datasets/Read Dataset and Make Numpy Files.ipynb

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      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
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    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n"
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     "text": [
      "test Directory Classes: ['0:Normal', '1:Bacteria', '2:Viral', '3:COVID19']\n",
      "Reading Data for 0:Normal class\n"
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      "Reading Data for 1:Bacteria class\n"
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      "Reading Data for 3:COVID19 class\n"
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      "test Data: (639, 224, 224, 3)\n",
      "test Labels: (639,)\n",
      "train Directory Classes: ['0:Normal', '1:Bacteria', '2:Viral', '3:COVID19']\n",
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      "Reading Data for 1:Bacteria class\n"
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      "Reading Data for 2:Viral class\n"
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      "Reading Data for 3:COVID19 class\n"
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      "\n",
      "train Data: (5310, 224, 224, 3)\n",
      "train Labels: (5310,)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "os.chdir('/content/drive/My Drive/GitHub Repositories/COVID-19')\n",
    "dataDir = './datasets'\n",
    "directories = os.listdir(dataDir)\n",
    "\n",
    "def Read_Images_and_Make_Numpy_Files(directory):\n",
    "  images = []\n",
    "  labels = []\n",
    "  classes = os.listdir(os.path.join(dataDir, directory))\n",
    "  print('{} Directory Classes: {}'.format(directory, classes))\n",
    "  for class_ in classes:\n",
    "    if class_ == '0:Normal':\n",
    "      print('Reading Data for {} class'.format(class_))\n",
    "      imageNames = os.listdir(os.path.join(dataDir, directory, class_))\n",
    "      for imgName in tqdm(imageNames):\n",
    "        img = load_img(os.path.join(dataDir, directory, class_, imgName), target_size=(224, 224), color_mode='rgb', interpolation='lanczos')\n",
    "        img_array = img_to_array(img, data_format='channels_last', dtype='float32')\n",
    "        images.append(img_array)\n",
    "        labels.append(0)\n",
    "    elif class_ == '1:Bacteria':\n",
    "      print('Reading Data for {} class'.format(class_))\n",
    "      imageNames = os.listdir(os.path.join(dataDir, directory, class_))\n",
    "      for imgName in tqdm(imageNames):\n",
    "        img = load_img(os.path.join(dataDir, directory, class_, imgName), target_size=(224, 224), color_mode='rgb', interpolation='lanczos')\n",
    "        img_array = img_to_array(img, data_format='channels_last', dtype='float32')\n",
    "        images.append(img_array)\n",
    "        labels.append(1)\n",
    "    elif class_ == '2:Viral':\n",
    "      print('Reading Data for {} class'.format(class_))\n",
    "      imageNames = os.listdir(os.path.join(dataDir, directory, class_))\n",
    "      for imgName in tqdm(imageNames):\n",
    "        img = load_img(os.path.join(dataDir, directory, class_, imgName), target_size=(224, 224), color_mode='rgb', interpolation='lanczos')\n",
    "        img_array = img_to_array(img, data_format='channels_last', dtype='float32')\n",
    "        images.append(img_array)\n",
    "        labels.append(2)\n",
    "    elif class_ == '3:COVID19':\n",
    "      print('Reading Data for {} class'.format(class_))\n",
    "      imageNames = os.listdir(os.path.join(dataDir, directory, class_))\n",
    "      for imgName in tqdm(imageNames):\n",
    "        img = load_img(os.path.join(dataDir, directory, class_, imgName), target_size=(224, 224), color_mode='rgb', interpolation='lanczos')\n",
    "        img_array = img_to_array(img, data_format='channels_last', dtype='float32')\n",
    "        images.append(img_array)\n",
    "        labels.append(3)\n",
    "  \n",
    "  images = np.array(images).reshape(-1, 224, 224, 3)\n",
    "  print('{} Data: {}'.format(directory, images.shape))\n",
    "  labels = np.array(labels)\n",
    "  print('{} Labels: {}'.format(directory, labels.shape))\n",
    "  np.save('./datasets/{}_images'.format(directory), images)\n",
    "  np.save('./datasets/{}_labels'.format(directory), labels)\n",
    "\n",
    "\n",
    "for directory in directories:\n",
    "    if directory=='train':\n",
    "      Read_Images_and_Make_Numpy_Files(directory)\n",
    "    elif directory=='test':\n",
    "      Read_Images_and_Make_Numpy_Files(directory)\n",
    "      \n",
    "\n"
   ]
  }
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