Jiaxiang Li 2019-03-14
suppressMessages(library(tidyverse))
read_lines("L04808.inf", n_max = 6)
## [1] "k\t-48.470881\t32.011461\t95.238590"
## [2] "i\t-21.382022\t29.339303\t104.449558"
## [3] "j\t19.595904\t31.194517\t102.236371"
## [4] "l\t45.550416\t32.628547\t97.349969"
## [5] "v\t-28.168727\t-40.454120\t110.929256"
## [6] "w\t25.815436\t-41.598142\t108.601852"
read_lines("L04808.vtx", n_max = 6)
## [1] "-7.438540\t-91.531036\t84.705287" "-6.838273\t-91.779326\t84.984915"
## [3] "-4.882652\t-93.316424\t85.449459" "-4.779594\t-93.432092\t85.415700"
## [5] "-4.126869\t-93.563063\t85.388501" "-3.469838\t-93.705728\t85.336970"
vtx <- read_delim("L04808.vtx",delim = '\t',col_names = c('x1','y1','z1'))
## Parsed with column specification:
## cols(
## x1 = col_double(),
## y1 = col_double(),
## z1 = col_double()
## )
inf <- read_delim("L04808.inf",delim = '\t',col_names = c('l','x2','y2','z2'))
## Parsed with column specification:
## cols(
## l = col_character(),
## x2 = col_double(),
## y2 = col_double(),
## z2 = col_double()
## )
vtx %>% dim
## [1] 32251 3
inf %>% dim
## [1] 17 4
vtx是一个立体的脸。
inf %>% mutate(by = 1) %>%
full_join(vtx %>% mutate(by = 1) %>%
mutate(vtx_index = row_number())
, by = 'by') %>%
group_by(l) %>%
mutate(
dist = (x1-x2)^2 + (y1-y2)^2 + (z1-z2)^2
) %>%
filter(
dist == min(dist)
)
## # A tibble: 17 x 10
## # Groups: l [17]
## l x2 y2 z2 by x1 y1 z1 vtx_index
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 k -4.85e+1 32.0 95.2 1 -4.86e+1 32.1 95.3 23786
## 2 i -2.14e+1 29.3 104. 1 -2.13e+1 29.9 104. 23814
## 3 j 1.96e+1 31.2 102. 1 1.94e+1 31.7 103. 23860
## 4 l 4.56e+1 32.6 97.3 1 4.52e+1 32.3 97.7 23889
## 5 v -2.82e+1 -40.5 111. 1 -2.83e+1 -40.1 111. 10292
## 6 w 2.58e+1 -41.6 109. 1 2.60e+1 -42.1 109. 10349
## 7 o 0. 0 140 1 5.77e-2 -0.832 140. 17346
## 8 p -1.42e-3 38.6 113. 1 9.09e-3 38.0 113. 24265
## 9 m -1.90e+1 -7.42 116. 1 -1.91e+1 -7.43 116. 16834
## 10 n 2.00e+1 -7.76 114. 1 1.99e+1 -7.78 114. 16879
## 11 r -1.00e+0 -39.4 126. 1 -8.56e-1 -39.3 126. 10823
## 12 s 4.14e-1 -35.2 130. 1 9.09e-1 -35.3 130. 12597
## 13 t 7.99e-1 -46.3 127. 1 3.03e-1 -46.6 127. 9073
## 14 q -1.06e-3 -14.6 126. 1 3.82e-2 -14.3 127. 15373
## 15 z -1.00e+0 -82.6 116. 1 -7.60e-1 -82.2 117. 3206
## 16 x -7.33e+1 -26.1 35.9 1 -7.34e+1 -25.9 36.1 15000
## 17 y 6.28e+1 -30.9 30.1 1 6.27e+1 -31.0 30.3 15496
## # ... with 1 more variable: dist <dbl>
如果运算量大，运算速度慢，可以采用下面的方法。 round 图片加速运算。
library(magrittr)
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
##
## set_names
## The following object is masked from 'package:tidyr':
##
## extract
library(wrapr)
##
## Attaching package: 'wrapr'
## The following object is masked from 'package:dplyr':
##
## coalesce
## The following object is masked from 'package:tibble':
##
## view
# vtx
vtx %>% round(-1) %>% distinct() -> vtx_n1;dim(vtx_n1)
## [1] 707 3
vtx %>% round( 0) %>% distinct() -> vtx_00;dim(vtx_00)
## [1] 29632 3
vtx %>% round( 1) %>% distinct() -> vtx_01;dim(vtx_01)
## [1] 32235 3
vtx %>% round( 2) %>% distinct() -> vtx_02;dim(vtx_02)
## [1] 32240 3
vtx %>% round( 3) %>% distinct() -> vtx_03;dim(vtx_03)
## [1] 32240 3
然后按照round后的图片，先找 local 最优，再调试。