Ashish_nb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import io\n",
    "import cv2\n",
    "from sklearn.model_selection import train_test_split\n",
    "import glob\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D,Conv3D\n",
    "from keras.layers import MaxPool2D,AvgPool2D,BatchNormalization\n",
    "from keras.layers import Flatten,LeakyReLU\n",
    "from keras.layers import Dense,Dropout\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from keras import backend as K\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_name</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y1</th>\n",
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       "      <td>1467891670691DSC_0582.png</td>\n",
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       "      <th>1</th>\n",
       "      <td>1475151786691DSC00560.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1480334114405_R2A3620.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JPEG_20160712_161047_1000606860114.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JPEG_20160403_174522_-1137349501.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                               image_name  x1  x2  y1  y2\n",
       "0               1467891670691DSC_0582.png NaN NaN NaN NaN\n",
       "1               1475151786691DSC00560.png NaN NaN NaN NaN\n",
       "2               1480334114405_R2A3620.png NaN NaN NaN NaN\n",
       "3  JPEG_20160712_161047_1000606860114.png NaN NaN NaN NaN\n",
       "4    JPEG_20160403_174522_-1137349501.png NaN NaN NaN NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_name</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>combine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1467891670691DSC_0582.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[nan, nan, nan, nan]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1475151786691DSC00560.png</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>1480334114405_R2A3620.png</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>JPEG_20160403_174522_-1137349501.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "                               image_name  x1  x2  y1  y2  \\\n",
       "0               1467891670691DSC_0582.png NaN NaN NaN NaN   \n",
       "1               1475151786691DSC00560.png NaN NaN NaN NaN   \n",
       "2               1480334114405_R2A3620.png NaN NaN NaN NaN   \n",
       "3  JPEG_20160712_161047_1000606860114.png NaN NaN NaN NaN   \n",
       "4    JPEG_20160403_174522_-1137349501.png NaN NaN NaN NaN   \n",
       "\n",
       "                combine  \n",
       "0  [nan, nan, nan, nan]  \n",
       "1  [nan, nan, nan, nan]  \n",
       "2  [nan, nan, nan, nan]  \n",
       "3  [nan, nan, nan, nan]  \n",
       "4  [nan, nan, nan, nan]  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['combine'] = df[['x1', 'x2','y1','y2']].values.tolist()\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img_list = glob.glob(\"images/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-7c8b6f6f2f72>\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[1;34m'data.txt'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0mmy_loaded_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data.txt'"
     ]
    }
   ],
   "source": [
    "with open('data.txt', 'rb') as f:\n",
    "    my_loaded_array = np.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "         [0.47058824, 0.44313725, 0.43137255]],\n",
       "\n",
       "        [[0.01960784, 0.75686275, 0.89411765],\n",
       "         [0.01176471, 0.74117647, 0.88235294],\n",
       "         [0.        , 0.70980392, 0.85882353],\n",
       "         ...,\n",
       "         [0.4627451 , 0.44313725, 0.43137255],\n",
       "         [0.46666667, 0.44705882, 0.44313725],\n",
       "         [0.45882353, 0.43921569, 0.43137255]]],\n",
       "\n",
       "\n",
       "       [[[0.78039216, 0.78039216, 0.78039216],\n",
       "         [0.77254902, 0.78039216, 0.78039216],\n",
       "         [0.76862745, 0.77647059, 0.77647059],\n",
       "         ...,\n",
       "         [0.81176471, 0.81960784, 0.81960784],\n",
       "         [0.81176471, 0.82352941, 0.81568627],\n",
       "         [0.81176471, 0.82352941, 0.81960784]],\n",
       "\n",
       "        [[0.77647059, 0.77647059, 0.77647059],\n",
       "         [0.77254902, 0.78039216, 0.78039216],\n",
       "         [0.77254902, 0.78039216, 0.78039216],\n",
       "         ...,\n",
       "         [0.81960784, 0.82745098, 0.82745098],\n",
       "         [0.81568627, 0.82352941, 0.82352941],\n",
       "         [0.81568627, 0.82745098, 0.81960784]],\n",
       "\n",
       "        [[0.78431373, 0.78431373, 0.78431373],\n",
       "         [0.77647059, 0.78431373, 0.78431373],\n",
       "         [0.77254902, 0.78039216, 0.78039216],\n",
       "         ...,\n",
       "         [0.81960784, 0.82745098, 0.82745098],\n",
       "         [0.81960784, 0.82745098, 0.82745098],\n",
       "         [0.81960784, 0.82745098, 0.82745098]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.75686275, 0.76470588, 0.76470588],\n",
       "         [0.76078431, 0.76862745, 0.76862745],\n",
       "         [0.76078431, 0.76862745, 0.76862745],\n",
       "         ...,\n",
       "         [0.81960784, 0.82745098, 0.82745098],\n",
       "         [0.81960784, 0.82745098, 0.82745098],\n",
       "         [0.82352941, 0.83137255, 0.83137255]],\n",
       "\n",
       "        [[0.76078431, 0.76862745, 0.76862745],\n",
       "         [0.76078431, 0.76862745, 0.76862745],\n",
       "         [0.76078431, 0.76862745, 0.76862745],\n",
       "         ...,\n",
       "         [0.81568627, 0.82352941, 0.82352941],\n",
       "         [0.81568627, 0.82352941, 0.82352941],\n",
       "         [0.81568627, 0.82352941, 0.82352941]],\n",
       "\n",
       "        [[0.76470588, 0.77254902, 0.77254902],\n",
       "         [0.76078431, 0.76862745, 0.76862745],\n",
       "         [0.76078431, 0.76862745, 0.76862745],\n",
       "         ...,\n",
       "         [0.81568627, 0.82352941, 0.82352941],\n",
       "         [0.81568627, 0.82352941, 0.82352941],\n",
       "         [0.81568627, 0.82352941, 0.82352941]]],\n",
       "\n",
       "\n",
       "       [[[0.67058824, 0.68235294, 0.68627451],\n",
       "         [0.68235294, 0.68235294, 0.69411765],\n",
       "         [0.67843137, 0.68235294, 0.69803922],\n",
       "         ...,\n",
       "         [0.68235294, 0.69411765, 0.69803922],\n",
       "         [0.68235294, 0.69803922, 0.70196078],\n",
       "         [0.67843137, 0.69411765, 0.69803922]],\n",
       "\n",
       "        [[0.66666667, 0.68235294, 0.68627451],\n",
       "         [0.67843137, 0.68235294, 0.69803922],\n",
       "         [0.68235294, 0.68627451, 0.70196078],\n",
       "         ...,\n",
       "         [0.67843137, 0.69411765, 0.69803922],\n",
       "         [0.6745098 , 0.69411765, 0.69019608],\n",
       "         [0.67843137, 0.69411765, 0.69803922]],\n",
       "\n",
       "        [[0.68627451, 0.69411765, 0.69803922],\n",
