DistanceToBoundary
HHanh
April 20, 2017
Read file tps
read.tps = function(data) {
a = readLines(data)
LM = grep("LM", a)
ID.ind = grep("ID", a)
images = basename(gsub("(IMAGE=)(.*)", "\\2", a[ID.ind - 1]))
skip = LM
nrows = as.numeric(gsub("(LM=)([0-9])", "\\2", grep("LM", a, value=T)))
l = length(LM)
landmarks = vector("list", l)
for (i in 1:l) {
landmarks[i] = list(data.frame(
read.table(file=data, header=F, skip=LM[i],
nrows=nrows[i], col.names=c("X", "Y")),
IMAGE = images[i],
ID = read.table(file=data, header=F, skip=ID.ind[i]-1,
nrows=1, sep="=", col.names="ID")[2,],
Scale = read.table(file=data, header=F, skip=ID.ind[i],
nrows=1, sep="=")[,2]))
}
do.call(rbind, landmarks)
}Read the file TPS and convert it to csv, delete some unnecessary information
setwd("C:/Users/Admin/Documents/Intern/Intern")
data = read.tps("totlm4_F_Juillet_SL_CLIC.TPS")
write.csv(data,file="data1.csv")Add isLandmark column to data2.csv
data1 <- read.csv("data2.csv")
data2 <- data.matrix(data1[,c("X","Y")])
head(data2)## X Y
## [1,] 1698 1279
## [2,] 2103 1223
## [3,] 2290 1110
## [4,] 2364 1024
## [5,] 2408 927
## [6,] 2311 835Distance to the center
center <- colMeans(data2)
center## X Y
## 1535.8889 911.8175temp = data.frame
a = data.frame()
for (i in 1:length(data2[,1])){
#print(i)
distance <- sqrt((data2[i,1]-center[1])^2 + (data2[i,2]-center[2])^2)
a <- rbind(a, distance)
#print(distance)
}
colnames(a) <- c("Distance")
X <- cbind(data1,a)
d <- X[,c("X","Y","Distance","isLandmark")]
#write.csv(d, file= "Test.csv")
d1 <- data.matrix(d[,c("X","Y","Distance","isLandmark")])
head(d1)## X Y Distance isLandmark
## 1 1698 1279 401.3764 1
## 2 2103 1223 646.8768 1
## 3 2290 1110 779.7178 1
## 4 2364 1024 835.6751 1
## 5 2408 927 872.2433 1
## 6 2311 835 778.9083 1library(shapes)##
## Attaching package: 'shapes'## The following object is masked from 'package:stats':
##
## sigmaplotshapes(d1,joinline=1:126)
#plot(d1)Randomize position of landmark coordinates in matrix
rand.rowx <- d1[sample(nrow(d1)),]
rand <- write.csv(rand.rowx,file= "ranData.csv",quote=F,row.names=F,col.names=T)## Warning in write.csv(rand.rowx, file = "ranData.csv", quote = F, row.names
## = F, : attempt to set 'col.names' ignoredrand.rowx## X Y Distance isLandmark
## 106 1501 514 399.34442 0
## 4 2364 1024 835.67514 1
## 89 2142 1217 678.60671 0
## 102 1807 563 441.78598 0
## 103 1691 536 406.56884 0
## 7 2155 726 646.39516 1
## 126 1004 1086 559.68326 0
## 75 490 1109 1064.31411 0
## 35 2276 1016 747.40783 0
## 25 616 776 929.86125 0
## 70 2245 839 712.84006 0
## 32 2181 834 649.78758 0
## 116 2424 895 888.27033 0
## 124 1097 954 440.91136 0
## 51 1585 518 396.86785 0
## 86 1899 1270 510.04354 0
## 107 1712 539 412.32024 0
## 23 538 834 1000.91847 0
## 112 2231 1238 767.83755 0
## 93 2374 869 839.20413 0
## 79 1001 1198 606.63545 0
## 9 1725 552 406.48692 