{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":45867,"databundleVersionId":6924515},{"sourceType":"kernelVersion","sourceId":148683476}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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os\nfrom glob import glob\nfrom pathlib import Path\nfrom tqdm.notebook import tqdm\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-23T06:39:24.205974Z","iopub.execute_input":"2024-10-23T06:39:24.207305Z","iopub.status.idle":"2024-10-23T06:39:25.449133Z","shell.execute_reply.started":"2024-10-23T06:39:24.207252Z","shell.execute_reply":"2024-10-23T06:39:25.447897Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"competition_dataset_directory = Path('/kaggle/input/UBC-OCEAN')\njpeg_dataset_directory = Path('/kaggle/input/ubc-ocean-jpeg-dataset-pipeline')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-23T06:39:31.254398Z","iopub.execute_input":"2024-10-23T06:39:31.254788Z","iopub.status.idle":"2024-10-23T06:39:31.260315Z","shell.execute_reply.started":"2024-10-23T06:39:31.254751Z","shell.execute_reply":"2024-10-23T06:39:31.25891Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"df_train = pd.read_csv(competition_dataset_directory / 'train.csv')\ndf_train","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-23T06:39:34.502485Z","iopub.execute_input":"2024-10-23T06:39:34.502917Z","iopub.status.idle":"2024-10-23T06:39:34.548443Z","shell.execute_reply.started":"2024-10-23T06:39:34.502856Z","shell.execute_reply":"2024-10-23T06:39:34.547129Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"     image_id label  image_width  image_height  is_tma\n0           4  HGSC        23785         20008   False\n1          66  LGSC        48871         48195   False\n2          91  HGSC         3388          3388    True\n3         281  LGSC        42309         15545   False\n4         286    EC        37204         30020   False\n..        ...   ...          ...           ...     ...\n533     65022  LGSC        53355         46675   False\n534     65094    MC        55042         45080   False\n535     65300  HGSC        75860         27503   False\n536     65371  HGSC        42551         41800   False\n537     65533  HGSC        45190         33980   False\n\n[538 rows x 5 columns]","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>label</th>\n      <th>image_width</th>\n      <th>image_height</th>\n      <th>is_tma</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>HGSC</td>\n      <td>23785</td>\n      <td>20008</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>LGSC</td>\n      <td>48871</td>\n      <td>48195</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>91</td>\n      <td>HGSC</td>\n      <td>3388</td>\n      <td>3388</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>281</td>\n      <td>LGSC</td>\n      <td>42309</td>\n      <td>15545</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>286</td>\n      <td>EC</td>\n      <td>37204</td>\n      <td>30020</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>533</th>\n      <td>65022</td>\n      <td>LGSC</td>\n      <td>53355</td>\n      <td>46675</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>534</th>\n      <td>65094</td>\n      <td>MC</td>\n      <td>55042</td>\n      <td>45080</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>535</th>\n      <td>65300</td>\n      <td>HGSC</td>\n      <td>75860</td>\n      <td>27503</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>536</th>\n      <td>65371</td>\n      <td>HGSC</td>\n      <td>42551</td>\n      <td>41800</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>537</th>\n      <td>65533</td>\n      <td>HGSC</td>\n      <td>45190</td>\n      <td>33980</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>538 rows × 5 columns</p>\n</div>"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\n\ndef visualize_categorical_column_distribution(df, column, title):\n    plt.figure(figsize=(8, 6))\n    sns.countplot(x=column, data=df)\n    plt.title(title)\n    plt.xticks(rotation=45)\n    plt.show()\n\n# Example usage\nvisualize_categorical_column_distribution(df=df_train, column='label', title='Training Set Target Frequencies')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-23T06:39:50.893532Z","iopub.execute_input":"2024-10-23T06:39:50.894022Z","iopub.status.idle":"2024-10-23T06:39:51.189529Z","shell.execute_reply.started":"2024-10-23T06:39:50.893906Z","shell.execute_reply":"2024-10-23T06:39:51.188237Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"training_set_sample_count = df_train.shape[0]\ntraining_set_image_count = len(os.listdir(competition_dataset_directory / 'train_images'))\ntraining_set_thumbnail_count = len(os.listdir(competition_dataset_directory / 'train_thumbnails'))\n\nprint(f'Training Set Sample Count: {training_set_sample_count}')\nprint(f'Training Set Image Count: {training_set_image_count}')\nprint(f'Training Set Thumbnail Count: {training_set_thumbnail_count}')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-23T06:39:54.890278Z","iopub.execute_input":"2024-10-23T06:39:54.890723Z","iopub.status.idle":"2024-10-23T06:39:54.900678Z","shell.execute_reply.started":"2024-10-23T06:39:54.890681Z","shell.execute_reply":"2024-10-23T06:39:54.899209Z"}},"outputs":[{"name":"stdout","text":"Training Set Sample Count: 538\nTraining Set Image Count: 538\nTraining Set Thumbnail Count: 513\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"visualize_categorical_column_distribution(\n    df=df_train,\n    column='is_tma',\n    title='Training Set Image Type Frequencies (is_tma)'\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-23T06:39:57.970158Z","iopub.execute_input":"2024-10-23T06:39:57.970625Z","iopub.status.idle":"2024-10-23T06:39:58.159718Z","shell.execute_reply.started":"2024-10-23T06:39:57.970578Z","shell.execute_reply":"2024-10-23T06:39:58.158311Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"for