LOC Colors - Production.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOC Colors - Production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Calculate color swatches for historic postcards*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from sys import exit\n",
    "from io import BytesIO\n",
    "from colorsys import rgb_to_hsv, hsv_to_rgb\n",
    "from scipy.cluster.vq import kmeans\n",
    "from numpy import array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEFAULT_NUM_COLORS = 6\n",
    "# default minimum and maximum values are used to clamp the color values to a specific range\n",
    "# originally this was set to 170 and 200, but I'm running with 0 and 256 in order to \n",
    "# not clamp the values. This can also be set as a parameter. \n",
    "DEFAULT_MINV = 0\n",
    "DEFAULT_MAXV = 256\n",
    "\n",
    "THUMB_SIZE = (200, 200)\n",
    "SCALE = 256.0\n",
    "\n",
    "def down_scale(x):\n",
    "    return x / SCALE\n",
    "\n",
    "def up_scale(x):\n",
    "    return int(x * SCALE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original code by Laura Wrubel uses the [RGB (red, green, blue) color space](https://en.wikipedia.org/wiki/RGB_color_space) for most color computations.\n",
    "\n",
    "We're using the [HSV (hue, saturation, value) color space](https://en.wikipedia.org/wiki/HSL_and_HSV) for clustering in the hope of getting prettier and more colorful results for our historic postcards.\n",
    "\n",
    "That necessitates modifying some utility functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clamp_hsv(color, min_v, max_v):\n",
    "    \"\"\"\n",
    "    Clamps a color such that the value (lightness) is between min_v and max_v.\n",
    "    \"\"\"\n",
    "    # use down_scale to convert color to value between 0-1 as expected by rgb_hsv\n",
    "    h, s, v = [down_scale(c) for c in color]\n",
    "    # also convert the min_v and max_v to values between 0-1\n",
    "    min_v, max_v = map(down_scale, (min_v, max_v))\n",
    "    # get the maximum of the min value and the color's value (therefore bumping it up if needed)\n",
    "    # then get the minimum of that number and the max_v (bumping the value down if needed)\n",
    "    v = min(max(min_v, v), max_v)\n",
    "    # apply upscale to get the h, s, v(which has been clamped) back to 0-255, return as tuple\n",
    "    return tuple(map(up_scale, (h, s, v)))\n",
    "\n",
    "\n",
    "def order_by_hue_hsv(colors):\n",
    "    \"\"\"\n",
    "    Orders colors by hue.\n",
    "    \"\"\"\n",
    "    hsvs = [list(map(down_scale, color)) for color in colors]\n",
    "    hsvs.sort(key=lambda t: t[0])\n",
    "    return [tuple(map(up_scale, hsv_to_rgb(*hsv))) for hsv in hsvs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All postcards are scanned in front of a black background, and many contain a lot of very dark colors. This function lets us experiment on removing all colors under a certain saturation or value threshold: colorless (grey-ish) and dark colors, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clip_hsv(colors_hsv, min_s, min_v):\n",
    "    min_s = down_scale(min_s)\n",
    "    min_v = down_scale(min_v)\n",
    "    hsvs = [tuple(map(down_scale, color)) for color in colors_hsv]\n",
    "    hsvs = filter(lambda hsv: (hsv[1] >= min_s) and (hsv[2] >= min_v), hsvs)\n",
    "    return [tuple(map(up_scale, hsv)) for hsv in hsvs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a certain color appears more than once in the picture (when `count >= 1`), we add it more than once to the dataset. This way, large areas of a single color factor in heavily in the resulting clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_colors(img, colorspace='HSV'):\n",
    "    \"\"\"\n",
    "    Returns a list of all the image's colors.\n",
    "    \"\"\"\n",
    "    w, h = img.size\n",
    "    # convert('RGB') converts the image's pixels info to RGB \n",
    "    # getcolors() returns an unsorted list of (count, pixel) values\n",
    "    # w * h ensures that maxcolors parameter is set so that each pixel could be unique\n",
    "    # there are three values returned in a list\n",
    "    # return [color for count, color in img.convert(colorspace).getcolors(w * h)]\n",
    "    return [single_color for count, color in img.convert(colorspace).getcolors(w * h) for single_color in [color] * count]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hexify(rgb):\n",
    "    return \"#{0:02x}{1:02x}{2:02x}\".format(*rgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For experimentation, allow scaling of the colorspace (effectively making clustering along scaled down axes more likely, and along scaled up axes less likely), clipping of pixels with low saturation and/or low value.\n",
