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ECG Image to Signal Conversion Pipeline\n \nThis notebook demonstrates a robust, multi-stage deep learning pipeline for digitizing ECG signals from images. It is designed for the PhysioNet ECG Image Digitization Challenge and is suitable for both competition and local testing environments.\n\n---\n\n## Overview\n\nThe pipeline consists of three main stages:\n\n- **Stage 0:** Image normalization and alignment (preprocessing, rotation, and homography correction)\n- **Stage 1:** Grid rectification and further normalization (removes grid artifacts, enhances signal visibility)\n- **Stage 2:** Signal extraction from rectified images (deep learning-based extraction of ECG traces)\n\nEach stage uses a custom-trained deep learning model. The notebook is modular, with clear error handling and fallback mechanisms to ensure robust processing.\n\n---\n\n## Key Features\n\n- Fully automated, end-to-end ECG image digitization\n- Custom deep learning models for each stage\n- Handles both competition and local test datasets\n- Robust error handling and fallback mechanisms\n- Produces a submission file in the required format for the challenge\n- Deterministic setup for reproducibility\n- Modular code for easy adaptation and extension\n\n**Note:** This notebook is intended for use in a Kaggle environment with the required datasets and model weights available in the specified input paths.","metadata":{"papermill":{"duration":0.007395,"end_time":"2025-12-25T04:34:00.304165","exception":false,"start_time":"2025-12-25T04:34:00.29677","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# --- Environment Setup ---\n# Uninstall TensorFlow (if present) and install required package for connected components 3D.\n# Note: The custom package is installed from a Kaggle dataset directory.\n# This ensures a clean environment and the correct dependencies for the pipeline.\n\n!pip uninstall -y tensorflow\n!uv pip install --no-deps --system --no-index --find-links='/kaggle/input/hengck23-submit-physionet/hengck23-submit-physionet/setup' 'connected-components-3d'\n\n# Optional: Check CUDA availability for PyTorch\nimport torch\nprint('CUDA available:', torch.cuda.is_available())","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2025-12-26T16:43:22.518974Z","iopub.execute_input":"2025-12-26T16:43:22.519635Z","iopub.status.idle":"2025-12-26T16:44:11.398643Z","shell.execute_reply.started":"2025-12-26T16:43:22.519608Z","shell.execute_reply":"2025-12-26T16:44:11.397672Z"},"papermill":{"duration":44.33977,"end_time":"2025-12-25T04:34:44.65064","exception":false,"start_time":"2025-12-25T04:34:00.31087","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# --- Imports and Data Preparation ---\n# Import all required libraries, set up deterministic behavior, and prepare paths for test data.\n# The code dynamically selects between competition and local test datasets.\n# Additional comments and structure for clarity and reproducibility.\n\nimport kagglehub\nfrom scipy.interpolate import CubicSpline\ndeterministic = kagglehub.package_import('wasupandceacar/deterministic').deterministic\ndeterministic.init_all(67, disable_list=['cuda_block'])\n\nimport sys\nsys.path.append('/kaggle/input/hengck23-submit-physionet/hengck23-submit-physionet')\n\nimport os\nimport gc\nimport cv2\nimport torch\nimport traceback\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom tqdm.auto import tqdm\nfrom shutil import copyfile\nfrom scipy.signal import resample\n\n# Stage 0: Image Normalization and Alignment\nfrom stage0_model import Net as Stage0Net\nfrom stage0_common import *\n\nif_submit = os.getenv('KAGGLE_IS_COMPETITION_RERUN')\nif if_submit:\n    test_meta = Path(\"/kaggle/input/physionet-ecg-image-digitization/test.csv\")\n    test_dir = Path(\"/kaggle/input/physionet-ecg-image-digitization/test\")\nelse:\n    test_meta = Path(\"/kaggle/input/physio-test-fake-dataset/test_fake/test.csv\")\n    test_dir = Path(\"/kaggle/input/physio-test-fake-dataset/test_fake\")\n\nvalid_df = pd.read_csv(test_meta)\nvalid_df['id'] = valid_df['id'].astype(str) \nvalid_id = valid_df['id'].unique().tolist()\n\nglobal_dict = {\n    \"stage0_dir\": \"/kaggle/working/stage0\",\n    \"stage1_dir\": \"/kaggle/working/stage1\",\n    \"stage2_dir\": \"/kaggle/working/stage2\",\n}\n\n# --- Utility Function ---\n# Enhance image contrast and denoise for model input.