├── README.md ├── _init_paths.py ├── config ├── h2t.config ├── j-hmdb.config ├── ucf-101.config └── ucf-sports.config ├── data ├── embedding-example.png ├── h2t │ ├── annotations │ │ ├── all_videos.txt │ │ ├── testlist.txt │ │ └── trainlist.txt │ └── wtv │ │ └── h2t-wtv.npy ├── j-hmdb │ ├── annotations │ │ ├── all_videos.txt │ │ ├── test-split1.txt │ │ └── train-split1.txt │ └── wtv │ │ └── jhmdb-wtv.npy ├── mscoco │ ├── mscoco-wtv.npy │ ├── mscoco_classes.txt │ └── mscoco_priors_new.npy ├── ucf-101 │ ├── annotations │ │ ├── all_videos.txt │ │ ├── test_split01.txt │ │ └── train_split01.txt │ └── wtv │ │ └── ucf-101-wtv.npy ├── ucf-sports │ ├── annotations │ │ ├── all_videos.txt │ │ ├── test_split.txt │ │ └── train_split.txt │ └── wtv │ │ └── sports-wtv.npy └── visualization-example.png ├── helper.py ├── local_detect.py ├── local_link.py ├── local_score.py ├── visualize ├── example │ ├── frames │ │ ├── 0001.jpg │ │ ├── 0002.jpg │ │ ├── 0003.jpg │ │ ├── 0004.jpg │ │ ├── 0005.jpg │ │ ├── 0006.jpg │ │ ├── 0007.jpg │ │ ├── 0008.jpg │ │ ├── 0009.jpg │ │ ├── 0010.jpg │ │ ├── 0011.jpg │ │ ├── 0012.jpg │ │ ├── 0013.jpg │ │ ├── 0014.jpg │ │ ├── 0015.jpg │ │ ├── 0016.jpg │ │ ├── 0017.jpg │ │ ├── 0018.jpg │ │ ├── 0019.jpg │ │ ├── 0020.jpg │ │ ├── 0021.jpg │ │ ├── 0022.jpg │ │ ├── 0023.jpg │ │ ├── 0024.jpg │ │ ├── 0025.jpg │ │ ├── 0026.jpg │ │ ├── 0027.jpg │ │ ├── 0028.jpg │ │ ├── 0029.jpg │ │ ├── 0030.jpg │ │ ├── 0031.jpg │ │ ├── 0032.jpg │ │ ├── 0033.jpg │ │ ├── 0034.jpg │ │ ├── 0035.jpg │ │ ├── 0036.jpg │ │ ├── 0037.jpg │ │ ├── 0038.jpg │ │ ├── 0039.jpg │ │ ├── 0040.jpg │ │ ├── 0041.jpg │ │ ├── 0042.jpg │ │ ├── 0043.jpg │ │ ├── 0044.jpg │ │ ├── 0045.jpg │ │ ├── 0046.jpg │ │ ├── 0047.jpg │ │ ├── 0048.jpg │ │ ├── 0049.jpg │ │ ├── 0050.jpg │ │ ├── 0051.jpg │ │ ├── 0052.jpg │ │ ├── 0053.jpg │ │ ├── 0054.jpg │ │ └── 0055.jpg │ └── tube.hdf5 └── visualize_video_tube.py └── word_to_vec.py /README.md: -------------------------------------------------------------------------------- 1 | # spatial-aware-object-embeddings 2 | This repository contains the basis for computing spatial-aware object embeddings for zero-shot action localization. 3 | 4 | The code is from the paper: 5 |
6 | _"Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions"_ 7 |
8 | Pascal Mettes and Cees G. M. Snoek, International Conference on Computer Vision (ICCV), 2017 9 |

10 | Drawing 11 |

12 | The repository includes: 13 | * Object and actor extraction from pre-trained MS-COCO models. 14 | * Scoring of boxes in video frames for specified action names. 15 | * Linking of boxes into zero-shot action tubes. 16 | * word2vec and MS-COCO statistics required for zero-shot action localization and classification. 17 | * Config files, video lists, and train/test splits for common action datasets. 18 | 19 | ## Running the code 20 | 21 | The __first step__ is to adjust the paths for the action dataset of interest in the config file. This can be done either by personalizing the 'framedir' path, or by copying the frames per video to the pre-specified 'framedir' location. 22 | 23 | The __second step__ is to extract actor and object instances in each video frame. The code uses Faster R-CNN for such extraction. See their [Github page](https://github.com/rbgirshick/py-faster-rcnn/tree/master/data) for installation. 24 | 25 | The code is not limited to a specific model. Throughout the paper we employ a pre-trained Faster R-CNN model based on MS-COCO, which can be downloaded as follows: 26 | ``` 27 | cd data/ 28 | wget https://dl.dropboxusercontent.com/s/cotx0y81zvbbhnt/coco_vgg16_faster_rcnn_final.caffemodel?dl=0 29 | wget https://github.com/rbgirshick/py-faster-rcnn/blob/master/models/coco/VGG16/faster_rcnn_end2end/test.prototxt 30 | cd .. 31 | ``` 32 | 33 | Given the Caffe models, the code for extraction can be run as follows (using UCF Sports as running example): 34 | ``` 35 | python local_detect.py -c config/ucf-sports.config -p PROTOTXTFILE -m CAFFEMODELFILE 36 | ``` 37 | 38 | The __third step__ is to score individual bounding boxes in video frames. There are multiple parameters here, the default parameters are as in the paper (see help option of the script for details): 39 | ``` 40 | python local_score.py -c config/ucf-sports.config -t 1 41 | ``` 42 | 43 | The __fourth step__ is to link local box scores into action tubes: 44 | ``` 45 | python local_link.py -c config/ucf-sports.config -b PATH_FROM_LOCAL_SCORE -s 1 -t 5 46 | ``` 47 | 48 | The local_detect.py, local_score.py, and local_link.py script all have a help option to see the parameters. 49 | 50 | ## Visualizing action tubes 51 | 52 | To facilitate a graphical display of action tubes and to gain quick insight into the correctness of a tube approach, we additionally provide a visualization script for action tubes. 53 | 54 | You can run the script directly to yield an example: 55 | ``` 56 | python visualize_video_tube.py 57 | ``` 58 | This should give the following result: 59 |

60 | Drawing 61 |

62 | For personalized visualizations, please read the script itself. 63 | -------------------------------------------------------------------------------- /_init_paths.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Fast R-CNN 3 | # Copyright (c) 2015 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ross Girshick 6 | # -------------------------------------------------------- 7 | 8 | """Set up paths for Fast R-CNN.""" 9 | 10 | import os.path as osp 11 | import sys 12 | 13 | def add_path(path): 14 | if path not in sys.path: 15 | sys.path.insert(0, path) 16 | 17 | # Add caffe to PYTHONPATH 18 | caffe_path = osp.join('../../deep_localization/', 'py-faster-rcnn', 'caffe-fast-rcnn', 'python') 19 | #print caffe_path 20 | add_path(caffe_path) 21 | 22 | # Add lib to PYTHONPATH 23 | lib_path = osp.join('../../deep_localization/', 'py-faster-rcnn', 'lib') 24 | add_path(lib_path) 25 | #print lib_path 26 | -------------------------------------------------------------------------------- /config/h2t.config: -------------------------------------------------------------------------------- 1 | [actions] 2 | # Dominant object / person detections. 3 | domdir = data/h2t/rpn/ 4 | 5 | # Ground truth directory. 6 | gtdir = data/h2t/gt/ 7 | 8 | # Directory for the action tubes. 9 | scoredir = data/h2t/boxscores/ 10 | 11 | # Directory for the action tubes. 12 | tubedir = data/h2t/tubes/ 13 | 14 | # Frames. 15 | framedir = data/h2t/videos/ 16 | 17 | # Wordtovec file for the actions. 18 | wtvfile = data/h2t/wtv/sports-wtv.npy 19 | 20 | # Directory for the averaged supporting object scores. 21 | supportdir = data/h2t/global-objects/shuffle-avg/ 22 | 23 | # Contextual video scores. 24 | videoscores = data/h2t/wtv/videoscores-wtv-shuffle.npy 25 | 26 | # Video file. 27 | vidfile = data/h2t/annotations/all_videos.txt 28 | 29 | # Train and test splits. 30 | trainsplit = data/h2t/annotations/trainlist.txt 31 | testsplit = data/h2t/annotations/testlist.txt 32 | -------------------------------------------------------------------------------- /config/j-hmdb.config: -------------------------------------------------------------------------------- 1 | [actions] 2 | # Dominant object / person detections. 