CoALTP2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import math\n",
    "from skimage import io,color,feature,exposure\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LTP2(image,scale1,scale2,row,col,thres):\n",
    "    \n",
    "    ltpmat1 = np.zeros(shape=(r-2,c-2),dtype=np.uint8)\n",
    "    ltpmat2 = np.zeros(shape=(r-2,c-2),dtype=np.uint8)\n",
    "    ltpmat3 = np.zeros(shape=(r-2,c-2),dtype=np.uint8)\n",
    "    ltpmat4 = np.zeros(shape=(r-2,c-2),dtype=np.uint8)\n",
    "\n",
    "    for i in range(1,row-1):\n",
    "        for j in range(1,col-1):\n",
    "            window = image[i-1:i+2,j-1:j+2]\n",
    "            a=window[1,1]-thres\n",
    "            b=window[1,1]+thres\n",
    "            binary1=window>=b\n",
    "            binary2=window<=a\n",
    "            ltpmat1[i-1,j-1] = (sum(sum(binary1*scale1)))\n",
    "            ltpmat2[i-1,j-1] = (sum(sum(binary2*scale1)))\n",
    "            ltpmat3[i-1,j-1] = (sum(sum(binary1*scale2)))\n",
    "            ltpmat4[i-1,j-1] = (sum(sum(binary2*scale2)))\n",
    "    \n",
    "    return ltpmat1,ltpmat2,ltpmat3,ltpmat4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GLCM(image,d,theta,levels):\n",
    "    return feature.greycomatrix(image,d,theta,levels=levels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CoLTP2_feature_vector(image):\n",
    "    scale1 = np.array([[2,0,1],[0,0,0],[4,0,8]],dtype=np.uint8)\n",
    "    scale2 = np.array([[0,1,0],[2,0,8],[0,4,0]],dtype=np.uint8)\n",
    "    (r,c) = image.shape\n",
    "    threshold=5\n",
    "    ltpmat1,ltpmat2,ltpmat3,ltpmat4 = LTP2(image,scale1,scale2,r,c,threshold)\n",
    "    \n",
    "    d=[4]\n",
    "    theta=[0,np.pi/4,np.pi/2,3*np.pi/4]\n",
    "    levels=16\n",
    "    ltpg1 = GLCM(ltpmat1,d,theta,levels).flatten()\n",
    "    ltpg2 = GLCM(ltpmat2,d,theta,levels).flatten()\n",
    "    ltpg3 = GLCM(ltpmat3,d,theta,levels).flatten()\n",
    "    ltpg4 = GLCM(ltpmat4,d,theta,levels).flatten()\n",
    "    ltpg2+=32\n",
    "    ltpg3+=48\n",
    "    ltpg4+=64\n",
    "    \n",
    "    return np.concatenate((ltpg1,ltpg2,ltpg3,ltpg4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2304x2304 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = io.imread('orl_faces\\\\s01\\\\1.pgm')\n",
    "scale1 = np.array([[2,0,1],[0,0,0],[4,0,8]],dtype=np.uint8)\n",
    "scale2 = np.array([[0,1,0],[2,0,8],[0,4,0]],dtype=np.uint8)\n",
    "(r,c) = image.shape\n",
    "threshold=5\n",
    "ltp_image1,ltp_image2,ltp_image3,ltp_image4 = LTP2(image,scale1,scale2,r,c,threshold)\n",
    "d=[4]\n",
    "theta=[0,np.pi/4,np.pi/2,3*np.pi/4]\n",
    "levels=16\n",
    "ltpg1 = GLCM(ltp_image1,d,theta,levels)\n",
    "ltpg2 = GLCM(ltp_image2,d,theta,levels)\n",
    "ltpg3 = GLCM(ltp_image3,d,theta,levels)\n",
    "ltpg4 = GLCM(ltp_image4,d,theta,levels)\n",
    "ltpg2+=32\n",
    "ltpg3+=48\n",
    "ltpg4+=64\n",
    "\n",
    "fig = plt.figure(figsize=(32,32))\n",
    "gs=GridSpec(5,4)\n",
    "\n",
    "ax1=fig.add_subplot(gs[0,0])\n",
    "ax2=fig.add_subplot(gs[0,1])\n",
    "ax3=fig.add_subplot(gs[1,0])\n",
    "ax4=fig.add_subplot(gs[1,1])\n",
    "ax5=fig.add_subplot(gs[2,0])\n",
    "ax6=fig.add_subplot(gs[2,1])\n",
    "ax7=fig.add_subplot(gs[3,0])\n",
    "ax8=fig.add_subplot(gs[3,1])\n",
    "ax9=fig.add_subplot(gs[4,0])\n",
    "ax10=fig.add_subplot(gs[4,1])\n",
    "ax11=fig.add_subplot(gs[0,2])\n",
    "ax12=fig.add_subplot(gs[0,3])\n",
    "ax13=fig.add_subplot(gs[1,2])\n",
    "ax14=fig.add_subplot(gs[1,3])\n",
    "ax15=fig.add_subplot(gs[2,2])\n",
