vanishing point detector.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detect lines in the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to show an image\n",
    "from matplotlib import pyplot as plt\n",
    "def show(img, title = \"\", save=False):\n",
    "    plt.imshow(img,  cmap='gray')\n",
    "    plt.title(title)\n",
    "    plt.axis('off')\n",
    "    if save:\n",
    "        plt.savefig(title, dpi = 200)\n",
    "#         cv2.imwrite(title,img)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "path = 'imgs'\n",
    "\n",
    "# Detect lines using Canny & Hough \n",
    "def detect_lines(img_file, filter_params, canny_params, hough_params):\n",
    "    cnt = 0\n",
    "    valid_lines = []\n",
    "    f_p1, f_p2, f_p3 = filter_params \n",
    "    c_p1, c_p2 = canny_params\n",
    "    h_p1, h_p2, h_p3 = hough_params\n",
    "    img = cv2.imread(os.path.join(path, img_file),0)  # read image in grey scale\n",
    "#     show(img, 'Original '+img_file, save=True)\n",
    "    edges = cv2.Canny(img, c_p1, c_p2,apertureSize=3,L2gradient=True)\n",
    "#     show(edges, 'No filtered Canny'+img_file, save=True)\n",
    "    filtered = cv2.bilateralFilter(img,f_p1, f_p2, f_p3)\n",
    "#     show(filtered, 'Filtered image '+ img_file, save=True)\n",
    "    filtered_edges = cv2.Canny(filtered, c_p1, c_p2, apertureSize=3,L2gradient=True)\n",
    "#     show(filtered_edges, 'Filtered Canny'+img_file, save=True)\n",
    "    \n",
    "    color_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) \n",
    "    \n",
    "    lines = cv2.HoughLinesP(filtered_edges,1,np.pi/180, h_p1, h_p2, h_p3)\n",
    "    \n",
    "    for line in lines:\n",
    "        for x1,y1,x2,y2 in line:\n",
    "            slope = (y2-y1)/(x2-x1)\n",
    "            \n",
    "            if abs(slope) <= 0.1:        # exclude horizontal lines\n",
    "                cv2.line(color_img,(x1,y1),(x2,y2),(0,255,0),2) \n",
    "            elif abs(slope) > 4:                # exclude vertical lines\n",
    "                cv2.line(color_img,(x1,y1),(x2,y2),(255,0,0),2)\n",
    "            else:\n",
    "                cv2.line(color_img,(x1,y1),(x2,y2),(0, 0, 255),2)\n",
    "                valid_lines.append([x1,y1,x2,y2])\n",
    "                cnt += 1\n",
    "                \n",
    "#     plt.imshow(color_img) \n",
    "#     plt.title(\"Hough lines \"+ img_file)\n",
    "#     plt.axis('off')\n",
    "#     cv2.imwrite('Hough lines'+ img_file, color_img)\n",
    "#     plt.show()\n",
    "    \n",
    "    return valid_lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RANSAC to locate the vanishing point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Find an intersection point of line1 and line2\n",
    "def find_intersection_point(line1, line2, xlims, ylims):\n",
    "    l1_x1, l1_y1, l1_x2, l1_y2 = line1\n",
    "    l2_x1, l2_y1, l2_x2, l2_y2 = line2\n",
    "    xdiff = (l1_x1 - l1_x2, l2_x1 - l2_x2)\n",
    "    ydiff = (l1_y1 - l1_y2, l2_y1 - l2_y2)\n",
    "\n",
    "    def det(a, b):\n",
    "        x1, y1 = a\n",
    "        x2, y2 = b\n",
    "        return x1 * y2 - y1 * x2\n",
    "    div = det(xdiff, ydiff)\n",
    "    \n",
    "    if div == 0:                  # parallel lines\n",
    "        return None\n",
    "    \n",
    "    d = (det((l1_x1, l1_y1), (l1_x2, l1_y2)), det((l2_x1, l2_y1), (l2_x2, l2_y2)))\n",
    "    x = det(d, xdiff) / div\n",
    "    y = det(d, ydiff) / div\n",
    "    \n",
    "    if x < xlims[0] or x > xlims[1] or y < ylims[1] or y > ylims[0]:  # points are out of image plane\n",
    "        return None\n",
    "                \n",
    "    return (x, y)\n",
    "\n",
    "# Find the distance from a point to a line (code below is from worksheet07)\n",
    "def find_dist_to_line(slope, y_intercept, x0, y0):\n",
    "    x = (x0 + slope*y0 - slope*y_intercept)/(1 + slope**2)\n",
    "    y = (slope*x0 + (slope**2)*y0 - (slope**2)*y_intercept)/(1 + slope**2) + y_intercept\n",
    "    dist = math.sqrt((x - x0)**2 + (y - y0)**2)\n",
    " \n",
    "    return dist\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RANSAC loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Find the vanishing point \n",
    "def ransac_loop(lines, n_iteration, n_sample, dist_t, xlim, ylim, img_ = None):\n",
    "    max_num_inlier = -np.inf\n",
    "    vanishing_point = None\n",
    "    vanishing_point_total_dist = 0 \n",
    "    total_points = []\n",
    "    for i in range(n_iteration):\n",
    "        random_lines = random.sample(lines, n_sample) if n_sample < len(lines) else lines\n",
    "        points = []     # potential vanishing points\n",
    "\n",
    "        for i in range(len(random_lines)):\n",
    "            line1 = random_lines[i]\n",
    "            \n",
    "            for k in range(len(random_lines)):\n",
    "                if i == k:\n",
    "                    continue\n",
    "                line2 = random_lines[k]\n",
    "                point = find_intersection_point(line1, line2, xlim, ylim)\n",
    "                if point != None:\n",
    "                    points.append(point)\n",
    "                    total_points.append(point)\n",
    "                    \n",
    "        # to choose the better point with less total distance when there are multiple points \n",
    "        # having the same number of inliers\n",
    "    \n",
    "        # count inliers within a pre-defined distance\n",
    "        for point in points:\n",
    "            x, y = point\n",
    "            num_inliers = 0\n",
    "            total_dist = 0\n",
    "            \n",
    "            for line in lines:\n",
    "                x1, y1, x2, y2 = line\n",
    "                slope = (y2-y1)/(x2-x1)\n",
    "                y_intercept = -x1*slope+y1\n",
    "                dist = find_dist_to_line(slope, y_intercept, x, y)\n",
    "\n",
    "                if dist <= dist_t:\n",
    "                    num_inliers += 1\n",
    "                    total_dist += dist\n",
    "                    \n",
    "            if (num_inliers == max_num_inlier and total_dist < vanishing_point_total_dist) \\\n",
    "                or (num_inliers > max_num_inlier):\n",
    "\n",
    "                max_num_inlier = num_inliers\n",
    "                vanishing_point = point\n",
    "                vanishing_point_total_dist = total_dist\n",
    "                \n",
    "        total_points.append(points)\n",
    "\n",
    "## code below is for error check and debugging        \n",
    "#     if intersection_point == None:\n",
    "#         print('Error')\n",
    "    \n",
    "#     print(max_num_inlier, vanishing_point)\n",
    "#     print('total points # ', len(total_points))\n",
    "    \n",
    "#     if img_ != None:\n",
    "#         img = cv2.imread(os.path.join(path, img_),0)\n",
    "#         plt.imshow(img, cmap='gray')\n",
    "#         for i in range(len(total_points)):\n",
    "#             x, y = total_points[i][0], total_points[i][1]\n",
    "#             plt.scatter(x,y,zorder=i+2, s = 3.0, c = 'lightyellow')\n",
    "#         if xlim[0] <= vanishing_point[0] <= xlim[1] and ylim[1] <= vanishing_point[1] <= ylim[0]:\n",
    "#             plt.scatter(gt_dic[img_][0], gt_dic[img_][1],zorder=i+2, s = 6.0, c = 'green')\n",
    "#             plt.scatter(vanishing_point[0], vanishing_point[1],zorder=i+2, s = 6.0, c = 'red')\n",
    "#             plt.axis('off')\n",
    "#             plt.savefig('Intersection points (10_10) '+ img_,dpi=250)\n",
    "#             plt.show()\n",
    "\n",
    "    return vanishing_point\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main function and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns: ﻿image, x, y\n",
      "Processed totla 13 lines.\n"
     ]
    }
   ],
   "source": [
    "# read GT locations of images\n",
    "import csv\n",
    "gt_dic = {}    \n",
    "with open('vanishing_points.csv') as gt_file:\n",
    "    ground_truth = csv.reader(gt_file, delimiter=',')\n",
    "    cnt = 0\n",
    "    for gt in ground_truth:\n",
    "        if cnt == 0:\n",
    "            print(f'Columns: {gt[0]}, {gt[1]}, {gt[2]}')\n",
    "        else:\n",
    "            gt_dic[gt[0]] = [int(gt[1]), int(gt[2])]\n",
    "        cnt += 1\n",
    "    print(f'Processed totla {cnt} lines.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to calculate mean squared euclidean distance (MSED) between the predicted and GT vanishing points\n",
    "import math\n",
    "msed_dic = {}\n",
    "def compute_MSED(pred_dic, gt_dic): \n",
    "    sum_dist = 0\n",
    "    num_dist = len(gt_dic.keys())\n",
    "    for img in pred_dic.keys():\n",
    "        gt_x, gt_y = gt_dic[img] \n",
    "        pred_x, pred_y = pred_dic[img]\n",
    "        squared_euclidean_dist = (pred_x - gt_x)**2 + (pred_y - gt_y)**2  # squared distance\n",
    "        sum_dist += squared_euclidean_dist\n",
    "        msed_dic[img] = squared_euclidean_dist\n",
    "    print(msed_dic)\n",
    "    return (sum_dist/num_dist), msed_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# main function \n",
    "def main(imgs, n_iteration, n_sample, dist_t, filter_params, canny_paramsm, hough_params):\n",
    "    pred_dic = {} \n",
    "    for img in imgs:\n",
    "        lines = detect_lines(img, filter_params, canny_params, hough_params)\n",
    "        height, width = cv2.imread(os.path.join(path, img),0).shape[:2]\n",
    "        point = ransac_loop(lines, n_iteration, n_sample, dist_t,(0,width), (height,0), img)\n",
    "        pred_dic[img] = point\n",
    "        print(img, \":\",point)\n",
    "    msed, msed_dic = compute_MSED(pred_dic, gt_dic)    \n",
    "    print(f'Accuracy(msed): {msed}\\n')\n",
    "    return msed, msed_dic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# experimental parameters from the experiments below\n",
    "filter_params = (3, 90 , 90) \n",
    "canny_params = (50, 150)        # 50, 150\n",
    "hough_params = (80, 100, 15)    # 80, 100, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/heekim/opt/anaconda3/envs/CV/lib/python3.7/site-packages/ipykernel_launcher.py:34: RuntimeWarning: divide by zero encountered in int_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "img1.jpg : (404.9604743083004, 182.64822134387353)\n",
