{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":45867,"databundleVersionId":6924515,"sourceType":"competition"}],"dockerImageVersionId":31192,"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 session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T07:53:33.606259Z","iopub.execute_input":"2025-12-04T07:53:33.606548Z","iopub.status.idle":"2025-12-04T07:53:37.155418Z","shell.execute_reply.started":"2025-12-04T07:53:33.606525Z","shell.execute_reply":"2025-12-04T07:53:37.154609Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/UBC-OCEAN/updated_image_ids.json\n/kaggle/input/UBC-OCEAN/sample_submission.csv\n/kaggle/input/UBC-OCEAN/train.csv\n/kaggle/input/UBC-OCEAN/test.csv\n/kaggle/input/UBC-OCEAN/test_thumbnails/41_thumbnail.png\n/kaggle/input/UBC-OCEAN/train_images/14127.png\n/kaggle/input/UBC-OCEAN/train_images/34649.png\n/kaggle/input/UBC-OCEAN/train_images/15221.png\n/kaggle/input/UBC-OCEAN/train_images/52375.png\n/kaggle/input/UBC-OCEAN/train_images/17487.png\n/kaggle/input/UBC-OCEAN/train_images/32112.png\n/kaggle/input/UBC-OCEAN/train_images/22290.png\n/kaggle/input/UBC-OCEAN/train_images/48734.png\n/kaggle/input/UBC-OCEAN/train_images/39146.png\n/kaggle/input/UBC-OCEAN/train_images/64950.png\n/kaggle/input/UBC-OCEAN/train_images/32042.png\n/kaggle/input/UBC-OCEAN/train_images/63429.png\n/kaggle/input/UBC-OCEAN/train_images/13526.png\n/kaggle/input/UBC-OCEAN/train_images/53859.png\n/kaggle/input/UBC-OCEAN/train_images/63836.png\n/kaggle/input/UBC-OCEAN/train_images/56117.png\n/kaggle/input/UBC-OCEAN/train_images/7490.png\n/kaggle/input/UBC-OCEAN/train_images/60685.png\n/kaggle/input/UBC-OCEAN/train_images/7955.png\n/kaggle/input/UBC-OCEAN/train_images/29888.png\n/kaggle/input/UBC-OCEAN/train_images/51679.png\n/kaggle/input/UBC-OCEAN/train_images/10077.png\n/kaggle/input/UBC-OCEAN/train_images/19030.png\n/kaggle/input/UBC-OCEAN/train_images/49281.png\n/kaggle/input/UBC-OCEAN/train_images/45185.png\n/kaggle/input/UBC-OCEAN/train_images/41801.png\n/kaggle/input/UBC-OCEAN/train_images/10896.png\n/kaggle/input/UBC-OCEAN/train_images/63941.png\n/kaggle/input/UBC-OCEAN/train_images/62828.png\n/kaggle/input/UBC-OCEAN/train_images/18138.png\n/kaggle/input/UBC-OCEAN/train_images/37655.png\n/kaggle/input/UBC-OCEAN/train_images/20463.png\n/kaggle/input/UBC-OCEAN/train_images/52931.png\n/kaggle/input/UBC-OCEAN/train_images/36302.png\n/kaggle/input/UBC-OCEAN/train_images/21373.png\n/kaggle/input/UBC-OCEAN/train_images/39208.png\n/kaggle/input/UBC-OCEAN/train_images/61823.png\n/kaggle/input/UBC-OCEAN/train_images/24617.png\n/kaggle/input/UBC-OCEAN/train_images/3098.png\n/kaggle/input/UBC-OCEAN/train_images/29147.png\n/kaggle/input/UBC-OCEAN/train_images/28028.png\n/kaggle/input/UBC-OCEAN/train_images/1952.png\n/kaggle/input/UBC-OCEAN/train_images/51128.png\n/kaggle/input/UBC-OCEAN/train_images/12244.png\n/kaggle/input/UBC-OCEAN/train_images/11417.png\n/kaggle/input/UBC-OCEAN/train_images/33976.png\n/kaggle/input/UBC-OCEAN/train_images/18568.png\n/kaggle/input/UBC-OCEAN/train_images/281.png\n/kaggle/input/UBC-OCEAN/train_images/24759.png\n/kaggle/input/UBC-OCEAN/train_images/11557.png\n/kaggle/input/UBC-OCEAN/train_images/45630.png\n/kaggle/input/UBC-OCEAN/train_images/36063.png\n/kaggle/input/UBC-OCEAN/train_images/63015.png\n/kaggle/input/UBC-OCEAN/train_images