MainVizNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re \n",
    "\n",
    "\n",
    "csv_list = pd.read_csv('list.csv', header=None)\n",
    "csv_list = csv_list.iloc[:,0].values\n",
    "frames = []\n",
    "for csv in csv_list:\n",
    "    frames.append(pd.read_csv(csv))\n",
    "\n",
    "full_validation = pd.concat(frames)\n",
    "threshold_list = full_validation['threshold'].unique()\n",
    "\n",
    "fcn8 = full_validation.loc[full_validation.model_name.str.contains('FCMN8'),:]\n",
    "fcn16 = full_validation.loc[full_validation.model_name.str.contains('FCMN16'),:]\n",
    "fcn32 = full_validation.loc[full_validation.model_name.str.contains('FCMN32'),:]\n",
    "\n",
    "\n",
    "#     Lista de Plots a Generar:\n",
    "#         PixelWise Metrics:\n",
    "#             Barplots de Precision, Recall e IoU\n",
    "#                 x: 3 arquitecturas a cada threshold\n",
    "#                 y: valor de la metrica [0,1]\n",
    "#         BudWise Metrics:\n",
    "#             Distancia Euclideana entre centros de masa normalizada\n",
    "#                 x: thresholds\n",
    "#                 y: valor de distancia normalizado promedio y error promedio\n",
    "#             Una vez definido cuando se detecta una yema\n",
    "#             ROC Curve Precision X Recall:\n",
    "#                 x: Recall\n",
    "#                 y: Precision\n",
    "#                 z: Threshold\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['valid0FCMN8rmsprop_lr0.0001_prep_mobilenet_ep150.csv',\n",
       "       'valid0FCMN16rmsprop_lr0.0001_prep_mobilenet_ep150.csv',\n",
       "       'valid0FCMN32adam_lr0.0001_prep_wences_ep150.csv'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iou_mean_8s = []\n",
    "iou_std_8s = []\n",
    "iou_mean_16s = []\n",
    "iou_std_16s = []\n",
    "iou_mean_32s = []\n",
    "iou_std_32s = []\n",
    "\n",
    "# fcn8_rms = fcn8.loc[fcn8.model_name.str.contains('rmsprop'),:]\n",
    "# iou_mean = []\n",
    "# prec_mean = []\n",
    "# rec_mean = []\n",
    "# iou_std = []\n",
    "# prec_std = []\n",
    "# rec_std = []\n",
    "for th in threshold_list:\n",
    "    th_slice = fcn8.loc[fcn8.threshold == th, :]\n",
    "    iou_mean_8s.append(th_slice.iou.mean())\n",
    "    iou_std_8s.append(th_slice.iou.std())\n",
    "for th in threshold_list:\n",
    "    th_slice = fcn16.loc[fcn16.threshold == th, :]\n",
    "    iou_mean_16s.append(th_slice.iou.mean())\n",
    "    iou_std_16s.append(th_slice.iou.std())\n",
    "for th in threshold_list:\n",
    "    th_slice = fcn32.loc[fcn32.threshold == th, :]\n",
    "    iou_mean_32s.append(th_slice.iou.mean())\n",
    "    iou_std_32s.append(th_slice.iou.std())\n",
    "\n",
    "#     prec_list = []\n",
    "#     for i in np.arange(th_slice.shape[0]):\n",
    "#         if th_slice.segmentation_x_center.values[i] == np.nan:\n",
    "#             continue\n",
    "#         elif(th_slice.segmentation_area.values[i] == 0):\n",
    "#             prec_list.append(0)\n",
    "#         else:\n",
    "#             prec_list.append(th_slice.intersection.values[i] / th_slice.segmentation_area.values[i])\n",
    "#     prec_mean.append(np.mean(prec_list))\n",
    "#     prec_std.append(np.std(prec_list))\n",
    "#     rec_mean.append(np.mean(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "#     rec_std.append(np.std(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "ind = np.arange(len(threshold_list))*3  # the x locations for the groups\n",
    "width = 0.85  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(ind - width, iou_mean_8s, width, yerr=iou_std_8s,\n",
    "                color='#cecece', label='FCN8s')\n",
    "ax.bar(ind, iou_mean_16s, width, yerr=iou_std_16s,\n",
    "                color='#827f7f', label='FCN16s')\n",
    "ax.bar(ind + width, iou_mean_32s, width, yerr=iou_std_32s,\n",
    "                color='#000000', label='FCN32s')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('FCN8s - FCN16s - FCN32s')\n",
    "ax.set_ylabel('Mean IoU')\n",
    "ax.set_title('Average IoU for the three implemented architectures')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(threshold_list)\n",
    "ax.legend()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "plt.savefig('plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prec_mean_8s = []\n",
    "prec_std_8s = []\n",
    "prec_mean_16s = []\n",
    "prec_std_16s = []\n",
    "prec_mean_32s = []\n",
    "prec_std_32s = []\n",
    "\n",
    "for th in threshold_list:\n",
    "    prec_list = []\n",
    "    th_slice = fcn8.loc[fcn8.threshold == th, :]\n",
    "    \n",
    "    for i in np.arange(th_slice.shape[0]):\n",
    "        if(th_slice.segmentation_area.values[i] == 0):\n",
    "            prec_list.append(0)\n",
    "        else:\n",
    "            prec_list.append(th_slice.intersection.values[i] / th_slice.segmentation_area.values[i])\n",
    "            \n",
    "    prec_mean_8s.append(np.mean(prec_list))\n",
    "    prec_std_8s.append(np.std(prec_list))\n",
    "    \n",
    "for th in threshold_list:\n",
    "    prec_list = []\n",
    "    th_slice = fcn16.loc[fcn16.threshold == th, :]\n",
    "    \n",
    "    for i in np.arange(th_slice.shape[0]):\n",
    "        if(th_slice.segmentation_area.values[i] == 0):\n",
    "            prec_list.append(0)\n",
    "        else:\n",
    "            prec_list.append(th_slice.intersection.values[i] / th_slice.segmentation_area.values[i])\n",
    "            \n",
    "    prec_mean_16s.append(np.mean(prec_list))\n",
    "    prec_std_16s.append(np.std(prec_list))\n",
    "    \n",
    "for th in threshold_list:\n",
    "    prec_list = []\n",
    "    th_slice = fcn32.loc[fcn32.threshold == th, :]\n",
    "    \n",
    "    for i in np.arange(th_slice.shape[0]):\n",
    "        if(th_slice.segmentation_area.values[i] == 0):\n",
    "            prec_list.append(0)\n",
    "        else:\n",
    "            prec_list.append(th_slice.intersection.values[i] / th_slice.segmentation_area.values[i])\n",
    "            \n",
    "    prec_mean_32s.append(np.mean(prec_list))\n",
    "    prec_std_32s.append(np.std(prec_list))\n",
    "\n",
    "#     prec_list = []\n",
    "#     for i in np.arange(th_slice.shape[0]):\n",
    "#         if th_slice.segmentation_x_center.values[i] == np.nan:\n",
    "#             continue\n",
    "#         elif(th_slice.segmentation_area.values[i] == 0):\n",
    "#             prec_list.append(0)\n",
    "#         else:\n",
    "#             prec_list.append(th_slice.intersection.values[i] / th_slice.segmentation_area.values[i])\n",
    "#     prec_mean.append(np.mean(prec_list))\n",
    "#     prec_std.append(np.std(prec_list))\n",
    "#     rec_mean.append(np.mean(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "#     rec_std.append(np.std(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "ind = np.arange(len(threshold_list))*3  # the x locations for the groups\n",
    "width = 0.85  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(ind - width, prec_mean_8s, width, yerr=prec_std_8s,\n",
    "                color='#cecece', label='FCN8s')\n",
    "ax.bar(ind, prec_mean_16s, width, yerr=prec_std_16s,\n",
    "                color='#827f7f', label='FCN16s')\n",
    "ax.bar(ind + width, prec_mean_32s, width, yerr=prec_std_32s,\n",
    "                color='#000000', label='FCN32s')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('FCN8s - FCN16s - FCN32s')\n",
    "ax.set_title('Average Precision for the three implemented architectures')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(threshold_list)\n",
    "ax.legend()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "plt.savefig('plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rec_mean_8s = []\n",
    "rec_std_8s = []\n",
    "rec_mean_16s = []\n",
    "rec_std_16s = []\n",
    "rec_mean_32s = []\n",
    "rec_std_32s = []\n",
    "\n",
    "for th in threshold_list:\n",
    "    th_slice = fcn8.loc[fcn8.threshold == th, :]\n",
    "    rec_mean_8s.append(np.mean(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "    rec_std_8s.append(np.std(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "for th in threshold_list:\n",
    "    th_slice = fcn16.loc[fcn16.threshold == th, :]\n",
    "    rec_mean_16s.append(np.mean(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "    rec_std_16s.append(np.std(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "for th in threshold_list:\n",
    "    th_slice = fcn32.loc[fcn32.threshold == th, :]\n",
    "    rec_mean_32s.append(np.mean(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "    rec_std_32s.append(np.std(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "\n",
    "#     prec_list = []\n",
    "#     for i in np.arange(th_slice.shape[0]):\n",
    "#         if th_slice.segmentation_x_center.values[i] == np.nan:\n",
    "#             continue\n",
    "#         elif(th_slice.segmentation_area.values[i] == 0):\n",
    "#             prec_list.append(0)\n",
    "#         else:\n",
    "#             prec_list.append(th_slice.intersection.values[i] / th_slice.segmentation_area.values[i])\n",
    "#     prec_mean.append(np.mean(prec_list))\n",
    "#     prec_std.append(np.std(prec_list))\n",
    "#     rec_mean.append(np.mean(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "#     rec_std.append(np.std(th_slice.intersection.values / th_slice.gt_area.values))\n",
    "ind = np.arange(len(threshold_list))*3  # the x locations for the groups\n",
    "width = 0.85  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(ind - width, rec_mean_8s, width, yerr=rec_std_8s,\n",
    "                color='#cecece', label='FCN8s')\n",
    "ax.bar(ind, rec_mean_16s, width, yerr=rec_std_16s,\n",
    "                color='#827f7f', label='FCN16s')\n",
    "ax.bar(ind + width, rec_mean_32s, width, yerr=rec_std_32s,\n",
    "                color='#000000', label='FCN32s')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('FCN8s - FCN16s - FCN32s')\n",
    "ax.set_ylabel('Mean Recall')\n",
    "ax.set_title('Average Recall for the three implemented architectures')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(threshold_list)\n",
    "ax.legend()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "plt.savefig('plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'threshold', 'sample', 'iou', 'intersection', 'union',\n",
       "       'gt_area', 'segmentation_area', 'gt_x_center', 'gt_y_center',\n",
       "       'segmentation_x_center', 'segmentation_y_center', 'x_distance',\n",
       "       'y_distance', 'euclidean_distance', 'x_size', 'y_size'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid = pd.read_csv('./0FCMN8rmsprop_lr0.0001_decay0_ep120_th_no_binarize.csv')\n",
    "valid.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>threshold</th>\n",
       "      <th>sample</th>\n",
       "      <th>iou</th>\n",
       "      <th>intersection</th>\n",
       "      <th>union</th>\n",
       "      <th>gt_area</th>\n",
       "      <th>segmentation_area</th>\n",
       "      <th>gt_x_center</th>\n",
       "      <th>gt_y_center</th>\n",
       "      <th>segmentation_x_center</th>\n",
       "      <th>segmentation_y_center</th>\n",
       "      <th>x_distance</th>\n",
       "      <th>y_distance</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>x_size</th>\n",
       "      <th>y_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0766.jpg</td>\n",
       "      <td>0.348560</td>\n",
       "      <td>2167</td>\n",
       "      <td>6217</td>\n",
       "      <td>5712</td>\n",
       "      <td>2672</td>\n",
       "      <td>639.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>18.027756</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0485.jpg</td>\n",
       "      <td>0.612738</td>\n",
       "      <td>10525</td>\n",
       "      <td>17177</td>\n",
       "      <td>10949</td>\n",
       "      <td>16753</td>\n",
       "      <td>486.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>9.433981</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0674.jpg</td>\n",
       "      <td>0.593740</td>\n",
       "      <td>5615</td>\n",
       "      <td>9457</td>\n",
       "      <td>6158</td>\n",
       "      <td>8914</td>\n",
       "      <td>532.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>14.764823</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0417.jpg</td>\n",
       "      <td>0.875591</td>\n",
       "      <td>9079</td>\n",
       "      <td>10369</td>\n",
       "      <td>9470</td>\n",
       "      <td>9978</td>\n",
       "      <td>551.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0136.jpg</td>\n",
       "      <td>0.667444</td>\n",
       "      <td>2577</td>\n",
       "      <td>3861</td>\n",
       "      <td>2822</td>\n",
       "      <td>3616</td>\n",
       "      <td>523.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0570.jpg</td>\n",
       "      <td>0.058015</td>\n",
       "      <td>1533</td>\n",