      "img3.jpg : (396.02836879432624, 275.8936170212766)\n",
      "img6.jpg : (377.6380051590714, 256.78675838349096)\n",
      "img7.jpg : (379.53710247349824, 423.9328621908127)\n",
      "img9.jpg : (440.8291457286432, 369.72361809045225)\n",
      "img10.jpg : (237.2222808870116, 471.5844772967265)\n",
      "img13.jpg : (388.44615384615383, 258.46153846153845)\n",
      "img14.jpg : (363.2178217821782, 267.9504950495049)\n",
      "img15.jpg : (411.46715328467155, 272.45985401459853)\n",
      "img18.jpg : (290.98924731182797, 214.86021505376345)\n",
      "img20.jpg : (399.9268292682927, 289.8536585365854)\n",
      "img21.jpg : (392.85714285714283, 206.14285714285714)\n",
      "{'img1.jpg': 1.0462591198112325, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 1.6029954848883974, 'img7.jpg': 1.158710934085846, 'img9.jpg': 15.422161056538801, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.482021042895163, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.639286685310136\n",
      "\n",
      "run 2\n",
      "img1.jpg : (401.6, 180.4)\n",
      "img3.jpg : (395.86129334582944, 276.98687910028116)\n",
      "img6.jpg : (377.420640569395, 257.4797153024911)\n",
      "img7.jpg : (379.64864864864865, 423.9189189189189)\n",
      "img9.jpg : (439.8250807319699, 369.3471474703983)\n",
      "img10.jpg : (237.2222808870116, 471.5844772967265)\n",
      "img13.jpg : (388.44615384615383, 258.46153846153845)\n",
      "img14.jpg : (363.2178217821782, 267.9504950495049)\n",
      "img15.jpg : (411.46715328467155, 272.45985401459853)\n",
      "img18.jpg : (290.93774834437085, 214.78940397350993)\n",
      "img20.jpg : (400.52021563342316, 288.5148247978437)\n",
      "img21.jpg : (392.85714285714283, 206.14285714285714)\n",
      "{'img1.jpg': 12.519999999999861, 'img3.jpg': 0.9931698944829392, 'img6.jpg': 0.606353516292856, 'img7.jpg': 1.2651570489408033, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 30.293081276654696, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.20632046456687\n",
      "\n",
      "24.422803574938502\n"
     ]
    }
   ],
   "source": [
    "dic = {'id': [], 'msed': []}\n",
    "msed_list = []\n",
    "for i in range(2):\n",
    "    print(f'run {i+1}')\n",
    "    msed, msed_dic = main(gt_dic.keys(), 10, 10, 5, filter_params, canny_params, hough_params)\n",
    "    for k in gt_dic.keys():\n",
    "        dic['id'].append(k)\n",
    "        dic['msed'].append(msed_dic[k])\n",
    "    msed_list.append(msed)\n",
    "print(np.mean(msed_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dic = pd.DataFrame(dic)\n",
    "df_dic.rename(columns = {'id' :'image', 'msed':'MSED'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set(font_scale=5) \n",
    "sns.set_style(\"whitegrid\",)\n",
    "ax = sns.boxplot(x=\"image\", y=\"MSED\",\\\n",
    "                 data=df_dic, showfliers = True, palette = \"Set2\", linewidth = 3)\n",
    "# sns.despine(offset=20)\n",
    "sns.set(rc={'figure.figsize':(50,30)})\n",
    "# ax.legend(title = 'inlier threshold', fontsize=50, title_fontsize = 50, \\\n",
    "#          frameon = True,bbox_to_anchor=(1.15, 0.5),loc= 'center right' ,\\\n",
    "#           facecolor='white', borderaxespad=0.)\n",
    "ax.set_xlabel('image', fontsize=70)\n",
    "ax.set_ylabel('MSED',fontsize=70)\n",
    "plt.yscale('log')\n",
    "plt.tight_layout()\n",
    "plt.savefig('msed_foval', dpi = 250 )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The following blocks are for parameter settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dist threshold :  2\n",
      "run 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/heekim/opt/anaconda3/envs/CV/lib/python3.7/site-packages/ipykernel_launcher.py:34: RuntimeWarning: divide by zero encountered in int_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 113.87654320987595, 'img3.jpg': 2.9140625, 'img6.jpg': 0.34793907760141396, 'img7.jpg': 29.24819882345141, 'img9.jpg': 2.985410693202873, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 35.12401400797024, 'img18.jpg': 5.258834029778099, 'img20.jpg': 32.90379708259057, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 67.48798580626172\n",
      "\n",
      "run 2\n",
      "{'img1.jpg': 113.87654320987595, 'img3.jpg': 0.500422114448938, 'img6.jpg': 0.1293563834104213, 'img7.jpg': 29.136352158909403, 'img9.jpg': 2.4549257767931736, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 33.94919772647241, 'img18.jpg': 3.777777777777841, 'img20.jpg': 34.24173553718987, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 67.10527841129567\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 143.99099539086126, 'img3.jpg': 2.016732965268951, 'img6.jpg': 1.149113854595386, 'img7.jpg': 29.519999999999783, 'img9.jpg': 3.9286913580246163, 'img10.jpg': 50.671014640036645, 'img13.jpg': 67.77228238234044, 'img14.jpg': 464.3797530864188, 'img15.jpg': 33.61491052344004, 'img18.jpg': 4.739644970414126, 'img20.jpg': 34.24173553718987, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 74.08833591321583\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 207.75711219594288, 'img3.jpg': 1.3210349161543922, 'img6.jpg': 0.9118861661192518, 'img7.jpg': 27.451795841209744, 'img9.jpg': 2.7349836111724874, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 33.94919772647241, 'img18.jpg': 5.0, 'img20.jpg': 29.441749328783253, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 74.64706583637704\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 115.99999999999972, 'img3.jpg': 0.17121516805516107, 'img6.jpg': 0.23916232515739771, 'img7.jpg': 29.769578995020154, 'img9.jpg': 6.061103731433358, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 35.12401400797024, 'img18.jpg': 3.7038071472722356, 'img20.jpg': 32.90379708259057, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 67.59747572568074\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 143.99099539086126, 'img3.jpg': 1.3210349161543922, 'img6.jpg': 1.558685032139538, 'img7.jpg': 28.12483005131358, 'img9.jpg': 3.0921782590587017, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 33.94919772647241, 'img18.jpg': 5.0, 'img20.jpg': 29.441749328783253, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 69.4729750796211\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 5.423305588585055, 'img3.jpg': 1.3210349161543922, 'img6.jpg': 0.375765850373395, 'img7.jpg': 29.24819882345141, 'img9.jpg': 3.9286913580246163, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 34.11376953125, 'img18.jpg': 5.0, 'img20.jpg': 29.441749328783253, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 58.00412880394101\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 113.99086656270836, 'img3.jpg': 1.3210349161543922, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 26.081298828125, 'img9.jpg': 2.4459876543209997, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 33.94919772647241, 'img18.jpg': 9.100314349112443, 'img20.jpg': 18.604498335976274, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 66.07064833442764\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 201.11727591670456, 'img3.jpg': 2.4390477202678085, 'img6.jpg': 0.23916232515739771, 'img7.jpg': 27.451795841209744, 'img9.jpg': 2.5, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 34.11376953125, 'img18.jpg': 5.0, 'img20.jpg': 29.441749328783253, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 74.12498590950356\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 113.87654320987595, 'img3.jpg': 2.614014625424987, 'img6.jpg': 0.23916232515739771, 'img7.jpg': 29.24819882345141, 'img9.jpg': 3.8800000000000594, 'img10.jpg': 50.671014640036645, 'img13.jpg': 19.111106274214624, 'img14.jpg': 464.3797530864188, 'img15.jpg': 33.94919772647241, 'img18.jpg': 5.0, 'img20.jpg': 18.604498335976274, 'img21.jpg': 53.03515625}\n",
      "Accuracy(msed): 66.21738710808572\n",
      "\n",
      "dist threshold :  3\n",
      "run 1\n",
      "{'img1.jpg': 113.99086656270836, 'img3.jpg': 0.28871961863232276, 'img6.jpg': 0.864293628808848, 'img7.jpg': 11.222222222222083, 'img9.jpg': 2.3419172199587672, 'img10.jpg': 35.08353753619988, 'img13.jpg': 100.95061728395098, 'img14.jpg': 464.3797530864188, 'img15.jpg': 47.4453125, 'img18.jpg': 6.45858343337334, 'img20.jpg': 39.77767960771218, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 70.09416944268585\n",
      "\n",
      "run 2\n",
      "{'img1.jpg': 143.4615384615385, 'img3.jpg': 0.3511929537440921, 'img6.jpg': 1.3387518984111741, 'img7.jpg': 22.568705033973302, 'img9.jpg': 2.951789627465259, 'img10.jpg': 35.08353753619988, 'img13.jpg': 100.95061728395098, 'img14.jpg': 464.3797530864188, 'img15.jpg': 46.922520661157336, 'img18.jpg': 7.402275105878058, 'img20.jpg': 38.36268220674843, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 73.50832453897756\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 136.384615384615, 'img3.jpg': 4.420221077047018, 'img6.jpg': 1.3387518984111741, 'img7.jpg': 11.177849331915512, 'img9.jpg': 2.4459876543209997, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 47.4453125, 'img18.jpg': 6.45858343337334, 'img20.jpg': 27.1490514905146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 65.09291404905723\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 136.384615384615, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 2.2594118679613286, 'img7.jpg': 15.771947295654538, 'img9.jpg': 5.934027777777798, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 46.922520661157336, 'img18.jpg': 7.880000000000059, 'img20.jpg': 25.607176232338556, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 65.43843176031488\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 135.18525519848777, 'img3.jpg': 4.25, 'img6.jpg': 0.528954857268094, 'img7.jpg': 10.544792398755048, 'img9.jpg': 1.4908692476259597, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 47.4453125, 'img18.jpg': 10.349482248520802, 'img20.jpg': 36.351597791733944, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 65.87007167174006\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 128.6734693877553, 'img3.jpg': 7.109739634657072, 'img6.jpg': 2.657375171267436, 'img7.jpg': 6.850415512465437, 'img9.jpg': 2.5, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 47.4453125, 'img18.jpg': 7.959865880448576, 'img20.jpg': 