calcificationTestImageProcessing_LBP2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/emyesme/CalcificationDetection/blob/main/calcificationTestImageProcessing_LBP2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Libraries and Data Setup"
      ],
      "metadata": {
        "id": "kXWym_EKAKUN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import libraries"
      ],
      "metadata": {
        "id": "P5KF-GomVYsN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Wlt8za56vXZ",
        "outputId": "a89ddb7e-a166-4513-e99a-05cf0072f0a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (4.1.2.30)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python) (1.21.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.8)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.6)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (0.18.3)\n",
            "Collecting scikit-image\n",
            "  Downloading scikit_image-0.19.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (13.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.5 MB 14.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (2.6.3)\n",
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            "Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (1.21.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (2021.11.2)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (2.4.1)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (7.1.2)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (1.4.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->scikit-image) (3.0.8)\n",
            "Installing collected packages: scikit-image\n",
            "  Attempting uninstall: scikit-image\n",
            "    Found existing installation: scikit-image 0.18.3\n",
            "    Uninstalling scikit-image-0.18.3:\n",
            "      Successfully uninstalled scikit-image-0.18.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed scikit-image-0.19.2\n"
          ]
        }
      ],
      "source": [
        "# just once to install opencv\n",
        "!pip install opencv-python\n",
        "!pip install matplotlib\n",
        "!pip install numpy\n",
        "#!pip install google-colab\n",
        "\n",
        "#!pip install PyWavelets\n",
        "#!pip install image_dehazer\n",
        "!pip install -U scikit-image\n",
        "#!pip install fastprogress\n",
        "from fastprogress import master_bar, progress_bar\n",
        "\n",
        "# import opencv\n",
        "import cv2\n",
        "# import numpy\n",
        "import numpy as np\n",
        "import math\n",
        "from skimage import feature\n",
        "import itertools\n",
        "#import show special for google colab\n",
        "from google.colab.patches import cv2_imshow\n",
        "#import plt for display\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Drive Setup"
      ],
      "metadata": {
        "id": "XIR8JLtxAB3j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5i-G98GNpyYt",
        "outputId": "09aee3dd-2984-4792-8bf2-185ebbdfbf79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive') # This will prompt for authorization."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We put a shortcut in our drive to the image processing folder for this."
      ],
      "metadata": {
        "id": "lPVc5BwPAacs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "5st-OLpSqZ6A"
      },
      "outputs": [],
      "source": [
        "import os \n",
        "DATA_DIR = os.path.join('/content',\n",
        "                        'drive',\n",
        "                        'MyDrive',\n",
        "                        'Image Processing and Analysis 2022',\n",
        "                        'projects',\n",
        "                        'Calcification Detection',\n",
        "                        'dataset')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Setup"
      ],
      "metadata": {
        "id": "9qro11DuuHQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#go into de directory of the images\n",
        "\n",
        "# this have 3 outputs root directory, the folders in the path and the files in the path.\n",
        "# we ignore _ the two first because we are not interested in those\n",
        "_, _, images = next(os.walk(os.path.join(DATA_DIR,'images')))\n",
        "_, _, breastMasks = next(os.walk(os.path.join(DATA_DIR,'masks')))\n",
