pan> 
+        video_path = args.video_path
140 
+        if args.verbose:
141 
+            print('Opening '+video_path+" .... ")
142 
+        start_video(video_path)
143 
+    if image:
144 
+        image_path = args.image_path
145 
+        if args.verbose:
146 
+            print("Opening "+image_path+" .... ")
147 
+        image_detect(image_path)
148 
+
149 
+
150 
+    cv2.destroyAllWindows()

+ 182 - 0
yolov3-tiny.cfg
Bestand weergeven 

@@ -0,0 +1,182 @@
1 
+[net]
2 
+# Testing
3 
+batch=1
4 
+subdivisions=1
5 
+# Training
6 
+# batch=64
7 
+# subdivisions=2
8 
+width=416
9 
+height=416
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=16
28 
+size=3
29 
+stride=1
30 
+pad=1
31 
+activation=leaky
32 
+
33 
+[maxpool]
34 
+size=2
35 
+stride=2
36 
+
37 
+[convolutional]
38 
+batch_normalize=1
39 
+filters=32
40 
+size=3
41 
+stride=1
42 
+pad=1
43 
+activation=leaky
44 
+
45 
+[maxpool]
46 
+size=2
47 
+stride=2
48 
+
49 
+[convolutional]
50 
+batch_normalize=1
51 
+filters=64
52 
+size=3
53 
+stride=1
54 
+pad=1
55 
+activation=leaky
56 
+
57 
+[maxpool]
58 
+size=2
59 
+stride=2
60 
+
61 
+[convolutional]
62 
+batch_normalize=1
63 
+filters=128
64 
+size=3
65 
+stride=1
66 
+pad=1
67 
+activation=leaky
68 
+
69 
+[maxpool]
70 
+size=2
71 
+stride=2
72 
+
73 
+[convolutional]
74 
+batch_normalize=1
75 
+filters=256
76 
+size=3
77 
+stride=1
78 
+pad=1
79 
+activation=leaky
80 
+
81 
+[maxpool]
82 
+size=2
83 
+stride=2
84 
+
85 
+[convolutional]
86 
+batch_normalize=1
87 
+filters=512
88 
+size=3
89 
+stride=1
90 
+pad=1
91 
+activation=leaky
92 
+
93 
+[maxpool]
94 
+size=2
95 
+stride=1
96 
+
97 
+[convolutional]
98 
+batch_normalize=1
99 
+filters=1024
100 
+size=3
101 
+stride=1
102 
+pad=1
103 
+activation=leaky
104 
+
105 
+###########
106 
+
107 
+[convolutional]
108 
+batch_normalize=1
109 
+filters=256
110 
+size=1
111 
+stride=1
112 
+pad=1
113 
+activation=leaky
114 
+
115 
+[convolutional]
116 
+batch_normalize=1
117 
+filters=512
118 
+size=3
119 
+stride=1
120 
+pad=1
121 
+activation=leaky
122 
+
123 
+[convolutional]
124 
+size=1
125 
+stride=1
126 
+pad=1
127 
+filters=255
128 
+activation=linear
129 
+
130 
+
131 
+
132 
+[yolo]
133 
+mask = 3,4,5
134 
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
135 
+classes=80
136 
+num=6
137 
+jitter=.3
138 
+ignore_thresh = .7
139 
+truth_thresh = 1
140 
+random=1
141 
+
142 
+[route]
143 
+layers = -4
144 
+
145 
+[convolutional]
146 
+batch_normalize=1
147 
+filters=128
148 
+size=1
149 
+stride=1
150 
+pad=1
151 
+activation=leaky
152 
+
153 
+[upsample]
154 
+stride=2
155 
+
156 
+[route]
157 
+layers = -1, 8
158 
+
159 
+[convolutional]
160 
+batch_normalize=1
161 
+filters=256
162 
+size=3
163 
+stride=1
164 
+pad=1
165 
+activation=leaky
166 
+
167 
+[convolutional]
168 
+size=1
169 
+stride=1
170 
+pad=1
171 
+filters=255
172 
+activation=linear
173 
+
174 
+[yolo]
175 
+mask = 0,1,2
176 
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
177 
+classes=80
178 
+num=6
179 
+jitter=.3
180 
+ignore_thresh = .7
181 
+truth_thresh = 1
182 
+random=1

+ 788 - 0
yolov3.cfg
Bestand weergeven 

@@ -0,0 +1,788 @@
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 
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+random=1

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  1. MIT License
    2. 

  2. 

  3. Copyright (c) Nicolas Gallagher
    4. 

  4. Copyright (c) Jonathan Neal
    5. 

  5. Copyright (c) Sindre Sorhus <sindresorhus@gmail.com> (sindresorhus.com)
    6. 

  6. Copyright (c) Adam Wathan
    7. 

  7. Copyright (c) Jonathan Reinink
    8. 

  8. 

  9. Permission is hereby granted, free of charge, to any person obtaining a copy
    10. 

  10. of this software and associated documentation files (the "Software"), to deal
    11. 

  11. in the Software without restriction, including without limitation the rights
    12. 

  12. to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    13. 

  13. copies of the Software, and to permit persons to whom the Software is
    14. 

  14. furnished to do so, subject to the following conditions:
    15. 

  15. 

  16. The above copyright notice and this permission notice shall be included in all
    17. 

  17. copies or substantial portions of the Software.
    18. 

  18. 

  19. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    20. 

  20. IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    21. 

  21. FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    22. 

  22. AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    23. 

  23. LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    24. 

  24. OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    25. 

  25. SOFTWARE.
    26. 

  26.