├── .gitignore ├── LICENSE ├── README.md ├── cfg └── yolov3.cfg ├── coco.names ├── images ├── dog.jpg ├── eagle.jpg ├── giraffe.jpg ├── horses.jpg ├── kite.jpg ├── person.jpg └── scream.jpg ├── yolo_detection_images.py ├── yolo_detection_video.py └── yolo_detection_webcam.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.weights 2 | *.mp4 3 | *.avi 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # YOLO-v3-Object-Detection 2 | YOLO - You Only Look Once 3 | This repository has the code for YOLO v3 Object detection, and is capable of fast object detection. Input can be given through images, videos and webcam input feed. 4 | 5 | View a demo of the project at: https://www.youtube.com/watch?v=iT1yJTk77CQ 6 | -------------------------------------------------------------------------------- /cfg/yolov3.cfg: -------------------------------------------------------------------------------- 1 | [net] 2 | # Testing 3 | # batch=1 4 | # subdivisions=1 5 | # Training 6 | batch=64 7 | subdivisions=16 8 | width=608 9 | height=608 10 | channels=3 11 | momentum=0.9 12 | decay=0.0005 13 | angle=0 14 | saturation = 1.5 15 | exposure = 1.5 16 | hue=.1 17 | 18 | learning_rate=0.001 19 | burn_in=1000 20 | max_batches = 500200 21 | policy=steps 22 | steps=400000,450000 23 | scales=.1,.1 24 | 25 | [convolutional] 26 | batch_normalize=1 27 | filters=32 28 | size=3 29 | stride=1 30 | pad=1 31 | activation=leaky 32 | 33 | # Downsample 34 | 35 | [convolutional] 36 | batch_normalize=1 37 | filters=64 38 | size=3 39 | stride=2 40 | pad=1 41 | activation=leaky 42 | 43 | [convolutional] 44 | batch_normalize=1 45 | filters=32 46 | size=1 47 | stride=1 48 | pad=1 49 | activation=leaky 50 | 51 | [convolutional] 52 | batch_normalize=1 53 | filters=64 54 | size=3 55 | stride=1 56 | pad=1 57 | activation=leaky 58 | 59 | [shortcut] 60 | from=-3 61 | activation=linear 62 | 63 | # Downsample 64 | 65 | [convolutional] 66 | batch_normalize=1 67 | filters=128 68 | size=3 69 | stride=2 70 | pad=1 71 | activation=leaky 72 | 73 | [convolutional] 74 | batch_normalize=1 75 | filters=64 76 | size=1 77 | stride=1 78 | pad=1 79 | activation=leaky 80 | 81 | [convolutional] 82 | batch_normalize=1 83 | filters=128 84 | size=3 85 | stride=1 86 | pad=1 87 | activation=leaky 88 | 89 | [shortcut] 90 | from=-3 91 | activation=linear 92 | 93 | [convolutional] 94 | batch_normalize=1 95 | filters=64 96 | size=1 97 | stride=1 98 | pad=1 99 | activation=leaky 100 | 101 | [convolutional] 102 | batch_normalize=1 103 | filters=128 104 | size=3 105 | stride=1 106 | pad=1 107 | activation=leaky 108 | 109 | [shortcut] 110 | from=-3 111 | activation=linear 112 | 113 | # Downsample 114 | 115 | [convolutional] 116 | batch_normalize=1 117 | filters=256 118 | size=3 119 | stride=2 120 | pad=1 121 | activation=leaky 122 | 123 | [convolutional] 124 | batch_normalize=1 125 | filters=128 126 | size=1 127 | stride=1 128 | pad=1 129 | activation=leaky 130 | 131 | [convolutional] 132 | batch_normalize=1 133 | filters=256 134 | size=3 135 | stride=1 136 | pad=1 137 | activation=leaky 138 | 139 | [shortcut] 140 | from=-3 141 | activation=linear 142 | 143 | [convolutional] 144 | batch_normalize=1 145 | filters=128 146 | size=1 147 | stride=1 148 | pad=1 149 | activation=leaky 150 | 151 | [convolutional] 152 | batch_normalize=1 153 | filters=256 154 | size=3 155 | stride=1 156 | pad=1 157 | activation=leaky 158 | 159 | [shortcut] 160 | from=-3 161 | activation=linear 162 | 163 | [convolutional] 164 | batch_normalize=1 165 | filters=128 166 | size=1 167 | stride=1 168 | pad=1 169 | activation=leaky 170 | 171 | [convolutional] 172 | batch_normalize=1 173 | filters=256 174 | size=3 175 | stride=1 176 | pad=1 177 | activation=leaky 178 | 179 | [shortcut] 180 | from=-3 181 | activation=linear 182 | 183 | [convolutional] 184 | batch_normalize=1 185 | filters=128 186 | size=1 187 | stride=1 188 | pad=1 189 | activation=leaky 190 | 191 | [convolutional] 192 | batch_normalize=1 193 | filters=256 194 | size=3 195 | stride=1 196 | pad=1 197 | activation=leaky 198 | 199 | [shortcut] 200 | from=-3 201 | activation=linear 202 | 203 | 204 | [convolutional] 205 | batch_normalize=1 206 | filters=128 207 | size=1 208 | stride=1 209 | pad=1 210 | activation=leaky 211 | 212 | [convolutional] 213 | batch_normalize=1 214 | filters=256 215 | size=3 216 | stride=1 217 | pad=1 218 | activation=leaky 219 | 220 | [shortcut] 221 | from=-3 222 | activation=linear 223 | 224 | [convolutional] 225 | batch_normalize=1 226 | filters=128 227 | size=1 228 | stride=1 229 | pad=1 230 | activation=leaky 231 | 232 | [convolutional] 233 | batch_normalize=1 234 | filters=256 235 | size=3 236 | stride=1 237 | pad=1 238 | activation=leaky 239 | 240 | [shortcut] 241 | from=-3 242 | activation=linear 243 | 244 | [convolutional] 245 | batch_normalize=1 246 | filters=128 247 | size=1 248 | stride=1 249 | pad=1 250 | activation=leaky 251 | 252 | [convolutional] 253 | batch_normalize=1 254 | filters=256 255 | size=3 256 | stride=1 257 | pad=1 258 | activation=leaky 259 | 260 | [shortcut] 261 | from=-3 262 | activation=linear 263 | 264 | [convolutional] 265 | batch_normalize=1 266 | filters=128 267 | size=1 268 | stride=1 269 | pad=1 270 | activation=leaky 271 | 272 | [convolutional] 273 | batch_normalize=1 274 | filters=256 275 | size=3 276 | stride=1 277 | pad=1 278 | activation=leaky 279 | 280 | [shortcut] 281 | from=-3 282 | activation=linear 283 | 284 | # Downsample 285 | 286 | [convolutional] 287 | batch_normalize=1 288 | filters=512 289 | size=3 290 | stride=2 291 | pad=1 292 | activation=leaky 293 | 294 | [convolutional] 295 | batch_normalize=1 296 | filters=256 297 | size=1 298 | stride=1 299 | pad=1 300 | activation=leaky 301 | 302 | [convolutional] 303 | batch_normalize=1 304 | filters=512 305 | size=3 306 | stride=1 307 | pad=1 308 | activation=leaky 309 | 310 | [shortcut] 311 | from=-3 312 | activation=linear 313 | 314 | 315 | [convolutional] 316 | batch_normalize=1 317 | filters=256 318 | size=1 319 | stride=1 320 | pad=1 321 | activation=leaky 322 | 323 | [convolutional] 324 | batch_normalize=1 325 | filters=512 326 | size=3 327 | stride=1 328 | pad=1 329 | activation=leaky 330 | 331 | [shortcut] 332 | from=-3 333 | activation=linear 334 | 335 | 336 | [convolutional] 337 | batch_normalize=1 338 | filters=256 339 | size=1 340 | stride=1 341 | pad=1 342 | activation=leaky 343 | 344 | [convolutional] 345 | batch_normalize=1 346 | filters=512 347 | size=3 348 | stride=1 349 | pad=1 350 | activation=leaky 351 | 352 | [shortcut] 353 | from=-3 354 | activation=linear 355 | 356 | 357 | [convolutional] 358 | batch_normalize=1 359 | filters=256 360 | size=1 361 | stride=1 362 | pad=1 363 | activation=leaky 364 | 365 | [convolutional] 366 | batch_normalize=1 367 | filters=512 368 | size=3 369 | stride=1 370 | pad=1 371 | activation=leaky 372 | 373 | [shortcut] 374 | from=-3 375 | activation=linear 376 | 377 | [convolutional] 378 | batch_normalize=1 379 | filters=256 380 | size=1 381 | stride=1 382 | pad=1 383 | activation=leaky 384 | 385 | [convolutional] 386 | batch_normalize=1 387 | filters=512 388 | size=3 389 | stride=1 390 | pad=1 391 | activation=leaky 392 | 393 | [shortcut] 394 | from=-3 395 | activation=linear 396 | 397 | 398 | [convolutional] 399 | batch_normalize=1 400 | filters=256 401 | size=1 402 | stride=1 403 | pad=1 404 | activation=leaky 405 | 406 | [convolutional] 407 | batch_normalize=1 408 | filters=512 409 | size=3 410 | stride=1 411 | pad=1 412 | activation=leaky 413 | 414 | [shortcut] 415 | from=-3 416 | activation=linear 417 | 418 | 419 | [convolutional] 420 | batch_normalize=1 421 | filters=256 422 | size=1 423 | stride=1 424 | pad=1 