Convert_images_CSV.ipynb

{
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   "cell_type": "code",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import PIL.Image as Image\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "deletable": true,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    "editable": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving as train_neutral.csv\n",
      "Saving as train_sadness.csv\n",
      "Saving as train_happiness.csv\n",
      "Saving as train_surprise.csv\n",
      "Saving as train_disgust.csv\n",
      "Saving as train_angry.csv\n",
      "Saving as train_fear.csv\n",
      "Saving as validation_neutral.csv\n",
      "Saving as validation_sadness.csv\n",
      "Saving as validation_happiness.csv\n",
      "Saving as validation_surprise.csv\n",
      "Saving as validation_disgust.csv\n",
      "Saving as validation_angry.csv\n",
      "Saving as validation_fear.csv\n",
      "Saving as test_neutral.csv\n",
      "Saving as test_sadness.csv\n",
      "Saving as test_happiness.csv\n",
      "Saving as test_surprise.csv\n",
      "Saving as test_disgust.csv\n",
      "Saving as test_angry.csv\n",
      "Saving as test_fear.csv\n"
     ]
    }
   ],
   "source": [
    "root, dirs, files = os.walk(\"/home/asad/Desktop/Facial/try_try_again/fer2013_grayscale/fer2013_data/\").__next__()\n",
    "\n",
    "for i in range(len(dirs)):\n",
    "    subroot, subdirs, subfiles = os.walk(root+\"/\"+dirs[i]).__next__()\n",
    "    for j in range(len(subdirs)):\n",
    "        filelist = glob.glob((subroot+\"/\"+subdirs[j]+'/*.png'))\n",
    "        x = np.array([np.array(Image.open(fname)) for fname in filelist])\n",
    "        x.shape\n",
    "        print(\"Saving as \"+dirs[i]+\"_\"+subdirs[j]+\".csv\")\n",
    "        ds = x.reshape(x.shape[0],x.shape[2]*x.shape[1])\n",
    "        numpy.savetxt(dirs[i]+\"_\"+subdirs[j]+\".csv\", ds, delimiter=\" \",fmt='%i')\n",
    "        df = pd.read_csv(dirs[i]+\"_\"+subdirs[j]+\".csv\", header=None, names=['Pixels'])\n",
    "        df.insert(0, 'labels', j)\n",
    "        # print(dirs[i]+'_csv/'+dirs[i]+\"_\"+subdirs[j]+\".csv\")\n",
    "        df.to_csv(dirs[i]+'_csv/'+dirs[i]+\"_\"+subdirs[j]+\".csv\", sep='\\t',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
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   "source": [
    "fout=open(\"out.csv\",\"a\")\n",
    "path ='validation_csv' # use your path\n",
    "filenames = glob.glob(path + \"/*.csv\")\n",
    "combined_csv = pd.concat([pd.read_csv(f) for f in filenames])\n",
    "combined_csv.to_csv('valid_all.csv',sep=',', index=False, header=None )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
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       "      <td>0</td>\n",
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       "887       0  0 3 6 14 30 37 42 36 56 60 74 104 121 132 165 ...\n",
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       "\n",
       "[890 rows x 2 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#f = pd.Series([1], index=['labels'])\n",
    "#df['labels']=0\n",
    "df = pd.read_csv('validation_happiness.csv', header=None, names=['Pixels'])\n",
    "df.insert(0, 'labels', 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We're on time 0\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-98-2e0656792c82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mfilelist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdirs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/*.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfilelist\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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[0m\u001b[1;32m      6\u001b[0m     \u001b[0;31m# name = print(dirs[x])+\"_valid.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_valid.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%i'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
     ]
    }
   ],
   "source": [
    "for i in range(len(dirs)):\n",
    "    print(\"We're on time %d\" % (i))\n",
    "    filelist = glob.glob((root+'/'+dirs[i]+'/*.png'))\n",
    "    x = np.array([np.array(Image.open(fname)) for fname in filelist])\n",
    "    ds = x.reshape(x.shape[0],x.shape[2]*x.shape[1])\n",
    "    # name = print(dirs[x])+\"_valid.csv\"\n",
    "    numpy.savetxt(dirs[i]+\"_valid.csv\", ds, delimiter=\" \",fmt='%i')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neutral_valid.csv\n"
     ]
    }
   ],
   "source": [
    "print(dirs[0]+\"_valid.csv\")\n",
    "\"test/neutral/neutral\\\" + str(i) + \\\".png\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "filelist = glob.glob((root+'/'+dirs[0]+'/*.png'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/asad/Desktop/Facial/try_try_again/fer2013_grayscale/fer2013_data/validation/neutral\n"
     ]
    }
   ],
   "source": [
    "print(root+'/'+dirs[0])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "filelist.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = np.array([np.array(Image.open(fname)) for fname in filelist])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2304"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape[2]*x.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ds = x.reshape(x.shape[0],x.shape[2]*x.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "numpy.savetxt(\"neutral.csv\", ds, delimiter=\" \",fmt='%i')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 36,  50,  53, ...,  70, 104, 120],\n",
       "       [ 36,  61,  51, ...,  41,  47,  44],\n",
       "       [193, 192, 153, ..., 254, 255, 255],\n",
       "       ..., \n",
       "       [  1,  13,  24, ..., 200, 202, 204],\n",
       "       [233, 230, 192, ...,  41,  41,  47],\n",
       "       [120,   6,   4, ..., 178, 173, 174]], dtype=uint8)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y=zeros((496,2304))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "w= zeros(496)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'zip' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-83-623b94a727f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrows\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'zip' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "rows = zip(y,w)\n",
    "rows.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "numpy.savetxt(\"test_valid.csv\", np.hstack([w, ds[1]]), delimiter=\",\",fmt='%i')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 36,  50,  53, ...,  70, 104, 120],\n",
       "       [ 36,  61,  51, ...,  41,  47,  44],\n",
       "       [193, 192, 153, ..., 254, 255, 255],\n",
       "       ..., \n",
       "       [ 52,  51,  52, ...,  89,  90, 104],\n",
       "       [180, 135, 117, ...,  43,  41,  42],\n",
       "       [255, 254, 253, ..., 135, 134, 137]], dtype=uint8)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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