instaclust.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import join\n",
    "from pprint import pprint as pp\n",
    "from glob import glob\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from scipy import misc\n",
    "from scipy import ndimage\n",
    "\n",
    "from sklearn import cluster\n",
    "\n",
    "import pywt\n",
    "\n",
    "from IPython.display import Image, HTML, display\n",
    "\n",
    "from caffe_feature_extractor import get_imagenet_features\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def list_path(base):\n",
    "    return [join(base, x) for x in listdir(base)]\n",
    "\n",
    "filenames_letters = list_path('./data/letters/')\n",
    "filenames_pictures = list_path('./data/pictures/')\n",
    "names = filenames_letters + filenames_pictures\n",
    "\n",
    "#pp(filenames_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The plan:\n",
    "# 1. Scale image to 64x64\n",
    "# 2. Normalize an image by scaling the maximum value of each color channel to cover the full range of intensities\n",
    "# 3. Obtain Haar wavelet\n",
    "# 4. Take 60 largets coeffs, quantize the rest to ±1\n",
    "# 5. Use random projection to reduce 64x64x3 dimensions to 100\n",
    "# 6. Add features:\n",
    "#    - avg of each color channel (3 features)\n",
    "#    - aspect ratio: w/(w+h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data/letters/2016-01-01T17:06:49-illustratedmonthly-1152997288828311536_352141636.jpg',\n",
       " './data/letters/2016-01-01T22:34:38-draw_ul-1153162286760646225_15934020.jpg',\n",
       " './data/letters/2016-01-02T00:48:17-emmalinnea_art-1153229556651573244_291638246.jpg',\n",
       " './data/letters/2016-01-01T15:14:27-mateuszwitczakdesigns-1152940736491420170_497752429.jpg',\n",
       " './data/letters/2016-01-03T14:07:52-type.gang-1154356775748064883_1685135468.jpg',\n",
       " './data/letters/2016-01-04T12:33:02-thedailytype-1155033817724560220_36220583.jpg',\n",
       " './data/letters/2016-01-01T23:56:56-adantype-1153203712614149560_1562755458.jpg',\n",
       " './data/letters/2016-01-03T10:10:50-type.gang-1154237470364620275_1685135468.jpg',\n",
       " './data/letters/2016-01-01T04:37:00-adhex5150-1152619897569003341_31197416.jpg',\n",
       " './data/letters/2016-01-01T02:40:11-lujancarloz-1152561100791230348_177631546.jpg',\n",
       " './data/letters/2016-01-01T04:36:26-thiago_bianchini-1152619607507437701_685007145.jpg',\n",
       " './data/letters/2016-01-04T05:36:33-bhack13-1154824191311429791_183087383.jpg',\n",
       " './data/letters/2016-01-01T18:44:09-remrk-1153046279708422682_190971739.jpg',\n",
       " './data/letters/2016-01-03T02:18:36-jonbensondesigns-1153999784802677103_1572585667.jpg',\n",
       " './data/letters/2016-01-01T10:56:01-raitg-1152810656216475607_16281112.jpg',\n",
       " './data/letters/2016-01-01T00:09:04-khairulitie-1152485042808668080_219586006.jpg',\n",
       " './data/letters/2016-01-03T23:39:27-khairulitie-1154644462439219042_219586006.jpg',\n",
       " './data/letters/2016-01-01T01:00:42-handmadefont-1152511027210227221_1300751410.jpg',\n",
       " './data/letters/2016-01-02T19:57:57-jonbensondesigns-1153808195765563915_1572585667.jpg',\n",
       " './data/letters/2016-01-03T23:26:20-madebymarzipan-1154637858624625948_11515630.jpg',\n",
       " './data/pictures/2016-01-01T00:18:51-erica_calardo-1152489961723387138_34938702.jpg',\n",
       " './data/pictures/2016-01-03T14:22:05-belovstyle-1154363929159233911_343325987.jpg',\n",
       " './data/pictures/2016-01-04T11:54:44-sherry_bobbins-1155014537846883088_625209511.jpg',\n",
       " './data/pictures/2016-01-02T00:34:48-_irina_rumyantseva-1153222767541076137_290277927.jpg',\n",
       " './data/pictures/2016-01-02T01:13:32-australia-1153242260293309622_7199116.jpg',\n",
       " './data/pictures/2016-01-02T19:51:11-earlboykins-1153804795502291427_10455921.jpg',\n",
       " './data/pictures/2016-01-04T05:43:01-ogabel-1154827452305474564_3840608.jpg',\n",
       " './data/pictures/2016-01-01T18:56:51-kjl_shokeen-1153052668750145445_1637827760.jpg',\n",
       " './data/pictures/2016-01-01T17:42:08-prohorenkoff-1153015066228924581_1470044071.jpg',\n",
       " './data/pictures/2016-01-01T05:08:20-jasonminauro-1152635665398157658_10530051.jpg',\n",
       " './data/pictures/2016-01-03T10:37:12-letter_it_down-1154250741790766958_7537780.jpg',\n",
       " './data/pictures/2016-01-03T02:44:02-earlboykins-1154012591078402212_10455921.jpg',\n",
       " './data/pictures/2016-01-01T15:54:17-bobrovmaxim-1152960780053712840_55625962.jpg',\n",
       " './data/pictures/2016-01-01T10:07:57-totorofox-1152786468766533761_40033377.jpg',\n",
       " './data/pictures/2016-01-01T02:51:06-ylianna-1152566589691486373_29580727.jpg',\n",
       " './data/pictures/2016-01-01T01:07:56-australia-1152514668397352024_7199116.jpg',\n",
       " './data/pictures/2016-01-01T23:07:04-starwars-1153178605714667126_625833324.jpg',\n",
       " './data/pictures/2016-01-03T23:27:04-klctvefusion-1154638228991460842_5742000.jpg',\n",
