encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.applications import vgg19\n",
    "import os\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import imutils\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nicholas-roerich_maleine-s-room-1913.jpg',\n",
       " 'henri-de-toulouse-lautrec_miss-may-belfort-welcoming-1895.jpg',\n",
       " 'raphael-kirchner_single-items-6.jpg',\n",
       " 'nicholas-roerich_sketch-of-costumes-for-snow-maiden-3.jpg',\n",
       " 'pablo-picasso_the-cries-of-virgins-1900.jpg',\n",
       " 'nicholas-roerich_crown-of-the-world-1921.jpg',\n",
       " 'ivan-bilibin_magazine-russian-opera-in-paris-1930.jpg',\n",
       " 'raphael-kirchner_girls-faces-with-red-border-3.jpg',\n",
       " 'heorhiy-narbut_playing-cards-1917-1.jpg',\n",
       " 'raphael-kirchner_the-embrace.jpg',\n",
       " 'ferdinand-hodler_self-portrait-1912-1.jpg',\n",
       " 'boris-kustodiev_on-the-volga-1910.jpg',\n",
       " 'nicholas-roerich_king-s-room-1913.jpg',\n",
       " 'felix-vallotton_portrait-of-french-writer-victor-charbonnel-1898.jpg',\n",
       " 'aubrey-beardsley_the-yellow-book-1894.jpg',\n",
       " 'nicholas-roerich_yaroslavl-entrance-to-the-st-nicholas-wet-church-1903.jpg',\n",
       " 'jozsef-rippl-ronai_aunt-manci-1913.jpg',\n",
       " 'zinaida-serebriakova_in-ballet-dressing-room-1924.jpg',\n",
       " 'j.-e.-h.-macdonald_the-little-falls-1919.jpg',\n",
       " 'ivan-bilibin_chernomor-s-gardens-sketches-of-scenery-for-mikhail-glinka-s-ruslan-and-ludmilla-1913(1).jpg',\n",
       " 'paul-mathiopoulos_woman-with-pearls.jpg',\n",
       " 'aubrey-beardsley_ballet-of-marionettes-ii.jpg',\n",
       " 'konstantin-korovin_margaret-s-garden-1910.jpg',\n",
       " 'boris-kustodiev_the-guy-from-tula-1926.jpg',\n",
       " 'anna-ostroumova-lebedeva_evening-1923.jpg',\n",
       " 'raphael-kirchner_unknown-1906.jpg',\n",
       " 'raphael-kirchner_christmas-pictures-signed-with-paris.jpg',\n",
       " 'valentin-serov_portrait-of-henrietta-girshman-1906.jpg',\n",
       " 'zinaida-serebriakova_portrait-of-gm-balanchivadze-dzh-balanchina-in-a-suit-of-bacchus.jpg',\n",
       " 'heorhiy-narbut_illustration-for-the-book-how-mice-buried-the-cat-by-zhukovsky-1910-4.jpg',\n",
       " 'raphael-kirchner_ivory-sculpture-1.jpg',\n",
       " 'alexandre-benois_letter-l-1904(3).jpg',\n",
       " 'henri-de-toulouse-lautrec_at-the-moulin-rouge-the-dance-1890.jpg',\n",
       " 'alphonse-mucha_hamlet-1899.jpg',\n",
       " 'nicholas-roerich_throne-of-the-invisible-god-1914.jpg',\n",
       " 'henri-de-toulouse-lautrec_portrait-of-oscar-wilde-1895.jpg',\n",
       " 'serge-sudeikin_pancake-parsley.jpg',\n",
       " 'alphonse-mucha_iris-1.jpg',\n",
       " 'wilhelm-kotarbinski_a-roman-bacchanal-detail.jpg',\n",
       " 'otto-eckmann_carpet-design-1898.jpg',\n",
       " 'wilhelm-kotarbinski_the-evening-silence.jpg',\n",
       " 'raphael-kirchner_roma-3.jpg',\n",
       " 'edmund-dulac_the-arabian-nights-daylight-faded-from-view.jpg',\n",
       " 'wilhelm-kotarbinski_two-girls-in-yellow-and-pink.jpg',\n",
       " 'henri-de-toulouse-lautrec_nude-girl-1893.jpg',\n",
       " 'raphael-kirchner_girls-with-purple-surrounds-1900-4.jpg',\n",
       " 'jozsef-rippl-ronai_busy-city-scene-1914.jpg',\n",
       " 'aubrey-beardsley_frontispiece-for-venus-and-tannhauser.jpg',\n",
       " 'raphael-kirchner_greek-girls-6.jpg',\n",
       " 'carlos-saenz-de-tejada_the-portal-n-vitoria-fifties.jpg',\n",
       " 'nicholas-roerich_the-scene-with-three-figures-in-costumes-1920-1.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-russian-fairy-story-maria-morevna-1900-7(1).jpg',\n",
       " 'gustav-klimt_philosophy-final-state-1907.jpg',\n",
       " 'mikhail-vrubel_archangel-gabriel-1885.jpg',\n",
       " 'edmund-dulac_the-big-spider-s-diamonds-illustration-to-fairies-i-have-met.jpg',\n",
       " 'wilhelm-kotarbinski_in-the-land-of-pyramids.jpg',\n",
       " 'jules-cheret_l-etendard-fran-ais-quai-d-orsay-bicycle-shop-1891.jpg',\n",
       " 'mikhail-vrubel_mamyrov-the-old-deacon-costume-design-for-the-opera-the-enchantress-1900.jpg',\n",
       " 'serge-sudeikin_costume-design-for-venetian-madmen-jester-1915.jpg',\n",
       " 'raphael-kirchner_two-in-a-car-1916.jpg',\n",
       " 'jacques-villon_comedy-of-society-1903.jpg',\n",
       " 'koloman-moser_drafts-for-metal-relief-1904.jpg',\n",
       " 'koloman-moser_backhausen-interior-texiles-1899.jpg',\n",
       " 'heorhiy-narbut_illustration-to-the-song-of-roland-1903.jpg',\n",
       " 'raphael-kirchner_victory.jpg',\n",
       " 'nicholas-roerich_old-pskov-1904.jpg',\n",
       " 'raphael-kirchner_dolly-sister.jpg',\n",
       " 'konstantin-korovin_refectory-of-the-house-of-ivan-khovansky-1910.jpg',\n",
       " 'boris-kustodiev_portrait-of-peter-kapitza-1926.jpg',\n",
       " 'aubrey-beardsley_how-la-beale-isoud-nursed-sir-tristram.jpg',\n",
       " 'carl-larsson_los-deberes-1898(1).jpg',\n",
       " 'alphonse-mucha_byzantine-head-the-blonde-1897.jpg',\n",
       " 'nicholas-roerich_a-guy-who-plays-the-horn-1913.jpg',\n",
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       " 'alphonse-mucha_8th-sokol-festival-1912.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-historical-revolutionary-almanac-of-publisher-rosehip-1906(1).jpg',\n",
       " 'zinaida-serebriakova_at-breakfast-1914.jpg',\n",
       " 'boris-kustodiev_children-of-the-artist-1913.jpg',\n",
       " 'raphael-kirchner_unknown-18.jpg',\n",
