-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathRandomArt.nb
5168 lines (5150 loc) · 300 KB
/
RandomArt.nb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
(* Content-type: application/vnd.wolfram.mathematica *)
(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)
(* CreatedBy='Mathematica 10.0' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 158, 7]
NotebookDataLength[ 306789, 5159]
NotebookOptionsPosition[ 306070, 5130]
NotebookOutlinePosition[ 306428, 5146]
CellTagsIndexPosition[ 306385, 5143]
WindowFrame->Normal*)
(* Beginning of Notebook Content *)
Notebook[{
Cell[CellGroupData[{
Cell[BoxData["Today"], "Input",
CellChangeTimes->{{3.734630649266306*^9, 3.734630649667811*^9}}],
Cell[BoxData[
TemplateBox[{RowBox[{"\"Sun 6 May 2018\""}],RowBox[{"DateObject", "[",
RowBox[{"{",
RowBox[{"2018", ",", "5", ",", "6"}], "}"}], "]"}]},
"DateObject",
Editable->False]], "Output",
CellChangeTimes->{3.734630650377069*^9}]
}, Open ]],
Cell[CellGroupData[{
Cell["Random Art", "Title",
CellChangeTimes->{{3.7346306607483883`*^9, 3.7346306620591927`*^9}}],
Cell[TextData[{
"To perform a random coordinate transformation on the pixels of a raster \
image, each point {",
StyleBox["x",
FontSlant->"Italic"],
", ",
StyleBox["y}",
FontSlant->"Italic"],
" inside the unit rectangle is mapped into another points {",
StyleBox["x",
FontSlant->"Italic"],
"\[CloseCurlyQuote], ",
StyleBox["y",
FontSlant->"Italic"],
"\[CloseCurlyQuote]} in the unit rectangle using a series of functions "
}], "Text",
CellChangeTimes->{{3.734630684476192*^9, 3.734630951687311*^9}, {
3.734631051709547*^9, 3.734631070973343*^9}, {3.734631185861486*^9,
3.734631225275105*^9}, {3.734631639726556*^9, 3.734631648461948*^9}, {
3.734631858965806*^9, 3.7346319404670258`*^9}, 3.734632923878442*^9},
TextJustification->1.],
Cell[BoxData[{
RowBox[{
RowBox[{"tau", " ", "=", " ",
RowBox[{"N", "[",
RowBox[{"2", "\[Pi]"}], "]"}]}], ";"}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"normalize", "[",
RowBox[{"{",
RowBox[{"x_", ",", "y_"}], "}"}], "]"}], " ", ":=", " ",
RowBox[{"{",
RowBox[{
RowBox[{"Mod", "[",
RowBox[{"x", ",", "1"}], "]"}], ",", " ",
RowBox[{"Mod", "[",
RowBox[{"y", ",", "1"}], "]"}]}], "}"}]}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"(*",
RowBox[{
RowBox[{
RowBox[{"normalize", "[",
RowBox[{"{",
RowBox[{"x_", ",", "y_"}], "}"}], "]"}], " ", ":=", " ",
RowBox[{"{",
RowBox[{
RowBox[{"TriangleWave", "[",
RowBox[{
RowBox[{"{",
RowBox[{"0", ",", "1"}], "}"}], ",",
RowBox[{"x", "/", "2"}]}], "]"}], ",",
RowBox[{"TriangleWave", "[",
RowBox[{
RowBox[{"{",
RowBox[{"0", ",", "1"}], "}"}], ",",
RowBox[{"y", "/", "2"}]}], "]"}]}], "}"}]}], ";"}],
"*)"}]}]}], "Input",
CellChangeTimes->{{3.734631603080426*^9, 3.734631616326433*^9}, {