       "      <td>26424</td>\n",
       "      <td>25933</td>\n",
       "      <td>2024</td>\n",
       "      <td>415.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>-85.0</td>\n",
       "      <td>95.257546</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0119.jpg</td>\n",
       "      <td>0.822630</td>\n",
       "      <td>2152</td>\n",
       "      <td>2616</td>\n",
       "      <td>2184</td>\n",
       "      <td>2584</td>\n",
       "      <td>525.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0595.jpg</td>\n",
       "      <td>0.932285</td>\n",
       "      <td>23130</td>\n",
       "      <td>24810</td>\n",
       "      <td>24013</td>\n",
       "      <td>23927</td>\n",
       "      <td>497.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0758.jpg</td>\n",
       "      <td>0.582997</td>\n",
       "      <td>3614</td>\n",
       "      <td>6199</td>\n",
       "      <td>3620</td>\n",
       "      <td>6193</td>\n",
       "      <td>607.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>611.0</td>\n",
       "      <td>527.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0479.jpg</td>\n",
       "      <td>0.843697</td>\n",
       "      <td>8534</td>\n",
       "      <td>10115</td>\n",
       "      <td>8673</td>\n",
       "      <td>9976</td>\n",
       "      <td>473.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0330.jpg</td>\n",
       "      <td>0.013855</td>\n",
       "      <td>253</td>\n",
       "      <td>18260</td>\n",
       "      <td>17885</td>\n",
       "      <td>628</td>\n",
       "      <td>546.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>77.878110</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0379.jpg</td>\n",
       "      <td>0.102101</td>\n",
       "      <td>1842</td>\n",
       "      <td>18041</td>\n",
       "      <td>17199</td>\n",
       "      <td>2684</td>\n",
       "      <td>540.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54.083269</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0443.jpg</td>\n",
       "      <td>0.763481</td>\n",
       "      <td>6088</td>\n",
       "      <td>7974</td>\n",
       "      <td>6109</td>\n",
       "      <td>7953</td>\n",
       "      <td>548.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0025.jpg</td>\n",
       "      <td>0.794521</td>\n",
       "      <td>1508</td>\n",
       "      <td>1898</td>\n",
       "      <td>1548</td>\n",
       "      <td>1858</td>\n",
       "      <td>556.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0678.jpg</td>\n",
       "      <td>0.788193</td>\n",
       "      <td>4860</td>\n",
       "      <td>6166</td>\n",
       "      <td>4860</td>\n",
       "      <td>6166</td>\n",
       "      <td>584.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0547.jpg</td>\n",
       "      <td>0.798352</td>\n",
       "      <td>34694</td>\n",
       "      <td>43457</td>\n",
       "      <td>42516</td>\n",
       "      <td>35635</td>\n",
       "      <td>440.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.083046</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0122.jpg</td>\n",
       "      <td>0.833681</td>\n",
       "      <td>2000</td>\n",
       "      <td>2399</td>\n",
       "      <td>2029</td>\n",
       "      <td>2370</td>\n",
       "      <td>496.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0465.jpg</td>\n",
       "      <td>0.006161</td>\n",
       "      <td>69</td>\n",
       "      <td>11200</td>\n",
       "      <td>11200</td>\n",
       "      <td>69</td>\n",
       "      <td>561.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.510562</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0163.jpg</td>\n",
       "      <td>0.774870</td>\n",
       "      <td>3287</td>\n",
       "      <td>4242</td>\n",
       "      <td>3299</td>\n",
       "      <td>4230</td>\n",
       "      <td>526.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0476.jpg</td>\n",
       "      <td>0.862405</td>\n",
       "      <td>8311</td>\n",
       "      <td>9637</td>\n",
       "      <td>8604</td>\n",
       "      <td>9344</td>\n",
       "      <td>487.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0763.jpg</td>\n",
       "      <td>0.748173</td>\n",
       "      <td>5324</td>\n",
       "      <td>7116</td>\n",
       "      <td>6802</td>\n",
       "      <td>5638</td>\n",
       "      <td>543.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0067.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2478</td>\n",
       "      <td>2470</td>\n",
       "      <td>8</td>\n",
       "      <td>601.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>-319.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>401.617978</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0771.jpg</td>\n",
       "      <td>0.618298</td>\n",
       "      <td>8637</td>\n",
       "      <td>13969</td>\n",
       "      <td>8965</td>\n",
       "      <td>13641</td>\n",
       "      <td>539.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>536.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>10.440307</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0714.jpg</td>\n",
       "      <td>0.335995</td>\n",
       "      <td>2062</td>\n",
       "      <td>6137</td>\n",
       "      <td>5634</td>\n",
       "      <td>2565</td>\n",
       "      <td>538.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>24.351591</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0106.jpg</td>\n",
       "      <td>0.706859</td>\n",
       "      <td>2257</td>\n",
       "      <td>3193</td>\n",
       "      <td>2269</td>\n",
       "      <td>3181</td>\n",
       "      <td>476.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0440.jpg</td>\n",
       "      <td>0.049094</td>\n",
       "      <td>187</td>\n",
       "      <td>3809</td>\n",
       "      <td>3522</td>\n",
       "      <td>474</td>\n",
       "      <td>491.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>41.231056</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0468.jpg</td>\n",
       "      <td>0.852747</td>\n",
       "      <td>10447</td>\n",
       "      <td>12251</td>\n",
       "      <td>11556</td>\n",
       "      <td>11142</td>\n",
       "      <td>435.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>7.615773</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0321.jpg</td>\n",
       "      <td>0.683649</td>\n",
       "      <td>5381</td>\n",
       "      <td>7871</td>\n",
       "      <td>7146</td>\n",
       "      <td>6106</td>\n",
       "      <td>437.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>13.038405</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0688.jpg</td>\n",
       "      <td>0.573066</td>\n",
       "      <td>11102</td>\n",
       "      <td>19373</td>\n",
       "      <td>11133</td>\n",
       "      <td>19342</td>\n",
       "      <td>586.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>20.615528</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0049.jpg</td>\n",
       "      <td>0.778854</td>\n",
       "      <td>2011</td>\n",
       "      <td>2582</td>\n",
       "      <td>2023</td>\n",
       "      <td>2570</td>\n",
       "      <td>411.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</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",
       "      <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>1230</th>\n",
       "      <td>1230</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0559.jpg</td>\n",
       "      <td>0.141619</td>\n",
       "      <td>3922</td>\n",
       "      <td>27694</td>\n",
       "      <td>27648</td>\n",
       "      <td>3968</td>\n",
       "      <td>550.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-86.0</td>\n",
       "      <td>86.371292</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>1231</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0456.jpg</td>\n",
       "      <td>0.766662</td>\n",
       "      <td>6039</td>\n",
       "      <td>7877</td>\n",
       "      <td>7749</td>\n",
       "      <td>6167</td>\n",
       "      <td>407.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>1232</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0735.jpg</td>\n",
       "      <td>0.089524</td>\n",
       "      <td>717</td>\n",
       "      <td>8009</td>\n",
       "      <td>8008</td>\n",
       "      <td>718</td>\n",
       "      <td>475.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.591260</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>1233</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0441.jpg</td>\n",
       "      <td>0.911125</td>\n",
       "      <td>10221</td>\n",
       "      <td>11218</td>\n",
       "      <td>11105</td>\n",
       "      <td>10334</td>\n",
       "      <td>631.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>633.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>1234</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0463.jpg</td>\n",
       "      <td>0.036623</td>\n",
       "      <td>820</td>\n",
       "      <td>22390</td>\n",
       "      <td>11491</td>\n",
       "      <td>11719</td>\n",
       "      <td>459.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>126.372465</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>1235</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0453.jpg</td>\n",
       "      <td>0.818307</td>\n",
       "      <td>8904</td>\n",
       "      <td>10881</td>\n",
       "      <td>10628</td>\n",
       "      <td>9157</td>\n",
       "      <td>401.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>1236</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0599.jpg</td>\n",
       "      <td>0.445545</td>\n",
       "      <td>14838</td>\n",
       "      <td>33303</td>\n",
       "      <td>32625</td>\n",
       "      <td>15516</td>\n",
       "      <td>470.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-47.0</td>\n",
       "      <td>47.381431</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>1237</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0764.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>860</td>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>1238</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0078.jpg</td>\n",
       "      <td>0.719184</td>\n",
       "      <td>1657</td>\n",
       "      <td>2304</td>\n",
       "      <td>2276</td>\n",
       "      <td>1685</td>\n",
       "      <td>518.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>1239</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0619.jpg</td>\n",
       "      <td>0.116759</td>\n",
       "      <td>8070</td>\n",
       "      <td>69117</td>\n",
       "      <td>69117</td>\n",
       "      <td>8070</td>\n",
       "      <td>544.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>119.012604</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>1240</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0182.jpg</td>\n",
       "      <td>0.745700</td>\n",
       "      <td>2818</td>\n",
       "      <td>3779</td>\n",
       "      <td>3779</td>\n",
       "      <td>2818</td>\n",
       "      <td>541.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>1241</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0282.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1856</td>\n",
       "      <td>1624</td>\n",
       "      <td>232</td>\n",
       "      <td>520.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>-422.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>522.444255</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>1242</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0493.jpg</td>\n",
       "      <td>0.858877</td>\n",
       "      <td>9129</td>\n",
       "      <td>10629</td>\n",
       "      <td>10581</td>\n",
       "      <td>9177</td>\n",
       "      <td>552.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.123106</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>1243</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0747.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1566</td>\n",
       "      <td>1566</td>\n",
       "      <td>0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>1244</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0120.jpg</td>\n",
       "      <td>0.791251</td>\n",
       "      <td>1664</td>\n",
       "      <td>2103</td>\n",
       "      <td>2103</td>\n",
       "      <td>1664</td>\n",
       "      <td>445.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>1245</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0523.jpg</td>\n",
       "      <td>0.824161</td>\n",
       "      <td>43922</td>\n",
       "      <td>53293</td>\n",
       "      <td>53165</td>\n",
       "      <td>44050</td>\n",
       "      <td>529.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>1246</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0430.jpg</td>\n",
       "      <td>0.838992</td>\n",
