2_image = cv2.imread(imagepath)
36
+ pil_image = Image.open(imagepath).convert("RGB")
37
+ # orig_width, orig_height = pil_image.size
38
+ image_np = np.array(pil_image)
39
+
40
+ lmks = self.face_detector.get_landmarks(image_np) # list
41
+
42
+ if lmks is not None:
43
+ lmks = np.array(lmks)
44
+ np.save(lmk_path, lmks)
45
+ else:
46
+ open(
47
+ os.path.join(
48
+ fan_npy_folder,
49
+ imagename + '.npy.empty'),
50
+ 'a'
51
+ ).close()
52
+
53
+ if save_vis:
54
+ image_torch = F.to_tensor(pil_image)
55
+ ret_img = SHOW.tensor_vis_landmarks(
56
+ image_torch,
57
+ torch.from_numpy(lmks[0])
58
+ )
59
+ m_img = SHOW.merge_views([[ret_img[0]]])
60
+ cv2.imwrite(f'{fan_vis_dir}/{imagename}.jpg', m_img)
61
+
62
+
SHOW/SHOW/detector/pifpaf_detector.py ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import openpifpaf
2
+ import matplotlib.pyplot as plt
3
+ import PIL
4
+ import json
5
+ import cv2
6
+
7
+ class pifpaf_detector(object):
8
+ # chk_type:str = 'shufflenetv2k30-wholebody'
9
+
10
+ # def __post_init__(self):
11
+ def __init__(self):
12
+ chk_type:str = 'shufflenetv2k30-wholebody'
13
+ self.predictor = openpifpaf.Predictor(checkpoint=chk_type)
14
+
15
+ def process(self,full_file_name,out_file_name,out_img_name):
16
+ pil_im = PIL.Image.open(full_file_name).convert('RGB')
17
+ predictions, _, _ = self.predictor.pil_image(pil_im)
18
+
19
+ # sample_img=cv2.cvtColor(sample_img,cv2.COLOR_BGR2RGB)
20
+ # pil_im = PIL.Image.fromarray(sample_img)
21
+ # predictions, gt_anns, image_meta = self.pose_predictor.pil_image(pil_im)
22
+ # ret = [i.json_data() for i in predictions]
23
+
24
+ with open(out_file_name, "w") as fp:
25
+ r = [i.json_data() for i in predictions]
26
+ json.dump(r, fp)
27
+
28
+ self.save_vis(pil_im,predictions,out_img_name)
29
+
30
+ def save_vis(self,pil_im,predictions,out_img_name):
31
+ annotation_painter = openpifpaf.show.AnnotationPainter()
32
+ with openpifpaf.show.image_canvas(pil_im) as ax:
33
+ annotation_painter.annotations(ax, predictions)
34
+ ax.figure.savefig(out_img_name)
35
+
36
+ def predict( self, sample_img):
37
+
38
+ # self.pose_ret_list.append(ret)
39
+ annotation_painter = openpifpaf.show.AnnotationPainter()
40
+ with openpifpaf.show.image_canvas(pil_im) as ax:
41
+ annotation_painter.annotations(ax, predictions)
42
+ canvas = FigureCanvasAgg(ax.figure)
43
+ canvas.draw()
44
+ buf = canvas.buffer_rgba()
45
+ X = np.asarray(buf)
46
+ X=cv2.cvtColor(X,cv2.COLOR_RGBA2RGB)
47
+ self.pose_ret_img=cv2.resize(X,self.predict_size)
48
+ # PIL.Image.fromarray(X).show()
49
+ # fig.set_tight_layout(True)
50
+ # ax.figure.savefig('./test.jpg')
51
+ # import pdb;pdb.set_trace()
52
+ return ret,self.pose_ret_img
53
+
SHOW/SHOW/face_iders/__init__.py ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from .builder import build_ider,build_ider2
2
+ from .arcface_ider import arcface_ider
3
+ from .base import insightface_ider
4
+ from .utils import match_faces
5
+
6
+ __all__ = ['build_ider','build_ider2',
7
+ 'base','arcface_ider']
8
+
9
+ # from .builder import *
10
+ # from .base import *
11
+ # from .arcface_ider import *
SHOW/SHOW/face_iders/arcface_ider.py ADDED
@@ -0,0 +1,122 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+ import torch
4
+ import mmcv
5
+ import cv2
6
+ import numpy as np
7
+ from pathlib import Path
8
+ from easydict import EasyDict
9
+ from .builder import IDER
10
+ from .base import ider_base
11
+ from loguru import logger
12
+
13
+ default_weight_path=os.path.join(os.path.dirname(__file__),
14
+ '../../../models/arcface/glink360k_cosface_r100_fp16_0.1.pth')
15
+
16
+ @IDER.register_module()
17
+ class arcface_ider(ider_base):
18
+ def __init__(self,
19
+ weight=default_weight_path,
20
+ name='r100', fp16=True,
21
+ det='fan', threshold=0.45, **kwargs
22
+ ):
23
+
24
+ self.threshold = threshold
25
+ self.det = det
26
+
27
+ from modules.arcface_torch.backbones import get_model
28
+ self.net = get_model(name, fp16=fp16)
29
+ self.net.load_state_dict(torch.load(weight))
30
+ self.net.eval()
31
+
32
+ if self.det == 'fan':
33
+ import face_alignment
34
+ self.fan = face_alignment.FaceAlignment(
35
+ face_alignment.LandmarksType._2D,
36
+ flip_input=False)
37
+ if self.det == 'mtcnn':
38
+ from facenet_pytorch import MTCNN as mtcnn
39
+ self.mt = mtcnn(keep_all=True)
40
+
41
+ @torch.no_grad()
42
+ def get_bbox_mtcnn(self, img):
43
+ # image: 0-255, uint8, rgb, [h, w, 3]
44
+ out = self.mt.detect(img[None, ...])
45
+ # [747.456 94.97711 889.748 282.031 ]a[0]
46
+ if out[0].any():
47
+ return out[0].squeeze(0), 'bbox'
48
+ else:
49
+ logger.warning('img det return None bbox')
50
+ return (None, None)
51
+
52
+ @torch.no_grad()
53
+ def get_bbox_fan(self, img):
54
+ # image: 0-255, uint8, rgb, [h, w, 3]
55
+ h, w, _ = img.shape
56
+ lmk_list = self.fan.get_landmarks(img)
57
+
58
+ if lmk_list:
59
+ bbox_list = []
60
+ for lmk in lmk_list:
61
+ kpt = lmk.squeeze()
62
+ left = np.min(kpt[:, 0])
63
+ right = np.max(kpt[:, 0])
64
+ top = np.min(kpt[:, 1])
65
+ bottom = np.max(kpt[:, 1])
66
+ bbox = [left, top, right, bottom]
67
+ bbox_list.append(bbox)
68
+ # [[746.0, 140.0, 894.0, 283.0]]
69
+ return bbox_list, 'kpt68'
70
+
71
+ logger.warning('img det return None bbox')
72
+ return (None, None)
73
+
74
+ @torch.no_grad()
75
+ def get_face_info_from_img(self, img):
76
+ # img: rgb,hw3,uint8
77
+ ret_list = []
78
+
79
+ if self.det == 'fan':
80
+ bboxes, _ = self.get_bbox_fan(img)
81
+ if self.det == 'mtcnn':
82
+ bboxes, _ = self.get_bbox_mtcnn(img)
83
+
84
+ if bboxes is None or (
85
+ isinstance(bboxes,np.ndarray) and
86
+ not bboxes.any()
87
+ ):
88
+ logger.warning(f'img det return None bbox')
89
+ return None
90
+
91
+ crop_im_bs = mmcv.image.imcrop(img, np.array(bboxes))
92
+
93
+ for crop_im, bbox in zip(crop_im_bs, bboxes):
94
+ _img = cv2.resize(crop_im, (112, 112))
95
+ _img = np.transpose(_img, (2, 0, 1))
96
+ _img = torch.from_numpy(_img).unsqueeze(0).float()
97
+ _img.div_(255).sub_(0.5).div_(0.5)
98
+
99
+
100
+ feat = self.net(_img).numpy()[0]
101
+ feat = feat/np.linalg.norm(feat)
102
+
103
+ ret_list.append(EasyDict({
104
+ 'normed_embedding': feat,
105
+ 'crop_im': crop_im,
106
+ 'bbox': bbox}))
107
+
108
+ return ret_list
109
+
110
+ def get(self, img):
111
+ # img: bgr,hw3,uint8
112
+ if isinstance(img, str):
113
+ if Path(img).exists():
114
+ img = cv2.imread(img)
115
+ else:
116
+ logger.info(f'img not exists: {img}')
117
+ return None
118
+
119
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
120
+ face_info = self.get_face_info_from_img(img)
121
+
122
+ return face_info
SHOW/SHOW/face_iders/base.py ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+ import mmcv
4
+ import cv2
5
+ import torch
6
+ import numpy as np
7
+ from pathlib import Path
8
+ from typing import Optional, Union, NewType
9
+ from .builder import IDER
10
+
11
+
12
+ class ider_base(object):
13
+ def get_all_emb(self, im: np.ndarray = None) -> np.ndarray:
14
+ # im：bgr
15
+ faces = self.app.get(im)
16
+ return faces
17
+
18
+ def get_face_emb(self, im: np.ndarray = None) -> np.ndarray:
19
+ # im：bgr
20
+ faces = self.app.get(im)
21
+ emb = faces[0].normed_embedding
22
+ return emb
23
+
24
+ def cal_emb_sim(self, emb1: np.ndarray = None, emb2: np.ndarray = None) -> np.ndarray:
25
+ return np.dot(emb1, emb2.T)
26
+
27
+ def get(self, img):
28
+ return self.app.get(img)
29
+
30
+
31
+ @IDER.register_module()
32
+ class insightface_ider(ider_base):
33
+ def __init__(self, threshold=0.6, **kwargs):
34
+ self.threshold = threshold
35
+
36
+ from insightface.app import FaceAnalysis
37
+ self.app = FaceAnalysis(providers=['CUDAExecutionProvider'])
38
+ self.app.prepare(ctx_id=-1, det_size=(640, 640))
SHOW/SHOW/face_iders/builder.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ from mmcv.utils import Registry
2
+ import mmcv
3
+
4
+ IDER = Registry('ider')
5
+
6
+ def build_ider(config):
7
+ return mmcv.build_from_cfg(config,IDER)
8
+
9
+ def build_ider2(config):
10
+ return IDER.build(config)
SHOW/SHOW/face_iders/utils.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from loguru import logger
3
+
4
+
5