3.7346316508464193`*^9, 3.734631652350198*^9}, {3.734631981710326*^9,
3.734632023866398*^9}, {3.7347679270617456`*^9, 3.734767929195889*^9}, {
3.734768259306842*^9, 3.734768264688652*^9}, {3.734768446462367*^9,
3.7347684663470097`*^9}, {3.73476875371984*^9, 3.734768772670034*^9}}],
Cell[BoxData[
RowBox[{
RowBox[{"twist", ":=", " ",
RowBox[{"Module", "[",
RowBox[{
RowBox[{"{",
RowBox[{"cp", ",", "rot", ",", "twister", ",", "df", ",", " ", "off"}],
"}"}], ",", "\[IndentingNewLine]",
RowBox[{
RowBox[{"cp", " ", "=", " ",
RowBox[{"RandomReal", "[",
RowBox[{
RowBox[{"{",
RowBox[{"0", ",", "1"}], "}"}], ",",
RowBox[{"{",
RowBox[{"10", ",", "2"}], "}"}]}], "]"}]}], ";",
"\[IndentingNewLine]",
RowBox[{"off", " ", "=", " ",
RowBox[{"RandomReal", "[",
RowBox[{
RowBox[{"{",
RowBox[{
RowBox[{"-", "1"}], ",", "1"}], "}"}], ",",
RowBox[{"{",
RowBox[{"10", ",", "4"}], "}"}]}], "]"}]}], ";",
"\[IndentingNewLine]",
RowBox[{"df", " ", "=", " ",
RowBox[{"Table", "[",
RowBox[{
RowBox[{"RandomChoice", "[",
RowBox[{"{",
RowBox[{
"EuclideanDistance", ",", " ", "ManhattanDistance", ",", " ",
"ChessboardDistance"}], "}"}], "]"}], ",",
RowBox[{"{",
RowBox[{"i", ",", "1", ",", "10"}], "}"}]}], "]"}]}], ";",
"\[IndentingNewLine]",
RowBox[{
RowBox[{"rot", "[",
RowBox[{"p_", ",", "i_"}], "]"}], " ", ":=", " ",
RowBox[{"Module", "[",
RowBox[{
RowBox[{"{",
RowBox[{"dv", ",", "angle", ",", "dist"}], "}"}], ",",
"\[IndentingNewLine]",
RowBox[{
RowBox[{"dv", " ", "=", " ",
RowBox[{"p", "-",
RowBox[{"cp", "[",
RowBox[{"[", "i", "]"}], "]"}]}]}], ";", "\[IndentingNewLine]",
RowBox[{"angle", " ", "=", " ",
RowBox[{"Arg", "[",
RowBox[{
RowBox[{"dv", "[",
RowBox[{"[", "1", "]"}], "]"}], " ", "+", " ",
RowBox[{"I", " ",
RowBox[{"dv", "[",
RowBox[{"[", "2", "]"}], "]"}]}]}], "]"}]}], ";",
"\[IndentingNewLine]",
RowBox[{"dist", " ", "=", " ",
RowBox[{"EuclideanDistance", "[",
RowBox[{"p", ",",
RowBox[{"cp", "[",
RowBox[{"[", "i", "]"}], "]"}]}], "]"}]}], ";",
"\[IndentingNewLine]",
RowBox[{"With", "[",
RowBox[{
RowBox[{"{",
RowBox[{"a", " ", "=", " ",
RowBox[{"angle", " ", "+",
RowBox[{
RowBox[{"Total", "[",
RowBox[{"Table", "[",
RowBox[{
RowBox[{
RowBox[{"Cos", "[",
RowBox[{
RowBox[{"tau", " ", "*", " ", "dist"}], " ", "+", " ",
RowBox[{"off", "[",
RowBox[{"[",
RowBox[{"i", ",", "j"}], "]"}], "]"}]}], "]"}], "/",
"j"}], ",",
RowBox[{"{",
RowBox[{"j", ",", "1", ",", "4"}], "}"}]}], "]"}], "]"}],
"/", "16"}]}]}], "}"}], ",", "\[IndentingNewLine]",
RowBox[{"normalize", "[",
RowBox[{
RowBox[{"cp", "[",
RowBox[{"[", "i", "]"}], "]"}], " ", "+", " ",
RowBox[{"dist", " ",
RowBox[{"{",
RowBox[{
RowBox[{"Cos", "[", "a", "]"}], ",",
RowBox[{"Sin", "[", "a", "]"}]}], "}"}]}]}], "]"}]}],
"]"}]}]}], "\[IndentingNewLine]", "]"}]}], ";",