3 | domdir = data/j-hmdb/rpn/ 4 | 5 | # Ground truth directory. 6 | gtdir = data/j-hmdb/gt/ 7 | 8 | # Directory for the action tubes. 9 | scoredir = data/j-hmdb/boxscores/ 10 | 11 | # Directory for the action tubes. 12 | tubedir = data/j-hmdb/tubes/ 13 | 14 | # Frames. 15 | framedir = data/j-hmdb/videos/ 16 | 17 | # Wordtovec file for the actions. 18 | wtvfile = data/j-hmdb/wtv/sports-wtv.npy 19 | 20 | # Directory for the averaged supporting object scores. 21 | supportdir = data/j-hmdb/global-objects/shuffle-avg/ 22 | 23 | # Contextual video scores. 24 | videoscores = data/j-hmdb/wtv/videoscores-wtv-shuffle.npy 25 | 26 | # Video file. 27 | vidfile = data/j-hmdb/annotations/all_videos.txt 28 | 29 | # Train and test splits. 30 | trainsplit = data/j-hmdb/annotations/train-split1.txt 31 | testsplit = data/j-hmdb/annotations/test-split1.txt 32 | -------------------------------------------------------------------------------- /config/ucf-101.config: -------------------------------------------------------------------------------- 1 | [actions] 2 | # Dominant object / person detections. 3 | domdir = data/ucf-101/rpn/ 4 | 5 | # Ground truth directory. 6 | gtdir = data/ucf-101/gt/ 7 | 8 | # Directory for the action tubes. 9 | scoredir = data/ucf-101/boxscores/ 10 | 11 | # Directory for the action tubes. 12 | tubedir = data/ucf-101/tubes/ 13 | 14 | # Frames. 15 | framedir = data/ucf-101/videos/ 16 | 17 | # Wordtovec file for the actions. 18 | wtvfile = data/ucf-101/wtv/sports-wtv.npy 19 | 20 | # Directory for the averaged supporting object scores. 21 | supportdir = data/ucf-101/global-objects/shuffle-avg/ 22 | 23 | # Contextual video scores. 24 | videoscores = data/ucf-101/wtv/videoscores-wtv-shuffle.npy 25 | 26 | # Video file. 27 | vidfile = data/ucf-101/annotations/all_videos.txt 28 | 29 | # Train and test splits. 30 | trainsplit = data/ucf-101/annotations/train_split01.txt 31 | testsplit = data/ucf-101/annotations/test_split01.txt 32 | -------------------------------------------------------------------------------- /config/ucf-sports.config: -------------------------------------------------------------------------------- 1 | [actions] 2 | # Dominant object / person detections. 3 | domdir = data/ucf-sports/rpn/ 4 | 5 | # Ground truth directory. 6 | gtdir = data/ucf-sports/gt/ 7 | 8 | # Directory for the action tubes. 9 | scoredir = data/ucf-sports/boxscores/ 10 | 11 | # Directory for the action tubes. 12 | tubedir = data/ucf-sports/tubes/ 13 | 14 | # Frames. 15 | framedir = data/ucf-sports/videos/ 16 | 17 | # Wordtovec file for the actions. 18 | wtvfile = data/ucf-sports/wtv/sports-wtv.npy 19 | 20 | # Directory for the averaged supporting object scores. 21 | supportdir = data/ucf-sports/global-objects/shuffle-avg/ 22 | 23 | # Contextual video scores. 24 | videoscores = data/ucf-sports/wtv/videoscores-wtv-shuffle.npy 25 | 26 | # Video file. 27 | vidfile = data/ucf-sports/annotations/all_videos.txt 28 | 29 | # Train and test splits. 30 | trainsplit = data/ucf-sports/annotations/train_split.txt 31 | testsplit = data/ucf-sports/annotations/test_split.txt 32 | -------------------------------------------------------------------------------- /data/embedding-example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/embedding-example.png -------------------------------------------------------------------------------- /data/h2t/annotations/all_videos.txt: -------------------------------------------------------------------------------- 1 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wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_8 901 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_9 902 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_le_med_6 903 | wave/CharlieAndTheChocolateFactory_wave_u_nm_np1_fr_bad_1 904 | wave/Chris_Benoit_waving_goodbye_wave_u_nm_np1_fr_med_0 905 | wave/Chris_Benoit_waving_goodbye_wave_u_nm_np1_fr_med_1 906 | wave/DONNIE_DARKO_wave_u_nm_np1_fr_goo_9 907 | wave/EVOLUTION_wave_u_cm_np1_fr_med_4 908 | wave/EVOLUTION_wave_u_nm_np1_fr_med_18 909 | wave/Hands_Up__Clap_Hands_And_Wave!_wave_f_cm_np1_ri_med_0 910 | wave/Hitch_Part_2_wave_h_nm_np1_fr_goo_12 911 | wave/Maddin_winkt_wave_h_cm_np1_fr_med_1 912 | wave/Maya_beim_Winken_wave_f_cm_np1_fr_med_0 913 | wave/Maya_beim_Winken_wave_f_cm_np1_fr_med_1 914 | wave/Maya_beim_Winken_wave_f_cm_np1_fr_med_2 915 | wave/OldSchool_wave_f_cm_np1_fr_med_13 916 | wave/Pirates_5_wave_h_nm_np1_fr_med_8 917 | wave/RATRACE_wave_f_nm_np1_ri_med_10 918 | wave/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_wave_u_cm_np1_fr_med_0 919 | wave/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_wave_u_cm_np1_fr_med_1 920 | wave/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_wave_u_cm_np1_fr_med_2 921 | wave/TrumanShow_wave_u_nm_np1_fr_med_0 922 | wave/TrumanShow_wave_u_nm_np1_fr_med_25 923 | wave/WeddingCrashers_wave_u_nm_np1_fr_goo_0 924 | wave/Wie_man_winkt!!_wave_u_cm_np1_fr_med_0 925 | wave/eddieizzardcircle_wave_u_nm_np1_ri_med_8 926 | wave/prideandprejudice1_wave_f_nm_np1_ri_med_14 927 | wave/veoh_harold_and_kumar_wave_u_nm_np1_fr_goo_5 928 | wave/wave_and_say_hi_wave_u_nm_np1_fr_med_0 929 | -------------------------------------------------------------------------------- /data/j-hmdb/annotations/test-split1.txt: -------------------------------------------------------------------------------- 1 | 1 2 3 8 9 10 16 17 18 22 33 34 46 47 52 55 56 57 58 83 84 85 86 87 88 89 90 91 98 99 100 101 102 104 105 108 109 119 120 136 144 145 146 147 148 149 167 168 171 172 173 174 175 178 179 181 193 194 198 203 212 213 214 235 236 237 238 239 240 244 245 251 252 253 254 259 265 266 267 268 269 277 286 287 288 290 294 298 299 300 301 303 305 306 309 310 311 320 331 332 333 334 335 336 337 342 343 350 351 352 356 365 366 367 397 398 399 403 404 405 406 407 408 425 426 427 428 429 430 431 441 442 457 458 464 465 466 467 476 477 478 479 490 495 497 500 502 503 504 515 516 517 518 540 541 542 554 555 556 557 558 559 560 561 567 568 569 570 571 572 594 595 602 603 604 605 606 607 615 629 630 631 632 633 634 635 636 637 638 639 649 650 651 660 661 673 674 675 682 683 686 690 691 692 693 703 705 713 715 722 727 732 734 735 736 743 744 745 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 830 831 832 833 834 835 836 841 842 843 844 847 849 850 852 855 856 862 863 864 869 883 884 887 889 891 893 894 904 905 912 913 914 923 927 2 | -------------------------------------------------------------------------------- /data/j-hmdb/annotations/train-split1.txt: -------------------------------------------------------------------------------- 1 | 4 5 6 7 19 20 21 26 36 37 38 39 11 12 13 23 24 25 30 31 32 27 28 29 40 41 35 14 15 53 54 71 72 73 74 75 76 59 60 61 62 63 64 65 66 67 77 78 79 80 81 82 45 48 49 50 51 42 43 44 68 69 70 115 116 129 130 117 118 103 110 111 112 113 97 95 96 124 125 126 127 106 92 93 94 121 114 128 122 123 131 132 133 107 159 160 161 169 170 156 157 158 162 163 164 165 166 152 153 154 155 142 143 150 151 134 137 138 139 140 141 135 186 187 188 189 190 191 209 196 205 206 197 215 202 177 201 176 185 