+ def match_faces(img, face_ider, person_face_emb):
6
+ # img: bgr,hw3,uint8
7
+ faces = face_ider.get(img)
8
+ if faces is None:
9
+ return None, None
10
+ # face_ider: 1.func:get(np_img) --> {2.normed_embedding,3.bbox}
11
+ for face in faces:
12
+ cur_emb = face.normed_embedding
13
+ sim = face_ider.cal_emb_sim(cur_emb, person_face_emb)
14
+ if sim >= face_ider.threshold:
15
+ logger.info(f'found sim:{sim}')
16
+ correspond_bbox = face.bbox
17
+ xmin, ymin, xmax, ymax = correspond_bbox
18
+ correspond_center = [
19
+ int((xmin+xmax)/2),
20
+ int((ymin+ymax)/2)]
21
+ return correspond_center, correspond_bbox
22
+ logger.info(f'not found: {sim}')
23
+ return None, None
SHOW/SHOW/flame/FLAME.py ADDED
@@ -0,0 +1,283 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ #
3
+ # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
4
+ # holder of all proprietary rights on this computer program.
5
+ # Using this computer program means that you agree to the terms
6
+ # in the LICENSE file included with this software distribution.
7
+ # Any use not explicitly granted by the LICENSE is prohibited.
8
+ #
9
+ # Copyright©2019 Max-Planck-Gesellschaft zur Förderung
10
+ # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
11
+ # for Intelligent Systems. All rights reserved.
12
+ #
13
+ # For comments or questions, please email us at [email protected]
14
+ # For commercial licensing contact, please contact [email protected]
15
+
16
+ import pickle
17
+
18
+ import numpy as np
19
+ # Modified from smplx code for FLAME
20
+ import torch
21
+ import torch.nn as nn
22
+ import torch.nn.functional as F
23
+ from pytorch3d.transforms import rotation_6d_to_matrix, matrix_to_rotation_6d
24
+
25
+ from .lbs import lbs
26
+
27
+
28
+
29
+ def to_tensor(array, dtype=torch.float32):
30
+ if 'torch.tensor' not in str(type(array)):
31
+ return torch.tensor(array, dtype=dtype)
32
+
33
+
34
+ def to_np(array, dtype=np.float32):
35
+ if 'scipy.sparse' in str(type(array)):
36
+ array = array.todense()
37
+ return np.array(array, dtype=dtype)
38
+
39
+
40
+ class Struct(object):
41
+ def __init__(self, **kwargs):
42
+ for key, val in kwargs.items():
43
+ setattr(self, key, val)
44
+
45
+
46
+ def rot_mat_to_euler(rot_mats):
47
+ # Calculates rotation matrix to euler angles
48
+ # Careful for extreme cases of eular angles like [0.0, pi, 0.0]
49
+
50
+ sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +
51
+ rot_mats[:, 1, 0] * rot_mats[:, 1, 0])
52
+ return torch.atan2(-rot_mats[:, 2, 0], sy)
53
+
54
+
55
+ class FLAME(nn.Module):
56
+ """
57
+ borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py
58
+ Given FLAME parameters for shape, pose, and expression, this class generates a differentiable FLAME function
59
+ which outputs the a mesh and 2D/3D facial landmarks
60
+ """
61
+
62
+ def __init__(self, config):
63
+ super(FLAME, self).__init__()
64
+ print("Creating the FLAME Decoder")
65
+ with open(config.flame_geom_path, 'rb') as f:
66
+ ss = pickle.load(f, encoding='latin1')
67
+ flame_model = Struct(**ss)
68
+
69
+ self.dtype = torch.float32
70
+ self.register_buffer('faces', to_tensor(to_np(flame_model.f, dtype=np.int64), dtype=torch.long))
71
+ # The vertices of the template model
72
+ self.register_buffer('v_template', to_tensor(to_np(flame_model.v_template), dtype=self.dtype))
73
+ # The shape components and expression
74
+ shapedirs = to_tensor(to_np(flame_model.shapedirs), dtype=self.dtype)
75
+ shapedirs = torch.cat([shapedirs[:, :, :config.num_shape_params], shapedirs[:, :, 300:300 + config.num_exp_params]], 2)
76
+ self.register_buffer('shapedirs', shapedirs)
77
+ # The pose components
78
+ num_pose_basis = flame_model.posedirs.shape[-1]
79
+ posedirs = np.reshape(flame_model.posedirs, [-1, num_pose_basis]).T
80
+ self.register_buffer('posedirs', to_tensor(to_np(posedirs), dtype=self.dtype))
81
+ #
82
+ self.register_buffer('J_regressor', to_tensor(to_np(flame_model.J_regressor), dtype=self.dtype))
83
+ parents = to_tensor(to_np(flame_model.kintree_table[0])).long();
84
+ parents[0] = -1
85
+ self.register_buffer('parents', parents)
86
+ self.register_buffer('lbs_weights', to_tensor(to_np(flame_model.weights), dtype=self.dtype))
87
+
88
+ # Register default parameters
89
+ self._register_default_params('neck_pose_params', 6)
90
+ self._register_default_params('jaw_pose_params', 6)
91
+ self._register_default_params('eye_pose_params', 12)
92
+ self._register_default_params('shape_params', config.num_shape_params)
93
+ self._register_default_params('expression_params', config.num_exp_params)
94
+
95
+ # Static and Dynamic Landmark embeddings for FLAME
96
+ # static_lmk_embedding = np.load(config.flame_static_lmk_path,allow_pickle=True)
97
+ static_lmk_embedding = np.load(config.flame_static_lmk_path)
98
+ dynamic_lmk_embedding = np.load(config.flame_dynamic_lmk_path, allow_pickle=True, encoding='latin1').item()
99
+ self.num_lmk_angles = len(dynamic_lmk_embedding['lmk_face_idx'])
100
+
101
+ self.register_buffer('lmk_faces_idx', torch.from_numpy(static_lmk_embedding['lmk_face_idx'][17:].astype(int)).to(torch.int64))
102
+ self.register_buffer('lmk_bary_coords', torch.from_numpy(static_lmk_embedding['lmk_b_coords'][17:, :]).to(self.dtype))
103
+ self.register_buffer('dynamic_lmk_faces_idx', torch.from_numpy(np.array(dynamic_lmk_embedding['lmk_face_idx']).astype(int)).to(torch.int64))
104
+ self.register_buffer('dynamic_lmk_bary_coords', torch.from_numpy(np.array(dynamic_lmk_embedding['lmk_b_coords'])).to(self.dtype))
105
+
106
+ neck_kin_chain = [];
107
+ NECK_IDX = 1
108
+ curr_idx = torch.tensor(NECK_IDX, dtype=torch.long)
109
+ while curr_idx != -1:
110
+ neck_kin_chain.append(curr_idx)
111
+ curr_idx = self.parents[curr_idx]
112
+ self.register_buffer('neck_kin_chain', torch.stack(neck_kin_chain))
113
+
114
+ def _find_dynamic_lmk_idx_and_bcoords(self, pose, dynamic_lmk_faces_idx,
115
+ dynamic_lmk_b_coords,
116
+ neck_kin_chain, dtype=torch.float32):
117
+ """
118
+ Selects the face contour depending on the reletive position of the head
119
+ Input:
120
+ vertices: N X num_of_vertices X 3
121
+ pose: N X full pose
122
+ dynamic_lmk_faces_idx: The list of contour face indexes
123
+ dynamic_lmk_b_coords: The list of contour barycentric weights
124
+ neck_kin_chain: The tree to consider for the relative rotation
125
+ dtype: Data type
126
+ return:
127
+ The contour face indexes and the corresponding barycentric weights
128
+ """
129
+
130
+ batch_size = pose.shape[0]
131
+
132
+ aa_pose = torch.index_select(pose.view(batch_size, -1, 6), 1,
133
+ neck_kin_chain)
134
+
135
+ rot_mats = rotation_6d_to_matrix(aa_pose.view(-1, 6)).view([batch_size, -1, 3, 3])
136
+ rel_rot_mat = torch.eye(3, device=pose.device, dtype=dtype).unsqueeze_(dim=0).expand(batch_size, -1, -1)
137
+
138
+ for idx in range(len(neck_kin_chain)):
139
+ rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat)
140
+
141
+ y_rot_angle = torch.round(
142
+ torch.clamp(rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi,
143
+ max=(self.num_lmk_angles - 1) // 2)).to(dtype=torch.long)
144
+
145
+ neg_mask = y_rot_angle.lt(0).to(dtype=torch.long)
146
+ mask = y_rot_angle.lt(-(self.num_lmk_angles - 1) // 2).to(dtype=torch.long)
147
+ neg_vals = mask * (self.num_lmk_angles - 1) + (1 - mask) * ((self.num_lmk_angles - 1) // 2 - y_rot_angle)
148
+ y_rot_angle = (neg_mask * neg_vals + (1 - neg_mask) * y_rot_angle)
149
+
150
+ dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx,
151
+ 0, y_rot_angle)
152
+ dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords,
153
+ 0, y_rot_angle)
154
+ return dyn_lmk_faces_idx, dyn_lmk_b_coords
155
+
156
+ def _vertices2landmarks(self, vertices, faces, lmk_faces_idx, lmk_bary_coords):
157
+ """
158
+ Calculates landmarks by barycentric interpolation
159
+ Input:
160
+ vertices: torch.tensor NxVx3, dtype = torch.float32
161
+ The tensor of input vertices
162
+ faces: torch.tensor (N*F)x3, dtype = torch.long
163
+ The faces of the mesh
164
+ lmk_faces_idx: torch.tensor N X L, dtype = torch.long
165
+ The tensor with the indices of the faces used to calculate the
166
+ landmarks.