       " 'boris-kustodiev_fontanka-1916.jpg',\n",
       " 'ferdinand-hodler_weisshorn-of-montana-1915.jpg',\n",
       " 'egon-schiele_portrait-of-edith-schiele-the-artist-s-wife-1915.jpg',\n",
       " 'henri-de-toulouse-lautrec_jane-avril-dancing-1892.jpg',\n",
       " 'leon-bakst_scheherazade-la-sultane-bleue-1910.jpg',\n",
       " 'konstantin-korovin_dancer-costume-design-for-rubinstein-s-opera.jpg',\n",
       " 'boris-kustodiev_the-merchant-s-wife-at-tea-1920.jpg',\n",
       " 'carl-larsson_under-the-birches-1902(1).jpg',\n",
       " 'raphael-kirchner_lillian-lorraine-hearst-s-sunday-american-1917.jpg',\n",
       " 'raphael-kirchner_deadly-sins-3.jpg',\n",
       " 'raphael-kirchner_nude-drawings-8.jpg',\n",
       " 'zinaida-serebriakova_harvesting-1915.jpg',\n",
       " 'ferdinand-hodler_cavalryman-striding-a-horse-1908.jpg',\n",
       " 'alphonse-mucha_soap-factory-of-bagnolet-1897.jpg',\n",
       " 'ivan-milev_unknown-title-2.jpg',\n",
       " 'nicholas-roerich_terem-of-yaroslavna-by-the-monastery-wall-1908.jpg',\n",
       " 'nicholas-roerich_winter-1914.jpg',\n",
       " 'raphael-kirchner_nude-drawings-16.jpg',\n",
       " 'raphael-kirchner_bellage-of-sect-1905.jpg',\n",
       " 'natalia-goncharova_liturgy-six-winged-seraph.jpg',\n",
       " 'sergey-solomko_temptation.jpg',\n",
       " 'raphael-kirchner_love-flowers-1903.jpg',\n",
       " 'raphael-kirchner_sweet-smoke-1913.jpg',\n",
       " 'koloman-moser_dancer-1903.jpg',\n",
       " 'koloman-moser_design-for-the-anniversary-stamp-austrian-with-empress-maria-theresa-1908.jpg',\n",
       " 'frantisek-kupka_boudoir-scene-1903.jpg',\n",
       " 'alphonse-mucha_the-slavs-in-their-original-homeland-1912.jpg',\n",
       " 'nicholas-roerich_sketch-of-tsar-dodon.jpg',\n",
       " 'konstantin-korovin_costume-design-for-igor-in-the-production-of-prince-igor-at-the-mariinsky-theatre-1909.jpg',\n",
       " 'leon-bakst_la-peri-natasha-trouhanova-as-the-peri-1911.jpg',\n",
       " 'natalia-goncharova_liturgy-the-seraph-s-costume.jpg',\n",
       " 'ivan-bilibin_sketch-for-the-opera-the-golden-cockerel-by-nikolai-rimsky-korsakov-1909-1.jpg',\n",
       " 'aubrey-beardsley_poster-of-girl-and-a-bookshop.jpg',\n",
       " 'aubrey-beardsley_caricature-of-felix-mendelssohn.jpg',\n",
       " 'boris-kustodiev_province-1919.jpg',\n",
       " 'michel-simonidy_le-figaro-1900.jpg',\n",
       " 'egon-schiele_peasants-jug-1918.jpg',\n",
       " 'nicholas-roerich_light-night-castle-of-prince-judas-iscariot-1909.jpg',\n",
       " 'pablo-picasso_couple-walking-1901.jpg',\n",
       " 'zinaida-serebriakova_girls-at-the-piano-1922.jpg',\n",
       " 'wilhelm-kotarbinski_resting-by-the-shore.jpg',\n",
       " 'anders-zorn_madame-clara-rikoff.jpg',\n",
       " 'nicholas-roerich_sage-1916.jpg',\n",
       " 'wilhelm-kotarbinski_cleopatra.jpg',\n",
       " 'edmund-dulac_princess-orchid-s-party-illustration-to-fairies-i-have-met.jpg',\n",
       " 'anders-zorn_frances-cleveland.jpg',\n",
       " 'raphael-kirchner_santoy-1900-3.jpg',\n",
       " 'carlos-saenz-de-tejada_the-life-of-st-ignatius-loyola-plate-5-ignatius-and-the-first-companions-take-vows-at-montmatre.jpg',\n",
       " 'aubrey-beardsley_moska.jpg',\n",
       " 'natalia-goncharova_design-for-final-backcloth.jpg',\n",
       " 'zinaida-serebriakova_peasant-woman-with-rocker-1917.jpg',\n",
       " 'nicholas-roerich_archers-1908.jpg',\n",
       " 'alphonse-mucha_emerald.jpg',\n",
       " 'theodor-severin-kittelsen_rugga.jpg',\n",
       " 'max-kurzweil_the-fisherman.jpg',\n",
       " 'zinaida-serebriakova_portrait-of-e-m-edwards-1912.jpg',\n",
       " 'michel-simonidy_femeie-pe-malul-m-rii.jpg',\n",
       " 'carl-larsson_summer-morning-published-in-lasst-licht-hinin-let-in-more-light-1908(1).jpg',\n",
       " 'ivan-bilibin_complimentary-ticket-to-a-solo-exhibition-by-ivan-bilibin-at-alexandria-1924(1).jpg',\n",
       " 'aubrey-beardsley_scarlet_pastorale.jpg',\n",
       " 'aubrey-beardsley_cinesias-entreating-myrrhina-to-coition.jpg',\n",
       " 'leon-bakst_design-for-a-lady-s-dress-1912.jpg',\n",
       " 'ferdinand-hodler_forest-with-a-mountain-stream-1902.jpg',\n",
       " 'sergey-solomko_for-you-brave-soldier.jpg',\n",
       " 'alexandre-benois_letter-s-1904(3).jpg',\n",
       " 'kay-nielsen_the-princess-and-the-pea.jpg',\n",
       " 'max-kurzweil_bettina-bauer-1908.jpg',\n",
       " 'henri-de-toulouse-lautrec_the-ambassadors-people-chics-1893.jpg',\n",
       " 'alphonse-mucha_summer-1896.jpg',\n",
       " 'raphael-kirchner_akropolis-6.jpg',\n",
       " 'raphael-kirchner_santoy-1900-2.jpg',\n",
       " 'nicholas-roerich_girl-1920-1.jpg',\n",
       " 'raphael-kirchner_players.jpg',\n",
       " 'zinaida-serebriakova_self-portrait-1907.jpg',\n",
       " 'nicholas-roerich_polovets-and-girls-1943.jpg',\n",
       " 'raphael-kirchner_apples-sweet-fruits-1900.jpg',\n",
       " 'sergey-solomko_the-admired-beauty.jpg',\n",
       " 'felix-vallotton_portrait-of-communard-raoul-rigault-1897.jpg',\n",
       " 'carlos-saenz-de-tejada_illustration-for-the-living-forest-1957.jpg',\n",
       " 'zinaida-serebriakova_portrait-of-a-ballerina-l-a-ivanova-1921.jpg',\n",
       " 'serge-sudeikin_cover-of-the-catalog-of-the-exhibition-scarlet-rose-1904.jpg',\n",
       " 'nicholas-roerich_study-to-conquest-of-kazan.jpg',\n",
       " 'henri-de-toulouse-lautrec_the-coastal-bus-company-1888.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-magazine-golden-fleece-1906(1).jpg',\n",