       "      <td>3830</td>\n",
       "      <td>4565</td>\n",
       "      <td>4399</td>\n",
       "      <td>3996</td>\n",
       "      <td>574.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>1247</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0070.jpg</td>\n",
       "      <td>0.618927</td>\n",
       "      <td>1465</td>\n",
       "      <td>2367</td>\n",
       "      <td>2367</td>\n",
       "      <td>1465</td>\n",
       "      <td>531.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>1248</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0689.jpg</td>\n",
       "      <td>0.015980</td>\n",
       "      <td>128</td>\n",
       "      <td>8010</td>\n",
       "      <td>8010</td>\n",
       "      <td>128</td>\n",
       "      <td>605.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>-69.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>69.028979</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>1249</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0554.jpg</td>\n",
       "      <td>0.106439</td>\n",
       "      <td>3617</td>\n",
       "      <td>33982</td>\n",
       "      <td>33982</td>\n",
       "      <td>3617</td>\n",
       "      <td>489.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>74.040529</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>1250</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0036.jpg</td>\n",
       "      <td>0.778584</td>\n",
       "      <td>2145</td>\n",
       "      <td>2755</td>\n",
       "      <td>2527</td>\n",
       "      <td>2373</td>\n",
       "      <td>467.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.082763</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>1251</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0354.jpg</td>\n",
       "      <td>0.474247</td>\n",
       "      <td>10782</td>\n",
       "      <td>22735</td>\n",
       "      <td>14412</td>\n",
       "      <td>19105</td>\n",
       "      <td>571.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>68.359345</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>1252</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0189.jpg</td>\n",
       "      <td>0.678349</td>\n",
       "      <td>1742</td>\n",
       "      <td>2568</td>\n",
       "      <td>2556</td>\n",
       "      <td>1754</td>\n",
       "      <td>523.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>1253</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0768.jpg</td>\n",
       "      <td>0.585838</td>\n",
       "      <td>2573</td>\n",
       "      <td>4392</td>\n",
       "      <td>4392</td>\n",
       "      <td>2573</td>\n",
       "      <td>592.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1254</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0717.jpg</td>\n",
       "      <td>0.634031</td>\n",
       "      <td>4572</td>\n",
       "      <td>7211</td>\n",
       "      <td>7008</td>\n",
       "      <td>4775</td>\n",
       "      <td>488.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>14.866069</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1255</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0494.jpg</td>\n",
       "      <td>0.856642</td>\n",
       "      <td>9782</td>\n",
       "      <td>11419</td>\n",
       "      <td>11351</td>\n",
       "      <td>9850</td>\n",
       "      <td>601.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>1256</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0121.jpg</td>\n",
       "      <td>0.707748</td>\n",
       "      <td>1763</td>\n",
       "      <td>2491</td>\n",
       "      <td>2491</td>\n",
       "      <td>1763</td>\n",
       "      <td>510.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>1257</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0084.jpg</td>\n",
       "      <td>0.647834</td>\n",
       "      <td>1376</td>\n",
       "      <td>2124</td>\n",
       "      <td>2084</td>\n",
       "      <td>1416</td>\n",
       "      <td>515.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>1258</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0396.jpg</td>\n",
       "      <td>0.901564</td>\n",
       "      <td>9745</td>\n",
       "      <td>10809</td>\n",
       "      <td>10781</td>\n",
       "      <td>9773</td>\n",
       "      <td>507.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>1259</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0370.jpg</td>\n",
       "      <td>0.052974</td>\n",
       "      <td>334</td>\n",
       "      <td>6305</td>\n",
       "      <td>6305</td>\n",
       "      <td>334</td>\n",
       "      <td>560.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>569.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>38.052595</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1260 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  threshold    sample       iou  intersection  union  gt_area  \\\n",
       "0              0        0.1  0766.jpg  0.348560          2167   6217     5712   \n",
       "1              1        0.1  0485.jpg  0.612738         10525  17177    10949   \n",
       "2              2        0.1  0674.jpg  0.593740          5615   9457     6158   \n",
       "3              3        0.1  0417.jpg  0.875591          9079  10369     9470   \n",
       "4              4        0.1  0136.jpg  0.667444          2577   3861     2822   \n",
       "5              5        0.1  0570.jpg  0.058015          1533  26424    25933   \n",
       "6              6        0.1  0119.jpg  0.822630          2152   2616     2184   \n",
       "7              7        0.1  0595.jpg  0.932285         23130  24810    24013   \n",
       "8              8        0.1  0758.jpg  0.582997          3614   6199     3620   \n",
       "9              9        0.1  0479.jpg  0.843697          8534  10115     8673   \n",
       "10            10        0.1  0330.jpg  0.013855           253  18260    17885   \n",
       "11            11        0.1  0379.jpg  0.102101          1842  18041    17199   \n",
       "12            12        0.1  0443.jpg  0.763481          6088   7974     6109   \n",
       "13            13        0.1  0025.jpg  0.794521          1508   1898     1548   \n",
       "14            14        0.1  0678.jpg  0.788193          4860   6166     4860   \n",
       "15            15        0.1  0547.jpg  0.798352         34694  43457    42516   \n",
       "16            16        0.1  0122.jpg  0.833681          2000   2399     2029   \n",
       "17            17        0.1  0465.jpg  0.006161            69  11200    11200   \n",
       "18            18        0.1  0163.jpg  0.774870          3287   4242     3299   \n",
       "19            19        0.1  0476.jpg  0.862405          8311   9637     8604   \n",
       "20            20        0.1  0763.jpg  0.748173          5324   7116     6802   \n",
       "21            21        0.1  0067.jpg  0.000000             0   2478     2470   \n",
       "22            22        0.1  0771.jpg  0.618298          8637  13969     8965   \n",
       "23            23        0.1  0714.jpg  0.335995          2062   6137     5634   \n",
       "24            24        0.1  0106.jpg  0.706859          2257   3193     2269   \n",
       "25            25        0.1  0440.jpg  0.049094           187   3809     3522   \n",
       "26            26        0.1  0468.jpg  0.852747         10447  12251    11556   \n",
       "27            27        0.1  0321.jpg  0.683649          5381   7871     7146   \n",
       "28            28        0.1  0688.jpg  0.573066         11102  19373    11133   \n",
       "29            29        0.1  0049.jpg  0.778854          2011   2582     2023   \n",
       "...          ...        ...       ...       ...           ...    ...      ...   \n",
       "1230        1230        0.9  0559.jpg  0.141619          3922  27694    27648   \n",
       "1231        1231        0.9  0456.jpg  0.766662          6039   7877     7749   \n",
       "1232        1232        0.9  0735.jpg  0.089524           717   8009     8008   \n",
       "1233        1233        0.9  0441.jpg  0.911125         10221  11218    11105   \n",
       "1234        1234        0.9  0463.jpg  0.036623           820  22390    11491   \n",
       "1235        1235        0.9  0453.jpg  0.818307          8904  10881    10628   \n",
       "1236        1236        0.9  0599.jpg  0.445545         14838  33303    32625   \n",
       "1237        1237        0.9  0764.jpg  0.000000             0    860      860   \n",
       "1238        1238        0.9  0078.jpg  0.719184          1657   2304     2276   \n",
       "1239        1239        0.9  0619.jpg  0.116759          8070  69117    69117   \n",
       "1240        1240        0.9  0182.jpg  0.745700          2818   3779     3779   \n",
       "1241        1241        0.9  0282.jpg  0.000000             0   1856     1624   \n",
       "1242        1242        0.9  0493.jpg  0.858877          9129  10629    10581   \n",
       "1243        1243        0.9  0747.jpg  0.000000             0   1566     1566   \n",
       "1244        1244        0.9  0120.jpg  0.791251          1664   2103     2103   \n",
       "1245        1245        0.9  0523.jpg  0.824161         43922  53293    53165   \n",
       "1246        1246        0.9  0430.jpg  0.838992          3830   4565     4399   \n",
       "1247        1247        0.9  0070.jpg  0.618927          1465   2367     2367   \n",
       "1248        1248        0.9  0689.jpg  0.015980           128   8010     8010   \n",
       "1249        1249        0.9  0554.jpg  0.106439          3617  33982    33982   \n",
       "1250        1250        0.9  0036.jpg  0.778584          2145   2755     2527   \n",
       "1251        1251        0.9  0354.jpg  0.474247         10782  22735    14412   \n",
       "1252        1252        0.9  0189.jpg  0.678349          1742   2568     2556   \n",
       "1253        1253        0.9  0768.jpg  0.585838          2573   4392     4392   \n",
       "1254        1254        0.9  0717.jpg  0.634031          4572   7211     7008   \n",
       "1255        1255        0.9  0494.jpg  0.856642          9782  11419    11351   \n",
       "1256        1256        0.9  0121.jpg  0.707748          1763   2491     2491   \n",
       "1257        1257        0.9  0084.jpg  0.647834          1376   2124     2084   \n",
       "1258        1258        0.9  0396.jpg  0.901564          9745  10809    10781   \n",
       "1259        1259        0.9  0370.jpg  0.052974           334   6305     6305   \n",
       "\n",
       "      segmentation_area  gt_x_center  gt_y_center  segmentation_x_center  \\\n",
       "0                  2672        639.0        448.0                  638.0   \n",
       "1                 16753        486.0        550.0                  481.0   \n",
       "2                  8914        532.0        456.0                  539.0   \n",
       "3                  9978        551.0        502.0                  551.0   \n",
       "4                  3616        523.0        515.0                  517.0   \n",
       "5                  2024        415.0        583.0                  458.0   \n",
       "6                  2584        525.0        514.0                  524.0   \n",
       "7                 23927        497.0        501.0                  497.0   \n",
       "8                  6193        607.0        524.0                  611.0   \n",
       "9                  9976        473.0        460.0                  475.0   \n",
       "10                  628        546.0        439.0                  563.0   \n",
       "11                 2684        540.0        421.0                  495.0   \n",
       "12                 7953        548.0        526.0                  547.0   \n",
       "13                 1858        556.0        429.0                  558.0   \n",
       "14                 6166        584.0        507.0                  585.0   \n",
       "15                35635        440.0        615.0                  435.0   \n",
       "16                 2370        496.0        510.0                  496.0   \n",
       "17                   69        561.0        507.0                  596.0   \n",
       "18                 4230        526.0        550.0                  524.0   \n",
       "19                 9344        487.0        518.0                  487.0   \n",
       "20                 5638        543.0        546.0                  529.0   \n",
       "21                    8        601.0        467.0                  920.0   \n",
       "22                13641        539.0        570.0                  536.0   \n",
       "23                 2565        538.0        565.0                  546.0   \n",
       "24                 3181        476.0        526.0                  478.0   \n",
       "25                  474        491.0        520.0                  517.0   \n",
       "26                11142        435.0        478.0                  442.0   \n",
