characteristics/characteristics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/charlie/projects/entropy/preprocessed/canny/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/treatment/active/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/treatment/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/treatment/active/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/treatment/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/reactive/inactive/\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_787165/4830768.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0mdict_name_reactive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_reactive'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0mdict_name_reactive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skimage import io\n",
    "import util\n",
    "import cv2\n",
    "\n",
    "\n",
    "parent_dir = r\"/home/charlie/projects/entropy/preprocessed/\"\n",
    "\n",
    "\n",
    "# Create dictionaries to store the results\n",
    "#active_stats: list[tuple[tuple, float]]\n",
    "#inactive_stats: list[tuple[tuple, float]]\n",
    "\n",
    "faz_area = {}\n",
    "faz_circularity = {}\n",
    "vessel_density = {}\n",
    "vessel_length_density = {}\n",
    "\n",
    "\n",
    "#第一層 列出前處理方式種類\n",
    "for dirname in os.listdir(parent_dir):\n",
    "    if (dirname.endswith(\".DS_store\")):\n",
    "        continue\n",
    "    \n",
    "    #each image has up to two parameters, use 'parameter' list to store them\n",
    "    parameter = []\n",
    "    \n",
    "    #each image has up to two parameters, use four lists to store the metrics\n",
    "    reactive_active1 = []\n",
    "    reactive_inactive1 = []\n",
    "    treatment_active1 = []\n",
    "    treatment_inactive1 = []\n",
    "    \n",
    "    reactive_active2 = []\n",
    "    reactive_inactive2 = []\n",
    "    treatment_active2 = []\n",
    "    treatment_inactive2 = []\n",
    "       \n",
    "    type_dir = parent_dir + dirname+'/'\n",
    "    print(type_dir)\n",
    "    \n",
    "    #第二層 列出前處理方式\n",
    "    for dirname1 in os.listdir(type_dir):\n",
    "        if (dirname1.endswith(\".DS_store\")):\n",
    "            continue\n",
    "        \n",
    "        pre_dir = type_dir + dirname1+'/'\n",
    "        print(pre_dir)\n",
    "        \n",
    "        #第三層 列出reactive/treatment\n",
    "        for dirname2 in os.listdir(pre_dir):\n",
    "            if (dirname2.endswith(\".DS_store\")):\n",
    "                continue\n",
    "            \n",
    "            cond_dir = pre_dir + dirname2+'/'\n",
    "            print(cond_dir)\n",
    "            \n",
    "            #第四層 列出active/inactive\n",
    "            for dirname3 in os.listdir(cond_dir):\n",
    "                if (dirname3.endswith(\".DS_Store\")):\n",
    "                    continue\n",
    "                    \n",
    "                amd_dir = cond_dir + dirname3+'/'\n",
    "                print(amd_dir)\n",
    "\n",
    "                #列出目錄中的所有圖像\n",
    "                for filename in os.listdir(amd_dir):\n",
    "                    if not (filename.endswith(\".bmp\") or filename.endswith(\".jpg\")):\n",
    "                        continue\n",
    "                        \n",
    "                    # 組合文件路徑\n",
    "                    filepath = os.path.join(amd_dir, filename)\n",
    "                    image = cv2.imread(filepath)\n",
    "                    #print(filepath)\n",
    "                    \n",
    "                    \n",
    "                    #column名稱\n",
    "                    column_name = dirname1+ dirname2 + '_' + dirname3\n",
    "                        \n",
    "                    #initialize the parameters\n",
    "                    img_parameter_1 = 0\n",
    "                    img_parameter_2 = 0\n",
    "                    image_name = filename.split('_')\n",
    "                    \n",
    "                    #分類：binary，parameter為entropy和VD\n",
    "                    if(dirname == 'binary'):\n",
    "                        # Read the image and calculate the entropy\n",
    "                        img_parameter_1 = util.image_entropy(filepath)\n",
    "                        parameter.append(\"entropy\")\n",
    "                            \n",
    "                    # Add the results to the dictionary\n",
    "                    #entropy[(column_name, image_name[0], image.shape[0], image.shape[1])] = img_entropy\n",
    "                    if dirname2 == 'reactive':\n",
    "                        if dirname3 == 'active':\n",
    "                            reactive_active1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            reactive_active2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                        if dirname3 == 'inactive':\n",