       "         [0.6745098 , 0.68627451, 0.70196078],\n",
       "         [0.68627451, 0.69019608, 0.70588235],\n",
       "         ...,\n",
       "         [0.68235294, 0.69803922, 0.70196078],\n",
       "         [0.68235294, 0.69803922, 0.70196078],\n",
       "         [0.68235294, 0.69411765, 0.70588235]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.69803922, 0.69803922, 0.69803922],\n",
       "         [0.69803922, 0.69803922, 0.69803922],\n",
       "         [0.70588235, 0.70196078, 0.70980392],\n",
       "         ...,\n",
       "         [0.6745098 , 0.68235294, 0.68627451],\n",
       "         [0.67058824, 0.6745098 , 0.68235294],\n",
       "         [0.6745098 , 0.67058824, 0.67843137]],\n",
       "\n",
       "        [[0.69411765, 0.69411765, 0.69411765],\n",
       "         [0.70196078, 0.70196078, 0.70196078],\n",
       "         [0.70196078, 0.70196078, 0.70196078],\n",
       "         ...,\n",
       "         [0.6745098 , 0.68235294, 0.68627451],\n",
       "         [0.67058824, 0.67843137, 0.68235294],\n",
       "         [0.6745098 , 0.6745098 , 0.6745098 ]],\n",
       "\n",
       "        [[0.69803922, 0.69803922, 0.69803922],\n",
       "         [0.69803922, 0.69803922, 0.70196078],\n",
       "         [0.69411765, 0.70196078, 0.70196078],\n",
       "         ...,\n",
       "         [0.68235294, 0.67843137, 0.68627451],\n",
       "         [0.6745098 , 0.67058824, 0.67843137],\n",
       "         [0.67058824, 0.67058824, 0.67058824]]]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_loaded_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-7-d15625eda39b>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-7-d15625eda39b>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    from scikit-image import feature\u001b[0m\n\u001b[1;37m               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import scipy.misc\n",
    "from skimage import feature\n",
    "from scipy import misc\n",
    "from PIL import Image\n",
    "from skimage import color\n",
    "edges=[]\n",
    "\n",
    "\n",
    "\n",
    "label=[]\n",
    "data=[]\n",
    "i=0\n",
    "for imagePath in df['image_name'].values:\n",
    "    imgn='images\\\\'+ imagePath\n",
    "    if imgn in train_img_list :\n",
    "        image = cv2.imread(imgn)\n",
    "        imgne=np.array(image)\n",
    "        #imgnew=imgne.flatten().reshape(1,480*640*3)\n",
    "        image = cv2.resize(image, (120,160))\n",
    "        #dat=(rgb.convert('1',dither=Image.NONE))\n",
    "        img = color.rgb2gray(image)\n",
    "        data.append(cv2.canny(img, sigma=2))\n",
    "        #data.append(np.array(dat))\n",
    "        print(image.shape)\n",
    "        i=1+i\n",
    "        label.append(imagePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=np.array(data)/255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24045, 28, 28)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24045, 28, 28, 1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data_28_grey_out.txt', 'wb') as f:\n",
    "    np.save(f, d, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data.txt', 'rb') as f:\n",
    "    my_loaded_array = np.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14000, 48, 64, 3)"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_loaded_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24045, 60, 80, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                      ...\n",
       "1                                                      ...\n",
       "2                                                      ...\n",
       "3                                                      ...\n",
       "4                                                      ...\n",
       "5                                                      ...\n",
       "6                                                      ...\n",
       "7                                                      ...\n",
       "8                                                      ...\n",
       "9                                                      ...\n",
       "10                                                     ...\n",
       "11                                                     ...\n",
       "12                                                     ...\n",
       "13                                                     ...\n",
       "14                                                     ...\n",
       "15                                                     ...\n",
       "16                                                     ...\n",
       "17                                                     ...\n",
       "18                                                     ...\n",
       "19                                                     ...\n",
       "20                                                     ...\n",
       "21                                                     ...\n",
       "22                                                     ...\n",
       "23                                                     ...\n",
       "24                                                     ...\n",
       "25                                                     ...\n",
       "26                                                     ...\n",
       "27                                                     ...\n",
       "28                                                     ...\n",
       "29                                                     ...\n",
       "                               ...                        \n",
       "13970                                                  NaN\n",
       "13971                                                  NaN\n",
       "13972                                                  NaN\n",
       "13973                                                  NaN\n",
       "13974                                                  NaN\n",
       "13975                                                  NaN\n",
       "13976                                                  NaN\n",
       "13977                                                  NaN\n",
       "13978                                                  NaN\n",
       "13979                                                  NaN\n",
       "13980                                                  NaN\n",
       "13981                                                  NaN\n",
       "13982                                                  NaN\n",
       "13983                                                  NaN\n",
       "13984                                                  NaN\n",
       "13985                                                  NaN\n",
       "13986                                                  NaN\n",
       "13987                                                  NaN\n",
       "13988                                                  NaN\n",
       "13989                                                  NaN\n",
       "13990                                                  NaN\n",
       "13991                                                  NaN\n",