167
+ lmk_bary_coords: torch.tensor N X L X 3, dtype = torch.float32
168
+ The tensor of barycentric coordinates that are used to interpolate
169
+ the landmarks
170
+
171
+ Returns:
172
+ landmarks: torch.tensor NxLx3, dtype = torch.float32
173
+ The coordinates of the landmarks for each mesh in the batch
174
+ """
175
+ # Extract the indices of the vertices for each face
176
+ # NxLx3
177
+ batch_size, num_verts = vertices.shape[:2]
178
+ lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view(
179
+ 1, -1, 3).view(batch_size, lmk_faces_idx.shape[1], -1)
180
+
181
+ lmk_faces += torch.arange(batch_size, dtype=torch.long).view(-1, 1, 1).to(
182
+ device=vertices.device) * num_verts
183
+
184
+ lmk_vertices = vertices.view(-1, 3)[lmk_faces]
185
+ landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords])
186
+ return landmarks
187
+
188
+ def forward(self, shape_params, trans_params=None, rot_params=None, neck_pose_params=None, jaw_pose_params=None, eye_pose_params=None, expression_params=None):
189
+
190
+ """
191
+ Input:
192
+ trans_params: N X 3 global translation
193
+ rot_params: N X 3 global rotation around the root joint of the kinematic tree (rotation is NOT around the origin!)
194
+ neck_pose_params (optional): N X 3 rotation of the head vertices around the neck joint
195
+ jaw_pose_params (optional): N X 3 rotation of the jaw
196
+ eye_pose_params (optional): N X 6 rotations of left (parameters [0:3]) and right eyeball (parameters [3:6])
197
+ shape_params (optional): N X number of shape parameters
198
+ expression_params (optional): N X number of expression parameters
199
+ return:d
200
+ vertices: N X V X 3
201
+ landmarks: N X number of landmarks X 3
202
+ """
203
+ batch_size = shape_params.shape[0]
204
+ I = matrix_to_rotation_6d(torch.eye(3)[None].cuda())
205
+
206
+ if trans_params is None:
207
+ trans_params = torch.zeros(batch_size, 3).cuda()
208
+ if rot_params is None:
209
+ rot_params = I.clone()
210
+ if neck_pose_params is None:
211
+ neck_pose_params = I.clone()
212
+ if jaw_pose_params is None:
213
+ jaw_pose_params = self.jaw_pose_params.expand(batch_size, -1)
214
+ if eye_pose_params is None:
215
+ eye_pose_params = self.eye_pose_params.expand(batch_size, -1)
216
+ if shape_params is None:
217
+ shape_params = self.shape_params.expand(batch_size, -1)
218
+ if expression_params is None:
219
+ expression_params = self.expression_params.expand(batch_size, -1)
220
+
221
+ # Concatenate identity shape and expression parameters
222
+ betas = torch.cat([shape_params, expression_params], dim=1)
223
+
224
+ # The pose vector contains global rotation, and neck, jaw, and eyeball rotations
225
+ full_pose = torch.cat([rot_params, neck_pose_params, jaw_pose_params, eye_pose_params], dim=1)
226
+
227
+ # FLAME models shape and expression deformations as vertex offset from the mean face in 'zero pose', called v_template
228
+ template_vertices = self.v_template.unsqueeze(0).expand(batch_size, -1, -1)
229
+
230
+ # Use linear blendskinning to model pose roations
231
+ vertices, _ = lbs(betas, full_pose, template_vertices,
232
+ self.shapedirs, self.posedirs,
233
+ self.J_regressor, self.parents,
234
+ self.lbs_weights, dtype=self.dtype)
235
+
236
+ # Add translation to the vertices
237
+ vertices = vertices + trans_params[:, None, :]
238
+
239
+ lmk_faces_idx = self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1)
240
+ lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).expand(batch_size, -1, -1)
241
+
242
+ dyn_lmk_faces_idx, dyn_lmk_bary_coords = self._find_dynamic_lmk_idx_and_bcoords(
243
+ full_pose, self.dynamic_lmk_faces_idx,
244
+ self.dynamic_lmk_bary_coords,
245
+ self.neck_kin_chain, dtype=self.dtype)
246
+
247
+ lmk_faces_idx = torch.cat([dyn_lmk_faces_idx, lmk_faces_idx], 1)
248
+ lmk_bary_coords = torch.cat([dyn_lmk_bary_coords, lmk_bary_coords], 1)
249
+ landmarks2d = self._vertices2landmarks(vertices, self.faces, lmk_faces_idx, lmk_bary_coords)
250
+
251
+ return vertices, landmarks2d
252
+
253
+ def _register_default_params(self, param_fname, dim):
254
+ default_params = torch.zeros([1, dim], dtype=self.dtype, requires_grad=False)
255
+ self.register_parameter(param_fname, nn.Parameter(default_params, requires_grad=False))
256
+
257
+
258
+ class FLAMETex(nn.Module):
259
+ """
260
+ current FLAME texture are adapted from BFM Texture Model
261
+ """
262
+
263
+ def __init__(self, config):
264
+ super(FLAMETex, self).__init__()
265
+ tex_space = np.load(config.tex_space_path)
266
+ mu_key = 'MU'
267
+ pc_key = 'PC'
268
+ n_pc = 199
269
+ texture_mean = tex_space[mu_key].reshape(1, -1)
270
+ texture_basis = tex_space[pc_key].reshape(-1, n_pc)
271
+ n_tex = config.tex_params
272
+ texture_mean = torch.from_numpy(texture_mean).float()[None, ...] * 255.0
273
+ texture_basis = torch.from_numpy(texture_basis[:, :n_tex]).float()[None, ...] * 255.0
274
+ self.register_buffer('texture_mean', texture_mean)
275
+ self.register_buffer('texture_basis', texture_basis)
276
+ self.image_size = config.image_size
277
+
278
+ def forward(self, texcode):
279
+ texture = self.texture_mean + (self.texture_basis * texcode[:, None, :]).sum(-1)
280
+ texture = texture.reshape(texcode.shape[0], 512, 512, 3).permute(0, 3, 1, 2)
281
+ texture = F.interpolate(texture, self.image_size, mode='bilinear')
282
+ texture = texture[:, [2, 1, 0], :, :]
283
+ return texture
SHOW/SHOW/flame/__init__.py ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ from .FLAME import FLAME
2
+ from .lbs import lbs
SHOW/SHOW/flame/lbs.py ADDED
@@ -0,0 +1,406 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+
3
+ # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
4
+ # holder of all proprietary rights on this computer program.