39.77767960771218, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 65.6057044593996\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 135.18525519848777, 'img3.jpg': 0.28871961863232276, 'img6.jpg': 2.5, 'img7.jpg': 7.907569676069913, 'img9.jpg': 0.971713488615288, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 47.4453125, 'img18.jpg': 10.349482248520802, 'img20.jpg': 36.351597791733944, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 65.44118719504577\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 115.99999999999972, 'img3.jpg': 4.420221077047018, 'img6.jpg': 3.449253606484171, 'img7.jpg': 17.369584815355182, 'img9.jpg': 2.5507692307692293, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 47.4453125, 'img18.jpg': 6.45858343337334, 'img20.jpg': 36.351597791733944, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 64.86165985610431\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 135.18525519848777, 'img3.jpg': 0.3511929537440921, 'img6.jpg': 2.2594118679613286, 'img7.jpg': 12.931090299456692, 'img9.jpg': 1.95400551418478, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 47.4453125, 'img18.jpg': 6.45858343337334, 'img20.jpg': 39.77767960771218, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 65.88809393278412\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 136.384615384615, 'img3.jpg': 0.26872503102372675, 'img6.jpg': 1.3387518984111741, 'img7.jpg': 7.907569676069913, 'img9.jpg': 4.0, 'img10.jpg': 35.08353753619988, 'img13.jpg': 26.504774583625746, 'img14.jpg': 464.3797530864188, 'img15.jpg': 48.15914966963516, 'img18.jpg': 7.880000000000059, 'img20.jpg': 39.77767960771218, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 65.83425725716305\n",
      "\n",
      "dist threshold :  5\n",
      "run 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 1.19847623966939, 'img7.jpg': 1.193877551020377, 'img9.jpg': 16.19045873194271, 'img10.jpg': 38.90211014831479, 'img13.jpg': 31.249949777193706, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 18.031830886751973, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.137840756683417\n",
      "\n",
      "run 2\n",
      "{'img1.jpg': 91.10183958132151, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 1.766048074016691, 'img9.jpg': 9.591337377119316, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 5.046939515433516, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 32.229909188837546\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 7.53061224489797, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 0.8364047646379325, 'img7.jpg': 1.766048074016691, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.338292396195754\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.7270242247148118, 'img9.jpg': 15.565288449703221, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.694957075610063\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.6925379477277819, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.1686297041080185, 'img9.jpg': 16.19045873194271, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.412973571172055, 'img20.jpg': 34.962428867265764, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.317996660064882\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 1.2785161674270376, 'img6.jpg': 0.8364047646379325, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 11.227251705399619, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.971048202862292, 'img18.jpg': 2.5289212463904427, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.76181418245352\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 0.2882639264467225, 'img3.jpg': 0.500422114448938, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.7557849768601448, 'img9.jpg': 9.591337377119316, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 8.807630248485019, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.583851799325615\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.500653215270722, 'img7.jpg': 0.9847252529260344, 'img9.jpg': 15.565288449703221, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.482021042895163, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.08195353810974\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 2.874098845974684, 'img3.jpg': 1.2785161674270376, 'img6.jpg': 1.1026267846519557, 'img7.jpg': 4.32433208848022, 'img9.jpg': 16.19045873194271, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 5.046939515433516, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.67908034662524\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.8243527091907314, 'img6.jpg': 1.2913631633714786, 'img7.jpg': 1.6879012345678643, 'img9.jpg': 18.906076734694057, 'img10.jpg': 38.90211014831479, 'img13.jpg': 31.249949777193706, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.482021042895163, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 27.76491462920933\n",
      "\n",
      "dist threshold :  7\n",
      "run 1\n",
      "{'img1.jpg': 42.91715976331379, 'img3.jpg': 10.336708703589188, 'img6.jpg': 6.434437611585011, 'img7.jpg': 4.336734693877487, 'img9.jpg': 4.266540642722141, 'img10.jpg': 50.671014640036645, 'img13.jpg': 9.254720585573743, 'img14.jpg': 291.48084837742294, 'img15.jpg': 27.55654320987657, 'img18.jpg': 7.402275105878058, 'img20.jpg': 19.26680567784523, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 41.02085996866379\n",
      "\n",
      "run 2\n",
      "{'img1.jpg': 17.0, 'img3.jpg': 3.7949171517245666, 'img6.jpg': 12.853920118343378, 'img7.jpg': 5.0, 'img9.jpg': 19.455624801397356, 'img10.jpg': 50.671014640036645, 'img13.jpg': 9.254720585573743, 'img14.jpg': 291.48084837742294, 'img15.jpg': 25.27180899908164, 'img18.jpg': 8.201730665224472, 'img20.jpg': 19.26680567784523, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 40.04816013574123\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 17.47337278106525, 'img3.jpg': 10.336708703589188, 'img6.jpg': 5.879292403746185, 'img7.jpg': 3.5829476682093198, 'img9.jpg': 4.8696185196997055, 'img10.jpg': 50.671014640036645, 'img13.jpg': 9.254720585573743, 'img14.jpg': 291.48084837742294, 'img15.jpg': 48.191522762951564, 'img18.jpg': 10.0, 'img20.jpg': 19.26680567784523, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 40.777781894365376\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 16.33431952662701, 'img3.jpg': 12.556781772872927, 'img6.jpg': 6.434437611585011, 'img7.jpg': 4.42627218934907, 'img9.jpg': 3.1096236965256376, 'img10.jpg': 50.671014640036645, 'img13.jpg': 9.254720585573743, 'img14.jpg': 291.48084837742294, 'img15.jpg': 48.191522762951564, 'img18.jpg': 7.602948245473568, 'img20.jpg': 19.26680567784523, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 40.63798547487567\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 19.007439396138597, 'img3.jpg': 10.336708703589188, 'img6.jpg': 8.827932098765485, 'img7.jpg': 6.533139755081327, 'img9.jpg': 4.8696185196997055, 'img10.jpg': 50.671014640036645, 'img13.jpg': 5.173340536340211, 'img14.jpg': 291.48084837742294, 'img15.jpg': 26.33952892164737, 'img18.jpg': 7.880000000000059, 'img20.jpg': 13.814827706230286, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 38.605077438933044\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 24.25, 'img3.jpg': 10.336708703589188, 'img6.jpg': 8.309082433934591, 'img7.jpg': 6.196956536185099, 'img9.jpg': 2.4434400826447247, 'img10.jpg': 50.671014640036645, 'img13.jpg': 10.441734476401304, 'img14.jpg': 291.48084837742294, 'img15.jpg': 26.33952892164737, 'img18.jpg': 7.602948245473568, 'img20.jpg': 19.26680567784523, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 39.63879989228544\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 17.746877054569254, 'img3.jpg': 16.743801652892344, 'img6.jpg': 5.211238293444399, 'img7.jpg': 3.6938775510204334, 'img9.jpg': 9.031633847968045, 'img10.jpg': 50.671014640036645, 'img13.jpg': 9.254720585573743, 'img14.jpg': 291.48084837742294, 'img15.jpg': 48.191522762951564, 'img18.jpg': 7.880000000000059, 'img20.jpg': 22.711147369034208, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 41.74526772892986\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 17.0, 'img3.jpg': 13.483399044405273, 'img6.jpg': 8.90641757648249, 'img7.jpg': 3.611111111111155, 'img9.jpg': 21.27426391168363, 'img10.jpg': 50.671014640036645, 'img13.jpg': 9.254720585573743, 'img14.jpg': 291.48084837742294, 'img15.jpg': 25.27180899908164, 'img18.jpg': 8.201730665224472, 'img20.jpg': 0.6701030927834899, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 39.012662384670854\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 24.25, 'img3.jpg': 10.336708703589188, 'img6.jpg': 6.540786690092881, 'img7.jpg': 6.944499054820569, 'img9.jpg': 14.76550475328215, 'img10.jpg': 50.671014640036645, 'img13.jpg': 6.294286172087795, 'img14.jpg': 291.48084837742294, 'img15.jpg': 48.191522762951564, 'img18.jpg': 8.755461195393782, 'img20.jpg': 19.26680567784523, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 42.15199738664729\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 2.4463111760409246, 'img3.jpg': 3.7949171517245666, 'img6.jpg': 5.879292403746185, 'img7.jpg': 3.611111111111155, 'img9.jpg': 14.76550475328215, 'img10.jpg': 50.671014640036645, 'img13.jpg': 9.254720585573743, 'img14.jpg': 291.48084837742294, 'img15.jpg': 25.27180899908164, 'img18.jpg': 7.880000000000059, 'img20.jpg': 19.26680567784523, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 37.72073879067583\n",
      "\n",
      "dist threshold :  10\n",
      "run 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 5.447086801426904, 'img3.jpg': 1.0421699291961584, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 42.46968590211815, 'img9.jpg': 0.8712792184075757, 'img10.jpg': 200.5799420819995, 'img13.jpg': 10.157024793388466, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 3.853618053560667, 'img20.jpg': 5.116770281465621, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 37.88586205533776\n",
      "\n",
      "run 2\n",
