KNN-leave-one-out-grayscale.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Leave-One-Out Grayscale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robots Autónomos. Mapas Topológicos visuales\n",
    "\n",
    "Autores:\n",
    "    Alejandro Benítez López, Elena Benito Frey, Mario González Carbayo, Isidro López Dominguez, Blanca Martínez Donoso y Ángel Pavón Pérez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "import statistics as stats\n",
    "\n",
    "directory = os.getcwd() + \"/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se obtienen todas las rutas a las imágenes y se calculan cuantas hay:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de imagenes: 664\n"
     ]
    }
   ],
   "source": [
    "# numero de imagenes totales en el dataset\n",
    "total_img = 0\n",
    "# lista con todas las rutas a las imagenes del dataset\n",
    "all_filenames = []\n",
    "\n",
    "for i in range(9):\n",
    "    \n",
    "    filenames = [img for img in glob.glob(directory + \"landmarks_img/\"+ str(i) + \"/*.jpg\")]\n",
    "    \n",
    "    for file in filenames:\n",
    "        all_filenames.append(file)\n",
    "        \n",
    "    total_img += len(filenames)\n",
    "\n",
    "all_filenames.sort()\n",
    "print(f\"Total de imagenes: {total_img}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se define la función de KNN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn(img, hist_dict):\n",
    "\n",
    "    # se calcula el histograma en escala de grises\n",
    "    hist = cv2.calcHist([img],[0],None,[256],[0,256])\n",
    "    \n",
    "    # se calcula el historgama normalizado\n",
    "    hist = hist / (output_dimension[0] * output_dimension[1])\n",
    "   \n",
    "    min_distance = 1000000\n",
    "    min_key = ''\n",
    "    \n",
    "    # busca el histograma mas parecido por la distancia euclidea\n",
    "    for key, value in hist_dict.items():\n",
    "        \n",
    "        distance = np.linalg.norm(value-hist)\n",
    "        \n",
    "        if distance < min_distance:\n",
    "            min_distance = distance\n",
    "            min_key = key\n",
    "    \n",
    "    # devuelve el numero del landmark mas cercano\n",
    "    return int(min_key[9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se calculan los histogramas y se normalizan:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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      "Porcentaje: 87.65060240963855\n",
      "Imagen 583 = landmark_7_frame_51.jpg\n",
      "Porcentaje: 87.8012048192771\n",
      "Imagen 584 = landmark_7_frame_52.jpg\n",
      "Porcentaje: 87.95180722891567\n",
      "Imagen 585 = landmark_7_frame_53.jpg\n",
      "Porcentaje: 88.10240963855422\n",
      "Imagen 586 = landmark_7_frame_54.jpg\n",
      "Porcentaje: 88.25301204819277\n",
      "Imagen 587 = landmark_7_frame_55.jpg\n",
      "Porcentaje: 88.40361445783132\n",
      "Imagen 588 = landmark_7_frame_56.jpg\n",
      "Porcentaje: 88.55421686746988\n",
      "Imagen 589 = landmark_7_frame_57.jpg\n",
      "Porcentaje: 88.70481927710843\n",
      "Imagen 590 = landmark_7_frame_58.jpg\n",
      "Porcentaje: 88.855421686747\n",
      "Imagen 591 = landmark_7_frame_59.jpg\n",
      "Porcentaje: 89.00602409638554\n",
      "Imagen 592 = landmark_7_frame_6.jpg\n",
