Annotations_Image_Metadata_Judaya.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8153aa07-bff3-4230-a0a3-0aa70cb46038",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "# Annotations and Metadata\n",
    "def load_json_files(folder_path):\n",
    "    data = []\n",
    "    for file_name in os.listdir(folder_path):\n",
    "        if file_name.endswith('.json'):\n",
    "            with open(os.path.join(folder_path, file_name), 'r') as f:\n",
    "                data.append(json.load(f))\n",
    "    return data\n",
    "\n",
    "# Folders\n",
    "annotations_folder = r\"B:\\annotations\\sample\\coco\\annotations_03\"\n",
    "metadata_folder = r\"B:\\annotations\\sample\\ocular-json\"\n",
    "\n",
    "# Load all annotations and metadata\n",
    "annotations = load_json_files(annotations_folder)\n",
    "metadata = load_json_files(metadata_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "878359a9-3312-4a05-8c33-71c0fadfb5d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "450eb10a-9bff-438d-b2b8-10986dac16ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract timestamp from file name or file path\n",
    "def extract_timestamp(file_path):\n",
    "    match = re.search(r'Ocular_(\\d{4}-\\d{2}-\\d{2}_\\d{2}_\\d{2}_\\d{2})', file_path)\n",
    "    return match.group(1) if match else None\n",
    "\n",
    "metadata_list = []\n",
    "for meta in metadata:\n",
    "    if isinstance(meta, dict):\n",
    "        # Processing each key-value pair in the dictionary\n",
    "        timestamp = extract_timestamp(meta.get('file_path', None))\n",
    "        if timestamp:\n",
    "            metadata_list.append({\n",
    "                'timestamp': timestamp,\n",
    "                'zoom_ration': meta.get('zoom_ration', None),\n",
    "                'slide_name': meta.get('slide_name', None),\n",
    "                'username': meta.get('username', None),\n",
    "                'stain': meta.get('stain', None)\n",
    "            })\n",
    "        else:\n",
    "            print('No timestamp found')\n",
    "\n",
    "metadata_df = pd.DataFrame(metadata_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d169608-5d86-4659-a19e-de152d7177d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6634fc35-01c6-4685-acdb-52157d21850b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Based on the timestamp pattern in the file paths match annotations to metadata\n",
    "annotation_list = []\n",
    "for annotation_file in annotations:\n",
    "    for image in annotation_file['images']:\n",
    "        file_name = image['file_name']\n",
    "        image_id = image['id']\n",
    "        timestamp = extract_timestamp(file_name)\n",
    "        if timestamp:\n",
    "            matching_metadata = metadata_df[metadata_df['timestamp'] == timestamp]\n",
    "            if not matching_metadata.empty:\n",
    "                for annotation in annotation_file['annotations']:\n",
    "                    if annotation['image_id'] == image['id']:\n",
    "                        for category in annotation_file['categories']:\n",
    "                            category_name = category['name']\n",
    "                            category_id = category['id']\n",
    "                            if annotation['category_id'] == category_id:\n",
    "                             for _, row in matching_metadata.iterrows():\n",
    "                                annotation_list.append({\n",
    "                                'file_name': file_name,\n",
    "                                'category_id': annotation['category_id'],\n",
    "                                'category_name':category_name,\n",
    "                                'image_id': image_id,\n",
    "                                'zoom_ration': row['zoom_ration']\n",
    "                                })\n",
    "\n",
    "\n",
    "judaya_annotations_df = pd.DataFrame(annotation_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b0ebac8-e05d-4a89-88c0-df2f7b45bcc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#judaya_annotations_df.to_csv('judaya_annotations_df.csv', sep=',', header=True, index=False, na_rep='NA', float_format='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfaf0ebd-5289-4d80-8a63-b949ffcc8362",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d6d0bd8b-e88d-4885-a60c-006b39acba94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slide_name</th>\n",