       " 'nicholas-roerich_arrows-of-sky-spears-of-land-1915.jpg',\n",
       " 'raphael-kirchner_behind-the-scenes-1900-1.jpg',\n",
       " 'jacek-malczewski_three-heads-love.jpg',\n",
       " 'koloman-moser_the-abersee-1913.jpg',\n",
       " 'wilhelm-kotarbinski_the-crucifixion-of-jesus-christ.jpg',\n",
       " 'boris-kustodiev_masha-the-daughter-of-merchant-1924.jpg',\n",
       " 'raphael-kirchner_nude-drawings-17.jpg',\n",
       " 'wilhelm-kotarbinski_portrait-of-young-woman.jpg',\n",
       " 'nicholas-roerich_the-court-of-prince-vladimir-galitsky-study-of-scene-design-for-prince-igor-1914.jpg',\n",
       " 'leon-bakst_scheherezade-odalisque-1910.jpg',\n",
       " 'jacek-malczewski_country-1903.jpg',\n",
       " 'nicholas-roerich_the-elders-weared-in-bear-hides-1944.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-fairy-tale-vasilisa-the-beautiful(1).jpg',\n",
       " 'eugã¨ne-grasset_inqui-tude.jpg',\n",
       " 'raphael-kirchner_nude-drawings-9.jpg',\n",
       " 'nicholas-roerich_viking-s-tomb-1908.jpg',\n",
       " 'gustav-klimt_the-beethoven-frieze-the-longing-for-happiness-finds-repose-in-poetry-right-wall.jpg',\n",
       " 'frantisek-kupka_front-cover-of-the-la-libert-issue-from-l-assiette-au-beurre-1906.jpg',\n",
       " 'edmund-dulac_dreamer-of-dreams-by-the-queen-of-romania.jpg',\n",
       " 'raphael-kirchner_deadly-sins-2.jpg',\n",
       " 'wilhelm-kotarbinski_war.jpg',\n",
       " 'valentin-serov_apollo-and-diana-beating-children-niobe.jpg',\n",
       " 'raphael-kirchner_fanny-brice-1916-1.jpg',\n",
       " 'heorhiy-narbut_100-karbovanets-of-the-ukrainian-state-revers-1918.jpg',\n",
       " 'ivan-bilibin_illustration-for-alexander-pushkin-s-fairytale-of-the-tsar-saltan(1).jpg',\n",
       " 'koloman-moser_fabric-design-with-floral-awakening-for-backhausen-1900.jpg',\n",
       " 'gustav-klimt_farm-garden-with-crucifix-1912.jpg',\n",
       " 'henri-de-toulouse-lautrec_jane-avril-dancing-1893.jpg',\n",
       " 'gustav-klimt_the-dancer-1918.jpg',\n",
       " 'konstantin-korovin_terem-dadon-1909.jpg',\n",
       " 'ivan-bilibin_boy-fellah-1923(1).jpg',\n",
       " 'edmund-dulac_daughters-of-the-stars-by-mary-crary.jpg',\n",
       " 'ivan-bilibin_costume-design-for-the-opera-legend-of-the-invisible-city-of-kitezh-and-maiden-fevronia-by-1934(1).jpg',\n",
       " 'raphael-kirchner_lollotte_02-1916.jpg',\n",
       " 'carl-larsson_christmas-morning-1894(1).jpg',\n",
       " 'sergey-solomko_meeting.jpg',\n",
       " 'sergey-solomko_two-sketches-of-costumes.jpg',\n",
       " 'anna-ostroumova-lebedeva_landscape-around-the-viscontiev-bridge-1923.jpg',\n",
       " 'henri-de-toulouse-lautrec_a-day-out-1893.jpg',\n",
       " 'raphael-kirchner_happy-new-year-1899-4.jpg',\n",
       " 'eugã¨ne-grasset_spring-1894.jpg',\n",
       " 'felix-vallotton_portrait-of-french-writer-hugues-rebell-1898.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-tale-of-prince-ivan-the-firebird-and-the-grey-wolf-1899(1).jpg',\n",
       " 'leon-bakst_costume-for-the-englishman-from-la-boutique-fantastique-1917.jpg',\n",
       " 'wilhelm-kotarbinski_st-pafnuty.jpg',\n",
       " 'nicholas-roerich_the-painting-of-the-southern-wall-of-the-chapel-of-st-anastasia-1913.jpg',\n",
       " 'nicholas-roerich_foreign-lords-1907-1.jpg',\n",
       " 'raphael-kirchner_parfums-cubin.jpg',\n",
       " 'edmund-dulac_a-brave-heart-from-beauty-and-the-beast.jpg',\n",
       " 'raphael-kirchner_surrender.jpg',\n",
       " 'wilhelm-kotarbinski_mused.jpg',\n",
       " 'ferdinand-hodler_running-women-1895.jpg',\n",
       " 'jacek-malczewski_self-portrait-in-an-armour.jpg',\n",
       " 'koloman-moser_the-rax.jpg',\n",
       " 'serge-sudeikin_study-for-eugene-onegin.jpg',\n",
       " 'jacques-villon_the-game-of-solitaire-1904.jpg',\n",
       " 'raphael-kirchner_greek-girls-7.jpg',\n",
       " 'alphonse-mucha_cover-composed-by-mucha-for-the-french-literary-and-artistic-review-la-plume-1898.jpg',\n",
       " 'gustav-klimt_chruch-in-cassone.jpg',\n",
       " 'nicholas-roerich_study-to-flying-carpet-1915.jpg',\n",
       " 'raphael-kirchner_girls-with-purple-surrounds-1900-5.jpg',\n",
       " 'felix-vallotton_portrait-of-french-writer-andr-ferdinand-herold-1896.jpg',\n",
       " 'edmund-dulac_selene-and-endymion.jpg',\n",
       " 'raphael-kirchner_gleaner.jpg',\n",
       " 'wilhelm-kotarbinski_ballade.jpg',\n",
       " 'raphael-kirchner_roma-2.jpg',\n",
       " 'paul-mathiopoulos_portrait-of-a-woman.jpg',\n",
       " 'raphael-kirchner_girls-with-ancient-musical-instruments.jpg',\n",
       " 'kay-nielsen_fisherman-and-his-wife.jpg',\n",
       " 'valentin-serov_a-recruit-1906.jpg',\n",
       " 'heorhiy-narbut_illustration-for-the-book-how-mice-buried-the-cat-by-zhukovsky-1910-5.jpg',\n",
       " 'a.y.-jackson_indian-home-1927.jpg',\n",
       " 'valentin-serov_portrait-of-henrietta-girshman-1907.jpg',\n",
       " 'nicholas-roerich_konchak-1909.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-magazine-golden-fleece-1906-1(1).jpg',\n",
       " 'henri-de-toulouse-lautrec_they.jpg',\n",
       " 'ivan-bilibin_crane-1900(1).jpg',\n",
       " 'alphonse-mucha_the-autumn-1896.jpg',\n",
       " 'ferdinand-hodler_the-forest-near-reichenbach-1903.jpg',\n",
       " 'jacek-malczewski_the-tribute-money-triptych-middle.jpg',\n",
       " 'aladar-korosfoi-kriesch_vanity-1904.jpg',\n",
       " 'anna-ostroumova-lebedeva_construction-of-the-palace-bridge-1922.jpg',\n",
       " 'boris-kustodiev_self-portrait-1912.jpg',\n",
