TF03-histogram-exploration-sliding-window.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import filters\n",
    "import handler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # for debugging purposes\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import normalize\n",
    "from math import ceil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, _ = handler.getImageAsArray(handler.FOETUS_PATH_ORIGINAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create and Apply SWAHE Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "swahe = filters.SWAHE(maskSize = 127)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgNew = swahe.filter(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "handler.plotFigs([img, imgNew], swahe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import os.path\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "currentDir = Path().absolute()\n",
    "root = str(currentDir) + '\\\\..\\outputs\\\\hist_Adaptive_Equalise\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(root):\n",
    "    os.makedirs(root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create pillow image object from filtered image array\n",
    "img_PIL = Image.fromarray(imgNew[0], 'L')\n",
    "\n",
    "# Save filtered image from pillow image object\n",
    "filePath = root+'filtered_foetus_maskSize_52.png'\n",
    "img_PIL.save(filePath, 'PNG')\n",
    "print(\"Saved filtered image to... \\n{}\\n\\n\".format(filePath))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3rd Part Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use CV2 library built-in equalise and CLAHe functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import third_party_filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "third_party_filters.equalise(handler.FOETUS_PATH_ORIGINAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "third_party_filters.CLAHE(handler.FOETUS_PATH_ORIGINAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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