       "      <th>zoom_ration</th>\n",
       "      <th>Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126_f_eu</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1487</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DSI AYE 013-G-NU</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         slide_name zoom_ration  Number\n",
       "0                 1         1.6       1\n",
       "1          126_f_eu         1.0      20\n",
       "2              1487         1.0       1\n",
       "3                 2         1.2       1\n",
       "4  DSI AYE 013-G-NU         2.8       1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_2 = metadata_df.groupby(['slide_name','zoom_ration']).size().reset_index(name='count')\n",
    "grouped_2.columns = ['slide_name','zoom_ration','Number']\n",
    "#print(grouped_2)\n",
    "slides = pd.DataFrame(grouped_2)\n",
    "slides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "626ed8df-df52-4027-8ea2-559908a15f1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0ba2952c-b4e7-493a-9b43-5f5bf7b77b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>zoom_ration</th>\n",
       "      <th>category_name</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Artifact</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PF</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>WBC</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WBC</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   image_id zoom_ration category_name  number\n",
       "0         1           1      Artifact       1\n",
       "1         1           1            PF       3\n",
       "2         1           1           WBC       2\n",
       "3         1         1.0   Trophozoite      93\n",
       "4         1         1.0           WBC      35"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_annotations =judaya_annotations_df.groupby(['image_id','zoom_ration','category_name']).size().reset_index(name='number')\n",
    "#print(grouped_annotations)\n",
    "slides_2 = pd.DataFrame(grouped_annotations)\n",
    "slides_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "344d498e-d3b6-4c6b-a1bd-f28d958a6896",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "73d04c20-69f4-456d-a3ad-ef3b0e1524c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_id\n",
      "121    9\n",
      "11     9\n",
      "38     8\n",
      "7      8\n",
      "45     8\n",
      "46     8\n",
      "22     8\n",
      "58     8\n",
      "101    7\n",
      "60     7\n",
      "36     7\n",
      "39     7\n",
      "43     7\n",
      "48     7\n",
      "52     7\n",
      "59     7\n",
      "83     7\n",
      "128    7\n",
      "32     7\n",
      "157    7\n",
      "98     7\n",
      "2      7\n",
      "129    7\n",
      "119    7\n",
      "120    7\n",
      "122    7\n",
      "123    7\n",
      "124    7\n",
      "126    7\n",
      "34     7\n",
      "64     7\n",
      "10     7\n",
      "5      7\n",
      "24     7\n",
      "12     7\n",
      "6      7\n",
      "23     7\n",
      "4      7\n",
      "13     7\n",
      "3      7\n",
      "156    6\n",
      "87     6\n",
      "92     6\n",
      "91     6\n",
      "15     6\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "grouped_annotations = judaya_annotations_df.groupby(['image_id','zoom_ration','category_name']).size().reset_index(name='number')\n",
    "#print(grouped_annotations)\n",
    "slides_2 = pd.DataFrame(grouped_annotations)\n",
    "#slides_2.head(26)\n",
    "slides_number = slides_2['image_id'].value_counts()\n",
    "print(slides_number.head(45))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e005bb31-9cea-46a8-a50c-ece5065d09ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ec302c06-bcec-411e-acdf-d3418d2c917d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_id\n",
      "121    9\n",
      "11     9\n",
      "38     8\n",
      "7      8\n",
      "45     8\n",
      "      ..\n",
      "85     3\n",
      "153    3\n",
      "159    3\n",
      "115    3\n",
      "179    3\n",
      "Name: count, Length: 200, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Number of images\n",
    "category_counts = slides_2['image_id'].value_counts()\n",
    "\n",
    "# Print the counts\n",
    "print(category_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03cb2f7d-a5c9-4a2c-b02c-2350cb46017f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b2b8f5ff-7d67-411a-8175-5609d8cda66a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category_name\n",
