3D_Cnn.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import model\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_training_data():\n",
    "    count = 0\n",
    "    train_array = np.zeros(shape=(32,32,32,4))\n",
    "    flag_first_element = 1\n",
    "    for voxel_file in glob.glob('./data/*.csv'):\n",
    "        csv = pd.read_csv(voxel_file)\n",
    "        (x_norm, y_norm, z_norm, occupany) = (csv.channel2.tolist(), csv.channel3.tolist(), csv.channel4.tolist(), csv.channel4.tolist())\n",
    "        (x_cord, y_cord, z_cord) = (csv['ix'].tolist(), csv['iy'].tolist(), csv['iz'].tolist())\n",
    "        _3d_x_norm =  _3d_y_norm = _3d_z_norm = _3d_occupancy = np.zeros(shape=(32,32,32))\n",
    "\n",
    "        for i in range(len(x_cord)):\n",
    "            _3d_x_norm[x_cord[i],y_cord[i],z_cord[i]] = x_norm[i]\n",
    "            _3d_y_norm[x_cord[i],y_cord[i],z_cord[i]] = y_norm[i]\n",
    "            _3d_z_norm[x_cord[i],y_cord[i],z_cord[i]] = z_norm[i]\n",
    "            _3d_occupancy[x_cord[i],y_cord[i],z_cord[i]] = occupany[i]\n",
    "\n",
    "        voxel = np.stack(( _3d_x_norm, _3d_y_norm, _3d_z_norm, _3d_occupancy),-1)\n",
    "\n",
    "        if(flag_first_element):\n",
    "            train_array = voxel\n",
    "            flag_first_element = 0\n",
    "        else:\n",
    "            train_array = np.concatenate((train_array, voxel), -1)\n",
    "        count = count + 1\n",
    "\n",
    "    train_array = train_array.reshape(count, 32, 32, 32, 4)\n",
    "\n",
    "    return train_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_training_labels():\n",
    "    csv = pd.read_csv('Path_to_labels_csv')\n",
    "    labels = csv['labels']\n",
    "    return labels.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_labels(y):\n",
    "    labels = np.array(['No', 'Yes'])\n",
    "    y = labels[y.argmax(1)]\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "1/1 [==============================] - 1s 892ms/step - loss: 0.6169 - acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    X_train = get_training_data()\n",
    "    Y_train = [1,0]   #get_training_labels()\n",
    "    Y_train = to_categorical(Y_train, 2)    #One hot encoding the data\n",
    "    modell = model.create_model()\n",
    "    modell.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    modell.fit(X_train, Y_train)\n",
    "    y_test = modell.predict(X_train)\n",
    "    y_test = get_labels(a)\n",
    "    print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
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