README.Rmd

---
output: github_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warn = FALSE,
  message = FALSE
)

requireNamespace("ggplot2", quietly = TRUE)
requireNamespace("geomtextpath", quietly = TRUE)
requireNamespace("smoothr", quietly = TRUE)
```

# centerline <img src="man/figures/logo.png" align="right" width="120" />

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The `centerline` R package simplifies the extraction of linear features from complex polygons, such as roads or rivers, by computing their centerlines (or median-axis) based on skeletons. It uses the super-fast [`geos`](https://paleolimbot.github.io/geos/index.html) library in the background and have bindings for your favorite spatial data library ([`sf`](https://r-spatial.github.io/sf/), [`terra`](https://rspatial.github.io/terra/) and [`geos`](https://paleolimbot.github.io/geos/index.html)).

## Installation

```{r, eval = FALSE}
# The easiest way to get centerline is to install it from CRAN:
install.packages("centerline")

# Or the development version from GitHub:
# install.packages("pak")
pak::pak("atsyplenkov/centerline")
```

## Examples for closed geometries

At the heart of this package is the `cnt_skeleton` function, which efficiently computes the skeleton of closed 2D polygonal geometries. The function uses [`geos::geos_simplify`](https://paleolimbot.github.io/geos/reference/geos_centroid.html) by default to keep the most important nodes and reduce noise from the beginning. However, it has option to densify the amount of points using [`geos::geos_densify`](https://paleolimbot.github.io/geos/reference/geos_centroid.html), which can produce more smooth results. Otherwise, you can set the parameter `keep = 1` to work with the initial geometry.

```{r skeleton_lake, warn=FALSE, message=FALSE}
library(sf)
library(centerline)

lake <-
  sf::st_read(
    system.file("extdata/example.gpkg", package = "centerline"),
    layer = "lake",
    quiet = TRUE
  )

# Original
lake_skeleton <-
  cnt_skeleton(lake, keep = 1)

# Simplified
lake_skeleton_s <-
  cnt_skeleton(lake, keep = 0.1)

# Densified
lake_skeleton_d <-
  cnt_skeleton(lake, keep = 2)

```

<details>
<summary>cnt_skeleton() code 👇</summary>
```{r skeletons_plot}
library(ggplot2)

skeletons <-
  rbind(lake_skeleton, lake_skeleton_s, lake_skeleton_d)
skeletons$type <- factor(
  c("Original", "Simplified", "Densified"),
  levels = c("Original", "Simplified", "Densified")
)

skeletons_plot <-
  ggplot() +
  geom_sf(
    data = lake,
    fill = "#c8e8f1",
    color = NA
  ) +
  geom_sf(
    data = skeletons,
    lwd = 0.2,
    alpha = 0.5,
    color = "#263238"
  ) +
  coord_sf(expand = FALSE, clip = "off") +
  labs(caption = "cnt_skeleton() example") +
  facet_wrap(~type) +
  theme_void() +
  theme(
    plot.caption = element_text(family = "mono", size = 6),
    plot.background = element_rect(fill = "white", color = NA),
    strip.text = element_text(face = "bold", hjust = 0.25, size = 12),
    plot.margin = margin(0.2, -0.5, 0.2, -0.5, unit = "lines"),
    panel.spacing.x = unit(-2, "lines")
  )
```
</details>
```{r save_skeletons, include=FALSE}
ggsave(
  "man/figures/README-skeletons_plot.png",
  skeletons_plot,
  dpi = 500,
  width = 10,
  height = 6,
  units = "cm"
)
```
```{r, echo=FALSE, out.width = "80%", fig.align = "center"}
knitr::include_graphics("man/figures/README-skeletons_plot.png")
```

<br>

However, the above-generated lines are not exactly a centerline of a polygon. One way to find the centerline of a closed polygon is to define both `start` and `end` points with the `cnt_path()` function. For example, in the case of landslides, it could be the landslide initiation point and landslide terminus.

```{r}
# Load Polygon Of Interest (POI)
polygon <-
  sf::st_read(
    system.file(
      "extdata/example.gpkg",
      package = "centerline"
    ),
    layer = "polygon",
    quiet = TRUE
  )

# Load points data
points <-
  sf::st_read(
    system.file(
      "extdata/example.gpkg",
      package = "centerline"
    ),
    layer = "polygon_points",
    quiet = TRUE
  ) |>
  head(n = 2)
points$id <- seq_len(nrow(points))