      "Trophozoite    445\n",
      "WBC            403\n",
      "PF             134\n",
      "Other           82\n",
      "Artifact        46\n",
      "Schizont         1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Get the count of each category\n",
    "category_counts = slides_2['category_name'].value_counts()\n",
    "\n",
    "# Print the counts\n",
    "print(category_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2100ad8b-bac2-445f-aba8-4cc0bf1cfe69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "98ca5e32-2c02-42d2-8084-d8c47a8bd712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the count of each category\n",
    "category_counts = slides_2['category_name'].value_counts()\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(category_counts.index, category_counts.values)\n",
    "plt.xlabel('Category Name')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Category Counts')\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d39a384b-85d9-4c11-94c3-c77ffe75637f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "04c896e7-9ee6-414f-a093-60441fdb2a5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "659\n",
      "351\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Thick and Thin in annotations_df\n",
    "judaya_annotations_df['is_thick'] = judaya_annotations_df['file_name'].str.contains('THICK', case=False)\n",
    "judaya_annotations_df['is_thin'] = judaya_annotations_df['file_name'].str.contains('THIN', case=False)\n",
    "annotation_thick = judaya_annotations_df['is_thick']\n",
    "annotation_thin = judaya_annotations_df['is_thin']\n",
    "\n",
    "thick_annotations = judaya_annotations_df[annotation_thick]\n",
    "thick_annotations = thick_annotations.drop('is_thick', axis = 1)\n",
    "thick_annotations = thick_annotations.drop('is_thin', axis = 1)\n",
    "thick_annotations = thick_annotations.drop('category_id', axis = 1)\n",
    "thick_annotations= thick_annotations.drop('category_name', axis = 1)\n",
    "thick_annotations= thick_annotations.groupby(['file_name','image_id']).size().reset_index(name='count')\n",
    "length_thick_annotations = len(thick_annotations)\n",
    "print(length_thick_annotations)\n",
    "\n",
    "thin_annotations = judaya_annotations_df[annotation_thin]\n",
    "thin_annotations = thin_annotations.drop('is_thick', axis = 1)\n",
    "thin_annotations = thin_annotations.drop('is_thin', axis = 1)\n",
    "thin_annotations = thin_annotations.drop('category_id', axis = 1)\n",
    "thin_annotations = thin_annotations.drop('category_name', axis = 1)\n",
    "length_thin_annotations = len(thin_annotations)\n",
    "print(length_thin_annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "328a0c30-06c6-4c46-8f68-3814ed75d212",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9612426e-9a2a-42cd-8a32-66edb9235d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2661\n",
      "2472\n"
     ]
    }
   ],
   "source": [
    "# Thick and Thin in metadata_df\n",
    "metadata_df['is_thick'] = metadata_df['slide_name'].str.contains('THICK', case=False)\n",
    "metadata_df['is_thin'] = metadata_df['slide_name'].str.contains('THIN', case=False)\n",
    "metadata_thick = metadata_df['is_thick']\n",
    "metadata_thin = metadata_df['is_thin']\n",
    "\n",
    "thick_meta = metadata_df[metadata_thick]\n",
    "thick_meta = thick_meta.drop('is_thick', axis = 1)\n",
    "thick_meta = thick_meta.drop('is_thin', axis = 1)\n",
    "thick_meta = thick_meta.drop('stain', axis = 1)\n",
    "thick_meta = thick_meta.drop('username', axis = 1)\n",
    "thick_meta = thick_meta.groupby(['timestamp','slide_name','zoom_ration']).size().reset_index(name='Number')\n",
    "thick_meta = thick_meta.drop('Number', axis = 1)\n",
    "length_thick_meta = len(thick_meta)\n",
    "print(length_thick_meta)\n",
    "\n",
    "thin_meta = metadata_df[metadata_thin]\n",
    "thin_meta = thin_meta.drop('is_thick', axis = 1)\n",
    "thin_meta = thin_meta.drop('is_thin', axis = 1)\n",
    "thin_meta = thin_meta.drop('stain', axis = 1)\n",
    "thin_meta = thin_meta.drop('username', axis = 1)\n",
    "thin_meta = thin_meta.groupby(['timestamp','slide_name','zoom_ration']).size().reset_index(name='Number')\n",
    "thin_meta = thin_meta.drop('Number', axis = 1)\n",
    "length_thin_meta = len(thin_meta)\n",