       "13992                                                  NaN\n",
       "13993                                                  NaN\n",
       "13994                                                  NaN\n",
       "13995                                                  NaN\n",
       "13996                                                  NaN\n",
       "13997                                                  NaN\n",
       "13998                                                  NaN\n",
       "13999                                                  NaN\n",
       "Name: array, Length: 14000, dtype: object"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('datain.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train=df.iloc[:,1:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train=df.iloc[:,1:5]\n",
    "Y_train['centerx']=(Y_train['x1']+Y_train['x2'])/2\n",
    "Y_train['centery']=(Y_train['y1']+Y_train['y2'])/2\n",
    "Y_train['width']=(Y_train['x2']-Y_train['x1'])\n",
    "Y_train['height']=(Y_train['y2']-Y_train['y1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train=Y_train.drop(['x1','x2','y1','y2'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Y_train.head()\n",
    "Y=df.iloc[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centerx</th>\n",
       "      <th>centery</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>305.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>380</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>434</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319.0</td>\n",
       "      <td>241.5</td>\n",
       "      <td>564</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>314.5</td>\n",
       "      <td>273.5</td>\n",
       "      <td>575</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>323.0</td>\n",
       "      <td>249.5</td>\n",
       "      <td>526</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centerx  centery  width  height\n",
       "0    305.0    234.0    380     182\n",
       "1    240.0    235.0    434     348\n",
       "2    319.0    241.5    564     457\n",
       "3    314.5    273.5    575     223\n",
       "4    323.0    249.5    526     151"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_iou(y_true, y_pred):\n",
    "   score, up_opt = tf.metrics.mean_iou(y_true, y_pred,num_classes=2)\n",
    "   K.get_session().run(tf.local_variables_initializer())\n",
    "   with tf.control_dependencies([up_opt]):\n",
    "       score = tf.identity(score)\n",
    "   return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data.txt', 'wb') as f:\n",
    "    np.save(f, my_array, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "(trainX, valX, trainY, valY) = train_test_split(my_loaded_array,Y_train, test_size=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 48, 64, 32)        864       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 48, 64, 32)        128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 48, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 21, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 21, 64)        8192      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 16, 21, 64)        256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 16, 21, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 10, 64)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 10, 64)         36864     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 8, 10, 64)         256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 8, 10, 64)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 10, 128)        73728     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 8, 10, 128)        512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 8, 10, 128)        0         \n",
      "_________________________________________________________________\n",
      "average_pooling2d_1 (Average (None, 4, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 4, 5, 128)         147456    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 4, 5, 128)         512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 4, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 2, 2, 256)         131072    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 2, 2, 256)         1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 2, 2, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 4)                 1028      \n",
      "=================================================================\n",
      "Total params: 730,084\n",
      "Trainable params: 728,740\n",
      "Non-trainable params: 1,344\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3,3),strides=(1,1), padding='same',  input_shape=[48,64,3],use_bias=False))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(MaxPool2D(pool_size=(3,3)))\n",
    "model.add(Conv2D(64, kernel_size=(2,2),strides=(1,1), padding='same',use_bias=False))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(64, kernel_size=(3,3),strides=(1,1), padding='same',use_bias=False))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(Conv2D(128, kernel_size=(3,3),strides=(1,1), padding='same',use_bias=False))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(AvgPool2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(128, kernel_size=(3,3),strides=(1,1), padding='same',use_bias=False))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(256, kernel_size=(2,2),strides=(1,1), padding='same',use_bias=False))\n",
    "model.add(BatchNormalization())\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256,activation='relu'))\n",
    "model.add(Dense(256,activation='relu'))\n",
    "model.add(Dense(4))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13986 samples, validate on 14 samples\n",
      "Epoch 1/10\n",
      "   30/13986 [..............................] - ETA: 3:13:36 - loss: 109444.3438 - acc: 0.2333"
     ]
    },
    {
     "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-21-cdb63585120f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m               metrics=['accuracy'])\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\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[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1039\u001b[1;33m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1040\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2713\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2715\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2717\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2674\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2675\u001b[1;33m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2676\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1380\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[0;32m   1381\u001b[0m               \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1382\u001b[1;33m               run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1383\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1384\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.compile(loss=keras.losses.mse,\n",