       " 'nicholas-roerich_captive-1909.jpg',\n",
       " 'boris-kustodiev_portrait-of-rene-ivanovna-notgaft-1914.jpg',\n",
       " 'nicholas-roerich_knight-of-entourage-1912-1.jpg',\n",
       " 'sergey-solomko_zabava-putyatichna.jpg',\n",
       " 'nicholas-roerich_oze-s-room-1912-2.jpg',\n",
       " 'nicholas-roerich_two-girls-1930.jpg',\n",
       " 'nicholas-roerich_sketch-of-costumes-for-the-rite-of-spring-1912.jpg',\n",
       " 'heorhiy-narbut_design-of-five-hundred-hryvnias-bill-of-the-ukrainian-national-republic-revers-1918.jpg',\n",
       " 'edmund-dulac_out-of-this-world-stealers-of-light-by-the-queen-of-romania.jpg',\n",
       " 'nicholas-roerich_sketch-of-costumes-for-snow-maiden-2.jpg',\n",
       " 'carl-larsson_by-the-cellar-1917(1).jpg',\n",
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       " 'ferdinand-hodler_portrait-of-sculptor-james-vibert.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-fairy-tale-vasilisa-the-beautiful-1900-1(1).jpg',\n",
       " 'richard-gerstl_mathilde-schoenberg-1907.jpg',\n",
       " 'felix-vallotton_self-portrait-1895.jpg',\n",
       " 'alphonse-mucha_dusk.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-russian-fairy-story-white-duck-1902(1).jpg',\n",
       " 'heorhiy-narbut_illustration-for-the-book-how-mice-buried-the-cat-by-zhukovsky-1910-3.jpg',\n",
       " 'raphael-kirchner_magentic-pole.jpg',\n",
       " 'nicholas-roerich_polovtsian-camp-1914-2.jpg',\n",
       " 'carl-larsson_my-mother-1893(1).jpg',\n",
       " 'wilhelm-kotarbinski_roman-holiday.jpg',\n",
       " 'boris-kustodiev_tsar-nicholas-ii-1915.jpg',\n",
       " 'raphael-kirchner_single-items-1.jpg',\n",
       " 'nicholas-roerich_sketch-of-costumes-for-snow-maiden-4.jpg',\n",
       " 'koloman-moser_the-light.jpg',\n",
       " 'wilhelm-kotarbinski_on-the-terrace.jpg',\n",
       " 'koloman-moser_picture-book-for-the-niece-of-ditha-mautner-von-markhof-1904.jpg',\n",
       " 'raphael-kirchner_girls-faces-with-red-border-4.jpg',\n",
       " 'raphael-kirchner_deadly-sins.jpg',\n",
       " 'raphael-kirchner_love-flowers-1903-1.jpg',\n",
       " 'boris-kustodiev_meeting-stage-design-for-the-ostrovsky-s-play-the-easy-money-1917.jpg',\n",
       " 'alphonse-mucha_the-west-end-review-1898.jpg',\n",
       " 'aubrey-beardsley_the-toilette-of-salome.jpg',\n",
       " 'wilhelm-kotarbinski_dream.jpg',\n",
       " 'wilhelm-kotarbinski_pilate-s-court.jpg',\n",
       " 'anna-ostroumova-lebedeva_pavlovsk-novosilviysky-bridge-1921.jpg',\n",
       " 'boris-kustodiev_hard-rime-1917.jpg',\n",
       " 'raphael-kirchner_love-flowers-1903-3.jpg',\n",
       " 'ivan-bilibin_donkey-illustration-for-the-magazine-bogey-1906(1).jpg',\n",
       " 'nicholas-roerich_sketches-of-costumes-for-prince-igor.jpg',\n",
       " 'ivan-bilibin_costume-design-for-the-opera-the-golden-cockerel-by-nikolai-rimsky-korsakov-1909(1).jpg',\n",
       " 'raphael-kirchner_single-items-3.jpg',\n",
       " 'felix-vallotton_the-beautiful-sunday-1895.jpg',\n",
       " 'nicholas-roerich_novgorod-bargaining-1920.jpg',\n",
       " 'felix-vallotton_portrait-of-french-writer-rachilde-1898.jpg',\n",
       " 'koloman-moser_square-vignette-1896.jpg',\n",
       " 'edmund-dulac_mohammad-the-rubaiyat-of-omar-khayyam.jpg',\n",
       " 'konstantin-korovin_murman-coast-pomor-crosses-1900.jpg',\n",
       " 'raphael-kirchner_girls-faces-with-red-border-6.jpg',\n",
       " 'sergey-solomko_tough-response.jpg',\n",
       " 'nicholas-roerich_sketch-of-costumes-for-snow-maiden-6.jpg',\n",
       " 'zinaida-serebriakova_apples-on-the-branches-1910.jpg',\n",
       " 'jacek-malczewski_portrait-of-stanislaw-bryniarski.jpg',\n",
       " 'heorhiy-narbut_illustration-for-the-book-how-mice-buried-the-cat-by-zhukovsky-1910-1.jpg',\n",
       " 'alexandre-benois_cover-page-1904(4).jpg',\n",
       " 'filipp-malyavin_laughting-1898.jpg',\n",
       " 'lawren-harris_aftermath-of-storm-lake-superior-sketch-xxxiv-1926.jpg',\n",
       " 'carl-larsson_solrosorna(1).jpg',\n",
       " 'sergey-solomko_maria-swan-white.jpg',\n",
       " 'nicholas-roerich_corridors-of-the-castle-dungeons-1913.jpg',\n",
       " 'koloman-moser_self-portrait.jpg',\n",
       " 'zinaida-serebriakova_four-female-figures-1916.jpg',\n",
       " 'jacek-malczewski_elle-death-1907.jpg',\n",
       " 'ferdinand-hodler_portrait-of-berthe-jacques-1894.jpg',\n",
       " 'carl-larsson_the-day-before-christmas-1892(1).jpg',\n",
       " 'raphael-kirchner_greek-girls-3.jpg',\n",
       " 'carl-larsson_lazy-nook-1897(1).jpg',\n",
       " 'gustav-klimt_tragedy.jpg',\n",
       " 'j.-e.-h.-macdonald_the-wild-river-1919.jpg',\n",
       " 'heorhiy-narbut_illustration-fairy-tales-teremok-mizgir-1910.jpg',\n",
       " 'nicholas-roerich_entourage-1912.jpg',\n",
       " 'boris-kustodiev_stepan-razin-1918.jpg',\n",
       " 'nicholas-roerich_the-hut-of-dead-1909.jpg',\n",
       " 'aleksandra-ekster_costume-design-for-the-play-famira-kifared-by-innokentiy-annenski-maenad-1916.jpg',\n",
       " 'raphael-kirchner_roma-6.jpg',\n",
       " 'jacek-malczewski_portrait-of-aleksander-wielopolski-1903.jpg',\n",
       " 'jacek-malczewski_death-1917.jpg',\n",
       " 'jacek-malczewski_the-resurrection-immortality.jpg',\n",
       " 'raphael-kirchner_ivory-sculpture.jpg',\n",
       " 'ferdinand-hodler_swearing-to-the-left.jpg',\n",
       " 'martiros-saryan_costume-design-for-the-opera-by-rimsky-korsakov-s-golden-cockerel-1931.jpg',\n",
