edges2shoes_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code are tested on Google colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1804,
     "status": "ok",
     "timestamp": 1524063755386,
     "user": {
      "displayName": "Lu SA",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "109275333798683015269"
     },
     "user_tz": -480
    },
    "id": "SGd-EubNNFDm",
    "outputId": "e3c1e9c3-d6ce-4e01-e13f-10f48def3120",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datalab\t\t       instance_normalization.py  __pycache__\r\n",
      "GroupNormalization.py  models\r\n",
      "image_augmentation.py  pixel_shuffler.py\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download edge2shoes dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 38636,
     "status": "ok",
     "timestamp": 1524063797420,
     "user": {
      "displayName": "Lu SA",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "109275333798683015269"
     },
     "user_tz": -480
    },
    "id": "30G4vx_qNFMG",
    "outputId": "ec95e243-2ba6-45f9-94f2-08e31aa1d57f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-04-18 15:02:39--  https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/edges2shoes.tar.gz\n",
      "Resolving people.eecs.berkeley.edu (people.eecs.berkeley.edu)... 128.32.189.73\n",
      "Connecting to people.eecs.berkeley.edu (people.eecs.berkeley.edu)|128.32.189.73|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2165283376 (2.0G) [application/x-gzip]\n",
      "Saving to: ‘edges2shoes.tar.gz’\n",
      "\n",
      "edges2shoes.tar.gz  100%[===================>]   2.02G  41.5MB/s    in 37s     \n",
      "\n",
      "2018-04-18 15:03:16 (56.3 MB/s) - ‘edges2shoes.tar.gz’ saved [2165283376/2165283376]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/edges2shoes.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unzip data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "XTyh_4IVNFJu"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!tar -zxvf edges2shoes.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "afy-8u_fNFGm"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "VlDf8kekNFAe"
   },
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "xl9F75wRMok0"
   },
   "outputs": [],
   "source": [
    "from multiprocessing import Pool, cpu_count\n",
    "from glob import glob\n",
    "from matplotlib import pyplot as plt\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split training image into `./train/edges` and `./train/shoes` folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_size = 128 # 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "RVDlhmOPMolG"
   },
   "outputs": [],
   "source": [
    "dir_imgs = \"edges2shoes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "lzdT0guGMolS"
   },
   "outputs": [],
   "source": [
    "fn_train_imgs = glob(f\"{dir_imgs}/train/*.*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 554,
     "status": "ok",
     "timestamp": 1524063841006,
     "user": {
      "displayName": "Lu SA",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "109275333798683015269"
     },
     "user_tz": -480
    },
    "id": "8ol7z5_2Molc",
    "outputId": "deaf1664-aa9a-4069-fe35-b50f724acb71"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49825"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fn_train_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "JOeUQvDFMom8"
   },
   "outputs": [],
   "source": [
    "!mkdir -p ./train/edges\n",
    "!mkdir -p ./train/shoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "uMBEfOarOr5a"
   },
   "outputs": [],
   "source": [
    "edges_save_path = \"train/edges/\"\n",
    "shoes_save_path = \"train/shoes/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "vxe36aIiMonE"
   },
   "outputs": [],
   "source": [
    "def split_image(fn):\n",
    "    raw_fn = fn.split(\"/\")[-1]\n",
    "    im = plt.imread(fn)[..., :3]\n",
    "    \n",
    "    edges_im = im[:,:256,:]\n",
    "    shoes_im = im[:,256:,:]\n",
    "    if output_size == 128:\n",
    "        edges_im = cv2.resize(edges_im, (output_size,output_size))\n",
    "        shoes_im = cv2.resize(shoes_im, (output_size,output_size))\n",
    "    elif output_size == 64:\n",
    "        edges_im = cv2.resize(cv2.erode(edges_im, np.ones((2,2),np.uint8), iterations = 1), (output_size,output_size))\n",
    "        shoes_im = cv2.resize(shoes_im, (output_size,output_size))\n",
    "    else:\n",
    "        assert (output_size == 128) or (output_size == 64), \"output_size should be either 128 or 64.\"\n",
    "    plt.imsave(f\"{edges_save_path}{raw_fn}\", edges_im, format=\"jpg\")\n",
    "    plt.imsave(f\"{shoes_save_path}{raw_fn}\", shoes_im, format=\"jpg\")\n",
    "    \n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3762,
     "status": "ok",
     "timestamp": 1524063850664,
     "user": {
      "displayName": "Lu SA",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "109275333798683015269"
     },
     "user_tz": -480
    },
    "id": "tdJPYubWOTM4",
    "outputId": "72b48d60-4e23-498f-d753-8fc9a8b0e342"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting joblib\n",
      "  Downloading https://files.pythonhosted.org/packages/4f/51/870b2ec270fc29c5d89f85353da420606a9cb39fba4747127e7c7d7eb25d/joblib-0.11-py2.py3-none-any.whl (176kB)\n",
      "\u001b[K    100% |████████████████████████████████| 184kB 3.3MB/s \n",
      "\u001b[?25hInstalling collected packages: joblib\n",
      "Successfully installed joblib-0.11\n"
     ]
    }
   ],
   "source": [
    "#!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "YtTuquOmMooE"
   },
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 124
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 382710,
     "status": "ok",
     "timestamp": 1524065544222,
     "user": {
      "displayName": "Lu SA",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "109275333798683015269"
     },
     "user_tz": -480
    },
    "id": "JMrvL0nuMooM",
    "outputId": "8d873168-17cb-414a-ba3d-27df235cc904"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1336 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=-1)]: Done 6436 tasks      | elapsed:   50.3s\n",
      "[Parallel(n_jobs=-1)]: Done 14936 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 26836 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 42136 tasks      | elapsed:  5.4min\n",
      "[Parallel(n_jobs=-1)]: Done 49825 out of 49825 | elapsed:  6.4min finished\n"
     ]
    }
   ],
   "source": [
    "_ = Parallel(n_jobs=-1, verbose=1)(map(delayed(split_image), fn_train_imgs))\n",
    "# It seems like multiprocessing is not available in jupyter on windows\n",
    "# Use the following if error occurs\n",
    "# _ = Parallel(n_jobs=-1, verbose=1, backend=\"threading\")(map(delayed(split_image), fn_train_imgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 574,
     "status": "ok",
     "timestamp": 1524065544882,
     "user": {
      "displayName": "Lu SA",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "109275333798683015269"
     },
     "user_tz": -480
    },
    "id": "8oc05QRJMop-",
    "outputId": "7bb36157-0fbf-439f-be5f-e6682b146fde"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49825\n"
     ]
    }
   ],
   "source": [
    "print(len(glob(\"train/edges/*.*\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 772,
     "status": "ok",
     "timestamp": 1524065546104,
     "user": {
      "displayName": "Lu SA",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "109275333798683015269"
     },
     "user_tz": -480
    },
    "id": "sQwW8x8jQBhK",
    "outputId": "eda59b1b-6858-45f7-80f4-1faf13133611"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert len(glob(\"train/edges/*.*\")) == len(glob(\"train/shoes/*.*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "Ugfch70qQElU"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "edge_shoes_split.ipynb",
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