      "{'img1.jpg': 38.429444444444556, 'img3.jpg': 0.9305537922225946, 'img6.jpg': 0.7396449704142314, 'img7.jpg': 43.55555555555591, 'img9.jpg': 7.785347230948335, 'img10.jpg': 200.5799420819995, 'img13.jpg': 4.982849870993898, 'img14.jpg': 143.02019409861853, 'img15.jpg': 38.736829761566504, 'img18.jpg': 3.777777777777841, 'img20.jpg': 11.410701749031073, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 42.68961432881815\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 12.984552517264419, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.5172683843684001, 'img7.jpg': 40.55555555555557, 'img9.jpg': 2.196676420843553, 'img10.jpg': 200.5799420819995, 'img13.jpg': 10.157024793388466, 'img14.jpg': 143.02019409861853, 'img15.jpg': 21.90082644628088, 'img18.jpg': 5.0, 'img20.jpg': 5.116770281465621, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 38.380185606484524\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 0.2882639264467225, 'img3.jpg': 0.308980841722849, 'img6.jpg': 1.1026267846519557, 'img7.jpg': 38.3125, 'img9.jpg': 1.0288332527085953, 'img10.jpg': 200.5799420819995, 'img13.jpg': 4.982849870993898, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 5.0, 'img20.jpg': 5.568806613662353, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 36.80876214738368\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 0.2882639264467225, 'img3.jpg': 0.41812622246796566, 'img6.jpg': 0.500653215270722, 'img7.jpg': 48.26921033053456, 'img9.jpg': 10.269571799308086, 'img10.jpg': 200.5799420819995, 'img13.jpg': 4.982849870993898, 'img14.jpg': 143.02019409861853, 'img15.jpg': 36.126390703121224, 'img18.jpg': 2.3055149744338985, 'img20.jpg': 3.627067289483723, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 39.05952626041029\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 10.672437076806567, 'img3.jpg': 1.0421699291961584, 'img6.jpg': 0.5172683843684001, 'img7.jpg': 43.55555555555591, 'img9.jpg': 2.220408163265245, 'img10.jpg': 200.5799420819995, 'img13.jpg': 4.982849870993898, 'img14.jpg': 143.02019409861853, 'img15.jpg': 45.802812937542484, 'img18.jpg': 3.853618053560667, 'img20.jpg': 5.116770281465621, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 39.974213087134814\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 0.2882639264467225, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.5172683843684001, 'img7.jpg': 66.72303660515038, 'img9.jpg': 9.89697265625, 'img10.jpg': 200.5799420819995, 'img13.jpg': 4.982849870993898, 'img14.jpg': 143.02019409861853, 'img15.jpg': 38.736829761566504, 'img18.jpg': 3.777777777777841, 'img20.jpg': 5.568806613662353, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 41.04623854748585\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 11.988769174579957, 'img3.jpg': 0.308980841722849, 'img6.jpg': 1.1026267846519557, 'img7.jpg': 66.72303660515038, 'img9.jpg': 9.89697265625, 'img10.jpg': 200.5799420819995, 'img13.jpg': 10.157024793388466, 'img14.jpg': 143.02019409861853, 'img15.jpg': 38.736829761566504, 'img18.jpg': 5.258834029778099, 'img20.jpg': 5.116770281465621, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 42.601375976784716\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 8.763469064698857, 'img3.jpg': 0.4288321799308176, 'img6.jpg': 0.17754504864692183, 'img7.jpg': 43.55555555555591, 'img9.jpg': 13.090489207276883, 'img10.jpg': 200.5799420819995, 'img13.jpg': 4.982849870993898, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 3.853618053560667, 'img20.jpg': 5.116770281465621, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 38.75678447837896\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 5.447086801426904, 'img3.jpg': 0.308980841722849, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 42.46968590211815, 'img9.jpg': 9.277337330964468, 'img10.jpg': 200.5799420819995, 'img13.jpg': 4.982849870993898, 'img14.jpg': 143.02019409861853, 'img15.jpg': 38.736829761566504, 'img18.jpg': 3.853618053560667, 'img20.jpg': 5.116770281465621, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 39.356448085522814\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# test block for iteration number \n",
    "import time\n",
    "dist = [2, 3, 5, 7, 10]\n",
    "dist_result = []\n",
    "dist_dic = {'id': [], 'msed': [], 'dist': []}\n",
    "for d in dist:\n",
    "    print('dist threshold : ', d)\n",
    "    msed_list = []\n",
    "    for i in range(10):\n",
    "        print(f'run {i+1}')\n",
    "        msed, msed_dic = main(gt_dic.keys(), 10, 10, d, filter_params, canny_params, hough_params)\n",
    "        msed_list.append(msed)\n",
    "        msed_dic\n",
    "        for k in msed_dic.keys():\n",
    "            dist_dic['id'].append(k)\n",
    "            dist_dic['msed'].append(msed_dic[k])\n",
    "            dist_dic['dist'].append(d)\n",
    "\n",
    "#     print('list all', msed_list)\n",
    "#     print('mean', np.mean(msed_list), \"\\n\\n\\n\")\n",
    "#     dist_result.append(np.mean(msed_list))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = df[df.dist_threshold != 10]\n",
    "# df.rename(columns = {'id' :'image', 'msed':'MSED', 'dist':'dist_threshold'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=4) \n",
    "sns.set_style(\"whitegrid\",)\n",
    "ax = sns.boxplot(x=\"image\", y=\"MSED\", hue=\"dist_threshold\", \\\n",
    "                 data=df, showfliers = False, palette=\"flare\", linewidth = 2)\n",
    "# sns.despine(offset=20)\n",
    "sns.set(rc={'figure.figsize':(50,30)})\n",
    "ax.legend(title = 'inlier threshold', fontsize=50, title_fontsize = 50, \\\n",
    "         frameon = True,bbox_to_anchor=(1.15, 0.5),loc= 'center right' ,\\\n",
    "          facecolor='white', borderaxespad=0.)\n",
    "ax.set_xlabel('image', fontsize=70)\n",
    "ax.set_ylabel('MSED',fontsize=70)\n",
    "plt.yscale('log')\n",
    "plt.tight_layout()\n",
    "plt.savefig('msed_dist_box_log', dpi = 250 )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = dist_result[1:]  # exclude 2 from the dist values\n",
    "x = dist[1:]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(x, y, color=\"red\")\n",
    "ax.set_xlabel(\"distance threshold for inliers\")\n",
    "ax.set_ylabel(\"MSED\", c = 'r')\n",
    "# ax.set_ylim([20,75])\n",
    "ax.set_xticks(x)\n",
    "\n",
    "plt.savefig('dist_inliers.png', dpi = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num sample :  2\n",
      "run 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/heekim/opt/anaconda3/envs/CV/lib/python3.7/site-packages/ipykernel_launcher.py:34: RuntimeWarning: divide by zero encountered in int_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 19.007439396138597, 'img3.jpg': 4.505252621882078, 'img6.jpg': 6.638893122660839, 'img7.jpg': 11.09261186264297, 'img9.jpg': 0.5129889226546737, 'img10.jpg': 102.6801426872766, 'img13.jpg': 54.89196675900324, 'img14.jpg': 230.9792387543251, 'img15.jpg': 68.15637246070008, 'img18.jpg': 23.51511892349003, 'img20.jpg': 23.876593295139806, 'img21.jpg': 14.660771251641895}\n",
      "Accuracy(msed): 46.70978250479633\n",
      "\n",
      "run time: 0.5083889961242676\n",
      "run 2\n",
      "{'img1.jpg': 70.79591836734696, 'img3.jpg': 4.329212577264123, 'img6.jpg': 8.56453516149642, 'img7.jpg': 7.210540578753726, 'img9.jpg': 959.5606420927458, 'img10.jpg': 38.90211014831479, 'img13.jpg': 38.944999999999624, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.58939207584227, 'img18.jpg': 37.96159122085031, 'img20.jpg': 34.962428867265764, 'img21.jpg': 14.660771251641895}\n",
      "Accuracy(msed): 115.625194703345\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 4.203450327186311, 'img6.jpg': 1.9818611742615229, 'img7.jpg': 14.897252630459956, 'img9.jpg': 4.188581314878777, 'img10.jpg': 76.28926264201353, 'img13.jpg': 45.34386819376197, 'img14.jpg': 143.02019409861853, 'img15.jpg': 121.80196425132625, 'img18.jpg': 10.363421874674852, 'img20.jpg': 9.70578637608537, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 37.55418868606265\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 14.855230457959475, 'img3.jpg': 8.619129522450105, 'img6.jpg': 23.466718229327416, 'img7.jpg': 42.525950413223036, 'img9.jpg': 27.35177744781191, 'img10.jpg': 50.671014640036645, 'img13.jpg': 17.436219735470246, 'img14.jpg': 230.9792387543251, 'img15.jpg': 40.828775267538994, 'img18.jpg': 41.42171789715597, 'img20.jpg': 96.08039188368936, 'img21.jpg': 14.660771251641895}\n",
      "Accuracy(msed): 50.74141129171918\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 13.16568047337293, 'img3.jpg': 1.3210349161543922, 'img6.jpg': 0.9782369951766575, 'img7.jpg': 28.704642045629164, 'img9.jpg': 7.41940474168266, 'img10.jpg': 34.12906804733723, 'img13.jpg': 5.173340536340211, 'img14.jpg': 230.9792387543251, 'img15.jpg': 61.55102040816344, 'img18.jpg': 24.236066804278593, 'img20.jpg': 11.410701749031073, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 36.449580506978016\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 1335.8906208008598, 'img3.jpg': 2.4116638299774187, 'img6.jpg': 3.1205452646161067, 'img7.jpg': 6.196956536185099, 'img9.jpg': 4.629324130965335, 'img10.jpg': 50.671014640036645, 'img13.jpg': 115.5555555555553, 'img14.jpg': 465.12500978933457, 'img15.jpg': 59.47029908391558, 'img18.jpg': 25.209879754884838, 'img20.jpg': 17.546811670799464, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 175.34618430578124\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 36.36094674556224, 'img3.jpg': 3.244704914756114, 'img6.jpg': 0.23916232515739771, 'img7.jpg': 19.966164464270765, 'img9.jpg': 3.4688292531441807, 'img10.jpg': 103.61248852157962, 'img13.jpg': 22.658344743958807, 'img14.jpg': 291.48084837742294, 'img15.jpg': 27.201428271371654, 'img18.jpg': 53.44562955254948, 'img20.jpg': 90.39265705769127, 'img21.jpg': 14.660771251641895}\n",
      "Accuracy(msed): 55.560997956592196\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 26.23341968103377, 'img3.jpg': 4.228398342013027, 'img6.jpg': 13.866724378034583, 'img7.jpg': 14.633851413911342, 'img9.jpg': 163.0407124196721, 'img10.jpg': 51.0093436374116, 'img13.jpg': 21.565150931523107, 'img14.jpg': 230.9792387543251, 'img15.jpg': 57.39483881787984, 'img18.jpg': 2.0000000000000115, 'img20.jpg': 28.88888888888917, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 52.68059148974486\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 250.14848347668664, 'img3.jpg': 12.556781772872927, 'img6.jpg': 2.5346648301193473, 'img7.jpg': 13.609511380371455, 'img9.jpg': 20.156804733727846, 'img10.jpg': 50.671014640036645, 'img13.jpg': 52.03550295857964, 'img14.jpg': 230.9792387543251, 'img15.jpg': 39.77368703226374, 'img18.jpg': 53.73504108637769, 'img20.jpg': 4.845429362880838, 'img21.jpg': 14.660771251641895}\n",
