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如何根据颜色对壁纸/照片进行分组？

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如何根据颜色对壁纸/照片进行分组？

我有几个工作区，我想通过壁纸来区分它们。

另一方面，我希望在每个工作区上都有某种形式的壁纸轮换。

是否有任何现成的软件可以将我的壁纸作为输入，并根据其颜色将它们分组为指定数量的组？

如果它可以从命令行运行（这样我就可以自动化它），则可以获得额外的奖励。

答案1

我用 R 编写了以下脚本：

#!/usr/bin/Rscript

makeSureInstalled<-function(package) {if (length(grep(paste("^",package,"$",sep=""),noquote(installed.packages())[,1]))==0) install.packages(package)
                                      library(package=package,character.only=TRUE)
}

makeSureInstalled("argparse")
makeSureInstalled("foreach")
makeSureInstalled("doMC")

arghandling<-function()
  # Reads command line arguments, verifies the input
{
  parser <- ArgumentParser(description="Reads all files in the specified directory (not-recursively) treats them as jpegs, and groups them by mean color.",
                           epilog="Example: ./wallpapers-grouping.R --cluster-count 9 --out-prefix 'cluster '")
  parser$add_argument("--cluster-count","-n", type="integer", dest="clustercount",
                      help="Number of groups to do. Must be (much) smaller than number of individual photos.")
  parser$add_argument("--feature-set", "-f", dest="mode", type="integer", 
                      help="Before running the actual clustering, the program pre-processes image and extracts features. There are 2 feature sets supported: 1 - one pixel (3 numbers for each colour). 2 - 4 pixel (image is divided into equal 4 parts, and each part is averaged to one pixel. 4x3=12 numbers in total.)")
  parser$add_argument("--out-prefix","-o", type="character", default='cluster ', dest='prefix',
                      help="The prefix appended to each grouped picture before grouping. Can be a directory name (absolute or relative) or name prefix.")
  parser$add_argument("--cpu-count",  type="integer", dest="cpu",
                      help="How many cores to use. Defaults to all available cores.")
  parser$add_argument("input", type="character", 
                      help="Input directory. Defaults to current directory. ")

  args<-commandArgs(trailingOnly = TRUE)

  if (length(args)==0)
  {
    parser$print_help()
    quit()
  } else
  {
    cat("Welcome to the wallpapers-grouping.R program.\n")
    args=parser$parse_args(args)
  }


  if (is.null(args$clustercount))
  {
    cat(paste0("Error! You must specify number of clusters with --cluster-count or -n argument.\n"))
    quit()    
  }

  if (is.null(args$input))
  {
    args$input<-cwd()
  } else {
    if(!file.exists(args$input))
    {
      cat(paste0("Cannot find the input directory (", args$input,")\n"))
      quit()
    }
  }

  photos=dir(path=args$input)
  cat(paste0(length(photos)," files found in ",args$input,".\n"))

  if (length(photos)<=args$clustercount)
  {
    cat(paste0("Number of clusters must be (much) smaller than number of photos.\n"))
    quit()
  }

  registerDoMC()
  howManyCores<-getDoParWorkers()
  if(is.null(args$cpu) || args$cpu==howManyCores)
  {
    cat(paste0(howManyCores," cores detected.\n"))
  } else {
    cat(paste0(howManyCores," cores detected, but will use ",args$cpu," independent threads.\n"))  
    howManyCores<-args$cpu
  }
  registerDoMC(cores=howManyCores)

  if(is.null(args$mode))
  {
    args$mode<-1
  }

  args$mode<-switch(args$mode,1,2)

  list(args=args,photos=photos)
}

ah<-arghandling()

photos<-ah$photos
args<-ah$args


makeSureInstalled('jpeg')
makeSureInstalled('plyr')

processphoto<-function(filename,mode=1) {
  photo=readJPEG(filename)
  if(mode==1)
  {
    apply(photo,3,mean)
  }else  {
    dims<-dim(obraz)[1:2]
    gr=round(dims/2)
    o11<-photo[1:gr[[1]],1:gr[[2]],]
    o12<-photo[(gr[[1]]+1):dims[[1]],1:gr[[2]],]
    o21<-photo[1:gr[[1]],(gr[[2]]+1):dims[[2]],]
    o22<-photo[(gr[[1]]+1):dims[[1]],(gr[[2]]+1):dims[[2]],]
    o11<-apply(o11,3,mean)
    o12<-apply(o12,3,mean)
    o21<-apply(o21,3,mean)
    o22<-apply(o22,3,mean)

    c(o11,o12,o21,o22)
  }
}

cat(paste0('Extracting features from all ',length(photos),' pictures...'))
results<-aaply(photos,1,processphoto,.parallel=TRUE,mode=args$mode)

cat(paste0('Grouping them into ',length(args$clustercount),' groups using k-means...'))
clusters<-kmeans(results,args$clustercount)

copycluster <- function(which.cluster) {
  files<-photos[clusters$cluster==which.cluster]
  todir <- paste0(args$prefix,which.cluster)
  if (!isTRUE(file.info(todir)$isdir)) dir.create(todir, recursive=TRUE)

  file.copy(from = files,  to = paste0(todir,'/',files ))
}

cat(paste0("Copying photos into their groups..."))
a_ply(1:args$clustercount,1,copycluster)

脚本会安装所需的库。在 Linux/Mac 上，它将使用所有可用核心（不幸的是，在 Windows 上不能）。

它应该可以在任何系统上开箱即用，并且在 Linux/Mac 上，它可以直接从终端运行。

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