Annotations_Image_Metadata_Kati.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "21e70e38-6f06-4768-b641-78c96d1d777e",
   "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_04\"\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": "85bf4ee1-a186-447c-a992-e5f90ee59302",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "aacb3013-b102-4810-bc4b-e335b53b8952",
   "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": "ce46fb28-0a33-4bcf-9669-8f999d49baca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "bd29659d-33d3-43d7-a39d-35b3f016e365",
   "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",
    "kati_annotations_df = pd.DataFrame(annotation_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e135d2b-a55c-4b0b-9afe-1503b169f017",
   "metadata": {},
   "outputs": [],
   "source": [
    "#kati_annotations_df.to_csv('kati_annotations_df.csv', sep=',', header=True, index=False, na_rep='NA', float_format='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cac12c0-0818-4864-8d37-a98807743a33",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "cd288c24-c2b2-40df-984a-5b35ad593d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DSI-Awach</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DSI-Awach</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DSI-Awach</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>THICK-19-KAMB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>THICK-20-KAMB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>THICK-21-KAMB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>THICK-28-ARRH</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>THICK-28-ARRH</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>THICK-28-ARRH</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>THICK-28-ARRH</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6</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\n",
       "5          DSI-Awach         1.0       1\n",
       "6          DSI-Awach         1.6       1\n",
       "7          DSI-Awach         1.8       1\n",
       "8      THICK-19-KAMB         1.0      24\n",
       "9      THICK-20-KAMB         1.0      21\n",
       "10     THICK-21-KAMB         1.0      39\n",
       "11     THICK-28-ARRH         1.0       1\n",
       "12     THICK-28-ARRH         1.8       2\n",
       "13     THICK-28-ARRH           2       1\n",
       "14     THICK-28-ARRH         2.2       6"
      ]
     },
     "execution_count": 99,
     "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(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16b26675-1080-4705-a673-3f837bfcb1ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "12e8f1d9-3625-45ec-9281-3bba70153ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>1.0</td>\n",
       "      <td>Artifact</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>WBC</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   image_id zoom_ration category_name  number\n",
       "0         1         1.0      Artifact       1\n",
       "1         1         1.0         Other       1\n",
       "2         1         1.0   Trophozoite       2\n",
       "3         1         1.4   Trophozoite       8\n",
       "4         1         1.4           WBC       4"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_annotations = kati_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": "2fbe200d-996d-41ee-8ee7-cff839342e05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "81c3b2a1-3faf-4d51-8331-1aa006490345",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_id\n",
      "1      9\n",
      "156    9\n",
      "16     9\n",
      "21     8\n",
      "14     8\n",
      "117    8\n",
      "32     8\n",
      "77     8\n",
      "62     8\n",
      "106    7\n",
      "97     7\n",
      "109    7\n",
      "42     7\n",
      "27     7\n",
      "80     7\n",
      "118    7\n",
      "65     7\n",
      "37     7\n",
      "36     7\n",
      "119    7\n",
      "34     7\n",
      "40     7\n",
      "29     7\n",
      "12     7\n",
      "2      7\n",
      "8      7\n",
      "4      7\n",
      "13     7\n",
      "17     7\n",
      "149    7\n",
      "152    7\n",
      "96     6\n",
      "158    6\n",
      "94     6\n",
      "95     6\n",
      "86     6\n",
      "85     6\n",
      "160    6\n",
      "161    6\n",
      "64     6\n",
      "154    6\n",
      "134    6\n",
      "112    6\n",
      "133    6\n",
      "150    6\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "grouped_annotations = kati_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": "eaea2a32-0fc2-4d0c-b17e-1f04694bef02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "fff638a7-ab2f-42a2-b685-356da9560a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_id\n",
      "1      9\n",
      "156    9\n",
      "16     9\n",
      "21     8\n",
      "14     8\n",
      "      ..\n",
      "279    1\n",
      "280    1\n",
      "281    1\n",
      "282    1\n",
      "410    1\n",
      "Name: count, Length: 410, 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": "8b002f41-c530-4111-90db-8c199f8197d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f9ac6ad0-a376-492b-bd80-f090d84fc9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category_name\n",
      "WBC            447\n",
      "Trophozoite    439\n",
      "Other          224\n",
      "Artifact       165\n",
      "PF               9\n",
      "PO               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": "ed6a1cd0-eaf5-48f9-8d9b-8b0cdf38f84b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "866205bd-8bdf-43a7-8622-004d0ed37962",
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b47154-e771-4b1b-9e39-0a22f5f61d98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ac7a0361-ee82-4d6d-b854-45ba52d40f0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "662\n",
      "1819\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Thick and Thin in annotations_df\n",
    "kati_annotations_df['is_thick'] = kati_annotations_df['file_name'].str.contains('THICK', case=False)\n",
    "kati_annotations_df['is_thin'] = kati_annotations_df['file_name'].str.contains('THIN', case=False)\n",
    "annotation_thick = kati_annotations_df['is_thick']\n",
    "annotation_thin = kati_annotations_df['is_thin']\n",
    "\n",
    "thick_annotations = kati_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 = kati_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": "8d5773d9-4448-4b08-a03c-a01f6f7f8596",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "91985b75-b447-40ea-8fea-57875a9e98ce",
   "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": "cbb976c1-68be-455f-b737-3a4fd288fa63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3226c59e-2040-4a4b-9353-107463d21c8c",
   "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>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Image_ID Category_name  Number\n",
       "0         1   Trophozoite      27\n",
       "1         2   Trophozoite      10\n",
       "2         3   Trophozoite      36\n",
       "3         4   Trophozoite      23\n",
       "4         5   Trophozoite      35\n",
       "5         6   Trophozoite      42\n",
       "6         7   Trophozoite      36\n",
       "7         8   Trophozoite      45\n",
       "8         9   Trophozoite      36\n",
       "9        10   Trophozoite      40"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "trophozoite_annotations = kati_annotations_df[kati_annotations_df['category_id'] == 1]\n",
    "\n",
    "# Create a mapping of category_id to category_name\n",
    "category_mapping = kati_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(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38ec299-6917-4600-b2e7-a8d2828be391",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "086e191c-e6fe-44ea-86e4-d664bc06de32",
   "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",
    "# Assuming trophozoite_annotations is already defined as in your code\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Select a range of images (e.g., the first 20 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": "raw",
   "id": "c9e648c2-d187-4bf7-a00a-566f678b2155",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb34eea6-d8c2-49d1-91ee-b1528616e904",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "183aeab0-7f34-442f-8790-7b52e61e2870",
   "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>27</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>10</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>36</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>23</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>35</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      27      Thick\n",
       "1         2   Trophozoite      10      Thick\n",
       "2         3   Trophozoite      36      Thick\n",
       "3         4   Trophozoite      23      Thick\n",
       "4         5   Trophozoite      35      Thick"
      ]
     },
     "execution_count": 109,
     "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",
    "\n",
    "trophozoite_annotations_thick.head()\n",
    "#trophozoite_annotations_thick.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e78d12ae-eabb-48d0-bdb4-1d864deee2d9",
   "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_thick['Image_ID'].unique()[:35]\n",
    "\n",
    "# Filter the data to only include the selected image range\n",
    "filtered_data_thick = trophozoite_annotations_thick[trophozoite_annotations_thick['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_thick, palette='viridis')\n",
    "plt.title('Number of Trophozoite for Each Thick 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": "0b9a618a-c29a-44b2-be1f-d5f5db2509b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f91037b2-87f3-4de3-a9a1-37b37f317009",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "0d40beb2-cf3e-4b69-a9d9-ccaefee01cdc",
   "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>31</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>27</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>30</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>11</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>18</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      31       Thin\n",
       "18        19   Trophozoite      27       Thin\n",
       "19        20   Trophozoite      30       Thin\n",
       "20        21   Trophozoite      11       Thin\n",
       "21        22   Trophozoite      18       Thin"
      ]
     },
     "execution_count": 111,
     "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",
    "trophozoite_annotations_thin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ac6f8bc-ab3e-427e-bd38-80fbc38dee1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b595cda5-8f39-4c98-9d94-a21ad440c46c",
   "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_thin['Image_ID'].unique()[:35]\n",
    "\n",
    "# Filter the data to only include the selected image range\n",
    "filtered_data_thin = trophozoite_annotations_thin[trophozoite_annotations_thin['Image_ID'].isin(image_range)]\n",
    "\n",
    "#annotation_range = filtered_data_thin['Number'].min(), filtered_data_thin['Number'].max()\n",
    "#annotation_range = (0,45)\n",
    "\n",
    "# Bar plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x='Image_ID', y='Number', hue='Category_name', data=filtered_data_thin, palette='viridis')\n",
    "plt.title('Number of Trophozoite for Each Thin 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": "286e3ce7-6de4-419e-a39d-34315ecacf4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5e508ee3-bba4-4d74-a4b6-23bb0d7b6b9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Image_ID, Number, Category_name]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "schizont_annotations = kati_annotations_df[kati_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 = kati_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": "48974728-77b6-42a3-bccb-2b6613a617bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b088caa3-f6e7-4fac-b27d-5f5e341e40c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Image_ID, Number, Category_name]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "gametocyte_annotations = kati_annotations_df[kati_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 = kati_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": "a6699645-1e46-4656-9272-24db29730747",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "44bcf03f-4c4f-48f7-b06d-54b0a41fc5d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   image_id  category_id  count\n",
      "0         1            4      5\n",
      "1         2            4      7\n",
      "2         3            4     10\n",
      "3         4            4     11\n",
      "4         5            4     18\n"
     ]
    }
   ],
   "source": [
    "wbc_annotations = kati_annotations_df[kati_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": "4839869e-d364-4c34-bde2-49b8af97265f",
   "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
}