       " 'raphael-kirchner_girls-with-purple-surrounds-1900-1.jpg',\n",
       " 'alphonse-mucha_nude-on-a-rock-1899.jpg',\n",
       " 'carlos-saenz-de-tejada_the-life-of-st-ignatius-loyola-plate-4-ignatius-and-his-friend-francis-xavier-at-the-university.jpg',\n",
       " 'kay-nielsen_the-man-who-never-laughed.jpg',\n",
       " 'nicholas-roerich_costume-design-for-tale-of-tsar-saltan-1919.jpg',\n",
       " 'nicholas-roerich_curtain-and-portal-for-snow-maiden-1919.jpg',\n",
       " 'mstislav-dobuzhinsky_vitebsk-1919.jpg',\n",
       " 'ivan-bilibin_saints-boris-and-gleb-on-the-ship.jpg',\n",
       " 'viktor-vasnetsov_seraphim-1896.jpg',\n",
       " 'raphael-kirchner_viennese-blood-1.jpg',\n",
       " 'alexandre-benois_interieur(1).jpg',\n",
       " 'alphonse-mucha_slavia-1896.jpg',\n",
       " 'mikhail-vrubel_portrait-of-n-zabela-1904.jpg',\n",
       " 'wilhelm-kotarbinski_girl-between-hollyhocks.jpg',\n",
       " 'wilhelm-kotarbinski_music.jpg',\n",
       " 'koloman-moser_standing-youth.jpg',\n",
       " 'theodor-severin-kittelsen_bokskap(2).jpg',\n",
       " 'konstantin-korovin_khan-s-wife-costume-design-for-c-pugni-s-ballet-1901.jpg',\n",
       " 'koloman-moser_backhausen-interior-texiles-for-the-bristol-hotel-in-bolzano-1898.jpg',\n",
       " 'boris-kustodiev_shrove-tide-1919-1.jpg',\n",
       " 'carl-larsson_the-cottage(1).jpg',\n",
       " 'frank-johnston_patterned-hillside-1918.jpg',\n",
       " 'sergey-solomko_design-of-male-medieval-costume.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-russian-fairy-story-the-frog-princess-1899(1).jpg',\n",
       " 'anna-ostroumova-lebedeva_venice-grand-canal-grey-day-1911.jpg',\n",
       " 'jozsef-rippl-ronai_terka-linzer-1923.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-russian-fairy-story-maria-morevna-1900-5(1).jpg',\n",
       " 'kay-nielsen_six-swans.jpg',\n",
       " 'heorhiy-narbut_poetry-headpiece-to-magazine-art-1919.jpg',\n",
       " 'boris-kustodiev_sailor-and-his-girl-1921.jpg',\n",
       " 'jacek-malczewski_self-portrait-with-the-muses.jpg',\n",
       " 'sergey-solomko_queen-azvyakovna.jpg',\n",
       " 'edmund-dulac_jason-appointed-tiphys-the-stargazer.jpg',\n",
       " 'jacek-malczewski_three-heads-hope.jpg',\n",
       " 'a.y.-jackson_wilderness-deese-bay.jpg',\n",
       " 'anna-ostroumova-lebedeva_exchange-1922.jpg',\n",
       " 'raphael-kirchner_merci-i-1916.jpg',\n",
       " 'carl-larsson_azalea-1906(1).jpg',\n",
       " 'edmund-dulac_came-shining-through-the-dusk-an-angel-shape-the-rubaiyat-of-omar-khayyam.jpg',\n",
       " 'franklin-carmichael_lake-wabagishik-1928.jpg',\n",
       " 'zinaida-serebriakova_sleeping-peasant-1917.jpg',\n",
       " 'boris-kustodiev_pancake-tuesday-in-a-village-maslenitsa-1916.jpg',\n",
       " 'raphael-kirchner_santoy-1900.jpg',\n",
       " 'egon-schiele_house-with-drying-laundry-1917.jpg',\n",
       " 'ivan-bilibin_sketch-for-the-opera-boris-godunov-by-modest-mussorgsky-1930.jpg',\n",
       " 'boris-kustodiev_meeting-easter-day-1917.jpg',\n",
       " 'nicholas-roerich_king-mark-1912-1.jpg',\n",
       " 'raphael-kirchner_temptation.jpg',\n",
       " 'raphael-kirchner_nude-drawings-13.jpg',\n",
       " 'ivan-milev_obruchenie-1923.jpg',\n",
       " 'a.y.-jackson_winter-morning-charlevoix-county-1933.jpg',\n",
       " 'henri-de-toulouse-lautrec_entry-of-cha-u-kao.jpg',\n",
       " 'koloman-moser_hut-in-leysin-1913.jpg',\n",
       " 'koloman-moser_poster-for-prince-richard-metternich-1899.jpg',\n",
       " 'anna-ostroumova-lebedeva_round-pond-1923.jpg',\n",
       " 'henri-de-toulouse-lautrec_gala-at-the-moulin-rouge-1894.jpg',\n",
       " 'nicholas-roerich_ostrovenka-suburbs-1903.jpg',\n",
       " 'gustav-klimt_the-tree-of-life-stoclet-frieze.jpg',\n",
       " 'konstantin-korovin_the-curtain-sketch-of-the-play-la-ronde-des-heures.jpg',\n",
       " 'raphael-kirchner_deadly-sins-6.jpg',\n",
       " 'kay-nielsen_a-snowdrift-carried-them-away.jpg',\n",
       " 'raphael-kirchner_girls-with-animals-1901-2.jpg',\n",
       " 'alphonse-mucha_not_detected_227797.jpg',\n",
       " 'anna-ostroumova-lebedeva_petersburg-fontanka-1922.jpg',\n",
       " 'gustav-klimt_pear-tree-1903.jpg',\n",
       " 'nicholas-roerich_idols-pagan-rus-1910.jpg',\n",
       " 'nicholas-roerich_rostov-the-great-1903.jpg',\n",
       " 'anders-zorn_portrait-of-mrs-eben-richards-1899.jpg',\n",
       " 'mikhail-vrubel_mikula-selianinovich-and-volha-1899.jpg',\n",
       " 'aladar-korosfoi-kriesch_the-story-of-kl-ra-z-ch-ii-1911.jpg',\n",
       " 'sergey-solomko_wedding-ceremony-middle-ages-1909.jpg',\n",
       " 'ivan-bilibin_st-prince-vladimir-1926-1.jpg',\n",
       " 'franklin-carmichael_autumn.jpg',\n",
       " 'edmund-dulac_the-face-of-wretched-man-the-rubaiyat-of-omar-khayyam.jpg',\n",
       " 'henri-de-toulouse-lautrec_the-sailor-s-sopng-at-the-star-le-havre-1899.jpg',\n",
       " 'zinaida-serebriakova_bather-1911.jpg',\n",
       " 'nicholas-roerich_mekheski-moon-people-1915.jpg',\n",
       " 'koloman-moser_poster-for-felix-salzens-jung-wiener-theatre-with-lieben-augustin-1901.jpg',\n",
       " 'franz-stuck_poster-for-the-international-hygiene-exhibition-1911-in-dresden-1911.jpg',\n",
       " 'ferdinand-hodler_pulling-soldiers-1908.jpg',\n",
       " 'natalia-goncharova_liturgy-st-andrew.jpg',\n",
       " 'alphonse-mucha_not_detected_227768.jpg',\n",
       " 'raphael-kirchner_akropolis-2.jpg',\n",
       " 'nicholas-roerich_saviour-almighty-and-saints-1906.jpg',\n",
