从其中R矩阵

Question

我已中的R计算，并且图像选择特定区域，我已经定义某些像素为0，I只希望“0”，是完全由像素配量包围测距：

inpixno0 <- filter(inx0, inx0$pixel!=0) 
range(inpixno0$pixel) 
# [1] 0.5000476 0.6998763` 

例如，我只想要红色区域包围的黑色区域。

我已创建包含所提到的像素和它们的索引的数据帧。它看起来像这样：

> inx0[326:333,] 
# row col  pixel 
# 326 36 34 0.5141253 
# 327 37 34 0.5039121 
# 328 38 34 0.0000000 
# 329 39 34 0.0000000 
# 330 40 34 0.0000000 
# 331 41 34 0.5376547 
# 332 42 34 0.5866648 
# 333 43 34 0.6188273 

dim(inx0) 
# [1] 12350  3 

我有很多这样的数据帧，而我的想法是提取ROW和COL索引等于0，我感兴趣的值，并有新的数据帧我拥有这些的每一个数据框。

我想知道如何使用高效的代码来完成，因此我可以将它应用于所有具有这些特性（超过1500）的数据帧。

可重复的数据集（500个观察）：

structure(list(row = c(87, 87, 92, 93, 94, 96, 101, 80, 91, 92, 
93, 94, 95, 96, 97, 98, 99, 100, 102, 91, 92, 93, 94, 95, 96, 
97, 98, 99, 100, 102, 104, 78, 80, 82, 83, 92, 93, 94, 95, 96, 
97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 79, 97, 98, 99, 
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 102, 104, 105, 
106, 107, 108, 109, 110, 105, 106, 107, 108, 109, 110, 111, 112, 
113, 116, 108, 110, 111, 112, 113, 114, 115, 54, 55, 56, 57, 
58, 59, 62, 112, 114, 115, 116, 117, 55, 56, 57, 58, 117, 53, 
54, 55, 56, 57, 58, 59, 60, 47, 48, 49, 50, 51, 52, 53, 54, 55, 
56, 57, 58, 59, 60, 121, 122, 45, 46, 47, 48, 49, 50, 51, 52, 
53, 54, 55, 56, 57, 58, 59, 60, 122, 123, 124, 126, 44, 45, 46, 
47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 124, 125, 
126, 127, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 
55, 56, 125, 126, 127, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 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, 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, 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, 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, 145, 146, 147, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 49, 146, 25, 
26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 
42, 43, 44, 45, 46, 47, 48, 145, 146, 147, 23, 26, 27, 28, 29, 
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 
46, 133, 145, 147, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 133, 134, 135, 150, 22, 
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 
39, 40, 41, 51, 53, 62, 63, 64, 77, 134, 135, 136, 150, 151, 
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 
39, 40, 41, 52, 53, 60, 62, 65, 83, 84, 135, 136, 137, 150, 151, 
152, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 
37, 38, 39, 49, 51, 52, 53), col = c(13, 14, 14, 14, 14, 14, 
14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 
16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 
19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 
21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 
23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 
26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 
27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 
27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 
29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 
30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 
31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 
32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 
33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 
33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 
34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 
35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 
35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 
36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 
37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 
38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 
38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 
39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 
39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 40, 
40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40), pixel = c(0.692499524081477, 
0.695745288406624, 0.694355606320198, 0.699857224443175, 0.692061679040548, 
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0.686769465067581, 0.691909385113268, 0.686398248619836, 0.683447553778793, 
0.682352941176471, 0.675623453264802, 0.678555111364934, 0.680392156862745, 
0.696449647820293, 0.698781648581763, 0.693318103940605, 0.695878545592994, 
0.684751570531125, 0.667628022082619, 0.665705311250714, 0.679392727964972, 
0.687235865219874, 0.65221778031601, 0.66082238720731, 0.69564058633162, 
0.695478774033885, 0.666542927850752, 0.694669712545212, 0.685722444317533, 
0.693565581572435, 0.69776318294308, 0.683761660003807, 0.687074052922141, 
0.676470588235294, 0.675889967637541, 0.683704549781079, 0.67843137254902, 
0.645040928992958, 0.666666666666667, 0.674538359032933, 0.683761660003807, 
0.662440510184657, 0.66749476489625, 0.67395773843518, 0.696354464115744, 
0.696906529602132, 0.684161431562916, 0.680192271083191, 0.68913953931087, 
0.695926137445269, 0.67365315058062, 0.664705882352941, 0.671844660194175, 
0.675414049114791, 0.685522558537979, 0.680944222349134, 0.688235294117647, 
0.681496287835522, 0.679840091376357, 0.688939653531315, 0.686274509803922, 
0.665810013325719, 0.696935084713497, 0.662745098039216, 0.671692366266895, 
0.665134209023415, 0.66317342470969, 0.695402627070246, 0.699552636588616, 
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0.696078431372549, 0.686931277365315, 0.697363411383971, 0.696040357890728, 
0.690196078431373, 0.677136874167143, 0.657490957548067, 0.686255473063011, 
0.662250142775557, 0.676708547496669, 0.68063011612412, 0.695821435370265, 
0.699743003997716, 0.688577955454026, 0.688235294117647, 0.651104130972778, 
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0.562859318484676, 0.555939463163906, 0.58927279649724, 0.597344374643061, 
0.619493622691795, 0.644060536836094, 0.646135541595279, 0.629525985151342, 
0.655939463163906, 0.681429659242338, 0.671397296782791, 0.679240434037693, 
0.694698267656577, 0.694346087949743, 0.664011041309728, 0.698039215686274, 
0.693080144679231, 0.668846373500857, 0.629944793451361, 0.578964401294499, 
0.541442984960975, 0.532438606510566, 0.536093660765277, 0.547058823529412, 
0.580972777460498, 0.603074433656958, 0.645050447363412, 0.631639063392347, 
0.667780316009899, 0.659137635636779, 0.682667047401484, 0.672549019607843, 
0.687654673519894, 0.67843137254902, 0.661897963068723, 0.654835332191128, 
0.663801637159717, 0.684951456310678, 0.634941937940225, 0.678012564249, 
0.652807919284217, 0.624367028364745, 0.605044736341138, 0.516105082809823, 
0.565343613173423, 0.538663620788119, 0.511897963068724, 0.579973348562726, 
0.614563106796117, 0.626965543498952, 0.641947458595088, 0.633333333333333, 
0.673320007614696, 0.67556634304207, 0.693413287645156, 0.674186179326099, 
0.681781838949172, 0.686521987435751, 0.676146963639825, 0.615810013325719, 
0.623653150580621, 0.614667808871121, 0.630801446792309, 0.632467161621931, 
0.626556253569389, 0.633009708737864, 0.609680182752713, 0.5368075385494, 
0.529735389301352, 0.538397106415382, 0.56294498381877, 0.618465638682657, 
0.614296592423377, 0.641871311631449, 0.622834570721493, 0.657062630877594, 
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0.665105653912051, 0.692156862745098, 0.692423377117838, 0.686541024176662, 
0.690329335617742)), .Names = c("row", "col", "pixel"), row.names = c(NA, 
500L), class = "data.frame") 

Answer 1

这可能是对于很少使用的矩阵索引很好的利用。下面是一个简单的例子：

> mat <- matrix(1:9, 3,3) 
> mat 
    [,1] [,2] [,3] 
[1,] 1 4 7 
[2,] 2 5 8 
[3,] 3 6 9 
> mat[cbind(1:2, 3:2)] 
[1] 7 5 

看起来你已经拥有你想要的像素的行和列，所以这将是这样的：

inpixno0[cbind(rows, cols)]