/50962.png\n/kaggle/input/UBC-OCEAN/train_images/30515.png\n/kaggle/input/UBC-OCEAN/train_images/35754.png\n/kaggle/input/UBC-OCEAN/train_images/57100.png\n/kaggle/input/UBC-OCEAN/train_images/24507.png\n/kaggle/input/UBC-OCEAN/train_images/33984.png\n/kaggle/input/UBC-OCEAN/train_images/39425.png\n/kaggle/input/UBC-OCEAN/train_images/52420.png\n/kaggle/input/UBC-OCEAN/train_images/63289.png\n/kaggle/input/UBC-OCEAN/train_images/4608.png\n/kaggle/input/UBC-OCEAN/train_images/46469.png\n/kaggle/input/UBC-OCEAN/train_images/14424.png\n/kaggle/input/UBC-OCEAN/train_images/61424.png\n/kaggle/input/UBC-OCEAN/train_images/15912.png\n/kaggle/input/UBC-OCEAN/train_images/61689.png\n/kaggle/input/UBC-OCEAN/train_images/4.png\n/kaggle/input/UBC-OCEAN/train_images/8279.png\n/kaggle/input/UBC-OCEAN/train_images/18914.png\n/kaggle/input/UBC-OCEAN/train_images/16325.png\n/kaggle/input/UBC-OCEAN/train_images/27249.png\n/kaggle/input/UBC-OCEAN/train_images/3222.png\n/kaggle/input/UBC-OCEAN/train_images/2097.png\n/kaggle/input/UBC-OCEAN/train_images/61280.png\n/kaggle/input/UBC-OCEAN/train_images/38535.png\n/kaggle/input/UBC-OCEAN/train_images/30369.png\n/kaggle/input/UBC-OCEAN/train_images/38018.png\n/kaggle/input/UBC-OCEAN/train_images/52275.png\n/kaggle/input/UBC-OCEAN/train_images/59002.png\n/kaggle/input/UBC-OCEAN/train_images/27747.png\n/kaggle/input/UBC-OCEAN/train_images/4877.png\n/kaggle/input/UBC-OCEAN/train_images/53059.png\n/kaggle/input/UBC-OCEAN/train_images/7204.png\n/kaggle/input/UBC-OCEAN/train_images/57468.png\n/kaggle/input/UBC-OCEAN/train_images/24563.png\n/kaggle/input/UBC-OCEAN/train_images/36204.png\n/kaggle/input/UBC-OCEAN/train_images/8805.png\n/kaggle/input/UBC-OCEAN/train_images/44432.png\n/kaggle/input/UBC-OCEAN/train_images/30203.png\n/kaggle/input/UBC-OCEAN/train_images/48502.png\n/kaggle/input/UBC-OCEAN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os\n\nroot = \"/kaggle/input\"\nfor path, dirs, files in os.walk(root):\n    print(path)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T07:56:58.325324Z","iopub.execute_input":"2025-12-04T07:56:58.325889Z","iopub.status.idle":"2025-12-04T07:56:58.913285Z","shell.execute_reply.started":"2025-12-04T07:56:58.32586Z","shell.execute_reply":"2025-12-04T07:56:58.912555Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input\n/kaggle/input/UBC-OCEAN\n/kaggle/input/UBC-OCEAN/test_thumbnails\n/kaggle/input/UBC-OCEAN/train_images\n/kaggle/input/UBC-OCEAN/train_thumbnails\n/kaggle/input/UBC-OCEAN/test_images\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"import os\n\nfor dirname, _, filenames in os.walk('/kaggle/input/UBC-OCEAN'):\n    print(dirname)\n    for filename in filenames:\n        print(\"  \", filename)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T07:58:03.117232Z","iopub.execute_input":"2025-12-04T07:58:03.118092Z","iopub.status.idle":"2025-12-04T07:58:03.703128Z","shell.execute_reply.started":"2025-12-04T07:58:03.118059Z","shell.execute_reply":"2025-12-04T07:58:03.702242Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/UBC-OCEAN\n   updated_image_ids.json\n   sample_submission.csv\n   train.csv\n   test.csv\n/kaggle/input/UBC-OCEAN/test_thumbnails\n   41_thumbnail.png\n/kaggle/input/UBC-OCEAN/train_images\n   14127.png\n   34649.png\n   15221.png\n   52375.png\n   17487.png\n   