-
Notifications
You must be signed in to change notification settings - Fork 3
Expand file tree
/
Copy pathdigital-research-methods-10b-image-processing.nb
More file actions
3422 lines (3299 loc) · 174 KB
/
digital-research-methods-10b-image-processing.nb
File metadata and controls
3422 lines (3299 loc) · 174 KB
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 10.2' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 158, 7]
NotebookDataLength[ 177489, 3413]
NotebookOptionsPosition[ 170881, 3214]
NotebookOutlinePosition[ 172537, 3262]
CellTagsIndexPosition[ 172457, 3257]
WindowFrame->Normal*)
(* Beginning of Notebook Content *)
Notebook[{
Cell[CellGroupData[{
Cell[BoxData[
TogglerBox[2, {1->
OverlayBox[{
TagBox[GridBox[{
{
GraphicsBox[RasterBox[CompressedData["
1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBLECOU2VJahIzkCEFxAJADBL8//8/
Qfzo0SP2GzduCF29elXs/Pnz0sgYn747d+5wA9VoArE/EB8B4uPoGJu+Fy9e
MF+4cEENKB8AxBeB+AMQ/wXif+gYi1tBdvoB8QUg/ghV9x8XRtb7/PlzFqje
t1D7cOrDpv/ixYvqUHuJ0ousHxpWAVA3E6UXWT80nC8S8i82/Q8ePOAgx26Y
/uvXrwsD2UdJ8Tey/kuXLolD0wNJbofpB6YVSUr0n4ek5QHTT6n7KQ0/SuOP
0vRDafqlRv6BxgPZ+Ret/HhHrBl4yi9QWHwiFB7Yyk+QO6B+CTxPYvmJjG/f
vs17nozyG1f9cR6t7gBhAPeiQ4A=
"], {{0, 26}, {16, 0}}, {0, 255},
ColorFunction->RGBColor],
BaseStyle->"ImageGraphics",
ImageSize->Magnification[1],
ImageSizeRaw->{16, 26},
PlotRange->{{0, 16}, {0, 26}}],
GraphicsBox[RasterBox[CompressedData["
1:eJzt2d9OVFcUB2DSNmkfQRFEiRQ1gCK1vW4fwT6BJu1tE9uk8fUUERE9aECC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"], {{0, 26}, {2000, 0}}, {0, 255},
ColorFunction->RGBColor],
BaseStyle->"ImageGraphics",
ImageSize->Magnification[1],
ImageSizeRaw->{2000, 26},
PlotRange->{{0, 2000}, {0, 26}}],
GraphicsBox[RasterBox[CompressedData["
1:eJy9lstOwkAUhhthQUJcuUBl5cr4GPoABvEJINGtCZoYng4Il4Z7gXAJYUHY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"], {{0, 26}, {24, 0}}, {0, 255},
ColorFunction->RGBColor],
BaseStyle->"ImageGraphics",
ImageSize->Magnification[1],
ImageSizeRaw->{24, 26},
PlotRange->{{0, 24}, {0, 26}}]}
},
AutoDelete->False,
GridBoxItemSize->{"Columns" -> {Automatic,
Scaled[0.6], Automatic}},
GridBoxSpacings->{"Columns" -> {{0}}, "Rows" -> {{0}}}],
"Grid"],
PaneBox[
StyleBox[
RowBox[{
"Background", " ", "images", " ", "and", " ", "text", " ", "colors", " ",
"have", " ", "been", " ", "adjusted", " ", "for", " ", "editing", " ",
RowBox[{"purposes", "."}]}],
LineBreakWithin->False,
FontFamily->"Helvetica",
FontSize->12,
FontColor->GrayLevel[0.4]],
Alignment->Center,
ImageSize->Scaled[0.5],
ScrollPosition->{0., 0.}]},
Alignment->{Center, Center}], 2->""}, "1"]], "SlideShowNavigationBar", \
"FirstSlide",
CellMargins->{{0, 0}, {0, 0}},
CellFrameLabelMargins->2,
CellSize->{Inherited, 30},
CellChangeTimes->{3.647361175465108*^9},
TextAlignment->Center,
CellTags->"SlideShowHeader"],
Cell["\<\
Digital Research Methods 10B:
