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+# IC Layout Skeleton Generation (prototype)
+
+This repo provides a prototype pipeline to handle strictly-Manhattan binary IC layout images: extract skeletons, vectorize skeletons into Manhattan polylines, train a skeleton-generation model, and expand skeletons back to images.
+
+## Project Structure
+
+- `scripts/`: Core processing scripts.
+  - `skeleton_extract.py`: Binarize & extract skeleton PNG.
+  - `vectorize_skeleton.py`: Trace skeleton PNG into JSON polylines.
+  - `expand_skeleton.py`: Rasterize polyline JSON back to a PNG.
+- `models/`: PyTorch models (e.g., `SkeletonVAE`).
+- `train/`: Training scripts.
+- `datasets/`: Dataset building helpers.
+- `out/`: Output directory for generated data and logs (ignored by git).
+
+## Setup
+
+This project uses `uv` for dependency management.
+
+1.  **Install uv**:
+    ```bash
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    ```
+
+2.  **Install dependencies**:
+    ```bash
+    uv sync
+    ```
+
+## Usage
+
+### 1. Prepare Data
+Ensure your dataset is available. For example, link your data folder:
+```bash
+ln -s ~/Documents/data/ICCAD2019/img ./ICCAD2019/img
+```
+(Note: The workspace already contains a link to `ICCAD2019` if configured).
+
+### 2. Run the Pipeline
+
+You can run individual scripts using `uv run`.
+
+**Extract Skeleton:**
+```bash
+uv run scripts/skeleton_extract.py path/to/image.png out/result_dir --denoise 3
+```
+
+**Vectorize Skeleton:**
+```bash
+uv run scripts/vectorize_skeleton.py out/result_dir/image_sk.png out/result_dir/image_vec.json
+```
+
+**Expand (Reconstruct) Image:**
+```bash
+uv run scripts/expand_skeleton.py out/result_dir/image_vec.json out/result_dir/image_recon.png
+```
+
+### 3. Build Dataset
+Batch process a folder of images:
+```bash
+uv run datasets/build_skeleton_dataset.py ICCAD2019/img out/dataset_processed
+```
+
+### 4. Train Model
+Train the VAE on the processed skeletons:
+```bash
+uv run train/train_skeleton_vae.py out/dataset_processed --epochs 20 --batch 16
+```
+
+## Debugging
+A debug script is provided to run the full pipeline on a few random images from the dataset:
+```bash
+uv run debug_pipeline.py
+```
+Results will be in `out/debug`.