f video_play: 139 
+        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
Целия файл 

@@ -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
Целия файл 

@@ -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 
+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

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latest

Tum 2 年之前
父節點
48cbdf015a 3ec06af091
當前提交
967b4432ea
共有 4 個文件被更改，包括 64 次插入 和 134 次删除
分割檢視 顯示文件統計
  1. 二進制
      app/Output/excel_out_test_excel_formatter_update.xlsx
  2. 34 132
      app/backend/templates/backend/index.html
  3. 8 2
      app/backend/views.py
  4. 22 0
      app/exfo/lib.py

二進制
app/Output/excel_out_test_excel_formatter_update.xlsx
查看文件


+ 34 - 132
app/backend/templates/backend/index.html
查看文件 

@@ -1,15 +1,16 @@
1 1 
 {% extends "base.html" %}
2 2 
 {% load backend_tags %}
3 3 
 {% block content %}
4 
-<h2>APIs</h2>
4 
+<h2>Exfo APIs</h2>
5 5 
       <div class='row row-cols-md-5 row-cols-2'>
6 6 
       {% for r in output.results %}
7 7 
       <div class='p-3 border text-center'><a href="/backend/service_status?cmd={{r.URI}}/v1&section=index">{{ r.display_name }}</a></div>
8 8 
       {% endfor %}
9 9 
       </div>
10 
+      <!--
10 11 
       <pre>
11 12 
       {{ output | pprint }}
12 
-      </pre>
13 
+      </pre> -->
13 14 
       <h1>SLA</h1>
14 15
15 16 
       {% for x  in sla.result %}
 
@@ -28,6 +29,37 @@
28 29 
           </ul>
29 30 
         {% endfor %}
30 31 
       {% endfor %}
32 
+      <hr>
33 
+<h2>Mikrotik APIs</h2>
34 
+<!--
35 
+<pre>
36 
+{{ mk_ips | pprint }}
37 
+</pre> -->
38 
+<h5 class='text-primary'>IP/ROUTE</h5>
39 
+{% for ip in mk_ips %}
40 
+<h4>{{ ip.gateway }}</h4>
41 
+  <div class='d-flex flex-row justify-content-start  flex-wrap align-content-stretch mb-5'>
42 
+    {% for k,v in ip.items %}
43 
+      <div class='border p-3 fw-bolder'>{{ k }}</div><div class='border p-3'>{{ v }}</div>
44 
+    {% endfor %}
45 
+  </div>
46 
+
47 
+{% endfor %}
48 
+
49 
+<h5 class='text-primary'>IP/Address</h5>
50 
+<!--
51 
+<pre>
52 
+{{ mk_address | pprint }}
53 
+</pre> -->
54 
+{% for ip in mk_address %}
55 
+<h4>{{ ip.interface }}</h4>
56 
+  <div class='d-flex flex-row justify-content-start  flex-wrap align-content-stretch mb-5'>
57 
+    {% for k,v in ip.items %}
58 
+      <div class='border p-3 fw-bolder'>{{ k }}</div><div class='border p-3'>{{ v }}</div>
59 
+    {% endfor %}
60 
+  </div>
61 
+
62 
+{% endfor %}
31 63
32 64 
 <div class='py-3'>
33 65 
   <p>
 