Image Processing\
\>", "Title",
CellChangeTimes->{
3.559948400406288*^9, {3.647331852839075*^9, 3.647331861295168*^9}, {
3.647331922221755*^9, 3.647331924532915*^9}, {3.647332658962614*^9,
3.64733266480881*^9}, {3.6473611327373047`*^9, 3.647361139759391*^9}, {
3.647448943135475*^9, 3.6474489472294493`*^9}, {3.655314259525436*^9,
3.655314265060014*^9}}],
Cell[TextData[{
"William J Turkel, ",
StyleBox["wturkel@uwo.ca",
FontSlant->"Italic",
FontColor->RGBColor[
0.396078431372549, 0.596078431372549, 0.796078431372549]],
"\nHistory 2816 / Digital Humanities 2130 / History 9877"
}], "Subtitle",
CellChangeTimes->{{3.647331955723979*^9, 3.6473319708190517`*^9},
3.647332103720562*^9}]
}, Open ]],
Cell[CellGroupData[{
Cell["", "SlideShowNavigationBar",
CellTags->"SlideShowHeader"],
Cell[CellGroupData[{
Cell["Overview", "Section",
CellChangeTimes->{{3.647361153919208*^9, 3.6473611548467712`*^9}}],
Cell["\<\
In addition to working with page images and OCRed text, digital images such \
as photographs, paintings, drawings, charts, etc. can be research sources in \
their own right. Here we learn some basic image processing techniques that we \
can use to extract and visualize digital pictures of various kinds.\
\>", "Text",
CellChangeTimes->{{3.647361157582405*^9, 3.647361159086673*^9}, {
3.655314310900227*^9, 3.6553144458132257`*^9}}]
}, Open ]]
}, Open ]],
Cell[CellGroupData[{
Cell["", "SlideShowNavigationBar",
CellTags->"SlideShowHeader"],
Cell[CellGroupData[{
Cell["Setup", "Section",
CellChangeTimes->{{3.647716369451888*^9, 3.647716370003084*^9}, {
3.647798474690284*^9, 3.6477984752570744`*^9}}],
Cell[TextData[{
"A collection of scanned page images from Darwin\[CloseCurlyQuote]s ",
StyleBox["Expression of the Emotions",
FontSlant->"Italic"],
" is on the Internet Archive at the following location"
}], "Text",
CellChangeTimes->{{3.6477163758185587`*^9, 3.647716376530898*^9}, {
3.656514330665957*^9, 3.656514377728553*^9}}],
Cell[TextData[ButtonBox["https://archive.org/download/\
expressionemoti01darwgoog",
BaseStyle->"Hyperlink",
ButtonData->{
URL["https://archive.org/download/expressionemoti01darwgoog"], None},
ButtonNote->
"https://archive.org/download/expressionemoti01darwgoog"]], "Text",
CellChangeTimes->{{3.6565149451340714`*^9, 3.656514959210992*^9}}],
Cell["Here is a single page for us to experiment with", "Text",
CellChangeTimes->{{3.656514379952529*^9, 3.656514388512184*^9}}],
Cell[BoxData[
RowBox[{
RowBox[{"expressionPage", "=",
RowBox[{
"Import", "[",
"\"\<https://github.com/williamjturkel/Digital-Research-Methods/blob/\
master/darwin-expression/expressionemoti01darwgoog_0113.tif?raw=true\>\"",
"]"}]}], ";"}]], "Input",
CellChangeTimes->{{3.656514245377735*^9, 3.6565142458957577`*^9}, {
3.656514304181292*^9, 3.656514325346678*^9}}]
}, Open ]]
}, Open ]],
Cell[CellGroupData[{