      "Accuracy(msed): 62.14224427332365\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 0.06910686728395571, 'img3.jpg': 2.9140625, 'img6.jpg': 12.853920118343378, 'img7.jpg': 4.825712992304196, 'img9.jpg': 279.33884297520717, 'img10.jpg': 51.41012396694238, 'img13.jpg': 16.47805747382487, 'img14.jpg': 464.3797530864188, 'img15.jpg': 33.94919772647241, 'img18.jpg': 4.906746973163498, 'img20.jpg': 36.351597791733944, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 77.15030442366162\n",
      "\n",
      "list all [46.70978250479633, 115.625194703345, 37.55418868606265, 50.74141129171918, 36.449580506978016, 175.34618430578124, 55.560997956592196, 52.68059148974486, 62.14224427332365, 77.15030442366162]\n",
      "mean 70.99604801420048 /////////\n",
      "\n",
      "\n",
      "\n",
      "num sample :  5\n",
      "run 1\n",
      "{'img1.jpg': 1.0058063579619434, 'img3.jpg': 1.2228031708550904, 'img6.jpg': 1.5885709077460506, 'img7.jpg': 1.8156025613632563, 'img9.jpg': 15.02015532544376, 'img10.jpg': 38.90211014831479, 'img13.jpg': 67.77228238234044, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 4.482021042895163, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 30.16931675538294\n",
      "\n",
      "run time: 1.2408099174499512\n",
      "run 2\n",
      "{'img1.jpg': 0.2882639264467225, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.500653215270722, 'img7.jpg': 7.32888888888892, 'img9.jpg': 22.59999999999984, 'img10.jpg': 38.90211014831479, 'img13.jpg': 39.050772653919545, 'img14.jpg': 143.02019409861853, 'img15.jpg': 39.77368703226374, 'img18.jpg': 12.99684059675866, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 30.27031533834997\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 74.29933522674095, 'img3.jpg': 0.6925379477277819, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 5.0, 'img9.jpg': 11.742603550295689, 'img10.jpg': 38.90211014831479, 'img13.jpg': 26.504774583625746, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.192867036011222, 'img18.jpg': 13.11999999999994, 'img20.jpg': 30.293081276654696, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 32.38787164052548\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 132.55555555555605, 'img3.jpg': 0.45437241817881757, 'img6.jpg': 1.0, 'img7.jpg': 7.8306008328375265, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 26.504774583625746, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 15.796051892269958, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 38.05584142707219\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.23037126530612864, 'img6.jpg': 1.149113854595386, 'img7.jpg': 1.0624999999999603, 'img9.jpg': 15.135461036031185, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.192867036011222, 'img18.jpg': 7.880000000000059, 'img20.jpg': 48.327264135395914, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.412884359408945\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 46.48760330578532, 'img3.jpg': 1.2228031708550904, 'img6.jpg': 2.524351476619204, 'img7.jpg': 8.594263991870239, 'img9.jpg': 11.227251705399619, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.971048202862292, 'img18.jpg': 4.33034516030002, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 30.604179297050564\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 1.5207100591715852, 'img3.jpg': 0.8243527091907314, 'img6.jpg': 0.8364047646379325, 'img7.jpg': 1.7482182209083053, 'img9.jpg': 2.7349836111724874, 'img10.jpg': 51.41012396694238, 'img13.jpg': 31.249949777193706, 'img14.jpg': 143.02019409861853, 'img15.jpg': 66.66264272420952, 'img18.jpg': 4.672353275868549, 'img20.jpg': 18.031830886751973, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 28.41985789224253\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 1.0624349635795443, 'img3.jpg': 1.2228031708550904, 'img6.jpg': 0.500653215270722, 'img7.jpg': 3.6062819576333776, 'img9.jpg': 15.02015532544376, 'img10.jpg': 50.671014640036645, 'img13.jpg': 13.144685292687818, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.971048202862292, 'img18.jpg': 8.201730665224472, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 27.738647487514047\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 1.3913103969225422, 'img3.jpg': 2.796270581233911, 'img6.jpg': 2.9200000000001003, 'img7.jpg': 1.7152199762187654, 'img9.jpg': 20.71525678541689, 'img10.jpg': 38.90211014831479, 'img13.jpg': 71.6191999999997, 'img14.jpg': 143.02019409861853, 'img15.jpg': 33.94919772647241, 'img18.jpg': 2.4232344805854327, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 31.955719298107017\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 89.74968558562227, 'img3.jpg': 0.308980841722849, 'img6.jpg': 1.1693455894754763, 'img7.jpg': 6.765432098765539, 'img9.jpg': 11.742603550295689, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 4.810149943505519, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 33.15677578385091\n",
      "\n",
      "list all [30.16931675538294, 30.27031533834997, 32.38787164052548, 38.05584142707219, 26.412884359408945, 30.604179297050564, 28.41985789224253, 27.738647487514047, 31.955719298107017, 33.15677578385091]\n",
      "mean 30.917140927950452 /////////\n",
      "\n",
      "\n",
      "\n",
      "num sample :  10\n",
      "run 1\n",
      "{'img1.jpg': 96.98741776937614, 'img3.jpg': 1.2785161674270376, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.7270242247148118, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.482021042895163, 'img20.jpg': 48.327264135395914, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 33.271422738239345\n",
      "\n",
      "run time: 4.613759994506836\n",
      "run 2\n",
      "{'img1.jpg': 3.0578512396693727, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 3.979892921670809, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.858306760381378\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.766048074016691, 'img9.jpg': 16.147580461240466, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 20.049714003248347, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.057602994768857\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 3.596307594442547, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 1.647387511028283, 'img7.jpg': 1.713280000000084, 'img9.jpg': 16.414992695398162, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.721096208732657\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 91.10183958132151, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.7557849768601448, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.830663577314225, 'img20.jpg': 43.84034651066873, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 31.85752624121992\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.4148907199456366, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.713280000000084, 'img9.jpg': 7.700374294985532, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 3.7457480026729426, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.14507593311551\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 1.0462591198112325, 'img3.jpg': 1.2228031708550904, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.7270242247148118, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 3.7457480026729426, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.49933725183098\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 1.0058063579619434, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.766048074016691, 'img9.jpg': 16.111111111111214, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 3.7457480026729426, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.305550881162006\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 12.519999999999861, 'img3.jpg': 1.608740894901134, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 2.7422013033339807, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.36210010015192\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 1.0, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 20.5875851560687, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.482021042895163, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.448162487770862\n",
      "\n",
      "list all [33.271422738239345, 24.858306760381378, 26.057602994768857, 25.721096208732657, 31.85752624121992, 25.14507593311551, 24.49933725183098, 25.305550881162006, 26.36210010015192, 26.448162487770862]\n",
      "mean 26.95261815973734 /////////\n",
      "\n",
      "\n",
      "\n",
      "num sample :  20\n",
      "run 1\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.55715854761078\n",
      "\n",
      "run time: 17.276978015899658\n",
      "run 2\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 18.5, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.27976978111521\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 1.5885709077460506, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 9.894182888184591, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 5.046939515433516, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.818098541813622\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.042953501889112\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 1.3951263850415092, 'img7.jpg': 1.766048074016691, 'img9.jpg': 18.5, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.443167171815187\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 18.5, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 5.046939515433516, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.41594588027021\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.920530286278293\n",
      "\n",
      "run 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.585783131854786\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.55715854761078\n",
      "\n",
      "run 10\n",
      "{'img1.jpg': 1.0624349635795443, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 9.894182888184591, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.56128864997231\n",
      "\n",
      "list all [24.55715854761078, 25.27976978111521, 24.818098541813622, 25.042953501889112, 25.443167171815187, 25.41594588027021, 24.920530286278293, 24.585783131854786, 24.55715854761078, 24.56128864997231]\n",