       "27                 6106        437.0        398.0                  430.0   \n",
       "28                19342        586.0        489.0                  581.0   \n",
       "29                 2570        411.0        544.0                  410.0   \n",
       "...                 ...          ...          ...                    ...   \n",
       "1230               3968        550.0        640.0                  542.0   \n",
       "1231               6167        407.0        522.0                  406.0   \n",
       "1232                718        475.0        506.0                  493.0   \n",
       "1233              10334        631.0        503.0                  633.0   \n",
       "1234              11719        459.0        464.0                  488.0   \n",
       "1235               9157        401.0        491.0                  399.0   \n",
       "1236              15516        470.0        578.0                  464.0   \n",
       "1237                  0        576.0        460.0                    NaN   \n",
       "1238               1685        518.0        503.0                  521.0   \n",
       "1239               8070        544.0        625.0                  494.0   \n",
       "1240               2818        541.0        494.0                  543.0   \n",
       "1241                232        520.0        472.0                  942.0   \n",
       "1242               9177        552.0        493.0                  556.0   \n",
       "1243                  0        590.0        477.0                    NaN   \n",
       "1244               1664        445.0        544.0                  445.0   \n",
       "1245              44050        529.0        471.0                  532.0   \n",
       "1246               3996        574.0        539.0                  571.0   \n",
       "1247               1465        531.0        540.0                  530.0   \n",
       "1248                128        605.0        491.0                  674.0   \n",
       "1249               3617        489.0        697.0                  468.0   \n",
       "1250               2373        467.0        446.0                  468.0   \n",
       "1251              19105        571.0        450.0                  639.0   \n",
       "1252               1754        523.0        509.0                  525.0   \n",
       "1253               2573        592.0        468.0                  595.0   \n",
       "1254               4775        488.0        479.0                  474.0   \n",
       "1255               9850        601.0        509.0                  598.0   \n",
       "1256               1763        510.0        532.0                  508.0   \n",
       "1257               1416        515.0        538.0                  520.0   \n",
       "1258               9773        507.0        532.0                  509.0   \n",
       "1259                334        560.0        531.0                  558.0   \n",
       "\n",
       "      segmentation_y_center  x_distance  y_distance  euclidean_distance  \\\n",
       "0                     466.0         1.0       -18.0           18.027756   \n",
       "1                     558.0         5.0        -8.0            9.433981   \n",
       "2                     469.0        -7.0       -13.0           14.764823   \n",
       "3                     502.0         0.0         0.0            0.000000   \n",
       "4                     517.0         6.0        -2.0            6.324555   \n",
       "5                     668.0       -43.0       -85.0           95.257546   \n",
       "6                     515.0         1.0        -1.0            1.414214   \n",
       "7                     504.0         0.0        -3.0            3.000000   \n",
       "8                     527.0        -4.0        -3.0            5.000000   \n",
       "9                     459.0        -2.0         1.0            2.236068   \n",
       "10                    515.0       -17.0       -76.0           77.878110   \n",
       "11                    391.0        45.0        30.0           54.083269   \n",
       "12                    529.0         1.0        -3.0            3.162278   \n",
       "13                    429.0        -2.0         0.0            2.000000   \n",
       "14                    508.0        -1.0        -1.0            1.414214   \n",
       "15                    604.0         5.0        11.0           12.083046   \n",
       "16                    510.0         0.0         0.0            0.000000   \n",
       "17                    501.0       -35.0         6.0           35.510562   \n",
       "18                    551.0         2.0        -1.0            2.236068   \n",
       "19                    517.0         0.0         1.0            1.000000   \n",
       "20                    546.0        14.0         0.0           14.000000   \n",
       "21                    223.0      -319.0       244.0          401.617978   \n",
       "22                    580.0         3.0       -10.0           10.440307   \n",
       "23                    588.0        -8.0       -23.0           24.351591   \n",
       "24                    528.0        -2.0        -2.0            2.828427   \n",
       "25                    552.0       -26.0       -32.0           41.231056   \n",
       "26                    481.0        -7.0        -3.0            7.615773   \n",
       "27                    409.0         7.0       -11.0           13.038405   \n",
       "28                    509.0         5.0       -20.0           20.615528   \n",
       "29                    544.0         1.0         0.0            1.000000   \n",
       "...                     ...         ...         ...                 ...   \n",
       "1230                  726.0         8.0       -86.0           86.371292   \n",
       "1231                  517.0         1.0         5.0            5.099020   \n",
       "1232                  496.0       -18.0        10.0           20.591260   \n",
       "1233                  502.0        -2.0         1.0            2.236068   \n",
       "1234                  341.0       -29.0       123.0          126.372465   \n",
       "1235                  492.0         2.0        -1.0            2.236068   \n",
       "1236                  625.0         6.0       -47.0           47.381431   \n",
       "1237                    NaN         NaN         NaN                 NaN   \n",
       "1238                  506.0        -3.0        -3.0            4.242641   \n",
       "1239                  517.0        50.0       108.0          119.012604   \n",
       "1240                  493.0        -2.0         1.0            2.236068   \n",
       "1241                  164.0      -422.0       308.0          522.444255   \n",
       "1242                  492.0        -4.0         1.0            4.123106   \n",
       "1243                    NaN         NaN         NaN                 NaN   \n",
       "1244                  544.0         0.0         0.0            0.000000   \n",
       "1245                  468.0        -3.0         3.0            4.242641   \n",
       "1246                  543.0         3.0        -4.0            5.000000   \n",
       "1247                  535.0         1.0         5.0            5.099020   \n",
       "1248                  493.0       -69.0        -2.0           69.028979   \n",
       "1249                  768.0        21.0       -71.0           74.040529   \n",
       "1250                  440.0        -1.0         6.0            6.082763   \n",
       "1251                  457.0       -68.0        -7.0           68.359345   \n",
       "1252                  508.0        -2.0         1.0            2.236068   \n",
       "1253                  469.0        -3.0        -1.0            3.162278   \n",
       "1254                  484.0        14.0        -5.0           14.866069   \n",
       "1255                  510.0         3.0        -1.0            3.162278   \n",
       "1256                  533.0         2.0        -1.0            2.236068   \n",
       "1257                  539.0        -5.0        -1.0            5.099020   \n",
       "1258                  531.0        -2.0         1.0            2.236068   \n",
       "1259                  569.0         2.0       -38.0           38.052595   \n",
       "\n",
       "      x_size  y_size  \n",
       "0       1024    1024  \n",
       "1       1024    1024  \n",
       "2       1024    1024  \n",
       "3       1024    1024  \n",
       "4       1024    1024  \n",
       "5       1024    1024  \n",
       "6       1024    1024  \n",
       "7       1024    1024  \n",
       "8       1024    1024  \n",
       "9       1024    1024  \n",
       "10      1024    1024  \n",
       "11      1024    1024  \n",
       "12      1024    1024  \n",
       "13      1024    1024  \n",
       "14      1024    1024  \n",
       "15      1024    1024  \n",
       "16      1024    1024  \n",
       "17      1024    1024  \n",
       "18      1024    1024  \n",
       "19      1024    1024  \n",
       "20      1024    1024  \n",
       "21      1024    1024  \n",
       "22      1024    1024  \n",
       "23      1024    1024  \n",
       "24      1024    1024  \n",
       "25      1024    1024  \n",
       "26      1024    1024  \n",
       "27      1024    1024  \n",
       "28      1024    1024  \n",
       "29      1024    1024  \n",
       "...      ...     ...  \n",
       "1230    1024    1024  \n",
       "1231    1024    1024  \n",
       "1232    1024    1024  \n",
       "1233    1024    1024  \n",
       "1234    1024    1024  \n",
       "1235    1024    1024  \n",
       "1236    1024    1024  \n",
       "1237    1024    1024  \n",
       "1238    1024    1024  \n",
       "1239    1024    1024  \n",
       "1240    1024    1024  \n",
       "1241    1024    1024  \n",
       "1242    1024    1024  \n",
       "1243    1024    1024  \n",
       "1244    1024    1024  \n",
       "1245    1024    1024  \n",
       "1246    1024    1024  \n",
       "1247    1024    1024  \n",
       "1248    1024    1024  \n",
       "1249    1024    1024  \n",
       "1250    1024    1024  \n",
       "1251    1024    1024  \n",
       "1252    1024    1024  \n",
       "1253    1024    1024  \n",
       "1254    1024    1024  \n",
       "1255    1024    1024  \n",
       "1256    1024    1024  \n",
       "1257    1024    1024  \n",
       "1258    1024    1024  \n",
       "1259    1024    1024  \n",
       "\n",
       "[1260 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'imageOrigin', 'mask', 'patch_name', 'x_center_orig',\n",
       "       'y_center_orig', 'x_center_resize', 'y_center_resize', 'diam_orig',\n",
       "       'diam_resize'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('./single_instance_dataset_wradius.csv')\n",
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wvillegas/miniconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:3652: RuntimeWarning: Invalid value encountered in percentile\n",
      "  interpolation=interpolation)\n",
      "/home/wvillegas/miniconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1872: RuntimeWarning: invalid value encountered in less_equal\n",
      "  wiskhi = np.compress(x <= hival, x)\n",
      "/home/wvillegas/miniconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1879: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  wisklo = np.compress(x >= loval, x)\n",
      "/home/wvillegas/miniconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1887: RuntimeWarning: invalid value encountered in less\n",
      "  np.compress(x < stats['whislo'], x),\n",
      "/home/wvillegas/miniconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1888: RuntimeWarning: invalid value encountered in greater\n",
      "  np.compress(x > stats['whishi'], x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxdata = [np.zeros(140) for std in range(len(threshold_list))]\n",
    "for i in np.arange(0,len(threshold_list)):\n",
    "    th_slice = valid.loc[valid['threshold'] == threshold_list[i]]\n",
    "    for j in np.arange(th_slice.shape[0]):\n",
    "        row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[j]]\n",
    "        boxdata[i][j] =((th_slice['euclidean_distance'].values[j] / (row['diam_resize'].values[0]/2)))\n",
    "# plt.errorbar(threshold_list, Y,yerr=yerr, linestyle='None', marker='.', ecolor='orange',markersize=10)\n",
    "# plt.xlabel('Thresholds')\n",
    "# plt.ylabel('Euclidean Distance Mean Norm')\n",
    "# plt.tight_layout()\n",
    "# rectangular box plot\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.boxplot(boxdata, vert=True, patch_artist=True, labels=threshold_list)\n",
    "ax1.set_yscale('log')\n",
    "ax1.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "247/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = []\n",
    "acumList = []\n",
    "yerr = []\n",
    "for i in np.arange(0,len(threshold_list)):\n",