Cell["", "SlideShowNavigationBar",
CellTags->"SlideShowHeader"],
Cell[CellGroupData[{
Cell["Interactively deblurring page images", "Section",
CellChangeTimes->{{3.647716384690867*^9, 3.6477163853144817`*^9}, {
3.655316067480979*^9, 3.655316073105068*^9}, {3.6553164822504053`*^9,
3.655316484545803*^9}}],
Cell["\<\
Here is some blurred text that I archived to the internet archive\
\>", "Text",
CellChangeTimes->{{3.6477163758185587`*^9, 3.647716376530898*^9}, {
3.655315971389436*^9, 3.655315976444623*^9}, {3.6553160329153633`*^9,
3.655316041130221*^9}}],
Cell[BoxData[
RowBox[{"blurredText", "=",
RowBox[{
"Import", "[",
"\"\<https://web.archive.org/web/20151031213342/http://i.imgur.com/cTDBnjy.\
png\>\"", "]"}]}]], "Input",
CellChangeTimes->{{3.655316042359393*^9, 3.6553160618493156`*^9}}],
Cell[TextData[{
"Variables q and r are pixel radius for ",
StyleBox["ImageDeconvolve",
FontWeight->"Bold"],
" kernel and ",
StyleBox["Sharpen",
FontWeight->"Bold"],
" commands"
}], "Text",
CellChangeTimes->{{3.655316265186298*^9, 3.655316289013029*^9}, {
3.655316518058992*^9, 3.655316518856778*^9}}],
Cell[BoxData[
RowBox[{"Manipulate", "[",
RowBox[{
RowBox[{"ImageAdjust", "[",
RowBox[{"Sharpen", "[",
RowBox[{
RowBox[{"ImageDeconvolve", "[",
RowBox[{"blurredText", ",",
RowBox[{"GaussianMatrix", "[", "q", "]"}]}], "]"}], ",", "r"}], "]"}],
"]"}], ",",
RowBox[{"{",
RowBox[{
RowBox[{"{",
RowBox[{"q", ",", "3"}], "}"}], ",", "0", ",", "6", ",", "1"}], "}"}],
",",
RowBox[{"{",
RowBox[{
RowBox[{"{",
RowBox[{"r", ",", "4"}], "}"}], ",", "0", ",", "6", ",", "1"}], "}"}]}],
"]"}]], "Input",
CellChangeTimes->{{3.655315643370988*^9, 3.6553159587092237`*^9}, {
3.655316078849998*^9, 3.655316152158012*^9}, {3.655316359239868*^9,
3.655316396461336*^9}, {3.655316581950939*^9, 3.655316585834529*^9}, {
3.655316636069193*^9, 3.6553166848833714`*^9}, {3.655317605466415*^9,
3.655317746348069*^9}, {3.655317786090713*^9, 3.65531779173921*^9}, {
3.655318042418722*^9, 3.655318055745529*^9}}]
}, Open ]]
}, Open ]],
Cell[CellGroupData[{
Cell["", "SlideShowNavigationBar",
CellTags->"SlideShowHeader"],
Cell[CellGroupData[{
Cell["Image restoration", "Section",
CellChangeTimes->{{3.647716384690867*^9, 3.6477163853144817`*^9}, {
3.6553145894819613`*^9, 3.655314594591407*^9}}],
Cell[TextData[{
"This example comes from the ",
StyleBox["Mathematica",
FontSlant->"Italic"],
" documentation"
}], "Text",
CellChangeTimes->{{3.6477163758185587`*^9, 3.647716376530898*^9}, {
3.655314596703269*^9, 3.655314606527514*^9}, {3.6553146627018147`*^9,
3.6553146666447372`*^9}, {3.6553146997565928`*^9, 3.655314705595257*^9}, {
3.655316493979108*^9, 3.655316494880995*^9}}],
Cell[BoxData[
RowBox[{"Inpaint", "[",
RowBox[{
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJwsvPdvnGm+5Xdh+wf/FQsY/m1hOABewzYuDNzd9Z2ZFkkx51Q558RilNQ9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