data.ipynb

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     "text": [
      "Requirement already satisfied: tensorflow==2.2.0 in ./ocrenv/lib/python3.6/site-packages (2.2.0)\n",
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      "Requirement already satisfied: setuptools>=41.0.0 in ./ocrenv/lib/python3.6/site-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (50.3.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in ./ocrenv/lib/python3.6/site-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.3.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in ./ocrenv/lib/python3.6/site-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.7.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in ./ocrenv/lib/python3.6/site-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.0.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in ./ocrenv/lib/python3.6/site-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.4.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in ./ocrenv/lib/python3.6/site-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2.24.0)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in ./ocrenv/lib/python3.6/site-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.22.1)\n",
      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in ./ocrenv/lib/python3.6/site-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2.0.0)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in ./ocrenv/lib/python3.6/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.3.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in ./ocrenv/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2.10)\n",
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      "Requirement already satisfied: chardet<4,>=3.0.2 in ./ocrenv/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.0.4)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in ./ocrenv/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.5\" in ./ocrenv/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (4.6)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in ./ocrenv/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (4.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in ./ocrenv/lib/python3.6/site-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.3.0)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in ./ocrenv/lib/python3.6/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.1.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in ./ocrenv/lib/python3.6/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.4.8)\n"
     ]
    }
   ],
   "source": [
    "!pip3 install tensorflow==2.2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import os \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from tqdm.notebook import tqdm\n",
    "import random\n",
    "def create_dir(base,ext):\n",
    "    '''\n",
    "        creates and external dir in base\n",
    "        args:\n",
    "            base   =    the main path\n",
    "            ext    =    the external path\n",
    "    '''\n",
    "    _path=os.path.join(base,ext)\n",
    "    if not os.path.exists(_path):\n",
    "        os.mkdir(_path)\n",
    "    return _path\n",
    "\n",
    "# dataset dir\n",
    "ds_path  =   create_dir(os.getcwd(),'OcrDTF')\n",
    "tf_train =   create_dir(ds_path,'Train')\n",
    "tf_eval  =   create_dir(ds_path,'Eval')\n",
    "tf_test  =   create_dir(ds_path,'Test')\n",
    "\n",
    "# image dir\n",
    "IDEN_START=172\n",
    "BasicFinalDatabase_path   =    os.path.join(os.getcwd(),'BasicFinalDatabase')\n",
    "train_path                =    os.path.join(BasicFinalDatabase_path,'Train')\n",
    "test_path                 =    os.path.join(BasicFinalDatabase_path,'Test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=3000.0), HTML(value='')))"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "9600 2400 3000\n"
     ]
    }
   ],
   "source": [
    "train_img_paths  =  [img_path for img_path in tqdm(glob(os.path.join(train_path,'*/*.bmp')))]\n",
    "test_img_paths   =  [img_path for img_path in tqdm(glob(os.path.join(test_path,'*/*.bmp')))]\n",
    "\n",
    "nb_train=int(len(train_img_paths)*0.8)\n",
    "random.shuffle(train_img_paths)\n",
    "\n",
    "eval_img_paths  =  train_img_paths[nb_train:]\n",
    "train_img_paths =  train_img_paths[:nb_train]\n",
    "\n",
    "def get_label(img_path):\n",
    "    '''\n",
    "        get label from data path\n",
    "    '''\n",
    "    _base  =  os.path.dirname(img_path)\n",
    "    _label =  os.path.basename(_base)\n",
    "    _label =  int(_label) - IDEN_START\n",
    "    return _label\n",
    "\n",
    "print(len(train_img_paths),len(eval_img_paths),len(test_img_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "\n",
    "print(tf.__version__)\n",
    "\n",
    "DATA_NUM=512\n",
    "IMG_DIM =128\n",
    "\n",
    "def _bytes_feature(value):\n",
    "    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n",
    "def _int64_feature(value):\n",
    "      return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n",
    "def _float_feature(value):\n",
    "      return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n",
    "\n",
    "\n",
    "def to_tfrecord(img_paths,tf_mode,r_num):\n",
    "    '''\n",
    "      Creates tfrecords from Provided Image Paths\n",
    "    '''\n",
    "    tfrecord_name='{}.tfrecord'.format(r_num)\n",
    "    tfrecord_path=os.path.join(tf_mode,tfrecord_name) \n",
    "    \n",
    "    with tf.io.TFRecordWriter(tfrecord_path) as writer:    \n",
    "        \n",
    "        for img_path in tqdm(img_paths):\n",
    "            \n",
    "            #label\n",
    "            label=get_label(img_path)\n",
    "            \n",
    "            # image data\n",
    "            img = Image.open(img_path).convert('RGB')\n",
    "            img = img.resize((IMG_DIM,IMG_DIM))\n",
    "            img.save( 'test.png', 'png')\n",
    "            \n",
    "            with(open('test.png','rb')) as fid:\n",
    "                image_png_bytes=fid.read()\n",
    "            \n",
    "            # feature desc\n",
    "            data ={ 'image':_bytes_feature(image_png_bytes),\n",
    "                    'label':_int64_feature(label)\n",
    "            }\n",
    "            \n",
    "            features=tf.train.Features(feature=data)\n",
    "            example= tf.train.Example(features=features)\n",
    "            serialized=example.SerializeToString()\n",
    "            writer.write(serialized)  \n",
    "            \n",
    "def create_df(img_paths,tf_mode):\n",
    "    '''\n",
    "        tf record wrapper\n",
    "    '''\n",
    "    for idx in range(0,len(img_paths),DATA_NUM):\n",
    "        \n",
    "        _paths=img_paths[idx:idx+DATA_NUM]\n",
    "        \n",
    "        to_tfrecord(_paths,tf_mode,idx//DATA_NUM)"
   ]
  },
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    "create_df(eval_img_paths,tf_eval)\n",
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