        "_, _, groundTruths = next(os.walk(os.path.join(DATA_DIR, 'groundtruths')))\n",
        "\n",
        "images.sort()\n",
        "breastMasks.sort()\n",
        "groundTruths.sort()\n",
        "\n",
        "# read numbers of normal images\n",
        "normals = []\n",
        "with open(os.path.join(DATA_DIR,'normals.txt')) as f:\n",
        "    for line in f:\n",
        "        normals.append(line[:-1])"
      ],
      "metadata": {
        "id": "qVa4SLoMuJnG"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Google Colab Like a Pro"
      ],
      "metadata": {
        "id": "UrNskk4euP8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# https://medium.com/@robertbracco1/configuring-google-colab-like-a-pro-d61c253f7573#a642\n",
        "%%javascript\n",
        "function ClickConnect(){\n",
        "console.log(\"Working\");\n",
        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
        "}setInterval(ClickConnect,60000)"
      ],
      "metadata": {
        "id": "F95xA4V4uUZI",
        "outputId": "27229a2f-ee64-4c5c-cb4f-3b496ad9e53e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "function ClickConnect(){\n",
              "console.log(\"Working\");\n",
              "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
              "}setInterval(ClickConnect,60000)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ],
      "metadata": {
        "id": "fLGkMjUYVev7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DeHazing Using Dark Channel Prior and Guided Filter"
      ],
      "metadata": {
        "id": "XYzla-c2R8l_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dehazing method proposed by the professor (also used in his paper)\n",
        "\n",
        "Taken from here:\n",
        "https://github.com/He-Zhang/image_dehaze\n",
        "Info on readme.md of the repo"
      ],
      "metadata": {
        "id": "0uY5uR3KSGBi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dark Channel"
      ],
      "metadata": {
        "id": "_9cQrJMPS5LT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here goes the theory behind this function "
      ],
      "metadata": {
        "id": "0E0GdS_BTOAb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Here goes inputs --> output types\n",
        "def DarkChannel(im,sz):\n",
        "    b,g,r = cv2.split(im)\n",
        "    dc = cv2.min(cv2.min(r,g),b);\n",
        "    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(sz,sz))\n",
        "    dark = cv2.erode(dc,kernel)\n",
        "    return dark"
      ],
      "metadata": {
        "id": "oahKNNk_TCQ1"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### AtmLight"
      ],
      "metadata": {
        "id": "PYBQgzyNkKDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Possibly change to grayscale would be nice\n",
        "def AtmLight(im,dark):\n",
        "    [h,w] = im.shape[:2]\n",
        "    imsz = h*w\n",
        "    numpx = int(max(math.floor(imsz/1000),1))\n",
        "    darkvec = dark.reshape(imsz);\n",
        "    imvec = im.reshape(imsz,3);\n",
        "\n",
        "    indices = darkvec.argsort();\n",
        "    indices = indices[imsz-numpx::]\n",
        "\n",
        "    atmsum = np.zeros([1,3])\n",
        "    for ind in range(1,numpx):\n",
        "       atmsum = atmsum + imvec[indices[ind]]\n",
        "\n",
        "    A = atmsum / numpx;\n",
        "    return A"
      ],
      "metadata": {
        "id": "3o_LF4EXj7ut"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TransmissionEstimate"
      ],
      "metadata": {
        "id": "HgNCi3U-kNhR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def TransmissionEstimate(im,A,sz):\n",
        "    omega = 0.95;# the closer to 1 the stronger the darkenning\n",
        "    im3 = np.empty(im.shape,im.dtype);\n",
        "\n",
        "    for ind in range(0,3):\n",
        "        im3[:,:,ind] = im[:,:,ind]/A[0,ind]\n",
        "\n",
        "    transmission = 1 - omega*DarkChannel(im3,sz);\n",
        "    return transmission"
      ],
      "metadata": {
        "id": "B9564anxkREK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GuidedFilter"
      ],
      "metadata": {
        "id": "ujgwt8eakTwm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Guidedfilter(im,p,r,eps):\n",
        "    mean_I = cv2.boxFilter(im,cv2.CV_64F,(r,r));\n",