    "print(length_thin_meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec960d23-5ddc-4019-a26b-b3b8913f5e23",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "66d11909-cfaf-4db6-91ba-714af390f28f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image_ID</th>\n",
       "      <th>Category_name</th>\n",
       "      <th>Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Image_ID Category_name  Number\n",
       "0         1   Trophozoite      93\n",
       "1         2   Trophozoite      79\n",
       "2         3   Trophozoite      99\n",
       "3         4   Trophozoite     107\n",
       "4         5   Trophozoite      75"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "trophozoite_annotations = judaya_annotations_df[judaya_annotations_df['category_id'] == 1]\n",
    "\n",
    "# Create a mapping of category_id to category_name\n",
    "category_mapping = judaya_annotations_df.groupby('category_id')['category_name'].first()\n",
    "\n",
    "# Replacing category_id with category_name in the groupby column\n",
    "trophozoite_annotations.loc[:,'category_id'] = trophozoite_annotations['category_id'].map(category_mapping)\n",
    "\n",
    "trophozoite_annotations = trophozoite_annotations.groupby(['image_id','category_id']).size().reset_index(name='Number')\n",
    "trophozoite_annotations.columns = ['Image_ID','Category_name','Number']\n",
    "trophozoite_annotations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b8316ec-e6cf-4290-b01a-7a9cef4f0c01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d5d88d37-595c-4686-b728-9907a75a2c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Range of images \n",
    "image_range = trophozoite_annotations['Image_ID'].unique()[:35]\n",
    "\n",
    "# Filter the data to only include the selected image range\n",
    "filtered_data = trophozoite_annotations[trophozoite_annotations['Image_ID'].isin(image_range)]\n",
    "\n",
    "# Bar plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x='Image_ID', y='Number', hue='Category_name', data=filtered_data, palette='viridis')\n",
    "plt.title('Number of Trophozoite for Each Image')\n",
    "plt.xlabel('Image ID')\n",
    "plt.ylabel('Number of Annotations')\n",
    "plt.legend(title='Category Name')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8f4d07f-f3eb-4c25-b265-fdfcb89ed844",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1dd84ab7-0bdd-4deb-b3f3-942e2f4344c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image_ID</th>\n",
       "      <th>Category_name</th>\n",
       "      <th>Number</th>\n",
       "      <th>Slide_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>93</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>79</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>99</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>107</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>75</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Image_ID Category_name  Number Slide_Name\n",
       "0         1   Trophozoite      93      Thick\n",
       "1         2   Trophozoite      79      Thick\n",
       "2         3   Trophozoite      99      Thick\n",
       "3         4   Trophozoite     107      Thick\n",
       "4         5   Trophozoite      75      Thick"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Thick\n",
    "trophozoite_annotations['is_thick'] = metadata_df['slide_name'].str.contains('THICK', case=False)\n",
    "trophozoite_thick = trophozoite_annotations['is_thick']\n",
    "\n",
    "# New column indicating whether the slide is thick or thin\n",
    "trophozoite_annotations.loc[:, 'Slide_Name'] = np.where(trophozoite_thick, 'Thick','Thin')\n",
    "trophozoite_annotations_thick = trophozoite_annotations[trophozoite_thick]\n",
    "trophozoite_annotations_thick = trophozoite_annotations_thick.drop('is_thick', axis = 1)\n",
    "#trophozoite_annotations_thick = trophozoite_annotations_thick.drop('is_thin', axis = 1)\n",
    "trophozoite_annotations_thick.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf4b8dfa-553f-402a-9cbc-6b73703b3d99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c7761290-066d-4dba-b85a-b4ec8db82b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image_ID</th>\n",
       "      <th>Category_name</th>\n",
       "      <th>Number</th>\n",