       " './data/pictures/2016-01-03T23:52:14-ma_ry-1154650894650821906_464082.jpg',\n",
       " './data/pictures/2016-01-01T11:12:09-alexander_vyborov-1152818780251366783_206029616.jpg']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6Vpqp8b33v+uce9Z8/B4Ad6/+/gSAz+NSGEw7z42/V6WJF73OPL8v0re3RManVqbJRwQd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\nGNKW8be0pceQ/n+dB8SnhTmbagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcf36965d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print('Reading %s' % filenames_letters[0])\n",
    "data = misc.imread(filenames_letters[0])\n",
    "data = misc.imresize(data, (64, 64))\n",
    "#data.resize(64, 64)\n",
    "\n",
    "plt.imshow(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "((64, 64, 3), dtype('uint8'))\n"
     ]
    }
   ],
   "source": [
    "print(type(data))\n",
    "print(data.shape, data.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_haar_features(name):\n",
    "    return map(get_features, names)\n",
    "\n",
    "def get_features(filename):\n",
    "    data = misc.imread(filename)\n",
    "    data = misc.imresize(data, (64, 64))\n",
    "    #data.resize(64, 64)\n",
    "\n",
    "    feature_layers = np.zeros((32, 32, 3), dtype=np.float32)\n",
    "    additional = np.empty(4)\n",
    "    \n",
    "    for index in range(3):\n",
    "        layer = data[:, :, index]\n",
    "        layer = np.float32(layer)\n",
    "        additional[index] = layer.mean()\n",
    "        #layer /= 255.0\n",
    "        #print(layer.min(), layer.max(), layer.mean())\n",
    "        #print(layer[:1])\n",
    "\n",
    "        haar = pywt.wavedec2(data=layer, wavelet='haar', level=1)\n",
    "        cA = haar[0]\n",
    "        feature_layers[:, :, index] = cA\n",
    "        #print(len(cA))\n",
    "        #print(cA.shape)\n",
    "        #pp(list(cA))\n",
    "        #pp(haar)\n",
    "\n",
    "    h, w, _ = data.shape\n",
    "    aspect = float(w)/(w+h)\n",
    "    additional[-1] = aspect\n",
    "    #print(aspect)\n",
    "    #print(data[:, :, 0].mean())\n",
    "    #print(feature_layers.shape)\n",
    "    features = np.concatenate((feature_layers.reshape(32*32*3), additional))\n",
    "    #print(features.shape)\n",
    "    #features\n",
    "    #feature_layers\n",
    "    \n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_labels(names, feature_extractor=get_haar_features):\n",
    "    #features = map(get_features, names)\n",
    "    #features = get_imagenet_features(names)\n",
    "    features = feature_extractor(names)\n",
    "    #kmeans = cluster.KMeans(n_clusters=2, random_state=0)\n",
    "    kmeans = cluster.KMeans(n_clusters=2)\n",
    "    kmeans.fit(features)\n",
    "    labels = kmeans.labels_\n",
    "    return labels\n",
    "\n",
    "labels = get_labels(names)\n",
    "#zip(labels, names)\n",
    "#kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.        ,  0.57921177,  0.21141213, ...,  0.56604099,\n",
       "         1.03993249,  0.        ], dtype=float32),\n",
       " array([ 0.01669463,  0.99755955,  0.52832484, ...,  1.31598198,\n",
       "         0.28433514,  1.65871346], dtype=float32),\n",
       " array([ 0.        ,  2.31613398,  0.51020658, ...,  0.24909726,\n",
       "         0.47023988,  0.08781585], dtype=float32)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_imagenet_features(names[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#imagesList=''.join(\n",
    "#    [\"<img style='width: 64px; margin: 0px; float: left; border: 1px solid black;' src='%s' />\" % str(s)\n",
    "#     for s in names])\n",
    "#     for s in sorted(glob('yourimage*.png')) ])\n",
    "#display(HTML(imagesList))\n",
    "#display(HTML('<br>HEELLO<br>iernstierns'))\n",
    "\n",
    "def show_clusters(names, labels):\n",
    "    IMG_TEMPLATE = \"<img style='width: 128px; margin: 0px; float: left; border: 1px solid black;' src='%s'/>\"\n",
    "\n",
    "    def img(name):\n",
    "        return IMG_TEMPLATE % name\n",
    "\n",
    "    for cluster in set(labels):\n",
    "        display(HTML('<br>CLUSTER %s<br>' % cluster))\n",
    "        items = [img(names[index]) for index in range(len(names))\n",
    "                 if labels[index] == cluster]\n",
    "        display(HTML(''.join(items)))\n",
    "        #for index in range(len(names)):\n",
    "        #    if labels[index] == cluster:\n",
    "            #print(names[index])\n",
    "            #pic = misc.imread(names[index])\n",
    "            #plt.figure()\n",
    "            #plt.imshow(pic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def clusterize(names, feature_extractor=get_haar_features):\n",
    "    labels = get_labels(names, feature_extractor=feature_extractor)\n",
    "    show_clusters(names, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br>CLUSTER 0<br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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