    "              optimizer=keras.optimizers.adam(),\n",
    "              metrics=['accuracy'])\n",
    " \n",
    "model.fit(trainX, trainY, batch_size=30,validation_data=(valX, valY), epochs=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json_cxy2 = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json_cxy2)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model_cxy2.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(trainX, trainY.drop(['height','width'],axis=1), batch_size=50,validation_data=(valX, valY.drop(['height','width'],axis=1)) , epochs=20, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json_grey = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json_grey)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model_grey.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "from keras.models import model_from_json\n",
    "\n",
    "json_file = open('model_my_hw_48_64_3.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"model_my_hw_48_64_3.h5\")\n",
    "print(\"Loaded model from disk\")\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model.compile(loss=keras.losses.MSE,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13300 samples, validate on 700 samples\n",
      "Epoch 1/100\n",
      "  840/13300 [>.............................] - ETA: 3:26 - loss: 127.3175 - acc: 0.7024"
     ]
    },
    {
     "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-75-a14c8b454fc0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'centerx'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'centery'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'centerx'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'centery'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\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[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1039\u001b[1;33m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1040\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2713\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2715\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2717\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2674\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2675\u001b[1;33m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2676\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1380\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[0;32m   1381\u001b[0m               \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1382\u001b[1;33m               run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1383\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1384\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(trainX, trainY.drop(['centerx','centery'],axis=1), batch_size=30,validation_data=(valX, valY.drop(['centerx','centery'],axis=1)) , epochs=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_99 (Conv2D)           (None, 46, 62, 2)         56        \n",
      "_________________________________________________________________\n",
      "conv2d_100 (Conv2D)          (None, 46, 62, 4)         12        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_36 (MaxPooling (None, 23, 31, 4)         0         \n",
      "_________________________________________________________________\n",
      "dropout_56 (Dropout)         (None, 23, 31, 4)         0         \n",
      "_________________________________________________________________\n",
      "flatten_43 (Flatten)         (None, 2852)              0         \n",
      "_________________________________________________________________\n",
      "dense_96 (Dense)             (None, 1)                 2853      \n",
      "=================================================================\n",
      "Total params: 2,921\n",
      "Trainable params: 2,921\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(2, kernel_size=(3,3), activation='relu',  input_shape=[48,64,3]))\n",
    "model.add(Conv2D(4, kernel_size=(1,1),activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "#model.add(Conv2D(1, kernel_size=(2,2), activation='relu'))\n",
    "#model.add(Conv2D(1, kernel_size=(3,3), activation='relu'))\n",
    "#model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "##model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "#model.add(Dense(32, activation='relu'))\n",
    "#model.add(Dense(64, activation='relu'))\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation='relu'))\n",
    "#model.add(LeakReLU(0.1))\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13930 samples, validate on 70 samples\n",
      "Epoch 1/50\n",
      "13930/13930 [==============================] - 47s 3ms/step - loss: 145.4723 - acc: 0.0024 - val_loss: 121.2357 - val_acc: 0.0000e+00\n",
      "Epoch 2/50\n",
      "13930/13930 [==============================] - 39s 3ms/step - loss: 108.2632 - acc: 0.0030 - val_loss: 110.7657 - val_acc: 0.0000e+00\n",
      "Epoch 3/50\n",
      " 8430/13930 [=================>............] - ETA: 15s - loss: 99.4703 - acc: 0.0040"
     ]
    },
    {
     "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-215-c269c288f107>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m               metrics=['accuracy'])\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'centerx'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'centery'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'height'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'height'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'centerx'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'centery'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\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[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1039\u001b[1;33m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1040\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m    183\u001b[0m                         \u001b[1;31m# Do not slice the training phase flag.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m                         ins_batch = slice_arrays(\n\u001b[1;32m--> 185\u001b[1;33m                             ins[:-1], batch_ids) + [ins[-1]]\n\u001b[0m\u001b[0;32m    186\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m                         \u001b[0mins_batch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mslice_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\utils\\generic_utils.py\u001b[0m in \u001b[0;36mslice_arrays\u001b[1;34m(arrays, start, stop)\u001b[0m\n\u001b[0;32m    524\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'shape'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m                 \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    527\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\DL\\lib\\site-packages\\keras\\utils\\generic_utils.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    524\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'shape'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m                 \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    527\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.compile(loss=keras.losses.mae,\n",