      "mean 24.918185404023028 /////////\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "# test block for sample number \n",
    "sample = [2, 5, 10, 20]\n",
    "sample_result = []\n",
    "sample_time = []\n",
    "for n in sample:\n",
    "    print('num sample : ', n)\n",
    "    msed_list = []\n",
    "    for i in range(10):\n",
    "        if i == 0:\n",
    "            start = time.time()\n",
    "        print(f'run {i+1}')\n",
    "        msed, msed_dic = main(gt_dic.keys(), 10, n, 5, filter_params, canny_params, hough_params)\n",
    "        msed_list.append(msed)\n",
    "        if i == 0:\n",
    "            run_time = time.time() -start\n",
    "            print('run time:', run_time)\n",
    "            sample_time.append(run_time)\n",
    "    print('list all', msed_list)\n",
    "    print('mean', np.mean(msed_list), \"/////////\\n\\n\\n\")\n",
    "    sample_result.append(np.mean(msed_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = sample_result\n",
    "x = sample\n",
    "t = sample_time\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(x, y, color=\"red\")\n",
    "ax.set_xlabel(\"the number of random samples\")\n",
    "ax.set_ylabel(\"MSED\", c = 'r')\n",
    "ax.set_ylim([20,75])\n",
    "ax.set_xticks(x)\n",
    "\n",
    "ax2=ax.twinx()\n",
    "ax2.plot(x, t,color=\"green\")\n",
    "ax2.set_ylabel(\"run time per iteration (sec)\", c = 'green')\n",
    "ax2.set_ylim([0,18])\n",
    "\n",
    "plt.savefig('n_sample.png', dpi = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                     | 0/7 [00:00<?, ?it/s]/Users/heekim/opt/anaconda3/envs/CV/lib/python3.7/site-packages/ipykernel_launcher.py:34: RuntimeWarning: divide by zero encountered in int_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num iter :  1\n",
      "run 1\n",
      "{'img1.jpg': 10.310249307479264, 'img3.jpg': 0.4148907199456366, 'img6.jpg': 3.9911966701353405, 'img7.jpg': 3.374807271684233, 'img9.jpg': 18.07999999999999, 'img10.jpg': 38.90211014831479, 'img13.jpg': 48.5, 'img14.jpg': 143.02019409861853, 'img15.jpg': 27.91182890653652, 'img18.jpg': 7.512396694214822, 'img20.jpg': 27.1490514905146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 28.9577713266407\n",
      "\n",
      "run time: 0.8946030139923096\n",
      "run 2\n",
      "{'img1.jpg': 195.40625, 'img3.jpg': 4.898458311075348, 'img6.jpg': 4.028099173553796, 'img7.jpg': 13.10911573198915, 'img9.jpg': 40.913892477059214, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.894608864372604, 'img18.jpg': 20.246850482656605, 'img20.jpg': 43.84034651066873, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 47.00948903304825\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 8.079999999999991, 'img3.jpg': 2.2577393992752093, 'img6.jpg': 4.8914812273307495, 'img7.jpg': 4.664359861591677, 'img9.jpg': 7.160493827160598, 'img10.jpg': 38.90211014831479, 'img13.jpg': 5.173340536340211, 'img14.jpg': 143.02019409861853, 'img15.jpg': 15.125, 'img18.jpg': 15.728732897084953, 'img20.jpg': 59.646320972290134, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.914691965020964\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 0.7324380165288813, 'img3.jpg': 2.0061991297198367, 'img6.jpg': 2.6960077863534475, 'img7.jpg': 2.432840041474233, 'img9.jpg': 9.997581295350704, 'img10.jpg': 51.41012396694238, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 33.94919772647241, 'img18.jpg': 1.790123456790143, 'img20.jpg': 63.25658641486509, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 28.678769544282137\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 8.079999999999991, 'img3.jpg': 4.679895377496132, 'img6.jpg': 1.3951263850415092, 'img7.jpg': 1.0686616290098894, 'img9.jpg': 27.6799999999999, 'img10.jpg': 38.90211014831479, 'img13.jpg': 8.295606819058431, 'img14.jpg': 143.02019409861853, 'img15.jpg': 27.91182890653652, 'img18.jpg': 6.357153715399191, 'img20.jpg': 39.77767960771218, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 27.12456560828598\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 1.0058063579619434, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 9.155124653739758, 'img7.jpg': 4.825712992304196, 'img9.jpg': 0.21530407663175324, 'img10.jpg': 38.90211014831479, 'img13.jpg': 48.5, 'img14.jpg': 143.02019409861853, 'img15.jpg': 33.61491052344004, 'img18.jpg': 1.6724813100747808, 'img20.jpg': 18.52999999999975, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.497921738259578\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 12.764462809917369, 'img3.jpg': 3.133316780596036, 'img6.jpg': 1.5353277233477545, 'img7.jpg': 4.190123456790093, 'img9.jpg': 10.00000000000012, 'img10.jpg': 38.90211014831479, 'img13.jpg': 71.6191999999997, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.992305324715325, 'img18.jpg': 41.51497443389342, 'img20.jpg': 34.24173553718987, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 33.85335674380231\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 96.98741776937614, 'img3.jpg': 2.6219004165839883, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 1.2369730104689975, 'img9.jpg': 22.878135707029667, 'img10.jpg': 38.90211014831479, 'img13.jpg': 39.050772653919545, 'img14.jpg': 143.02019409861853, 'img15.jpg': 27.91182890653652, 'img18.jpg': 12.945575062353818, 'img20.jpg': 53.40761062357367, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 38.120479074662335\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 7.53061224489797, 'img3.jpg': 0.3511929537440921, 'img6.jpg': 2.012686976270661, 'img7.jpg': 5.0996856518728535, 'img9.jpg': 1.8697045211819767, 'img10.jpg': 50.671014640036645, 'img13.jpg': 71.6191999999997, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.971048202862292, 'img18.jpg': 2.4232344805854327, 'img20.jpg': 48.327264135395914, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 31.685197376475898\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 14%|██████▍                                      | 1/7 [00:08<00:52,  8.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 1.0058063579619434, 'img3.jpg': 2.914686889152911, 'img6.jpg': 2.5887418609535358, 'img7.jpg': 8.594263991870239, 'img9.jpg': 16.75516744111498, 'img10.jpg': 50.671014640036645, 'img13.jpg': 10.871090492576378, 'img14.jpg': 143.02019409861853, 'img15.jpg': 41.01462809917333, 'img18.jpg': 1.0617063572558347, 'img20.jpg': 41.794689026141974, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 28.218209988925082\n",
      "\n",
      "list all [28.9577713266407, 47.00948903304825, 26.914691965020964, 28.678769544282137, 27.12456560828598, 26.497921738259578, 33.85335674380231, 38.120479074662335, 31.685197376475898, 28.218209988925082]\n",
      "mean 31.706045239940323 /////////\n",
      "\n",
      "\n",
      "\n",
      "num iter :  3\n",
      "run 1\n",
      "{'img1.jpg': 1.0624349635795443, 'img3.jpg': 3.0166621875839637, 'img6.jpg': 1.2913631633714786, 'img7.jpg': 2.36134216568187, 'img9.jpg': 15.085843681519606, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 27.78056788668048, 'img18.jpg': 17.117604090577025, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.821849373252636\n",
      "\n",
      "run time: 1.7101848125457764\n",
      "run 2\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 1.2228031708550904, 'img6.jpg': 0.7252947533104409, 'img7.jpg': 1.9792041094272075, 'img9.jpg': 15.09485569375692, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 66.66264272420952, 'img18.jpg': 20.459590461982437, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 29.78272708803034\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 8.691969323433682, 'img3.jpg': 1.6904551143127884, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 7.32888888888892, 'img9.jpg': 14.639209859319639, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 27.91182890653652, 'img18.jpg': 4.33034516030002, 'img20.jpg': 13.814827706230286, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 23.66650723328497\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 9.781293364873314, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 1.2369730104689975, 'img9.jpg': 14.96115755082603, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 17.234567901234513, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 27.147383368826656\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 91.10183958132151, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 1.149113854595386, 'img7.jpg': 12.792387543252584, 'img9.jpg': 13.557851239669462, 'img10.jpg': 38.90211014831479, 'img13.jpg': 48.5, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 22.80421849648449, 'img20.jpg': 30.293081276654696, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 36.98077789312188\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.45437241817881757, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 7.229962141698168, 'img9.jpg': 27.35177744781191, 'img10.jpg': 50.671014640036645, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 66.66264272420952, 'img18.jpg': 20.73629453801665, 'img20.jpg': 34.962428867265764, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 32.19174741700517\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 3.596307594442547, 'img3.jpg': 0.9305537922225946, 'img6.jpg': 2.4559179848765655, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 27.91182890653652, 'img18.jpg': 1.25, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.15975528749051\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 1.3913103969225422, 'img3.jpg': 0.6925379477277819, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 2.36134216568187, 'img9.jpg': 17.0, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 34.11376953125, 'img18.jpg': 7.880000000000059, 'img20.jpg': 34.962428867265764, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.29901878584181\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 0.2882639264467225, 'img3.jpg': 2.849629284539537, 'img6.jpg': 1.8601651142369926, 'img7.jpg': 1.158710934085846, 'img9.jpg': 0.7379692617787587, 'img10.jpg': 50.671014640036645, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 31.328077511098286, 'img18.jpg': 1.25, 'img20.jpg': 48.327264135395914, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.383260107802496\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 29%|████████████▊                                | 2/7 [00:25<01:08, 13.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 0.5555555555555871, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 1.1026267846519557, 'img7.jpg': 1.158710934085846, 'img9.jpg': 16.19045873194271, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.894608864372604, 'img18.jpg': 7.880000000000059, 'img20.jpg': 18.031830886751973, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 23.899743724029065\n",