    "                            reactive_inactive1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1)) \n",
    "                            reactive_inactive2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            \n",
    "                    if dirname2 == 'treatment':\n",
    "                        if dirname3 == 'active':\n",
    "                            treatment_active1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            treatment_active2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                        if dirname3 == 'inactive':\n",
    "                            treatment_inactive1.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            treatment_inactive2.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_parameter_1))\n",
    "                            \n",
    "    # After reading all data, sort them by patient_number\n",
    "    #先用方法分再用數字分\n",
    "    for a, b, c, d in ((reactive_active1, reactive_inactive1, treatment_active1, treatment_inactive1),\n",
    "                       (reactive_active2, reactive_inactive2, treatment_inactive1, treatment_inactive2)):\n",
    "        for k in a,b,c,d:\n",
    "            k.sort(key = lambda x: x[0][3])\n",
    "            k.sort(key = lambda x: int(x[0][0]))\n",
    "\n",
    "        dict_name_reactive = str(parameter[0]) + '_reactive'\n",
    "        dict_name_reactive = {}\n",
    "        \n",
    "        dict_name_treatment = str(parameter[0]) + '_treatment'\n",
    "        dict_name_treatment = {}\n",
    "        \n",
    "        #pair the active and inactive keys\n",
    "        for (active_key, active_value), (inactive_key, inactive_value) in zip(a, b):\n",
    "            pass\n",
    "            dict_name_reactive[(active_key[0], active_key[3], inactive_key[0], inactive_key[3])] = [active_value, inactive_value]\n",
    "        \n",
    "        for (active_key, active_value), (inactive_key, inactive_value) in zip(c, d):\n",
    "            pass\n",
    "            dict_name_treatment[(active_key[0], active_key[3], inactive_key[0], inactive_key[3])] = [active_value, inactive_value]\n",
    "            \n",
    "            #print(entropy)\n",
    "            \n",
    "        # Convert the results dictionary to a DataFrame with a multi-level index\n",
    "        df_reactive = pd.DataFrame.from_dict(dict_name_reactive, orient='index')\n",
    "        df_reactive.index = pd.MultiIndex.from_tuples(df_reactive.index, names=['active_number', 'active_process', 'inactive_number', 'inactive_process'])\n",
    "        df_treatment = pd.DataFrame.from_dict(dict_name_reactive, orient='index')\n",
    "        df_treatment.index = pd.MultiIndex.from_tuples(df_reactive.index, names=['active_number', 'active_process', 'inactive_number', 'inactive_process'])\n",
    "        \n",
    "        # Pivot the DataFrame to have directory names as columns\n",
    "        df_reactive = df_reactive.pivot_table(values=0, index=['active_number','inactive_number'], columns=['active_process'])\n",
    "        df_treatment = df_treatment.pivot_table(values=0, index=['active_number','inactive_number'], columns=['active_process'])\n",
    "\n",
    "        # Save the results to an Excel file\n",
    "        excel_name = dirname + parameter[a[-1]-1]\n",
    "        df_reactive.to_excel('/home/charlie/projects/entropy/Entropy/excel/'+ excel_name+ '.xlsx')\n",
    "        df_treatment.to_excel('/home/charlie/projects/entropy/Entropy/excel/'+ excel_name+ '.xlsx')\n",
    "\n",
    "\n",
    "\n",
    "# # Initialize\n",
    "# active_stats: list[tuple[tuple, float]]\n",
    "# inactive_stats: list[tuple[tuple, float]]\n",
    "# active_stats = []\n",
    "# inactive_stats = []\n",
    "\n",
    "# # Add/read a data entry \n",
    "# active_stats.append(( (image_name[0], image.shape[0], image.shape[1]), img_entropy))\n",
    "\n",
    "# # After reading all data, sort them by something\n",
    "# active_stats = [((\"file1\", \"700\", \"500\"), 1.1),\n",
    "#                 ((\"file2\", \"700\", \"500\"), 1.2)] # just an arbitrary example\n",
    "# # a = ((\"file1\", \"700\", \"500\"), 1.1) => a[0] = (\"file1\", \"700\", \"500\") => a[0][0] = \"file1\"\n",
    "# active_stats.sort(key = lambda x: x[0][0])\n",
    "\n",
    "# # do the same thing with inactive_stats\n",
    "\n",
    "# # lista = [1, 2, 3]\n",
    "# # listb = [\"a\", \"b\", \"c\"]\n",
    "# # for i, j in zip(lista, listb):\n",
    "# #     print(i, j)\n",
    "# # >>> 1 a\n",
    "# # >>> 2 b\n",
    "# # >>> 3 c\n",
    "\n",
    "# for (active_key, active_value), (inactive_key, inactive_value) in zip(active_stats, inactive_stats):\n",
    "#     pass\n",
    "#     entropy[(active_key[0], inactive_key[0])]\n",
    "#     # do something\n",
    "\n",
    "    \n",
    "\n",
    "            \n",
    "\n",
    "'''\n",
    "\n",
    "    if(dirname == 'binary'):\n",
    "    #第二層 列出前處理方式種類\n",
