Photomosaic

photomosaic.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Photomosaic",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/kylemcdonald/e2623f7a28773a4207103c43c54acbc0/photomosaic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "oNRxFgeQoOo7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/kylemcdonald/python-utils.git utils\n",
        "!wget -c http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-imgs.npz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nC9Rlk25ok29",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.spatial.distance import cdist\n",
        "from google.colab import files\n",
        "from utils.imutil import imshow, imread, imresize\n",
        "from utils.mosaic import make_mosaic, unmake_mosaic\n",
        "from utils.progress import progress\n",
        "from utils.color_conversion import to_single_gray\n",
        "\n",
        "tiles = 255 - np.load('kmnist-train-imgs.npz')['arr_0']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s_MNhxHCovu8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# block-reduces grayscale images in shape (N,H,W)\n",
        "# assumes square images divisible by output_size\n",
        "def block_reduce_all(images, output_size):\n",
        "  if images.shape[1] % output_size != 0:\n",
        "    print(images.shape[1:], 'not divisible by', output_size)\n",
        "  block_size = images.shape[1] // output_size\n",
        "  reduced = images.reshape(-1, output_size, block_size, output_size, block_size)\n",
        "  reduced = np.transpose(reduced, (0, 2, 4, 1, 3))\n",
        "  reduced = reduced.reshape(-1, block_size*block_size, output_size, output_size)\n",
        "  reduced = reduced.mean(axis=1)\n",
        "  return reduced\n",
        "\n",
        "# resized img such that the new width is nx x tile width\n",
        "# and the new height is a multiple of the tile height\n",
        "def imresize_to_tiles(img, nx, tilesize):\n",
        "  th, tw = tilesize\n",
        "  iw = img.shape[1]\n",
        "  scale = (tw * nx) / iw\n",
        "  img = imresize(img, scale)\n",
        "  ih = (img.shape[0] // th) * th\n",
        "  img = img[:ih]\n",
        "  return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RWI6LqiipZU_",
        "colab_type": "code",
        "outputId": "22292e4c-e765-420a-e5df-cacbfda1cd75",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "# load from upload dialog\n",
        "upload = files.upload()\n",
        "fn = list(upload.keys())[0]\n",
        "img = imread(fn)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4be7901c-d9cd-47c0-98b9-e67d4bb5d1fa\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-4be7901c-d9cd-47c0-98b9-e67d4bb5d1fa\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving bear.jpg to bear.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "guMb5aazpmgN",
        "colab_type": "code",
        "outputId": "409d096c-4667-4cfe-c9bb-a6294405a683",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        }
      },
      "cell_type": "code",
      "source": [
        "nx = 100\n",
        "small_size = 4 # for 28x28 tiles try => 1, 2, 4, 7\n",
        "\n",
        "img = to_single_gray(img)\n",
        "img = imresize_to_tiles(img, nx, tiles.shape[1:])\n",
        "img_pieces = unmake_mosaic(img, nx)\n",
        "\n",
        "tiles_small = block_reduce_all(tiles, small_size)\n",
        "tiles_small = tiles_small.reshape(len(tiles_small), -1)\n",
        "tiles_small -= tiles_small.mean(axis=0)\n",
        "tiles_small /= tiles_small.std(axis=0)\n",
        "\n",
        "img_pieces_small = block_reduce_all(img_pieces, small_size)\n",
        "img_pieces_small = img_pieces_small.reshape(len(img_pieces_small), -1)\n",
        "img_pieces_small -= img_pieces_small.mean(axis=0)\n",
        "img_pieces_small /= img_pieces_small.std(axis=0)\n",
        "\n",
        "selected = []\n",
        "for piece in progress(img_pieces_small):\n",
        "  best = cdist(piece[np.newaxis,:], tiles_small, 'sqeuclidean').argmin()\n",
        "  selected.append(np.copy(tiles[best]))\n",
        "  tiles_small[best] = np.inf # avoid tile reuse\n",
        "\n",
        "reconstructed = make_mosaic(np.asarray(selected), nx)\n",
        "imshow(reconstructed, fmt='jpg', retina=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6600 0:00:06 1105.71/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {