s="lines-code"> 
+    if 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
ファイルの表示 

@@ -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
ファイルの表示 

@@ -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 
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670 
+batch_normalize=1
671 
+filters=256
672 
+size=1
673 
+stride=1
674 
+pad=1
675 
+activation=leaky
676 
+
677 
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678 
+batch_normalize=1
679 
+size=3
680 
+stride=1
681 
+pad=1
682 
+filters=512
683 
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684 
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685 
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686 
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687 
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688 
+pad=1
689 
+filters=255
690 
+activation=linear
691 
+
692 
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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 
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765 
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766 
+size=3
767 
+stride=1
768 
+pad=1
769 
+filters=256
770 
+activation=leaky
771 
+
772 
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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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whitesports
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Requests
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IDNAEncoder....

IDNAEncoder.php 11KB
文件历史 原始文件

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  1. <?php
    2. 

  2. 

  3. /**
    4. 

  4.  * IDNA URL encoder
    5. 

  5.  *
    6. 

  6.  * Note: Not fully compliant, as nameprep does nothing yet.
    7. 

  7.  *
    8. 

  8.  * @package Requests
    9. 

  9.  * @subpackage Utilities
    10. 

  10.  * @see https://tools.ietf.org/html/rfc3490 IDNA specification
    11. 

  11.  * @see https://tools.ietf.org/html/rfc3492 Punycode/Bootstrap specification
    12. 

  12.  */
    13. 

  13. class Requests_IDNAEncoder {
    14. 

  14. 	/**
    15. 

  15. 	 * ACE prefix used for IDNA
    16. 

  16. 	 *
    17. 

  17. 	 * @see https://tools.ietf.org/html/rfc3490#section-5
    18. 

  18. 	 * @var string
    19. 

  19. 	 */
    20. 

  20. 	const ACE_PREFIX = 'xn--';
    21. 

  21. 

  22. 	/**#@+
    23. 

  23. 	 * Bootstrap constant for Punycode
    24. 

  24. 	 *
    25. 

  25. 	 * @see https://tools.ietf.org/html/rfc3492#section-5
    26. 

  26. 	 * @var int
    27. 

  27. 	 */
    28. 

  28. 	const BOOTSTRAP_BASE         = 36;
    29. 

  29. 	const BOOTSTRAP_TMIN         = 1;
    30. 

  30. 	const BOOTSTRAP_TMAX         = 26;
    31. 

  31. 	const BOOTSTRAP_SKEW         = 38;
    32. 

  32. 	const BOOTSTRAP_DAMP         = 700;
    33. 

  33. 	const BOOTSTRAP_INITIAL_BIAS = 72;
    34. 

  34. 	const BOOTSTRAP_INITIAL_N    = 128;
    35. 

  35. 	/**#@-*/
    36. 

  36. 

  37. 	/**
    38. 

  38. 	 * Encode a hostname using Punycode
    39. 

  39. 	 *
    40. 

  40. 	 * @param string $string Hostname
    41. 

  41. 	 * @return string Punycode-encoded hostname
    42. 

  42. 	 */
    43. 

  43. 	public static function encode($string) {
    44. 

  44. 		$parts = explode('.', $string);
    45. 

  45. 		foreach ($parts as &$part) {
    46. 

  46. 			$part = self::to_ascii($part);
    47. 

  47. 		}
    48. 

  48. 		return implode('.', $parts);
    49. 

  49. 	}
    50. 

  50. 

  51. 	/**
    52. 

  52. 	 * Convert a UTF-8 string to an ASCII string using Punycode
    53. 

  53. 	 *
    54. 

  54. 	 * @throws Requests_Exception Provided string longer than 64 ASCII characters (`idna.provided_too_long`)
    55. 

  55. 	 * @throws Requests_Exception Prepared string longer than 64 ASCII characters (`idna.prepared_too_long`)
    56. 

  56. 	 * @throws Requests_Exception Provided string already begins with xn-- (`idna.provided_is_prefixed`)
    57. 

  57. 	 * @throws Requests_Exception Encoded string longer than 64 ASCII characters (`idna.encoded_too_long`)
    58. 

  58. 	 *
    59. 

  59. 	 * @param string $string ASCII or UTF-8 string (max length 64 characters)
    60. 

  60. 	 * @return string ASCII string
    61. 

  61. 	 */
    62. 

  62. 	public static function to_ascii($string) {
    63. 

  63. 		// Step 1: Check if the string is already ASCII
    64. 

  64. 		if (self::is_ascii($string)) {
    65. 

