Annotations_Image_Metadata_Okwa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "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_05\"\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": "4a6658d4-e808-475c-b3a9-d9ecdb0e3209",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "efc350f9-2c7d-4af6-94c9-221473c7fcc9",
   "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": "c3a1a9ad-9c8d-45db-a444-0d78499d14e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54f4d151-712d-482e-bafc-2ad73bc0b485",
   "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",
    "okwa_annotations_df = pd.DataFrame(annotation_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "333c4f66-6279-4303-b742-be2aeb73f235",
   "metadata": {},
   "outputs": [],
   "source": [
    "#okwa_annotations_df.to_csv('okwa_annotations_df.csv', sep=',', header=True, index=False, na_rep='NA', float_format='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bacea6f6-d736-4ccc-9adb-6f68769753c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4cf38f5d-f46e-4e12-86d6-745ff96ad1e3",
   "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": 26,
     "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": "f6a40529-c9df-4adb-8b65-2a0cbe5df796",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40f8003e-b080-421f-8eaa-63373bee19b4",
   "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>1.0</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>PF</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WBC</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Artifact</td>\n",
       "      <td>1</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            PF       2\n",
       "2         1         1.0   Trophozoite      41\n",
       "3         1         1.0           WBC      12\n",
       "4         1         1.4      Artifact       1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_annotations = okwa_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": "b6a86865-56bb-4e71-8317-861c2ea5fdef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "86359bb2-363f-4994-b64d-1003156f1b39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_id\n",
      "19     9\n",
      "51     9\n",
      "34     9\n",
      "108    9\n",
      "46     9\n",
      "      ..\n",
      "168    3\n",
      "194    3\n",
      "195    3\n",
      "173    3\n",
      "181    2\n",
      "Name: count, Length: 200, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "grouped_annotations = okwa_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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc7f33f-7018-4412-b183-9b1616c3a38b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c59cbd9-1c6a-4d9d-95b2-bb6beb05a6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_id\n",
      "19     9\n",
      "51     9\n",
      "34     9\n",
      "108    9\n",
      "46     9\n",
      "      ..\n",
      "168    3\n",
      "194    3\n",
      "195    3\n",
      "173    3\n",
      "181    2\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": "bcb5ce7e-0b61-443f-8aaf-cbb8d93f5bfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bfa61e74-1a8e-44fc-9ced-b286a9ebd156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category_name\n",
      "WBC            434\n",
      "Trophozoite    384\n",
      "PF             272\n",
      "Artifact        79\n",
      "Other           36\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": "62f96cd8-8524-4ad7-87f6-e23cc66ed860",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e6118f24-af20-4144-9257-8c5785d078ab",
   "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": "382e4844-4baa-451f-a69b-c6310a8fb0cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9004c0c5-292e-4e4c-9dcf-834878a70f55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "462\n",
      "3049\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Thick and Thin in annotations_df\n",
    "okwa_annotations_df['is_thick'] = okwa_annotations_df['file_name'].str.contains('THICK', case=False)\n",
    "okwa_annotations_df['is_thin'] = okwa_annotations_df['file_name'].str.contains('THIN', case=False)\n",
    "annotation_thick = okwa_annotations_df['is_thick']\n",
    "annotation_thin = okwa_annotations_df['is_thin']\n",
    "\n",
    "thick_annotations = okwa_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 = okwa_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": "bffebcb4-76a5-4f35-8afb-84c116212d13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8cf3f7af-c2b7-434d-9fce-e13e7aaf5fe5",
   "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": "58bf094b-d5fe-4a13-a0f9-6eca85b7c709",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1b7d149d-3273-41c6-9bc2-3e3f067bd6d5",
   "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>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>103</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>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Image_ID Category_name  Number\n",
       "0         1   Trophozoite      96\n",
       "1         2   Trophozoite      87\n",
       "2         3   Trophozoite     103\n",
       "3         4   Trophozoite     107\n",
       "4         5   Trophozoite     125"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "trophozoite_annotations = okwa_annotations_df[okwa_annotations_df['category_id'] == 1]\n",
    "\n",
    "# Create a mapping of category_id to category_name\n",
    "category_mapping = okwa_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": "77daa4fc-bc1e-40c5-a0cb-88e6aee4351a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0df0847e-2cf9-4fa8-8b03-4b6bee7d5b42",
   "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": "642150f2-4cc6-4ef6-8136-121e76c10c2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "deca08fe-8358-4b5c-8474-49be0f9d5b13",
   "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>96</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>87</td>\n",
       "      <td>Thick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>103</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>125</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      96      Thick\n",
       "1         2   Trophozoite      87      Thick\n",
       "2         3   Trophozoite     103      Thick\n",
       "3         4   Trophozoite     107      Thick\n",
       "4         5   Trophozoite     125      Thick"
      ]
     },
     "execution_count": 36,
     "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.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2615b781-f2f4-4c04-9bd9-833bfc032add",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ba53beaf-1c76-446b-ba2b-2eec92fed17d",
   "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": "b82d74ed-1c62-48cb-8cb7-a382e7af98de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3be91ff2-14df-43be-b016-26d6834189a7",
   "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>55</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>61</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>47</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>21</td>\n",
       "      <td>Thin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>28</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      55       Thin\n",
       "18        19   Trophozoite      61       Thin\n",
       "19        20   Trophozoite      47       Thin\n",
       "20        21   Trophozoite      21       Thin\n",
       "21        22   Trophozoite      28       Thin"
      ]
     },
     "execution_count": 38,
     "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()\n",
    "#trophozoite_annotations_thin.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d8d47cc-af42-43c5-bcea-24849a1a2e75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8c21a3ea-019e-4846-891c-3c8edbf22cd3",
   "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()[:40]\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": "6c146de1-5ccb-4a6f-a209-6de68d4b1a39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "64471750-e958-4fa8-aa94-7d55eab49fa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Image_ID, Number, Category_name]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "schizont_annotations = annotations_df[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 = 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": "f4e053e5-b181-467e-9617-f159d27eae57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "26eeb43d-b224-4df2-bec8-82dbab8830d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Image_ID, Number, Category_name]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "gametocyte_annotations = okwa_annotations_df[okwa_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 = okwa_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": "240695f9-62ef-44e0-9cad-c9d2bc37f4c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9c6877e7-9212-4a03-8bf0-37fef739fa2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   image_id  category_id  count\n",
      "0         1            4     27\n",
      "1         2            4     26\n",
      "2         3            4     30\n",
      "3         4            4     23\n",
      "4         5            4     13\n"
     ]
    }
   ],
   "source": [
    "wbc_annotations = okwa_annotations_df[okwa_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": "02c83a62-8fef-4207-b359-35d082efcb6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a20f9ba4-a610-4b86-bd88-bef9cba92ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   image_id  category_id  count\n",
      "0         1            5      2\n",
      "1         2            5      2\n",
      "2         3            5      2\n",
      "3         4            5      3\n",
      "4         5            5      1\n"
     ]
    }
   ],
   "source": [
    "pf_annotations = okwa_annotations_df[okwa_annotations_df['category_id'] == 5]\n",
    "pf_annotations = pf_annotations.drop('is_thick', axis = 1)\n",
    "pf_annotations = pf_annotations.drop('is_thin', axis = 1)\n",
    "pf_annotations = pf_annotations.groupby(['image_id','category_id']).size().reset_index(name='count')\n",
    "\n",
    "print(pf_annotations.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c71cff0-a22c-4850-bcf2-32eacc6a94dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28e067d9-e377-4ce3-86d9-ee90d8b6c141",
   "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
}