5
+ # You can only use this computer program if you have closed
6
+ # a license agreement with MPG or you get the right to use the computer
7
+ # program from someone who is authorized to grant you that right.
8
+ # Any use of the computer program without a valid license is prohibited and
9
+ # liable to prosecution.
10
+ #
11
+ # Copyright©2019 Max-Planck-Gesellschaft zur Förderung
12
+ # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
13
+ # for Intelligent Systems. All rights reserved.
14
+ #
15
+ # Contact: [email protected]
16
+
17
+ from __future__ import absolute_import
18
+ from __future__ import division
19
+ from __future__ import print_function
20
+
21
+ import numpy as np
22
+ import torch
23
+ import torch.nn.functional as F
24
+
25
+
26
+ # in batch*5
27
+ # out batch*6
28
+ from pytorch3d.transforms import rotation_6d_to_matrix
29
+
30
+
31
+ def stereographic_unproject_old(a):
32
+ s2 = torch.pow(a, 2).sum(1) # batch
33
+ unproj = 2 * a / (s2 + 1).view(-1, 1).repeat(1, 5) # batch*5
34
+ w = (s2 - 1) / (s2 + 1) # batch
35
+ out = torch.cat((unproj, w.view(-1, 1)), 1) # batch*6
36
+
37
+ return out
38
+
39
+
40
+ # in a batch*5, axis int
41
+ def stereographic_unproject(a, axis=None):
42
+ """
43
+ Inverse of stereographic projection: increases dimension by one.
44
+ """
45
+ batch = a.shape[0]
46
+ if axis is None:
47
+ axis = a.shape[1]
48
+ s2 = torch.pow(a, 2).sum(1) # batch
49
+ ans = torch.autograd.Variable(torch.zeros(batch, a.shape[1] + 1).cuda()) # batch*6
50
+ unproj = 2 * a / (s2 + 1).view(batch, 1).repeat(1, a.shape[1]) # batch*5
51
+ if (axis > 0):
52
+ ans[:, :axis] = unproj[:, :axis] # batch*(axis-0)
53
+ ans[:, axis] = (s2 - 1) / (s2 + 1) # batch
54
+ ans[:, axis + 1:] = unproj[:, axis:] # batch*(5-axis) # Note that this is a no-op if the default option (last axis) is used
55
+ return ans
56
+
57
+
58
+ def rot_mat_to_euler(rot_mats):
59
+ # Calculates rotation matrix to euler angles
60
+ # Careful for extreme cases of eular angles like [0.0, pi, 0.0]
61
+
62
+ sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +
63
+ rot_mats[:, 1, 0] * rot_mats[:, 1, 0])
64
+ return torch.atan2(-rot_mats[:, 2, 0], sy)
65
+
66
+
67
+ def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,
68
+ dynamic_lmk_b_coords,
69
+ neck_kin_chain, dtype=torch.float32):
70
+ ''' Compute the faces, barycentric coordinates for the dynamic landmarks
71
+
72
+
73
+ To do so, we first compute the rotation of the neck around the y-axis
74
+ and then use a pre-computed look-up table to find the faces and the
75
+ barycentric coordinates that will be used.
76
+
77
+ Special thanks to Soubhik Sanyal ([email protected])
78
+ for providing the original TensorFlow implementation and for the LUT.
79
+
80
+ Parameters
81
+ ----------
82
+ vertices: torch.tensor BxVx3, dtype = torch.float32
83
+ The tensor of input vertices
84
+ pose: torch.tensor Bx(Jx3), dtype = torch.float32
85
+ The current pose of the body model
86
+ dynamic_lmk_faces_idx: torch.tensor L, dtype = torch.long
87
+ The look-up table from neck rotation to faces
88
+ dynamic_lmk_b_coords: torch.tensor Lx3, dtype = torch.float32
89
+ The look-up table from neck rotation to barycentric coordinates
90
+ neck_kin_chain: list
91
+ A python list that contains the indices of the joints that form the
92
+ kinematic chain of the neck.
93
+ dtype: torch.dtype, optional
94
+
95
+ Returns
96
+ -------
97
+ dyn_lmk_faces_idx: torch.tensor, dtype = torch.long
98
+ A tensor of size BxL that contains the indices of the faces that
99
+ will be used to compute the current dynamic landmarks.
100
+ dyn_lmk_b_coords: torch.tensor, dtype = torch.float32
101
+ A tensor of size BxL that contains the indices of the faces that
102
+ will be used to compute the current dynamic landmarks.
103
+ '''
104
+
105
+ batch_size = vertices.shape[0]
106
+
107
+ aa_pose = torch.index_select(pose.view(batch_size, -1, 6), 1, neck_kin_chain)
108
+ rot_mats = rotation_6d_to_matrix(aa_pose.view(-1, 6)).view(batch_size, -1, 3, 3)
109
+
110
+ rel_rot_mat = torch.eye(3, device=vertices.device,
111
+ dtype=dtype).unsqueeze_(dim=0)
112
+ for idx in range(len(neck_kin_chain)):
113
+ rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat)
114
+
115
+ y_rot_angle = torch.round(
116
+ torch.clamp(-rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi,
117
+ max=39)).to(dtype=torch.long)
118
+ neg_mask = y_rot_angle.lt(0).to(dtype=torch.long)
119
+ mask = y_rot_angle.lt(-39).to(dtype=torch.long)
120
+ neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle)
121
+ y_rot_angle = (neg_mask * neg_vals +
122
+ (1 - neg_mask) * y_rot_angle)
123
+
124
+ dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx,
125
+ 0, y_rot_angle)
126
+ dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords,
127
+ 0, y_rot_angle)
128
+
129
+ return dyn_lmk_faces_idx, dyn_lmk_b_coords
130
+
131
+
132
+ def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords):
133
+ ''' Calculates landmarks by barycentric interpolation
134
+
135
+ Parameters
136
+ ----------
137
+ vertices: torch.tensor BxVx3, dtype = torch.float32
138
+ The tensor of input vertices
139
+ faces: torch.tensor Fx3, dtype = torch.long
140
+ The faces of the mesh
141
+ lmk_faces_idx: torch.tensor L, dtype = torch.long
142
+ The tensor with the indices of the faces used to calculate the
143
+ landmarks.
144
+ lmk_bary_coords: torch.tensor Lx3, dtype = torch.float32
145
+ The tensor of barycentric coordinates that are used to interpolate
146
+ the landmarks
147
+
148
+ Returns
149
+ -------
150
+ landmarks: torch.tensor BxLx3, dtype = torch.float32
151
+ The coordinates of the landmarks for each mesh in the batch
152
+ '''
153
+ # Extract the indices of the vertices for each face
154
+ # BxLx3
155
+ batch_size, num_verts = vertices.shape[:2]
156
+ device = vertices.device
157
+
158
+ lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view(
159
+ batch_size, -1, 3)
160
+
161
+ lmk_faces += torch.arange(
162
+ batch_size, dtype=torch.long, device=device).view(-1, 1, 1) * num_verts
163
+
164
+ lmk_vertices = vertices.view(-1, 3)[lmk_faces].view(
165
+ batch_size, -1, 3, 3)
166
+
167
+ landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords])
168
+ return landmarks
169
+
170
+
171
+ def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents,
172
+ lbs_weights, pose2rot=True, dtype=torch.float32):
173
+ ''' Performs Linear Blend Skinning with the given shape and pose parameters
174
+
175
+ Parameters
176
+ ----------
177
+ betas : torch.tensor BxNB
178
+ The tensor of shape parameters
179
+ pose : torch.tensor Bx(J + 1) * 3
180
+ The pose parameters in axis-angle format
181
+ v_template torch.tensor BxVx3
182
+ The template mesh that will be deformed
183
+ shapedirs : torch.tensor 1xNB
184
+ The tensor of PCA shape displacements
185
+ posedirs : torch.tensor Px(V * 3)
186
+ The pose PCA coefficients
187
+ J_regressor : torch.tensor JxV
188
+ The regressor array that is used to calculate the joints from
189
+ the position of the vertices
190
+ parents: torch.tensor J
191
+ The array that describes the kinematic tree for the model
192
+ lbs_weights: torch.tensor N x V x (J + 1)
193
+ The linear blend skinning weights that represent how much the
194
+ rotation matrix of each part affects each vertex
195
+ pose2rot: bool, optional
196
+ Flag on whether to convert the input pose tensor to rotation
197
+ matrices. The default value is True. If False, then the pose tensor
198
+ should already contain rotation matrices and have a size of
199
+ Bx(J + 1)x9
200
+ dtype: torch.dtype, optional
201
+
202
+ Returns
203
+ -------
204
+ verts: torch.tensor BxVx3
205
+ The vertices of the mesh after applying the shape and pose
206
+ displacements.
