Data_load.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shirts\n",
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH0CAYAAADVH+85AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njP1/rnUUeBUdpjMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1bd455dd8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "index = 0\n",
    "index2 = 0\n",
    "basewidth = 32\n",
    "\n",
    "#wpercent = (basewidth / float(img.size[0]))\n",
    "hsize = 32#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([[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([[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_train.npy', out) #Saving train image arrays\n",
    "np.save('Y_train.npy', index_array) #Saving train labels\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3, 32, 32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#erstes anzeigen\n",
    "np.shape(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2a1bf12d0b8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH0CAYAAADVH+85AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS5JUsf8PWpReFZcs+50AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a1bf4d3b70>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "plt.imshow(out[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basewidth = 32\n",
    "img = Image.open(input_dir + '/' + 'Shirts/' + '1050.jpg')\n",
    "#wpercent = (basewidth / float(img.size[0]))\n",
    "hsize = 32#int((float(img.size[1]) * float(wpercent)))\n",
    "img = img.resize((basewidth, hsize), Image.ANTIALIAS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "cifar10_dataset_folder_path = 'cifar-10-batches-py'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'batch_id' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-5b64280b521a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcifar10_dataset_folder_path\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'/data_batch_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'latin1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'labels'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'batch_id' is not defined"
     ]
    }
   ],
   "source": [
    "with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "    batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "labels = batch['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load images with url\n",
    "import pandas as pd\n",
    "\n",
    "witt_url = pd.read_csv('C:/Users/Martin/Downloads/wittweidende_bilder-feedmartinde.csv', sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Artikel_ID</th>\n",
       "      <th>Bilder 1</th>\n",
       "      <th>Bilder 2</th>\n",
       "      <th>Bilder 3</th>\n",
       "      <th>Bilder 4</th>\n",
       "      <th>Artnr6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://click.cptrack.de/?rd=true&amp;k=YXOl0QS1rzv...</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://click.cptrack.de/?rd=true&amp;k=K-CDhOLhsZq...</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://click.cptrack.de/?rd=true&amp;k=a3XxikQQalI...</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://click.cptrack.de/?rd=true&amp;k=gw4zboV0IOh...</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://click.cptrack.de/?rd=true&amp;k=k3wgYCh-qtp...</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>http://www.witt-weiden.de/images/shop/003/003....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Artikel_ID  \\\n",
       "0  http://click.cptrack.de/?rd=true&k=YXOl0QS1rzv...   \n",
       "1  http://click.cptrack.de/?rd=true&k=K-CDhOLhsZq...   \n",
       "2  http://click.cptrack.de/?rd=true&k=a3XxikQQalI...   \n",
       "3  http://click.cptrack.de/?rd=true&k=gw4zboV0IOh...   \n",
       "4  http://click.cptrack.de/?rd=true&k=k3wgYCh-qtp...   \n",
       "\n",
       "                                            Bilder 1  \\\n",
       "0  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "1  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "2  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "3  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "4  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "\n",
       "                                            Bilder 2  \\\n",
       "0  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "1  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "2  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "3  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "4  http://www.witt-weiden.de/images/shop/003/003....   \n",
       "\n",
       "                                            Bilder 3 Bilder 4  Artnr6  \n",
       "0  http://www.witt-weiden.de/images/shop/003/003....      NaN  100009  \n",
       "1  http://www.witt-weiden.de/images/shop/003/003....      NaN  100009  \n",
       "2  http://www.witt-weiden.de/images/shop/003/003....      NaN  100009  \n",
       "3  http://www.witt-weiden.de/images/shop/003/003....      NaN  100009  \n",
       "4  http://www.witt-weiden.de/images/shop/003/003....      NaN  100009  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "witt_url.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grouped_df = witt_url.groupby('Artnr6')[ 'Bilder 1', 'Bilder 2'].first().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Artnr6', 'Bilder 1', 'Bilder 2']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(grouped_df)\n",
    "#group_obj.groups.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "\n",
    "for i in range(1000):\n",
    "    fullfilename = os.path.join('C:/Users/Martin/Untitled Folder 1/Raw_images', str(grouped_df['Artnr6'][i])+'.jpg')\n",
    "    urllib.request.urlretrieve(grouped_df['Bilder 1'][i], fullfilename)\n",
    "\n",
    "\n",
    "\n",
    "#image = urllib.URLopener()\n",
    "#urllib.urlopen('http://www.gunnerkrigg.com//comics/00000001.jpg')\n",
    "#image.urlretrieve(witt_url['Bilder 1'][1], \"00000001.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'100009.jpg'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(witt_url['Artnr6'][1])+'.jpg'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../Raw_images/100009.jpg'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fullfilename"
   ]
  }
 ],
 "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
}