200 211 204 199 207 208 210 192 182 183 184 195 180 228 229 230 231 232 233 250 241 220 221 222 223 224 225 226 227 216 217 218 219 249 242 243 246 247 248 234 263 264 255 256 260 276 270 257 258 271 272 273 274 275 278 279 280 281 282 283 284 285 289 261 262 295 291 314 308 292 293 319 307 313 317 318 315 316 312 324 325 326 296 297 302 304 329 330 327 328 321 322 323 338 359 360 339 340 341 383 384 385 362 363 364 349 380 381 382 346 347 348 344 345 374 375 376 357 358 371 372 373 353 354 355 361 377 378 379 368 369 370 413 414 415 412 416 417 418 386 387 388 419 420 421 409 410 411 422 423 424 395 396 392 393 394 389 390 391 435 436 437 438 439 440 432 433 434 400 401 402 452 472 470 471 443 473 474 475 450 451 447 448 449 468 469 459 460 461 462 463 480 481 482 444 445 446 453 454 455 456 483 501 522 498 505 499 506 507 508 509 510 514 496 519 520 521 484 511 512 513 485 486 487 488 489 494 491 492 493 538 539 536 537 523 524 525 526 527 528 529 530 531 532 533 534 535 562 543 544 545 546 547 548 549 550 551 552 553 563 564 596 597 598 599 600 601 592 593 573 574 565 566 608 609 610 611 612 613 614 591 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 628 646 647 648 617 662 663 664 665 666 667 668 669 670 655 656 657 658 659 652 653 654 640 641 642 643 644 645 618 619 620 621 623 624 625 626 627 616 622 704 706 707 694 680 687 689 698 688 701 709 696 681 676 677 678 679 697 684 685 708 702 671 672 699 700 695 712 725 733 721 737 738 711 718 719 720 714 717 723 724 728 726 710 739 740 741 716 729 730 731 742 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 765 758 759 760 761 762 763 764 746 766 747 748 749 750 751 752 753 754 755 756 757 800 801 825 826 845 838 839 837 827 828 829 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 840 822 823 824 854 865 876 858 859 861 860 870 871 872 873 874 875 881 882 878 879 885 868 867 853 857 880 848 846 877 886 866 851 915 925 906 895 896 897 898 899 900 901 902 926 918 919 920 892 909 916 888 917 890 928 910 903 907 908 924 911 921 922 2 | -------------------------------------------------------------------------------- /data/j-hmdb/wtv/jhmdb-wtv.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/j-hmdb/wtv/jhmdb-wtv.npy -------------------------------------------------------------------------------- /data/mscoco/mscoco-wtv.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/mscoco/mscoco-wtv.npy -------------------------------------------------------------------------------- /data/mscoco/mscoco_classes.txt: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorcycle 5 | airplane 6 | bus 7 | train 8 | truck 9 | boat 10 | traffic light 11 | fire hydrant 12 | stop sign 13 | parking meter 14 | bench 15 | bird 16 | cat 17 | dog 18 | horse 19 | sheep 20 | cow 21 | elephant 22 | bear 23 | zebra 24 | giraffe 25 | backpack 26 | umbrella 27 | handbag 28 | tie 29 | suitcase 30 | frisbee 31 | skis 32 | snowboard 33 | sports ball 34 | kite 35 | baseball bat 36 | baseball glove 37 | skateboard 38 | surfboard 39 | tennis racket 40 | bottle 41 | wine glass 42 | cup 43 | fork 44 | knife 45 | spoon 46 | bowl 47 | banana 48 | apple 49 | sandwich 50 | orange 51 | broccoli 52 | carrot 53 | hot dog 54 | pizza 55 | donut 56 | cake 57 | chair 58 | couch 59 | potted plant 60 | bed 61 | dining table 62 | toilet 63 | tv 64 | laptop 65 | mouse 66 | remote 67 | keyboard 68 | cell phone 69 | microwave 70 | oven 71 | toaster 72 | sink 73 | refrigerator 74 | book 75 | clock 76 | vase 77 | scissors 78 | teddy bear 79 | hair drier 80 | toothbrush 81 | -------------------------------------------------------------------------------- /data/mscoco/mscoco_priors_new.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/mscoco/mscoco_priors_new.npy -------------------------------------------------------------------------------- /data/ucf-101/annotations/test_split01.txt: -------------------------------------------------------------------------------- 1 | 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 2 | -------------------------------------------------------------------------------- /data/ucf-101/annotations/train_split01.txt: -------------------------------------------------------------------------------- 1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 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-------------------------------------------------------------------------------- /data/ucf-sports/annotations/all_videos.txt: -------------------------------------------------------------------------------- 1 | diving/Diving-Side/001 2 | diving/Diving-Side/002 3 | diving/Diving-Side/003 4 | diving/Diving-Side/004 5 | diving/Diving-Side/005 6 | diving/Diving-Side/006 7 | diving/Diving-Side/007 8 | diving/Diving-Side/008 9 | diving/Diving-Side/009 10 | diving/Diving-Side/010 11 | diving/Diving-Side/011 12 | diving/Diving-Side/012 13 | diving/Diving-Side/013 14 | diving/Diving-Side/014 15 | swinging-golf/Golf-Swing-Back/001 16 | swinging-golf/Golf-Swing-Back/002 17 | swinging-golf/Golf-Swing-Back/003 18 | swinging-golf/Golf-Swing-Back/004 19 | swinging-golf/Golf-Swing-Back/005 20 | swinging-golf/Golf-Swing-Front/001 21 | swinging-golf/Golf-Swing-Front/002 22 | swinging-golf/Golf-Swing-Front/003 23 | swinging-golf/Golf-Swing-Front/004 24 | swinging-golf/Golf-Swing-Front/005 25 | swinging-golf/Golf-Swing-Front/006 26 | swinging-golf/Golf-Swing-Front/007 27 | swinging-golf/Golf-Swing-Front/008 28 | swinging-golf/Golf-Swing-Side/001 29 | swinging-golf/Golf-Swing-Side/002 30 | swinging-golf/Golf-Swing-Side/003 31 | swinging-golf/Golf-Swing-Side/004 32 | swinging-golf/Golf-Swing-Side/005 33 | kicking/Kicking-Front/001 34 | kicking/Kicking-Front/002 35 | kicking/Kicking-Front/003 36 | kicking/Kicking-Front/004 37 | kicking/Kicking-Front/005 38 | kicking/Kicking-Front/006 39 | kicking/Kicking-Front/007 40 | kicking/Kicking-Front/008 41 | kicking/Kicking-Front/009 42 | kicking/Kicking-Front/010 43 | kicking/Kicking-Side/001 44 | kicking/Kicking-Side/002 45 | kicking/Kicking-Side/003 46 | kicking/Kicking-Side/004 47 | kicking/Kicking-Side/005 48 | kicking/Kicking-Side/006 49 | kicking/Kicking-Side/007 50 | kicking/Kicking-Side/008 51 | kicking/Kicking-Side/009 52 | kicking/Kicking-Side/010 53 | lifting/Lifting/001 54 | lifting/Lifting/002 55 | lifting/Lifting/003 56 | lifting/Lifting/004 57 | lifting/Lifting/005 58 | lifting/Lifting/006 59 | riding-horse/Riding-Horse/001 60 | riding-horse/Riding-Horse/002 61 | riding-horse/Riding-Horse/003 62 | riding-horse/Riding-Horse/004 63 | riding-horse/Riding-Horse/005 64 | riding-horse/Riding-Horse/006 65 | riding-horse/Riding-Horse/007 66 | riding-horse/Riding-Horse/008 67 | riding-horse/Riding-Horse/009 68 | riding-horse/Riding-Horse/010 69 | riding-horse/Riding-Horse/011 70 | riding-horse/Riding-Horse/012 71 | running/Run-Side/001 72 | running/Run-Side/002 73 | running/Run-Side/003 74 | running/Run-Side/004 75 | running/Run-Side/005 76 | running/Run-Side/006 77 | running/Run-Side/007 78 | running/Run-Side/008 79 | running/Run-Side/009 80 | running/Run-Side/010 81 | running/Run-Side/011 82 | running/Run-Side/012 83 | running/Run-Side/013 84 | skateboarding/SkateBoarding-Front/001 85 | skateboarding/SkateBoarding-Front/002 86 | skateboarding/SkateBoarding-Front/003 87 | skateboarding/SkateBoarding-Front/004 88 | skateboarding/SkateBoarding-Front/005 89 | skateboarding/SkateBoarding-Front/006 90 | skateboarding/SkateBoarding-Front/007 91 | skateboarding/SkateBoarding-Front/008 92 | skateboarding/SkateBoarding-Front/009 93 | skateboarding/SkateBoarding-Front/010 94 | skateboarding/SkateBoarding-Front/011 95 | skateboarding/SkateBoarding-Front/012 96 | swinging/Swing-Bench/001 97 | swinging/Swing-Bench/002 98 | swinging/Swing-Bench/003 99 | swinging/Swing-Bench/004 100 | swinging/Swing-Bench/005 101 | swinging/Swing-Bench/006 102 | swinging/Swing-Bench/007 103 | swinging/Swing-Bench/008 104 | swinging/Swing-Bench/009 105 | swinging/Swing-Bench/010 106 | swinging/Swing-Bench/011 107 | swinging/Swing-Bench/012 108 | swinging/Swing-Bench/013 109 | swinging/Swing-Bench/014 110 | swinging/Swing-Bench/015 111 | swinging/Swing-Bench/016 112 | swinging/Swing-Bench/017 113 | swinging/Swing-Bench/018 114 | swinging/Swing-Bench/019 115 | swinging/Swing-Bench/020 116 | swinging-bar/Swing-SideAngle/001 117 | swinging-bar/Swing-SideAngle/002 118 | swinging-bar/Swing-SideAngle/003 119 | swinging-bar/Swing-SideAngle/004 120 | swinging-bar/Swing-SideAngle/005 121 | swinging-bar/Swing-SideAngle/006 122 | swinging-bar/Swing-SideAngle/007 123 | swinging-bar/Swing-SideAngle/008 124 | swinging-bar/Swing-SideAngle/009 125 | swinging-bar/Swing-SideAngle/010 126 | swinging-bar/Swing-SideAngle/011 127 | swinging-bar/Swing-SideAngle/012 128 | swinging-bar/Swing-SideAngle/013 129 | walking/Walk-Front/001 130 | walking/Walk-Front/002 131 | walking/Walk-Front/003 132 | walking/Walk-Front/004 133 | walking/Walk-Front/005 134 | walking/Walk-Front/006 135 | walking/Walk-Front/007 136 | walking/Walk-Front/008 137 | walking/Walk-Front/009 138 | walking/Walk-Front/010 139 | walking/Walk-Front/011 140 | walking/Walk-Front/012 141 | walking/Walk-Front/013 142 | walking/Walk-Front/014 143 | walking/Walk-Front/015 144 | walking/Walk-Front/016 145 | walking/Walk-Front/017 146 | walking/Walk-Front/018 147 | walking/Walk-Front/019 148 | walking/Walk-Front/020 149 | walking/Walk-Front/021 150 | walking/Walk-Front/022 151 | -------------------------------------------------------------------------------- /data/ucf-sports/annotations/test_split.txt: -------------------------------------------------------------------------------- 1 | 1 2 3 4 15 16 17 18 19 20 33 34 35 36 37 38 53 54 59 60 61 62 71 72 73 74 84 85 86 87 96 97 98 99 100 101 116 117 118 119 129 130 131 132 133 134 135 2 | -------------------------------------------------------------------------------- /data/ucf-sports/annotations/train_split.txt: -------------------------------------------------------------------------------- 1 | 5 6 7 8 9 10 11 12 13 14 21 22 23 24 25 26 27 28 29 30 31 32 39 40 41 42 43 44 45 46 47 48 49 50 51 52 55 56 57 58 63 64 65 66 67 68 69 70 75 76 77 78 79 80 81 82 83 88 89 90 91 92 93 94 95 102 103 104 105 106 107 108 109 110 111 112 113 114 115 120 121 122 123 124 125 126 127 128 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 2 | -------------------------------------------------------------------------------- /data/ucf-sports/wtv/sports-wtv.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/ucf-sports/wtv/sports-wtv.npy -------------------------------------------------------------------------------- /data/visualization-example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/visualization-example.png -------------------------------------------------------------------------------- /helper.py: -------------------------------------------------------------------------------- 1 | # 2 | # Helper functions for boxes. 3 | # 4 | # Categories of helper functions: 5 | # 1. Overlap functions. 6 | # 2. Spatial-aware object embedding functions. 7 | # 3. Misc. 8 | # 9 | 10 | import numpy as np 11 | from scipy.stats import entropy 12 | 13 | from sklearn.metrics import average_precision_score 14 | 15 | # 16 | # Helper functions category 1: Overlap functions. 17 | # 18 | 19 | # 20 | # 1.1 21 | # Compute the intersection between two boxes. 22 | # 23 | def boxintersect(a,b): 24 | if a[0] > b[2] or b[0] > a[2] or a[1] > b[3] or b[1] > a[3]: 25 | return 0 26 | w = min(a[2], b[2]) - max(a[0], b[0]) 27 | h = min(a[3], b[3]) - max(a[1], b[1]) 28 | return w * h 29 | 30 | # 31 | # 1.2 32 | # Overlap between two boxes. 33 | # 34 | def box_overlap(a, b): 35 | if a[2] > b[0] and b[2] > a[0] and a[3] > b[1] and b[3] > a[1]: 36 | i = min(a[2],b[2]) - max(a[0],b[0]) 37 | i *= min(a[3],b[3]) - max(a[1],b[1]) 38 | i = float(i) 39 | a1 = ((a[2]-a[0]) * (a[3]-a[1])) 40 | a2 = ((b[2]-b[0]) * (b[3]-b[1])) 41 | return i / (a1 + a2 - i) 42 | return 0. 43 | 44 | # 45 | # 1.3 46 | # Overlap between box and list of other boxes. 47 | # 48 | def liou(a, b): 49 | iou = np.zeros(b.shape[0]) 50 | for i in xrange(b.shape[0]): 51 | iou[i] = box_overlap(a, b[i]) 52 | return iou 53 | 54 | # 55 | # 1.4 56 | # Compute the intersection-over-union score for two proposals from the 57 | # same video. 58 | # Optional parameter: ss (stride for first tube, in case of sparse annotation 59 | # for second tube. 60 | # 61 | def tube_iou(p1, p2, ss=1): 62 | # Frame indices. 63 | p2idxs = np.where(p2[:,2] >= 0)[0] 64 | p1 = p1[::ss,:] 65 | p1f, p2f = p1[:,0].astype(int), p2[:,0].astype(int) 66 | p2f = p2f[p2idxs] 67 | p2 = p2[p2idxs,:] 68 | 69 | # Determine union of frame span. 70 | tmin = min(np.min(p1f), np.min(p2f)) 71 | tmax = max(np.max(p1f), np.max(p2f)) 72 | 73 | # Initialize the overlap scores across frame span. 74 | span = np.arange(tmin, tmax+1, ss) 75 | overlaps = np.zeros(len(span), dtype=np.float) 76 | 77 | # Go through the frame span. 78 | for d in xrange(len(span)): 79 | i = span[d] 80 | p1i, p2i = np.where(p1f == i)[0], np.where(p2f == i)[0] 81 | # Compute the overlap if frame in both proposals. 82 | if len(p1i) == 1 and len(p2i) == 1: 83 | a,b = p1[p1i[0],1:], p2[p2i[0],1:] 84 | a = [min(a[0],a[2]), min(a[1],a[3]), max(a[0],a[2]), max(a[1],a[3])] 85 | b = [min(b[0],b[2]), min(b[1],b[3]), max(b[0],b[2]), max(b[1],b[3])] 86 | # Only compute overlap if there is any 87 | if a[2] > b[0] and b[2] > a[0] and a[3] > b[1] and b[3] > a[1]: 88 | intersection = (min(a[2],b[2]) - max(a[0],b[0])) 89 | intersection *= (min(a[3],b[3]) - max(a[1],b[1])) 90 | intersection = float(intersection) 91 | area1 = ((a[2]-a[0]) * (a[3]-a[1])) 92 | area2 = ((b[2]-b[0]) * (b[3]-b[1])) 93 | overlaps[d] = intersection / (area1 + area2 - intersection) 94 | 95 | # Return the mean overlap over the frame span 96 | return np.mean(overlaps) 97 | 98 | # 99 | # Helper functions category 2: Embedding functions. 100 | # 101 | 102 | # 103 | # 2.1 104 | # Minimal edge distance between two boxes. 105 | # 106 | def box_dist(a, b): 107 | if boxintersect(a,b) > 0: 108 | return 0 109 | ae = np.array([[a[0],a[1]], [a[2],a[1]], [a[0],a[3]], [a[2],a[3]]]) 110 | be = np.array([[b[0],b[1]], [b[2],b[1]], [b[0],b[3]], [b[2],b[3]]]) 111 | mind = np.min(np.linalg.norm(ae-be[0], axis=1)) 112 | for i in xrange(1, be.shape[0]): 113 | nd = np.min(np.linalg.norm(ae-be[i], axis=1)) 114 | mind = min(mind, nd) 115 | return mind 116 | 117 | # 118 | # 2.2 119 | # Tile distributions with 9 tiles. a=person, b=object. 