207
+ joints: torch.tensor BxJx3
208
+ The joints of the model
209
+ '''
210
+
211
+ batch_size = max(betas.shape[0], pose.shape[0])
212
+ device = betas.device
213
+
214
+ # Add shape contribution
215
+ v_shaped = v_template + blend_shapes(betas, shapedirs)
216
+
217
+ # Get the joints
218
+ # NxJx3 array
219
+ J = vertices2joints(J_regressor, v_shaped)
220
+
221
+ # 3. Add pose blend shapes
222
+ # N x J x 3 x 3
223
+ ident = torch.eye(3, dtype=dtype, device=device)
224
+ if pose2rot:
225
+ # rot_mats = batch_rodrigues(pose.view(-1, 3), dtype=dtype).view([batch_size, -1, 3, 3])
226
+ rot_mats = rotation_6d_to_matrix(pose.view(-1, 6)).view([batch_size, -1, 3, 3])
227
+
228
+ pose_feature = (rot_mats[:, 1:, :, :] - ident).view([batch_size, -1])
229
+ # (N x P) x (P, V * 3) -> N x V x 3
230
+ pose_offsets = torch.matmul(pose_feature, posedirs) \
231
+ .view(batch_size, -1, 3)
232
+ else:
233
+ pose_feature = pose[:, 1:].view(batch_size, -1, 3, 3) - ident
234
+ rot_mats = pose.view(batch_size, -1, 3, 3)
235
+
236
+ pose_offsets = torch.matmul(pose_feature.view(batch_size, -1),
237
+ posedirs).view(batch_size, -1, 3)
238
+
239
+ v_posed = pose_offsets + v_shaped
240
+ # 4. Get the global joint location
241
+ J_transformed, A = batch_rigid_transform(rot_mats, J, parents, dtype=dtype)
242
+
243
+ # 5. Do skinning:
244
+ # W is N x V x (J + 1)
245
+ W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1])
246
+ # (N x V x (J + 1)) x (N x (J + 1) x 16)
247
+ num_joints = J_regressor.shape[0]
248
+ T = torch.matmul(W, A.view(batch_size, num_joints, 16)) \
249
+ .view(batch_size, -1, 4, 4)
250
+
251
+ homogen_coord = torch.ones([batch_size, v_posed.shape[1], 1],
252
+ dtype=dtype, device=device)
253
+ v_posed_homo = torch.cat([v_posed, homogen_coord], dim=2)
254
+ v_homo = torch.matmul(T, torch.unsqueeze(v_posed_homo, dim=-1))
255
+
256
+ verts = v_homo[:, :, :3, 0]
257
+
258
+ return verts, J_transformed
259
+
260
+
261
+ def vertices2joints(J_regressor, vertices):
262
+ ''' Calculates the 3D joint locations from the vertices
263
+
264
+ Parameters
265
+ ----------
266
+ J_regressor : torch.tensor JxV
267
+ The regressor array that is used to calculate the joints from the
268
+ position of the vertices
269
+ vertices : torch.tensor BxVx3
270
+ The tensor of mesh vertices
271
+
272
+ Returns
273
+ -------
274
+ torch.tensor BxJx3
275
+ The location of the joints
276
+ '''
277
+
278
+ return torch.einsum('bik,ji->bjk', [vertices, J_regressor])
279
+
280
+
281
+ def blend_shapes(betas, shape_disps):
282
+ ''' Calculates the per vertex displacement due to the blend shapes
283
+
284
+
285
+ Parameters
286
+ ----------
287
+ betas : torch.tensor Bx(num_betas)
288
+ Blend shape coefficients
289
+ shape_disps: torch.tensor Vx3x(num_betas)
290
+ Blend shapes
291
+
292
+ Returns
293
+ -------
294
+ torch.tensor BxVx3
295
+ The per-vertex displacement due to shape deformation
296
+ '''
297
+
298
+ # Displacement[b, m, k] = sum_{l} betas[b, l] * shape_disps[m, k, l]
299
+ # i.e. Multiply each shape displacement by its corresponding beta and
300
+ # then sum them.
301
+ blend_shape = torch.einsum('bl,mkl->bmk', [betas, shape_disps])
302
+ return blend_shape
303
+
304
+
305
+ def _batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):
306
+ ''' Calculates the rotation matrices for a batch of rotation vectors
307
+ Parameters
308
+ ----------
309
+ rot_vecs: torch.tensor Nx3
310
+ array of N axis-angle vectors
311
+ Returns
312
+ -------
313
+ R: torch.tensor Nx3x3
314
+ The rotation matrices for the given axis-angle parameters
315
+ '''
316
+
317
+ batch_size = rot_vecs.shape[0]
318
+ device = rot_vecs.device
319
+
320
+ angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True)
321
+ rot_dir = rot_vecs / angle
322
+
323
+ cos = torch.unsqueeze(torch.cos(angle), dim=1)
324
+ sin = torch.unsqueeze(torch.sin(angle), dim=1)
325
+
326
+ # Bx1 arrays
327
+ rx, ry, rz = torch.split(rot_dir, 1, dim=1)
328
+ K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device)
329
+
330
+ zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device)
331
+ K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \
332
+ .view((batch_size, 3, 3))
333
+
334
+ ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0)
335
+ rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K)
336
+ return rot_mat
337
+
338
+
339
+ def transform_mat(R, t):
340
+ ''' Creates a batch of transformation matrices
341
+ Args:
342
+ - R: Bx3x3 array of a batch of rotation matrices
343
+ - t: Bx3x1 array of a batch of translation vectors
344
+ Returns:
345
+ - T: Bx4x4 Transformation matrix
346
+ '''
347
+ # No padding left or right, only add an extra row
348
+ return torch.cat([F.pad(R, [0, 0, 0, 1]),
349
+ F.pad(t, [0, 0, 0, 1], value=1)], dim=2)
350
+
351
+
352
+ def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32):
353
+ """
354
+ Applies a batch of rigid transformations to the joints
355
+
356
+ Parameters
357
+ ----------
358
+ rot_mats : torch.tensor BxNx3x3
359
+ Tensor of rotation matrices
360
+ joints : torch.tensor BxNx3
361
+ Locations of joints
362
+ parents : torch.tensor BxN
363
+ The kinematic tree of each object
364
+ dtype : torch.dtype, optional:
365
+ The data type of the created tensors, the default is torch.float32
366
+
367
+ Returns
368
+ -------
369
+ posed_joints : torch.tensor BxNx3
370
+ The locations of the joints after applying the pose rotations
371
+ rel_transforms : torch.tensor BxNx4x4
372
+ The relative (with respect to the root joint) rigid transformations
373
+ for all the joints
374
+ """
375
+
376
+ joints = torch.unsqueeze(joints, dim=-1)
377
+
378
+ rel_joints = joints.clone()
379
+ rel_joints[:, 1:] -= joints[:, parents[1:]]
380
+
381
+ transforms_mat = transform_mat(
382
+ rot_mats.view(-1, 3, 3),
383
+ rel_joints.reshape(-1, 3, 1)).reshape(-1, joints.shape[1], 4, 4)
384
+
385
+ transform_chain = [transforms_mat[:, 0]]
386
+ for i in range(1, parents.shape[0]):
387
+ # Subtract the joint location at the rest pose
388
+ # No need for rotation, since it's identity when at rest
389
+ curr_res = torch.matmul(transform_chain[parents[i]],
390
+ transforms_mat[:, i])
391
+ transform_chain.append(curr_res)
392
+
393
+ transforms = torch.stack(transform_chain, dim=1)
394
+
395
+ # The last column of the transformations contains the posed joints
396
+ posed_joints = transforms[:, :, :3, 3]
397
+
398
+ # The last column of the transformations contains the posed joints
399
+ posed_joints = transforms[:, :, :3, 3]
400
+
401
+ joints_homogen = F.pad(joints, [0, 0, 0, 1])
402
+
403
+ rel_transforms = transforms - F.pad(
404
+ torch.matmul(transforms, joints_homogen), [3, 0, 0, 0, 0, 0, 0, 0])
405
+
406
+ return posed_joints, rel_transforms
SHOW/SHOW/image.py ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+
3
+ # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
4
+ # holder of all proprietary rights on this computer program.
5
+ # You can only use this computer program if you have closed
6
+ # a license agreement with MPG or you get the right to use the computer
7
+ # program from someone who is authorized to grant you that right.
8
+ # Any use of the computer program without a valid license is prohibited and
9
+ # liable to prosecution.
10
+ #
11
+ # Copyright2023 Max-Planck-Gesellschaft zur Förderung
12
+ # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
13
+ # for Intelligent Systems. All rights reserved.
14
+ #
15
+ # Authors: paper author.