425 | activation=leaky 426 | 427 | [convolutional] 428 | batch_normalize=1 429 | filters=512 430 | size=3 431 | stride=1 432 | pad=1 433 | activation=leaky 434 | 435 | [shortcut] 436 | from=-3 437 | activation=linear 438 | 439 | [convolutional] 440 | batch_normalize=1 441 | filters=256 442 | size=1 443 | stride=1 444 | pad=1 445 | activation=leaky 446 | 447 | [convolutional] 448 | batch_normalize=1 449 | filters=512 450 | size=3 451 | stride=1 452 | pad=1 453 | activation=leaky 454 | 455 | [shortcut] 456 | from=-3 457 | activation=linear 458 | 459 | # Downsample 460 | 461 | [convolutional] 462 | batch_normalize=1 463 | filters=1024 464 | size=3 465 | stride=2 466 | pad=1 467 | activation=leaky 468 | 469 | [convolutional] 470 | batch_normalize=1 471 | filters=512 472 | size=1 473 | stride=1 474 | pad=1 475 | activation=leaky 476 | 477 | [convolutional] 478 | batch_normalize=1 479 | filters=1024 480 | size=3 481 | stride=1 482 | pad=1 483 | activation=leaky 484 | 485 | [shortcut] 486 | from=-3 487 | activation=linear 488 | 489 | [convolutional] 490 | batch_normalize=1 491 | filters=512 492 | size=1 493 | stride=1 494 | pad=1 495 | activation=leaky 496 | 497 | [convolutional] 498 | batch_normalize=1 499 | filters=1024 500 | size=3 501 | stride=1 502 | pad=1 503 | activation=leaky 504 | 505 | [shortcut] 506 | from=-3 507 | activation=linear 508 | 509 | [convolutional] 510 | batch_normalize=1 511 | filters=512 512 | size=1 513 | stride=1 514 | pad=1 515 | activation=leaky 516 | 517 | [convolutional] 518 | batch_normalize=1 519 | filters=1024 520 | size=3 521 | stride=1 522 | pad=1 523 | activation=leaky 524 | 525 | [shortcut] 526 | from=-3 527 | activation=linear 528 | 529 | [convolutional] 530 | batch_normalize=1 531 | filters=512 532 | size=1 533 | stride=1 534 | pad=1 535 | activation=leaky 536 | 537 | [convolutional] 538 | batch_normalize=1 539 | filters=1024 540 | size=3 541 | stride=1 542 | pad=1 543 | activation=leaky 544 | 545 | [shortcut] 546 | from=-3 547 | activation=linear 548 | 549 | ###################### 550 | 551 | [convolutional] 552 | batch_normalize=1 553 | filters=512 554 | size=1 555 | stride=1 556 | pad=1 557 | activation=leaky 558 | 559 | [convolutional] 560 | batch_normalize=1 561 | size=3 562 | stride=1 563 | pad=1 564 | filters=1024 565 | activation=leaky 566 | 567 | [convolutional] 568 | batch_normalize=1 569 | filters=512 570 | size=1 571 | stride=1 572 | pad=1 573 | activation=leaky 574 | 575 | [convolutional] 576 | batch_normalize=1 577 | size=3 578 | stride=1 579 | pad=1 580 | filters=1024 581 | activation=leaky 582 | 583 | [convolutional] 584 | batch_normalize=1 585 | filters=512 586 | size=1 587 | stride=1 588 | pad=1 589 | activation=leaky 590 | 591 | [convolutional] 592 | batch_normalize=1 593 | size=3 594 | stride=1 595 | pad=1 596 | filters=1024 597 | activation=leaky 598 | 599 | [convolutional] 600 | size=1 601 | stride=1 602 | pad=1 603 | filters=255 604 | activation=linear 605 | 606 | 607 | [yolo] 608 | mask = 6,7,8 609 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 610 | classes=80 611 | num=9 612 | jitter=.3 613 | ignore_thresh = .7 614 | truth_thresh = 1 615 | random=1 616 | 617 | 618 | [route] 619 | layers = -4 620 | 621 | [convolutional] 622 | batch_normalize=1 623 | filters=256 624 | size=1 625 | stride=1 626 | pad=1 627 | activation=leaky 628 | 629 | [upsample] 630 | stride=2 631 | 632 | [route] 633 | layers = -1, 61 634 | 635 | 636 | 637 | [convolutional] 638 | batch_normalize=1 639 | filters=256 640 | size=1 641 | stride=1 642 | pad=1 643 | activation=leaky 644 | 645 | [convolutional] 646 | batch_normalize=1 647 | size=3 648 | stride=1 649 | pad=1 650 | filters=512 651 | activation=leaky 652 | 653 | [convolutional] 654 | batch_normalize=1 655 | filters=256 656 | size=1 657 | stride=1 658 | pad=1 659 | activation=leaky 