      "\n",
      "list all [26.821849373252636, 29.78272708803034, 23.66650723328497, 27.147383368826656, 36.98077789312188, 32.19174741700517, 25.15975528749051, 25.29901878584181, 25.383260107802496, 23.899743724029065]\n",
      "mean 27.63327702786855 /////////\n",
      "\n",
      "\n",
      "\n",
      "num iter :  5\n",
      "run 1\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.500422114448938, 'img6.jpg': 3.6115889542778814, 'img7.jpg': 11.263533080864407, 'img9.jpg': 9.894182888184591, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 66.66264272420952, 'img18.jpg': 4.672353275868549, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 29.474746865180588\n",
      "\n",
      "run time: 2.599553108215332\n",
      "run 2\n",
      "{'img1.jpg': 12.679500520291205, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.1686297041080185, 'img9.jpg': 2.5, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 3.919762302766313, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.063633105514423\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 8.079999999999991, 'img3.jpg': 0.8243527091907314, 'img6.jpg': 1.8601651142369926, 'img7.jpg': 1.2651570489408033, 'img9.jpg': 11.227251705399619, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 4.810149943505519, 'img20.jpg': 48.327264135395914, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.805324127991142\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 11.678500017959626, 'img3.jpg': 2.9169833232822437, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.7875832100591649, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.971048202862292, 'img18.jpg': 3.7457480026729426, 'img20.jpg': 27.60005944004565, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.22730259556705\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 2.914201183431979, 'img6.jpg': 0.9304069229752496, 'img7.jpg': 1.193877551020377, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 26.504774583625746, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 2.5289212463904427, 'img20.jpg': 30.293081276654696, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.182275289365332\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 17.34552157773796, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.104694999167317, 'img9.jpg': 10.613965483039163, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 39.77368703226374, 'img18.jpg': 4.672353275868549, 'img20.jpg': 59.646320972290134, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 29.05834178978556\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 8.079999999999991, 'img3.jpg': 1.2785161674270376, 'img6.jpg': 1.2913631633714786, 'img7.jpg': 1.7270242247148118, 'img9.jpg': 15.920369620745292, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.482021042895163, 'img20.jpg': 48.327264135395914, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.87336552646279\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 7.53061224489797, 'img3.jpg': 1.6904551143127884, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.193877551020377, 'img9.jpg': 20.612244897959293, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 4.482021042895163, 'img20.jpg': 63.25658641486509, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 28.494917312137005\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 7.53061224489797, 'img3.jpg': 0.7351715929379669, 'img6.jpg': 0.8168450220570859, 'img7.jpg': 4.32433208848022, 'img9.jpg': 11.938599542018835, 'img10.jpg': 38.90211014831479, 'img13.jpg': 31.249949777193706, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.192867036011222, 'img18.jpg': 2.7422013033339807, 'img20.jpg': 63.25658641486509, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 29.086333323414507\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 43%|███████████████████▎                         | 3/7 [00:51<01:16, 19.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.6942145767315794, 'img6.jpg': 0.8364047646379325, 'img7.jpg': 1.7482182209083053, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 5.046939515433516, 'img20.jpg': 43.84034651066873, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.175147719778042\n",
      "\n",
      "list all [29.474746865180588, 26.063633105514423, 25.805324127991142, 24.22730259556705, 25.182275289365332, 29.05834178978556, 26.87336552646279, 28.494917312137005, 29.086333323414507, 25.175147719778042]\n",
      "mean 26.944138765519646 /////////\n",
      "\n",
      "\n",
      "\n",
      "num iter :  7\n",
      "run 1\n",
      "{'img1.jpg': 1.0462591198112325, 'img3.jpg': 1.608740894901134, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 1.104694999167317, 'img9.jpg': 18.5, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 5.046939515433516, 'img20.jpg': 18.031830886751973, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 23.093573587926603\n",
      "\n",
      "run time: 3.3626439571380615\n",
      "run 2\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 1.647387511028283, 'img7.jpg': 1.495299533525905, 'img9.jpg': 15.565288449703221, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 8.201730665224472, 'img20.jpg': 63.25658641486509, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.726602101278626\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.8243527091907314, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.7152199762187654, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.412973571172055, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.25425622386784\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.7160493827160269, 'img6.jpg': 0.7846172022684514, 'img7.jpg': 3.611111111111155, 'img9.jpg': 9.874218875180468, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 10.363421874674852, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.534393017482856\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 1.3951263850415092, 'img7.jpg': 1.7152199762187654, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.672353275868549, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.544986805787165\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 6.432098765432142, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.855167710335465, 'img7.jpg': 1.6879012345678643, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 3.7457480026729426, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.923803937348392\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 91.10183958132151, 'img3.jpg': 0.41812622246796566, 'img6.jpg': 1.1693455894754763, 'img7.jpg': 1.4546138898127903, 'img9.jpg': 8.840020926004762, 'img10.jpg': 50.671014640036645, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 4.3409887892203916, 'img20.jpg': 18.031830886751973, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 30.8662682713334\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 1.0462591198112325, 'img3.jpg': 0.5439696422786898, 'img6.jpg': 1.2913631633714786, 'img7.jpg': 1.766048074016691, 'img9.jpg': 6.920000000000099, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 8.201730665224472, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.472935979653347\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 1.0462591198112325, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 1.0, 'img7.jpg': 1.713280000000084, 'img9.jpg': 16.111111111111214, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.672353275868549, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.53693085519181\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 57%|█████████████████████████▋                   | 4/7 [01:23<01:12, 24.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 91.10183958132151, 'img3.jpg': 0.8243527091907314, 'img6.jpg': 0.9564931299651243, 'img7.jpg': 1.766048074016691, 'img9.jpg': 16.8405126379497, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.192867036011222, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 32.727725144528115\n",
      "\n",
      "list all [23.093573587926603, 26.726602101278626, 25.25425622386784, 24.534393017482856, 24.544986805787165, 24.923803937348392, 30.8662682713334, 24.472935979653347, 25.53693085519181, 32.727725144528115]\n",
      "mean 26.268147592439817 /////////\n",
      "\n",
      "\n",
      "\n",
      "num iter :  10\n",
      "run 1\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.7351715929379669, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.713280000000084, 'img9.jpg': 15.920369620745292, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.894608864372604, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.08330506423086\n",
      "\n",
      "run time: 4.302260875701904\n",