16
+ # Special Acknowlegement: Wojciech Zielonka and Justus Thies
17
+ # Contact: [email protected]
18
+
19
+ import numpy as np
20
+ import torch
21
+ import torch.nn.functional as F
22
+
23
+
24
+ def lmk2d_to_bbox(lmks,h,w,bb_scale=2.0):
25
+ # lmks:68,2
26
+ x_min, x_max, y_min, y_max = np.min(lmks[:, 0]), np.max(lmks[:, 0]), np.min(lmks[:, 1]), np.max(lmks[:, 1])
27
+ x_center, y_center = int((x_max + x_min) / 2.0), int((y_max + y_min) / 2.0)
28
+ size = int(bb_scale * 2 * max(x_center - x_min, y_center - y_min))
29
+ xb_min, xb_max, yb_min, yb_max = max(x_center - size // 2, 0), min(x_center + size // 2, w - 1), \
30
+ max(y_center - size // 2, 0), min(y_center + size // 2, h - 1)
31
+
32
+ yb_max = min(yb_max, h-1)
33
+ xb_max = min(xb_max, w-1)
34
+ yb_min = max(yb_min, 0)
35
+ xb_min = max(xb_min, 0)
36
+ return [xb_min,yb_min, xb_max,yb_max]
37
+
38
+
39
+ def landmark_crop(image, lmks, dense_lmk, bb_scale=2.0):
40
+ h, w = image.shape[1:]
41
+
42
+ xb_min,yb_min, xb_max,yb_max=lmk2d_to_bbox(lmks,h,w,bb_scale=bb_scale)
43
+
44
+ if (xb_max - xb_min) % 2 != 0:
45
+ xb_min += 1
46
+
47
+ if (yb_max - yb_min) % 2 != 0:
48
+ yb_min += 1
49
+
50
+ cropped_image = crop_image(image, xb_min, yb_min, xb_max, yb_max)
51
+ cropped_image_lmks = np.vstack((lmks[:, 0] - xb_min, lmks[:, 1] - yb_min)).T
52
+ cropped_dense_lmk = np.vstack((dense_lmk[:, 0] - xb_min, dense_lmk[:, 1] - yb_min)).T
53
+ return cropped_image_lmks, cropped_dense_lmk, cropped_image, {'xb_min': xb_min, 'xb_max': xb_max, 'yb_min': yb_min, 'yb_max': yb_max}
54
+
55
+
56
+ def crop_image(image, x_min, y_min, x_max, y_max):
57
+ # image：C,H,W or c,y,x
58
+ return image[:, max(y_min, 0):min(y_max, image.shape[1] - 1), max(x_min, 0):min(x_max, image.shape[2] - 1)]
59
+
60
+
61
+ def squarefiy(image, lmk, dense_lmk, size=512):
62
+ _, h, w = image.shape
63
+ px = py = 0
64
+ max_wh = max(w, h)
65
+
66
+ if w != h:
67
+ px = int((max_wh - w) / 2)
68
+ py = int((max_wh - h) / 2)
69
+ image = F.pad(image, (px, px, py, py), 'constant', 0)
70
+
71
+ img = F.interpolate(image[None], (size, size), mode='bilinear', align_corners=False)[0]
72
+
73
+ if False:
74
+ scale_x = size / (w + px)
75
+ scale_y = size / (h + py)
76
+ lmk[:, 0] *= scale_x
77
+ lmk[:, 1] *= scale_y
78
+ dense_lmk[:, 0] *= scale_x
79
+ dense_lmk[:, 1] *= scale_y
80
+ else:
81
+ lmk[:, 0] = (lmk[:, 0] + px)*size/max_wh
82
+ lmk[:, 1] = (lmk[:, 1] + py)*size/max_wh
83
+ dense_lmk[:, 0] = (dense_lmk[:, 0] + px)*size/max_wh
84
+ dense_lmk[:, 1] = (dense_lmk[:, 1] + py)*size/max_wh
85
+
86
+ return img, lmk, dense_lmk, px, py
87
+
88
+
89
+ def tensor2im(input_image, imtype=np.uint8):
90
+ if isinstance(input_image, torch.Tensor):
91
+ input_image = torch.clamp(input_image, -1.0, 1.0)
92
+ image_tensor = input_image.data
93
+ else:
94
+ return input_image.reshape(3, 512, 512).transpose()
95
+ image_numpy = image_tensor[0].cpu().float().numpy()
96
+ if image_numpy.shape[0] == 1:
97
+ image_numpy = np.tile(image_numpy, (3, 1, 1))
98
+ image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
99
+ return image_numpy.astype(imtype)
100
+
101
+ def get_heatmap( values):
102
+ import cv2
103
+ l2 = tensor2im(values)
104
+ l2 = cv2.cvtColor(l2, cv2.COLOR_RGB2BGR)
105
+ l2 = cv2.normalize(l2, None, 0, 255, cv2.NORM_MINMAX)
106
+ heatmap = cv2.applyColorMap(l2, cv2.COLORMAP_JET)
107
+ heatmap = cv2.cvtColor(cv2.addWeighted(heatmap, 0.75, l2, 0.25, 0).astype(np.uint8), cv2.COLOR_BGR2RGB) / 255.
108
+ heatmap = torch.from_numpy(heatmap).permute(2, 0, 1)
109
+ return heatmap
110
+
111
+ def crop_image_bbox(image, lmks, dense_lmk, bbox):
112
+ xb_min = bbox['xb_min']
113
+ yb_min = bbox['yb_min']
114
+ xb_max = bbox['xb_max']
115
+ yb_max = bbox['yb_max']
116
+ cropped = crop_image(image, xb_min, yb_min, xb_max, yb_max)
117
+ cropped_image_lmks = np.vstack((lmks[:, 0] - xb_min, lmks[:, 1] - yb_min)).T
118
+ cropped_image_dense_lmk = np.vstack((dense_lmk[:, 0] - xb_min, dense_lmk[:, 1] - yb_min)).T
119
+ return cropped_image_lmks, cropped_image_dense_lmk, cropped
SHOW/SHOW/load_assets.py ADDED
@@ -0,0 +1,243 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sys
2
+ import os
3
+ import pickle
4
+ import torch
5
+ import numpy as np
6
+ from plyfile import PlyData
7
+ from easydict import EasyDict
8
+ from pathlib import Path
9
+ import random
10
+ import string
11
+ import mmcv
12
+ from loguru import logger
13
+ from modules.MICA.api_MICA import api_MICA
14
+ from SHOW.video_filter.MMposer import MMPoseAnalyzer
15
+ import cv2
16
+ import SHOW
17
+ import tempfile
18
+
19
+
20
+ def get_possible_person(poser, template_im):
21
+
22
+ def is_small_person(bbox, org_im: np.ndarray):
23
+ img_height, img_width, _ = org_im.shape
24
+ box_height = bbox[3] - bbox[1]
25
+ box_width = bbox[2] - bbox[0]
26
+ is_small_person = 0
27
+ if ((box_height / img_height) < 0.4 or (box_width / img_width) < 0.3):
28
+ is_small_person = 1
29
+ return is_small_person
30
+
31
+ def is_kpts_whole(kpts):
32
+ is_whole = 1
33
+ poit_to_check = list(range(0, 13))
34
+ for idx in poit_to_check:
35
+ if kpts[idx][-1] < 0.3:
36
+ is_whole = 0
37
+ break
38
+ return is_whole
39
+
40
+ if isinstance(template_im, str):
41
+ org_im = cv2.imread(template_im)
42
+ else:
43
+ org_im = template_im
44
+
45
+ pose_results = poser.predict(template_im)
46
+
47
+ #################
48
+ logger.info(f'mmpose det length before: {len(pose_results)}')
49
+ # pose_results = [i for i in pose_results if i['bbox'][-1]>0.3]
50
+ # pose_results = [i for i in pose_results if is_small_person(i['bbox'],org_im)]
51
+ pose_results = [i for i in pose_results if is_kpts_whole(i['keypoints'])]
52
+ if len(pose_results) == 0:
53
+ logger.info(f'no whole person detected')
54
+ return None
55
+ logger.info(f'mmpose det length after: {len(pose_results)}')
56
+ #################
57
+
58
+ for idx in range(len(pose_results)):
59
+ bbox = pose_results[idx]['bbox']
60
+ box_height = bbox[3] - bbox[1]
61
+ box_width = bbox[2] - bbox[0]
62
+ pose_results[idx]['size'] = box_height * box_width
63
+
64
+ pose_results.sort(key=lambda x: x['size'], reverse=True)
65
+ pose_results_size_list = [i['size'] for i in pose_results]
66
+ logger.info(f'pose_results_size_list: {pose_results_size_list}')
67
+
68
+ max_ret = pose_results[0]
69
+ left, top, right, bottom = [int(i) for i in max_ret['bbox'][:4]]
70
+ max_person_crop_im = org_im[top:bottom, left:right, :]
71
+ logger.info(
72
+ f'cropped image from left:{left},top:{top},right:{right},bottom:{bottom}'
73
+ )
74
+ #################
75
+
76
+ return max_person_crop_im
77
+
78
+
79
+ def read_shape(speaker_ply_file_path):
80
+ # return： (5023, 3)
81
+ ply_data = PlyData.read(speaker_ply_file_path)
82
+ speaker_shape = np.stack([
