minixcali: Excalidraw Document Builder

library(imager)
library(tidyverse)
library(purrr)

# # Location of the photograph
# file="frankenstein.jpg"
# 
# # Load, convert to grayscale, filter image (to convert it to bw) and sample
# load.image(file) %>% 
#   grayscale() %>% 
#   threshold("45%") %>% 
#   as.cimg() %>% 
#   as.data.frame() -> franky
# 
# # This function cuts the dendrogram in x groups and removes clusters formed by just one point
# clustResultx <- function(x) {
#   clustCut <- tibble(cluster_id = cutree(clusters, x)) %>% bind_cols(data)
#   clustCut %>% group_by(cluster_id) %>% 
#     summarize(size = n()) %>% 
#     filter(size > 1) %>% 
#     select(cluster_id) %>% 
#     inner_join(clustCut, by = "cluster_id") -> clustCut
#   return(clustCut)
# }
# 
# # This function adds the resulting segments of comparing two consecutive clustering results
# add_segments <- function(x){
#   
#   df1 <- clustEvol[[x]]
#   df0 <- clustEvol[[x-1]]
#   
#   new_points <- anti_join(df1, df0, by = "id")
#   
#   # If a new point is added to an existing cluster
#   new_points %>% 
#     inner_join(df1, by = "cluster_id", suffix = c(".1", ".2")) %>% 
#     filter(id.1 != id.2) %>% 
#     mutate(d = sqrt((x.1 - x.2)^2 + (y.1 - y.2)^2)) %>% 
#     group_by(id.1) %>% 
#     arrange(d) %>% 
#     slice(1) %>% 
#     select(p1 = id.1, p2 = id.2) %>% 
#     ungroup -> new_segments1
#   
#   # If a new 2-points cluster is generated
#   new_points %>% anti_join(bind_rows(select(new_segments1, id = p1), 
#                                      select(new_segments1, id = p2)), by = "id") %>% 
#     group_by(cluster_id) %>% 
#     ungroup -> unpaired_points
#   
#   unpaired_points %>% inner_join(unpaired_points, by = "cluster_id", suffix = c(".1", ".2")) %>% 
#     filter(id.1 < id.2) %>% 
#     select(p1 = id.1, p2 = id.2) -> new_segments2
#   
#   # If two existing clusters are joined
#   new_points <- anti_join(df1, df0, by = c("id", "cluster_id"))
#   
#   new_points %>% 
#     inner_join(df1, by = "cluster_id", suffix = c(".1", ".2")) %>% 
#     filter(id.1 != id.2) %>% 
#     anti_join(new_points, by = c("id.2" = "id")) %>% 
#     mutate(d = sqrt((x.1 - x.2)^2 + (y.1 - y.2)^2)) %>% 
#     arrange(d) %>% 
#     slice(1) %>% 
#     select(p1 = id.1, p2 = id.2) %>% 
#     ungroup -> new_segments3
#   
#   bind_rows(new_segments1, new_segments2, new_segments3)
# }
# 
# # Sample size
# n <- 2500
# 
# # Random sample of points from protograph
# franky %>% 
#   sample_n(n, weight=(1-value)) %>% 
#   select(x,y) %>% mutate(id = row_number()) -> data
# 
# # Matriz of distance between points
# dist_data <- dist(data %>% select(-id), method = "euclidean")
# 
# # Hierarchical clustering
# clusters <- hclust(dist_data, method = 'single')
# 
# # List with all possible clusters from maximum to minimum number of clusters
# nrow(data):1 %>% 
#   map(function(x) clustResultx(x)) -> clustEvol
# 
# # Segments of clusters
# 2:length(clustEvol) %>% 
#   map(function(x) add_segments(x)) %>% 
#   bind_rows() -> segments_id
# 
# # Segments in (x, y) and (xend, yend) format
# segments_id %>% 
#   inner_join(data, by = c("p1" = "id"), suffix = c(".1", ".2")) %>% 
#   inner_join(data, by = c("p2" = "id"), suffix = c(".1", ".2")) %>% 
#   select(x = x.1, y = y.1, xend = x.2, yend = y.2) -> segments
# 
# # Plot
# p <- ggplot(segments) + 
#   geom_curve(aes(x = x, y = y, xend = xend, yend = yend),
#              ncp = 10) +
#   scale_x_continuous(expand=c(.1, .1)) +
#   scale_y_continuous(expand=c(.1, .1), trans = scales::reverse_trans()) +
#   coord_equal() +
#   theme_void()
# 
# gb <- ggplot_build(p)

# d <- gb$data
# saveRDS(d, 'franck.rds')

str(d)