      "Porcentaje: 89.1566265060241\n",
      "Imagen 593 = landmark_7_frame_60.jpg\n",
      "Fallo en landmark_7_frame_60.jpg\n",
      "Porcentaje: 89.30722891566265\n",
      "Imagen 594 = landmark_7_frame_61.jpg\n",
      "Porcentaje: 89.4578313253012\n",
      "Imagen 595 = landmark_7_frame_62.jpg\n",
      "Porcentaje: 89.60843373493977\n",
      "Imagen 596 = landmark_7_frame_7.jpg\n",
      "Porcentaje: 89.75903614457832\n",
      "Imagen 597 = landmark_7_frame_8.jpg\n",
      "Porcentaje: 89.90963855421687\n",
      "Imagen 598 = landmark_7_frame_9.jpg\n",
      "Porcentaje: 90.06024096385542\n",
      "Imagen 599 = landmark_8_frame_0.jpg\n",
      "Porcentaje: 90.21084337349397\n",
      "Imagen 600 = landmark_8_frame_1.jpg\n",
      "Porcentaje: 90.36144578313252\n",
      "Imagen 601 = landmark_8_frame_10.jpg\n",
      "Porcentaje: 90.51204819277109\n",
      "Imagen 602 = landmark_8_frame_11.jpg\n",
      "Porcentaje: 90.66265060240964\n",
      "Imagen 603 = landmark_8_frame_12.jpg\n",
      "Porcentaje: 90.8132530120482\n",
      "Imagen 604 = landmark_8_frame_13.jpg\n",
      "Porcentaje: 90.96385542168674\n",
      "Imagen 605 = landmark_8_frame_14.jpg\n",
      "Porcentaje: 91.1144578313253\n",
      "Imagen 606 = landmark_8_frame_15.jpg\n",
      "Porcentaje: 91.26506024096386\n",
      "Imagen 607 = landmark_8_frame_16.jpg\n",
      "Porcentaje: 91.41566265060241\n",
      "Imagen 608 = landmark_8_frame_17.jpg\n",
      "Porcentaje: 91.56626506024097\n",
      "Imagen 609 = landmark_8_frame_18.jpg\n",
      "Porcentaje: 91.71686746987952\n",
      "Imagen 610 = landmark_8_frame_19.jpg\n",
      "Porcentaje: 91.86746987951807\n",
      "Imagen 611 = landmark_8_frame_2.jpg\n",
      "Porcentaje: 92.01807228915662\n",
      "Imagen 612 = landmark_8_frame_20.jpg\n",
      "Porcentaje: 92.16867469879519\n",
      "Imagen 613 = landmark_8_frame_21.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentaje: 92.31927710843374\n",
      "Imagen 614 = landmark_8_frame_22.jpg\n",
      "Porcentaje: 92.46987951807229\n",
      "Imagen 615 = landmark_8_frame_23.jpg\n",
      "Porcentaje: 92.62048192771084\n",
      "Imagen 616 = landmark_8_frame_24.jpg\n",
      "Porcentaje: 92.7710843373494\n",
      "Imagen 617 = landmark_8_frame_25.jpg\n",
      "Porcentaje: 92.92168674698796\n",
      "Imagen 618 = landmark_8_frame_26.jpg\n",
      "Porcentaje: 93.07228915662651\n",
      "Imagen 619 = landmark_8_frame_27.jpg\n",
      "Porcentaje: 93.22289156626506\n",
      "Imagen 620 = landmark_8_frame_28.jpg\n",
      "Porcentaje: 93.37349397590361\n",
      "Imagen 621 = landmark_8_frame_29.jpg\n",
      "Porcentaje: 93.52409638554217\n",
      "Imagen 622 = landmark_8_frame_3.jpg\n",
      "Porcentaje: 93.67469879518072\n",
      "Imagen 623 = landmark_8_frame_30.jpg\n",
      "Porcentaje: 93.82530120481928\n",
      "Imagen 624 = landmark_8_frame_31.jpg\n",
      "Porcentaje: 93.97590361445783\n",
      "Imagen 625 = landmark_8_frame_32.jpg\n",
      "Porcentaje: 94.12650602409639\n",
      "Imagen 626 = landmark_8_frame_33.jpg\n",
      "Porcentaje: 94.27710843373494\n",
      "Imagen 627 = landmark_8_frame_34.jpg\n",
      "Porcentaje: 94.42771084337349\n",
      "Imagen 628 = landmark_8_frame_35.jpg\n",
      "Porcentaje: 94.57831325301204\n",
      "Imagen 629 = landmark_8_frame_36.jpg\n",