83
+ ply_data['vertex']['x'], ply_data['vertex']['y'],
84
+ ply_data['vertex']['z']
85
+ ], 1)
86
+ return speaker_shape
87
+
88
+
89
+ def load_assets(config, face_ider=None, template_im=None, **kwargs):
90
+
91
+ assets_root = config.assets_root
92
+ dtype = config.dtype
93
+ device = config.device
94
+ ret_dict = EasyDict({})
95
+
96
+ shape_res_factory_dir = f'{assets_root}/id_pic/shape_factory2'
97
+ emb_res_factory_dir = f'{assets_root}/id_pic/{config.ider_cfg.npy_folder_name}'
98
+ emb_res_factory_path = f'{assets_root}/id_pic/emb_factory2.pkl'
99
+
100
+ emb_res_factory_is_changed_flag = False
101
+ if Path(emb_res_factory_path).exists():
102
+ emb_res_factory = mmcv.load(emb_res_factory_path)
103
+ else:
104
+ emb_res_factory = {'0' * 12: [np.zeros((512, ))]}
105
+ emb_res_factory_is_changed_flag = True
106
+
107
+ use_direct_face_emb = False
108
+ max_person_crop_im = None
109
+ shape_id = config.speaker_name
110
+ logger.info(f'shape_id/speaker_name: {shape_id}')
111
+ registered_id = ['oliver', 'seth', 'conan', 'chemistry']
112
+
113
+ if shape_id not in registered_id:
114
+ logger.info(f'current shape_id: {shape_id}')
115
+
116
+ poser = MMPoseAnalyzer()
117
+ config.load_betas = False
118
+ config.save_betas = False
119
+
120
+ max_person_crop_im = get_possible_person(poser, template_im)
121
+ if max_person_crop_im is None:
122
+ logger.error(f'max_person_crop_im is None')
123
+ return
124
+
125
+ face_ider_ret = face_ider.get(max_person_crop_im)
126
+ if face_ider_ret is None:
127
+ logger.error(f'face_ider_ret is None')
128
+ ret_dict.person_face_emb = None
129
+ else:
130
+ cur_speaker_feat = face_ider_ret[0].normed_embedding
131
+ use_direct_face_emb = True
132
+
133
+ shape_id=os.urandom(24).hex()
134
+ mica_out_ply = os.path.join(tempfile.gettempdir(), f'{shape_id}.ply')
135
+
136
+ mica_out_img = None
137
+ mica_out_npy = None
138
+
139
+ ret_dict.person_face_emb = cur_speaker_feat
140
+
141
+ del poser
142
+
143
+ else:
144
+ person_face_emb_path = os.path.abspath(
145
+ os.path.join(assets_root, emb_res_factory_dir, f'{shape_id}.npy'))
146
+
147
+ if Path(person_face_emb_path).exists():
148
+ ret_dict.person_face_emb = np.load(person_face_emb_path)
149
+ else:
150
+ ret_dict.person_face_emb = None
151
+
152
+ logger.info(f'loaded specific speaker: {shape_id}')
153
+ mica_out_ply = os.path.join(shape_res_factory_dir, shape_id,
154
+ 'out.ply')
155
+ mica_out_img = os.path.join(shape_res_factory_dir, shape_id,
156
+ 'out.jpg')
157
+ mica_out_npy = os.path.join(shape_res_factory_dir, shape_id,
158
+ 'out.npy')
159
+
160
+ #######################################save pkl
161
+ if emb_res_factory_is_changed_flag:
162
+ logger.info(f'saving emb_res_factory...')
163
+ mmcv.dump(emb_res_factory, emb_res_factory_path)
164
+
165
+
166
+ #######################################run MICA
167
+ if ret_dict.person_face_emb is not None:
168
+ if not Path(mica_out_ply).exists():
169
+ mica_ins = api_MICA()
170
+ mica_ins.predict(
171
+ input_img_path=max_person_crop_im
172
+ if max_person_crop_im is not None else template_im,
173
+ output_ply_path=mica_out_ply,
174
+ output_render_path=mica_out_img,
175
+ output_param_npy_path=mica_out_npy
176
+ )
177
+ del mica_ins
178
+
179
+ if Path(mica_out_ply).exists():
180
+ ret_dict.speaker_shape_vertices = torch.from_numpy(
181
+ read_shape(mica_out_ply)).to(device).to(dtype)
182
+ else:
183
+ ret_dict.speaker_shape_vertices = None
184
+
185
+ if use_direct_face_emb:
186
+ # mica_out_ply_f.close()
187
+ if os.path.exists(mica_out_ply):
188
+ os.remove(mica_out_ply)
189
+ else:
190
+ ret_dict.speaker_shape_vertices = None
191
+
192
+ #######################################others
193
+ config.speaker_name = shape_id
194
+ logger.info(f'shape_id/speaker_name: {shape_id}')
195
+
196
+
197
+ flame2020to2019_exp_trafo = './flame2020to2019_exp_trafo.npy'
198
+ flame2020to2019_exp_trafo = os.path.abspath(
199
+ os.path.join(assets_root, flame2020to2019_exp_trafo))
200
+ flame2020to2019_exp_trafo = np.load(flame2020to2019_exp_trafo)
201
+ flame2020to2019_exp_trafo = torch.from_numpy(flame2020to2019_exp_trafo).to(
202
+ device).to(dtype)
203
+ ret_dict.flame2020to2019_exp_trafo = flame2020to2019_exp_trafo
204
+
205
+ mediapipe_landmark_embedding = './mediapipe_landmark_embedding__smplx.npz'
206
+ mediapipe_landmark_embedding = os.path.abspath(
207
+ os.path.join(assets_root, mediapipe_landmark_embedding))
208
+ mp_lmk_emb = {}
209
+ mediapipe_landmark_embedding = np.load(mediapipe_landmark_embedding)
210
+ mp_lmk_emb['lmk_face_idx'] = torch.from_numpy(
211
+ mediapipe_landmark_embedding['lmk_face_idx'].astype(int)).to(
212
+ device).to(dtype).long()
213
+ mp_lmk_emb['lmk_b_coords'] = torch.from_numpy(
214
+ mediapipe_landmark_embedding['lmk_b_coords'].astype(float)).to(
215
+ device).to(dtype)
216
+ mp_lmk_emb['landmark_indices'] = torch.from_numpy(
217
+ mediapipe_landmark_embedding['landmark_indices'].astype(int)).to(
218
+ device).to(dtype)
219
+ ret_dict.mp_lmk_emb = mp_lmk_emb
220
+
221
+ FLAME_masks = './FLAME_masks.pkl'
222
+ FLAME_masks = os.path.abspath(os.path.join(assets_root, FLAME_masks))
223
+ with open(FLAME_masks, 'rb') as f:
224
+ FLAME_masks = pickle.load(f, encoding='latin1')
225
+ for key in FLAME_masks.keys():
226
+ FLAME_masks[key] = torch.from_numpy(
227
+ FLAME_masks[key]).to(device).to(dtype)
228
+ ret_dict.FLAME_masks = FLAME_masks
229
+
230
+ smplx2flame_idx = './SMPL-X__FLAME_vertex_ids.npy'
231
+ smplx2flame_idx = os.path.abspath(
232
+ os.path.join(assets_root, smplx2flame_idx))
233
+ smplx2flame_idx = np.load(smplx2flame_idx)
234
+ smplx2flame_idx = torch.from_numpy(smplx2flame_idx).to(device).to(
235
+ dtype).long()
236
+ ret_dict.smplx2flame_idx = smplx2flame_idx
237
+
238
+ vertex_colors = r'./smplx_verts_colors.txt'
239
+ vertex_colors = os.path.abspath(os.path.join(assets_root, vertex_colors))
240
+ vertex_colors = np.loadtxt(vertex_colors)
241
+ ret_dict.vertex_colors = vertex_colors
242
+
243
+ return ret_dict
SHOW/SHOW/load_models.py ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import smplx
2
+ from human_body_prior.tools.model_loader import load_vposer
3
+ import torch
4
+ import torch.nn as nn
5
+ import os
6
+ import pickle
7
+ import os.path as osp
8
+ from .datasets import op_dataset
9
+ import numpy as np
10
+ import mmcv
11
+
12
+ DEFAULT_SMPLX_CONFIG2 = dict(
13
+ dtype=torch.float32,
14
+ num_betas=200,
15
+ num_expression_coeffs=50,
16
+ num_pca_comps=12,
17
+ flat_hand_mean=True,
18
+ use_pca=True,
19
+ model_type='smplx',
20
+ use_face_contour=True,
21
+ )
22
+
23
+ DEFAULT_SMPLX_CONFIG = dict(
24
+ create_global_orient=True,
25
+ create_body_pose=True,
26
+ create_betas=True,
27