    "ax16=fig.add_subplot(gs[2,3])\n",
    "ax17=fig.add_subplot(gs[3,2])\n",
    "ax18=fig.add_subplot(gs[3,3])\n",
    "ax19=fig.add_subplot(gs[4,2])\n",
    "ax20=fig.add_subplot(gs[4,3])\n",
    "\n",
    "\n",
    "ax1.set_title('LTP Image (Upper matrix) scale:a')\n",
    "ax1.imshow(ltp_image1,cmap='gray')\n",
    "ax1.set_yticklabels([])\n",
    "ax1.set_xticklabels([])\n",
    "\n",
    "ax2.set_title('LTP Image (Lower matrix) scale:a')\n",
    "ax2.imshow(ltp_image2,cmap='gray')\n",
    "ax2.set_yticklabels([])\n",
    "ax2.set_xticklabels([])\n",
    "\n",
    "ax11.set_title('LTP Image (Upper matrix) scale:b')\n",
    "ax11.imshow(ltp_image3,cmap='gray')\n",
    "ax11.set_yticklabels([])\n",
    "ax11.set_xticklabels([])\n",
    "\n",
    "ax12.set_title('LTP Image (Lower matrix) scale:b')\n",
    "ax12.imshow(ltp_image4,cmap='gray')\n",
    "ax12.set_yticklabels([])\n",
    "ax12.set_xticklabels([])\n",
    "\n",
    "ax3.matshow(ltpg1[:,:,0,0],cmap=plt.cm.gray)\n",
    "ax3.set_title('LTP GLCM (Upper matrix) d=4 theta=0 deg')\n",
    "ax3.set_xticks(np.arange(0,16))\n",
    "ax3.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax4.matshow(ltpg2[:,:,0,0],cmap=plt.cm.gray)\n",
    "ax4.set_title('LTP GLCM (Lower matrix) d=4 theta=0 deg')\n",
    "ax4.set_xticks(np.arange(0,16))\n",
    "ax4.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax13.matshow(ltpg3[:,:,0,0],cmap=plt.cm.gray)\n",
    "ax13.set_title('LTP GLCM (Upper matrix) d=4 theta=0 deg')\n",
    "ax13.set_xticks(np.arange(0,16))\n",
    "ax13.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax14.matshow(ltpg4[:,:,0,0],cmap=plt.cm.gray)\n",
    "ax14.set_title('LTP GLCM (Lower matrix) d=4 theta=0 deg')\n",
    "ax14.set_xticks(np.arange(0,16))\n",
    "ax14.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax5.matshow(ltpg1[:,:,0,1],cmap=plt.cm.gray)\n",
    "ax5.set_title('LTP GLCM (Upper matrix) d=4 theta=45 deg')\n",
    "ax5.set_xticks(np.arange(0,16))\n",
    "ax5.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax6.matshow(ltpg2[:,:,0,1],cmap=plt.cm.gray)\n",
    "ax6.set_title('LTP GLCM (Lower matrix) d=4 theta=45 deg')\n",
    "ax6.set_xticks(np.arange(0,16))\n",
    "ax6.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax15.matshow(ltpg3[:,:,0,1],cmap=plt.cm.gray)\n",
    "ax15.set_title('LTP GLCM (Upper matrix) d=4 theta=45 deg')\n",
    "ax15.set_xticks(np.arange(0,16))\n",
    "ax15.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax16.matshow(ltpg4[:,:,0,1],cmap=plt.cm.gray)\n",
    "ax16.set_title('LTP GLCM (Lower matrix) d=4 theta=45 deg')\n",
    "ax16.set_xticks(np.arange(0,16))\n",
    "ax16.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax7.matshow(ltpg1[:,:,0,2],cmap=plt.cm.gray)\n",
    "ax7.set_title('LTP GLCM (Upper matrix) d=4 theta=90 deg')\n",
    "ax7.set_xticks(np.arange(0,16))\n",
    "ax7.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax8.matshow(ltpg2[:,:,0,2],cmap=plt.cm.gray)\n",
    "ax8.set_title('LTP GLCM (Lower matrix) d=4 theta=90 deg')\n",
    "ax8.set_xticks(np.arange(0,16))\n",
    "ax8.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax17.matshow(ltpg3[:,:,0,2],cmap=plt.cm.gray)\n",
    "ax17.set_title('LTP GLCM (Upper matrix) d=4 theta=90 deg')\n",
    "ax17.set_xticks(np.arange(0,16))\n",
    "ax17.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax18.matshow(ltpg4[:,:,0,2],cmap=plt.cm.gray)\n",
    "ax18.set_title('LTP GLCM (Lower matrix) d=4 theta=90 deg')\n",
    "ax18.set_xticks(np.arange(0,16))\n",
    "ax18.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax9.matshow(ltpg1[:,:,0,3],cmap=plt.cm.gray)\n",