    "\n",
    "The scaling is inverted after the clustering algorithm is executed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colorz(image_url, n=DEFAULT_NUM_COLORS, min_v=DEFAULT_MINV, max_v=DEFAULT_MAXV,\n",
    "           order_colors=True, coefficients=[1.0, 1.0, 1.0], clip_colors=False, min_clip_s=20, min_clip_v=20):\n",
    "    \"\"\"\n",
    "    Get the n most dominant colors of an image.\n",
    "    Clamps value to between min_v and max_v.\n",
    "\n",
    "    Total number of colors returned is n, optionally ordered by hue.\n",
    "    Returns as a list of RGB triples.\n",
    "\n",
    "    \"\"\"\n",
    "    try:\n",
    "        r = requests.get(image_url)\n",
    "    except ValueError:\n",
    "        print(\"{0} was not a valid URL.\".format(image_file))\n",
    "        exit(1)\n",
    "    img = Image.open(BytesIO(r.content))\n",
    "    img.thumbnail(THUMB_SIZE) # replace with a thumbnail with same aspect ratio, no larger than THUMB_SIZE\n",
    "\n",
    "    obs = get_colors(img, 'HSV') # gets a list of RGB/HSV colors (e.g. (213, 191, 152)) for each pixel\n",
    "    # adjust the value of each color, if you've chosen to change minimum and maximum values\n",
    "    clamped = [clamp_hsv(color, min_v, max_v) for color in obs]\n",
    "    clipped = clip_hsv(clamped, min_clip_s, min_clip_v) if clip_colors else clamped\n",
    "    # turns the list of colors into a numpy array of floats, then applies scipy's k-means function\n",
    "    clusters, _ = kmeans(array(clipped).astype(float) * coefficients, n)\n",
    "    normalized_clusters = clusters / coefficients\n",
    "    colors = order_by_hue_hsv(normalized_clusters) if order_colors else normalized_clusters\n",
    "        \n",
    "    hex_colors = list(map(hexify, colors)) # turn RGB into hex colors for web\n",
    "    return hex_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_row_with_links(link_and_colors):\n",
    "    html = \"\"\n",
    "    url, colors = link_and_colors\n",
    "    for count, color in enumerate(colors):\n",
    "        square = '<rect x=\"{0}\" y=\"{1}\" width=\"30\" height=\"30\" fill=\"{2}\" />'.format(((count * 30)), 0, color)\n",
    "        html += square\n",
    "    full_html = '<a href=\"{0}\" target=\"_blank\"><svg height=\"30\" width=\"{2}\">{1}</svg>'.format(url, html, len(colors) * 30)\n",
    "    return full_html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at how different parameters affect how the swatches look."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need an image link. Grab a link to a IIIF manifest from [https://labs.onb.ac.at/en/dataset/akon/](https://labs.onb.ac.at/en/dataset/akon/) or take the one provided down below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'@context': 'https://iiif.io/api/presentation/2/context.json',\n",
       " '@id': 'https://iiif.onb.ac.at/presentation/AKON/AK115_479/manifest',\n",
       " '@type': 'sc:Manifest',\n",
       " 'label': 'Dresden',\n",
       " 'metadata': [{'label': [{'@value': 'Id', '@language': 'en'},\n",
       "    {'@value': 'Id', '@language': 'ger'}],\n",
       "   'value': 'AK115_479'},\n",
       "  {'label': [{'@value': 'Title', '@language': 'en'},\n",
       "    {'@value': 'Titel', '@language': 'ger'}],\n",
       "   'value': 'Dresden'},\n",
       "  {'label': [{'@value': 'Place', '@language': 'en'},\n",
       "    {'@value': 'Ort', '@language': 'ger'}],\n",
       "   'value': \"<a href='https://sws.geonames.org/2935022'>Dresden</a>\"},\n",
       "  {'label': [{'@value': 'Year', '@language': 'en'},\n",
       "    {'@value': 'Jahr', '@language': 'ger'}],\n",
       "   'value': '1906'},\n",
       "  {'label': [{'@value': 'Disseminator', '@language': 'en'},\n",
       "    {'@value': 'Anbieter', '@language': 'ger'}],\n",
       "   'value': \"<a href='https://akon.onb.ac.at/'>Ansichtskarten Online</a>\"},\n",
       "  {'label': [{'@value': 'Physical Location', '@language': 'en'},\n",
       "    {'@value': 'Standort', '@language': 'ger'}],\n",
       "   'value': 'Niederösterreichische Landesbibliothek 1672 - ÖNB'}],\n",
       " 'description': 'Ministerium, Dampferlandeplatz',\n",
       " 'viewingDirection': 'left-to-right',\n",
       " 'viewingHint': 'paged',\n",
       " 'license': 'http://creativecommons.org/publicdomain/mark/1.0/',\n",
       " 'attribution': [{'@value': 'Austrian National Library', '@language': 'en'},\n",
       "  {'@value': 'Österreichische Nationalbibliothek', '@language': 'ger'}],\n",
