Image_URL_to_NP_loader.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-24d9e741527f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     20\u001b[0m         \u001b[0mcurr_url\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_url\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"_9.jpg\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"_1.jpg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[1;31m#print(curr_url)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m         \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurr_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfullfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[0malread_load\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[1;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[0;32m    186\u001b[0m     \u001b[0murl_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplittype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    161\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    162\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 163\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    464\u001b[0m             \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    482\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    483\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 484\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    485\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    442\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    443\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 444\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    445\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1281\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1282\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1284\u001b[0m     \u001b[0mhttp_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1255\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1256\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1257\u001b[1;33m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1258\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1259\u001b[0m             \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1196\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1197\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1198\u001b[1;33m                 \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1199\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1200\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[1;31m# read until we get a non-100 response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    296\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 297\u001b[1;33m             \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    298\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 258\u001b[1;33m         \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iso-8859-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    259\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"status line\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Martin\\Anaconda3\\envs\\tf\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    574\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 576\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    577\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#load images with url\n",
    "import pandas as pd\n",
    "import os\n",
    "import urllib.request\n",
    "\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "alread_load = True\n",
    "#Witt Feed mit Artikelnr\n",
    "witt_url = pd.read_csv('C:/Users/Martin/Untitled Folder 1/merged_feed.csv', delimiter=',', encoding ='latin-1' )\n",
    "\n",
    "grouped_df = witt_url#[witt_url['OBERMKZ']==72]\n",
    "#schleife über die einzelnen Zeilen um bilder zu laden\n",
    "if(alread_load==True):\n",
    "    for i in range(20000):\n",
    "        fullfilename = os.path.join('C:/Users/Martin/Untitled Folder 1/Raw_images/', str(grouped_df['ARTIKELNR'].values[i])+'.jpg')\n",
    "        curr_url = grouped_df['Bilder 1'].values[i]\n",
    "        curr_url=curr_url.replace(\"_9.jpg\", \"_1.jpg\")\n",
    "        #print(curr_url)\n",
    "        urllib.request.urlretrieve(curr_url, fullfilename)\n",
    "alread_load = False\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        100009\n",
       "1        100010\n",
       "2        100021\n",
       "3        100032\n",
       "4        100097\n",
       "5        100101\n",
       "6        100112\n",
       "7        100123\n",
       "8        100144\n",
       "9        100155\n",
       "10       100166\n",
       "11       100172\n",
       "12       100177\n",
       "13       100188\n",
       "14       100214\n",
       "15       100224\n",
       "16       100235\n",
       "17       100279\n",
       "18       100291\n",
       "19       100304\n",
       "20       100310\n",
       "21       100326\n",
       "22       100359\n",
       "23       100360\n",
       "24       100371\n",
       "25       100387\n",
       "26       100393\n",
       "27       100406\n",
       "28       100412\n",
       "29       100417\n",
       "          ...  \n",
       "19624    888817\n",
       "19625    888828\n",
       "19626    888839\n",
       "19627    888840\n",
       "19628    888851\n",
       "19629    888873\n",
       "19630    888931\n",
       "19631    888985\n",
       "19632    888996\n",
       "19633    970174\n",
       "19634    970587\n",
       "19635    970963\n",
       "19636    973075\n",
       "19637    973117\n",
       "19638    973188\n",
       "19639    974390\n",
       "19640    974925\n",
       "19641    978422\n",
       "19642    978551\n",
       "19643    980810\n",
       "19644    980821\n",
       "19645    983029\n",
       "19646    983578\n",
       "19647    983915\n",
       "19648    984016\n",
       "19649    988516\n",
       "19650    990138\n",
       "19651    990819\n",
       "19652    993158\n",
       "19653    993307\n",
       "Name: ARTIKELNR, Length: 19654, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df['ARTIKELNR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no_label\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "#Bilder in numpy format laden\n",
    "#Directory containing images you wish to convert\n",
    "input_dir = 'C:/Users/Martin/Untitled Folder 1/Raw_images/'\n",
    "\n",
    "directories = os.listdir(input_dir)\n",
    "\n",
    "#Laden der Bilder\n",
    "\n",
    "index = 0\n",
    "index2 = 0\n",
    "basewidth = 64\n",
    "\n",
    "#wpercent = (basewidth / float(img.size[0]))\n",
    "hsize = 64#int((float(img.size[1]) * float(wpercent)))\n",
    "\n",
    "for folder in directories:\n",
    "\t#Ignoring .DS_Store dir\n",
    "\tif folder == '.DS_Store':\n",
    "\t\tpass\n",
    "\n",
    "\telse:\n",
    "\t\tprint(folder)\n",
    "\n",
    "\t\tfolder2 = os.listdir(input_dir + '/' + folder)\n",
    "\t\tindex += 1\n",
    "\n",
    "\t\tfor image in folder2:\n",
    "\t\t\tif image == \".DS_Store\":\n",
    "\t\t\t\tpass\n",
    "\n",
    "\t\t\telse:\n",
    "\t\t\t\tindex2 += 1\n",
    "\n",
    "\t\t\t\tim = Image.open(input_dir+\"/\"+folder+\"/\"+image) #Opening image\n",
    "\t\t\t\tim = im.resize((basewidth, hsize), Image.ANTIALIAS)\n",
    "\t\t\t\tim = (np.array(im)) #Converting to numpy array\n",
    "\t\t\t\tplt.imshow(im)                \n",
    "\t\t\t\ttry:\n",
    "\t\t\t\t\tr = im[:,:,0] #Slicing to get R data\n",
    "\t\t\t\t\tg = im[:,:,1] #Slicing to get G data\n",
    "\t\t\t\t\tb = im[:,:,2] #Slicing to get B data\n",
    "\n",
    "\t\t\t\t\tif index2 != 1:\n",
    "\t\t\t\t\t\tnew_array = np.array([im], np.uint8)#np.array([[r] + [g] + [b]], np.uint8) #Creating array with shape (3, 100, 100)\n",
    "\t\t\t\t\t\tout = np.append(out, new_array, 0) #Adding new image to array shape of (x, 3, 100, 100) where x is image number\n",
    "\n",
    "\t\t\t\t\telif index2 == 1:\n",
    "\t\t\t\t\t\tout = np.array([im], np.uint8)#np.array([[r] + [g] + [b]], np.uint8) #Creating array with shape (3, 100, 100)\n",
    "\n",
    "\t\t\t\t\tif index == 1 and index2 == 1:\n",
    "\t\t\t\t\t\tindex_array = np.array([[index]])\n",
    "\n",
    "\t\t\t\t\telse:\n",
    "\t\t\t\t\t\tnew_index_array = np.array([[index]], np.int8)\n",
    "\t\t\t\t\t\tindex_array = np.append(index_array, new_index_array, 0)\n",
    "\n",
    "\t\t\t\texcept Exception as e:\n",
    "\t\t\t\t\tprint(e)\n",
    "\t\t\t\t\tprint( \"Removing image\" + image)\n",
    "\t\t\t\t\tos.remove(input_dir+\"/\"+folder+\"/\"+image)\n",
    "\n",
    "print(index)\n",
    "\n",
    "np.save('X_64_train.npy', out) #Saving train image arrays\n",
    "np.save('Y_64_train.npy', index_array) #Saving train labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3511, 64, 64, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NbdlS/pkJzugBAMgaiR4AgIyR6AEAyFhNEr2Z/YmZPWBmB8xswMzazexJM/sj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\n7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f12d91ac18>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "plt.imshow(im)  \n",
    "np.shape(out)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}