        "    mean_p = cv2.boxFilter(p, cv2.CV_64F,(r,r));\n",
        "    mean_Ip = cv2.boxFilter(im*p,cv2.CV_64F,(r,r));\n",
        "    cov_Ip = mean_Ip - mean_I*mean_p;\n",
        "\n",
        "    mean_II = cv2.boxFilter(im*im,cv2.CV_64F,(r,r));\n",
        "    var_I   = mean_II - mean_I*mean_I;\n",
        "\n",
        "    a = cov_Ip/(var_I + eps);\n",
        "    b = mean_p - a*mean_I;\n",
        "\n",
        "    mean_a = cv2.boxFilter(a,cv2.CV_64F,(r,r));\n",
        "    mean_b = cv2.boxFilter(b,cv2.CV_64F,(r,r));\n",
        "\n",
        "    q = mean_a*im + mean_b;\n",
        "    return q;"
      ],
      "metadata": {
        "id": "pQwICn9skXtP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TransmissionRefine"
      ],
      "metadata": {
        "id": "4tB3quNxkbJv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def TransmissionRefine(im,et):\n",
        "    gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY);\n",
        "    gray = np.float64(gray)/255;\n",
        "    r = 60;\n",
        "    eps = 0.0001;\n",
        "    t = Guidedfilter(gray,et,r,eps);\n",
        "\n",
        "    return t;"
      ],
      "metadata": {
        "id": "r_GHwktLkbkv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Recover"
      ],
      "metadata": {
        "id": "6nOv5qYxkjqP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Recover(im,t,A,tx = 0.1):\n",
        "    res = np.empty(im.shape,im.dtype);\n",
        "    t = cv2.max(t,tx);\n",
        "\n",
        "    for ind in range(0,3):\n",
        "        res[:,:,ind] = (im[:,:,ind]-A[0,ind])/t + A[0,ind]\n",
        "    return res"
      ],
      "metadata": {
        "id": "VuIef3qHkn5I"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### deHazingDarkChannelPriorPy"
      ],
      "metadata": {
        "id": "S_V-ibO6koPC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "v9lVjTTcoC2C"
      },
      "outputs": [],
      "source": [
        "def deHazingDarkChannelPriorPy(matrix, mask):\n",
        "\n",
        "    I = matrix.astype(np.float64)/255\n",
        " \n",
        "    dark = DarkChannel(I,15)\n",
        "    A = AtmLight(I,dark)\n",
        "    te = TransmissionEstimate(I,A,15)\n",
        "    t = TransmissionRefine(matrix,te)\n",
        "    J = Recover(I,t,A,0.1)\n",
        "    preprocessed = J\n",
        "    return preprocessed\n",
        "\n",
        "# image = cv2.imread(DATA_DIR+\"/images/53582422_3f0db31711fc9795_MG_R_ML_ANON.tif\")\n",
        "# dark, t, matrix, J = deHazingDarkChannelPriorPy(image, image)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image Dilation"
      ],
      "metadata": {
        "id": "ZAa2ldjTlMWA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def imgDilation(matrix):\n",
        "  kernel = np.ones((3,3), np.uint8)\n",
        "  img_dilation = cv2.dilate(matrix, kernel, iterations=3)\n",
        "  return img_dilation"
      ],
      "metadata": {
        "id": "8D4EdMZolL_j"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CLAHE"
      ],
      "metadata": {
        "id": "TGh49rq8MvPt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def imgCLAHE(matrix):\n",
        "  matrix = matrix.astype(np.uint16)\n",
        "  #gray = cv2.cvtColor(matrix, cv2.COLOR_RGB2GRAY)\n",
        "  \n",
        "  clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
        "  cl1 = clahe.apply(matrix)\n",
        "  return cl1"
      ],
      "metadata": {
        "id": "J4nRkl0aM00c"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## De-noising"
      ],
      "metadata": {
        "id": "Li2hgonWoVIf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def gaussianBlur(matrix):\n",
        "  img_blurred = cv2.GaussianBlur(matrix, (5,5), 0)\n",
        "  return img_blurred"
      ],
      "metadata": {
        "id": "cTAZIg1RoauO"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Other Tried Methods"
      ],
      "metadata": {
        "id": "KFc9qVWuu2ng"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CLAHE (adaptive histogram equalization)**\n",
        "  * CLAHE + dehazing = bad results, black image \n",
        "  * points less visible with CLAHE\n",
        "\n",
        "**Linear stretching**\n",
        "  * still missing linear streching\n",
        "  * The code goes on forever (high computational cost)\n",
        "\n"
      ],
      "metadata": {
        "id": "OY0FcYiDvfZG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Candidate Extraction"
      ],
      "metadata": {
        "id": "vNWyVrjyVvuW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Hessian-Matrix-Based Analysis"
      ],
      "metadata": {
        "id": "_BG3WCxXZNpv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hessian-matrix-based analysis or difference of gaussians (DoH) blob detection from skimage:\n",
        "\n",
        "https://scikit-image.org/docs/stable/api/skimage.feature.html?highlight=local%20binary%20pattern#skimage.feature.blob_doh"
      ],
      "metadata": {
        "id": "_FPkl7iJlJCC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "f0KHFD9fFC9y"
      },
      "outputs": [],
      "source": [
        "def candidateExtraction(matrix, mask):\n",
        "\n",
        "  from skimage import feature\n",
        "  \n",
        "  # returns x,y,sigma of the blob\n",
        "  blobs = feature.blob_doh(matrix,\n",
        "                           min_sigma=1,\n",
        "                           max_sigma=30,\n",
        "                           num_sigma=10,\n",
        "                           # The absolute lower bound for scale space maxima.\n",
        "                           # Local maxima smaller than threshold are ignored.\n",
        "                           # Reduce this to detect blobs with lower intensities.\n",
        "                           # If threshold_rel is also specified, whichever threshold is larger will be used.\n",
        "                           # If None, threshold_rel is used instead.\n",
        "                           threshold=0.005,\n",
        "                           # lower more sensible, more false positives bad also tinier calcifications detected\n",
        "                           overlap=0.5,\n",
        "                           log_scale=False,\n",
        "                           threshold_rel=None\n",
        "                           )\n",
        "  # taken from the documentation\n",
        "  # ...The downside is that this method can’t be used for detecting blobs of radius less than 3px\n",
        "  # due to the box filters used in the approximation of Hessian Determinant.\n",
        "  result = blobs\n",
        "  return result"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Difference of Gaussians"
      ],
      "metadata": {
        "id": "Hukzu-tbZaLh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Taken from:\n",
        "\n",
        "https://scikit-image.org/docs/stable/api/skimage.feature.html?highlight=local%20binary%20pattern#skimage.feature.blob_dog"
      ],
      "metadata": {
        "id": "Ik72WpwDlUet"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from math import sqrt\n",
        "def candidateExtractionDoG(matrix, mask):\n",
        "  from skimage import data, feature\n",
        "\n",
        "  blobs = feature.blob_dog(matrix,\n",
        "                           min_sigma=0.005,\n",
        "                           max_sigma=50,\n",
        "                           threshold=0.04)\n",
        "\n",
        "  blobs[:, 2] = blobs[:, 2] * sqrt(2) # https://scikit-image.org/docs/stable/auto_examples/features_detection/plot_blob.html\n",
        "\n",
        "  result = blobs\n",
        "  return result"
      ],
      "metadata": {
        "id": "CK8xuxdkZEwU"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Extraction"
      ],
      "metadata": {
        "id": "von-0yGEXbzT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get Ground Truth for each patch\n",
        "def patchGroundTruth(candidate, groundTruth):\n",
        "\n",
        "  left = int((candidate[0] - candidate[2]) if ((candidate[0] - candidate[2]) > 0) else 0)\n",
        "  right = int((candidate[0] + candidate[2]) if ((candidate[0] + candidate[2]) < groundTruth.shape[0]) else groundTruth.shape[0])\n",
        "  top = int((candidate[1] - candidate[2]) if ((candidate[1] - candidate[2]) > 0) else 0)\n",
        "  bottom = int((candidate[1] + candidate[2]) if ((candidate[1] + candidate[2]) < groundTruth.shape[1]) else groundTruth.shape[1])\n",
        "\n",
        "  truePatch = groundTruth[left : right,\n",
        "                          top  : bottom]\n",
        "  sum = np.sum(truePatch)\n",
        "  if sum > 0:\n",
