v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import importlib\n",
    "import os\n",
    "from ipywidgets import interact\n",
    "from ipywidgets import interact_manual\n",
    "from IPython.display import display\n",
    "from ipywidgets import widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'ipynb.fs.full.dicomHandler' (/mnt/6884ACCB84AC9CD6/OneDrive - FEI/Aulas/Graduacao/8 - Semestre/CC8421 - TRABALHO FINAL DE CURSO II/image-segmentation/dicomHandler.ipynb)>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipynb.fs.full.wspFFA as wspFirefly\n",
    "import ipynb.fs.full.wspCS as wspCuckooSearch\n",
    "import ipynb.fs.full.wspKH as wspKrillHerd\n",
    "import ipynb.fs.full.wspEHO as wspElephantHerding\n",
    "import ipynb.fs.full.wspABC as wspArtificialBeeColony\n",
    "\n",
    "import ipynb.fs.full.plotGraph as plotGraph\n",
    "import ipynb.fs.full.wspMultithreshold as wspMultithreshold\n",
    "import ipynb.fs.full.dicomHandler as dicomHandler\n",
    "\n",
    "importlib.reload(wspFirefly)\n",
    "importlib.reload(wspCuckooSearch)\n",
    "importlib.reload(wspKrillHerd)\n",
    "importlib.reload(wspElephantHerding)\n",
    "importlib.reload(wspArtificialBeeColony)\n",
    "\n",
    "importlib.reload(plotGraph)\n",
    "importlib.reload(wspMultithreshold)\n",
    "importlib.reload(dicomHandler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_dir = \"../Database/2EFVJVCN/\"\n",
    "file = \"I1680000\"\n",
    "bio_algorithms = 'FFA'\n",
    "dimension = 2\n",
    "q = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = folder_dir + file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2947\n",
      "int16\n",
      "int16\n",
      "0 2947\n"
     ]
    }
   ],
   "source": [
    "dicom_image, pixel_array = dicomHandler.read_dicom_image(image)\n",
    "    \n",
    "hu_image = dicomHandler.transform_to_hu(dicom_image, pixel_array)\n",
    "\n",
    "hist, bin_edges, best_thresholds, img_thres = wspMultithreshold.wspMultithreshold(hu_image, bio_algorithms, dimension, q)\n",
    "\n",
    "high_intensity = wspMultithreshold.get_high_intensity_pixels(img_thres)\n",
    "\n",
    "pixel_array = dicomHandler.transform_to_pixel_array(dicom_image, high_intensity)\n",
    "\n",
    "print(pixel_array.min(), pixel_array.max())\n",
    "\n",
    "hard_tissue = wspMultithreshold.get_largest_region(pixel_array)\n",
    "print(pixel_array.dtype)\n",
    "print(hard_tissue.dtype)\n",
    "print(hard_tissue.min(), hard_tissue.max())\n",
    "\n",
    "dicomHandler.save_dicom(dicom_image, hard_tissue,\"images/temp2.dcm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ , original_image = dicomHandler.read_dicom_image(\"images/temp2.dcm\")\n",
    "dicomHandler.show_dicom_image(original_image)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('tcc_env')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "50c3a715107fe4dcdc7dcc1225e41dc08fefd31cb688eb0c1f730ec66093fb86"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}