32112.png\n   22290.png\n   48734.png\n   39146.png\n   64950.png\n   32042.png\n   63429.png\n   13526.png\n   53859.png\n   63836.png\n   56117.png\n   7490.png\n   60685.png\n   7955.png\n   29888.png\n   51679.png\n   10077.png\n   19030.png\n   49281.png\n   45185.png\n   41801.png\n   10896.png\n   63941.png\n   62828.png\n   18138.png\n   37655.png\n   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19157_thumbnail.png\n   38959_thumbnail.png\n   14542_thumbnail.png\n   19255_thumbnail.png\n   35592_thumbnail.png\n   32636_thumbnail.png\n   64824_thumbnail.png\n   56947_thumbnail.png\n   20670_thumbnail.png\n   27739_thumbnail.png\n   26124_thumbnail.png\n   33976_thumbnail.png\n   58974_thumbnail.png\n   30369_thumbnail.png\n   26219_thumbnail.png\n   52836_thumbnail.png\n   19569_thumbnail.png\n   6281_thumbnail.png\n   31033_thumbnail.png\n   18014_thumbnail.png\n   55287_thumbnail.png\n   36499_thumbnail.png\n   22489_thumbnail.png\n   27851_thumbnail.png\n   52420_thumbnail.png\n   18607_thumbnail.png\n   6843_thumbnail.png\n   15486_thumbnail.png\n   28736_thumbnail.png\n   63836_thumbnail.png\n   53059_thumbnail.png\n   19512_thumbnail.png\n   29331_thumbnail.png\n   30539_thumbnail.png\n   33839_thumbnail.png\n   50246_thumbnail.png\n   53859_thumbnail.png\n   65371_thumbnail.png\n   51128_thumbnail.png\n   3055_thumbnail.png\n   6793_thumbnail.png\n   34822_thumbnail.png\n   61961_thumbnail.png\n   23523_thumbnail.png\n   4797_thumbnail.png\n   35998_thumbnail.png\n   25561_thumbnail.png\n   42549_thumbnail.png\n   31473_thumbnail.png\n   24507_thumbnail.png\n   20858_thumbnail.png\n   38349_thumbnail.png\n   61100_thumbnail.png\n   14051_thumbnail.png\n   59031_thumbnail.png\n   12442_thumbnail.png\n/kaggle/input/UBC-OCEAN/test_images\n   41.png\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"!ls /kaggle/input","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T07:58:34.081008Z","iopub.execute_input":"2025-12-04T07:58:34.081803Z","iopub.status.idle":"2025-12-04T07:58:34.202674Z","shell.execute_reply.started":"2025-12-04T07:58:34.081774Z","shell.execute_reply":"2025-12-04T07:58:34.201822Z"}},"outputs":[{"name":"stdout","text":"UBC-OCEAN\n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"!ls /kaggle/input/UBC-OCEAN","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T07:58:52.024161Z","iopub.execute_input":"2025-12-04T07:58:52.024475Z","iopub.status.idle":"2025-12-04T07:58:52.148493Z","shell.execute_reply.started":"2025-12-04T07:58:52.024443Z","shell.execute_reply":"2025-12-04T07:58:52.147613Z"}},"outputs":[{"name":"stdout","text":"sample_submission.csv  test_images\ttrain.csv     train_thumbnails\ntest.csv\t       test_thumbnails\ttrain_images  updated_image_ids.json\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"import pandas as pd\n\nbase = \"/kaggle/input/UBC-OCEAN\"\n\ntrain_df = pd.read_csv(f\"{base}/train.csv\")\ntest_df  = pd.read_csv(f\"{base}/test.csv\")\nsample_df = pd.read_csv(f\"{base}/sample_submission.csv\")\n\ntrain_df.head(), test_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T07:59:49.084951Z","iopub.execute_input":"2025-12-04T07:59:49.085297Z","iopub.status.idle":"2025-12-04T07:59:49.132759Z","shell.execute_reply.started":"2025-12-04T07:59:49.085267Z","shell.execute_reply":"2025-12-04T07:59:49.132091Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"(   