      "run 2\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.5172683843684001, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 4.055805898491059, 'img20.jpg': 59.646320972290134, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.227518002385057\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 12.764462809917369, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.8364047646379325, 'img7.jpg': 1.8156025613632563, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 5.046939515433516, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.273374792848653\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 3.596307594442547, 'img3.jpg': 0.8243527091907314, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.4546138898127903, 'img9.jpg': 18.5, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.971048202862292, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.78642841262234\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 1.1111111111111174, 'img3.jpg': 0.45437241817881757, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.766048074016691, 'img9.jpg': 15.565288449703221, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 5.455131796090706, 'img20.jpg': 34.962428867265764, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 23.973973868819943\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.6942145767315794, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 1.7557849768601448, 'img9.jpg': 15.422161056538801, 'img10.jpg': 38.90211014831479, 'img13.jpg': 14.527411986025355, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 2.5289212463904427, 'img20.jpg': 34.962428867265764, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.907548197175487\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 7.53061224489797, 'img3.jpg': 0.5439696422786898, 'img6.jpg': 0.8364047646379325, 'img7.jpg': 1.713280000000084, 'img9.jpg': 18.07999999999999, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 4.672353275868549, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.892384329439665\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.7351715929379669, 'img6.jpg': 0.500653215270722, 'img7.jpg': 1.7875832100591649, 'img9.jpg': 16.19045873194271, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.672353275868549, 'img20.jpg': 30.293081276654696, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 23.983629903937317\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.7351715929379669, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 11.227251705399619, 'img10.jpg': 38.90211014831479, 'img13.jpg': 31.249949777193706, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.672353275868549, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 27.051360276955688\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 71%|████████████████████████████████▏            | 5/7 [02:05<01:01, 30.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 1.0462591198112325, 'img3.jpg': 0.500422114448938, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.766048074016691, 'img9.jpg': 19.125, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.939639177791438\n",
      "\n",
      "list all [25.08330506423086, 26.227518002385057, 25.273374792848653, 25.78642841262234, 23.973973868819943, 24.907548197175487, 25.892384329439665, 23.983629903937317, 27.051360276955688, 24.939639177791438]\n",
      "mean 25.311916202620644 /////////\n",
      "\n",
      "\n",
      "\n",
      "num iter :  15\n",
      "run 1\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.766048074016691, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 5.046939515433516, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.483181409699384\n",
      "\n",
      "run time: 6.914228916168213\n",
      "run 2\n",
      "{'img1.jpg': 1.0624349635795443, 'img3.jpg': 1.2785161674270376, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 1.7482182209083053, 'img9.jpg': 16.111111111111214, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 49.11623770571165, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.49259047927478\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8156025613632563, 'img9.jpg': 15.920369620745292, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.129196264535377\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 1.2913631633714786, 'img7.jpg': 1.7270242247148118, 'img9.jpg': 18.906076734694057, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.482128672790072\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.104694999167317, 'img9.jpg': 15.422161056538801, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.08459818818137\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 1.647387511028283, 'img7.jpg': 1.7270242247148118, 'img9.jpg': 16.19045873194271, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.318814575642758\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 1.2785161674270376, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 9.894182888184591, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.76788070263595\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 91.10183958132151, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 1.1026267846519557, 'img7.jpg': 1.713280000000084, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 5.046939515433516, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 32.29601912304524\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 7.53061224489797, 'img3.jpg': 1.2228031708550904, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 15.418290021821143, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 23.185617685555115, 'img18.jpg': 4.3409887892203916, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.36773678345013\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 86%|██████████████████████████████████████▌      | 6/7 [03:08<00:41, 41.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 1.0624349635795443, 'img3.jpg': 0.45437241817881757, 'img6.jpg': 0.15669988692767312, 'img7.jpg': 4.32433208848022, 'img9.jpg': 16.147580461240466, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 34.962428867265764, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.422159299841578\n",
      "\n",
      "list all [24.483181409699384, 25.49259047927478, 25.129196264535377, 25.482128672790072, 25.08459818818137, 25.318814575642758, 24.76788070263595, 32.29601912304524, 25.36773678345013, 24.422159299841578]\n",
      "mean 25.784430549909665 /////////\n",
      "\n",
      "\n",
      "\n",
      "num iter :  20\n",
      "run 1\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.158710934085846, 'img9.jpg': 18.5, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.21739314237979\n",
      "\n",
      "run time: 8.178591966629028\n",
      "run 2\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.855167710335465, 'img7.jpg': 1.7152199762187654, 'img9.jpg': 20.71525678541689, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.830663577314225, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.630588545483928\n",
      "\n",
      "run 3\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.713280000000084, 'img9.jpg': 15.565288449703221, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.672353275868549, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.135810293059908\n",
      "\n",
      "run 4\n",
      "{'img1.jpg': 11.678500017959626, 'img3.jpg': 1.608740894901134, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 16.19045873194271, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.672353275868549, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 26.12179449961184\n",
      "\n",
      "run 5\n",
      "{'img1.jpg': 1.0462591198112325, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.6879012345678643, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.33034516030002, 'img20.jpg': 30.293081276654696, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 23.66822273866661\n",
      "\n",
      "run 6\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 0.012122126653585485, 'img6.jpg': 0.15692185740968076, 'img7.jpg': 1.7875832100591649, 'img9.jpg': 8.840020926004762, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 28.239412244898116, 'img18.jpg': 4.33034516030002, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.551228305678986\n",
      "\n",
      "run 7\n",
      "{'img1.jpg': 0.5280907372400541, 'img3.jpg': 1.4705882352941577, 'img6.jpg': 0.15755138959190332, 'img7.jpg': 1.7875832100591649, 'img9.jpg': 14.669560377344713, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.672353275868549, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 25.050295979065115\n",
      "\n",
      "run 8\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 1.6029954848883974, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.712163926230797\n",
      "\n",
      "run 9\n",
      "{'img1.jpg': 1.9965397923876098, 'img3.jpg': 0.13639018075130943, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 15.565288449703221, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.830663577314225, 'img20.jpg': 40.319452706722146, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.73943436324619\n",
      "\n",
      "run 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 7/7 [04:31<00:00, 38.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img1.jpg': 2.200788140364089, 'img3.jpg': 0.20688608578468026, 'img6.jpg': 0.5604252060717672, 'img7.jpg': 1.8367346938775442, 'img9.jpg': 8.774836590942943, 'img10.jpg': 38.90211014831479, 'img13.jpg': 4.781301775147994, 'img14.jpg': 143.02019409861853, 'img15.jpg': 26.59758111780041, 'img18.jpg': 4.810149943505519, 'img20.jpg': 45.6901067712564, 'img21.jpg': 18.326530612244724}\n",
      "Accuracy(msed): 24.64230376532745\n",
      "\n",
      "list all [25.21739314237979, 25.630588545483928, 25.135810293059908, 26.12179449961184, 23.66822273866661, 24.551228305678986, 25.050295979065115, 24.712163926230797, 24.73943436324619, 24.64230376532745]\n",
      "mean 24.946923555875063 /////////\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# test block for iteration number \n",
    "import time\n",
    "iters = [1, 3, 5, 7, 10, 15, 20]\n",
    "iters_result = []\n",
    "iters_time = []\n",
    "for it in tqdm(iters):\n",
    "    print('num iter : ', it)\n",
    "    msed_list = []\n",
    "    for i in range(10):\n",
    "        if i == 0:\n",
    "            start = time.time()\n",
    "        print(f'run {i+1}')\n",
    "        msed, msed_dic = main(gt_dic.keys(), it , 10, 5, filter_params, canny_params, hough_params)\n",
    "        msed_list.append(msed)\n",
    "        if i == 0:\n",
    "            run_time = time.time() -start\n",
    "            print('run time:', run_time)\n",
    "            iters_time.append(run_time)\n",
    "            \n",
    "    print('list all', msed_list)\n",
    "    print('mean', np.mean(msed_list), \"/////////\\n\\n\\n\")\n",
    "    iters_result.append(np.mean(msed_list))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = iters_result\n",
    "x = iters\n",
    "t = iters_time\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(x, y, color=\"red\")\n",
    "ax.set_xlabel(\"the number of iterations\")\n",
    "ax.set_ylabel(\"MSED\", c = 'r')\n",
    "ax.set_ylim([22,33])\n",
    "ax.set_xticks(x)\n",
    "\n",
    "ax2=ax.twinx()\n",
    "ax2.plot(x, t,color=\"green\")\n",
    "ax2.set_ylabel(\"run time per iteration (sec)\", c = 'green')\n",
    "\n",
    "plt.savefig('n_iter.png', dpi = 200)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}