    "              optimizer=keras.optimizers.adam(),\n",
    "              metrics=['accuracy'])\n",
    " \n",
    "model.fit(trainX, trainY.drop(['centerx','centery','height'],axis=1), batch_size=30,validation_data=(valX, valY.drop(['height','centerx','centery'],axis=1)) , epochs=50, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "14000/14000 [==============================] - 8s 597us/step - loss: 19.4004 - acc: 0.9565\n",
      "Epoch 2/50\n",
      "14000/14000 [==============================] - 8s 598us/step - loss: 19.2186 - acc: 0.9541\n",
      "Epoch 3/50\n",
      "14000/14000 [==============================] - 8s 601us/step - loss: 19.0825 - acc: 0.9547\n",
      "Epoch 4/50\n",
      "14000/14000 [==============================] - 9s 620us/step - loss: 19.1397 - acc: 0.9535\n",
      "Epoch 5/50\n",
      "14000/14000 [==============================] - 9s 619us/step - loss: 19.3434 - acc: 0.9525\n",
      "Epoch 6/50\n",
      "14000/14000 [==============================] - 9s 621us/step - loss: 19.3564 - acc: 0.9527\n",
      "Epoch 7/50\n",
      "14000/14000 [==============================] - 9s 620us/step - loss: 19.1684 - acc: 0.9536\n",
      "Epoch 8/50\n",
      "14000/14000 [==============================] - 9s 620us/step - loss: 19.0883 - acc: 0.9559\n",
      "Epoch 9/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.0837 - acc: 0.9518\n",
      "Epoch 10/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.2073 - acc: 0.9559\n",
      "Epoch 11/50\n",
      "14000/14000 [==============================] - 9s 624us/step - loss: 19.2321 - acc: 0.9541\n",
      "Epoch 12/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.1290 - acc: 0.9544\n",
      "Epoch 13/50\n",
      "14000/14000 [==============================] - 9s 626us/step - loss: 19.3496 - acc: 0.9566\n",
      "Epoch 14/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.0696 - acc: 0.9566\n",
      "Epoch 15/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.2720 - acc: 0.9544\n",
      "Epoch 16/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.3864 - acc: 0.9524\n",
      "Epoch 17/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.2134 - acc: 0.9531\n",
      "Epoch 18/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.2698 - acc: 0.9556\n",
      "Epoch 19/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.1142 - acc: 0.9531\n",
      "Epoch 20/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.2503 - acc: 0.9548\n",
      "Epoch 21/50\n",
      "14000/14000 [==============================] - 9s 632us/step - loss: 19.1851 - acc: 0.9523\n",
      "Epoch 22/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.1256 - acc: 0.9523\n",
      "Epoch 23/50\n",
      "14000/14000 [==============================] - 9s 609us/step - loss: 19.2266 - acc: 0.9551\n",
      "Epoch 24/50\n",
      "14000/14000 [==============================] - 8s 606us/step - loss: 19.3608 - acc: 0.9559\n",
      "Epoch 25/50\n",
      "14000/14000 [==============================] - 9s 608us/step - loss: 19.2349 - acc: 0.9534\n",
      "Epoch 26/50\n",
      "14000/14000 [==============================] - 8s 606us/step - loss: 19.1982 - acc: 0.9544\n",
      "Epoch 27/50\n",
      "14000/14000 [==============================] - 9s 608us/step - loss: 19.3279 - acc: 0.9519\n",
      "Epoch 28/50\n",
      "14000/14000 [==============================] - 8s 606us/step - loss: 19.1858 - acc: 0.9538\n",
      "Epoch 29/50\n",
      "14000/14000 [==============================] - 8s 606us/step - loss: 19.3023 - acc: 0.9522\n",
      "Epoch 30/50\n",
      "14000/14000 [==============================] - 8s 605us/step - loss: 19.1505 - acc: 0.9569\n",
      "Epoch 31/50\n",
      "14000/14000 [==============================] - 8s 606us/step - loss: 19.1762 - acc: 0.9534\n",
      "Epoch 32/50\n",
      "14000/14000 [==============================] - 8s 605us/step - loss: 19.0264 - acc: 0.9554\n",
      "Epoch 33/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.3115 - acc: 0.9539\n",
      "Epoch 34/50\n",
      "14000/14000 [==============================] - 9s 647us/step - loss: 19.3940 - acc: 0.9543\n",
      "Epoch 35/50\n",
      "14000/14000 [==============================] - 9s 620us/step - loss: 19.2426 - acc: 0.9536\n",
      "Epoch 36/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.1602 - acc: 0.9549\n",
      "Epoch 37/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.0784 - acc: 0.9526\n",
      "Epoch 38/50\n",
      "14000/14000 [==============================] - 9s 621us/step - loss: 19.1325 - acc: 0.9559\n",
      "Epoch 39/50\n",
      "14000/14000 [==============================] - 9s 624us/step - loss: 19.2017 - acc: 0.9544\n",
      "Epoch 40/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.4997 - acc: 0.9535\n",
      "Epoch 41/50\n",
      "14000/14000 [==============================] - 9s 627us/step - loss: 19.1743 - acc: 0.9534\n",
      "Epoch 42/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.3525 - acc: 0.9541\n",
      "Epoch 43/50\n",
      "14000/14000 [==============================] - 9s 621us/step - loss: 19.3288 - acc: 0.9536\n",
      "Epoch 44/50\n",
      "14000/14000 [==============================] - 9s 621us/step - loss: 19.2441 - acc: 0.9530\n",
      "Epoch 45/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.2131 - acc: 0.9552\n",
      "Epoch 46/50\n",
      "14000/14000 [==============================] - 9s 624us/step - loss: 19.1001 - acc: 0.9541\n",
      "Epoch 47/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.1325 - acc: 0.9539\n",
      "Epoch 48/50\n",
      "14000/14000 [==============================] - 9s 623us/step - loss: 19.4575 - acc: 0.9521\n",
      "Epoch 49/50\n",
      "14000/14000 [==============================] - 9s 624us/step - loss: 19.0567 - acc: 0.9556\n",
      "Epoch 50/50\n",
      "14000/14000 [==============================] - 9s 622us/step - loss: 19.2409 - acc: 0.9531\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c26f3cca20>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(trainX, trainY.drop(['centerx','centery'],axis=1), batch_size=200 , epochs=50, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "model_json_my_hw = model.to_json()\n",
    "with open(\"model_my_hw_48_64_3.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json_my_hw)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model_my_hw_48_64_3.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "model_json_hw2 = model1.to_json()\n",
    "with open(\"model1.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json_hw2)\n",
    "# serialize weights to HDF5\n",
    "model1.save_weights(\"model_hw2.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "output=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12815, 5)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 80, 3) 14000\n",
      "(60, 80, 3) 14001\n",
      "(60, 80, 3) 14002\n",
      "(60, 80, 3) 14003\n",
      "(60, 80, 3) 14004\n",
      "(60, 80, 3) 14005\n",
      "(60, 80, 3) 14006\n",
      "(60, 80, 3) 14007\n",
