Layered_BDM_Python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Layered BDM - Python",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2O__gdBvHo-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import skimage.io as io\n",
        "from PIL import Image \n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HY5i_mjqxrIx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_url = 'https://raw.githubusercontent.com/andandandand/ImageAnalysisWithAlgorithmicInformation/master/fourByFourCTMs.csv'\n",
        "ctms_df = pd.read_csv(data_url, \n",
        "                      header=None, \n",
        "                      names=['block', 'ctm_value'],\n",
        "                      dtype={'block': object})\n",
        "\n",
        "ctms_dict = dict(zip(ctms_df['block'].values, ctms_df['ctm_value'].values))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PR_XibwvO4_",
        "colab_type": "code",
        "outputId": "4dec0c68-3cfc-418a-ca76-097c8985dd00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "imag_array = io.imread('https://github.com/andandandand/ImageAnalysisWithAlgorithmicInformation/blob/master/images/t2BrainSlice.png?raw=true')\n",
        "Image.fromarray(imag_array)"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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s3MYuB23kFJ1lmS2IzW1hT32ToW/+xiXXxy8lIIKyhGX0yMUqw/ZZ0FszP5Cw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\n7vCro3LZi2GUUwP1wc5dj2thdXAr3bx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NrCksgM6bfgqCrokrgp0E2aP+t7OL9XV9BvnD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\n4EhuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=256x256 at 0x7FCD3B74EDA0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-9YIA68ALOr",
        "colab_type": "code",
        "outputId": "91124400-b8ce-44f0-af10-d3343e83bc85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "imag_array.shape[0]"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "256"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yEGaFu18_-7l",
        "colab_type": "code",
        "outputId": "1e2c1f61-bdf3-4d5c-e901-a1172280c9e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "mini_array = imag_array[128:128+8, 128:128+8]\n",
        "mini_array"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Array([[ 77,  46,  58,  58,  74,  54,  78,  69],\n",
              "       [ 93,  53,  66,  71,  56,  56,  62,  64],\n",
              "       [ 91,  67,  68,  55,  57,  67,  75,  62],\n",
              "       [136,  71,  64,  64,  66,  63,  65,  80],\n",
              "       [146,  82,  63,  61,  57,  79,  48,  61],\n",
              "       [140, 102,  67,  57,  65,  52,  56,  43],\n",
              "       [160, 103,  54,  50,  64,  70,  65,  50],\n",
              "       [166,  81,  56,  56,  54,  63,  62,  49]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiRdke_SGtok",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from skimage.util.shape import view_as_windows\n",
        "\n",
        "def layered_bdm(imag_array):\n",
        "  raw_blocks       = view_as_windows(imag_array, (4, 4), step = 1)\n",
        "  i, j, _, _       = raw_blocks.shape\n",
        "  flattened_blocks = [raw_blocks[x][y].flatten() for x in range(i) for y in range(j)]\n",
        "\n",
        "  binary_blocks = []\n",
        "  for block in flattened_blocks:\n",
        "    for thresh in range(255):\n",
        "      bin_array = list((block == thresh).astype(np.uint8))    \n",
        "      binary_blocks.append(''.join(map(str, bin_array)))\n",
        "\n",
        "  unique_blocks, block_counts = np.unique(binary_blocks, return_counts=True)\n",
        "\n",
        "  return(np.sum([ctms_dict[block] for block in unique_blocks]) + np.sum(np.log2(block_counts)))\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pC59_xa2IVGn",
        "colab_type": "code",
        "outputId": "da74f28a-3386-407b-94aa-aa8f13423871",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "layered_bdm(mini_array)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1817.8776670932257"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    }
  ]
}