       " 'aubrey-beardsley_the-cover-of-the-studio-volume-1.jpg',\n",
       " 'koloman-moser_portrait-of-a-woman-in-profile.jpg',\n",
       " 'henri-de-toulouse-lautrec_m-praince-1893.jpg',\n",
       " 'raphael-kirchner_akropolis-3.jpg',\n",
       " 'aubrey-beardsley_katharina-klafsky.jpg',\n",
       " 'anna-ostroumova-lebedeva_cypress-trees-1902.jpg',\n",
       " 'alphonse-mucha_cycles-perfecta-1902.jpg',\n",
       " 'carl-larsson_self-portrait-1895(1).jpg',\n",
       " 'aubrey-beardsley_poster-advertising-the-spinster-s-scrip.jpg',\n",
       " 'aubrey-beardsley_the-repentance-of-mrs.jpg',\n",
       " 'nicholas-roerich_san-gimignano-1906.jpg',\n",
       " 'jan-toorop_the-desire-and-the-satisfaction-1893.jpg',\n",
       " 'raphael-kirchner_happy-new-year-1912.jpg',\n",
       " 'ferdinand-hodler_wetterhorn.jpg',\n",
       " 'carl-larsson_peasant-interior-in-winter-1890(1).jpg',\n",
       " 'koloman-moser_sketch-for-a-poster-of-the-vienna-workshop.jpg',\n",
       " 'henri-de-toulouse-lautrec_the-beauty-and-the-beast-the-bezique-1895.jpg',\n",
       " 'michel-simonidy_tulipes-1923.jpg',\n",
       " 'koloman-moser_cover-design-for-ewart-felicie-jugendschatz-german-seals-1897.jpg',\n",
       " 'henri-de-toulouse-lautrec_snobisme.jpg',\n",
       " 'carl-larsson_harvesting-ice-1905(1).jpg',\n",
       " 'a.y.-jackson_skeena-crossing-1926.jpg',\n",
       " 'ivan-bilibin_costume-design-for-the-opera-prince-igor-by-alexander-borodin-1929(1).jpg',\n",
       " 'raphael-kirchner_girls-with-purple-surrounds-1900.jpg',\n",
       " 'valentin-serov_portrait-of-maria-akimova-1908.jpg',\n",
       " 'anna-ostroumova-lebedeva_french-embankment-1922.jpg',\n",
       " 'wilhelm-kotarbinski_angel-of-sadness.jpg',\n",
       " 'zinaida-serebriakova_nude-girl.jpg',\n",
       " 'raphael-kirchner_behind-the-scenes-1900-4.jpg',\n",
       " 'aladar-korosfoi-kriesch_ego-sum-via-veritas-et-vita-1903.jpg',\n",
       " 'aubrey-beardsley_how-king-mark-and-sir-dinadan-heard-sir-palomides-ii.jpg',\n",
       " 'koloman-moser_fruit-bowl-with-three-red-and-yellow-pears.jpg',\n",
       " 'koloman-moser_design-for-the-bill-of-50-crowns-1911.jpg',\n",
       " 'ivan-bilibin_n-medtner-two-tales.jpg',\n",
       " 'felix-vallotton_italian-poet-and-philosopher-giacomo-leopardi-1895.jpg',\n",
       " 'edmund-dulac_the-emperor-s-new-clothes.jpg',\n",
       " 'alphonse-mucha_carnation-1.jpg',\n",
       " 'boris-kustodiev_shostakovich-museum-of-art.jpg',\n",
       " 'koloman-moser_draft-drawings-for-a-cabinet-in-the-headquarters-of-the-vienna-workshop-1903.jpg',\n",
       " 'zinaida-serebriakova_portrait-of-eugene-serebryakov-1909.jpg',\n",
       " 'konstantinos-parthenis_music-painting-and-decoration-of-a-piano-1920.jpg',\n",
       " 'paul-mathiopoulos_seated-girl.jpg',\n",
       " 'koloman-moser_stage-design-for-jeep-from-the-mountain-of-louis-holzberg-stage-2-night.jpg',\n",
       " 'edmund-dulac_stealers-of-light-by-the-queen-of-romania.jpg',\n",
       " 'aubrey-beardsley_prospectus-no-1.jpg',\n",
       " 'alphonse-mucha_calendar-of-cherry-blossom-1898.jpg',\n",
       " 'andrei-ryabushkin_nastassja-mikulichna.jpg',\n",
       " 'wilhelm-kotarbinski_evening-peace.jpg',\n",
       " 'ivan-bilibin_illustration-for-the-poem-the-tale-of-the-golden-cockerel-by-alexander-pushkin-1906-2(1).jpg',\n",
       " 'eliseu-visconti_carta-bilhete-300-r-is.jpg',\n",
       " 'zinaida-serebriakova_katya-still-life-1923.jpg',\n",
       " 'ivan-bilibin_illustration-for-alexander-pushkin-s-fairytale-of-the-tsar-saltan-1905-5(1).jpg',\n",
       " 'raphael-kirchner_figures-on-turquoise-background-1901.jpg',\n",
       " 'henri-de-toulouse-lautrec_the-mad-cow.jpg',\n",
       " 'raphael-kirchner_cigarettes-of-the-world-1900.jpg',\n",
       " 'heorhiy-narbut_sheet-ch-from-the-album-ukrainian-alphabet-1917.jpg',\n",
       " 'henri-de-toulouse-lautrec_troupe-de-mlle-elegantine-affiche-1896.jpg',\n",
       " 'raphael-kirchner_nude-drawings-12.jpg',\n",
       " 'nicholas-roerich_sorcerer-1909.jpg',\n",
       " 'henri-de-toulouse-lautrec_the-journal-white-poster-1896.jpg',\n",
       " 'boris-kustodiev_the-merchant-s-wife-at-tea-1918.jpg',\n",
       " ...]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('Data/Art_Nouveau_Modern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.listdir('Data/Art_Nouveau_Modern')\n",
    "image = cv2.imread('Data/Art_Nouveau_Modern/raphael-kirchner_santoy-1900-2.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdad294b0b8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "width, height = keras.preprocessing.image.load_img('Data/Art_Nouveau_Modern/raphael-kirchner_santoy-1900-2.jpg').size\n",
    "img_nrows = 512\n",
    "img_ncols = int(width * img_nrows / height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "544"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gram_matrix(x):\n",
    "    x = tf.transpose(x)\n",
    "    features = tf.reshape(x, (tf.shape(x)[0], -1))\n",
    "    gram = tf.matmul(features, tf.transpose(features))\n",
    "    gram = tf.reshape(gram,(tf.shape(gram)[0], -1))\n",
    "    return gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image_path):\n",
    "    img = keras.preprocessing.image.load_img(\n",
    "        image_path, target_size=(img_nrows, img_ncols)\n",
    "    )\n",
    "    img = keras.preprocessing.image.img_to_array(img)\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "    img = vgg19.preprocess_input(img)\n",
