#!/usr/bin/env python3
import os
import random
import subprocess
from pathlib import Path
import shutil

def run_command(cmd):
    print(f"Running: {' '.join(cmd)}")
    subprocess.run(cmd, check=True)

def main():
    # Setup paths
    base_dir = Path(__file__).parent
    img_dir = base_dir / "ICCAD2019" / "img"
    out_dir = base_dir / "out" / "debug1"
    
    if out_dir.exists():
        shutil.rmtree(out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)

    # Get images
    all_images = list(img_dir.glob("*.png"))
    if not all_images:
        print("No images found in ICCAD2019/img")
        return

    selected_images = random.sample(all_images, min(5, len(all_images)))
    
    print(f"Selected {len(selected_images)} images for debugging.")

    for img_path in selected_images:
        print(f"\nProcessing {img_path.name}...")
        case_dir = out_dir / img_path.stem
        case_dir.mkdir(exist_ok=True)
        
        # 1. Skeleton Extraction
        # Note: The user mentioned "black background, white subject" or vice versa.
        # The script has --invert. I'll try without invert first, assuming standard layout (often drawn dark on light or light on dark).
        # Let's check one image first? No, I'll just run it.
        # Actually, usually layouts are drawn objects. If background is black, objects are white.
        # My script assumes objects are white (value 1) for skeletonization.
        # If the image is white background, I need to invert.
        # I'll try both or just assume one. Let's assume we might need --invert if it's white background.
        # I'll run with --invert if the mean pixel value is high (white background).
        
        # But wait, I can't easily check mean here without loading. 
        # I'll just run the extraction script.
        
        extract_cmd = [
            "python3", "scripts/skeleton_extract.py",
            str(img_path),
            str(case_dir),
            "--denoise", "3"
        ]
        # Heuristic: if filename contains 'nonhotspot', it might be a clip.
        # Let's just try running it.
        run_command(extract_cmd)
        
        sk_png = case_dir / (img_path.stem + "_sk.png")
        if not sk_png.exists():
            print(f"Failed to generate skeleton for {img_path.name}")
            continue

        # 2. Vectorization
        vec_json = case_dir / (img_path.stem + "_vec.json")
        vec_cmd = [
            "python3", "scripts/vectorize_skeleton.py",
            str(sk_png),
            str(vec_json)
        ]
        run_command(vec_cmd)

        # 3. Expansion (Reconstruction)
        recon_png = case_dir / (img_path.stem + "_recon.png")
        expand_cmd = [
            "python3", "scripts/expand_skeleton.py",
            str(vec_json),
            str(recon_png),
            "--line-width", "3" # Adjust as needed
        ]
        run_command(expand_cmd)

    print(f"\nDebug run complete. Check results in {out_dir}")

if __name__ == "__main__":
    main()