120 | # 121 | def tiledist(a, b): 122 | d = np.zeros(9) 123 | e = 1e6 124 | # Above left. 125 | d[0] = boxintersect([0, 0, a[0], a[1]], b) 126 | # Above center. 127 | d[1] = boxintersect([a[0], 0, a[2], a[1]], b) 128 | # Above right. 129 | d[2] = boxintersect([a[2], 0, e, a[1]], b) 130 | # Left. 131 | d[3] = boxintersect([0, a[1], a[0], a[3]], b) 132 | # On. 133 | d[4] = boxintersect(a, b) 134 | # Right. 135 | d[5] = boxintersect([a[2], a[1], e, a[3]], b) 136 | # Below left. 137 | d[6] = boxintersect([0, a[3], a[0], e], b) 138 | # Below center. 139 | d[7] = boxintersect([a[0], a[3], a[2], e], b) 140 | # Below right. 141 | d[8] = boxintersect([a[2], a[3], e, e], b) 142 | return d / float((b[2] - b[0]) * (b[3] - b[1])) 143 | 144 | # 145 | # 2.3 146 | # Find pairs of high scoring and high overlapping boxes for Viterbi. 147 | # 148 | def viterbi_scores(b1, s1, b2, s2, iouw): 149 | scores = np.zeros((s1.shape[0], s2.shape[0])) 150 | for i in xrange(s1.shape[0]): 151 | iou = liou(b1[i], b2) 152 | scores[i] = s1[i] + s2 + iouw * iou 153 | return scores 154 | 155 | # 156 | # Helper functions category 3: Misc functions. 157 | # 158 | 159 | # 160 | # 3.1 161 | # Jensen-Shannon Divergence. 162 | # 163 | def jensen_shannon_divergence(p, q): 164 | apq = 0.5 * (p + q) 165 | return 0.5 * entropy(p, apq, 2) + 0.5 * entropy(q, apq, 2) 166 | 167 | # 168 | # 3.2 169 | # Remove elements from a tube that correspond to non-annotations. 170 | # Used for experiments on Hollywood2Tubes, where the lack of action is 171 | # annotated with -1 values for the coordindates in the frame. 172 | # 173 | def tube_trim(tube): 174 | keep = np.where(tube[:,1] >= 0)[0] 175 | return tube[keep,:] 176 | 177 | # 178 | # 3.3 179 | # Interpolate a tube (done e.g. for UCF-101 due to its many videos). 180 | # 181 | def tube_interpolate(tube, scores, stride, nr_frames): 182 | if tube.shape[0] == nr_frames: 183 | return tube 184 | 185 | ntube = np.zeros((nr_frames, tube.shape[1]), dtype=tube.dtype) 186 | nscores = np.zeros(nr_frames) 187 | 188 | for i in xrange(nr_frames): 189 | i1, i2 = i / stride, i / stride + 1 190 | w = (i % stride) / float(stride) 191 | 192 | ntube[i,0] = i 193 | if i2 < tube.shape[0]: 194 | ntube[i,1] = (1-w) * tube[i1,1] + w * tube[i2,1] 195 | ntube[i,2] = (1-w) * tube[i1,2] + w * tube[i2,2] 196 | ntube[i,3] = (1-w) * tube[i1,3] + w * tube[i2,3] 197 | ntube[i,4] = (1-w) * tube[i1,4] + w * tube[i2,4] 198 | nscores[i] = (1-w) * scores[i1] + w * nscores[i2] 199 | else: 200 | ntube[i,1] = tube[i1,1] 201 | ntube[i,2] = tube[i1,2] 202 | ntube[i,3] = tube[i1,3] 203 | ntube[i,4] = tube[i1,4] 204 | nscores[i] = scores[i1] 205 | 206 | return ntube, nscores 207 | 208 | -------------------------------------------------------------------------------- /local_detect.py: -------------------------------------------------------------------------------- 1 | # 2 | # Extract the boxes and scores for the objects from a Faster R-CNN model, 3 | # applied to a complete dataset. 4 | # 5 | # This is step 1 towards zero-shot action localization with spatial-aware 6 | # object embeddings. 7 | # 8 | # Pascal Mettes (2017). 9 | # 10 | # Please cite accordingly when using this code: 11 | # 12 | # @article{mettes2017spatial, 13 | # title={Spatial-Aware Object Embeddings for Zero-Shot Localization and 14 | # Classification of Actions}, 15 | # author={Mettes, Pascal and Snoek, Cees G M}, 16 | # journal={ICCV}, 17 | # year={2017} 18 | #} 19 | # 20 | 21 | import os 22 | import sys 23 | import cv2 24 | import h5py 25 | import numpy as np 26 | import scipy.io as sio 27 | import argparse 28 | from ConfigParser import SafeConfigParser 29 | 30 | import caffe 31 | import _init_paths 32 | from fast_rcnn.config import cfg 33 | from fast_rcnn.test import im_detect 34 | from fast_rcnn.nms_wrapper import nms 35 | from utils.timer import Timer 36 | 37 | # 38 | # Parse all arguments. 39 | # 40 | def parse_args(): 41 | parser = argparse.ArgumentParser(description="Detect local objects for a complete action dataset") 42 | parser.add_argument("-c", dest="configfile", help="Configuration file", default="config/ucf-sports.config", type=str) 43 | parser.add_argument("-s", dest="stride", help="Frame stride for object detection", default=1, type=int) 44 | parser.add_argument("-p", dest="prototxt", help="Location for Caffe prototxt (Faster R-CNN)", default="", type=str) 45 | parser.add_argument("-m", dest="caffemodel", help="Location for Caffe model (Faster R-CNN)", default="", type=str) 46 | args = parser.parse_args() 47 | return args 48 | 49 | # 50 | # Main entry point fo the script. In here: go through all videos and frames. 51 | # 52 | if __name__ == "__main__": 53 | # User parameters. 54 | args = parse_args() 55 | config = args.configfile 56 | stride = args.stride 57 | prototxt = args.prototxt 58 | caffemodel = args.caffemodel 59 | 60 | # Yield directories and videos. 61 | parser = SafeConfigParser() 62 | parser.read(config) 63 | framedir = parser.get('actions', 'framedir') 64 | resdir = parser.get('actions', 'scoredir') 65 | vidlist = parser.get('actions', 'vidfile') 66 | videos = np.array([v.strip().split()[0] for v in open(vidlist)]) 67 | 68 | # Caffe / Faster R-CNN settings. 69 | ### Use region proposals. 70 | cfg.TEST.HAS_RPN = True 71 | ### Set to CPU/GPU. 72 | caffe.set_mode_gpu() 73 | 74 | # Load the network. 75 | net = caffe.Net(prototxt, caffemodel, caffe.TEST) 76 | print "HERE" 77 | 78 | # Go over all videos and all frames. 79 | for video in videos: 80 | print video 81 | frames = sorted(os.listdir(framedir + video)) 82 | 83 | # Create resulting directory if not existing already. 84 | if not os.path.exists(resdir + video): 85 | os.makedirs(resdir + video) 86 | 87 | # Go over the frames. 88 | for i in xrange(0,len(frames),stride): 89 | print i, 90 | sys.stdout.flush() 91 | 92 | # Skip if done before. 93 | if os.path.exists(resdir + video + "/" + frames[i][:-3] + "npy"): 94 | continue 95 | 96 | # Load frame, get data, stack data, store data. 97 | try: 98 | frame = cv2.imread(basedir + video + "/" + frames[i]) 99 | s,b = im_detect(net, frame) 100 | t = np.hstack((s,b)) 101 | np.save(resdir + video + "/" + frames[i][:-3] + "npy", t) 102 | except: 103 | pass 104 | print 105 | -------------------------------------------------------------------------------- /local_link.py: -------------------------------------------------------------------------------- 1 | # 2 | # Use the box scores of individual frames to generate action tubes. 3 | # 4 | # This is step 3 towards zero-shot action localization with spatial-aware 5 | # object embeddings. 6 | # 7 | # Pascal Mettes (2017). 8 | # 9 | # Please cite accordingly when using this code: 10 | # 11 | # @article{mettes2017spatial, 12 | # title={Spatial-Aware Object Embeddings for Zero-Shot Localization and 13 | # Classification of Actions}, 14 | # author={Mettes, Pascal and Snoek, Cees G M}, 15 | # journal={ICCV}, 16 | # year={2017} 17 | #} 18 | # 19 | 20 | import os 21 | import sys 22 | import h5py 23 | import numpy as np 24 | import argparse 25 | 26 | import helper 27 | 28 | from ConfigParser import SafeConfigParser 29 | 30 | # 31 | # Parse all arguments. 