      "Porcentaje: 94.7289156626506\n",
      "Imagen 630 = landmark_8_frame_37.jpg\n",
      "Porcentaje: 94.87951807228916\n",
      "Imagen 631 = landmark_8_frame_38.jpg\n",
      "Porcentaje: 95.03012048192771\n",
      "Imagen 632 = landmark_8_frame_39.jpg\n",
      "Porcentaje: 95.18072289156626\n",
      "Imagen 633 = landmark_8_frame_4.jpg\n",
      "Porcentaje: 95.33132530120481\n",
      "Imagen 634 = landmark_8_frame_40.jpg\n",
      "Porcentaje: 95.48192771084338\n",
      "Imagen 635 = landmark_8_frame_41.jpg\n",
      "Porcentaje: 95.63253012048193\n",
      "Imagen 636 = landmark_8_frame_42.jpg\n",
      "Porcentaje: 95.78313253012048\n",
      "Imagen 637 = landmark_8_frame_43.jpg\n",
      "Porcentaje: 95.93373493975903\n",
      "Imagen 638 = landmark_8_frame_44.jpg\n",
      "Porcentaje: 96.08433734939759\n",
      "Imagen 639 = landmark_8_frame_45.jpg\n",
      "Porcentaje: 96.23493975903614\n",
      "Imagen 640 = landmark_8_frame_46.jpg\n",
      "Porcentaje: 96.3855421686747\n",
      "Imagen 641 = landmark_8_frame_47.jpg\n",
      "Porcentaje: 96.53614457831326\n",
      "Imagen 642 = landmark_8_frame_48.jpg\n",
      "Porcentaje: 96.6867469879518\n",
      "Imagen 643 = landmark_8_frame_49.jpg\n",
      "Porcentaje: 96.83734939759036\n",
      "Imagen 644 = landmark_8_frame_5.jpg\n",
      "Porcentaje: 96.98795180722891\n",
      "Imagen 645 = landmark_8_frame_50.jpg\n",
      "Porcentaje: 97.13855421686748\n",
      "Imagen 646 = landmark_8_frame_51.jpg\n",
      "Porcentaje: 97.28915662650603\n",
      "Imagen 647 = landmark_8_frame_52.jpg\n",
      "Porcentaje: 97.43975903614458\n",
      "Imagen 648 = landmark_8_frame_53.jpg\n",
      "Porcentaje: 97.59036144578313\n",
      "Imagen 649 = landmark_8_frame_54.jpg\n",
      "Porcentaje: 97.74096385542168\n",
      "Imagen 650 = landmark_8_frame_55.jpg\n",
      "Porcentaje: 97.89156626506023\n",
      "Imagen 651 = landmark_8_frame_56.jpg\n",
      "Porcentaje: 98.0421686746988\n",
      "Imagen 652 = landmark_8_frame_57.jpg\n",
      "Porcentaje: 98.19277108433735\n",
      "Imagen 653 = landmark_8_frame_58.jpg\n",
      "Porcentaje: 98.3433734939759\n",
      "Imagen 654 = landmark_8_frame_59.jpg\n",
      "Porcentaje: 98.49397590361446\n",
      "Imagen 655 = landmark_8_frame_6.jpg\n",
      "Porcentaje: 98.644578313253\n",
      "Imagen 656 = landmark_8_frame_60.jpg\n",
      "Porcentaje: 98.79518072289157\n",
      "Imagen 657 = landmark_8_frame_61.jpg\n",
      "Porcentaje: 98.94578313253012\n",
      "Imagen 658 = landmark_8_frame_62.jpg\n",
      "Porcentaje: 99.09638554216868\n",
      "Imagen 659 = landmark_8_frame_63.jpg\n",
      "Porcentaje: 99.24698795180723\n",
      "Imagen 660 = landmark_8_frame_64.jpg\n",
      "Porcentaje: 99.39759036144578\n",
      "Imagen 661 = landmark_8_frame_7.jpg\n",
      "Porcentaje: 99.54819277108433\n",
      "Imagen 662 = landmark_8_frame_8.jpg\n",
      "Porcentaje: 99.6987951807229\n",
      "Imagen 663 = landmark_8_frame_9.jpg\n",
      "Porcentaje: 99.84939759036145\n",
      "Porcentaje por cada landmark:\n",
      "\t[100.0, 100.0, 98.9247311827957, 98.78048780487805, 98.36065573770492, 98.85057471264368, 98.71794871794872, 96.82539682539682, 100.0]\n",
      "Porcentaje medio:\n",
      "\t98.93997722015199\n"
     ]
    }
   ],