       "      <th>Slide_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>62</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>40</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>49</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>61</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>31</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Image_ID Category_name  Number Slide_Name\n",
       "17        18   Trophozoite      62       Thin\n",
       "18        19   Trophozoite      40       Thin\n",
       "19        20   Trophozoite      49       Thin\n",
       "20        21   Trophozoite      61       Thin\n",
       "21        22   Trophozoite      31       Thin"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Thin\n",
    "trophozoite_annotations['is_thin'] = metadata_df['slide_name'].str.contains('THIN', case=False)\n",
    "trophozoite_thin = trophozoite_annotations['is_thin']\n",
    "# For Zoomratio\n",
    "#trophozoite_annotations['zoom_ration'] = metadata_df['zoom_ration']\n",
    "\n",
    "# New column indicating whether the slide is thick or thin\n",
    "trophozoite_annotations.loc[:, 'Slide_Name'] = np.where(trophozoite_thin, 'Thin','Thick')\n",
    "trophozoite_annotations_thin = trophozoite_annotations[trophozoite_thin]\n",
    "trophozoite_annotations_thin = trophozoite_annotations_thin.drop('is_thin', axis = 1)\n",
    "trophozoite_annotations_thin = trophozoite_annotations_thin.drop('is_thick', axis = 1)\n",
    "\n",
    "trophozoite_annotations_thin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2f55b85-0ed3-4770-9a68-ef5306573bd7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "55274b1d-ba59-420a-8d10-ce4581e37932",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Image_ID  Number Category_name\n",
      "0       145       1      Schizont\n"
     ]
    }
   ],
   "source": [
    "schizont_annotations = judaya_annotations_df[judaya_annotations_df['category_id'] == 2]\n",
    "\n",
    "schizont_annotations = schizont_annotations.groupby(['image_id']).size().reset_index(name='Number')\n",
    "schizont_annotations['category_id'] = 2\n",
    "\n",
    "category_mapping = judaya_annotations_df[['category_id', 'category_name']].drop_duplicates()\n",
    "schizont_annotations.loc[:,'category_id'] = schizont_annotations['category_id'].replace(category_mapping.set_index('category_id')['category_name'])\n",
    "schizont_annotations.columns = ['Image_ID','Number','Category_name']\n",
    "print(schizont_annotations.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cec2995e-ab7f-4cc8-bb49-ff28329c12bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "529e6c93-f5b3-4e7a-b731-bfb159dd53dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Image_ID, Number, Category_name]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "gametocyte_annotations = judaya_annotations_df[judaya_annotations_df['category_id'] == 3]\n",
    "\n",
    "gametocyte_annotations = gametocyte_annotations.groupby(['image_id']).size().reset_index(name='Number')\n",
    "gametocyte_annotations['category_id'] = 3\n",
    "\n",
    "category_mapping = judaya_annotations_df[['category_id', 'category_name']].drop_duplicates()\n",
    "gametocyte_annotations.loc[:,'category_id'] = gametocyte_annotations['category_id'].replace(category_mapping.set_index('category_id')['category_name'])\n",
    "gametocyte_annotations.columns = ['Image_ID','Number','Category_name']\n",
    "print(gametocyte_annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f603ed-3c89-4a18-9f00-50eb5cefea3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2e9dcf5f-4067-472a-bde1-94eeafe0c23b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   image_id  category_id  count\n",
      "0         1            4     37\n",
      "1         2            4     30\n",
      "2         3            4     38\n",
      "3         4            4     36\n",
      "4         5            4     28\n"
     ]
    }
   ],
   "source": [
    "wbc_annotations = judaya_annotations_df[judaya_annotations_df['category_id'] == 4]\n",
    "wbc_annotations = wbc_annotations.drop('is_thick', axis = 1)\n",
    "wbc_annotations = wbc_annotations.drop('is_thin', axis = 1)\n",
    "wbc_annotations = wbc_annotations.groupby(['image_id','category_id']).size().reset_index(name='count')\n",
    "\n",
    "print(wbc_annotations.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62b86a0c-8414-40ea-98c4-fce216ea7a1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2791b45e-d5bb-4cb5-a355-f037b4dc263a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}