        "    return str(1)\n",
        "  else:\n",
        "    return str(0)"
      ],
      "metadata": {
        "id": "fe7aJb2YhZ9a"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Documentation and example on LBP:\n",
        "\n",
        "\n",
        "\n",
        "*   https://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.local_binary_pattern\n",
        "*   https://scikit-image.org/docs/dev/auto_examples/features_detection/plot_local_binary_pattern.html\n",
        "\n",
        "Reference Paper:\n",
        "\n",
        "\n",
        "\n",
        "*   Sadad T, Munir A, Saba T, Hussain A, Fuzzy C-Means and Region Growing based Classification of Tumor from Mammograms using Hybrid Texture Features, Journal of Computational Science (2018), https://doi.org/10.1016/j.jocs.2018.09.015\n",
        "\n",
        "Input of LBP:\n",
        "\n",
        "Grayscale image, radius (pixel distance from center point), points (number of surrounding pixels to be taken at the defined radius, usually 8*radius), and method.\n",
        "\n",
        "Output of LBP:\n",
        "\n",
        "LBP grayscale image for textural classification\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "a1WRbRlQ3LXV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Video tutorial on how to use GLCM:\n",
        "\n",
        "* https://www.youtube.com/watch?v=5x-CIHRmMNY\n",
        "\n",
        "Documentation and example on GLCM:\n",
        "\n",
        "* https://scikit-image.org/docs/dev/auto_examples/features_detection/plot_glcm.html\n",
        "\n",
        "Paper:\n",
        "\n",
        "*  https://ijcrr.com/uploads/3454_pdf.pdf\n",
        "\n",
        "GLCM Properties documentation:\n",
        "\n",
        "* https://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.graycoprops\n",
        "\n",
        "GLCM Gray co-matrix documentation:\n",
        "\n",
        "* https://stackoverflow.com/questions/54512617/creating-gray-level-co-occurrence-matrix-from-16-bit-image\n",
        "(why can't we have 16 bit type for the coocurrence matrix computation)\n",
        "\n",
        "* https://www.sciencedirect.com/topics/engineering/cooccurrence-matrix (simple explanation GLCM)"
      ],
      "metadata": {
        "id": "MfBTU_XZoIsZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Output of the GLCM command: \n",
        "\n",
        "the gray-level co-occurrence histogram. The value P[i,j,d,theta]\n",
        "is the number of times that gray-level j occurs at a distance d\n",
        "and at an angle theta from gray-level i.\n",
        "If normed is False, the output is of type uint32, otherwise it is float64.\n",
        "The dimensions are: levels x levels x number of distances x number of angles."
      ],
      "metadata": {
        "id": "PUWQRdESsaXf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "23WbyaI2FhzF"
      },
      "outputs": [],
      "source": [
        "# Using GLCM with the given candidates\n",
        "def featuresExtraction(matrix, candidates, features, mask, groundTruth, image, folder):\n",
        "    \n",
        "  from skimage.feature import local_binary_pattern\n",
        "\n",
        "  # No candidates, no extraction needed\n",
        "  if (len(candidates) == 0):\n",
        "    return []\n",
        "  # angles\n",
        "  angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]\n",
        "\n",
        "  flag = True\n",
        "  for index, candidate in enumerate(progress_bar(candidates)):\n",
        "\n",
        "    distances = [1] # probably we need bigger\n",
        "\n",
        "    # combination of distances and angles as couples of values\n",
        "    distancesAngles = list(itertools.product(distances, angles))\n",
        "\n",
        "    # to use them as coordinates they have to be integers\n",
        "    \n",
        "    candidate = candidate.astype(np.int64)\n",
        "\n",
        "    # changed for LBP+GLCM\n",
        "    \n",
        "    if candidate[2] == 0:\n",
        "      candidate[2] = 1\n",
        "\n",
        "\n",
        "    # candidates are y,x and sigma\n",
        "    left = int((candidate[0] - candidate[2]) if ((candidate[0] - candidate[2]) > 0) else 0)\n",
        "    right = int((candidate[0] + candidate[2]) if ((candidate[0] + candidate[2]) < matrix.shape[0]) else matrix.shape[0])\n",