   "source": [
    "# numero de imagenes por cada landmark\n",
    "imgs_per_landmark         = [0] * 9\n",
    "# numero de aciertos por cada landmark\n",
    "aciertos_img_per_landmark = [0] * 9\n",
    "\n",
    "# la dimension de todas las imagenes\n",
    "output_dimension = (848, 480)\n",
    "\n",
    "for i in range(total_img):\n",
    "    \n",
    "    # se guarda y se saca la imagen a comprobar\n",
    "    leave_one_out_file = os.path.basename(all_filenames[i])\n",
    "    leave_one_out_img  = cv2.imread(all_filenames[i], cv2.IMREAD_GRAYSCALE)\n",
    "    real_landmark     = int(leave_one_out_file[9])\n",
    "    \n",
    "    print(f\"Imagen {i} = {leave_one_out_file}\")\n",
    "    \n",
    "    # diccionario donde se guardaran todos los histogramas\n",
    "    hist_dict = {}\n",
    "    \n",
    "    for k in range(9):\n",
    "        # se cargan las imagenes del landmark i\n",
    "        landmark_filenames = [img for img in glob.glob(directory + \"landmarks_img/\"+ str(k) + \"/*.jpg\")]\n",
    "        \n",
    "        # quitamos el leave_one_out_file\n",
    "        if all_filenames[i] in landmark_filenames:\n",
    "            landmark_filenames.remove(all_filenames[i])\n",
    "        \n",
    "        \n",
    "        landmark_filenames.sort()\n",
    "        images    = [cv2.imread(f, cv2.IMREAD_GRAYSCALE) for f in landmark_filenames]\n",
    "        img_names = [os.path.basename(f)                 for f in landmark_filenames]\n",
    "      \n",
    "        # se calcula el histograma de todas las demas imagenes\n",
    "        for j, img in enumerate(images):\n",
    "            \n",
    "            # se calcula el histograma en escala de grises\n",
    "            hist = cv2.calcHist([img],[0],None,[256],[0,256])\n",
    "\n",
    "            # se calcula el historgama normalizado\n",
    "            hist_norm = hist / (output_dimension[0] * output_dimension[1])\n",
    "\n",
    "            # se añade al diccionario. La clave es el nombre de la imagen y el valor es su histograma normalizado \n",
    "            hist_dict[img_names[j]] = hist_norm \n",
    "\n",
    "    \n",
    "    # se comprueba la imagen seleccionada con el knn\n",
    "    landmark = knn(leave_one_out_img, hist_dict)\n",
    "    \n",
    "    # se suma uno a las iteraciones hechas en el landmark real\n",
    "    imgs_per_landmark[real_landmark] += 1\n",
    "    \n",
    "    # si el landmark predecido por knn es igual al real, se suma uno en los aciertos de ese landmark\n",
    "    if landmark == real_landmark:\n",
    "        aciertos_img_per_landmark[real_landmark] += 1\n",
    "    else:\n",
    "        print(f\"Fallo en {leave_one_out_file}\")\n",
    "    \n",
    "    print(f\"Porcentaje: {(i * 100) / total_img}\")\n",
    "        \n",
    "# se saca el porcentaje parcial y total:\n",
    "porcentaje_total = []\n",
    "\n",
    "for i in range(len(imgs_per_landmark)):\n",
    "    x = (aciertos_img_per_landmark[i] * 100) / imgs_per_landmark[i]\n",
    "    porcentaje_total.append(x)\n",
    "\n",
    "print(\"Porcentaje por cada landmark:\")\n",
    "print(\"\\t\" + str(porcentaje_total))\n",
    "print(\"Porcentaje medio:\")\n",
    "print(\"\\t\" + str(stats.mean(porcentaje_total)))"
   ]
  }
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