    "ax9.set_title('LTP GLCM (Upper matrix) d=4 theta=135 deg')\n",
    "ax9.set_xticks(np.arange(0,16))\n",
    "ax9.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax10.matshow(ltpg2[:,:,0,3],cmap=plt.cm.gray)\n",
    "ax10.set_title('LTP GLCM (Lower matrix) d=4 theta=135 deg')\n",
    "ax10.set_xticks(np.arange(0,16))\n",
    "ax10.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax19.matshow(ltpg3[:,:,0,3],cmap=plt.cm.gray)\n",
    "ax19.set_title('LTP GLCM (Upper matrix) d=4 theta=135 deg')\n",
    "ax19.set_xticks(np.arange(0,16))\n",
    "ax19.set_yticks(np.arange(0,16))\n",
    "\n",
    "ax20.matshow(ltpg4[:,:,0,3],cmap=plt.cm.gray)\n",
    "ax20.set_title('LTP GLCM (Lower matrix) d=4 theta=135 deg')\n",
    "ax20.set_xticks(np.arange(0,16))\n",
    "ax20.set_yticks(np.arange(0,16))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing image :  353 :  orl_faces\\s36\\2.pgm\n",
      "processing image :  354 :  orl_faces\\s36\\3.pgm\n",
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      "processing image :  400 :  orl_faces\\s40\\9.pgm\n"
     ]
    }
   ],
   "source": [
    "coltp2_feature_matrix = []\n",
    "k=1\n",
    "for img_file in glob.glob(\"orl_faces\\\\**\\\\*.pgm\"):\n",
    "    print(\"processing image : \",k,\": \",img_file)\n",
    "    image = io.imread(img_file)\n",
    "    coltp2_feature_matrix.append(CoLTP2_feature_vector(image))\n",
    "    k+=1\n",
    "    \n",
    "np.savetxt(\"CSV\\\\orl_faces_CoLTP2_FeatureMatrix.csv\",coltp2_feature_matrix,fmt='%f',delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cityBlockDistance(feature_matrix):\n",
    "    (row,col)=np.shape(feature_matrix)\n",
    "    city_block_distance_matrix = np.zeros(shape=(row,row),dtype=np.double)\n",
    "    for i in range(row):\n",
    "        for j in range(i+1,row):\n",
    "            city_block_distance_matrix[i,j] = city_block_distance_matrix[j,i] = sum(np.abs(feature_matrix[i]-feature_matrix[j]))\n",
    "    return city_block_distance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclideanDistance(feature_matrix):\n",
    "    (row,col)=np.shape(feature_matrix)\n",
    "    euclidean_distance_matrix = np.zeros(shape=(row,row),dtype=np.double)\n",
    "    for i in range(row):\n",
    "        for j in range(i+1,row):\n",
    "            euclidean_distance_matrix[i,j] = euclidean_distance_matrix[j,i] = math.sqrt(sum(np.square(feature_matrix[i]-feature_matrix[j])))\n",
    "    return euclidean_distance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d1Distance(feature_matrix):\n",
    "    (row,col)=np.shape(feature_matrix)\n",
    "    d1_distance_matrix = np.zeros(shape=(row,row),dtype=np.double)\n",
    "    for i in range(row):\n",
    "        for j in range(i+1,row):\n",
    "            d1_distance_matrix[i,j] = d1_distance_matrix[j,i] = sum(abs((feature_matrix[i]-feature_matrix[j])/(1+feature_matrix[i]+feature_matrix[j])))\n",
    "    return d1_distance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_block_distance_matrix = cityBlockDistance(np.array(coltp2_feature_matrix,dtype=np.double))\n",
    "euclidean_distance_matrix = euclideanDistance(np.array(coltp2_feature_matrix,dtype=np.double))\n",
    "d1_distance_matrix = d1Distance(np.array(coltp2_feature_matrix,dtype=np.double))\n",
    "\n",
    "np.savetxt(\"CSV\\\\coltp2_distance\\\\orl_faces_coltp2_CityBlockDistanceMatrix.csv\",city_block_distance_matrix,fmt='%f',delimiter=\",\")\n",
    "np.savetxt(\"CSV\\\\coltp2_distance\\\\orl_faces_coltp2_EuclideanDistanceMatrix.csv\",euclidean_distance_matrix,fmt='%f',delimiter=\",\")\n",