    "    return tf.convert_to_tensor(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deprocess_image(x):\n",
    "    # Util function to convert a tensor into a valid image\n",
    "    x = x.reshape((img_nrows, img_ncols, 3))\n",
    "    # Remove zero-center by mean pixel\n",
    "    x[:, :, 0] += 103.939\n",
    "    x[:, :, 1] += 116.779\n",
    "    x[:, :, 2] += 123.68\n",
    "    # 'BGR'->'RGB'\n",
    "    x = x[:, :, ::-1]\n",
    "    x = np.clip(x, 0, 255).astype(\"uint8\")\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = vgg19.VGG19(weights=\"imagenet\", include_top=False)\n",
    "outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])\n",
    "feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['input_1', 'block1_conv1', 'block1_conv2', 'block1_pool', 'block2_conv1', 'block2_conv2', 'block2_pool', 'block3_conv1', 'block3_conv2', 'block3_conv3', 'block3_conv4', 'block3_pool', 'block4_conv1', 'block4_conv2', 'block4_conv3', 'block4_conv4', 'block4_pool', 'block5_conv1', 'block5_conv2', 'block5_conv3', 'block5_conv4', 'block5_pool'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, None, None, 3)]   0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv4 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv4 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv4 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "=================================================================\n",
      "Total params: 20,024,384\n",
      "Trainable params: 20,024,384\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "feature_extractor.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def style_encoding(image_path,feature_extractor):\n",
    "    image_tensor = preprocess_image(image_path)\n",
    "    features = feature_extractor(image_tensor)\n",
    "    layer_features = features['block5_pool']\n",
    "    gram = gram_matrix(layer_features)\n",
    "    gram = np.array(tf.reshape(gram, [-1]))\n",
    "    return gram\n",
    "\n",
    "\n",
    "def color_encoding(image_path):\n",
    "    image = cv2.imread(image_path)\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
    "    features = []\n",
    "    (h, w) = image.shape[:2]\n",
    "    (cX, cY) = (int(w * 0.5), int(h * 0.5))\n",
    "    segments = [(0, cX, 0, cY), (cX, w, 0, cY), (cX, w, cY, h),(0, cX, cY, h)]\n",
    "    \n",
    "    (axesX, axesY) = (int(w * 0.75) // 2, int(h * 0.75) // 2)\n",
    "    \n",
    "    ellipMask = np.zeros(image.shape[:2], dtype = \"uint8\")\n",
    "    \n",
    "    cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1)\n",
    "    \n",
    "    for (startX, endX, startY, endY) in segments:\n",
    "        \n",
    "# construct a mask for each corner of the image, subtracting\n",
    "# the elliptical center from it\n",
    "        cornerMask = np.zeros(image.shape[:2], dtype = \"uint8\")\n",
    "        cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1)\n",
    "        cornerMask = cv2.subtract(cornerMask, ellipMask)\n",
    "        # extract a color histogram from the image, then update the\n",
    "        # feature vector\n",
    "        hist = histogram(image, mask = cornerMask)\n",
    "        features.extend(hist)\n",
    "    hist = histogram(image, ellipMask)\n",
    "    features.extend(hist)\n",
    "    return np.array(features)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histogram(image, mask):\n",
    "    # extract a 3D color histogram from the masked region of the\n",
    "    # image, using the supplied number of bins per channel\n",
    "    hist = cv2.calcHist([image], [0, 1, 2], mask, (8,10,9),\n",
    "        [0, 180, 0, 256, 0, 256])\n",
    "    # normalize the histogram if we are using OpenCV 2.4\n",
    "    if imutils.is_cv2():\n",
    "        hist = cv2.normalize(hist).flatten()\n",
    "    # otherwise handle for OpenCV 3+\n",
    "    else:\n",
    "        hist = cv2.normalize(hist, hist).flatten()\n",
    "    # return the histogram\n",
    "    return hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Dataframe Outline\n",
    "\n",
    "#### [Primary Key{image path}, Folder_encompassed{Folder path}, Encodings]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_dir = os.getcwd()\n",
    "data_dir = os.path.join(current_dir, 'Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/pradyumnaelavarthi/Downloads/Projects/DSC/Similar_art/Data'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['Art_Nouveau_Modern',\n",
       "  'Baroque',\n",
       "  'Contemporary_Realism',\n",
       "  'Cubism',\n",
       "  'Early_Renaissance',\n",
       "  'Fauvism',\n",
       "  'High_Renaissance',\n",
       "  'Impressionism',\n",
       "  'Mannerism_Late_Renaissance',\n",
       "  'Minimalism'],\n",
       " 22)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_folders = [i for i in sorted(os.listdir(data_dir)) if not i.startswith('.')]\n",