        "    top = int((candidate[1] - candidate[2]) if ((candidate[1] - candidate[2]) > 0) else 0)\n",
        "    bottom = int((candidate[1] + candidate[2]) if ((candidate[1] + candidate[2]) < matrix.shape[1]) else matrix.shape[1])\n",
        "\n",
        "    patchCandidate = matrix[left: right,\n",
        "                            top : bottom]\n",
        "\n",
        "    \n",
        "    # LBP received grayscale\n",
        "\n",
        "    radius= 1\n",
        "    points= 8 * radius\n",
        "\n",
        "    patchCandidate = cv2.cvtColor(patchCandidate, cv2.COLOR_BGR2GRAY)\n",
        "    patchCandidate = local_binary_pattern(patchCandidate, points, radius, method='default')\n",
        "\n",
        "    # graycomatrix, glcm, receive unsigned integer type\n",
        "    patchCandidate = patchCandidate.astype(np.uint8)\n",
        "\n",
        "    \n",
        "    dictFeatures = {}\n",
        "    dictFeatures = {'name': 'patch_' + str(index) + '_' + str(image.split(\".\")[0]),\n",
        "                    'label': patchGroundTruth(candidate, groundTruth)}\n",
        "\n",
        "    for distanceAngle in distancesAngles:\n",
        "      distance = distanceAngle[0]\n",
        "      angle = distanceAngle[1]\n",
        "      \n",
        "      # get the degree to use it as name for the column\n",
        "      name = str(angle*(180.0/np.pi))\n",
        "\n",
        "      # input image, distance in pixels, angles\n",
        "      glcm = feature.graycomatrix(patchCandidate, [ distance ], [ angle ])\n",
        "\n",
        "      # properties\n",
        "      dictFeatures['LBP' + 'contrast'+ str(distance) + name] = feature.graycoprops(glcm, 'contrast')[0][0]\n",
        "      dictFeatures['LBP' +'dissimilarity' + str(distance) + name] = feature.graycoprops(glcm, 'dissimilarity')[0][0]\n",
        "      dictFeatures['LBP' +'homogeneity' + str(distance) + name] = feature.graycoprops(glcm, 'homogeneity')[0][0]\n",
        "      dictFeatures['LBP' +'energy' + str(distance) + name] = feature.graycoprops(glcm, 'energy')[0][0]\n",
        "      dictFeatures['LBP' +'correlation' + str(distance) + name] = feature.graycoprops(glcm, 'correlation')[0][0]\n",
        "      dictFeatures['LBP' +'ASM' + str(distance) + name] = feature.graycoprops(glcm, 'ASM')[0][0]\n",
        "\n",
        "    # add to the dataframe the features for this patch\n",
        "    features = features.append(dictFeatures, ignore_index=True)\n",
        "\n",
        "  # save in the csv\n",
        "  flag = writeFeatures(features, flag, folder, image, \"LBPfeatures\")\n",
        "\n",
        "  return features"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def writeFeatures(features, flag, folder, image, name):\n",
        "  if(flag):\n",
        "    features.to_csv(os.path.join('/content',\n",
        "                                 'drive',\n",
        "                                 'MyDrive',\n",
        "                                 'Results',\n",
        "                                 folder,\n",
        "                                 str(image.split('.')[0]) + '_'+ name + '.csv'),\n",
        "                    mode='a',\n",
        "                    index=False)\n",
        "    flag = False\n",
        "  else:\n",
        "    features.to_csv(os.path.join('/content',\n",
        "                                 'drive',\n",
        "                                 'MyDrive',\n",
        "                                 'Results',\n",
        "                                 folder,\n",
        "                                 str(image.split('.')[0]) + '_'+ name + '.csv'),\n",
        "                  mode='a',\n",
        "                  header=False,\n",
        "                  index=False)\n",
        "  return flag"
      ],
      "metadata": {
        "id": "KacVrfAgo1Lr"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Connected Components"
      ],
      "metadata": {
        "id": "rYRi_ffVWClo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "d7G2psxqargQ"
      },
      "outputs": [],
      "source": [
        "# function to get connected components of the ground truth binary image\n",
        "def componentsStatsGroundTruth(matrix):\n",
        "    # getting the info of the components in the ground truth\n",