    "np.savetxt(\"CSV\\\\coltp2_distance\\\\orl_faces_coltp2_D1DistanceMatrix.csv\",d1_distance_matrix,fmt='%f',delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def averagePrecision(distance_matrix,classes,retrive_images,relevant_images_DB):\n",
    "    \n",
    "    class_precision = [0]*classes\n",
    "    \n",
    "    i=0\n",
    "    for distmat in distance_matrix:\n",
    "        relevant_images=sum(np.floor(np.argsort(distmat)[:retrive_images]/relevant_images_DB)==math.floor(i/relevant_images_DB))\n",
    "        class_precision[math.floor(i/relevant_images_DB)]+=relevant_images/retrive_images\n",
    "        l=i\n",
    "        i=i+1;\n",
    "        if math.floor(i/relevant_images_DB)!=math.floor(l/relevant_images_DB):\n",
    "            class_precision[math.floor(l/relevant_images_DB)]/=relevant_images_DB\n",
    "            \n",
    "    class_precision*=100\n",
    "    average_precision = sum(class_precision)/classes\n",
    "    \n",
    "    return class_precision,average_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def averageRecall(distance_matrix,classes,retrive_images,relevant_images_DB):\n",
    "    \n",
    "    class_recall = [0]*classes\n",
    "    \n",
    "    i=0\n",
    "    for distmat in distance_matrix:\n",
    "        relevant_images=sum(np.floor(np.argsort(distmat)[:retrive_images]/relevant_images_DB)==math.floor(i/relevant_images_DB))\n",
    "        class_recall[math.floor(i/relevant_images_DB)]+=relevant_images/relevant_images_DB\n",
    "        l=i\n",
    "        i=i+1;\n",
    "        if math.floor(i/relevant_images_DB)!=math.floor(l/relevant_images_DB):\n",
    "            class_recall[math.floor(l/relevant_images_DB)]/=relevant_images_DB\n",
    "    class_recall*=100\n",
    "    average_recall = sum(class_recall)/classes\n",
    "    return class_recall,average_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City Block Average Precision: 25.79999999999962%\n",
      "City Block Average Recall: 77.39999999999951%\n",
      "Euclidean Average Precision: 20.891666666666147%\n",
      "Euclidean Average Recall: 62.674999999999685%\n",
      "D1 Average Precision: 27.008333333333127%\n",
      "D1 Average Recall: 81.02499999999937%\n"
     ]
    }
   ],
   "source": [
    "classes = 40\n",
    "retrive_images = 30\n",
    "relevant_images_DB = 10\n",
    "\n",
    "city_block_class_precision,city_block_average_precision = averagePrecision(city_block_distance_matrix,classes,retrive_images,relevant_images_DB)\n",
    "city_block_class_recall,city_block_average_recall = averageRecall(city_block_distance_matrix,classes,retrive_images,relevant_images_DB)\n",
    "\n",
    "euclidean_class_precision,euclidean_average_precision = averagePrecision(euclidean_distance_matrix,classes,retrive_images,relevant_images_DB)\n",
    "euclidean_class_recall,euclidean_average_recall = averageRecall(euclidean_distance_matrix,classes,retrive_images,relevant_images_DB)\n",
    "\n",
    "d1_class_precision,d1_average_precision = averagePrecision(d1_distance_matrix,classes,retrive_images,relevant_images_DB)\n",
    "d1_class_recall,d1_average_recall = averageRecall(d1_distance_matrix,classes,retrive_images,relevant_images_DB)\n",
    "print('City Block Average Precision: '+str(city_block_average_precision)+'%')\n",
    "print('City Block Average Recall: '+str(city_block_average_recall)+'%')\n",
    "print('Euclidean Average Precision: '+str(euclidean_average_precision)+'%')\n",
    "print('Euclidean Average Recall: '+str(euclidean_average_recall)+'%')\n",
