TPclassifImages (original).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP reconnaissance d'images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from skimage import io\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "import time\n",
    "import glob\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retour sur SIFT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va commencer avec une seule image:\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x129fc5f27f0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Adaptez la variable 'basedir' à votre cas\n",
    "basedir= r\"C:\\Users\\stand\\Desktop\\Homework\\Polytech - Informatique\\Cours 2020-2021\\S6\\_8 Signals, Sounds and Images for the Informatician\\2021.04.16 - TP #10 - Catégorisations d'images\\raw-img\" + '\\\\'\n",
    "#img = io.imread(basedir+\"train/farfalla/OIP-Zq_q8o8cw50F1x6S7AmlsQHaFi.jpeg\")\n",
    "#img = io.imread(basedir+\"train/farfalla/eb35b30729f1073ed1584d05fb1d4e9fe777ead218ac104497f5c97faeebb5bb_640.png\")\n",
    "img = io.imread(basedir+\"train/ragno/eb3cb3082af0073ed1584d05fb1d4e9fe777ead218ac104497f5c97ca5edb3bd_640.png\")\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 640, 4)\n"
     ]
    }
   ],
   "source": [
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "durée = 0.18 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x129fc7c57f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "sift = cv2.SIFT_create()\n",
    "now = time.time()\n",
    "kp, des = sift.detectAndCompute(gray,None)\n",
    "duration = time.time()-now\n",
    "print(\"durée = %.2f s\" %duration)\n",
    "cv2.drawKeypoints(gray, kp, img, flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "plt.imshow(gray, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que contiennent les variables kp et des ? Quelles sont les dimensions ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "502\n",
      "<class 'cv2.KeyPoint'>\n",
      "502\n",
      "<class 'numpy.ndarray'>\n",
      "(502, 128)\n"
     ]
    }
   ],
   "source": [
    "print(len(kp))\n",
    "print(type(kp[0]))\n",
    "print(len(des))\n",
    "print(type(des[0]))\n",
    "print(des.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chargement de vos classes d'images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of images:  5312\n",
      "class:  pecora  :  1456  images.\n",
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      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "test\n",
      "classe ragno: 12.25 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "baseTrain = basedir+'train/'\n",
    "classes = ['pecora','ragno']\n",
    "\n",
    "lesSifts = np.empty(shape=(0, 128), dtype=int)\n",
    "dimImgs = []\n",
    "\n",
    "nbImages = 0\n",
    "for cl in classes:\n",
    "    nbImages += len(glob.glob(baseTrain+cl+'/*'))\n",
    "#xTrain = np.empty(shape=(nbImages,size2))\n",
    "print(\"total number of images: \", nbImages)\n",
    "i = 0\n",
    "nb = [] # pour stocker le nombre d'images de chaque classe\n",
    "\n",
    "for cl in classes:\n",
    "    now = time.time()\n",
    "    listImages = glob.glob(baseTrain+cl+'/*')\n",
    "    print(\"class: \", cl, \" : \", len(listImages), \" images.\") \n",
    "    nb.append(len(listImages))\n",
    "    for pathImg in tqdm(listImages, position=0, leave=True): # POUR DEBUGGUER, VOUS POUVEZ REDUIRE LE NB D'IMAGES!!\n",
    "        img = io.imread(pathImg)\n",
    "        \n",
    "    duration = time.time()-now\n",
    "    print('classe {}: {:.2f} s'.format(cl, duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(lesSifts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sac de descripteurs (BoF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans, MiniBatchKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MiniBatchKMeans(batch_size=100, compute_labels=True, init='k-means++',\n",
       "                init_size=None, max_iter=100, max_no_improvement=10,\n",
       "                n_clusters=8, n_init=3, random_state=None,\n",
       "                reassignment_ratio=0.01, tol=0.0, verbose=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k=50: 20 minutes avec KMeans / 2 minutes avec MiniBatchKMeans\n",
    "MiniBatchKMeans()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calcul des bows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification par regression logistique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dico = {name:[] for name in classes}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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