        "    # second value is connectivity 4 or 8\n",
        "    connectedComponentsGroundTruth = cv2.connectedComponentsWithStats(matrix, 8, cv2.CV_32S)\n",
        "\n",
        "    # Get the results\n",
        "    # The first cell is the number of labels\n",
        "    num_labels = connectedComponentsGroundTruth[0]\n",
        "    # The second cell is the label matrix\n",
        "    labels = connectedComponentsGroundTruth[1]\n",
        "    # The third cell is the stat matrix\n",
        "    stats = connectedComponentsGroundTruth[2]\n",
        "    # The fourth cell is the centroid matrix\n",
        "    centroids = connectedComponentsGroundTruth[3]\n",
        "\n",
        "    return num_labels, labels, stats, centroids"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Show Images"
      ],
      "metadata": {
        "id": "pqItorT9Wr06"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "G6mejNA1fgIq"
      },
      "outputs": [],
      "source": [
        "from matplotlib.patches import Circle\n",
        "import matplotlib.patches as mpatches\n",
        "\n",
        "# function to draw the grid to display\n",
        "def display_grid(figure, axis, img, imgGroundTruth, preprocessed, candidates, features):\n",
        "  # draw in the axis the img\n",
        "  axis[0][0].imshow(img)\n",
        "  # switch off the axis of the plot\n",
        "  axis[0][0].axis('off')\n",
        "  # set a title for the plot\n",
        "  axis[0][0].set_title('Image')\n",
        "\n",
        "  axis[0][1].imshow(imgGroundTruth, cmap='gray')\n",
        "  axis[0][1].axis('off')\n",
        "  axis[0][1].set_title('Ground Truth')\n",
        "\n",
        "  axis[0][2].imshow(imgMask)\n",
        "  axis[0][2].axis('off')\n",
        "  axis[0][2].set_title('Breast Mask')\n",
        "\n",
        "  axis[1][0].imshow(preprocessed, cmap='gray')\n",
        "  axis[1][0].axis('off')\n",
        "  axis[1][0].set_title('Preprocessed')\n",
        "\n",
        "  # draw candidates as circles\n",
        "  axis[1][1].imshow(preprocessed, cmap='gray')\n",
        "  axis[1][1].axis('off')\n",
        "  axis[1][1].set_title('Candidates')\n",
        "\n",
        "  # Now, loop through coord arrays, and create a circle at each x,y pair\n",
        "  for y,x,sigma in candidates:\n",
        "\n",
        "    blob = Circle((x,y), sigma*5, color='blue', fill=False)\n",
        "    axis[1][1].add_patch(blob)\n",
        "\n",
        "    rect=mpatches.Rectangle((x,y),sigma,sigma, \n",
        "                        fill=False,\n",
        "                        color=\"red\",\n",
        "                       linewidth=2)\n",
        "    axis[1][1].add_patch(rect)\n",
        "\n",
        "  axis[1][2].imshow(imgGroundTruth, cmap='gray')\n",
        "  axis[1][2].axis('off')\n",
        "  axis[1][2].set_title('compare with ground truth and candidates')\n",
        "\n",
        "  # Now, loop through coord arrays, and create a circle at each x,y pair\n",
        "  for y,x,sigma in candidates:\n",
        "    blob = Circle((x,y), sigma, color='blue', fill=False)\n",
        "    axis[1][2].add_patch(blob)\n",
        "  \n",
        "  return figure, axis"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Main"
      ],
      "metadata": {
        "id": "7GHlP95OW6e6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 891
        },
        "id": "qqqX2PGi6-X8",
        "outputId": "71e41073-b7bb-415a-964e-e9c3960ace02"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='410' class='' max='410' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [410/410 01:58<00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='2653' class='' max='2653' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [2653/2653 00:40<00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2653\n"
          ]
        }
      ],
      "source": [
        "import copy\n",
        "import pandas as pd\n",
        "\n",
        "#go through the image files \n",
        "for image, breastMask, groundTruth in zip(progress_bar(images), breastMasks, groundTruths):\n",
        "\n",
        "  # to save the features generated with the glcm\n",
        "  features = pd.DataFrame(dtype=np.float64)\n",
        "\n",
        "  # 20588020, 7717, 5328, 3787, 5725, 3859, 6934, 50995872\n",
        "  digits = '6934'\n",
        "\n",