    "    acumList = []\n",
    "    th_slice = valid.loc[valid['threshold'] == threshold_list[i]]\n",
    "    for j in np.arange(th_slice.shape[0]):\n",
    "        row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[j]]\n",
    "        if np.isnan(th_slice['euclidean_distance'].values[j]):\n",
    "            continue\n",
    "        else:\n",
    "            acumList.append(th_slice['euclidean_distance'].values[j] / ((123.5)))\n",
    "\n",
    "            \n",
    "            \n",
    "    Y.append(np.mean(acumList))\n",
    "    yerr.append(np.std(acumList))\n",
    "\n",
    "plt.errorbar(threshold_list, Y,yerr=yerr, linestyle='None', marker='.', ecolor='orange',markersize=10)\n",
    "plt.xticks(threshold_list)\n",
    "plt.xlabel('Thresholds')\n",
    "plt.ylabel('Euclidean Distance Normalized by Bud Diameter')\n",
    "plt.tight_layout()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Unnamed: 0</th>\n",
       "      <th>threshold</th>\n",
       "      <th>sample</th>\n",
       "      <th>iou</th>\n",
       "      <th>intersection</th>\n",
       "      <th>union</th>\n",
       "      <th>gt_area</th>\n",
       "      <th>segmentation_area</th>\n",
       "      <th>gt_x_center</th>\n",
       "      <th>gt_y_center</th>\n",
       "      <th>segmentation_x_center</th>\n",
       "      <th>segmentation_y_center</th>\n",
       "      <th>x_distance</th>\n",
       "      <th>y_distance</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>x_size</th>\n",
       "      <th>y_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>1120</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0766.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>5712</td>\n",
       "      <td>5712</td>\n",
       "      <td>0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>1121</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0485.jpg</td>\n",
       "      <td>0.712660</td>\n",
       "      <td>9896</td>\n",
       "      <td>13886</td>\n",
       "      <td>10949</td>\n",
       "      <td>12833</td>\n",
       "      <td>486.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>12.083046</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>1122</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0674.jpg</td>\n",
       "      <td>0.366154</td>\n",
       "      <td>2406</td>\n",
       "      <td>6571</td>\n",
       "      <td>6158</td>\n",
       "      <td>2819</td>\n",
       "      <td>532.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>1123</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0417.jpg</td>\n",
       "      <td>0.828880</td>\n",
       "      <td>7910</td>\n",
       "      <td>9543</td>\n",
       "      <td>9470</td>\n",
       "      <td>7983</td>\n",
       "      <td>551.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>1124</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0136.jpg</td>\n",
       "      <td>0.148831</td>\n",
       "      <td>420</td>\n",
       "      <td>2822</td>\n",
       "      <td>2822</td>\n",
       "      <td>420</td>\n",
       "      <td>523.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>7.615773</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>1125</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0570.jpg</td>\n",
       "      <td>0.010257</td>\n",
       "      <td>266</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>266</td>\n",
       "      <td>415.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>-46.0</td>\n",
       "      <td>-85.0</td>\n",
       "      <td>96.648849</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>1126</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0119.jpg</td>\n",
       "      <td>0.756208</td>\n",
       "      <td>1675</td>\n",
       "      <td>2215</td>\n",
       "      <td>2184</td>\n",
       "      <td>1706</td>\n",
       "      <td>525.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>1127</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0595.jpg</td>\n",
       "      <td>0.781476</td>\n",
       "      <td>18807</td>\n",
       "      <td>24066</td>\n",
       "      <td>24013</td>\n",
       "      <td>18860</td>\n",
       "      <td>497.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>1128</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0758.jpg</td>\n",
       "      <td>0.770400</td>\n",
       "      <td>3446</td>\n",
       "      <td>4473</td>\n",
       "      <td>3620</td>\n",
       "      <td>4299</td>\n",
       "      <td>607.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>611.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>1129</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0479.jpg</td>\n",
       "      <td>0.845632</td>\n",
       "      <td>7472</td>\n",
       "      <td>8836</td>\n",
       "      <td>8673</td>\n",
       "      <td>7635</td>\n",
       "      <td>473.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>1130</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0330.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>17885</td>\n",
       "      <td>17885</td>\n",
       "      <td>0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>1131</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0379.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>17199</td>\n",
       "      <td>17199</td>\n",
       "      <td>0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>1132</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0443.jpg</td>\n",
       "      <td>0.590872</td>\n",
       "      <td>3664</td>\n",
       "      <td>6201</td>\n",
       "      <td>6109</td>\n",
       "      <td>3756</td>\n",
       "      <td>548.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>8.602325</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>1133</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0025.jpg</td>\n",
       "      <td>0.571154</td>\n",
       "      <td>891</td>\n",
       "      <td>1560</td>\n",
       "      <td>1548</td>\n",
       "      <td>903</td>\n",
       "      <td>556.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>1134</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0678.jpg</td>\n",
       "      <td>0.867016</td>\n",
       "      <td>4303</td>\n",
       "      <td>4963</td>\n",
       "      <td>4860</td>\n",
       "      <td>4406</td>\n",
       "      <td>584.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>586.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>1135</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0547.jpg</td>\n",
       "      <td>0.556049</td>\n",
       "      <td>23656</td>\n",
       "      <td>42543</td>\n",
       "      <td>42516</td>\n",
       "      <td>23683</td>\n",
       "      <td>440.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>1136</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0122.jpg</td>\n",
       "      <td>0.729423</td>\n",
       "      <td>1480</td>\n",
       "      <td>2029</td>\n",
       "      <td>2029</td>\n",
       "      <td>1480</td>\n",
       "      <td>496.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>1137</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0465.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>11200</td>\n",
       "      <td>11200</td>\n",
       "      <td>0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>1138</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0163.jpg</td>\n",
       "      <td>0.825536</td>\n",
       "      <td>2735</td>\n",
       "      <td>3313</td>\n",
       "      <td>3299</td>\n",
       "      <td>2749</td>\n",
       "      <td>526.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>1139</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0476.jpg</td>\n",
       "      <td>0.814976</td>\n",
       "      <td>7096</td>\n",
       "      <td>8707</td>\n",
       "      <td>8604</td>\n",
       "      <td>7199</td>\n",
       "      <td>487.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>1140</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0763.jpg</td>\n",
       "      <td>0.544653</td>\n",
       "      <td>3708</td>\n",
       "      <td>6808</td>\n",
       "      <td>6802</td>\n",
       "      <td>3714</td>\n",
       "      <td>543.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.029386</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>1141</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0067.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2470</td>\n",
       "      <td>2470</td>\n",
       "      <td>0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>1142</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0771.jpg</td>\n",
       "      <td>0.782595</td>\n",
       "      <td>7509</td>\n",
       "      <td>9595</td>\n",
       "      <td>8965</td>\n",
       "      <td>8139</td>\n",
       "      <td>539.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>1143</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0714.jpg</td>\n",
       "      <td>0.045438</td>\n",
       "      <td>256</td>\n",
       "      <td>5634</td>\n",
       "      <td>5634</td>\n",
       "      <td>256</td>\n",
       "      <td>538.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>39.849718</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>1144</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0106.jpg</td>\n",
       "      <td>0.817528</td>\n",
       "      <td>1931</td>\n",
       "      <td>2362</td>\n",
       "      <td>2269</td>\n",
       "      <td>2024</td>\n",
       "      <td>476.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>527.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>1145</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0440.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3522</td>\n",
       "      <td>3522</td>\n",
       "      <td>0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>1146</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0468.jpg</td>\n",
       "      <td>0.779400</td>\n",
       "      <td>9020</td>\n",
       "      <td>11573</td>\n",
       "      <td>11556</td>\n",
       "      <td>9037</td>\n",
       "      <td>435.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>8.246211</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>1147</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0321.jpg</td>\n",
       "      <td>0.253289</td>\n",
       "      <td>1810</td>\n",
       "      <td>7146</td>\n",
       "      <td>7146</td>\n",
       "      <td>1810</td>\n",
       "      <td>437.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>21.540659</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>1148</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0688.jpg</td>\n",
       "      <td>0.694740</td>\n",
       "      <td>10328</td>\n",
       "      <td>14866</td>\n",
       "      <td>11133</td>\n",
       "      <td>14061</td>\n",
       "      <td>586.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>16.124515</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>1149</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0049.jpg</td>\n",
       "      <td>0.798328</td>\n",
       "      <td>1623</td>\n",
       "      <td>2033</td>\n",
       "      <td>2023</td>\n",
       "      <td>1633</td>\n",
       "      <td>411.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</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",
       "      <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>1230</th>\n",
       "      <td>1230</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0559.jpg</td>\n",
       "      <td>0.141619</td>\n",
       "      <td>3922</td>\n",
       "      <td>27694</td>\n",
       "      <td>27648</td>\n",
       "      <td>3968</td>\n",
       "      <td>550.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-86.0</td>\n",
       "      <td>86.371292</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>1231</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0456.jpg</td>\n",
       "      <td>0.766662</td>\n",
       "      <td>6039</td>\n",
       "      <td>7877</td>\n",
       "      <td>7749</td>\n",
       "      <td>6167</td>\n",
       "      <td>407.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>1232</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0735.jpg</td>\n",
       "      <td>0.089524</td>\n",
       "      <td>717</td>\n",
       "      <td>8009</td>\n",
       "      <td>8008</td>\n",
       "      <td>718</td>\n",
       "      <td>475.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.591260</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>1233</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0441.jpg</td>\n",
       "      <td>0.911125</td>\n",
       "      <td>10221</td>\n",
       "      <td>11218</td>\n",
       "      <td>11105</td>\n",
       "      <td>10334</td>\n",
       "      <td>631.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>633.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>1234</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0463.jpg</td>\n",