    "        for dirname2 in os.listdir(pre_dir):\n",
    "            if (dirname2.endswith(\".DS_store\")):\n",
    "                continue\n",
    "            \n",
    "    if(dirname == 'binary'):\n",
    "    #第二層 列出前處理方式種類\n",
    "        for dirname2 in os.listdir(pre_dir):\n",
    "            if (dirname2.endswith(\".DS_store\")):\n",
    "                continue\n",
    "    \n",
    "\n",
    "# Define the directories to search for images\n",
    "directories = ['/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/active/',\n",
    "               '/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/inactive/',\n",
    "               '/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/treatment/active/']\n",
    "\n",
    "# Create a dictionary to store the results\n",
    "results = {}\n",
    "\n",
    "# Loop over the directories and read the images\n",
    "for directory in directories:\n",
    "    for filename in os.listdir(directory):\n",
    "        if filename.endswith('.jpg') or filename.endswith('.png'):\n",
    "            # Read the image and calculate the average intensity\n",
    "            image = io.imread(os.path.join(directory, filename))\n",
    "            average_intensity = np.mean(image)\n",
    "            # Add the result to the dictionary\n",
    "            results[(directory, filename, image.shape[0], image.shape[1])] = average_intensity\n",
    "\n",
    "# Convert the results dictionary to a DataFrame with a multi-level index\n",
    "df = pd.DataFrame.from_dict(results, orient='index')\n",
    "df.index = pd.MultiIndex.from_tuples(df.index, names=['Directory', 'Filename', 'Height', 'Width'])\n",
    "\n",
    "# Pivot the DataFrame to have directory names as columns\n",
    "df = df.pivot_table(values=0, index=['Filename', 'Height', 'Width'], columns=['Directory'])\n",
    "\n",
    "# Save the results to an Excel file\n",
    "df.to_excel('/home/charlie/projects/entropy/Entropy/excel/image_average_intensity.xlsx')\n",
    "\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/charlie/projects/entropy/preprocessed/canny/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/edge/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/canny/gabor_edge/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_otsu/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_otsu/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_otsu/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_otsu/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_otsu/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/otsu/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/otsu/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/otsu/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/otsu/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/otsu/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/mean/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/mean/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/mean/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/mean/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/mean/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_mean/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_mean/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_mean/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_mean/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_mean/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gaussmean/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gaussmean/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gaussmean/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gaussmean/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gaussmean/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_triangle/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_triangle/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_triangle/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_triangle/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/gabor_triangle/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/triangle/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/triangle/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/triangle/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/triangle/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/binary/triangle/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/gabor/\n",
      "/home/charlie/projects/entropy/preprocessed/gabor/gabor/\n",