        "  if ((digits in image) and (digits in breastMask) and ('mask' in breastMask)):\n",
        "  #if ('mask' in breastMask):\n",
        "  #if image not in already:\n",
        "    #upload images\n",
        "    img = cv2.imread(os.path.join(DATA_DIR,'images',image))\n",
        "    imgMask = cv2.imread(os.path.join(DATA_DIR, 'masks', breastMask))\n",
        "    imgGroundTruth = cv2.imread(os.path.join(DATA_DIR, 'groundtruths', image), cv2.IMREAD_GRAYSCALE)\n",
        "    imgCopy = copy.deepcopy(img)\n",
        "\n",
        "    imgCopy = cv2.cvtColor(imgCopy, cv2.COLOR_RGB2GRAY)\n",
        "    preprocessed = imgCLAHE(imgCopy)\n",
        "    preprocessed = cv2.cvtColor(preprocessed, cv2.COLOR_GRAY2RGB)\n",
        "    preprocessed = deHazingDarkChannelPriorPy(preprocessed, imgMask)\n",
        "       \n",
        "    \n",
        "    preprocessedBlurDil = imgDilation(preprocessed)\n",
        "\n",
        "\n",
        "    # candidate extraction #\n",
        "    copyPreprocessed = copy.deepcopy(preprocessed)\n",
        "    copyPreprocessedBlurDil = copy.deepcopy(preprocessedBlurDil)\n",
        "\n",
        "         # we have to change np.float64 to np.float32 for the grayscale conversion\n",
        "    copyPreprocessedBlurDil = copyPreprocessedBlurDil.astype(np.float32)\n",
        "    copyPreprocessedBlurDil = cv2.cvtColor(copyPreprocessedBlurDil, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "    candidates = candidateExtractionDoG(copyPreprocessedBlurDil, imgMask)\n",
        "\n",
        "    # feature extraction #\n",
        "\n",
        "    copyPreprocessed = copyPreprocessed.astype(np.float32)\n",
        "    \n",
        "\n",
        "#    copyPreprocessed = cv2.cvtColor(copyPreprocessed, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "    features = featuresExtraction(copyPreprocessed, candidates, features, imgMask, imgGroundTruth, image, \"TestLBP\")\n",
        "    \n",
        "    # ML must be applied for the classification of the features extracted\n",
        "\n",
        "    ################ ERASE MEMORY\n",
        "    # import gc\n",
        "    # del features\n",
        "    # del preprocessed\n",
        "    # del candidates\n",
        "    # # del blobs\n",
        "    # del copyPreprocessed\n",
        "    # del imgCopy\n",
        "    # del img\n",
        "    # del imgMask\n",
        "    # del imgGroundTruth\n",
        "    # gc.collect()\n",
        "    ############################\n",
        "\n",
        "    # end image processing part #\n",
        "\n",
        "    # display related #\n",
        "\n",
        "    # matrix of plots and size of the figure\n",
        "    figure, axis = plt.subplots(2, 3, figsize=(15,15))\n",
        "    display_grid(figure, axis, img, imgGroundTruth, preprocessed, candidates, features)\n",
        "    plt.subplots_adjust(wspace=0, hspace=0)\n",
        "\n",
        "    # display figure with image\n",
        "    plt.show() \n",
        "    print(len(candidates))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CONCLUSIONS FOR PREPROCESSING"
      ],
      "metadata": {
        "id": "VRSMyeuNzT_W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* still missing quantum noise supression\n",
        "\n",
        "* details in the phd defense file\n",
        "\n",
        "* Observations from the results:\n",
        "    * fiber intersections may also appear as bright spots (false positives)\n",
        "\n",
        "* THINGS WE NOTICE BETWEEN BOTH DEHAZING METHODS\n",
        "    * Better suppression of fatty tissue (noise) and greater enhancement of brightness of desired feature (microcalcifications)\n",
        "    * sometimes for the other dehazing method black patches become present in the fatty tissue\n",
        "    * this did not happen in the dehazing with dark channel prior (and guided filter)\n",
        "    * sharper\n",
        "    * enhances the contrast and details\n",
        "\n",
        "* Observations from the results:\n",
        "    * images with pectoral muscule cause false positives"
      ],
      "metadata": {
        "id": "7KoeGI1Nzbtn"
      }
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "calcificationTestImageProcessing_LBP2.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
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