1
## 17 506 1018 1035.34818 0
## 78 886 1174 700.78189 0
## 45 1166 1230 487.91179 0
## 96 2226 763 705.97449 0
## 72 1298 1024 263.01339 0
## 27 1410 572 362.38642 0
## 122 1697 1147 285.07476 0
## 41 1488 1022 120.13966 0
## 110 229 953 1307.53760 0
## 10 1242 847 300.95179 1
## 80 1089 1217 541.15253 0
## 90 2340 1066 818.75939 0
## 77 793 1158 782.61724 0
## 65 1815 1203 403.34884 0
## 14 881 944 655.67917 1
## 5 2408 927 872.24326 1
## 95 2266 789 740.36907 0
## 29 1777 557 428.98718 0
## 22 572 836 966.86611 0
## 91 2398 981 864.88253 0
## 117 2288 806 759.51857 0
## 56 1083 594 553.27776 0
## 55 1179 568 495.56042 0
## 52 1542 520 391.86511 0
## 121 1405 1057 195.47345 0
## 120 1491 882 53.88964 0
## 94 2347 852 813.31382 0
## 16 786 1092 771.23219 1
## 54 1284 547 443.32809 0
## 44 575 1127 984.68827 0
## 50 1864 586 462.40017 0
## 119 1923 658 462.90206 0
## 118 2101 722 596.13860 0
## 82 1430 1266 369.67246 0
## 105 1561 517 395.61521 0
## 64 1735 1204 353.57583 0
## 100 1907 602 483.43595 0
## 97 2199 737 685.76781 0
## 98 2126 705 625.30359 0
## 92 2401 895 865.27456 0
## 59 881 676 696.05268 0
## 39 1561 937 35.56302 0
## 125 1093 991 449.91159 0
## 30 1693 848 169.57762 0
## 101 1989 633 532.02336 0
## 85 1830 1282 472.79642 0
## 28 1699 563 385.06994 0
## 11 1218 1041 343.13478 1
## 62 456 845 1081.95406 0
## 123 1048 914 487.89377 0
## 24 546 808 995.31808 0
## 67 1785 1209 387.78062 0
## 46 1620 1288 385.47112 0
## 36 2021 1013 495.55090 0
## 40 1653 931 118.67174 0
## 47 1965 1264 555.12961 0
## 20 567 915 968.89412 0
## 115 2502 990 969.26941 0
## 37 1719 1018 211.67052 0
## 42 1403 523 410.89959 0
## 63 343 884 1193.21319 0
## 69 1989 1051 474.00576 0
## 58 951 650 640.81463 0
## 104 1643 525 401.37331 0
## 68 1635 1243 345.69479 0
## 71 2012 729 510.00394 0
## 1 1698 1279 401.37642 1
## 61 733 733 822.56079 0
## 73 302 1079 1245.16336 0
## 111 1882 1287 510.44573 0
## 31 1783 842 256.78469 0
## 66 1801 1214 401.99277 0
## 18 517 992 1022.03904 0
## 8 1947 626 500.70347 1
## 81 1308 1251 408.62959 0
## 53 1472 515 401.92771 0
## 12 1491 1135 227.65206 1
## 83 1532 1280 368.20308 0
## 48 2195 1195 717.37006 0
## 74 429 1101 1122.93947 0
## 57 1021 617 593.31939 0
## 49 2047 657 571.10989 0
## 33 2160 934 624.50520 0
## 21 585 876 951.56322 0
## 87 2049 1249 613.98296 0
## 34 2339 929 803.29490 0
## 99 2087 679 598.27036 0
## 76 683 1143 883.66556 0
## 3 2290 1110 779.71783 1
## 88 2260 1148 761.65550 0
## 38 1530 937 25.86193 0
## 2 2103 1223 646.87679 1
## 84 1764 1283 435.67322 0
## 113 2393 1145 888.26435 0
## 114 2461 1067 938.03635 0
## 108 1604 517 400.64941 0
## 19 600 963 937.28740 0
## 26 1445 699 231.41318 0
## 6 2311 835 778.90831 1
## 60 798 710 764.99039 0
## 43 1377 526 417.25387 0
## 13 831 906 704.91289 1
## 109 223 979 1314.60668 0
## 15 877 985 662.94061 1