      "(60, 80, 3) 14008\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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     "text": [
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "(60, 80, 3) 26814\n"
     ]
    }
   ],
   "source": [
    "label_out=[]\n",
    "data_out=[]\n",
    "\n",
    "for imagePath in output['image_name'].values:\n",
    "    imgn='images\\\\'+ imagePath\n",
    "    if imgn in train_img_list :\n",
    "        image = cv2.imread(imgn)\n",
    "        imgne=np.array(image)\n",
    "        #imgnew=imgne.flatten().reshape(1,480*640*3)\n",
    "        image = cv2.resize(image, (80,60))\n",
    "        #blur = cv2.GaussianBlur(image,(5,5),0)\n",
    "        #gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "        #gray=gray.resize(gray,(160,120))\n",
    "        print(image.shape,i)\n",
    "        i=1+i\n",
    "        data_out.append(image)\n",
    "        label_out.append(imagePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_out=np.array(data_out)/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_out=d_out.reshape(-1,120,160,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data_60_80_out.txt', 'wb') as f:\n",
    "    np.save(f, d_out, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_out_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "output=pd.read_csv('cxy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "hist1=(loaded_model.predict(d_out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist2=loaded_model.predict(d_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "#output['centerx']=hist1[:,0]\n",
    "#output['centery']=hist1[:,1]\n",
    "output['width']=hist2[:,0]\n",
    "output['height']=hist2[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258.77000</td>\n",
       "      <td>238.44987</td>\n",
       "      <td>285.442230</td>\n",
       "      <td>456.290161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>JPEG_20160622_110649_1000527459853.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>324.88920</td>\n",
       "      <td>243.77745</td>\n",
       "      <td>366.021027</td>\n",
       "      <td>220.488434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>JPEG_20160823_120737_1000784898268.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.09450</td>\n",
       "      <td>238.55482</td>\n",
       "      <td>366.927979</td>\n",
       "      <td>373.805359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1480965956645IMG_3094.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361.80383</td>\n",
       "      <td>258.45245</td>\n",
       "      <td>590.274170</td>\n",
       "      <td>304.344147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>14732348976417a565e40d545452688130062b267a2d2.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325.39413</td>\n",
       "      <td>245.04796</td>\n",
       "      <td>535.108032</td>\n",
       "      <td>473.717804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1474723374370DSC07852.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.24707</td>\n",
       "      <td>236.42413</td>\n",
       "      <td>184.039780</td>\n",
       "      <td>439.083099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>JPEG_20160820_111118_100011668802.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.39896</td>\n",
       "      <td>252.54895</td>\n",
       "      <td>125.344307</td>\n",
       "      <td>318.076233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>JPEG_20160706_105226_100087855419.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>281.34552</td>\n",
       "      <td>217.56606</td>\n",
       "      <td>350.518250</td>\n",
       "      <td>304.391479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>JPEG_20160621_155839_100073846506.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.14710</td>\n",
       "      <td>241.87646</td>\n",
       "      <td>238.952301</td>\n",
       "      <td>333.972015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1480940089861_R2A1904.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.76086</td>\n",
       "      <td>290.78130</td>\n",
       "      <td>296.754944</td>\n",
       "      <td>265.671143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1474637513144DSC06780.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>308.18942</td>\n",
       "      <td>223.31877</td>\n",
       "      <td>229.850555</td>\n",
       "      <td>369.953125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>JPEG_20161123_170015_1000183903221.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330.67133</td>\n",
       "      <td>185.59811</td>\n",
       "      <td>148.840652</td>\n",
       "      <td>410.469421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>JPEG_20161205_152655_1000319785884.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295.17703</td>\n",
       "      <td>229.49718</td>\n",
       "      <td>441.304321</td>\n",
       "      <td>316.935974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>14806550588121473324459p2-500x500.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349.98538</td>\n",
       "      <td>233.87770</td>\n",
       "      <td>354.011200</td>\n",
       "      <td>516.690796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>JPEG_20160913_110021_1000554801748.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>367.44604</td>\n",
       "      <td>220.29813</td>\n",
       "      <td>534.494202</td>\n",
       "      <td>285.180969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>JPEG_20161028_113656_1000884495593.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295.03525</td>\n",
       "      <td>247.34917</td>\n",
       "      <td>458.482025</td>\n",
       "      <td>377.273590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>1480485006674IMG_20161129_181135.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334.59247</td>\n",
       "      <td>232.54710</td>\n",
       "      <td>574.558594</td>\n",
       "      <td>347.017578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>1468666217374JPEG_20160712_164026_100095190148...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>323.73022</td>\n",
       "      <td>239.97441</td>\n",
       "      <td>581.291931</td>\n",
       "      <td>83.447853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>1475159286472DSC08150.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>327.47888</td>\n",
       "      <td>253.50700</td>\n",
       "      <td>145.316025</td>\n",
       "      <td>293.496857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>1473662770499DeeplearnS11593.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.30463</td>\n",
       "      <td>239.14757</td>\n",
       "      <td>637.836548</td>\n",
       "      <td>512.502869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>JPEG_20161121_172423_1000451802749.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>353.73447</td>\n",
       "      <td>261.66537</td>\n",
       "      <td>424.579865</td>\n",