\ndef change_color(image_rgb):\n    gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)\n    denoised = cv2.fastNlMeansDenoising(gray, h=10)\n    clahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(8,8))\n    contrast_enhanced = clahe.apply(denoised)\n    return cv2.cvtColor(contrast_enhanced, cv2.COLOR_GRAY2RGB)\n\n# --- Stage 0: Image Normalization and Alignment ---\nstage0_dir = Path(global_dict[\"stage0_dir\"])\nstage0_dir.mkdir(exist_ok=True)\nstage0_net = Stage0Net(pretrained=False)\nstage0_net = load_net(stage0_net, '/kaggle/input/hengck23-submit-physionet/hengck23-submit-physionet/weight/stage0-last.checkpoint.pth')\nstage0_net.to(\"cuda:0\")\nstage0_net.eval()\n\nfor n, sample_id in enumerate(tqdm(valid_id)):\n    path = test_dir / f'{sample_id}.png'\n    output_path = stage0_dir / f'{sample_id}.png'\n    image_original = cv2.imread(str(path), cv2.IMREAD_COLOR)\n    image_original = cv2.cvtColor(image_original, cv2.COLOR_BGR2RGB)\n    image_for_model = change_color(image_original)\n    batch = image_to_batch(image_for_model)\n    try:\n        with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.float32):\n            output = stage0_net(batch)\n        rotated, keypoint = output_to_predict(image_original, batch, output)\n        normalised, _, _ = normalise_by_homography(rotated, keypoint)\n        cv2.imwrite(str(output_path), cv2.cvtColor(normalised, cv2.COLOR_RGB2BGR))\n    except Exception as e:\n        traceback.print_exc()\n        copyfile(path, output_path)\n\n# --- Stage 1: Grid Rectification ---\nfrom stage1_model import Net as Stage1Net\nfrom stage1_common import *\nstage0_dir = Path(global_dict[\"stage0_dir\"])\nstage1_dir = Path(global_dict[\"stage1_dir\"])\nstage1_dir.mkdir(exist_ok=True)\nstage1_net = Stage1Net(pretrained=False)\nstage1_net = load_net(stage1_net, '/kaggle/input/hengck23-submit-physionet/hengck23-submit-physionet/weight/stage1-last.checkpoint.pth')\nstage1_net.to(\"cuda:0\")\n\nfor n, sample_id in enumerate(tqdm(valid_id)):\n    path = stage0_dir / f'{sample_id}.png'\n    output_path = stage1_dir / f'{sample_id}.png'\n    image = cv2.imread(str(path), cv2.IMREAD_COLOR) # Corrected imread\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Explicit BGR to RGB conversion\n    batch = {'image': torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))).unsqueeze(0)}\n    try:\n        with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.float32):\n            output = stage1_net(batch)\n        gridpoint_xy, _ = output_to_predict(image, batch, output)\n        rectified = rectify_image(image, gridpoint_xy)\n        cv2.imwrite(output_path, cv2.cvtColor(rectified, cv2.COLOR_RGB2BGR))\n    except:\n        traceback.print_exc()\n        copyfile(path, output_path)\n\n# --- Stage 2: Signal Extraction ---\nimport torchvision.transforms as T\nfrom stage2_model import *\nfrom stage2_common import *\nfrom scipy.signal import savgol_filter, medfilt\n\nclass Net3(torch.nn.Module):\n    \"\"\"Custom U-Net based model for signal extraction from rectified ECG images.\"\"\"\n    def __init__(self, pretrained=True):\n        super(Net3, self).__init__()\n        encoder_dim = [64, 128, 256, 512]\n        decoder_dim = [128, 64, 32, 16]\n        self.encoder = timm.create_model(\n            model_name='resnet34.a3_in1k', pretrained=pretrained, in_chans=3, num_classes=0, global_pool=''\n        )\n        self.decoder = MyCoordUnetDecoder(\n            in_channel=encoder_dim[-1],\n            skip_channel=encoder_dim[:-1][::-1] + [0],\n            out_channel=decoder_dim,\n            scale=[2, 2, 2, 2]\n        )\n        self.pixel = torch.nn.Conv2d(decoder_dim[-1], 4, 1)\n    def forward(self, image):\n        encode = encode_with_resnet(self.encoder, image)\n        last, _ = self.decoder(feature=encode[-1], skip=encode[:-1][::-1] + [None])\n        pixel = self.pixel(last)\n        return pixel\n\nstage1_dir = Path(global_dict[\"stage1_dir\"])\nstage2_dir = Path(global_dict[\"stage2_dir\"])\nstage2_dir.mkdir(exist_ok=True)\nstage2_net = Net3(pretrained=False).to(\"cuda:0\")\nmodel_path = \"/kaggle/input/physio-seg-public/pytorch/net3_009_4200/1/iter_0004200.pt\"\nstage2_net.load_state_dict(torch.load(model_path))\nstage2_net.eval()\n\nx0, x1 = 0, 2176\ny0, y1 = 0, 1696\nzero_mv = [703.5, 987.5, 1271.5, 1531.5]\nmv_to_pixel = 78.5\nt0, t1 = 235, 4161\nresize = T.Resize((1696, 4352), interpolation=T.InterpolationMode.BILINEAR)\n\nfor n, sample_id in enumerate(tqdm(valid_id)):\n    path = stage1_dir / f'{sample_id}.png'\n    output_path = stage2_dir / f'{sample_id}.npy'\n    image = cv2.imread(str(path), cv2.IMREAD_COLOR) # Corrected imread\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Explicit BGR to RGB conversion\n    length = valid_df[(valid_df['id']==sample_id) & (valid_df['lead']=='II')].iloc[0].number_of_rows\n    image = image[y0:y1, x0:x1] / 