  65. 			// Skip to step 7
    66. 

  66. 			if (strlen($string) < 64) {
    67. 

  67. 				return $string;
    68. 

  68. 			}
    69. 

  69. 

  70. 			throw new Requests_Exception('Provided string is too long', 'idna.provided_too_long', $string);
    71. 

  71. 		}
    72. 

  72. 

  73. 		// Step 2: nameprep
    74. 

  74. 		$string = self::nameprep($string);
    75. 

  75. 

  76. 		// Step 3: UseSTD3ASCIIRules is false, continue
    77. 

  77. 		// Step 4: Check if it's ASCII now
    78. 

  78. 		if (self::is_ascii($string)) {
    79. 

  79. 			// Skip to step 7
    80. 

  80. 			if (strlen($string) < 64) {
    81. 

  81. 				return $string;
    82. 

  82. 			}
    83. 

  83. 

  84. 			throw new Requests_Exception('Prepared string is too long', 'idna.prepared_too_long', $string);
    85. 

  85. 		}
    86. 

  86. 

  87. 		// Step 5: Check ACE prefix
    88. 

  88. 		if (strpos($string, self::ACE_PREFIX) === 0) {
    89. 

  89. 			throw new Requests_Exception('Provided string begins with ACE prefix', 'idna.provided_is_prefixed', $string);
    90. 

  90. 		}
    91. 

  91. 

  92. 		// Step 6: Encode with Punycode
    93. 

  93. 		$string = self::punycode_encode($string);
    94. 

  94. 

  95. 		// Step 7: Prepend ACE prefix
    96. 

  96. 		$string = self::ACE_PREFIX . $string;
    97. 

  97. 

  98. 		// Step 8: Check size
    99. 

  99. 		if (strlen($string) < 64) {
    100. 

  100. 			return $string;
    101. 

  101. 		}
    102. 

  102. 

  103. 		throw new Requests_Exception('Encoded string is too long', 'idna.encoded_too_long', $string);
    104. 

  104. 	}
    105. 

  105. 

  106. 	/**
    107. 

  107. 	 * Check whether a given string contains only ASCII characters
    108. 

  108. 	 *
    109. 

  109. 	 * @internal (Testing found regex was the fastest implementation)
    110. 

  110. 	 *
    111. 

  111. 	 * @param string $string
    112. 

  112. 	 * @return bool Is the string ASCII-only?
    113. 

  113. 	 */
    114. 

  114. 	protected static function is_ascii($string) {
    115. 

  115. 		return (preg_match('/(?:[^\x00-\x7F])/', $string) !== 1);
    116. 

  116. 	}
    117. 

  117. 

  118. 	/**
    119. 

  119. 	 * Prepare a string for use as an IDNA name
    120. 

  120. 	 *
    121. 

  121. 	 * @todo Implement this based on RFC 3491 and the newer 5891
    122. 

  122. 	 * @param string $string
    123. 

  123. 	 * @return string Prepared string
    124. 

  124. 	 */
    125. 

  125. 	protected static function nameprep($string) {
    126. 

  126. 		return $string;
    127. 

  127. 	}
    128. 

  128. 

  129. 	/**
    130. 

  130. 	 * Convert a UTF-8 string to a UCS-4 codepoint array
    131. 

  131. 	 *
    132. 

  132. 	 * Based on Requests_IRI::replace_invalid_with_pct_encoding()
    133. 

  133. 	 *
    134. 

  134. 	 * @throws Requests_Exception Invalid UTF-8 codepoint (`idna.invalidcodepoint`)
    135. 

  135. 	 * @param string $input
    136. 

  136. 	 * @return array Unicode code points
    137. 

  137. 	 */
    138. 

  138. 	protected static function utf8_to_codepoints($input) {
    139. 

  139. 		$codepoints = array();
    140. 

  140. 

  141. 		// Get number of bytes
    142. 

  142. 		$strlen = strlen($input);
    143. 

  143. 

  144. 		// phpcs:ignore Generic.CodeAnalysis.JumbledIncrementer -- This is a deliberate choice.
    145. 

  145. 		for ($position = 0; $position < $strlen; $position++) {
    146. 

  146. 			$value = ord($input[$position]);
    147. 

  147. 

  148. 			// One byte sequence:
    149. 

  149. 			if ((~$value & 0x80) === 0x80) {
    150. 

  150. 				$character = $value;
    151. 

  151. 				$length    = 1;
    152. 

  152. 				$remaining = 0;
    153. 

  153. 			}
    154. 

  154. 			// Two byte sequence:
    155. 