       " 'logo': 'https://iiif.onb.ac.at/logo/',\n",
       " 'seeAlso': [{'@id': 'http://data.onb.ac.at/AKON/AK115_479',\n",
       "   'format': 'text/html'},\n",
       "  {'@id': 'http://data.onb.ac.at/AKON/AK115_479.rdf',\n",
       "   'format': 'application/rdf+xml'}],\n",
       " 'sequences': [{'@context': 'https://iiif.io/api/presentation/2/context.json',\n",
       "   '@id': 'https://iiif.onb.ac.at/presentation/AKON/AK115_479/sequence/normal',\n",
       "   '@type': 'sc:Sequence',\n",
       "   'startCanvas': 'https://iiif.onb.ac.at/presentation/AKON/AK115_479/canvas/479',\n",
       "   'canvases': [{'@context': 'https://iiif.io/api/presentation/2/context.json',\n",
       "     '@id': 'https://iiif.onb.ac.at/presentation/AKON/AK115_479/canvas/479',\n",
       "     '@type': 'sc:Canvas',\n",
       "     'label': 'Dresden',\n",
       "     'height': 1462,\n",
       "     'width': 2200,\n",
       "     'images': [{'@context': 'https://iiif.io/api/presentation/2/context.json',\n",
       "       '@id': 'https://iiif.onb.ac.at/presentation/AKON/AK115_479/annotation/479',\n",
       "       '@type': 'oa:Annotation',\n",
       "       'motivation': 'sc:painting',\n",
       "       'resource': {'@id': 'https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/full/0/native.jpg',\n",
       "        '@type': 'dctypes:Image',\n",
       "        'height': 1462,\n",
       "        'width': 2200,\n",
       "        'format': 'image/jpeg',\n",
       "        'service': {'@context': 'https://iiif.io/api/image/2/context.json',\n",
       "         '@id': 'https://iiif.onb.ac.at/images/AKON/AK115_479/479',\n",
       "         'profile': 'https://iiif.io/api/image/2/level2.json'}},\n",
       "       'on': 'https://iiif.onb.ac.at/presentation/AKON/AK115_479/canvas/479'}]}]}]}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = requests.get('https://iiif.onb.ac.at/presentation/AKON/AK115_479/manifest/')\n",
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image link can be found under `sequences[*].canvases[*].images[*].resource.@id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_link = 'https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at it. For our calculation, we'll use a much smaller variant of the image. Using the [IIIF Image API](https://iiif.io/api/image/2.1/), we can request an image of a certain size. To do this, we'll substitute the second `full` parameter by `!200,200`, meaning the resulting image should fit inside a 200x200 square."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import IPython.display as ipd\n",
    "\n",
    "im_r = requests.get(image_link)\n",
    "ipd.display(ipd.Image(im_r.content))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the color swatches..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#beaea3', '#494440', '#211d11', '#313c3f', '#8e9da2', '#534a4e']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols1 = colorz(image_link)\n",
    "cols1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and display them as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_colors(color_array, link):\n",
    "    html = draw_row_with_links((link, color_array))\n",
    "    ipd.display(ipd.HTML(html))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#beaea3\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#494440\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#211d11\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#313c3f\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#8e9da2\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#534a4e\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_colors(cols1, image_link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#c4aa9e', '#5b5651', '#242411', '#8a9597', '#435158', '#5d5559']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols2 = colorz(image_link, coefficients=[1.0, 2.0, 0.6])\n",
    "cols2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#c4aa9e\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#5b5651\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#242411\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#8a9597\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#435158\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#5d5559\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_colors(cols2, image_link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#c8aea2', '#4b4540', '#3d3422', '#4b5e62', '#829396', '#594f52']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols3 = colorz(image_link, coefficients=[1.0, 2.0, 0.6], clip_colors=True, min_clip_v=30)\n",