      "/home/charlie/projects/entropy/preprocessed/gabor/gabor/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/gabor/gabor/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/gabor/gabor/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/gabor/gabor/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/gabor_faz/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/gabor_faz/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/gabor_faz/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/gabor_faz/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/gabor_faz/reactive/inactive/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/faz/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/faz/treatment/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/faz/reactive/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/faz/reactive/active/\n",
      "/home/charlie/projects/entropy/preprocessed/faz/faz/reactive/inactive/\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n\\n    if(dirname == \\'binary\\'):\\n    #第二層 列出前處理方式種類\\n        for dirname2 in os.listdir(pre_dir):\\n            if (dirname2.endswith(\".DS_store\")):\\n                continue\\n            \\n    if(dirname == \\'binary\\'):\\n    #第二層 列出前處理方式種類\\n        for dirname2 in os.listdir(pre_dir):\\n            if (dirname2.endswith(\".DS_store\")):\\n                continue\\n    \\n\\n# Define the directories to search for images\\ndirectories = [\\'/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/active/\\',\\n               \\'/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/inactive/\\',\\n               \\'/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/treatment/active/\\']\\n\\n# Create a dictionary to store the results\\nresults = {}\\n\\n# Loop over the directories and read the images\\nfor directory in directories:\\n    for filename in os.listdir(directory):\\n        if filename.endswith(\\'.jpg\\') or filename.endswith(\\'.png\\'):\\n            # Read the image and calculate the average intensity\\n            image = io.imread(os.path.join(directory, filename))\\n            average_intensity = np.mean(image)\\n            # Add the result to the dictionary\\n            results[(directory, filename, image.shape[0], image.shape[1])] = average_intensity\\n\\n# Convert the results dictionary to a DataFrame with a multi-level index\\ndf = pd.DataFrame.from_dict(results, orient=\\'index\\')\\ndf.index = pd.MultiIndex.from_tuples(df.index, names=[\\'Directory\\', \\'Filename\\', \\'Height\\', \\'Width\\'])\\n\\n# Pivot the DataFrame to have directory names as columns\\ndf = df.pivot_table(values=0, index=[\\'Filename\\', \\'Height\\', \\'Width\\'], columns=[\\'Directory\\'])\\n\\n# Save the results to an Excel file\\ndf.to_excel(\\'/home/charlie/projects/entropy/Entropy/excel/image_average_intensity.xlsx\\')\\n\\n'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skimage import io\n",
    "import util\n",
    "import cv2\n",
    "\n",
    "\n",
    "parent_dir = r\"/home/charlie/projects/entropy/preprocessed/\"\n",
    "\n",
    "\n",
    "# Create dictionaries to store the results\n",
    "inactive_entropy = {}\n",
    "active_entropy = {}\n",
    "faz_area = {}\n",
    "faz_circularity = {}\n",
    "vessel_density = {}\n",
    "vessel_length_density = {}\n",
    "\n",
    "\n",
    "#第一層 列出前處理方式種類\n",
    "for dirname in os.listdir(parent_dir):\n",
    "    if (dirname.endswith(\".DS_store\")):\n",
    "        continue\n",
    "    \n",
    "    type_dir = parent_dir + dirname+'/'\n",
    "    print(type_dir)\n",
    "    \n",
    "    #第二層 列出前處理方式\n",
    "    for dirname1 in os.listdir(type_dir):\n",
    "        if (dirname1.endswith(\".DS_store\")):\n",
    "            continue\n",
    "        \n",
    "        pre_dir = type_dir + dirname1+'/'\n",
    "        print(pre_dir)\n",
    "        \n",
    "        #第三層 列出reactive/treatment\n",
    "        for dirname2 in os.listdir(pre_dir):\n",
    "            if (dirname2.endswith(\".DS_store\")):\n",
    "                continue\n",
    "            \n",
    "            cond_dir = pre_dir + dirname2+'/'\n",
    "            print(cond_dir)\n",
    "            \n",
    "            if dirname2 == 'reactive':\n",
    "                #第四層 列出active/inactive\n",
    "                for dirname3 in os.listdir(cond_dir):\n",
    "                    if (dirname3.endswith(\".DS_Store\")):\n",
    "                        continue\n",
    "                        \n",
    "                    amd_dir = cond_dir + dirname3+'/'\n",
    "                    print(amd_dir)\n",
    "                    \n",
    "                    #列出目錄中的所有圖像\n",
    "                    for filename in os.listdir(amd_dir):\n",
    "                        if not (filename.endswith(\".bmp\") or filename.endswith(\".jpg\")):\n",
    "                            continue\n",
    "                            \n",
    "                        # 組合文件路徑\n",
    "                        filepath = os.path.join(amd_dir, filename)\n",
    "                        image = cv2.imread(filepath)\n",
    "                        #print(filepath)\n",
    "                        \n",
    "                        #分類：binary\n",
    "                        if(dirname == 'binary'):\n",