  155. 			elseif (($value & 0xE0) === 0xC0) {
    156. 

  156. 				$character = ($value & 0x1F) << 6;
    157. 

  157. 				$length    = 2;
    158. 

  158. 				$remaining = 1;
    159. 

  159. 			}
    160. 

  160. 			// Three byte sequence:
    161. 

  161. 			elseif (($value & 0xF0) === 0xE0) {
    162. 

  162. 				$character = ($value & 0x0F) << 12;
    163. 

  163. 				$length    = 3;
    164. 

  164. 				$remaining = 2;
    165. 

  165. 			}
    166. 

  166. 			// Four byte sequence:
    167. 

  167. 			elseif (($value & 0xF8) === 0xF0) {
    168. 

  168. 				$character = ($value & 0x07) << 18;
    169. 

  169. 				$length    = 4;
    170. 

  170. 				$remaining = 3;
    171. 

  171. 			}
    172. 

  172. 			// Invalid byte:
    173. 

  173. 			else {
    174. 

  174. 				throw new Requests_Exception('Invalid Unicode codepoint', 'idna.invalidcodepoint', $value);
    175. 

  175. 			}
    176. 

  176. 

  177. 			if ($remaining > 0) {
    178. 

  178. 				if ($position + $length > $strlen) {
    179. 

  179. 					throw new Requests_Exception('Invalid Unicode codepoint', 'idna.invalidcodepoint', $character);
    180. 

  180. 				}
    181. 

  181. 				for ($position++; $remaining > 0; $position++) {
    182. 

  182. 					$value = ord($input[$position]);
    183. 

  183. 

  184. 					// If it is invalid, count the sequence as invalid and reprocess the current byte:
    185. 

  185. 					if (($value & 0xC0) !== 0x80) {
    186. 

  186. 						throw new Requests_Exception('Invalid Unicode codepoint', 'idna.invalidcodepoint', $character);
    187. 

  187. 					}
    188. 

  188. 

  189. 					--$remaining;
    190. 

  190. 					$character |= ($value & 0x3F) << ($remaining * 6);
    191. 

  191. 				}
    192. 

  192. 				$position--;
    193. 

  193. 			}
    194. 

  194. 

  195. 			if (// Non-shortest form sequences are invalid
    196. 

  196. 				$length > 1 && $character <= 0x7F
    197. 

  197. 				|| $length > 2 && $character <= 0x7FF
    198. 

  198. 				|| $length > 3 && $character <= 0xFFFF
    199. 

  199. 				// Outside of range of ucschar codepoints
    200. 

  200. 				// Noncharacters
    201. 

  201. 				|| ($character & 0xFFFE) === 0xFFFE
    202. 

  202. 				|| $character >= 0xFDD0 && $character <= 0xFDEF
    203. 

  203. 				|| (
    204. 

  204. 					// Everything else not in ucschar
    205. 

  205. 					$character > 0xD7FF && $character < 0xF900
    206. 

  206. 					|| $character < 0x20
    207. 

  207. 					|| $character > 0x7E && $character < 0xA0
    208. 

  208. 					|| $character > 0xEFFFD
    209. 

  209. 				)
    210. 

  210. 			) {
    211. 

  211. 				throw new Requests_Exception('Invalid Unicode codepoint', 'idna.invalidcodepoint', $character);
    212. 

  212. 			}
    213. 

  213. 

  214. 			$codepoints[] = $character;
    215. 

  215. 		}
    216. 

  216. 

  217. 		return $codepoints;
    218. 

  218. 	}
    219. 

  219. 

  220. 	/**
    221. 

  221. 	 * RFC3492-compliant encoder
    222. 

  222. 	 *
    223. 

  223. 	 * @internal Pseudo-code from Section 6.3 is commented with "#" next to relevant code
    224. 

  224. 	 * @throws Requests_Exception On character outside of the domain (never happens with Punycode) (`idna.character_outside_domain`)
    225. 

  225. 	 *
    226. 

  226. 	 * @param string $input UTF-8 encoded string to encode
    227. 

  227. 	 * @return string Punycode-encoded string
    228. 

  228. 	 */
    229. 

  229. 	public static function punycode_encode($input) {
    230. 

  230. 		$output = '';
    231. 

  231. 		// let n = initial_n
    232. 

  232. 		$n = self::BOOTSTRAP_INITIAL_N;
    233. 

  233. 		// let delta = 0
    234. 

  234. 		$delta = 0;
    235. 

  235. 		// let bias = initial_bias
    236. 