660 | 661 | [convolutional] 662 | batch_normalize=1 663 | size=3 664 | stride=1 665 | pad=1 666 | filters=512 667 | activation=leaky 668 | 669 | [convolutional] 670 | batch_normalize=1 671 | filters=256 672 | size=1 673 | stride=1 674 | pad=1 675 | activation=leaky 676 | 677 | [convolutional] 678 | batch_normalize=1 679 | size=3 680 | stride=1 681 | pad=1 682 | filters=512 683 | activation=leaky 684 | 685 | [convolutional] 686 | size=1 687 | stride=1 688 | pad=1 689 | filters=255 690 | activation=linear 691 | 692 | 693 | [yolo] 694 | mask = 3,4,5 695 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 696 | classes=80 697 | num=9 698 | jitter=.3 699 | ignore_thresh = .7 700 | truth_thresh = 1 701 | random=1 702 | 703 | 704 | 705 | [route] 706 | layers = -4 707 | 708 | [convolutional] 709 | batch_normalize=1 710 | filters=128 711 | size=1 712 | stride=1 713 | pad=1 714 | activation=leaky 715 | 716 | [upsample] 717 | stride=2 718 | 719 | [route] 720 | layers = -1, 36 721 | 722 | 723 | 724 | [convolutional] 725 | batch_normalize=1 726 | filters=128 727 | size=1 728 | stride=1 729 | pad=1 730 | activation=leaky 731 | 732 | [convolutional] 733 | batch_normalize=1 734 | size=3 735 | stride=1 736 | pad=1 737 | filters=256 738 | activation=leaky 739 | 740 | [convolutional] 741 | batch_normalize=1 742 | filters=128 743 | size=1 744 | stride=1 745 | pad=1 746 | activation=leaky 747 | 748 | [convolutional] 749 | batch_normalize=1 750 | size=3 751 | stride=1 752 | pad=1 753 | filters=256 754 | activation=leaky 755 | 756 | [convolutional] 757 | batch_normalize=1 758 | filters=128 759 | size=1 760 | stride=1 761 | pad=1 762 | activation=leaky 763 | 764 | [convolutional] 765 | batch_normalize=1 766 | size=3 767 | stride=1 768 | pad=1 769 | filters=256 770 | activation=leaky 771 | 772 | [convolutional] 773 | size=1 774 | stride=1 775 | pad=1 776 | filters=255 777 | activation=linear 778 | 779 | 780 | [yolo] 781 | mask = 0,1,2 782 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 783 | classes=80 784 | num=9 785 | jitter=.3 786 | ignore_thresh = .7 787 | truth_thresh = 1 788 | random=1 789 | 790 | -------------------------------------------------------------------------------- /coco.names: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorbike 5 | aeroplane 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 | sofa 59 | pottedplant 60 | bed 61 | diningtable 62 | toilet 63 | tvmonitor 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 | -------------------------------------------------------------------------------- /images/dog.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/msindev/YOLO-v3-Object-Detection/2bebf8d77c6244f4ec32337d5cbfac1fb56a317b/images/dog.jpg -------------------------------------------------------------------------------- /images/eagle.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/msindev/YOLO-v3-Object-Detection/2bebf8d77c6244f4ec32337d5cbfac1fb56a317b/images/eagle.jpg -------------------------------------------------------------------------------- /images/giraffe.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/msindev/YOLO-v3-Object-Detection/2bebf8d77c6244f4ec32337d5cbfac1fb56a317b/images/giraffe.jpg -------------------------------------------------------------------------------- /images/horses.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/msindev/YOLO-v3-Object-Detection/2bebf8d77c6244f4ec32337d5cbfac1fb56a317b/images/horses.jpg -------------------------------------------------------------------------------- /images/kite.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/msindev/YOLO-v3-Object-Detection/2bebf8d77c6244f4ec32337d5cbfac1fb56a317b/images/kite.jpg -------------------------------------------------------------------------------- /images/person.