PetDetectionPy/ImageUsing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50cc3f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiUAAAGiCAYAAAA4MLYWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9SYxk2XUe/L2IyJjHjMh5qKyhq7t6YDebpDkYNCnbkk1BsmVtDBsQvJRAwIBAGIYFbUiDoGAtZC0kGxBgwLINwV7ZC0O/bEqWxFEcmmSzh6ruyqrKqTIjM2OeIzIy4l8kvlMnTt0XmdVsmio6L5DIiBfv3ende853xuuNx+MxLstluSyX5bJclstyWX7CJfCT7sBluSyX5bJclstyWS4LcAlKLstluSyX5bJclsvy16RcgpLLclkuy2W5LJflsvy1KJeg5LJclstyWS7LZbksfy3KJSi5LJflslyWy3JZLstfi3IJSi7LZbksl+WyXJbL8teiXIKSy3JZLstluSyX5bL8tSiXoOSyXJbLclkuy2W5LH8tyiUouSyX5bJclstyWS7LX4tyCUouy2W5LJflslyWy/LXovxEQcm/+3f/DlevXkU0GsWHPvQhfPWrX/1JdueyXJbLclkuy2W5LD/B8hMDJf/tv/03/Pqv/zp+8zd/E9///vfxyU9+Ep/5zGews7Pzk+rSZbksl+WyXJbLcll+gsX7SR3I99GPfhSvvvoq/v2///dy7datW/ilX/ol/NZv/dZPokuX5bJclstyWS7LZfkJltBPotHBYIDXXnsN/+pf/auJ6z/3cz+Hb3zjG4/d3+/30e/35ftoNEKlUkE+n4fneT/2/l6Wy3JZLstluSyX5b2V8XiMZrOJ5eVlBALTDTQ/EVBSKpVwenqKhYWFiesLCwsoFouP3f9bv/Vb+MIXvvB/q3uX5bJclstyWS7LZXmfy+7uLlZXV6fe8xMBJSxWyzEej52aj9/4jd/A5z73Ofler9exvr6Oz372s4hGowgGgxgOh+j1ehiNRgCAQCCAUCiEmZkZRKNRZDIZpNNpzMzMwGWx0tfYh/F4jEAgAM/zMBqNcHp6ikajgXq9jtPTU4TDYfkNAILBIDzPkz+Wk5MT9Pt9nJycIBgMSp3sp+d58p+f+af7xmtPanEbj8cYj8cYjUbOZ+2cczz8jb97nodgMCjjnjaHHCfb1u1wLtkffY/tA8ccDAYRDAal7tPTU8RiMYxGI/R6PWxubiKXyyESiUzMo30Xun62wXXC/3rsnLfhcIjT01Ocnp5OjIfrjOPVY9b3sS7Wx7q63S6GwyGy2Szi8fhEu6x/NBqh3++jXC4jEokgFovJPeFwGKFQSO4fDocIBoMYj8dotVo4PT1FKBTC6ekpgsGgrMXxeIyZmRn5G4/HGAwG6PV6iEQiGI1GCIVC8DwP1WoVw+EQw+EQyWQSc3Nz0q/xeIx+v49AIICZmRkEAgFEIhEEg0F0u13UajV0u12EQiEkEglEo1FEIhEAwHA4xMnJCU5PT3FycoJ2u435+Xm88soryOVyGA6HE/Ns15hdn9PKeXuG88l79ZoHgGazKeuQ71m/03A4jMFggJOTE4zHY7mP9emxWBrBfWXXJtccC+drMBhIW6enp6jX62g0Guj3+2i1Wuh0Ouj1ekJzwuEwxuOx7DkAE9Kq3u8cI99lIBDA6enpY/OhC/ec7ruuk+3qa6FQaIIOut6r7p9rT+t54xriOgQg69xFX/yKixZyz3O963bH4zHi8TgSiQRCoTN2yv03Go2QyWQwHo8RiUQQDofR7XZRr9cRCASQTCYBQOY6GAxiMBjIWstkMvA8DzMzM7LPdf+4rs4bI/tt373mB/a//V2/V0sD+ZxeX3Y/9Xo9fOlLX0IqlTr3HfxEQEmhUEAwGHxMK3J0dPSY9gQ4W/QkZLpEo1HEYjEEAgHZ9Kenp7JwQ6EQQqEQYrEYYrEYotEowuHwxOaYVkhcAEy8kH6//xgo0RvGMkNuFm5E1mmJk+63JcR6U1607/qz/ZtGyDke9lH/DwaDE0TIbhIWy6D1eDXRdW0oCxAJSDj2UCiE0WiEaDQqzJVrhAzbD5TojaLBJDd9JBKZ6K8FJVxnLMFgUEAJQalrTHrzalBGIMH1ad85AVggEEAsFkMkEkEikZB6Z2ZmJhgawQffI0EKwchoNBLCnUgkkEwm4XmeEEOOhWPjePr9PjqdDobDIcLh8ARQrNfriEQisr4JdCgscB64X7l+yEgI0Nj/QCCAVColc6CZkh/zuqgZ1z6via79Xa+ffD4va0AzJ70OtJk5HA4LGLBMFTgDGAQXbIvXer0eut2uAItqtYpyuSzzxXYGgwFGoxHC4TCCwaDsK70nAoGAzDHnye6nk5OTifm0QFtf13uCf7zXvgM+Q3rJfmjGZcGZfk7Pv58gyT/2WY9vZmYGJycnvsKPbs8yWf1fCxGWFpMOcV3r93B6eopIJDLBxNPpNJLJpABGAg/yGgrZ4/EYiURC6AvfsR0/63aBChYLSvR1K4DqwjWjn7egxNblouMukD+t/ERASTgcxoc+9CF8+ctfxj/6R/9Irn/5y1/GP/yH//CJ6rJMXQMETeC5qCjJ6Mnxk/pdqFEzCtsHywT1f8scXYDEjsv27aKEVzNAl6bCb5Pbvl20HVvvNMahCTPf17TiAmF23rhxLcFxzbsFSLp/0wiTJURs27aj63K9b01EAUysJXu/liT9xqP7RBCo+0hpajgcot1uC2CIRqOIx+OIRqMCHMLhMGZmZjAYDCZAUzQaBQBhor1eTwA+GRcZgh4/mQPHqImg7ivHEQqFRNo/PT0ViXfaWnySPaHfiV2j1BZp4qqJrybMerz8zUWUOe/D4VAYJDW6nU4HnU5HwGCxWMRgMBDA0ev1hIkTYNjxhMPhiXY4rxTGNDC09EvPG9eLHiPBEt+/fk8umsvP9rdAICAaQNJg9selVdHFtVddzJdj0t/ZXxcD1UXTFxdo8dvfunBtc+7JuAOBANrttoDwwWAg80GwSCGZAJP7he+KGlyrmXMJe3ZeNK3Rz2rBygUk9dywH7otS7d5j0tL7GrjvPITM9987nOfw6/8yq/gwx/+MD7+8Y/jD/7gD7Czs4Nf+7Vfu3Ad2syhTSf8TU8UFwowqQFhsRNnNQoW7GimYTUluk4X6vd7SdMWyLRrfve4NqMlzq77ngQI+Y3HTzVrmaxtw240F3O2JiytpbBz/KQM7TzAxrHZPrradY2F60WP5bw27bqaNh5NkIFHDHMwGMDzPNEmUdWsGVYkEkE0GhXJmYQ2FouJCpbghlIhwYwfcSdj1MxIS5uaQc7MzGA4HKLZbOLk5EQYsWVaLmJ8kaIJs+0nf9dmQn2P1mjwj6bDwWCAeDyOk5MTdLtdDAYDeabb7aLZbOLBgweibep2uxPCUTgcFlMk50eDCi3luoAw519LstSSWQZEDcLMzIzMHUGD1YDwe6/XA4AJjSAFAc/zRBPsxxhZtAZDa5y0lkyDVysk+IF0TY8J/DSY5H3T6Nw0Ju/ap/q/FXb1Hmy324hGowL+qenyPE/mnXVrwZnXrFbLVSzNsX20tIcmOTsHtk4NMvQcahDI8evfXSDzSfbpTwyU/ON//I9RLpfxr//1v8bBwQFefPFF/PEf/zGuXLly4TpcTNwFDi5aj2XGRJkabWpVpW3PMky7QO1CZ/HTsEwb53sprucsEv5R6n+Sfui50eYiPwBjnwUgkoUFpfY5S3wsM5sG0Fx9P+8eW6ftt2Uq1mQzrR59n18/9FwSDAwGAwEkZKD0waJkPR6Pxb5NAjYajUR7Qmm/1WohnU4DOAM12uQFYEIA0OYgq7onMNKankAggGaziX6/j1QqJfVo6ey9rk+XFMlrJycnE4TVSvXpdBq9Xg/NZlPMK51OB81mE51OB5VKRTQg2lzR6/UQCAQwNzcn80IfATLo0WiETqczQUu0dGz9syxo6Ha78izNZ5oGsg09Xg1erK+C3l8Eq/b9aiBgfcwsIyMg1nROawTYP865Bixsz7Zr36Uev15/+r1bkOMq7B/nhH23QNWaNFy
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Directory to Pictures\n",
    "DATA_DIR = \"C:/Users/muhle/Desktop/Code/DataSets/PetAI\"\n",
    "# Kategorien die zu erkennen sind\n",
    "CATEGORIES = [\"DOG\", \"CAT\"]\n",
    "\n",
    "# Die Ordner mit den Kategorien im Namen Suchen, anschließend alle Bilder in Schwarz-Weiß umformen \n",
    "for category in CATEGORIES:\n",
    "    path = os.path.join(DATA_DIR, category)\n",
    "    for img in os.listdir(path):\n",
    "        img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)\n",
    "        # Anzeigen eines Bildes mit Matplotlib\n",
    "        plt.imshow(img_array, cmap=\"gray\")\n",
    "        plt.show()\n",
    "        break\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7847ac85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Größe des Bildes definieren\n",
    "IMG_SIZE = 50\n",
    "# Größe des Bild Array's ändern\n",
    "new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "# Neues verpixeltes Bild anzeigen\n",
    "plt.imshow(new_array, cmap = 'gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "709419fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12501/12501 [01:18<00:00, 159.58it/s]\n",
      "100%|██████████| 12501/12501 [01:16<00:00, 163.02it/s]\n"
     ]
    }
   ],
   "source": [
    "# Liste für Trainingsdaten definieren\n",
    "training_data = []\n",
    "\n",
    "def create_training_data():\n",
    "    for category in CATEGORIES:  # do dogs and cats\n",
    "\n",
    "        path = os.path.join(DATA_DIR,category)  # Pfad zu Hunde und Katzen erstellen\n",
    "        class_num = CATEGORIES.index(category)  # Klassifikation für Hund und Katze bekommen, Hund=0; Katze=1;\n",
    "\n",
    "        for img in tqdm(os.listdir(path)):  # Bilder von Katzen und Hunden wiederholen\n",
    "            try:\n",
    "                img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # Bild zu einen Array\n",
    "                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # Größe zu einer normalen Datengröße machen (50,50)\n",
    "                training_data.append([new_array, class_num])  # Hinzufügen zu unserer Liste trainings_data\n",
    "            except Exception as e:  # Wenn ein Fehler auftritt soll es einfach übersrpingen und weitermachen\n",