  236. 		$bias = self::BOOTSTRAP_INITIAL_BIAS;
    237. 

  237. 		// let h = b = the number of basic code points in the input
    238. 

  238. 		$h = 0;
    239. 

  239. 		$b = 0; // see loop
    240. 

  240. 		// copy them to the output in order
    241. 

  241. 		$codepoints = self::utf8_to_codepoints($input);
    242. 

  242. 		$extended   = array();
    243. 

  243. 

  244. 		foreach ($codepoints as $char) {
    245. 

  245. 			if ($char < 128) {
    246. 

  246. 				// Character is valid ASCII
    247. 

  247. 				// TODO: this should also check if it's valid for a URL
    248. 

  248. 				$output .= chr($char);
    249. 

  249. 				$h++;
    250. 

  250. 			}
    251. 

  251. 			// Check if the character is non-ASCII, but below initial n
    252. 

  252. 			// This never occurs for Punycode, so ignore in coverage
    253. 

  253. 			// @codeCoverageIgnoreStart
    254. 

  254. 			elseif ($char < $n) {
    255. 

  255. 				throw new Requests_Exception('Invalid character', 'idna.character_outside_domain', $char);
    256. 

  256. 			}
    257. 

  257. 			// @codeCoverageIgnoreEnd
    258. 

  258. 			else {
    259. 

  259. 				$extended[$char] = true;
    260. 

  260. 			}
    261. 

  261. 		}
    262. 

  262. 		$extended = array_keys($extended);
    263. 

  263. 		sort($extended);
    264. 

  264. 		$b = $h;
    265. 

  265. 		// [copy them] followed by a delimiter if b > 0
    266. 

  266. 		if (strlen($output) > 0) {
    267. 

  267. 			$output .= '-';
    268. 

  268. 		}
    269. 

  269. 		// {if the input contains a non-basic code point < n then fail}
    270. 

  270. 		// while h < length(input) do begin
    271. 

  271. 		$codepointcount = count($codepoints);
    272. 

  272. 		while ($h < $codepointcount) {
    273. 

  273. 			// let m = the minimum code point >= n in the input
    274. 

  274. 			$m = array_shift($extended);
    275. 

  275. 			//printf('next code point to insert is %s' . PHP_EOL, dechex($m));
    276. 

  276. 			// let delta = delta + (m - n) * (h + 1), fail on overflow
    277. 

  277. 			$delta += ($m - $n) * ($h + 1);
    278. 

  278. 			// let n = m
    279. 

  279. 			$n = $m;
    280. 

  280. 			// for each code point c in the input (in order) do begin
    281. 

  281. 			for ($num = 0; $num < $codepointcount; $num++) {
    282. 

  282. 				$c = $codepoints[$num];
    283. 

  283. 				// if c < n then increment delta, fail on overflow
    284. 

  284. 				if ($c < $n) {
    285. 

  285. 					$delta++;
    286. 

  286. 				}
    287. 

  287. 				// if c == n then begin
    288. 

  288. 				elseif ($c === $n) {
    289. 

  289. 					// let q = delta
    290. 

  290. 					$q = $delta;
    291. 

  291. 					// for k = base to infinity in steps of base do begin
    292. 

  292. 					for ($k = self::BOOTSTRAP_BASE; ; $k += self::BOOTSTRAP_BASE) {
    293. 

  293. 						// let t = tmin if k <= bias {+ tmin}, or
    294. 

  294. 						//     tmax if k >= bias + tmax, or k - bias otherwise
    295. 

  295. 						if ($k <= ($bias + self::BOOTSTRAP_TMIN)) {
    296. 

  296. 							$t = self::BOOTSTRAP_TMIN;
    297. 

  297. 						}
    298. 

  298. 						elseif ($k >= ($bias + self::BOOTSTRAP_TMAX)) {
    299. 

  299. 							$t = self::BOOTSTRAP_TMAX;
    300. 

  300. 						}
    301. 

  301. 						else {
    302. 

  302. 							$t = $k - $bias;
    303. 

  303. 						}
    304. 

  304. 						// if q < t then break
    305. 

  305. 						if ($q < $t) {
    306. 

  306. 							break;
    307. 

  307. 						}
    308. 

  308. 						// output the code point for digit t + ((q - t) mod (base - t))
    309. 

  309. 						$digit   = $t + (($q - $t) % (self::BOOTSTRAP_BASE - $t));
    310. 

  310. 						$output .= self::digit_to_char($digit);
    311. 

  311. 						// let q = (q - t) div (base - t)
    312. 