    "print('D1 Average Precision: '+str(d1_average_precision)+'%')\n",
    "print('D1 Average Recall: '+str(d1_average_recall)+'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_groups = 40\n",
    " \n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.25\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, city_block_class_precision[0:40], bar_width, alpha=opacity, color='b', label='City Block Distance')\n",
    "rects2 = plt.bar(index + bar_width, euclidean_class_precision[0:40], bar_width, alpha=opacity, color='y', label='Euclidean Distance')\n",
    "rects3 = plt.bar(index + 2*bar_width, d1_class_precision[0:40], bar_width, alpha=opacity, color='r', label='D1 Distance')\n",
    " \n",
    "plt.xlabel('Classes')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Class wise precisions for different distances')\n",
    "plt.xticks(index + bar_width, range(1,41))\n",
    "plt.legend()\n",
    " \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_groups = 40\n",
    " \n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.25\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, city_block_class_recall[0:40], bar_width, alpha=opacity, color='b', label='City Block Distance')\n",
    "rects2 = plt.bar(index + bar_width, euclidean_class_recall[0:40], bar_width, alpha=opacity, color='y', label='Euclidean Distance')\n",
    "rects3 = plt.bar(index + 2*bar_width, d1_class_recall[0:40], bar_width, alpha=opacity, color='r', label='D1 Distance')\n",
    " \n",
    "plt.xlabel('Classes')\n",
    "plt.ylabel('Recall')\n",
    "plt.title('Class wise recall rate for different distances')\n",
    "plt.xticks(index + bar_width, range(1,41))\n",
    "plt.legend()\n",
    " \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distances = ('City Block','Euclidean','D1')\n",
    "y_pos = np.arange(len(distances))\n",
    "average_precisions = [city_block_average_precision,euclidean_average_precision,d1_average_precision]\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(y_pos, average_precisions,0.2 ,align='center')\n",
    "plt.xticks(y_pos, distances)\n",
    "plt.ylabel('Average Precision')\n",
    "plt.title('Comparision of average precisions using different distance formulae')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_recalls = [city_block_average_recall,euclidean_average_recall,d1_average_recall]\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(y_pos, average_recalls,0.2 ,align='center')\n",
    "plt.xticks(y_pos, distances)\n",
    "plt.ylabel('Average Recall Rate')\n",
    "plt.title('Comparision of average recall rate using different distance formulae')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_block_average_precisions = []\n",
    "city_block_average_recalls = []\n",
    "euclidean_average_precisions = []\n",
    "euclidean_average_recalls = []\n",
    "d1_average_precisions = []\n",
    "d1_average_recalls = []\n",
    "for retrive_images in range(10,101,10):\n",
    "    city_block_average_precisions.append(averagePrecision(city_block_distance_matrix,classes,retrive_images,relevant_images_DB)[1])\n",
    "    city_block_average_recalls.append(averageRecall(city_block_distance_matrix,classes,retrive_images,relevant_images_DB)[1])\n",
    "    euclidean_average_precisions.append(averagePrecision(euclidean_distance_matrix,classes,retrive_images,relevant_images_DB)[1])\n",
    "    euclidean_average_recalls.append(averageRecall(euclidean_distance_matrix,classes,retrive_images,relevant_images_DB)[1])\n",