32 | # 33 | def parse_args(): 34 | parser = argparse.ArgumentParser(description="Detect local objects for a complete action dataset") 35 | parser.add_argument("-c", dest="configfile", help="Configuration file", default="config/ucf-sports.config", type=str) 36 | parser.add_argument("-b", dest="basedir", help="Directory where data from step 2 is stored", default="", type=str) 37 | parser.add_argument("-i", dest="iou_weight", help="Weight for the overlap score when linking boxes", default=1.0, type=float) 38 | parser.add_argument("-s", dest="stride", help="Stride between frames when computing scores (for interpolation)", default=1, type=int) 39 | parser.add_argument("-t", dest="nr_tubes", help="Number of tubes per action per video", default=5, type=int) 40 | args = parser.parse_args() 41 | return args 42 | 43 | # 44 | # Generate tubes by linking high scoring spatial-aware object embedding boxes. 45 | # 46 | class GenerateTubes(object): 47 | 48 | # 49 | # Initialize the class by settng the paths and loading the videos. 50 | # 51 | def __init__(self, configfile, basedir): 52 | # Parse the configurationfile. 53 | parser = SafeConfigParser() 54 | parser.read(configfile) 55 | self.dataset = configfile.split("/")[1].split(".")[0] 56 | 57 | # Yield the appropriate directories. 58 | self.gtdir = parser.get('actions', 'gtdir') 59 | self.domdir = parser.get('actions', 'domdir') 60 | self.scoredir = parser.get('actions', 'scoredir') 61 | self.tubedir = parser.get('actions', 'tubedir') 62 | self.framedir = parser.get('actions', 'framedir') 63 | self.wtvfile = parser.get('actions', 'wtvfile') 64 | self.split = parser.get('actions', 'testsplit') 65 | self.vidlist = parser.get('actions', 'vidfile') 66 | 67 | self.basedir = basedir 68 | 69 | # Load the split indices and set the training videos. 70 | self.teidxs = np.loadtxt(self.split, dtype=int) - 1 71 | self.videos = [line.strip().split()[0] for line in open(self.vidlist)] 72 | self.videos = np.array(self.videos) 73 | self.videos = self.videos[self.teidxs] 74 | 75 | # Yield action names. 76 | self.actions = np.array([vid.split("/")[0] for vid in self.videos]) 77 | self.actions, self.aidxs = np.unique(self.actions, return_inverse=True) 78 | 79 | # 80 | # Generate the tubes from scores. 81 | # 82 | def generate(self, nr_tubes, iou_weight, stride): 83 | # Do the scoring for each action for each video. 84 | for i in xrange(len(self.actions)): 85 | print "Action %d/%d: %s" %(i+1, len(self.actions), self.actions[i]) 86 | 87 | # Go through all videos. 88 | for j in xrange(len(self.videos)): 89 | print "\tVideo %d/%d\r" %(j+1, len(self.videos)), 90 | sys.stdout.flush() 91 | 92 | # Initialize the path wherr the tubes will be stored. 93 | bsplit = self.basedir.split("/") 94 | h5d = self.tubedir + "%s/%s/nrtubes-%d_iouw-%.2f/%s/%s/" \ 95 | %(bsplit[-3], bsplit[-2], nr_tubes, iou_weight, \ 96 | self.actions[i], self.videos[j]) 97 | h5f = h5d + "/tubes.hdf5" 98 | if os.path.exists(h5f): 99 | continue 100 | if not os.path.exists(h5d): 101 | os.makedirs(h5d) 102 | 103 | # Yield frames. 104 | bdir = self.basedir + self.actions[i] + "/" + self.videos[j] 105 | frames = sorted(os.listdir(bdir)) 106 | framescores = [] 107 | frameboxes = [] 108 | print bdir 109 | 110 | # Number of frames (for interpolation). 111 | if self.dataset == "h2t": 112 | flist = os.listdir(self.framedir + self.videos[j].split("/")[1]) 113 | nrframes = len(flist) 114 | elif self.dataset == "ucf-101": 115 | flist = os.listdir(self.framedir + self.videos[j].split("/")[1][:-4]) 116 | nrframes = len(flist) 117 | else: 118 | nrframes = len(frames) 119 | 120 | # Go over all frames. 121 | for k in xrange(len(frames)): 122 | framedata = np.load(bdir + "/" + frames[k]) 123 | framescores.append(framedata['scores']) 124 | frameboxes.append(framedata['boxes']) 125 | 126 | # Open the file where to save the tubes. 127 | h5f = h5py.File(h5f, "w") 128 | 129 | # Generate the tubes. 130 | for k in xrange(nr_tubes): 131 | ds, di = [], [] 132 | print k, nr_tubes 133 | 134 | # Initialize. 135 | for l in xrange(len(frameboxes)): 136 | frsize = frameboxes[l].shape[0] 137 | ds.append(np.zeros(frsize)) 138 | di.append(np.zeros(frsize, dtype=int)) 139 | 140 | # Viterbi. 141 | for l in xrange(len(frameboxes)-2, -1, -1): 142 | cscores = helper.viterbi_scores(frameboxes[l], \ 143 | framescores[l], frameboxes[l+1], \ 144 | framescores[l+1], iou_weight) 145 | cscores += ds[l+1] 146 | ds[l] = np.max(cscores, 1) 147 | di[l] = np.argmax(cscores, 1) 148 | 149 | # Start from frame 0. 150 | sf, si = 0, np.argmax(ds[0]) 151 | tube = [frameboxes[sf][si]] 152 | tscore = [framescores[sf][si]] 153 | tubeidxs = [sf*stride] 154 | idx = si 155 | framescores[sf][si] = 0 156 | 157 | # Connect. 158 | for l in xrange(sf, len(frameboxes)-1): 159 | idx = di[l][idx] 160 | tube.append(frameboxes[l+1][idx]) 161 | tscore.append(framescores[l+1][idx]) 162 | tubeidxs.append((l+1)*stride) 163 | framescores[l+1][idx] = 0 164 | 165 | # Numpy format and save. 166 | tube = np.vstack(tube) 167 | tscore = np.array(tscore) 168 | tubeidxs = np.array(tubeidxs) 169 | tube = np.hstack((tubeidxs[:,np.newaxis], tube)) 170 | 171 | # Interpolation. 172 | if stride > 1: 173 | tube, tscore = helper.tube_interpolate(tube, tscore, stride, nrframes) 174 | 175 | d1 = h5f.create_dataset(str(k), tube.shape) 176 | d1[:,:] = tube.copy() 177 | d2 = h5f.create_dataset("scores-%d" %(k), tscore.shape) 178 | d2[:] = tscore.copy() 179 | 180 | # Close the file. 181 | h5f.close() 182 | print 183 | 184 | # 185 | # Entry point of the script. 186 | # 187 | if __name__ == "__main__": 188 | # User parameters. 189 | args = parse_args() 190 | 191 | # Specify which dataset to use. 192 | configfile = args.configfile 193 | 194 | # Hyperparameters. 195 | nr_tubes = args.nr_tubes 196 | basedir = args.basedir 197 | iou_weight = args.iou_weight 198 | stride = args.stride 199 | 200 | tg = GenerateTubes(args.configfile, args.basedir) 201 | tg.generate(args.nr_tubes, args.iou_weight, args.stride) 202 | -------------------------------------------------------------------------------- /local_score.py: -------------------------------------------------------------------------------- 1 | # 2 | # Score the boxes based on person detection and interaction with dominant 3 | # objects. 4 | # 5 | # This is step 2 towards zero-shot action localization with spatial-aware 6 | # object embeddings. 7 | # 8 | # Pascal Mettes (2017). 9 | # 10 | # Please cite accordingly when using this code: 11 | # 12 | # @article{mettes2017spatial, 13 | # title={Spatial-Aware Object Embeddings for Zero-Shot Localization and 14 | # Classification of Actions}, 15 | # author={Mettes, Pascal and Snoek, Cees G M}, 16 | # journal={ICCV}, 17 | # year={2017} 18 | #} 19 | # 20 | 21 | import os 22 | import sys 23 | import h5py 24 | import numpy as np 25 | import argparse 26 | 27 | import _init_paths 28 | from fast_rcnn.nms_wrapper import nms 29 | 30 | import helper 31 | import word_to_vec 32 | 33 | from ConfigParser import SafeConfigParser 34 | 35 | # 36 | # Parse all arguments. 