goes16-contrail-detection-rle-encoding.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/patimejia/goes16-contrail-rle-encoding-submission?scriptVersionId=136584508\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"fce8ef91","metadata":{"execution":{"iopub.execute_input":"2023-07-12T18:58:19.111794Z","iopub.status.busy":"2023-07-12T18:58:19.111399Z","iopub.status.idle":"2023-07-12T18:58:19.586514Z","shell.execute_reply":"2023-07-12T18:58:19.585332Z"},"papermill":{"duration":0.481276,"end_time":"2023-07-12T18:58:19.589093","exception":false,"start_time":"2023-07-12T18:58:19.107817","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from pathlib import Path\n","\n","def rle_encode(image, foreground_value=1):\n","    # Run length encoding.\n","    dots = np.where(image.T.flatten() == foreground_value)[0]\n","    run_lengths = []\n","    prev = -2\n","    for dot in dots:\n","        if (dot > prev + 1): run_lengths.extend((dot + 1, 0))\n","        run_lengths[-1] += 1\n","        prev = dot\n","    return run_lengths\n","\n","def rle_to_string(rle):\n","    # Converts RLE to a string.\n","    return '-'.join(map(str, rle)) if rle else '-'\n","\n","def create_mask(data_path, record, num_pixels, shape=(256, 256)):\n","    # Creates a mask for the given record.\n","    band_08 = np.load(data_path / 'test' / record / 'band_08.npy').sum(axis=2)\n","    preds = np.c_[np.unravel_index(np.argpartition(band_08.ravel(), -num_pixels)[-num_pixels:], band_08.shape)]\n","    mask = np.zeros(shape)\n","    mask[preds[:, 0], preds[:, 1]] = 1\n","    return mask\n","\n","def visualize_mask(mask):\n","    # Visualizes the mask using matplotlib.\n","    plt.imshow(mask, cmap='Greys')\n","    plt.title(\"Obviously Not Contrails\")\n","    plt.show()\n","\n","def prepare_submission(submission, record, mask):\n","    # Prepares and updates the submission DataFrame.\n","    rle_mask = rle_encode(mask)\n","    submission.loc[int(record), 'encoded_pixels'] = rle_to_string(rle_mask)\n","\n","def main(data_path, num_plots=1):\n","    # Main function.\n","    data_path = Path(data_path)\n","    test_records = [entry.name for entry in data_path.glob('test/*') if entry.is_dir()]\n","    num_pixels = 1000\n","\n","    submission = pd.read_csv(data_path / 'sample_submission.csv', index_col='record_id')\n","\n","    for i, record in enumerate(test_records):\n","        mask = create_mask(data_path, record, num_pixels)\n","        if i < num_plots:  # Visualize the masks for the first 'num_plots' records\n","            visualize_mask(mask)\n","        prepare_submission(submission, record, mask)\n","\n","    submission.to_csv('submission.csv')\n","\n","# Directly call main with your data_path and num_plots as arguments\n","main('/kaggle/input/google-research-identify-contrails-reduce-global-warming', num_plots=2)"]},{"cell_type":"markdown","id":"b63cb53c","metadata":{"papermill":{"duration":0.001786,"end_time":"2023-07-12T18:58:19.593267","exception":false,"start_time":"2023-07-12T18:58:19.591481","status":"completed"},"tags":[]},"source":["# Contrail Detection Submission with Run-Length Encoding (RLE)\n","- The script imports necessary libraries such as `numpy`, `pandas`, and `pathlib`.\n","- Defines several functions to perform RLE, create and visualize masks, and prepare the submission `DataFrame`.\n","- The main function calls these functions and saves the submission in a CSV file.\n","\n","## What is RLE?\n","- Run-length encoding (RLE) is a form of lossless data compression.\n","- It stores runs of data as a single data value and count.\n","- For example, a string of characters `AAABBCCCC` can be compressed to `3A2B4C`.\n","- RLE excels at compressing data with runs: numerous consecutive repetitions.\n","\n","## Handling Empty Predictions\n","- Empty predictions are handled by the `rle_to_string` function.\n","- If the input RLE mask is `empty`, the function returns a string containing a single dash (`'-'`).\n","\n","# Acknowledgement\n","- This script is inspired by [INVERSION’s notebook](https://www.kaggle.com/code/inversion/contrails-rle-submission) on Kaggle.\n","- GitHub: To run this code locally, visit [src/utils/rle_encoding_submission.py](https://github.com/patmejia/contrails-vision/blob/main/src/utils/rle_encoding_submission.py)."]},{"cell_type":"markdown","id":"b79a1574","metadata":{"papermill":{"duration":0.00165,"end_time":"2023-07-12T18:58:19.596846","exception":false,"start_time":"2023-07-12T18:58:19.595196","status":"completed"},"tags":[]},"source":["<div style=\"background-color: #f2f2f2; padding: 53px; border-radius: 5px;\">\n","  <h3>If you found this notebook helpful...</h3>\n","  <p>\n","  Please consider giving it a star. Your support helps me continue to develop high-quality code and pursue my career as a data analyst/engineer. Feedback is always welcome and appreciated. Thank you for taking the time to read my work! \n","  </p> \n","  <h4>\n","  <p style=\"text-align: right;\">\n","  <a href=\"https://github.com/patmejia\"> - pat [¬º-°]¬ </a>\n","  </h4>\n","  </p>\n","</div>"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.10.10"},"papermill":{"default_parameters":{},"duration":10.687847,"end_time":"2023-07-12T18:58:20.219182","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-07-12T18:58:09.531335","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}