Apply random deep style filters to a bunch of images

random-filters.rb

#!/usr/bin/env ruby
#
# Takes an arbitrary number of files as arguments
#   and randomly generates PER_IMAGE filtered versions
#   of each image while using each filter an equal
#   number of times (approx).
#
# It uses batch inputs to minimize API calls,
#   but still expect at least 1 API call per filter
#
# Example usage:
#
#   export ALGORITHIA_API_KEY=sim.....
#   ./random-filter ~/Pictures/*.jpg
#

require 'algorithmia'
require 'pathname'
require 'pp'

# Change this to set the number of filters to apply to each image
PER_IMAGE = 4
# Filters that just never seemed to appeal to me
IGNORE_FILTERS = %w(plentiful alien_goggles colorful_blocks green_zuma aqua cinnamon_rolls really_hot space_pizza purp_paper plentiful)

ALL_FILTERS = %w(alien_goggles aqua blue_brush blue_granite bright_sand cinnamon_rolls clean_view colorful_blocks colorful_dream crafty_painting creativity crunch_paper dark_rain dark_soul deep_connections dry_skin far_away gan_vogh gred_mash green_zuma hot_spicy neo_instinct oily_mcoilface plentiful post_modern purp_paper purple_pond purple_storm rainbow_festival really_hot sand_paper smooth_ride space_pizza spagetti_accident sunday yellow_collage yellow_paper) - IGNORE_FILTERS


all_paths = ARGV
client = Algorithmia.client(ENV['ALGORITHMIA_API_KEY'])
data_dir_uri = 'data://.my/test'
data_dir = client.dir(data_dir_uri)

# Pre-upload any files that aren't already uploaded
# So that we can use them in multiple algo calls
# without re-uploading them
dir_listing = data_dir.each_file.map{|f| f.basename}.to_a
all_paths.each do |p|
  basename = Pathname.new(p).basename
  unless dir_listing.include?(basename.to_s)
    puts "Uploading #{basename}"
    data_file = data_dir.file(basename)
    data_dir.put_file(p)
  end
end

def generated_filename(source, filter)
    "#{Pathname.new(source).basename('.*')}-#{filter}.jpg"
end

# Generate pairings of filters with paths such that
# each image has PER_IMAGE unique filters
# and each filter gets assigned to approx same number of images
# Also, avoid re-generating images from previus runs
filter_paths = {}
filter_set = []
dir_listing = data_dir.each_file.map{|f| f.basename}.to_a
all_paths.shuffle.each do |p|
  redoes = 0
  PER_IMAGE.times do
    filter_set = ALL_FILTERS.shuffle if filter_set.empty?
    f = filter_set.pop
    if !dir_listing.include?(generated_filename(p, f))
      filter_paths[f] ||= []
      filter_paths[f] << p
    elsif redoes < ALL_FILTERS.length
      redo
    end
  end
end

# Now call the algorithm for each filter-path pariring
# since the algorithm allows batch operation per-filter
filter_paths.each do |filter, paths|
  return if paths.empty?

  src_paths = paths.map do |p|
    basename = Pathname.new(p).basename
    data_dir.file(basename).data_uri
  end
  save_paths = paths.map do |p|
    "#{data_dir_uri}/#{generated_filename(p, filter)}"
  end
  data = {
    images: src_paths,
    savePaths: save_paths,
    filterName: filter,
  }

  puts "\n### Rendering #{src_paths.length} images with #{filter} filter..."
  for i in 0..3
    begin
      algo = client.algo('deeplearning/DeepFilter/0.5.7')
      algo.set_timeout(300)
      resp = algo.pipe(data)
      resp.result['savePaths'].each { |p| puts p }
      break
    rescue => e
      puts "Error: #{e.inspect}"
      if i == 3
        pp data
      else
        puts "Retrying..."
      end
    end
  end
end