image_id label  image_width  image_height  is_tma\n 0         4  HGSC        23785         20008   False\n 1        66  LGSC        48871         48195   False\n 2        91  HGSC         3388          3388    True\n 3       281  LGSC        42309         15545   False\n 4       286    EC        37204         30020   False,\n    image_id  image_width  image_height\n 0        41        28469         16987)"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"import pandas as pd\n\n# Correct paths\ntrain_df = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\ntest_df  = pd.read_csv('/kaggle/input/UBC-OCEAN/test.csv')\n\ntrain_df.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:01:11.203481Z","iopub.execute_input":"2025-12-04T08:01:11.203849Z","iopub.status.idle":"2025-12-04T08:01:11.227067Z","shell.execute_reply.started":"2025-12-04T08:01:11.203824Z","shell.execute_reply":"2025-12-04T08:01:11.226267Z"}},"outputs":[{"execution_count":11,"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","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  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"import os\n\nbase = \"/kaggle/input/UBC-OCEAN/train_images\"\n\nfor root, dirs, files in os.walk(base):\n    print(root, \"->\", len(files), \"files\")\n    break\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:05:21.294153Z","iopub.execute_input":"2025-12-04T08:05:21.294453Z","iopub.status.idle":"2025-12-04T08:05:21.621479Z","shell.execute_reply.started":"2025-12-04T08:05:21.294429Z","shell.execute_reply":"2025-12-04T08:05:21.620636Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/UBC-OCEAN/train_images -> 538 files\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"!ls /kaggle/input/UBC-OCEAN/train_images | head\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:05:31.796111Z","iopub.execute_input":"2025-12-04T08:05:31.796755Z","iopub.status.idle":"2025-12-04T08:05:31.925764Z","shell.execute_reply.started":"2025-12-04T08:05:31.796726Z","shell.execute_reply":"2025-12-04T08:05:31.924791Z"}},"outputs":[{"name":"stdout","text":"10077.png\n10143.png\n1020.png\n10246.png\n10252.png\n10469.png\n10548.png\n10642.png\n10800.png\n1080.png\n","output_type":"stream"}],"execution_count":14},{"cell_type":"code","source":"image_id = train_df.iloc[0][\"image_id\"]\npath = \"/kaggle/input/UBC-OCEAN/train_images\"\n\nprint(\"Searching for:\", image_id)\n\nmatches = []\nfor root, dirs, files in os.walk(path):\n    for f in files:\n        if f.startswith(str(image_id)):\n            matches.append(os.path.join(root, f))\n\nmatches[:10], len(matches)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:06:06.628954Z","iopub.execute_input":"2025-12-04T08:06:06.629336Z","iopub.status.idle":"2025-12-04T08:06:07.33654Z","shell.execute_reply.started":"2025-12-04T08:06:06.629303Z","shell.execute_reply":"2025-12-04T08:06:07.335825Z"}},"outputs":[{"name":"stdout","text":"Searching for: 4\n","output_type":"stream"},{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"(['/kaggle/input/UBC-OCEAN/train_images/48734.png',\n  '/kaggle/input/UBC-OCEAN/train_images/49281.png',\n  '/kaggle/input/UBC-OCEAN/train_images/45185.png',\n  '/kaggle/input/UBC-OCEAN/train_images/41801.png',\n  '/kaggle/input/UBC-OCEAN/train_images/45630.png',\n  '/kaggle/input/UBC-OCEAN/train_images/4608.png',\n  '/kaggle/input/UBC-OCEAN/train_images/46469.png',\n  '/kaggle/input/UBC-OCEAN/train_images/4.png',\n  '/kaggle/input/UBC-OCEAN/train_images/4877.png',\n  '/kaggle/input/UBC-OCEAN/train_images/44432.png'],\n 81)"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"import pandas as pd\n\n# Correct paths for the CSVs\ntrain_df = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\ntest_df = pd.read_csv('/kaggle/input/UBC-OCEAN/test.csv')\n\ntrain_df.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:06:47.411761Z","iopub.execute_input":"2025-12-04T08:06:47.412083Z","iopub.status.idle":"2025-12-04T08:06:47.433488Z","shell.execute_reply.started":"2025-12-04T08:06:47.412059Z","shell.execute_reply":"2025-12-04T08:06:47.432696Z"}},"outputs":[{"execution_count":16,"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","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  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":16},{"cell_type":"code","source":"import cv2\nimport os\n\n# Get an example image_id from the train CSV\nimage_id = train_df.iloc[0][\"image_id\"]  # e.g., 4, 4608, etc.\n\n# Build correct path\nimage_path = f\"/kaggle/input/UBC-OCEAN/train_images/{image_id}.png\"\n\n# Load the image\nimg = cv2.imread(image_path)\n\nprint(\"Loaded:\", image_path)\nprint(\"Shape:\", img.shape)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:07:07.408006Z","iopub.execute_input":"2025-12-04T08:07:07.408283Z","iopub.status.idle":"2025-12-04T08:07:27.220037Z","shell.execute_reply.started":"2025-12-04T08:07:07.408264Z","shell.execute_reply":"2025-12-04T08:07:27.219149Z"}},"outputs":[{"name":"stdout","text":"Loaded: /kaggle/input/UBC-OCEAN/train_images/4.png\nShape: (20008, 23785, 3)\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"# 1.1 Explore directory structure\nimport os\nfrom pprint import pprint\n\nbase = \"/kaggle/input/UBC-OCEAN\"\n\nprint(\"Base exists:\", os.path.exists(base))\nprint(\"\\nTop-level files/folders under /kaggle/input:\")\npprint(sorted(os.listdir(\"/kaggle/input\")))\n\nprint(\"\\nContents of UBC-OCEAN:\")\nfor root, dirs, files in os.walk(base):\n    # print first few directories and files only for readability\n    rel = os.path.relpath(root, base)\n    if rel == \".\":\n        rel = \"/\"\n    print(f\"\\nFolder: {rel}\")\n    if dirs:\n        print(\"  subdirs:\", dirs[:10])\n    if files:\n        print(\"  files (first 20):\", files[:20])\n    # stop descending after 3 levels for brevity\n    if root.count(os.sep) - base.count(os.sep) >= 2:\n        continue\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:48:50.285629Z","iopub.execute_input":"2025-12-04T08:48:50.286529Z","iopub.status.idle":"2025-12-04T08:48:52.120953Z","shell.execute_reply.started":"2025-12-04T08:48:50.286494Z","shell.execute_reply":"2025-12-04T08:48:52.12007Z"}},"outputs":[{"name":"stdout","text":"Base exists: True\n\nTop-level files/folders under /kaggle/input:\n['UBC-OCEAN']\n\nContents of UBC-OCEAN:\n\nFolder: /\n  subdirs: ['test_thumbnails', 'train_images', 'train_thumbnails', 'test_images']\n  files (first 20): ['updated_image_ids.json', 'sample_submission.csv', 'train.csv', 'test.csv']\n\nFolder: test_thumbnails\n  files (first 20): ['41_thumbnail.png']\n\nFolder: train_images\n  