       "      <td>0.036623</td>\n",
       "      <td>820</td>\n",
       "      <td>22390</td>\n",
       "      <td>11491</td>\n",
       "      <td>11719</td>\n",
       "      <td>459.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>126.372465</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>1235</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0453.jpg</td>\n",
       "      <td>0.818307</td>\n",
       "      <td>8904</td>\n",
       "      <td>10881</td>\n",
       "      <td>10628</td>\n",
       "      <td>9157</td>\n",
       "      <td>401.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>1236</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0599.jpg</td>\n",
       "      <td>0.445545</td>\n",
       "      <td>14838</td>\n",
       "      <td>33303</td>\n",
       "      <td>32625</td>\n",
       "      <td>15516</td>\n",
       "      <td>470.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-47.0</td>\n",
       "      <td>47.381431</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>1237</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0764.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>860</td>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>1238</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0078.jpg</td>\n",
       "      <td>0.719184</td>\n",
       "      <td>1657</td>\n",
       "      <td>2304</td>\n",
       "      <td>2276</td>\n",
       "      <td>1685</td>\n",
       "      <td>518.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>1239</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0619.jpg</td>\n",
       "      <td>0.116759</td>\n",
       "      <td>8070</td>\n",
       "      <td>69117</td>\n",
       "      <td>69117</td>\n",
       "      <td>8070</td>\n",
       "      <td>544.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>119.012604</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>1240</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0182.jpg</td>\n",
       "      <td>0.745700</td>\n",
       "      <td>2818</td>\n",
       "      <td>3779</td>\n",
       "      <td>3779</td>\n",
       "      <td>2818</td>\n",
       "      <td>541.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>1241</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0282.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1856</td>\n",
       "      <td>1624</td>\n",
       "      <td>232</td>\n",
       "      <td>520.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>-422.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>522.444255</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>1242</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0493.jpg</td>\n",
       "      <td>0.858877</td>\n",
       "      <td>9129</td>\n",
       "      <td>10629</td>\n",
       "      <td>10581</td>\n",
       "      <td>9177</td>\n",
       "      <td>552.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.123106</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>1243</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0747.jpg</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1566</td>\n",
       "      <td>1566</td>\n",
       "      <td>0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>1244</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0120.jpg</td>\n",
       "      <td>0.791251</td>\n",
       "      <td>1664</td>\n",
       "      <td>2103</td>\n",
       "      <td>2103</td>\n",
       "      <td>1664</td>\n",
       "      <td>445.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>1245</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0523.jpg</td>\n",
       "      <td>0.824161</td>\n",
       "      <td>43922</td>\n",
       "      <td>53293</td>\n",
       "      <td>53165</td>\n",
       "      <td>44050</td>\n",
       "      <td>529.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>1246</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0430.jpg</td>\n",
       "      <td>0.838992</td>\n",
       "      <td>3830</td>\n",
       "      <td>4565</td>\n",
       "      <td>4399</td>\n",
       "      <td>3996</td>\n",
       "      <td>574.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>1247</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0070.jpg</td>\n",
       "      <td>0.618927</td>\n",
       "      <td>1465</td>\n",
       "      <td>2367</td>\n",
       "      <td>2367</td>\n",
       "      <td>1465</td>\n",
       "      <td>531.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>1248</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0689.jpg</td>\n",
       "      <td>0.015980</td>\n",
       "      <td>128</td>\n",
       "      <td>8010</td>\n",
       "      <td>8010</td>\n",
       "      <td>128</td>\n",
       "      <td>605.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>-69.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>69.028979</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>1249</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0554.jpg</td>\n",
       "      <td>0.106439</td>\n",
       "      <td>3617</td>\n",
       "      <td>33982</td>\n",
       "      <td>33982</td>\n",
       "      <td>3617</td>\n",
       "      <td>489.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>74.040529</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>1250</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0036.jpg</td>\n",
       "      <td>0.778584</td>\n",
       "      <td>2145</td>\n",
       "      <td>2755</td>\n",
       "      <td>2527</td>\n",
       "      <td>2373</td>\n",
       "      <td>467.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.082763</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>1251</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0354.jpg</td>\n",
       "      <td>0.474247</td>\n",
       "      <td>10782</td>\n",
       "      <td>22735</td>\n",
       "      <td>14412</td>\n",
       "      <td>19105</td>\n",
       "      <td>571.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>68.359345</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>1252</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0189.jpg</td>\n",
       "      <td>0.678349</td>\n",
       "      <td>1742</td>\n",
       "      <td>2568</td>\n",
       "      <td>2556</td>\n",
       "      <td>1754</td>\n",
       "      <td>523.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>1253</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0768.jpg</td>\n",
       "      <td>0.585838</td>\n",
       "      <td>2573</td>\n",
       "      <td>4392</td>\n",
       "      <td>4392</td>\n",
       "      <td>2573</td>\n",
       "      <td>592.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1254</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0717.jpg</td>\n",
       "      <td>0.634031</td>\n",
       "      <td>4572</td>\n",
       "      <td>7211</td>\n",
       "      <td>7008</td>\n",
       "      <td>4775</td>\n",
       "      <td>488.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>14.866069</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1255</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0494.jpg</td>\n",
       "      <td>0.856642</td>\n",
       "      <td>9782</td>\n",
       "      <td>11419</td>\n",
       "      <td>11351</td>\n",
       "      <td>9850</td>\n",
       "      <td>601.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>1256</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0121.jpg</td>\n",
       "      <td>0.707748</td>\n",
       "      <td>1763</td>\n",
       "      <td>2491</td>\n",
       "      <td>2491</td>\n",
       "      <td>1763</td>\n",
       "      <td>510.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>1257</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0084.jpg</td>\n",
       "      <td>0.647834</td>\n",
       "      <td>1376</td>\n",
       "      <td>2124</td>\n",
       "      <td>2084</td>\n",
       "      <td>1416</td>\n",
       "      <td>515.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>1258</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0396.jpg</td>\n",
       "      <td>0.901564</td>\n",
       "      <td>9745</td>\n",
       "      <td>10809</td>\n",
       "      <td>10781</td>\n",
       "      <td>9773</td>\n",
       "      <td>507.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>1259</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0370.jpg</td>\n",
       "      <td>0.052974</td>\n",
       "      <td>334</td>\n",
       "      <td>6305</td>\n",
       "      <td>6305</td>\n",
       "      <td>334</td>\n",
       "      <td>560.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>569.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>38.052595</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  threshold    sample       iou  intersection  union  gt_area  \\\n",
       "1120        1120        0.9  0766.jpg  0.000000             0   5712     5712   \n",
       "1121        1121        0.9  0485.jpg  0.712660          9896  13886    10949   \n",
       "1122        1122        0.9  0674.jpg  0.366154          2406   6571     6158   \n",
       "1123        1123        0.9  0417.jpg  0.828880          7910   9543     9470   \n",
       "1124        1124        0.9  0136.jpg  0.148831           420   2822     2822   \n",
       "1125        1125        0.9  0570.jpg  0.010257           266  25933    25933   \n",
       "1126        1126        0.9  0119.jpg  0.756208          1675   2215     2184   \n",
       "1127        1127        0.9  0595.jpg  0.781476         18807  24066    24013   \n",
       "1128        1128        0.9  0758.jpg  0.770400          3446   4473     3620   \n",
       "1129        1129        0.9  0479.jpg  0.845632          7472   8836     8673   \n",
       "1130        1130        0.9  0330.jpg  0.000000             0  17885    17885   \n",
       "1131        1131        0.9  0379.jpg  0.000000             0  17199    17199   \n",
       "1132        1132        0.9  0443.jpg  0.590872          3664   6201     6109   \n",
       "1133        1133        0.9  0025.jpg  0.571154           891   1560     1548   \n",
       "1134        1134        0.9  0678.jpg  0.867016          4303   4963     4860   \n",
       "1135        1135        0.9  0547.jpg  0.556049         23656  42543    42516   \n",
       "1136        1136        0.9  0122.jpg  0.729423          1480   2029     2029   \n",
       "1137        1137        0.9  0465.jpg  0.000000             0  11200    11200   \n",
       "1138        1138        0.9  0163.jpg  0.825536          2735   3313     3299   \n",
       "1139        1139        0.9  0476.jpg  0.814976          7096   8707     8604   \n",
       "1140        1140        0.9  0763.jpg  0.544653          3708   6808     6802   \n",
       "1141        1141        0.9  0067.jpg  0.000000             0   2470     2470   \n",
       "1142        1142        0.9  0771.jpg  0.782595          7509   9595     8965   \n",
       "1143        1143        0.9  0714.jpg  0.045438           256   5634     5634   \n",
       "1144        1144        0.9  0106.jpg  0.817528          1931   2362     2269   \n",
       "1145        1145        0.9  0440.jpg  0.000000             0   3522     3522   \n",
       "1146        1146        0.9  0468.jpg  0.779400          9020  11573    11556   \n",
       "1147        1147        0.9  0321.jpg  0.253289          1810   7146     7146   \n",
       "1148        1148        0.9  0688.jpg  0.694740         10328  14866    11133   \n",
       "1149        1149        0.9  0049.jpg  0.798328          1623   2033     2023   \n",
       "...          ...        ...       ...       ...           ...    ...      ...   \n",
       "1230        1230        0.9  0559.jpg  0.141619          3922  27694    27648   \n",
       "1231        1231        0.9  0456.jpg  0.766662          6039   7877     7749   \n",
       "1232        1232        0.9  0735.jpg  0.089524           717   8009     8008   \n",
       "1233        1233        0.9  0441.jpg  0.911125         10221  11218    11105   \n",
       "1234        1234        0.9  0463.jpg  0.036623           820  22390    11491   \n",
       "1235        1235        0.9  0453.jpg  0.818307          8904  10881    10628   \n",
       "1236        1236        0.9  0599.jpg  0.445545         14838  33303    32625   \n",
       "1237        1237        0.9  0764.jpg  0.000000             0    860      860   \n",
       "1238        1238        0.9  0078.jpg  0.719184          1657   2304     2276   \n",
       "1239        1239        0.9  0619.jpg  0.116759          8070  69117    69117   \n",
       "1240        1240        0.9  0182.jpg  0.745700          2818   3779     3779   \n",
       "1241        1241        0.9  0282.jpg  0.000000             0   1856     1624   \n",
       "1242        1242        0.9  0493.jpg  0.858877          9129  10629    10581   \n",