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/msindev/YOLO-v3-Object-Detection/2bebf8d77c6244f4ec32337d5cbfac1fb56a317b/images/person.jpg -------------------------------------------------------------------------------- /images/scream.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/msindev/YOLO-v3-Object-Detection/2bebf8d77c6244f4ec32337d5cbfac1fb56a317b/images/scream.jpg -------------------------------------------------------------------------------- /yolo_detection_images.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | 4 | confidenceThreshold = 0.5 5 | NMSThreshold = 0.3 6 | 7 | modelConfiguration = 'cfg/yolov3.cfg' 8 | modelWeights = 'yolov3.weights' 9 | 10 | labelsPath = 'coco.names' 11 | labels = open(labelsPath).read().strip().split('\n') 12 | 13 | np.random.seed(10) 14 | COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") 15 | 16 | net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights) 17 | 18 | image = cv2.imread('images/person.jpg') 19 | (H, W) = image.shape[:2] 20 | 21 | #Determine output layer names 22 | layerName = net.getLayerNames() 23 | layerName = [layerName[i[0] - 1] for i in net.getUnconnectedOutLayers()] 24 | 25 | blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB = True, crop = False) 26 | net.setInput(blob) 27 | layersOutputs = net.forward(layerName) 28 | 29 | boxes = [] 30 | confidences = [] 31 | classIDs = [] 32 | 33 | for output in layersOutputs: 34 | for detection in output: 35 | scores = detection[5:] 36 | classID = np.argmax(scores) 37 | confidence = scores[classID] 38 | if confidence > confidenceThreshold: 39 | box = detection[0:4] * np.array([W, H, W, H]) 40 | (centerX, centerY, width, height) = box.astype('int') 41 | x = int(centerX - (width/2)) 42 | y = int(centerY - (height/2)) 43 | 44 | boxes.append([x, y, int(width), int(height)]) 45 | confidences.append(float(confidence)) 46 | classIDs.append(classID) 47 | 48 | #Apply Non Maxima Suppression 49 | detectionNMS = cv2.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold) 50 | 51 | if(len(detectionNMS) > 0): 52 | for i in detectionNMS.flatten(): 53 | (x, y) = (boxes[i][0], boxes[i][1]) 54 | (w, h) = (boxes[i][2], boxes[i][3]) 55 | 56 | color = [int(c) for c in COLORS[classIDs[i]]] 57 | cv2.rectangle(image, (x, y), (x + w, y + h), color, 2) 58 | text = '{}: {:.4f}'.format(labels[classIDs[i]], confidences[i]) 59 | cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) 60 | 61 | cv2.imshow('Image', image) 62 | cv2.waitKey(0) 63 | -------------------------------------------------------------------------------- /yolo_detection_video.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | 4 | confidenceThreshold = 0.5 5 | NMSThreshold = 0.3 6 | 7 | modelConfiguration = 'cfg/yolov3.cfg' 8 | modelWeights = 'yolov3.weights' 9 | 10 | labelsPath = 'coco.names' 11 | labels = open(labelsPath).read().strip().split('\n') 12 | 13 | np.random.seed(10) 14 | COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") 15 | 16 | net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights) 17 | 18 | outputLayer = net.getLayerNames() 19 | outputLayer = [outputLayer[i[0] - 1] for i in net.getUnconnectedOutLayers()] 20 | 21 | video = cv2.VideoCapture('chase.mp4') 22 | writer = None 23 | (W, H) = (None, None) 24 | 25 | try: 26 | prop = cv2.CAP_PROP_FRAME_COUNT 27 | total = int(video.get(prop)) 28 | print("[INFO] {} total frames in video".format(total)) 29 | except: 30 | printf("Could not determine no. of frames in video") 31 | 32 | count = 0 33 | while True: 34 | (ret, frame) = video.read() 35 | if not ret: 36 | break 37 | if W is None or H is None: 38 | (H,W) = frame.shape[:2] 39 | 40 | blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB = True, crop = False) 41 | net.setInput(blob) 42 | layersOutputs = net.forward(outputLayer) 43 | 44 | boxes = [] 45 | confidences = [] 46 | classIDs = [] 47 | 48 | for output in layersOutputs: 49 | for detection in output: 50 | scores = detection[5:] 51 | classID = np.argmax(scores) 52 | confidence = scores[classID] 53 | if confidence > confidenceThreshold: 54 | box = detection[0:4] * np.array([W, H, W, H]) 55 | (centerX, centerY, width, height) = box.astype('int') 56 | x = int(centerX - (width/2)) 57 | y = int(centerY - (height/2)) 58 | 59 | boxes.append([x, y, int(width), int(height)]) 60 | confidences.append(float(confidence)) 61 | classIDs.append(classID) 62 | 63 | #Apply Non Maxima Suppression 64 | detectionNMS = cv2.