{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "JIGSAW_baseline.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_UoYN5PzsZh"
      },
      "source": [
        "![AIcrowd-Logo](https://raw.githubusercontent.com/AIcrowd/AIcrowd/master/app/assets/images/misc/aicrowd-horizontal.png)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnktOs5tzsZi"
      },
      "source": [
        "# Getting Started Code for [JIGSAW Challenge](www.aicrowd.com/challenges/jigsaw) on AIcrowd\n",
        "#### Author : Sharada Mohanty"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FSQaFNqj4Xqg"
      },
      "source": [
        "This baseline creates the image of desired size and places the puzzle pieces randomly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3NTJjh6zsZj"
      },
      "source": [
        "## Download Necessary Packages 📚"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52VNLEdxzsZl"
      },
      "source": [
        "!pip install numpy\n",
        "!pip install pandas"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1252SIXzsZp"
      },
      "source": [
        "## Download Data\n",
        "The first step is to download out train test data. We will be training a model on the train data and make predictions on test data. We submit our predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "y2jOSZWezsZq"
      },
      "source": [
        "!rm -rf data\n",
        "!mkdir data\n",
        "!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/jigsaw/v0.1/puzzles.tar.gz\n",
        "!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/jigsaw/v0.1/metadata.csv\n",
        "\n",
        "!mkdir data/puzzles   \n",
        "!tar -C data/puzzles -xvzf puzzles.tar.gz \n",
        "\n",
        "!mv metadata.csv data/metadata.csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2HnIJcEHzsZu"
      },
      "source": [
        "\n",
        "## Import packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKUyBzLWzsZv"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from PIL import Image\n",
        "import glob\n",
        "import tempfile\n",
        "import os \n",
        "import random\n",
        "import tqdm\n",
        "import tarfile\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYmcu6cezsZy"
      },
      "source": [
        "## Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIvcQSYNzsZz"
      },
      "source": [
        "PUZZLES_DIRECTORY = \"data/puzzles\"\n",
        "METADATA_FILE = \"data/metadata.csv\"\n",
        "\n",
        "OUTPUT_PATH = \"data/submission.tar.gz\"\n",
        "metadata_df = pd.read_csv(METADATA_FILE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YsvIsudZ4j-d"
      },
      "source": [
        "Create directory to store the solved image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Da3JIibY3W-9"
      },
      "source": [
        "TEMP_SUBMISSION_DIR = tempfile.TemporaryDirectory()\n",
        "TEMP_SUBMISSION_DIR_PATH = TEMP_SUBMISSION_DIR.name\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGBW_ioK4tPd"
      },
      "source": [
        "**This is a very naive approach of creating a new image of the desired size, and paste all the individual puzzle pieces at random locations.**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qh7mGFfr3bbA"
      },
      "source": [
        "for index, row in tqdm.tqdm(metadata_df.iterrows(), total=metadata_df.shape[0]):\n",
        "    # Get the height and widhts of all the images from metadata.csv.\n",
        "    \n",
        "\n",
        "    puzzle_id = row[\"puzzle_id\"]\n",
        "    image_width = row[\"width\"]\n",
        "    image_height = row[\"height\"]\n",
        "\n",
        "    puzzle_directory = os.path.join(\n",
        "        PUZZLES_DIRECTORY,\n",
        "        str(puzzle_id)\n",
        "    )\n",
        "    solved_puzzle_im = Image.new(\"RGBA\",\n",
        "        (image_width, image_height)\n",
        "    ) # Initially create RGBA images, and then drop A channel later\n",
        "\n",
        "    for _puzzle_piece in glob.glob(os.path.join(puzzle_directory, \"*.png\")):\n",
        "        puzzle_piece_im = Image.open(_puzzle_piece)\n",
        "        pp_width, pp_height = puzzle_piece_im.size\n",
        "\n",
        "        # Find Random location \n",
        "        random_x = random.randint(0, image_width - pp_width)\n",
        "        random_y = random.randint(0, image_height - pp_height)\n",
        "\n",
        "        solved_puzzle_im.paste(puzzle_piece_im, (random_x, random_y))\n",
        "\n",
        "        del puzzle_piece_im\n",
        "\n",
        "    solved_puzzle_im.convert(\"RGB\").save(\n",
        "        os.path.join(TEMP_SUBMISSION_DIR_PATH, \"{}.jpg\".format(str(puzzle_id)))\n",
        "    )\n",
        "    del solved_puzzle_im\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4GEh4rb_Irj"
      },
      "source": [
        "# Visualize the generated Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FSJixiE6M7y"
      },
      "source": [
        "img_path  = os.path.join(TEMP_SUBMISSION_DIR_PATH,'2.jpg')\n",
        "img = Image.open(img_path)\n",
        "img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhEwJBmW5VGR"
      },
      "source": [
        "# Create the tar file with all the solved images and store it in the OUTPUT PATH."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMRVv9Cw3n29"
      },
      "source": [
        "with tarfile.open(OUTPUT_PATH, mode=\"w:gz\") as tar_file:\n",
        "    for _filepath in glob.glob(os.path.join(TEMP_SUBMISSION_DIR_PATH, \"*.jpg\")):\n",
        "        print(_filepath)\n",
        "        _, filename = os.path.split(_filepath)\n",
        "        tar_file.add(_filepath, arcname=filename)\n",
        "\n",
        "print(\"Wrote output file to : \", OUTPUT_PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-WPsT0Szsag"
      },
      "source": [
        "## To download the generated tar.gz in colab run the below command"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2lFyA17zsah"
      },
      "source": [
        "try:\n",
        "    from google.colab import files\n",
        "    files.download(OUTPUT_PATH) \n",
        "except:\n",
        "    print(\"Option Only avilable in Google Colab\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKZfXEPUzsak"
      },
      "source": [
        "### Well Done! 👍 We are all set to make a submission and see your name on leaderborad. Lets navigate to [challenge page](https://www.aicrowd.com/challenges/jigsaw) and make one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pi2lTDqd51u6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}