    "    d1_average_precisions.append(averagePrecision(d1_distance_matrix,classes,retrive_images,relevant_images_DB)[1])\n",
    "    d1_average_recalls.append(averageRecall(d1_distance_matrix,classes,retrive_images,relevant_images_DB)[1])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "gs=GridSpec(1,2)\n",
    "index = range(10,101,10)\n",
    "ax1=fig.add_subplot(gs[0,0])\n",
    "ax2=fig.add_subplot(gs[0,1])\n",
    "\n",
    "ax1.plot(index,city_block_average_precisions,'-g.',label='City Block')\n",
    "ax1.plot(index,euclidean_average_precisions,'-r.',label='Euclidean')\n",
    "ax1.plot(index,d1_average_precisions,'-b.',label='D1')\n",
    "ax1.set_xlabel('Number of retrive images')\n",
    "ax1.set_ylabel('Precision')\n",
    "ax1.set_title('Average Precision for different numbers of retrival images')\n",
    "ax1.set_xticks(range(10,101,10))\n",
    "ax1.set_yticks(np.arange(math.floor(min(min(d1_average_precisions),min(euclidean_average_precisions),min(city_block_average_precisions))), max(max(d1_average_precisions),max(euclidean_average_precisions),max(city_block_average_precisions))+1, 5.0))\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(index,city_block_average_recalls,'-g.',label='City Block')\n",
    "ax2.plot(index,euclidean_average_recalls,'-r.',label='Euclidean')\n",
    "ax2.plot(index,d1_average_recalls,'-b.',label='D1')\n",
    "ax2.set_xlabel('Number of retrive images')\n",
    "ax2.set_ylabel('Recall Rate')\n",
    "ax2.set_title('Average Recall Rate for different numbers of retrival images')\n",
    "ax2.set_xticks(range(10,101,10))\n",
    "ax2.set_yticks(np.arange(math.floor(min(min(d1_average_recalls),min(euclidean_average_recalls),min(city_block_average_recalls))), max(max(d1_average_recalls),max(euclidean_average_recalls),max(city_block_average_recalls))+1, 5.0))\n",
    "ax2.grid()\n",
    "ax2.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter an image: (0-399):223\n"
     ]
    }
   ],
   "source": [
    "query=int(input(\"Enter an image: (0-399):\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "queryclass=math.floor(query/10)\n",
    "queryimg=query%10\n",
    "queryimage=io.imread(\"orl_faces\\\\s\"+str(queryclass+1).zfill(2)+\"\\\\\"+str(queryimg)+\".pgm\")\n",
    "\n",
    "query_coltp2_feature=CoLTP2_feature_vector(queryimage)\n",
    "\n",
    "coltp2_feature_matrix=np.array(coltp2_feature_matrix,dtype=np.double)\n",
    "distance_matrix_city_block=np.zeros(shape=(coltp2_feature_matrix.shape[0]),dtype=np.double)\n",
    "for i in range(coltp2_feature_matrix.shape[0]):\n",
    "    distance_matrix_city_block[i]=sum(np.abs(query_coltp2_feature-coltp2_feature_matrix[i]))\n",
    "\n",
    "best=8\n",
    "bestimgs=np.argsort(distance_matrix_city_block)[:best]\n",
    "classes=np.floor(bestimgs/10).astype(np.intc)+1\n",
    "imgs=bestimgs%10\n",
    "\n",
    "retrieve_images=[]\n",
    "for i in range(best):\n",
    "    image=io.imread(\"orl_faces\\\\s\"+str(classes[i]).zfill(2)+\"\\\\\"+str(imgs[i])+\".pgm\")\n",
    "    retrieve_images.append(image)\n",
    "    \n",
    "print(\"User Given Image:-\")\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "gs=GridSpec(int(math.floor(best/4))+1,4)\n",
    "\n",
    "ax1=fig.add_subplot(gs[0,:])\n",
    "ax1.imshow(queryimage,cmap='gray')\n",
    "ax1.set_yticklabels([])\n",
    "ax1.set_xticklabels([])\n",
    "\n",
    "for i in range(best):\n",
    "    ax=fig.add_subplot(gs[math.floor(i/4)+1,i%4])\n",
    "    ax.imshow(retrieve_images[i],cmap='gray')\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([])\n",
    "\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}