    "list_of_folders[:10], len(list_of_folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc_dict = {'image_path' : [], 'folder_path' : [], 'encoding' : []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/22 [03:57<?, ?it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-eb46bd984581>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mimage_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mstyle_encs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstyle_encodings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeature_extractor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0mcolor_encs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_encodings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mencs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstyle_encs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor_encs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-14-6d50e0283e92>\u001b[0m in \u001b[0;36mstyle_encodings\u001b[0;34m(image_path, feature_extractor)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstyle_encodings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeature_extractor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mimage_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocess_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_extractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mlayer_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'block5_pool'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mgram\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgram_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1010\u001b[0m         with autocast_variable.enable_auto_cast_variables(\n\u001b[1;32m   1011\u001b[0m             self._compute_dtype_object):\n\u001b[0;32m-> 1012\u001b[0;31m           \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    423\u001b[0m     \"\"\"\n\u001b[1;32m    424\u001b[0m     return self._run_internal_graph(\n\u001b[0;32m--> 425\u001b[0;31m         inputs, training=training, mask=mask)\n\u001b[0m\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\u001b[0m in \u001b[0;36m_run_internal_graph\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0;31m# Update tensor_dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1010\u001b[0m         with autocast_variable.enable_auto_cast_variables(\n\u001b[1;32m   1011\u001b[0m             self._compute_dtype_object):\n\u001b[0;32m-> 1012\u001b[0;31m           \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/keras/layers/convolutional.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    246\u001b[0m       \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_causal_padding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convolution_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py\u001b[0m in \u001b[0;36mconvolution_v2\u001b[0;34m(input, filters, strides, padding, data_format, dilations, name)\u001b[0m\n\u001b[1;32m   1018\u001b[0m       \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m       \u001b[0mdilations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdilations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1020\u001b[0;31m       name=name)\n\u001b[0m\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py\u001b[0m in \u001b[0;36mconvolution_internal\u001b[0;34m(input, filters, strides, padding, data_format, dilations, name, call_from_convolution, num_spatial_dims)\u001b[0m\n\u001b[1;32m   1148\u001b[0m           \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m           \u001b[0mdilations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdilations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m           name=name)\n\u001b[0m\u001b[1;32m   1151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mchannel_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py\u001b[0m in \u001b[0;36m_conv2d_expanded_batch\u001b[0;34m(input, filters, strides, padding, data_format, dilations, name)\u001b[0m\n\u001b[1;32m   2602\u001b[0m         \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2603\u001b[0m         \u001b[0mdilations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdilations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2604\u001b[0;31m         name=name)\n\u001b[0m\u001b[1;32m   2605\u001b[0m   return squeeze_batch_dims(\n\u001b[1;32m   2606\u001b[0m       \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/dsc/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d\u001b[0;34m(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)\u001b[0m\n\u001b[1;32m    927\u001b[0m         \u001b[0;34m\"use_cudnn_on_gpu\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"padding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m         \u001b[0;34m\"explicit_paddings\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexplicit_paddings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"data_format\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m         \"dilations\", dilations)\n\u001b[0m\u001b[1;32m    930\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = vgg19.VGG19(weights=\"imagenet\", include_top=False)\n",
    "outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])\n",
    "feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)\n",
    "\n",
    "for path in tqdm(list_of_folders):\n",
    "    folder_path = os.path.join(data_dir,path)\n",
    "    list_images = [image for image in sorted(os.listdir(folder_path)) if not image.startswith('.')]\n",
    "    for image in list_images:\n",
    "        image_path = os.path.join(folder_path,image)\n",
    "        style_encs = style_encodings(image_path,feature_extractor)\n",
    "        color_encs = color_encodings(image_path)\n",
    "        encs = np.concatenate([style_encs,color_encs])\n",
    "        enc_dict['image_path'].append(os.path.join(folder_path,image))\n",
    "        enc_dict['folder_path'].append(folder_path)\n",
    "        enc_dict['encoding'].append(encs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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