    "cols3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#c8aea2\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#4b4540\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#3d3422\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#4b5e62\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#829396\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#594f52\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_colors(cols3, image_link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#c9ac9f', '#49413b', '#332c1c', '#8ba9b0', '#2f393d', '#5d6b72']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols5 = colorz(image_link, clip_colors=True, min_clip_s=30, min_clip_v=30)\n",
    "cols5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#c9ac9f\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#49413b\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#332c1c\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#8ba9b0\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#2f393d\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#5d6b72\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_colors(cols5, image_link)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is getting tedious.\n",
    "\n",
    "Let's define a function that computes swatches and then displays the original image and the swatches side by side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colorize_and_display(image_link=image_link, **kwargs):\n",
    "    cols = colorz(image_link, **kwargs)\n",
    "    display_colors(cols, image_link)\n",
    "    ipd.display(ipd.Image(requests.get(image_link).content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#caa797\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#332c23\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#1c1a0a\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#3a4a4d\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#7c9fa8\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#0f0a11\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorize_and_display(clip_colors=True, min_clip_s=50, min_clip_v=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#cba898\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#443b31\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#302a18\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#2a3638\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#7da1aa\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#4e5f65\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorize_and_display(clip_colors=True, min_clip_s=50, min_clip_v=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#c7ada1\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#4e4742\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#352d1e\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#414f52\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#8ba2a7\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#594f52\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorize_and_display(clip_colors=True, min_clip_s=20, min_clip_v=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#c8aea2\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#4b4540\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#3d3421\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#4c5f63\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#819194\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#594f52\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorize_and_display(clip_colors=True, min_clip_s=20, min_clip_v=30, coefficients=[1.0, 2.0, 0.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://iiif.onb.ac.at/images/AKON/AK115_479/479/full/!200,200/0/native.jpg\" target=\"_blank\"><svg height=\"30\" width=\"180\"><rect x=\"0\" y=\"0\" width=\"30\" height=\"30\" fill=\"#c7ada1\" /><rect x=\"30\" y=\"0\" width=\"30\" height=\"30\" fill=\"#47413c\" /><rect x=\"60\" y=\"0\" width=\"30\" height=\"30\" fill=\"#32301a\" /><rect x=\"90\" y=\"0\" width=\"30\" height=\"30\" fill=\"#7e8e91\" /><rect x=\"120\" y=\"0\" width=\"30\" height=\"30\" fill=\"#485b5f\" /><rect x=\"150\" y=\"0\" width=\"30\" height=\"30\" fill=\"#51484b\" /></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorize_and_display(clip_colors=True, coefficients=[1.0, 2.0, 0.6], min_clip_s=20, min_clip_v=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks like a winner to me. We'll use `create_swatches.py` to create swatches for all available images in batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 User Default",
   "language": "python",
   "name": "python_3_user_default"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}