{"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":123966,"databundleVersionId":14902028,"sourceType":"competition"}],"dockerImageVersionId":31239,"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,"execution":{"iopub.status.busy":"2025-12-21T10:01:52.259102Z","iopub.execute_input":"2025-12-21T10:01:52.259958Z","iopub.status.idle":"2025-12-21T10:01:56.828180Z","shell.execute_reply.started":"2025-12-21T10:01:52.259924Z","shell.execute_reply":"2025-12-21T10:01:56.827038Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport pandas as pd\n\n# ---------------------------------------------------------\n# Find dataset root\n# ---------------------------------------------------------\nDATASET_ROOT = \"/kaggle/input\"\n\n# Find the competition folder automatically\ncomp_dir = None\nfor d in os.listdir(DATASET_ROOT):\n    if \"duality\" in d.lower() and \"geospatial\" in d.lower():\n        comp_dir = os.path.join(DATASET_ROOT, d)\n        break\n\nif comp_dir is None:\n    raise RuntimeError(\"Competition dataset folder not found\")\n\nprint(\"📁 Competition folder:\", comp_dir)\n\n# ---------------------------------------------------------\n# Find test images directory automatically\n# ---------------------------------------------------------\ntest_img_dir = None\nfor root, dirs, files in os.walk(comp_dir):\n    for f in files:\n        if f.lower().endswith((\".jpg\", \".png\")):\n            test_img_dir = root\n            break\n    if test_img_dir:\n        break\n\nif test_img_dir is None:\n    raise RuntimeError(\"No test images found\")\n\nprint(\"🖼️ Test image folder:\", test_img_dir)\n\n# ---------------------------------------------------------\n# Collect image IDs\n# ---------------------------------------------------------\nimage_ids = sorted(\n    os.path.splitext(f)[0]\n    for f in os.listdir(test_img_dir)\n    if f.lower().endswith((\".jpg\", \".png\"))\n)\n\nprint(f\"Found {len(image_ids)} test images\")\n\n# ---------------------------------------------------------\n# Dummy YOLO prediction\n# Format:\n# class confidence x_center y_center width height\n# ---------------------------------------------------------\nDUMMY_PRED = \"0 0.50 0.50 0.50 0.30 0.30\"\n\nsubmission = pd.DataFrame({\n    \"image_id\": image_ids,\n    \"prediction_string\": [DUMMY_PRED] * len(image_ids)\n})\n\n# REQUIRED output filename\nsubmission.to_csv(\"submission.csv\", index=False)\n\nprint(\"✅ submission.csv created successfully\")\nsubmission.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-21T10:02:46.838453Z","iopub.execute_input":"2025-12-21T10:02:46.838764Z","iopub.status.idle":"2025-12-21T10:02:47.374355Z","shell.execute_reply.started":"2025-12-21T10:02:46.838739Z","shell.execute_reply":"2025-12-21T10:02:47.373544Z"}},"outputs":[],"execution_count":null}]}