create_augment.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = np.load('trainx.npy')\n",
    "L = np.load('trainy.npy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "flow() got an unexpected keyword argument 'target_size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-d33c0d374287>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel_count\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         for batch in datagen.flow(x, target_size=(300, 300), batch_size=1,\n\u001b[0;32m----> 8\u001b[0;31m                                 save_to_dir='augmentresize/covid', save_prefix='covid', save_format='jpeg'):\n\u001b[0m\u001b[1;32m      9\u001b[0m             \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: flow() got an unexpected keyword argument 'target_size'"
     ]
    }
   ],
   "source": [
    "label_count = 0 \n",
    "for x in I:\n",
    "    i=0\n",
    "    x = x.reshape((1,) + x.shape)\n",
    "\n",
    "    if(L[label_count] == 0):\n",
    "        for batch in datagen.flow(x, target_size=(300, 300), batch_size=1,\n",
    "                                save_to_dir='augmentresize/covid', save_prefix='covid', save_format='jpeg'):\n",
    "            i += 1\n",
    "            if i > 10:\n",
    "                break\n",
    "    \n",
    "    label_count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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