37 | # 38 | def parse_args(): 39 | parser = argparse.ArgumentParser(description="Detect local objects for a complete action dataset") 40 | parser.add_argument("-c", dest="configfile", help="Configuration file", default="config/ucf-sports.config", type=str) 41 | parser.add_argument("-t", dest="topn", help="Number of objects to used per action", default=1, type=int) 42 | parser.add_argument("-d", dest="bdist", help="Maximum allowed distance between actors and objects (border distance in pixels)", default=25, type=int) 43 | parser.add_argument("-n", dest="nmst", help="Threshold for non-maximum suppression to score less boxes", default=0.5, type=int) 44 | parser.add_argument("-w", dest="ow", help="Relative weight of the object score compared to actor scores", default=1.0, type=float) 45 | args = parser.parse_args() 46 | return args 47 | 48 | # 49 | # Reweight scores based on person scores and relations to objects. 50 | # 51 | def frame_scores(pdata, odata, priors, oweights, ow, bdist): 52 | scores = pdata[:,-1].copy() 53 | for i in xrange(pdata.shape[0]): 54 | for j in xrange(len(odata)): 55 | bs, bi = -1, -1 56 | for k in xrange(odata[j].shape[0]): 57 | if odata[j][k,-1] > bs and helper.box_dist(pdata[i,:4], \ 58 | odata[j][k,:4]) <= bdist: 59 | bs, bi = odata[j][k,-1], k 60 | distribution = helper.tiledist(odata[j][bi,:4], pdata[i,:4]) 61 | scores[i] += ow * bs * oweights[j] * (1 - \ 62 | helper.jensen_shannon_divergence(priors[j], distribution)) 63 | return scores 64 | 65 | # 66 | # Main object for scoring boxes in video frames based on actors, objects, 67 | # and spatial relations between them. 68 | # 69 | class ScoreBoxes(object): 70 | 71 | # 72 | # Initialize the class by reading the configfile, yielding the videos, 73 | # and loading the word2vec data + spatial relations. 74 | # 75 | def __init__(self, configfile): 76 | # Parse the configurationfile. 77 | parser = SafeConfigParser() 78 | parser.read(configfile) 79 | self.dataset = configfile.split("/")[1].split(".")[0] 80 | 81 | # Yield the appropriate directories. 82 | self.domdir = parser.get('actions', 'domdir') 83 | self.scoredir = parser.get('actions', 'scoredir') 84 | self.wtvfile = parser.get('actions', 'wtvfile') 85 | self.split = parser.get('actions', 'testsplit') 86 | self.vidlist = parser.get('actions', 'vidfile') 87 | 88 | # Load the split indices and set the training videos. 89 | self.teidxs = np.loadtxt(self.split, dtype=int) - 1 90 | self.videos = [line.strip().split()[0] for line in open(self.vidlist)] 91 | self.videos = np.array(self.videos) 92 | self.videos = self.videos[self.teidxs] 93 | 94 | # Yield action names. 95 | self.actions = np.array([vid.split("/")[0] for vid in self.videos]) 96 | self.actions, self.aidxs = np.unique(self.actions, return_inverse=True) 97 | 98 | # Load the wordtovec file for the actions. 99 | self.wtva = np.load(self.wtvfile) 100 | 101 | # Actor indices (box coordinates and scores). 102 | self.pidxs = np.array([85,86,87,88,1]) 103 | 104 | # Load the prior spatial distributions from file. 105 | self.priors = np.load("data/mscoco/mscoco_priors_new.npy") 106 | self.priors = self.priors.reshape(-1)[0] 107 | 108 | # 109 | # For each action in the dataset, find the top n most relevant objects. 110 | # Relevancy is determined through word2vec. 111 | # 112 | def select_local_objects(self, topn): 113 | # Store for logging purposes. 114 | self.topn = topn 115 | 116 | # MS-COCO class names and vectors. 117 | self.objectnames = open("data/mscoco/mscoco_classes.txt").readlines() 118 | self.objectnames = np.array([on.strip() for on in self.objectnames]) 119 | wtvo = np.load("data/mscoco/mscoco-wtv.npy") 120 | 121 | # Find the top dominant objects per action. 122 | self.action_to_object_i = [] 123 | self.action_to_object_w = [] 124 | for i in xrange(len(self.actions)): 125 | ai, aw = word_to_vec.topn(self.wtva[i], wtvo, topn) 126 | self.action_to_object_i.append(ai+1) 127 | self.action_to_object_w.append(aw) 128 | 129 | # 130 | # Perform the actual scoring in here. 131 | # Score each box in each video frame. 132 | # 133 | def score(self, bdist, nmst, ow): 134 | # Do the scoring for each action for each video. 135 | for i in xrange(len(self.actions)): 136 | print "Action %d/%d: %s" %(i+1, len(self.actions), self.actions[i]) 137 | 138 | # Yield the object indices for the score and box extraction. 139 | oidxs = [] 140 | for j in xrange(len(self.action_to_object_i[i])): 141 | oidxs.append(list(range(self.action_to_object_i[i][j]*4+81, \ 142 | (self.action_to_object_i[i][j]+1)*4+81)) + \ 143 | [self.action_to_object_i[i][j]]) 144 | 145 | # Yield the corresponding priors (spatial relations). 146 | opriors = [] 147 | for j in xrange(len(self.action_to_object_i[i])): 148 | opriors.append(self.priors[self.objectnames[self.action_to_object_i[i][j]-1]]) 149 | 150 | # Go over all videos. 151 | for j in xrange(len(self.videos)): 152 | print "\tVideo %d/%d\r" %(j+1, len(self.videos)), 153 | sys.stdout.flush() 154 | print 155 | 156 | # Generate result directory. 157 | rdir = self.scoredir + "topn-%d/ow-%.2f_bdist-%d_nms-%.3f/%s/%s/" \ 158 | %(self.topn, ow, bdist, nmst, self.actions[i], self.videos[j]) 159 | if not os.path.exists(rdir): 160 | os.makedirs(rdir) 161 | 162 | # Yield frames. 163 | if self.dataset == "ucf-sports": 164 | ddir = self.domdir + self.videos[j] 165 | elif self.dataset == "j-hmdb": 166 | ddir = self.domdir + self.videos[j] + ".avi" 167 | elif self.dataset == "h2t": 168 | ddir = self.domdir + self.videos[j].split("/")[1] 169 | elif self.dataset == "ucf-101": 170 | ddir = self.domdir + self.videos[j].split("/")[1][:-4] 171 | frames = sorted(os.listdir(ddir)) 172 | 173 | # Go over all frames. 174 | for k in xrange(len(frames)): 175 | # Skip done work. 176 | if os.path.exists(rdir + "frame-%05d-data.npz" %(k)): 177 | continue 178 | 179 | # Load the boxes and scores. 180 | framedata = np.load(ddir + "/" + frames[k]) 181 | 182 | # Person boxes and scores. 183 | persondata = framedata[:,self.pidxs] 184 | # Reduce computational effort with non-maximum suppression. 185 | pkeep = nms(persondata, nmst) 186 | persondata = persondata[pkeep,:] 187 | 188 | # Compute scores. 189 | if self.topn == 0: 190 | # Only use actor (baseline in the paper). 191 | newscores = persondata[:,-1].copy() 192 | else: 193 | # Object boxes and scores. 194 | objectdata = [] 195 | for l in xrange(len(oidxs)): 196 | od = framedata[:,oidxs[l]] 197 | okeep = nms(od, nmst) 198 | od = od[okeep,:] 199 | objectdata.append(od) 200 | 201 | # Score per box. 202 | newscores = frame_scores(persondata, objectdata, \ 203 | opriors, self.action_to_object_w[i], ow, bdist) 204 | 205 | # Store the indices, boxes, and scores. 206 | np.savez(rdir + "frame-%05d-data.npz" %(k), boxes=persondata[:,:4], \ 207 | boxidxs=pkeep, scores=newscores) 208 | print 209 | # 210 | # Entry point of the script. 211 | # 212 | if __name__ == "__main__": 213 | # User parameters. 214 | args = parse_args() 215 | 216 | # Specify which dataset to use. 217 | configfile = args.configfile 218 | 219 | # Hyperparameters. 