       "1243        1243        0.9  0747.jpg  0.000000             0   1566     1566   \n",
       "1244        1244        0.9  0120.jpg  0.791251          1664   2103     2103   \n",
       "1245        1245        0.9  0523.jpg  0.824161         43922  53293    53165   \n",
       "1246        1246        0.9  0430.jpg  0.838992          3830   4565     4399   \n",
       "1247        1247        0.9  0070.jpg  0.618927          1465   2367     2367   \n",
       "1248        1248        0.9  0689.jpg  0.015980           128   8010     8010   \n",
       "1249        1249        0.9  0554.jpg  0.106439          3617  33982    33982   \n",
       "1250        1250        0.9  0036.jpg  0.778584          2145   2755     2527   \n",
       "1251        1251        0.9  0354.jpg  0.474247         10782  22735    14412   \n",
       "1252        1252        0.9  0189.jpg  0.678349          1742   2568     2556   \n",
       "1253        1253        0.9  0768.jpg  0.585838          2573   4392     4392   \n",
       "1254        1254        0.9  0717.jpg  0.634031          4572   7211     7008   \n",
       "1255        1255        0.9  0494.jpg  0.856642          9782  11419    11351   \n",
       "1256        1256        0.9  0121.jpg  0.707748          1763   2491     2491   \n",
       "1257        1257        0.9  0084.jpg  0.647834          1376   2124     2084   \n",
       "1258        1258        0.9  0396.jpg  0.901564          9745  10809    10781   \n",
       "1259        1259        0.9  0370.jpg  0.052974           334   6305     6305   \n",
       "\n",
       "      segmentation_area  gt_x_center  gt_y_center  segmentation_x_center  \\\n",
       "1120                  0        639.0        448.0                    NaN   \n",
       "1121              12833        486.0        550.0                  481.0   \n",
       "1122               2819        532.0        456.0                  540.0   \n",
       "1123               7983        551.0        502.0                  550.0   \n",
       "1124                420        523.0        515.0                  526.0   \n",
       "1125                266        415.0        583.0                  461.0   \n",
       "1126               1706        525.0        514.0                  524.0   \n",
       "1127              18860        497.0        501.0                  497.0   \n",
       "1128               4299        607.0        524.0                  611.0   \n",
       "1129               7635        473.0        460.0                  476.0   \n",
       "1130                  0        546.0        439.0                    NaN   \n",
       "1131                  0        540.0        421.0                    NaN   \n",
       "1132               3756        548.0        526.0                  553.0   \n",
       "1133                903        556.0        429.0                  558.0   \n",
       "1134               4406        584.0        507.0                  586.0   \n",
       "1135              23683        440.0        615.0                  432.0   \n",
       "1136               1480        496.0        510.0                  495.0   \n",
       "1137                  0        561.0        507.0                    NaN   \n",
       "1138               2749        526.0        550.0                  523.0   \n",
       "1139               7199        487.0        518.0                  488.0   \n",
       "1140               3714        543.0        546.0                  526.0   \n",
       "1141                  0        601.0        467.0                    NaN   \n",
       "1142               8139        539.0        570.0                  534.0   \n",
       "1143                256        538.0        565.0                  550.0   \n",
       "1144               2024        476.0        526.0                  476.0   \n",
       "1145                  0        491.0        520.0                    NaN   \n",
       "1146               9037        435.0        478.0                  443.0   \n",
       "1147               1810        437.0        398.0                  429.0   \n",
       "1148              14061        586.0        489.0                  584.0   \n",
       "1149               1633        411.0        544.0                  410.0   \n",
       "...                 ...          ...          ...                    ...   \n",
       "1230               3968        550.0        640.0                  542.0   \n",
       "1231               6167        407.0        522.0                  406.0   \n",
       "1232                718        475.0        506.0                  493.0   \n",
       "1233              10334        631.0        503.0                  633.0   \n",
       "1234              11719        459.0        464.0                  488.0   \n",
       "1235               9157        401.0        491.0                  399.0   \n",
       "1236              15516        470.0        578.0                  464.0   \n",
       "1237                  0        576.0        460.0                    NaN   \n",
       "1238               1685        518.0        503.0                  521.0   \n",
       "1239               8070        544.0        625.0                  494.0   \n",
       "1240               2818        541.0        494.0                  543.0   \n",
       "1241                232        520.0        472.0                  942.0   \n",
       "1242               9177        552.0        493.0                  556.0   \n",
       "1243                  0        590.0        477.0                    NaN   \n",
       "1244               1664        445.0        544.0                  445.0   \n",
       "1245              44050        529.0        471.0                  532.0   \n",
       "1246               3996        574.0        539.0                  571.0   \n",
       "1247               1465        531.0        540.0                  530.0   \n",
       "1248                128        605.0        491.0                  674.0   \n",
       "1249               3617        489.0        697.0                  468.0   \n",
       "1250               2373        467.0        446.0                  468.0   \n",
       "1251              19105        571.0        450.0                  639.0   \n",
       "1252               1754        523.0        509.0                  525.0   \n",
       "1253               2573        592.0        468.0                  595.0   \n",
       "1254               4775        488.0        479.0                  474.0   \n",
       "1255               9850        601.0        509.0                  598.0   \n",
       "1256               1763        510.0        532.0                  508.0   \n",
       "1257               1416        515.0        538.0                  520.0   \n",
       "1258               9773        507.0        532.0                  509.0   \n",
       "1259                334        560.0        531.0                  558.0   \n",
       "\n",
       "      segmentation_y_center  x_distance  y_distance  euclidean_distance  \\\n",
       "1120                    NaN         NaN         NaN                 NaN   \n",
       "1121                  561.0         5.0       -11.0           12.083046   \n",
       "1122                  462.0        -8.0        -6.0           10.000000   \n",
       "1123                  505.0         1.0        -3.0            3.162278   \n",
       "1124                  522.0        -3.0        -7.0            7.615773   \n",
       "1125                  668.0       -46.0       -85.0           96.648849   \n",
       "1126                  515.0         1.0        -1.0            1.414214   \n",
       "1127                  509.0         0.0        -8.0            8.000000   \n",
       "1128                  526.0        -4.0        -2.0            4.472136   \n",
       "1129                  460.0        -3.0         0.0            3.000000   \n",
       "1130                    NaN         NaN         NaN                 NaN   \n",
       "1131                    NaN         NaN         NaN                 NaN   \n",
       "1132                  533.0        -5.0        -7.0            8.602325   \n",
       "1133                  429.0        -2.0         0.0            2.000000   \n",
       "1134                  506.0        -2.0         1.0            2.236068   \n",
       "1135                  600.0         8.0        15.0           17.000000   \n",
       "1136                  510.0         1.0         0.0            1.000000   \n",
       "1137                    NaN         NaN         NaN                 NaN   \n",
       "1138                  550.0         3.0         0.0            3.000000   \n",
       "1139                  518.0        -1.0         0.0            1.000000   \n",
       "1140                  545.0        17.0         1.0           17.029386   \n",
       "1141                    NaN         NaN         NaN                 NaN   \n",
       "1142                  575.0         5.0        -5.0            7.071068   \n",
       "1143                  603.0       -12.0       -38.0           39.849718   \n",
       "1144                  527.0         0.0        -1.0            1.000000   \n",
       "1145                    NaN         NaN         NaN                 NaN   \n",
       "1146                  480.0        -8.0        -2.0            8.246211   \n",
       "1147                  418.0         8.0       -20.0           21.540659   \n",
       "1148                  505.0         2.0       -16.0           16.124515   \n",
       "1149                  542.0         1.0         2.0            2.236068   \n",
       "...                     ...         ...         ...                 ...   \n",
       "1230                  726.0         8.0       -86.0           86.371292   \n",
       "1231                  517.0         1.0         5.0            5.099020   \n",
       "1232                  496.0       -18.0        10.0           20.591260   \n",
       "1233                  502.0        -2.0         1.0            2.236068   \n",
       "1234                  341.0       -29.0       123.0          126.372465   \n",
       "1235                  492.0         2.0        -1.0            2.236068   \n",
       "1236                  625.0         6.0       -47.0           47.381431   \n",
       "1237                    NaN         NaN         NaN                 NaN   \n",
       "1238                  506.0        -3.0        -3.0            4.242641   \n",
       "1239                  517.0        50.0       108.0          119.012604   \n",
       "1240                  493.0        -2.0         1.0            2.236068   \n",
       "1241                  164.0      -422.0       308.0          522.444255   \n",
       "1242                  492.0        -4.0         1.0            4.123106   \n",
       "1243                    NaN         NaN         NaN                 NaN   \n",
       "1244                  544.0         0.0         0.0            0.000000   \n",
       "1245                  468.0        -3.0         3.0            4.242641   \n",
       "1246                  543.0         3.0        -4.0            5.000000   \n",
       "1247                  535.0         1.0         5.0            5.099020   \n",
       "1248                  493.0       -69.0        -2.0           69.028979   \n",
       "1249                  768.0        21.0       -71.0           74.040529   \n",
       "1250                  440.0        -1.0         6.0            6.082763   \n",
       "1251                  457.0       -68.0        -7.0           68.359345   \n",
       "1252                  508.0        -2.0         1.0            2.236068   \n",
       "1253                  469.0        -3.0        -1.0            3.162278   \n",
       "1254                  484.0        14.0        -5.0           14.866069   \n",
       "1255                  510.0         3.0        -1.0            3.162278   \n",
       "1256                  533.0         2.0        -1.0            2.236068   \n",
       "1257                  539.0        -5.0        -1.0            5.099020   \n",
       "1258                  531.0        -2.0         1.0            2.236068   \n",
       "1259                  569.0         2.0       -38.0           38.052595   \n",
       "\n",
       "      x_size  y_size  \n",
       "1120    1024    1024  \n",
       "1121    1024    1024  \n",
       "1122    1024    1024  \n",
       "1123    1024    1024  \n",
       "1124    1024    1024  \n",
       "1125    1024    1024  \n",
       "1126    1024    1024  \n",
       "1127    1024    1024  \n",
       "1128    1024    1024  \n",
       "1129    1024    1024  \n",
       "1130    1024    1024  \n",
       "1131    1024    1024  \n",
       "1132    1024    1024  \n",
       "1133    1024    1024  \n",
       "1134    1024    1024  \n",
       "1135    1024    1024  \n",
       "1136    1024    1024  \n",