  312. 						$q = floor(($q - $t) / (self::BOOTSTRAP_BASE - $t));
    313. 

  313. 					} // end
    314. 

  314. 					// output the code point for digit q
    315. 

  315. 					$output .= self::digit_to_char($q);
    316. 

  316. 					// let bias = adapt(delta, h + 1, test h equals b?)
    317. 

  317. 					$bias = self::adapt($delta, $h + 1, $h === $b);
    318. 

  318. 					// let delta = 0
    319. 

  319. 					$delta = 0;
    320. 

  320. 					// increment h
    321. 

  321. 					$h++;
    322. 

  322. 				} // end
    323. 

  323. 			} // end
    324. 

  324. 			// increment delta and n
    325. 

  325. 			$delta++;
    326. 

  326. 			$n++;
    327. 

  327. 		} // end
    328. 

  328. 

  329. 		return $output;
    330. 

  330. 	}
    331. 

  331. 

  332. 	/**
    333. 

  333. 	 * Convert a digit to its respective character
    334. 

  334. 	 *
    335. 

  335. 	 * @see https://tools.ietf.org/html/rfc3492#section-5
    336. 

  336. 	 * @throws Requests_Exception On invalid digit (`idna.invalid_digit`)
    337. 

  337. 	 *
    338. 

  338. 	 * @param int $digit Digit in the range 0-35
    339. 

  339. 	 * @return string Single character corresponding to digit
    340. 

  340. 	 */
    341. 

  341. 	protected static function digit_to_char($digit) {
    342. 

  342. 		// @codeCoverageIgnoreStart
    343. 

  343. 		// As far as I know, this never happens, but still good to be sure.
    344. 

  344. 		if ($digit < 0 || $digit > 35) {
    345. 

  345. 			throw new Requests_Exception(sprintf('Invalid digit %d', $digit), 'idna.invalid_digit', $digit);
    346. 

  346. 		}
    347. 

  347. 		// @codeCoverageIgnoreEnd
    348. 

  348. 		$digits = 'abcdefghijklmnopqrstuvwxyz0123456789';
    349. 

  349. 		return substr($digits, $digit, 1);
    350. 

  350. 	}
    351. 

  351. 

  352. 	/**
    353. 

  353. 	 * Adapt the bias
    354. 

  354. 	 *
    355. 

  355. 	 * @see https://tools.ietf.org/html/rfc3492#section-6.1
    356. 

  356. 	 * @param int $delta
    357. 

  357. 	 * @param int $numpoints
    358. 

  358. 	 * @param bool $firsttime
    359. 

  359. 	 * @return int New bias
    360. 

  360. 	 *
    361. 

  361. 	 * function adapt(delta,numpoints,firsttime):
    362. 

  362. 	 */
    363. 

  363. 	protected static function adapt($delta, $numpoints, $firsttime) {
    364. 

  364. 		// if firsttime then let delta = delta div damp
    365. 

  365. 		if ($firsttime) {
    366. 

  366. 			$delta = floor($delta / self::BOOTSTRAP_DAMP);
    367. 

  367. 		}
    368. 

  368. 		// else let delta = delta div 2
    369. 

  369. 		else {
    370. 

  370. 			$delta = floor($delta / 2);
    371. 

  371. 		}
    372. 

  372. 		// let delta = delta + (delta div numpoints)
    373. 

  373. 		$delta += floor($delta / $numpoints);
    374. 

  374. 		// let k = 0
    375. 

  375. 		$k = 0;
    376. 

  376. 		// while delta > ((base - tmin) * tmax) div 2 do begin
    377. 

  377. 		$max = floor(((self::BOOTSTRAP_BASE - self::BOOTSTRAP_TMIN) * self::BOOTSTRAP_TMAX) / 2);
    378. 

  378. 		while ($delta > $max) {
    379. 

  379. 			// let delta = delta div (base - tmin)
    380. 

  380. 			$delta = floor($delta / (self::BOOTSTRAP_BASE - self::BOOTSTRAP_TMIN));
    381. 

  381. 			// let k = k + base
    382. 

  382. 			$k += self::BOOTSTRAP_BASE;
    383. 

  383. 		} // end
    384. 

  384. 		// return k + (((base - tmin + 1) * delta) div (delta + skew))
    385. 

  385. 		return $k + floor(((self::BOOTSTRAP_BASE - self::BOOTSTRAP_TMIN + 1) * $delta) / ($delta + self::BOOTSTRAP_SKEW));
    386. 

  386. 	}
    387. 

  387. }
    388. 

  388.