255\n    batch = resize(torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))).unsqueeze(0)).float().to(\"cuda:0\")\n    try:\n        with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.float32):\n            output = stage2_net(batch)\n        pixel = torch.sigmoid(output).float().data.cpu().numpy()[0]\n        series_in_pixel = pixel_to_series(pixel[..., t0:t1], zero_mv, length)\n        series = (np.array(zero_mv).reshape(4, 1) - series_in_pixel) / mv_to_pixel\n        for i in range(series.shape[0]):\n            if i==3:\n                series[i] = savgol_filter(series[i], window_length=9, polyorder=2)\n            else:\n                series[i] = savgol_filter(series[i], window_length=5, polyorder=2)\n        np.save(output_path, series)\n    except:\n        traceback.print_exc()\n        series = np.zeros((4, length)) \n        np.save(output_path, series)\n\n# --- Helper: Series Dictionary ---\n# Converts extracted series into a dictionary by lead name.\ndef series_dict(series):\n    d = {}\n    for l in range(3):\n        lead_names = [\n            ['I',   'aVR', 'V1', 'V4'],\n            ['II',  'aVL', 'V2', 'V5'],\n            ['III', 'aVF', 'V3', 'V6'],\n        ][l]\n        split = np.array_split(series[l], 4)\n        for (k, s) in zip(lead_names, split):\n            d[k] = s\n    d['II'] = series[3]\n    return d\n\n# --- Submission File Creation ---\nstage2_dir = Path(global_dict[\"stage2_dir\"])\nres = []\ngb = valid_df.groupby('id')\nfor i, (sample_id, df) in enumerate(tqdm(gb)):\n    series = np.load(stage2_dir / f'{sample_id}.npy')\n    d_series = series_dict(series)\n    for _, d in df.iterrows():\n        s = d_series.get(d.lead, np.zeros(d.number_of_rows))\n        if len(s) != d.number_of_rows:\n            x_old = np.linspace(0, 1, len(s))\n            x_new = np.linspace(0, 1, d.number_of_rows)\n            # s = np.interp(x_new, x_old, s)\n            try:\n                cs = CubicSpline(x_old, s)\n                s = cs(x_new)\n            except:\n                s = np.interp(x_new, x_old, s)\n        row_id = [f'{sample_id}_{x}_{d.lead}' for x in range(d.number_of_rows)]\n        res.append(pd.DataFrame({'id': row_id, 'value': s}))\n    if i % 100 == 0:\n        gc.collect()\nsubmission = pd.concat(res, axis=0, ignore_index=True)\nsubmission.to_csv('submission.csv', index=False)\nsubmission.head(30)","metadata":{"execution":{"iopub.status.busy":"2025-12-26T16:44:11.400161Z","iopub.execute_input":"2025-12-26T16:44:11.400496Z","iopub.status.idle":"2025-12-26T16:46:02.911719Z","shell.execute_reply.started":"2025-12-26T16:44:11.400476Z","shell.execute_reply":"2025-12-26T16:46:02.910954Z"},"papermill":{"duration":130.998273,"end_time":"2025-12-25T04:36:55.658418","exception":false,"start_time":"2025-12-25T04:34:44.660145","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Additional Information\n\n- **Author:** Adapted for the PhysioNet ECG Image Digitization Challenge by the community.\n- **Requirements:** Kaggle environment, CUDA-enabled GPU, all referenced input datasets and model weights.\n- **References:**\n    - [PhysioNet Challenge 2024](https://physionetchallenges.org/2024/)\n    - [Kaggle competition page](https://www.kaggle.com/competitions/physionet-ecg-image-digitization)\n    - [Connected Components 3D](https://github.com/seung-lab/connected-components-3d)\n    - [PyTorch](https://pytorch.org/)\n    - [OpenCV](https://opencv.org/)\n    - [scipy.signal](https://docs.scipy.org/doc/scipy/reference/signal.html)\n\n- **Contact:** For questions or issues, please refer to the competition discussion forums or the official PhysioNet Challenge contact.\n\n---\n\n## How to Use This Notebook\n\n1. **Set up the Kaggle environment** with all required datasets and model weights in the specified input paths.\n2. **Run all cells in order**. The pipeline will process the test images and generate a submission file.\n3. **Check outputs and logs** for any errors or warnings. The notebook includes robust error handling and will fall back to default outputs if a stage fails.\n4. **Submit the generated CSV** to the competition platform.\n\n**Tip:** For best results, ensure all input paths and dependencies are available in your environment before running the notebook.\n\n---\n\n## Acknowledgements\n\nSpecial thanks to the PhysioNet Challenge organizers, the Kaggle community, and open-source contributors for providing resources and support.","metadata":{"papermill":{"duration":0.010315,"end_time":"2025-12-25T04:36:55.682761","exception":false,"start_time":"2025-12-25T04:36:55.672446","status":"completed"},"tags":[]}}]}