       "      <td>338.422485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>JPEG_20160711_112635_1000386061474.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.22653</td>\n",
       "      <td>239.48361</td>\n",
       "      <td>504.677277</td>\n",
       "      <td>141.352783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>JPEG_20161125_170806_1000294132098.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306.45790</td>\n",
       "      <td>271.70657</td>\n",
       "      <td>245.091354</td>\n",
       "      <td>409.102081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>1473664771684DeeplearnS11641.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.94302</td>\n",
       "      <td>244.51111</td>\n",
       "      <td>469.101715</td>\n",
       "      <td>194.147995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>JPEG_20161125_142000_1000881308475.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>271.84576</td>\n",
       "      <td>243.73705</td>\n",
       "      <td>506.922333</td>\n",
       "      <td>469.186279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12785</th>\n",
       "      <td>12785</td>\n",
       "      <td>JPEG_20160527_155707_1000436681221.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.61597</td>\n",
       "      <td>241.27410</td>\n",
       "      <td>567.203003</td>\n",
       "      <td>537.834656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12786</th>\n",
       "      <td>12786</td>\n",
       "      <td>1472036697076pyellow1.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.28955</td>\n",
       "      <td>237.28857</td>\n",
       "      <td>529.063232</td>\n",
       "      <td>498.430969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12787</th>\n",
       "      <td>12787</td>\n",
       "      <td>1473315151219DeeplearnS11541.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>338.07938</td>\n",
       "      <td>253.07550</td>\n",
       "      <td>586.743042</td>\n",
       "      <td>472.176758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12788</th>\n",
       "      <td>12788</td>\n",
       "      <td>JPEG_20161114_164147_1000717591566.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.61682</td>\n",
       "      <td>237.96675</td>\n",
       "      <td>358.926788</td>\n",
       "      <td>371.502319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12789</th>\n",
       "      <td>12789</td>\n",
       "      <td>JPEG_20160810_125457_1000618891946.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.30700</td>\n",
       "      <td>256.07590</td>\n",
       "      <td>253.545685</td>\n",
       "      <td>421.704407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12790</th>\n",
       "      <td>12790</td>\n",
       "      <td>1473662761056DeeplearnS11590.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>324.35890</td>\n",
       "      <td>235.83742</td>\n",
       "      <td>488.786774</td>\n",
       "      <td>213.698349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12791</th>\n",
       "      <td>12791</td>\n",
       "      <td>1474724287220DSC08133.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329.88574</td>\n",
       "      <td>232.54674</td>\n",
       "      <td>465.368927</td>\n",
       "      <td>362.676544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12792</th>\n",
       "      <td>12792</td>\n",
       "      <td>JPEG_20160924_140500_1000708658172.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.04138</td>\n",
       "      <td>242.42879</td>\n",
       "      <td>421.887207</td>\n",
       "      <td>314.179901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12793</th>\n",
       "      <td>12793</td>\n",
       "      <td>JPEG_20160920_160831_1000416032132.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.53656</td>\n",
       "      <td>264.07913</td>\n",
       "      <td>393.483521</td>\n",
       "      <td>463.545776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12794</th>\n",
       "      <td>12794</td>\n",
       "      <td>1475158952049DSC06915.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>308.20520</td>\n",
       "      <td>237.81181</td>\n",
       "      <td>214.741226</td>\n",
       "      <td>358.576965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12795</th>\n",
       "      <td>12795</td>\n",
       "      <td>JPEG_20161130_182058_1000451372797.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.63727</td>\n",
       "      <td>256.73920</td>\n",
       "      <td>575.913147</td>\n",
       "      <td>190.503967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12796</th>\n",
       "      <td>12796</td>\n",
       "      <td>JPEG_20160625_140951_1000716933208.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>372.47290</td>\n",
       "      <td>231.09370</td>\n",
       "      <td>249.058578</td>\n",
       "      <td>270.182739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12797</th>\n",
       "      <td>12797</td>\n",
       "      <td>147772335227620161028_162146.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309.04437</td>\n",
       "      <td>232.86832</td>\n",
       "      <td>596.273071</td>\n",
       "      <td>437.684265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12798</th>\n",
       "      <td>12798</td>\n",
       "      <td>JPEG_20160704_123939_1000567540935.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299.95898</td>\n",
       "      <td>233.68854</td>\n",
       "      <td>439.016205</td>\n",
       "      <td>221.075531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12799</th>\n",
       "      <td>12799</td>\n",
       "      <td>JPEG_20161123_190951_1000625148732.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>302.42947</td>\n",
       "      <td>251.02516</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>297.06018</td>\n",
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       "      <th>12808</th>\n",
       "      <td>12808</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>308.64310</td>\n",
       "      <td>291.06796</td>\n",
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       "      <th>12809</th>\n",
       "      <td>12809</td>\n",
       "      <td>1473751292174IMG_0982.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>355.13147</td>\n",
       "      <td>248.11858</td>\n",
       "      <td>507.000488</td>\n",
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       "      <th>12810</th>\n",
       "      <td>12810</td>\n",
       "      <td>JPEG_20161104_103201_1000965398606.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.23203</td>\n",
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       "      <td>476.573914</td>\n",
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       "      <th>12811</th>\n",
       "      <td>12811</td>\n",
       "      <td>JPEG_20160711_114732_1000663038627.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>304.85290</td>\n",
       "      <td>253.95900</td>\n",
       "      <td>117.446983</td>\n",
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       "    <tr>\n",
