## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(thinr)

## -----------------------------------------------------------------------------
# A 'V' shape — exercises diagonal preservation
make_v <- function() {
  m <- matrix(0L, 11, 11)
  for (k in seq(0, 5)) {
    m[2 + k, 2 + k] <- 1L
    m[2 + k, 10 - k] <- 1L
    m[3 + k, 2 + k] <- 1L
    m[3 + k, 10 - k] <- 1L
  }
  m
}
v <- make_v()
v

## -----------------------------------------------------------------------------
thin(v, method = "zhang_suen")

## -----------------------------------------------------------------------------
thin(v, method = "guo_hall")

## -----------------------------------------------------------------------------
thin(v, method = "hilditch")

## -----------------------------------------------------------------------------
thin(v, method = "k3m")

## -----------------------------------------------------------------------------
thin(v, method = "holt")

## -----------------------------------------------------------------------------
m <- matrix(0L, 9, 11)
m[3:7, 3:9] <- 1L      # 5x7 solid rectangle
medial_axis(m)

## -----------------------------------------------------------------------------
result <- medial_axis(m, return_distance = TRUE)
result$skeleton
round(result$distance, 3)

## -----------------------------------------------------------------------------
m <- matrix(1L, 5, 5)
m[1, 1] <- 0L
distance_transform(m, metric = "manhattan")
distance_transform(m, metric = "chessboard")
round(distance_transform(m, metric = "euclidean"), 3)

## ----eval = FALSE-------------------------------------------------------------
# library(bench)
# m <- matrix(0L, 200, 200)
# m[50:150, 50:150] <- 1L  # solid square
# 
# bench::mark(
#   zs       = thin(m, method = "zhang_suen"),
#   gh       = thin(m, method = "guo_hall"),
#   hild     = thin(m, method = "hilditch"),
#   k3m      = thin(m, method = "k3m"),
#   ma       = medial_axis(m),
#   dt_eucl  = distance_transform(m, metric = "euclidean"),
#   iterations = 5,
#   check = FALSE
# )