"\[IndentingNewLine]",
RowBox[{
RowBox[{"twister", "[",
RowBox[{"x_", ",", "y_"}], "]"}], " ", ":=", " ",
RowBox[{"Mean", "[",
RowBox[{"Table", "[",
RowBox[{
RowBox[{"rot", "[",
RowBox[{
RowBox[{"{",
RowBox[{"x", ",", "y"}], "}"}], ",", "i"}], " ", "]"}], ",",
RowBox[{"{",
RowBox[{"i", ",", "1", ",", "10"}], "}"}]}], "]"}], "]"}]}], ";",
"\[IndentingNewLine]", "twister"}]}], "\[IndentingNewLine]", "]"}]}],
";"}]], "Input",
CellChangeTimes->{{3.734630880800329*^9, 3.734630882698886*^9}, {
3.734630964695125*^9, 3.734631049998457*^9}, 3.7346311084003897`*^9, {
3.7346312349791183`*^9, 3.734631237355014*^9}, {3.7346314632196836`*^9,
3.734631465034375*^9}, {3.734631495569213*^9, 3.734631553775221*^9},
3.734631602137594*^9, {3.734632033946938*^9, 3.734632151794652*^9}, {
3.734632189642497*^9, 3.734632589484481*^9}, {3.7346329086577253`*^9,
3.734632909088237*^9}, {3.734633201595862*^9, 3.734633203395451*^9}, {
3.7346333557546873`*^9, 3.734633357760087*^9}, {3.734633397938017*^9,
3.734633399480063*^9}, {3.734633544927408*^9, 3.7346335551331997`*^9}, {
3.734633620511532*^9, 3.734633639554126*^9}, {3.7346336787409277`*^9,
3.7346336877551117`*^9}, {3.7346337315151043`*^9, 3.7346337382700167`*^9},
3.7346337828611403`*^9, {3.734633823252309*^9, 3.734633844356866*^9}, {
3.73463391105124*^9, 3.734633935161088*^9}, {3.7346339689290257`*^9,
3.734633973680052*^9}, {3.734634019232778*^9, 3.73463406349566*^9},
3.734634124406447*^9, 3.734634154517983*^9, {3.734634202481489*^9,
3.734634206946599*^9}, {3.7347666147160788`*^9, 3.7347666147905197`*^9}, {
3.7347666514914703`*^9, 3.734766654897607*^9}, {3.734766710273782*^9,
3.7347670370272617`*^9}, {3.734767078157589*^9, 3.734767104513874*^9}, {
3.7347671517783327`*^9, 3.734767151841256*^9}, {3.734767269600998*^9,
3.7347672741494837`*^9}, {3.734767455654509*^9, 3.734767471876177*^9}, {
3.734767509138482*^9, 3.734767510059692*^9}, {3.734767604565362*^9,
3.734767604738068*^9}, 3.734767830096202*^9, {3.734767869945653*^9,
3.734767887029932*^9}, {3.734767951829093*^9, 3.734767972268311*^9}, {
3.734768008798833*^9, 3.734768012235381*^9}, {3.734768073982048*^9,
3.734768076465858*^9}, {3.734768143677774*^9, 3.734768231615993*^9}, {
3.734768272928995*^9, 3.734768339230476*^9}, {3.734768379077793*^9,
3.7347684175896893`*^9}, {3.734768546645811*^9, 3.734768574193207*^9}, {
3.734768607668624*^9, 3.7347686190571404`*^9}, {3.734768677723372*^9,
3.734768719865419*^9}, {3.734768944086755*^9, 3.734769027245274*^9}, {
3.734769062699479*^9, 3.7347690636828403`*^9}, {3.7347690998456297`*^9,
3.734769158432571*^9}, {3.734769252672585*^9, 3.734769482203849*^9}, {
3.734769524516584*^9, 3.734769526114922*^9}}],
Cell[BoxData[
RowBox[{
RowBox[{
RowBox[{"transform", "[",