@@ -43,134 +75,4 @@
43 75 
     </div>
44 76 
   </div>
45 77 
 </div>
46 
-      <canvas class="my-4 w-100" id="myChart" width="900" height="380"></canvas>
47 
-
48 
-      <h2>Section title</h2>
49 
-      <div class="table-responsive">
50 
-        <table class="table table-striped table-sm">
51 
-          <thead>
52 
-            <tr>
53 
-              <th scope="col">#</th>
54 
-              <th scope="col">Header</th>
55 
-              <th scope="col">Header</th>
56 
-              <th scope="col">Header</th>
57 
-              <th scope="col">Header</th>
58 
-            </tr>
59 
-          </thead>
60 
-          <tbody>
61 
-            <tr>
62 
-              <td>1,001</td>
63 
-              <td>random</td>
64 
-              <td>data</td>
65 
-              <td>placeholder</td>
66 
-              <td>text</td>
67 
-            </tr>
68 
-            <tr>
69 
-              <td>1,002</td>
70 
-              <td>placeholder</td>
71 
-              <td>irrelevant</td>
72 
-              <td>visual</td>
73 
-              <td>layout</td>
74 
-            </tr>
75 
-            <tr>
76 
-              <td>1,003</td>
77 
-              <td>data</td>
78 
-              <td>rich</td>
79 
-              <td>dashboard</td>
80 
-              <td>tabular</td>
81 
-            </tr>
82 
-            <tr>
83 
-              <td>1,003</td>
84 
-              <td>information</td>
85 
-              <td>placeholder</td>
86 
-              <td>illustrative</td>
87 
-              <td>data</td>
88 
-            </tr>
89 
-            <tr>
90 
-              <td>1,004</td>
91 
-              <td>text</td>
92 
-              <td>random</td>
93 
-              <td>layout</td>
94 
-              <td>dashboard</td>
95 
-            </tr>
96 
-            <tr>
97 
-              <td>1,005</td>
98 
-              <td>dashboard</td>
99 
-              <td>irrelevant</td>
100 
-              <td>text</td>
101 
-              <td>placeholder</td>
102 
-            </tr>
103 
-            <tr>
104 
-              <td>1,006</td>
105 
-              <td>dashboard</td>
106 
-              <td>illustrative</td>
107 
-              <td>rich</td>
108 
-              <td>data</td>
109 
-            </tr>
110 
-            <tr>
111 
-              <td>1,007</td>
112 
-              <td>placeholder</td>
113 
-              <td>tabular</td>
114 
-              <td>information</td>
115 
-              <td>irrelevant</td>
116 
-            </tr>
117 
-            <tr>
118 
-              <td>1,008</td>
119 
-              <td>random</td>
120 
-              <td>data</td>
121 
-              <td>placeholder</td>
122 
-              <td>text</td>
123 
-            </tr>
124 
-            <tr>
125 
-              <td>1,009</td>
126 
-              <td>placeholder</td>
127 
-              <td>irrelevant</td>
128 
-              <td>visual</td>
129 
-              <td>layout</td>
130 
-            </tr>
131 
-            <tr>
132 
-              <td>1,010</td>
133 
-              <td>data</td>
134 
-              <td>rich</td>
135 
-              <td>dashboard</td>
136 
-              <td>tabular</td>
137 
-            </tr>
138 
-            <tr>
139 
-              <td>1,011</td>
140 
-              <td>information</td>
141 
-              <td>placeholder</td>
142 
-              <td>illustrative</td>
143 
-              <td>data</td>
144 
-            </tr>
145 
-            <tr>
146 
-              <td>1,012</td>
147 
-              <td>text</td>
148 
-              <td>placeholder</td>
149 
-              <td>layout</td>
150 
-              <td>dashboard</td>
151 
-            </tr>
152 
-            <tr>
153 
-              <td>1,013</td>
154 
-              <td>dashboard</td>
155 
-              <td>irrelevant</td>
156 
-              <td>text</td>
157 
-              <td>visual</td>
158 
-            </tr>
159 
-            <tr>
160 
-              <td>1,014</td>
161 
-              <td>dashboard</td>
162 
-              <td>illustrative</td>
163 
-              <td>rich</td>
164 
-              <td>data</td>
165 
-            </tr>
166 
-            <tr>
167 
-              <td>1,015</td>
168 
-              <td>random</td>
169 
-              <td>tabular</td>
170 
-              <td>information</td>
171 
-              <td>text</td>
172 
-            </tr>
173 
-          </tbody>
174 
-        </table>
175 
-      </div>
176 78 
 {% endblock %}

+ 8 - 2
app/backend/views.py
查看文件 

@@ -1,6 +1,6 @@
1 1 
 from django.shortcuts import render
2 2 
 from backend.mongodb import db
3 
-from exfo.lib import Exfo
3 
+from exfo.lib import Exfo, Mikrotik
4 4 
 from pprint import pprint
5 5
6 6 
 from ttp import ttp
 
@@ -9,6 +9,9 @@ from django.http import JsonResponse
9 9
10 10 
 exfo = Exfo("administrator", "exf0w0rxC@t4dm!n")
11 11 
 exfo.login()
12 
+
13 
+mkt = Mikrotik()
14 
+
12 15 
 def index(request):
13 16 
     collection  = db['mascot']
14 17 
     mascot_details = collection.find({})
 
@@ -96,8 +99,11 @@ interface {{ interface | contains("Vlan") }}
96 99 
     # # print result in JSON format
97 100 
     # results = parser.result(format='xlsx')[0]
98 101 
     # pprint(results)
102 
+    mk_ips = mkt.call_remote("ip/route")
103 
+    mk_address = mkt.call_remote("ip/address")
104 
+
99 105 
     return render(request, 'backend/index.html', {'objs': mascot_details, 'output': rapi.json(),\
100 
-            'sla': sla.json()})
106 
+            'sla': sla.json(), 'mk_ips': mk_ips, 'mk_address': mk_address})
101 107
102 108
103 109 
 #Define Collection

+ 22 - 0
app/exfo/lib.py
查看文件 

@@ -109,6 +109,22 @@ class Exfo:
109 109
110 110
111 111
112 
+from requests.auth import HTTPBasicAuth
113 
+
114 
+
115 
+class Mikrotik:
116 
+    BASE_URL = "https://110.77.145.239/rest/"
117 
+    basic = HTTPBasicAuth('brix', 'Digitins_01')
118 
+
119 
+    headers = {
120 
+      'Authorization': 'Basic YWRtaW46RGlnaXRpbnNfMDE='
121 
+    }
122 
+
123 
+    def call_remote(self, cmd, payload={}):
124 
+        url = self.BASE_URL + cmd
125 
+        pprint(url)
126 
+        response = requests.get(url, auth=self.basic,  data=payload, verify=False)
127 
+        return response.json()
112 128
113 129
114 130 
 if __name__ == '__main__':
 
@@ -120,3 +136,9 @@ if __name__ == '__main__':
120 136 
     e.test_avl_test_types()
121 137 
     e.logout()
122 138
139 
+    mkt = Mikrotik()
140 
+    pprint("--- ip/route ---")
141 
+    pprint(mkt.call_remote("ip/route"))
142 
+    pprint("--- ip/address -- ")
143 
+    pprint(mkt.call_remote("ip/address"))
144 
+