    "                            #column名稱\n",
    "                            column_name = dirname1+ '_' + dirname3\n",
    "                            \n",
    "                            # Read the image and calculate the average intensity\n",
    "                            img_entropy = util.image_entropy(filepath)\n",
    "                            \n",
    "                            image_name = filename.split('_')\n",
    "                            \n",
    "                            # Add the result to the dictionary\n",
    "                            if dirname3 == 'active':\n",
    "                                active_entropy[(column_name, image_name[0], image.shape[0], image.shape[1])] = img_entropy\n",
    "                                #active_entropy.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_entropy))\n",
    "                            \n",
    "                            if dirname3 == 'inactive':\n",
    "                                inactive_entropy[(column_name, image_name[0], image.shape[0], image.shape[1])] = img_entropy\n",
    "                                #inactive_entropy.append(((image_name[0], image.shape[0], image.shape[1], column_name), img_entropy)) \n",
    "    \n",
    "\n",
    "# Convert the results dictionary to a DataFrame with a multi-level index\n",
    "df = pd.DataFrame.from_dict(active_entropy, orient='index')\n",
    "df.index = pd.MultiIndex.from_tuples(df.index, names=['Directory', 'Filename', 'Height', 'Width'])\n",
    "\n",
    "# Pivot the DataFrame to have directory names as columns\n",
    "df = df.pivot_table(values=0, index=['Filename', 'Height', 'Width'], columns=['Directory'])\n",
    "\n",
    "# Save the results to an Excel file\n",
    "df.to_excel('/home/charlie/projects/entropy/Entropy/excel/active_entropy.xlsx')\n",
    "\n",
    "# Convert the results dictionary to a DataFrame with a multi-level index\n",
    "df2 = pd.DataFrame.from_dict(inactive_entropy, orient='index')\n",
    "df2.index = pd.MultiIndex.from_tuples(df2.index, names=['Directory', 'Filename', 'Height', 'Width'])\n",
    "\n",
    "# Pivot the DataFrame to have directory names as columns\n",
    "df2 = df2.pivot_table(values=0, index=['Filename', 'Height', 'Width'], columns=['Directory'])\n",
    "\n",
    "# Save the results to an Excel file\n",
    "df2.to_excel('/home/charlie/projects/entropy/Entropy/excel/inactive_entropy.xlsx')\n",
    "            \n",
    "\n",
    "'''\n",
    "\n",
    "    if(dirname == 'binary'):\n",
    "    #第二層 列出前處理方式種類\n",
    "        for dirname2 in os.listdir(pre_dir):\n",
    "            if (dirname2.endswith(\".DS_store\")):\n",
    "                continue\n",
    "            \n",
    "    if(dirname == 'binary'):\n",
    "    #第二層 列出前處理方式種類\n",
    "        for dirname2 in os.listdir(pre_dir):\n",
    "            if (dirname2.endswith(\".DS_store\")):\n",
    "                continue\n",
    "    \n",
    "\n",
    "# Define the directories to search for images\n",
    "directories = ['/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/active/',\n",
    "               '/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/reactive/inactive/',\n",
    "               '/home/charlie/projects/entropy/preprocessed/binary/gabor_gaussmean/treatment/active/']\n",
    "\n",
    "# Create a dictionary to store the results\n",
    "results = {}\n",
    "\n",
    "# Loop over the directories and read the images\n",
    "for directory in directories:\n",
    "    for filename in os.listdir(directory):\n",
    "        if filename.endswith('.jpg') or filename.endswith('.png'):\n",
    "            # Read the image and calculate the average intensity\n",
    "            image = io.imread(os.path.join(directory, filename))\n",
    "            average_intensity = np.mean(image)\n",
    "            # Add the result to the dictionary\n",
    "            results[(directory, filename, image.shape[0], image.shape[1])] = average_intensity\n",
    "\n",
    "# Convert the results dictionary to a DataFrame with a multi-level index\n",
    "df = pd.DataFrame.from_dict(results, orient='index')\n",
    "df.index = pd.MultiIndex.from_tuples(df.index, names=['Directory', 'Filename', 'Height', 'Width'])\n",
    "\n",
    "# Pivot the DataFrame to have directory names as columns\n",
    "df = df.pivot_table(values=0, index=['Filename', 'Height', 'Width'], columns=['Directory'])\n",
    "\n",
    "# Save the results to an Excel file\n",
    "df.to_excel('/home/charlie/projects/entropy/Entropy/excel/image_average_intensity.xlsx')\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g\n"
     ]
    }
   ],
   "source": [
    "a = 'string'\n",
    "print(a[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
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  "vscode": {
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