RowBox[{"img_", ",", " ", "transformations_"}], "]"}], " ", ":=", " ",
RowBox[{"Module", "[",
RowBox[{
RowBox[{"{",
RowBox[{"w", ",", "h", ",", " ", "result"}], "}"}], ",",
"\[IndentingNewLine]",
RowBox[{
RowBox[{"result", " ", "=", " ", "img"}], ";", "\[IndentingNewLine]",
RowBox[{"Do", "[", "\[IndentingNewLine]",
RowBox[{
RowBox[{"result", " ", "=",
RowBox[{"ImageTransformation", "[",
RowBox[{"result", ",", " ",
RowBox[{
RowBox[{"(",
RowBox[{
RowBox[{"(",
RowBox[{"transformations", "[",
RowBox[{"[", "i", "]"}], "]"}], ")"}], "[",
RowBox[{
RowBox[{"#", "[",
RowBox[{"[", "1", "]"}], "]"}], ",",
RowBox[{"#", "[",
RowBox[{"[", "2", "]"}], "]"}]}], "]"}], ")"}], "&"}]}],
"]"}]}], "\[IndentingNewLine]", ",",
RowBox[{"{",
RowBox[{"i", ",", "1", ",",
RowBox[{"Length", "[", "transformations", "]"}]}], "}"}]}],
"\[IndentingNewLine]", "]"}], ";", "\[IndentingNewLine]", "result"}]}],
"\[IndentingNewLine]", "]"}]}], ";"}]], "Input",
CellChangeTimes->{{3.734632651348246*^9, 3.734632784332304*^9}, {
3.734632865097548*^9, 3.73463286721595*^9}, {3.734632991088868*^9,
3.73463299460839*^9}, {3.7346332122534857`*^9, 3.734633234859907*^9}, {
3.7346332805818863`*^9, 3.734633287219636*^9}, {3.73463332883167*^9,
3.734633329251672*^9}, {3.7346334236814013`*^9, 3.734633519079867*^9}, {
3.73463358270179*^9, 3.734633585811946*^9}}],
Cell[CellGroupData[{
Cell[BoxData[{
RowBox[{"lena", " ", "=",
RowBox[{"ImageResize", "[",
RowBox[{
RowBox[{"ExampleData", "[",
RowBox[{"{",
RowBox[{"\"\<TestImage\>\"", ",", " ", "\"\<Lena\>\""}], "}"}], "]"}],
",", "200"}], "]"}]}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"r", " ", "=", " ", "twist"}], ";"}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"transform", "[",
RowBox[{"lena", ",",
RowBox[{"{", "r", "}"}]}], "]"}], " "}]}], "Input",
CellChangeTimes->{{3.7346326009331408`*^9, 3.734632611852849*^9}, {
3.734632790907632*^9, 3.7346328049683657`*^9}, {3.734632933673202*^9,
3.7346329356727753`*^9}, {3.734633006015181*^9, 3.7346330204132566`*^9}, {
3.734633112114995*^9, 3.734633164994788*^9}, {3.7346333183782263`*^9,
3.7346333184723177`*^9}, {3.734633365458503*^9, 3.7346333671454773`*^9}, {
3.734633496496687*^9, 3.734633501229323*^9}, {3.73463369158115*^9,
3.734633692370146*^9}, 3.734633848890758*^9, {3.734766993475318*^9,
3.734766996586689*^9}, {3.7347671545009823`*^9, 3.734767154557675*^9}, {
3.7347672788839083`*^9, 3.734767293454152*^9}, {3.734767460973662*^9,
3.734767461024901*^9}, {3.734767527118978*^9, 3.734767527178185*^9}, {
3.734769161482436*^9, 3.7347691615596724`*^9}, {3.734769416033719*^9,
3.734769416219808*^9}}],
Cell[BoxData[
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJx0vHdwHGl65qm4uz82LmJvp5ve9DhpNySNNK6nHUl478q79Ka8t/CEBwHQ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