files (first 20): ['14127.png', '34649.png', '15221.png', '52375.png', '17487.png', '32112.png', '22290.png', '48734.png', '39146.png', '64950.png', '32042.png', '63429.png', '13526.png', '53859.png', '63836.png', '56117.png', '7490.png', '60685.png', '7955.png', '29888.png']\n\nFolder: train_thumbnails\n  files (first 20): ['52275_thumbnail.png', '47960_thumbnail.png', '4_thumbnail.png', '57468_thumbnail.png', '20316_thumbnail.png', '27315_thumbnail.png', '46793_thumbnail.png', '43815_thumbnail.png', '25792_thumbnail.png', '48502_thumbnail.png', '55281_thumbnail.png', '56221_thumbnail.png', '29147_thumbnail.png', '43875_thumbnail.png', '33708_thumbnail.png', '57265_thumbnail.png', '15912_thumbnail.png', '60287_thumbnail.png', '6140_thumbnail.png', '27245_thumbnail.png']\n\nFolder: test_images\n  files (first 20): ['41.png']\n","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"# 1.2 Load train/test CSVs and show quick EDA\nimport pandas as pd\n\nbase = \"/kaggle/input/UBC-OCEAN\"\ntrain_csv = os.path.join(base, \"train.csv\")\ntest_csv  = os.path.join(base, \"test.csv\")\nsample_csv = os.path.join(base, \"sample_submission.csv\")\n\nprint(\"train.csv exists:\", os.path.exists(train_csv))\nprint(\"test.csv exists: \", os.path.exists(test_csv))\nprint(\"sample_submission exists:\", os.path.exists(sample_csv))\n\ntrain_df = pd.read_csv(train_csv)\ntest_df  = pd.read_csv(test_csv)\nsample_df = pd.read_csv(sample_csv)\n\nprint(\"\\ntrain_df.shape:\", train_df.shape)\nprint(\"test_df.shape:\", test_df.shape)\nprint(\"\\ntrain_df.columns:\", train_df.columns.tolist())\nprint(\"\\nFirst 5 rows of train_df:\")\ndisplay(train_df.head())\n\n# If there is a class/label column, show distribution. Try common names.\npossible_label_cols = [c for c in train_df.columns if c.lower() in (\"label\",\"target\",\"class\",\"subtype\")]\nprint(\"\\nDetected possible label columns:\", possible_label_cols)\n\nif possible_label_cols:\n    lab = possible_label_cols[0]\n    print(f\"\\nLabel distribution for '{lab}':\")\n    display(train_df[lab].value_counts().sort_index())\nelse:\n    # fallback: try to infer multiclass columns\n    print(\"\\nNo obvious label column detected. Show first few columns and sample values to inspect.\")\n    display(train_df.iloc[:, :6].head())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-04T08:54:09.834004Z","iopub.execute_input":"2025-12-04T08:54:09.834307Z","iopub.status.idle":"2025-12-04T08:54:10.985932Z","shell.execute_reply.started":"2025-12-04T08:54:09.834283Z","shell.execute_reply":"2025-12-04T08:54:10.984827Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_102/1429617276.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mbase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/kaggle/input/UBC-OCEAN\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"train.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mtest_csv\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"test.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0msample_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sample_submission.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'os' is not defined"],"ename":"NameError","evalue":"name 'os' is not defined","output_type":"error"}],"execution_count":2},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}