idx, row in tqdm(df_train.loc[~df_train['is_tma']].iterrows(), total=df_train.loc[~df_train['is_tma']].shape[0]):\n    \n    image_path = str(competition_dataset_directory / 'train_thumbnails' / f'{row[\"image_id\"]}_thumbnail.png')\n    image = cv2.imread(image_path)\n    \n    df_train.loc[idx, 'thumbnail_height'] = image.shape[0]\n    df_train.loc[idx, 'thumbnail_width'] = image.shape[1]\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from tqdm.notebook import tqdm\nimport cv2\n\n# Iterate with tqdm notebook version\nfor idx, row in tqdm(df_train.loc[~df_train['is_tma']].iterrows(), total=df_train.loc[~df_train['is_tma']].shape[0]):\n    \n    image_path = str(competition_dataset_directory / 'train_thumbnails' / f'{row[\"image_id\"]}_thumbnail.png')\n    image = cv2.imread(image_path)\n    \n    if image is not None:\n        df_train.loc[idx, 'thumbnail_height'] = image.shape[0]\n        df_train.loc[idx, 'thumbnail_width'] = image.shape[1]\n    else:\n        print(f\"Image not found or unable to load at {image_path}\")  # Optional for debugging\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from tqdm.notebook import tqdm\nimport cv2\n\n# Progress bar with `leave=False`\nfor idx, row in tqdm(df_train.loc[~df_train['is_tma']].iterrows(), total=df_train.loc[~df_train['is_tma']].shape[0], leave=False):\n    \n    image_path = str(competition_dataset_directory / 'train_thumbnails' / f'{row[\"image_id\"]}_thumbnail.png')\n    image = cv2.imread(image_path)\n    \n    if image is not None:\n        df_train.loc[idx, 'thumbnail_height'] = image.shape[0]\n        df_train.loc[idx, 'thumbnail_width'] = image.shape[1]\n    else:\n        print(f\"Image not found or unable to load at {image_path}\")\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\n\ndef visualize_continuous_columns_relationship(df, column1, column2, group=None, title=''):\n    plt.figure(figsize=(10, 6))\n    \n    if group:\n        sns.scatterplot(x=column1, y=column2, hue=group, data=df, palette='Set1', alpha=0.7)\n    else:\n        sns.scatterplot(x=column1, y=column2, data=df, alpha=0.7)\n        \n    plt.title(title)\n    plt.xlabel(column1)\n    plt.ylabel(column2)\n    plt.legend(title=group)\n    plt.show()\n\n# Example usage\nvisualize_continuous_columns_relationship(df=df_train, column1='image_width', column2='image_height', group='is_tma', title='Image Height vs Width (WSIs and TMAs)')\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"visualize_continuous_columns_relationship(\n    df=df_train.loc[df_train['is_tma']],\n    column1='image_width',\n    column2='image_height',\n    group=None,\n    title='Image Height vs Width (TMAs)'\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\n\ndef visualize_image(image, grayscale_image, title=''):\n    plt.figure(figsize=(12, 6))\n    \n    # RGB Image\n    plt.subplot(1, 2, 1)\n    plt.imshow(image)\n    plt.title(f'RGB - {title}')\n    plt.axis('off')\n    \n    # Grayscale Image\n    plt.subplot(1, 2, 2)\n    plt.imshow(grayscale_image, cmap='gray')\n    plt.title(f'Grayscale - {title}')\n    plt.axis('off')\n    \n    plt.show()\n\n# Example usage in your loop with additional metrics\nfor idx, row in training_wsis.iterrows():\n    \n    image_path = str(jpeg_dataset_directory / 'train_compressed_images' / f'{row[\"image_id\"]}.jpg')\n    image = cv2.imread(image_path)\n    \n    if image is not None:\n        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB for display\n        image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # Convert to Grayscale\n        \n        # Calculate additional statistics\n        mean = np.mean(image_rgb)\n        std = np.std(image_rgb)\n        min_val = np.min(image_rgb)\n        max_val = np.max(image_rgb)\n        median = np.median(image_rgb)\n        variance = np.var(image_rgb)\n        pixel_range = max_val - min_val\n        \n        # Visualize with updated metrics and grayscale view\n        visualize_image(\n            image=image_rgb,\n            grayscale_image=image_gray,\n            title=f'Image ID: {row[\"image_id\"]} Type: WSI Label: {row[\"label\"]}\\nHeight: {image_rgb.shape[0]} Width: {image_rgb.shape[1]}\\n'\n                  f'Mean: {mean:.2f} Std: {std:.2f} Median: {median:.2f}\\nVariance: {variance:.2f} Pixel Range: {pixel_range:.2f} Min: {min_val:.2f} Max: {max_val:.2f}'\n        )\n    else:\n        print(f\"Failed to load image at {image_path}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-23T06:40:27.753681Z","iopub.execute_input":"2024-10-23T06:40:27.754102Z","iopub.status.idle":"2024-10-23T06:40:27.804644Z","shell.execute_reply.started":"2024-10-23T06:40:27.754062Z","shell.execute_reply":"2024-10-23T06:40:27.803103Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[13], line 23\u001b[0m\n\u001b[1;32m     20\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# Example usage in your loop with additional metrics\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, row \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtraining_wsis\u001b[49m\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[1;32m     25\u001b[0m     image_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(jpeg_dataset_directory \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_compressed_images\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrow[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage_id\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     26\u001b[0m     image \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(image_path)\n","\u001b[0;31mNameError\u001b[0m: name 'training_wsis' is not defined"],"ename":"NameError","evalue":"name 'training_wsis' is not defined","output_type":"error"}],"execution_count":13}]}