+ create_left_hand_pose=True,
28
+ create_right_hand_pose=True,
29
+ create_expression=True,
30
+ create_jaw_pose=True,
31
+ create_leye_pose=True,
32
+ create_reye_pose=True,
33
+ create_transl=True,
34
+ )
35
+
36
+
37
+ class JointMapper(nn.Module):
38
+
39
+ def __init__(self, joint_maps=None):
40
+ super().__init__()
41
+ self.register_buffer('joint_maps',
42
+ torch.tensor(joint_maps, dtype=torch.long))
43
+
44
+ def forward(self, joints, **kwargs):
45
+ return torch.index_select(joints, 1, self.joint_maps)
46
+
47
+
48
+ def load_save_pkl(ours_pkl_file_path, device='cuda'):
49
+ data = mmcv.load(ours_pkl_file_path)[0]
50
+
51
+ for key in data.keys():
52
+ if isinstance(data[key], np.ndarray):
53
+ data[key] = torch.from_numpy(data[key]).to(device)
54
+ data['batch_size'] = data['expression'].shape[0]
55
+
56
+ return data
57
+
58
+
59
+ def load_smplx_model(device='cuda', **kwargs):
60
+ body_model = smplx.create(joint_mapper=JointMapper(
61
+ op_dataset.smpl_to_openpose()),
62
+ **DEFAULT_SMPLX_CONFIG,
63
+ **kwargs).to(device=device)
64
+ return body_model
65
+
66
+
67
+ def load_vposer_model(device='cuda', vposer_ckpt=''):
68
+ vposer_ckpt = osp.expandvars(vposer_ckpt)
69
+ vposer, _ = load_vposer(vposer_ckpt, vp_model='snapshot')
70
+ vposer = vposer.to(device=device)
71
+ vposer.eval()
72
+ return vposer
SHOW/SHOW/loggers/MyNeptuneLogger.py ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+
4
+ import torch
5
+ from pathlib import Path
6
+ import numpy as np
7
+ import os.path as osp
8
+ import shutil
9
+ import os
10
+
11
+ from mmcv.runner.hooks.logger import NeptuneLoggerHook
12
+ from loguru import logger
13
+ from .builder import MMYLOGGER
14
+ from .base import *
15
+
16
+
17
+
18
+ @MMYLOGGER.register_module()
19
+ class MyNeptuneLogger(NeptuneLoggerHook):
20
+ def __init__(self,*args, **kwargs):
21
+ super().__init__(*args, **kwargs)
22
+ from neptune.new.types import File
23
+ self.File=File
24
+
25
+ def initialize(self):
26
+ if self.init_kwargs:
27
+ self.run = self.neptune.init(**self.init_kwargs)
28
+ else:
29
+ self.run = self.neptune.init()
30
+
31
+ @logger.catch
32
+ def log(self, tag_name:str, tag_value, append=True):
33
+ if append:
34
+ self.run[tag_name].log(tag_value)
35
+ else:
36
+ self.run[tag_name]=(tag_value)
37
+
38
+ @logger.catch
39
+ def log_bs(self,dict_to_log={},append=True):
40
+ for key,val in dict_to_log.items():
41
+ # if self.is_scalar(val):
42
+ self.log(key,val,append=append)
43
+
44
+ @logger.catch
45
+ def log_image(self,key:str,img):
46
+ img=img_preprocess(img)
47
+ self.run[key].upload(self.File.as_image(img))
48
+
49
+ @logger.catch
50
+ def update_config(self,config_dict):
51
+ self.run['parameters']=config_dict
52
+
53
+ @logger.catch
54
+ def create_proj(self,
55
+ workshops='lithiumice',
56
+ proj_name='smplifyx',
57
+ ):
58
+ from neptune import management
59
+ api_token=self.init_kwargs.api_token
60
+
61
+ management.get_project_list(api_token=api_token)
62
+ management.create_project(
63
+ name=f'{workshops}/{proj_name}',
64
+ key=proj_name,
65
+ api_token=api_token,
66
+ visibility='pub')
SHOW/SHOW/loggers/MyTFLogger.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+
4
+ import torch
5
+ from pathlib import Path
6
+ import numpy as np
7
+ import os.path as osp
8
+ import shutil
9
+ import os
10
+
11
+ from mmcv.runner.hooks.logger import TensorboardLoggerHook
12
+ from loguru import logger
13
+ from .builder import MMYLOGGER
14
+ from .base import *
15
+
16
+
17
+
18
+ @MMYLOGGER.register_module()
19
+ class MyTFLogger(TensorboardLoggerHook,my_logger):
20
+ def __init__(self,*args, **kwargs):
21
+ super().__init__(*args, **kwargs)
22
+ if Path(self.log_dir).exists():
23
+ shutil.rmtree(self.log_dir)
24
+
25
+ @logger.catch
26
+ def log(self, tags:dict,iters=0):
27
+ for tag, val in tags.items():
28
+ if isinstance(val, str):
29
+ self.writer.add_text(tag, val, iters)
30
+ else:
31
+ self.writer.add_scalar(tag, val, iters)
SHOW/SHOW/loggers/MyTextLogger.py ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import torch
3
+ from pathlib import Path
4
+ import numpy as np
5
+ import os.path as osp
6
+ import shutil
7
+ import os
8
+
9
+ from mmcv.runner.hooks.logger import NeptuneLoggerHook
10
+ from loguru import logger
11
+ from .builder import MMYLOGGER
12
+ from .base import *
13
+
14
+
15
+ @MMYLOGGER.register_module()
16
+ class MyTextLogger(object):
17
+ def __init__(self,save_dir,filename="log.txt", mode="a",*args, **kwargs):
18
+ from SHOW.loggers.logger import setup_logger
19
+ setup_logger(save_dir,filename,mode=mode)
20
+
21
+ @logger.catch
22
+ def log(self, tag_name:str, tag_value,print_to_screen=False,**kwargs):
23
+ logger.log(f"{tag_name}:{tag_value}")
24
+
25
+ @logger.catch
26
+ def log_bs(self,append=True,print_to_screen=False,**kwargs):
27
+ for key,val in kwargs.items():
28
+ # if self.is_scalar(val):
29
+ self.log(key,val,print_to_screen=print_to_screen)
SHOW/SHOW/loggers/MyWandbLogger.py ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+
4
+ import torch
5
+ from pathlib import Path
6
+ import numpy as np
7
+ import os.path as osp
8
+ import shutil
9
+ import os
10
+
11
+ from mmcv.runner.hooks.logger import WandbLoggerHook
12
+ from loguru import logger
13
+ from .builder import MMYLOGGER
14
+ from .base import *
15
+
16
+
17
+
18
+ @MMYLOGGER.register_module()
19
+ class MyWandbLogger(WandbLoggerHook):
20
+ def __init__(self,wandb_key,wandb_name,*args, **kwargs):
21
+ os.environ['WANDB_API_KEY'] = wandb_key
22
+ os.environ['WANDB_NAME'] = wandb_name
23
+ super().__init__(*args, **kwargs)
24
+
25
+ def initialize(self):
26
+ if self.wandb is None:
27
+ self.import_wandb()
28
+ if self.init_kwargs:
29
+ self.wandb.init(**self.init_kwargs)
30
+ else:
31
+ self.wandb.init()
32
+
33
+ @logger.catch
34
+ def log(self, tag_name,tag_value,**kwargs):
35
+ self.wandb.log({tag_name:tag_value})
36
+
37
+ @logger.catch
38
+ def log_bs(self,dict_to_log={},**kwargs):
39
+ self.wandb.log(dict_to_log)
40
+
41
+ @logger.catch
42
+ def update_config(self,config_dict):
43
+ self.wandb.config.update(config_dict)
44
+
45
+ @logger.catch
46
+ def alert(self,title='',msg=''):
47
+ self.wandb.alert(
48
+ title=title,
49
+ text=msg
50
+ )
51
+
52
+ @logger.catch
53
+ def log_image(self,key:str,img):
54
+ img=img_preprocess(img)*255
55
+ upload_list=[self.wandb.Image(img,caption=key)]
56
+ self.wandb.log({key:upload_list})
57
+
58
+
59
+
SHOW/SHOW/loggers/__init__.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ from .builder import build_my_logger
2
+ from .MyNeptuneLogger import MyNeptuneLogger
3
+ from .MyWandbLogger import MyWandbLogger
4
+
5
+ # __all__ = ['build_my_logger',
6
+ # 'MyNeptuneLogger','MyWandbLogger','MyTFLogger']