       "      <th>12812</th>\n",
       "      <td>12812</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295.47080</td>\n",
       "      <td>219.14279</td>\n",
       "      <td>521.500183</td>\n",
       "      <td>423.216431</td>\n",
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       "    <tr>\n",
       "      <th>12813</th>\n",
       "      <td>12813</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>368.22610</td>\n",
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       "      <td>311.651062</td>\n",
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       "    <tr>\n",
       "      <th>12814</th>\n",
       "      <td>12814</td>\n",
       "      <td>1473751123070IMG_0992.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.74332</td>\n",
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       "      <td>647.358276</td>\n",
       "      <td>320.110840</td>\n",
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       "  </tbody>\n",
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       "<p>12815 rows × 10 columns</p>\n",
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       "       Unnamed: 0                                         image_name  x1  x2  \\\n",
       "0               0                          1474723840903DSC08089.png NaN NaN   \n",
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       "29             29             JPEG_20161125_142000_1000881308475.png NaN NaN   \n",
       "...           ...                                                ...  ..  ..   \n",
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       "12787       12787                   1473315151219DeeplearnS11541.png NaN NaN   \n",
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       "\n",
       "       y1  y2    centerx    centery       width      height  \n",
       "0     NaN NaN  352.85248  242.88140  259.019379  293.659302  \n",
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       "6     NaN NaN  324.88920  243.77745  366.021027  220.488434  \n",
       "7     NaN NaN  227.09450  238.55482  366.927979  373.805359  \n",
       "8     NaN NaN  361.80383  258.45245  590.274170  304.344147  \n",
       "9     NaN NaN  325.39413  245.04796  535.108032  473.717804  \n",
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       "11    NaN NaN  318.39896  252.54895  125.344307  318.076233  \n",
       "12    NaN NaN  281.34552  217.56606  350.518250  304.391479  \n",
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       "29    NaN NaN  271.84576  243.73705  506.922333  469.186279  \n",
       "...    ..  ..        ...        ...         ...         ...  \n",
       "12785 NaN NaN  301.61597  241.27410  567.203003  537.834656  \n",
       "12786 NaN NaN  318.28955  237.28857  529.063232  498.430969  \n",
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       "12789 NaN NaN  310.30700  256.07590  253.545685  421.704407  \n",
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       "12791 NaN NaN  329.88574  232.54674  465.368927  362.676544  \n",
       "12792 NaN NaN  286.04138  242.42879  421.887207  314.179901  \n",
       "12793 NaN NaN  313.53656  264.07913  393.483521  463.545776  \n",
       "12794 NaN NaN  308.20520  237.81181  214.741226  358.576965  \n",
       "12795 NaN NaN  307.63727  256.73920  575.913147  190.503967  \n",
       "12796 NaN NaN  372.47290  231.09370  249.058578  270.182739  \n",
       "12797 NaN NaN  309.04437  232.86832  596.273071  437.684265  \n",
       "12798 NaN NaN  299.95898  233.68854  439.016205  221.075531  \n",
       "12799 NaN NaN  302.42947  251.02516  260.470551  406.160278  \n",
       "12800 NaN NaN  326.02197  236.24075  213.130005  276.290894  \n",
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       "12802 NaN NaN  276.74933  197.42705  473.030090  336.055786  \n",
       "12803 NaN NaN  361.30542  228.76585  514.556458  394.131531  \n",
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       "12805 NaN NaN  345.97296  245.77588  539.988037  470.570129  \n",
       "12806 NaN NaN  319.00598  243.53722  425.908691  349.616699  \n",
       "12807 NaN NaN  297.06018  273.15573  512.462830  407.733765  \n",
       "12808 NaN NaN  308.64310  291.06796  592.176453  307.061401  \n",
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       "12814 NaN NaN  318.74332  236.64400  647.358276  320.110840  \n",
       "\n",
       "[12815 rows x 10 columns]"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "output['x1']=output['centerx']-output['width']/2\n",
    "output['x2']=output['centerx']+output['width']/2\n",
    "output['y1']=output['centery']-output['height']/2\n",
    "output['y2']=output['centery']+output['height']/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "output=output.drop(['Unnamed: 0','centerx','centery','width','height'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_csv('out12.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
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       "      <td>348.903532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JPEG_20161205_135307_1000155917326.png</td>\n",
       "      <td>79.192948</td>\n",
       "      <td>562.190292</td>\n",
       "      <td>29.467635</td>\n",
       "      <td>421.002425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JPEG_20160711_123440_1000518778437.png</td>\n",
       "      <td>225.479914</td>\n",
       "      <td>441.205026</td>\n",
       "      <td>127.633328</td>\n",
       "      <td>412.223232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JPEG_20160803_115329_100034020722.png</td>\n",
       "      <td>52.647156</td>\n",
       "      <td>530.662904</td>\n",
       "      <td>1.347165</td>\n",
       "      <td>453.366635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               image_name          x1          x2          y1  \\\n",
       "0               1474723840903DSC08089.png  223.342791  482.362169   96.051749   \n",
       "1        1473231475010DeeplearnS11276.png   97.351651  558.448209  139.703368   \n",
       "2  JPEG_20161205_135307_1000155917326.png   79.192948  562.190292   29.467635   \n",
       "3  JPEG_20160711_123440_1000518778437.png  225.479914  441.205026  127.633328   \n",
       "4   JPEG_20160803_115329_100034020722.png   52.647156  530.662904    1.347165   \n",
       "\n",
       "           y2  \n",
       "0  389.711051  \n",
       "1  348.903532  \n",
       "2  421.002425  \n",
       "3  412.223232  \n",
       "4  453.366635  "
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "columns = 4\n",
    "rows = 5\n",
    "for i in range(1+31,31+ columns * rows + 1):\n",
    "    img = plt.imread(\"images/\"+output['image_name'][i-1])\n",
    "    ax = fig.add_subplot(rows, columns, i-31)\n",
    "    rect = patches.Rectangle((output.iloc[i-1, 1], output.iloc[i-1, 3]), output.iloc[i-1, 2]-output.iloc[i-1, 1], output.iloc[i-1, 4]-output.iloc[i-1, 3], linewidth=1, edgecolor='r', facecolor='none')\n",
    "    ax.add_patch(rect)\n",
    "    plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}