220 | topn = args.topn 221 | bdist = args.bdist 222 | nmst = args.nmst 223 | ow = args.ow 224 | 225 | # Initialize and run the box scoring. 226 | boxmodel = ScoreBoxes(configfile) 227 | boxmodel.select_local_objects(topn) 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/visualize/visualize_video_tube.py: -------------------------------------------------------------------------------- 1 | # 2 | # This script visualizes a set of 3D tubes through a video. 3 | # 4 | # Written by: Pascal Mettes. 5 | # Original code: Jan van Gemert. 6 | # 7 | # 2017 8 | # 9 | 10 | import os 11 | import sys 12 | import glob 13 | import h5py 14 | from PIL import Image 15 | import numpy as np 16 | import mayavi 17 | from mayavi import mlab 18 | from tvtk.api import tvtk 19 | from tvtk.common import configure_input 20 | 21 | # 22 | # Load a set of figures from a directory. 23 | # 24 | # Input: 25 | # framedir (str) - Directory containing the frames. 26 | # ext (str) - Frame file extension. 27 | # 28 | # Output: 29 | # 4D tensor (nrf, nry, nrx, nrc). 30 | # 31 | def load_frames(framedir, ext="jpg"): 32 | # Load the sort the frames. 33 | frames = os.path.join(framedir, "*." + ext) 34 | frames = glob.glob(frames) 35 | frames.sort() 36 | 37 | # Load frame info. 38 | data = [] 39 | for i in xrange(len(frames)): 40 | frame = np.array(Image.open(frames[i])) 41 | data.append(frame) 42 | data = np.array(data) 43 | 44 | return data 45 | 46 | # 47 | # Convert frame data to an actor for 3D visualization. 48 | # 49 | # Input: 50 | # frame (numpy ndarray) - Frame data. 51 | # width (int) - Width. 52 | # height (int) - Height. 53 | # b (int) - Border. 54 | # 55 | # Output: 56 | # Tvtk actor of the frame. 57 | # 58 | def frame_to_actor(frame, width, height, b=5): 59 | # Dimensions with borders. 60 | w, h = width+2*b, height+2*b 61 | 62 | # Initialize array. 63 | data = np.zeros((w, h, 4)) 64 | if frame is None: 65 | data[:,:,-1] = 255 66 | data[b:-b,b:-b,-1] = 0 67 | else: 68 | data[b:-b,b:-b,:3] = frame 69 | data[:,:,-1] = 255 70 | data = np.transpose(data, (1,0,2)) 71 | colors = np.reshape(data, (w*h,4)) 72 | 73 | # Create actor. 74 | image = tvtk.ImageData() 75 | image.point_data.scalars=tvtk.UnsignedCharArray() 76 | image.point_data.scalars.from_array(colors) 77 | image.dimensions = np.array((w, h, 1)) 78 | actor = tvtk.ImageActor() 79 | configure_input(actor, image) 80 | 81 | return actor 82 | 83 | # 84 | # Visualize an individual tube through the video. 85 | # 86 | # Input: 87 | # tube (numpy ndarray) - Tube content. 88 | # color (rgb tuple) - Tube color. 89 | # width int) - Width. 90 | # height (int) - Height. 91 | # nr_frames (int) - Number of frames in whole video. 92 | # extent (float) - Temporal scale factor. 93 | # b (int) - Border. 94 | # 95 | # Output: 96 | # - 97 | # 98 | def visualize_tube(tube, color, width, height, nr_frames, extent, b=5): 99 | # Fill a volume with tube data. 100 | cube = np.zeros((width, height, nr_frames)) 101 | for i in xrange(tube.shape[0]): 102 | fidx = int(nr_frames - tube[i,0] - 1) 103 | box = tube[i,1:] + b 104 | box = box.astype(int) 105 | xmin, ymin, xmax, ymax = box 106 | cube[ymin:ymax,xmin:xmax, fidx] = 1.0 107 | 108 | # Create scalar field. 109 | field = mlab.pipeline.scalar_field(cube) 110 | # Re-adjust temporal scale. 111 | field.spacing = [1,1,extent] 112 | field.update_image_data = True 113 | 114 | # Plot the field. 115 | iso = mlab.pipeline.iso_surface(field, opacity=0.4, color=color) 116 | iso.scene.anti_aliasing_frames = 1 117 | iso.contour.number_of_contours = 2 118 | 119 | # 120 | # Visualize the 3D scene of a video and a set of colored tubes. 121 | # 122 | # Input: 123 | # frames (nrf, nrx, nry, nrc) - Frame data. 124 | # tubes (list) - List of tubes. 125 | # colors (list) - Color per tube. 126 | # extent (float) - Temporal scale. 127 | # nr_middle_frames (int) - Number of frames in the middle (border only). 128 | # 129 | # Output: 130 | # - 131 | # 132 | def visualize_scene(frames, tubes, colors, extent, nr_middle_frames=0): 133 | # Create the figure. 134 | figure = mlab.figure(fgcolor=(0,0,0), bgcolor=(1,1,1), size=(1600,900)) 135 | figure.scene.anti_aliasing_frames = 1 136 | 137 | # Retrieve frame dimensions. 138 | [nrf, nrx, nry, nrc] = frames.shape 139 | 140 | # Set camera position. 141 | figure.scene.z_plus_view() 142 | mayavi.mlab.roll(-90) 143 | figure.scene.parallel_projection = True 144 | figure.scene.camera.azimuth(25) 145 | figure.scene.camera.elevation(30) 146 | figure.scene.camera.zoom(1.5) 147 | 148 | # Show end frame. 149 | endframe = frame_to_actor(frames[-1], nrx, nry) 150 | endframe.position = [0,0,0] 151 | figure.scene.add_actor(endframe) 152 | 153 | # Show optionaly intermediate frame positions with only black borders. 154 | fs = nrf*extent / (nr_middle_frames+1) 155 | for i in xrange(nr_middle_frames): 156 | frame = frame_to_actor(None, nrx, nry) 157 | frame.position = [0,0,fs*(i+1)] 158 | figure.scene.add_actor(frame) 159 | 160 | # Show tubes. 161 | for i in xrange(len(tubes)): 162 | visualize_tube(tubes[i], colors[i], nrx, nry, nrf, extent) 163 | 164 | # Show start frame. 165 | startframe = frame_to_actor(frames[0], nrx, nry) 166 | startframe.position = [0,0,nrf*extent] 167 | figure.scene.add_actor(startframe) 168 | 169 | # Show the result and write it to file. 170 | mlab.show() 171 | 172 | # 173 | # Run an example visualization when running the script directly. 174 | # 175 | if __name__ == "__main__": 176 | # Directory with frames. 177 | framedir = "example/frames/" 178 | # Load a tube. 179 | tubes = [np.array(h5py.File("example/tube.hdf5", "r")["0"])] 180 | # Color of the tube. 181 | colors = [(1,0,0)] 182 | # Temporal extent of the video. 183 | extent = 30 184 | # Number of support frames in the middle. 185 | nr_middle_frames = 1 186 | 187 | # Load the frames. 188 | frames = load_frames(framedir) 189 | 190 | # Visualize. 191 | visualize_scene(frames, tubes, colors, extent, nr_middle_frames) 192 | -------------------------------------------------------------------------------- /word_to_vec.py: -------------------------------------------------------------------------------- 1 | # 2 | # Word2Vec vectors to distances. 3 | # 4 | # This file contains helper functions for selecting relevant objects from 5 | # arbitrary actions. 6 | # 7 | 8 | import numpy as np 9 | from scipy.spatial.distance import cosine 10 | 11 | # 12 | # Normalize word2vec vectors. 13 | # 14 | def normalize(a): 15 | for i in xrange(a.shape[0]): 16 | a[i] /= np.linalg.norm(a[i]) 17 | return a 18 | 19 | # 20 | # Cosine similarity between vectors. 21 | # 22 | def cos_sim(a, b): 23 | return 1 - cosine(a, b) 24 | 25 | # 26 | # Select top n vectors from b given a vector a. 27 | # 28 | def topn(a, b, n): 29 | cs = np.zeros(b.shape[0]) 30 | for i in xrange(b.shape[0]): 31 | cs[i] = cos_sim(a, b[i]) 32 | co = np.argsort(cs)[::-1] 33 | ci = co[:n] 34 | cw = cs[co[:n]] 35 | # Normalize the scores to add to one per action, not used in paper. 36 | #if len(cw) > 0: 37 | # cw /= np.sum(cw) 38 | return ci, cw 39 | --------------------------------------------------------------------------------