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold) 65 | if(len(detectionNMS) > 0): 66 | for i in detectionNMS.flatten(): 67 | (x, y) = (boxes[i][0], boxes[i][1]) 68 | (w, h) = (boxes[i][2], boxes[i][3]) 69 | 70 | color = [int(c) for c in COLORS[classIDs[i]]] 71 | cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) 72 | text = '{}: {:.4f}'.format(labels[classIDs[i]], confidences[i]) 73 | cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) 74 | 75 | if writer is None: 76 | fourcc = cv2.VideoWriter_fourcc(*'MJPG') 77 | writer = cv2.VideoWriter('chase_output.avi', fourcc, 30, (frame.shape[1], frame.shape[0]), True) 78 | if writer is not None: 79 | writer.write(frame) 80 | print("Writing frame" , count+1) 81 | count = count + 1 82 | 83 | writer.release() 84 | video.release() 85 | -------------------------------------------------------------------------------- /yolo_detection_webcam.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | 4 | confidenceThreshold = 0.5 5 | NMSThreshold = 0.3 6 | 7 | modelConfiguration = 'cfg/yolov3.cfg' 8 | modelWeights = 'yolov3.weights' 9 | 10 | labelsPath = 'coco.names' 11 | labels = open(labelsPath).read().strip().split('\n') 12 | 13 | np.random.seed(10) 14 | COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") 15 | 16 | net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights) 17 | 18 | outputLayer = net.getLayerNames() 19 | outputLayer = [outputLayer[i[0] - 1] for i in net.getUnconnectedOutLayers()] 20 | 21 | video_capture = cv2.VideoCapture(0) 22 | 23 | (W, H) = (None, None) 24 | 25 | while True: 26 | ret, frame = video_capture.read() 27 | frame = cv2.flip(frame, 1) 28 | if W is None or H is None: 29 | (H,W) = frame.shape[:2] 30 | 31 | blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB = True, crop = False) 32 | net.setInput(blob) 33 | layersOutputs = net.forward(outputLayer) 34 | 35 | boxes = [] 36 | confidences = [] 37 | classIDs = [] 38 | 39 | for output in layersOutputs: 40 | for detection in output: 41 | scores = detection[5:] 42 | classID = np.argmax(scores) 43 | confidence = scores[classID] 44 | if confidence > confidenceThreshold: 45 | box = detection[0:4] * np.array([W, H, W, H]) 46 | (centerX, centerY, width, height) = box.astype('int') 47 | x = int(centerX - (width/2)) 48 | y = int(centerY - (height/2)) 49 | 50 | boxes.append([x, y, int(width), int(height)]) 51 | confidences.append(float(confidence)) 52 | classIDs.append(classID) 53 | 54 | #Apply Non Maxima Suppression 55 | detectionNMS = cv2.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold) 56 | if(len(detectionNMS) > 0): 57 | for i in detectionNMS.flatten(): 58 | (x, y) = (boxes[i][0], boxes[i][1]) 59 | (w, h) = (boxes[i][2], boxes[i][3]) 60 | 61 | color = [int(c) for c in COLORS[classIDs[i]]] 62 | cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) 63 | text = '{}: {:.4f}'.format(labels[classIDs[i]], confidences[i]) 64 | cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) 65 | 66 | cv2.imshow('Output', frame) 67 | if(cv.waitKey(1) & 0xFF == ord('q')): 68 | break 69 | 70 | #Finally when video capture is over, release the video capture and destroyAllWindows 71 | video_capture.release() 72 | cv2.destroyAllWindows() 73 | --------------------------------------------------------------------------------