# 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`.