# Find POI's skeleton
pol_skeleton <- cnt_skeleton(polygon, keep = 1.5)

# Connect points
# For original skeleton
pol_path <-
  cnt_path(
    skeleton = pol_skeleton,
    start_point = subset(points, points$type == "start"),
    end_point = subset(points, points$type == "end")
  )
```

<details>
<summary>cnt_path() code 👇</summary>
```{r path_plot}
path_plot <- ggplot() +
  geom_sf(
    data = polygon,
    fill = "#d2d2d2",
    color = NA
  ) +
  geom_sf(
    data = pol_path,
    lwd = 1,
    color = "black"
  ) +
  geom_sf(
    data = points,
    aes(
      shape = type,
      fill = type
    ),
    color = "white",
    lwd = rel(1),
    size = rel(3)
  ) +
  scale_fill_manual(
    name = "",
    values = c(
      "start" = "dodgerblue",
      "end" = "firebrick"
    )
  ) +
  scale_shape_manual(
    name = "",
    values = c(
      "start" = 21,
      "end" = 22
    )
  ) +
  coord_sf(expand = FALSE, clip = "off") +
  labs(caption = "cnt_path() example") +
  theme_void() +
  theme(
    legend.position = "inside",
    legend.position.inside = c(0.85, 0.2),
    legend.key.spacing.y = unit(-0.5, "lines"),
    plot.caption = element_text(family = "mono", size = 6),
    plot.background = element_rect(fill = "white", color = NA),
    strip.text = element_text(face = "bold", hjust = 0.25, size = 12),
    plot.margin = margin(0.2, -0.5, 0.2, -0.5, unit = "lines"),
    panel.spacing.x = unit(-2, "lines")
  )
```
</details>

```{r save_path_plot, include=FALSE}
ggsave(
  "man/figures/README-path_plot.png",
  path_plot,
  dpi = 500,
  width = 7,
  height = 6,
  units = "cm"
)
```
```{r, echo=FALSE, out.width = "50%", fig.align = "center"}
knitr::include_graphics("man/figures/README-path_plot.png")
```

<br>

And what if we don't know the starting and ending locations? What if we just want to place our label accurately in the middle of our polygon? In this case, one may find the `cnt_path_guess` function useful. It returns the line connecting the most distant points, i.e., the polygon's length. Such an approach is used in limnology for measuring [lake lengths](https://www.lakescientist.com/lake-shape/), for example.

```{r}
lake_centerline <- cnt_path_guess(lake, keep = 1)
```

You can plot polygon centerlines with the `geom_cnt_*` functions family:

<details>
<summary>cnt_path_guess() code 👇</summary>
```{r centerline_plot}
library(ggplot2)

lakes <- rbind(lake, lake)
lakes$lc <- c("black", NA_character_) 

centerline_plot <- 
  ggplot() +
  geom_sf(
    data = lakes,
    fill = "#c8e8f1",
    color = NA
  ) +
  geom_cnt_text(
    data = lakes,
    aes(
      label = name,
      linecolor = lc
    ),
    keep = 1
  ) +
  facet_wrap(~lc) +
  labs(
    caption = "cnt_path_guess() and geom_cnt_text() examples"
  ) +
  theme_void() +
  theme(
    legend.position = "inside",
    legend.position.inside = c(0.85, 0.2),
    legend.key.spacing.y = unit(-0.5, "lines"),
    plot.caption = element_text(family = "mono", size = 6),
    plot.background = element_rect(fill = "white", color = NA),
    strip.text = element_blank(),
    plot.margin = margin(0.2, -0.5, 0.2, -0.5, unit = "lines"),
    panel.spacing.x = unit(-2, "lines")
  )
```
</details>

```{r save_centerline_plot, include=FALSE}
ggsave(
  "man/figures/README-centerline_plot.png",
  centerline_plot,
  dpi = 500,
  width = 8,
  height = 6,
  units = "cm"
)
```
```{r, echo=FALSE, out.width = "80%", fig.align = "center"}
knitr::include_graphics("man/figures/README-centerline_plot.png")
```


## Roadmap

```
centerline 📦
├── Closed geometries (e.g., lakes, landslides)
│   ├── When we do know starting and ending points (e.g., landslides) ✅
│   │   ├── centerline::cnt_skeleton ✅
│   │   └── centerline::cnt_path ✅
│   └── When we do NOT have points (e.g., lakes) ✅
│       ├── centerline::cnt_skeleton ✅
│       └── centerline::cnt_path_guess ✅
├── Linear objects (e.g., roads or rivers)  🔲
└── Collapse parallel lines to centerline 🔲
```

## Alternatives
- **R**
	- [midlines](https://github.com/RichardPatterson/midlines) - A more hydrology-oriented library that provides a multi-step approach to generate a smooth centerline of complex curved polygons (like rivers).
	- [cmgo](https://github.com/AntoniusGolly/cmgo) - The main aim of the package is to propose a workflow to extract channel bank metrics, and as a part of that workflow, centerline extraction was implemented.
  - [raybevel](https://github.com/tylermorganwall/raybevel) - Provides a way to generate **straight** skeletons of polygons. This approach is implemented in the `cnt_skeleton(method = "straight")` function of the current package.
- 🐍 Python:
	- [centerline](https://github.com/fitodic/centerline/tree/master) library
- 🦀 Rust:
	- [centerline_rs](https://codeberg.org/eadf/centerline_rs) library
- **JS** Javascript:
  - [Centerline labeling blogpost](https://observablehq.com/@veltman/centerline-labeling)