       "1137    1024    1024  \n",
       "1138    1024    1024  \n",
       "1139    1024    1024  \n",
       "1140    1024    1024  \n",
       "1141    1024    1024  \n",
       "1142    1024    1024  \n",
       "1143    1024    1024  \n",
       "1144    1024    1024  \n",
       "1145    1024    1024  \n",
       "1146    1024    1024  \n",
       "1147    1024    1024  \n",
       "1148    1024    1024  \n",
       "1149    1024    1024  \n",
       "...      ...     ...  \n",
       "1230    1024    1024  \n",
       "1231    1024    1024  \n",
       "1232    1024    1024  \n",
       "1233    1024    1024  \n",
       "1234    1024    1024  \n",
       "1235    1024    1024  \n",
       "1236    1024    1024  \n",
       "1237    1024    1024  \n",
       "1238    1024    1024  \n",
       "1239    1024    1024  \n",
       "1240    1024    1024  \n",
       "1241    1024    1024  \n",
       "1242    1024    1024  \n",
       "1243    1024    1024  \n",
       "1244    1024    1024  \n",
       "1245    1024    1024  \n",
       "1246    1024    1024  \n",
       "1247    1024    1024  \n",
       "1248    1024    1024  \n",
       "1249    1024    1024  \n",
       "1250    1024    1024  \n",
       "1251    1024    1024  \n",
       "1252    1024    1024  \n",
       "1253    1024    1024  \n",
       "1254    1024    1024  \n",
       "1255    1024    1024  \n",
       "1256    1024    1024  \n",
       "1257    1024    1024  \n",
       "1258    1024    1024  \n",
       "1259    1024    1024  \n",
       "\n",
       "[140 rows x 17 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = []\n",
    "acumList = []\n",
    "yerr = []\n",
    "for i in np.arange(0,len(threshold_list)):\n",
    "    acumList = []\n",
    "    th_slice = valid.loc[valid['threshold'] == threshold_list[i]]\n",
    "    for j in np.arange(th_slice.shape[0]):\n",
    "        row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[j]]\n",
    "\n",
    "        if (row['diam_resize'].values[0]/2 < 1000):\n",
    "            acumList.append(th_slice['euclidean_distance'].values[j] / ((row['diam_resize'].values[0]/2)))\n",
    "        else:\n",
    "            continue\n",
    "    Y.append(np.mean(acumList))\n",
    "    yerr.append(np.std(acumList))\n",
    "# for i in np.arange(0,len(threshold_list)):\n",
    "#     yerr.append(th_list[i]['euclidean_distance'].std())\n",
    "plt.errorbar(threshold_list, Y,yerr=yerr, linestyle='None', marker='.', ecolor='orange',markersize=10)\n",
    "plt.xticks(threshold_list)\n",
    "plt.xlabel('Thresholds')\n",
    "plt.ylabel('Euclidean Distance Normalized by Bud Diameter')\n",
    "plt.tight_layout()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_05 = valid.loc[valid['threshold'] == 0.5,:]\n",
    "distance_list = []\n",
    "colors = {'0.1': u'orchid', '0.2': u'darkcyan', '0.3': u'grey', '0.4': u'dodgerblue',\n",
    "         '0.5': u'turquoise', '0.6': u'darkviolet', '0.7':u'orange', '0.8':u'yellow','0.9':u'maroon','1.0':u'lime'}\n",
    "for sample in np.arange(th_05.shape[0]):\n",
    "    row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[sample]]\n",
    "    distance_list.append(th_slice['euclidean_distance'].values[sample] / ((row['diam_resize'].values[0]/2)))\n",
    "tp = 0\n",
    "fp = 0\n",
    "pr_dict = {\n",
    "    1:np.zeros((2,140)),\n",
    "    2:np.zeros((2,140)),\n",
    "    3:np.zeros((2,140)),\n",
    "    4:np.zeros((2,140)),\n",
    "    5:np.zeros((2,140)),\n",
    "    6:np.zeros((2,140)),\n",
    "    7:np.zeros((2,140)),\n",
    "    8:np.zeros((2,140)),\n",
    "    9:np.zeros((2,140)),\n",
    "    10:np.zeros((2,140))\n",
    "}\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    tp = 0\n",
    "    fp = 0\n",
    "    for j in np.arange(140):\n",
    "        if (distance_list[j] > i/10):\n",
    "            fp+=1\n",
    "        else:\n",
    "            tp+=1\n",
    "        pr_dict[i][0,j] = (tp/(tp+fp))\n",
    "        pr_dict[i][1,j] = (tp/140)\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    plt.plot(pr_dict[i][1,:],pr_dict[i][0,:], color=colors[str(i/10)])\n",
    "plt.ylim((0,1))\n",
    "plt.xlim((0,1))\n",
    "markers = [plt.Line2D([0,0],[0,0],color=color, marker='o', linestyle='') for color in colors.values()]\n",
    "plt.legend(markers, colors.keys(), numpoints=1)\n",
    "plt.title('Precision X Recall Curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.grid(True)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_05 = valid.loc[valid['threshold'] == 0.5,:]\n",
    "bud_recall = []\n",
    "distance_list = []\n",
    "\n",
    "th_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "for sample in np.arange(th_05.shape[0]):\n",
    "    row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[sample]]\n",
    "    distance_list.append(th_slice['euclidean_distance'].values[sample] / ((row['diam_resize'].values[0]/2)))\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    s = pd.Series(distance_list)\n",
    "    s = s.where(s<(i/10), False)\n",
    "    s = s.where(s == False, True)\n",
    "    bud_recall.append(s.sum()/140)\n",
    "plt.bar(x=[1,2,3,4,5,6,7,8,9,10],height=bud_recall,tick_label=['0.1', '0.2', '0.3' ,'0.4' ,'0.5' ,'0.6' ,'0.7' ,'0.8' ,'0.9', '1'])\n",
    "plt.xlabel('Distance Threshold (Bud Diameters)')\n",
    "plt.ylabel('Recall')\n",
    "plt.ylim((0,1))\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Unnamed: 0</th>\n",
       "      <th>x_center_orig</th>\n",
       "      <th>y_center_orig</th>\n",
       "      <th>x_center_resize</th>\n",
       "      <th>y_center_resize</th>\n",
       "      <th>diam_orig</th>\n",
       "      <th>diam_resize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>698.00000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>698.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>348.50000</td>\n",
       "      <td>2461.551576</td>\n",
       "      <td>1744.981375</td>\n",
       "      <td>1021.568768</td>\n",
       "      <td>1036.366762</td>\n",
       "      <td>469.785100</td>\n",
       "      <td>247.287658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>201.63953</td>\n",
       "      <td>401.465923</td>\n",
       "      <td>256.525842</td>\n",
       "      <td>97.013786</td>\n",
       "      <td>111.359334</td>\n",
       "      <td>320.558555</td>\n",
       "      <td>162.381768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1228.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>665.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>31.614458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>174.25000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>1574.000000</td>\n",
       "      <td>966.000000</td>\n",
       "      <td>977.250000</td>\n",
       "      <td>252.250000</td>\n",
       "      <td>131.415205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>348.50000</td>\n",
       "      <td>2559.500000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1023.000000</td>\n",
       "      <td>1036.500000</td>\n",
       "      <td>357.500000</td>\n",
       "      <td>202.195448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>522.75000</td>\n",
       "      <td>2740.000000</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>1079.500000</td>\n",
       "      <td>1091.000000</td>\n",
       "      <td>536.000000</td>\n",
       "      <td>306.377510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>697.00000</td>\n",
       "      <td>3480.000000</td>\n",
       "      <td>2552.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1434.000000</td>\n",
       "      <td>1716.000000</td>\n",
       "      <td>963.368421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  x_center_orig  y_center_orig  x_center_resize  \\\n",
       "count   698.00000     698.000000     698.000000       698.000000   \n",
       "mean    348.50000    2461.551576    1744.981375      1021.568768   \n",
       "std     201.63953     401.465923     256.525842        97.013786   \n",
       "min       0.00000    1228.000000     700.000000       665.000000   \n",
       "25%     174.25000    2277.000000    1574.000000       966.000000   \n",
       "50%     348.50000    2559.500000    1763.000000      1023.000000   \n",
       "75%     522.75000    2740.000000    1907.000000      1079.500000   \n",
       "max     697.00000    3480.000000    2552.000000      1309.000000   \n",
       "\n",
       "       y_center_resize    diam_orig  diam_resize  \n",
       "count       698.000000   698.000000   698.000000  \n",
       "mean       1036.366762   469.785100   247.287658  \n",
       "std         111.359334   320.558555   162.381768  \n",
       "min         415.000000    78.000000    31.614458  \n",
       "25%         977.250000   252.250000   131.415205  \n",
       "50%        1036.500000   357.500000   202.195448  \n",
       "75%        1091.000000   536.000000   306.377510  \n",
       "max        1434.000000  1716.000000   963.368421  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_05 = valid.loc[valid['threshold'] == 0.5,:]\n",
    "bud_recall = []\n",
    "distance_list = []\n",
    "\n",
    "th_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "for sample in np.arange(th_05.shape[0]):\n",
    "#     row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[sample]]\n",
    "    distance_list.append(th_slice['euclidean_distance'].values[sample] / 124)\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    s = pd.Series(distance_list)\n",
    "    s = s.where(s<(i/10), False)\n",
    "    s = s.where(s == False, True)\n",
    "    bud_recall.append(s.sum()/140)\n",
    "plt.bar(x=[1,2,3,4,5,6,7,8,9,10],height=bud_recall,tick_label=['0.1', '0.2', '0.3' ,'0.4' ,'0.5' ,'0.6' ,'0.7' ,'0.8' ,'0.9', '1'])\n",
    "plt.xlabel('Distance Threshold (Bud Diameters)')\n",
    "plt.ylabel('Recall')\n",
    "plt.ylim((0,1))\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_05 = valid.loc[valid['threshold'] == 0.5,:]\n",
    "bud_recall = []\n",
    "distance_list = []\n",
    "\n",
    "th_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "for sample in np.arange(th_05.shape[0]):\n",
    "    row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[sample]]\n",
    "    if row['diam_resize'].values[0]/2 < 100:\n",
    "        distance_list.append(th_slice['euclidean_distance'].values[sample] / ((row['diam_resize'].values[0]/2)))\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    s = pd.Series(distance_list)\n",
    "    s = s.where(s<(i/10), False)\n",
    "    s = s.where(s == False, True)\n",
    "    bud_recall.append(s.sum()/s.shape[0])\n",
    "plt.bar(x=[1,2,3,4,5,6,7,8,9,10],height=bud_recall,tick_label=['0.1', '0.2', '0.3' ,'0.4' ,'0.5' ,'0.6' ,'0.7' ,'0.8' ,'0.9', '1'])\n",
    "plt.xlabel('Distance Threshold (Bud Diameters < 100)')\n",
    "plt.ylabel('Recall')\n",
    "plt.ylim((0,1))\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_05 = valid.loc[valid['threshold'] == 0.5,:]\n",
    "bud_recall = []\n",
    "distance_list = []\n",
    "\n",
    "th_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "for sample in np.arange(th_05.shape[0]):\n",
    "    row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[sample]]\n",
    "    if row['diam_resize'].values[0]/2 < 200 and row['diam_resize'].values[0]/2 > 100:\n",
    "        distance_list.append(th_slice['euclidean_distance'].values[sample] / ((row['diam_resize'].values[0]/2)))\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    s = pd.Series(distance_list)\n",
    "    s = s.where(s<(i/10), False)\n",
    "    s = s.where(s == False, True)\n",
    "    bud_recall.append(s.sum()/s.shape[0])\n",
    "plt.bar(x=[1,2,3,4,5,6,7,8,9,10],height=bud_recall,tick_label=['0.1', '0.2', '0.3' ,'0.4' ,'0.5' ,'0.6' ,'0.7' ,'0.8' ,'0.9', '1'])\n",
    "plt.xlabel('Distance Threshold (Bud Diameters between 100 and 200)')\n",
    "plt.ylabel('Recall')\n",
    "plt.ylim((0,1))\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "th_05 = valid.loc[valid['threshold'] == 0.5,:]\n",
    "bud_recall = []\n",
    "distance_list = []\n",
    "\n",
    "th_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "for sample in np.arange(th_05.shape[0]):\n",
    "    row = dataset.loc[dataset['imageOrigin'] == th_slice['sample'].values[sample]]\n",
    "    if row['diam_resize'].values[0]/2 > 200:\n",
    "        distance_list.append(th_slice['euclidean_distance'].values[sample] / ((row['diam_resize'].values[0]/2)))\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    s = pd.Series(distance_list)\n",
    "    s = s.where(s<(i/10), False)\n",
    "    s = s.where(s == False, True)\n",
    "    bud_recall.append(s.sum()/s.shape[0])\n",
    "plt.bar(x=[1,2,3,4,5,6,7,8,9,10],height=bud_recall,tick_label=['0.1', '0.2', '0.3' ,'0.4' ,'0.5' ,'0.6' ,'0.7' ,'0.8' ,'0.9', '1'])\n",
    "plt.xlabel('Distance Threshold (Bud Diameters < 100)')\n",
    "plt.ylabel('Recall')\n",
    "plt.ylim((0,1))\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}