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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lane Boundary Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up Colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can delete this \"Setting up Colab\" section if you work locally and do not want to use Google Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colab_nb = 'google.colab' in str(get_ipython())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if colab_nb:\n",
    "  from google.colab import drive\n",
    "  drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if colab_nb:\n",
    "  %cd drive/My\\ Drive/aad/code/exercises/lane_detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if colab_nb:\n",
    "  !pip install segmentation-models-pytorch\n",
    "  !pip install albumentations --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import sys\n",
    "sys.path.append(\"../../util\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have collected data yourself in a folder \"data\" using `collect_data.py` and you want to use it for training, set the boolean in the next cell to `True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "own_data = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if own_data:\n",
    "    from seg_data_util import sort_collected_data\n",
    "    # copy and sort content of 'data' into 'data_lane_segmentation' folder:\n",
    "    sort_collected_data()\n",
    "    # Since data was copied, you can remove files in 'data' directory afterwards\n",
    "else:\n",
    "    # if you stopped the download before completion, please delete the 'data_lane_segmentation' folder and run this cell again\n",
    "    from seg_data_util import download_segmentation_data\n",
    "    download_segmentation_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independent of what you chose, you will have a directory 'data_lane_segmentation' now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"data_lane_segmentation\"\n",
    "\n",
    "x_train_dir = os.path.join(DATA_DIR, 'train')\n",
    "y_train_dir = os.path.join(DATA_DIR, 'train_label')\n",
    "\n",
    "x_valid_dir = os.path.join(DATA_DIR, 'val')\n",
    "y_valid_dir = os.path.join(DATA_DIR, 'val_label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the labels are regular png images with 3 color channels. The content of those color channels is identical, so when you load the png you should just load the first color channel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your code starts here: Train a deep learning segmentation model and evaluate its dice loss on the validation set. You should aim for a dice loss of 0.2 or less!"
   ]
  }
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