    "                pass\n",
    "create_training_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41c1245c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Länge der training_data\n",
    "print(len(training_data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdfe3e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "# Random Werte aus training_data nehmen\n",
    "random.shuffle(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1e9c8e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# Array ausgeben\n",
    "for sample in training_data[:10]:\n",
    "    print(sample[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5eaae186",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input (X) und Output (y) definieren\n",
    "X = []\n",
    "y = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "510602f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for features, label in training_data: \n",
    "    X.append(features)\n",
    "    y.append(label)\n",
    "    \n",
    "# X in einen Numpy Array umwandeln und ihn eine neue Größe geben, 1 Farmkanal (Grau)\n",
    "X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b59339a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# X und y in Pickle dateien schreiben\n",
    "pickle_out = open(\"X.pickle\", \"wb\")\n",
    "pickle.dump(X, pickle_out)\n",
    "pickle_out.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b487159",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open(\"y.pickle\", \"wb\")\n",
    "pickle.dump(y, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6aabb96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open(\"X.pickle\", \"wb\")\n",
    "pickle.dump(X, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f0e4ca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[102],\n",
       "        [139],\n",
       "        [155],\n",
       "        ...,\n",
       "        [113],\n",
       "        [ 98],\n",
       "        [ 32]],\n",
       "\n",
       "       [[108],\n",
       "        [146],\n",
       "        [160],\n",
       "        ...,\n",
       "        [114],\n",
       "        [ 90],\n",
       "        [ 16]],\n",
       "\n",
       "       [[116],\n",
       "        [148],\n",
       "        [161],\n",
       "        ...,\n",
       "        [ 92],\n",
       "        [ 14],\n",
       "        [ 13]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 99],\n",
       "        [ 97],\n",
       "        [105],\n",
       "        ...,\n",
       "        [ 77],\n",
       "        [ 75],\n",
       "        [ 71]],\n",
       "\n",
       "       [[102],\n",
       "        [108],\n",
       "        [103],\n",
       "        ...,\n",
       "        [ 76],\n",
       "        [ 70],\n",
       "        [ 73]],\n",
       "\n",
       "       [[107],\n",
       "        [102],\n",
       "        [110],\n",
       "        ...,\n",
       "        [ 86],\n",
       "        [ 74],\n",
       "        [ 80]]], dtype=uint8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7532702",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ff3800d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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