from __future__ import annotations

import argparse
import importlib.util
import json
import os
import sys
import types
from pathlib import Path

DEFAULT_PROJECT_ROOT = Path(__file__).resolve().parents[4]


def project_root() -> Path:
    raw = os.getenv("SEG_DATA_SERVER_ROOT")
    if not raw:
        return DEFAULT_PROJECT_ROOT
    path = Path(raw).expanduser()
    if path.is_absolute():
        return path.resolve()
    return (DEFAULT_PROJECT_ROOT / path).resolve()


PROJECT_ROOT = project_root()


def source_root() -> Path:
    raw = os.getenv("SEG_SOURCE_ROOT")
    if not raw:
        return (PROJECT_ROOT.parent / "Seg").resolve()
    path = Path(raw).expanduser()
    if path.is_absolute():
        return path.resolve()
    return (PROJECT_ROOT.parent / path).resolve()


SOURCE_ROOT = source_root()
YOLO_DIR = SOURCE_ROOT / "Seg_All_In_One_YoloModel"
LEGACY_HEATMAP = YOLO_DIR / "yolo_predict_visualize_nn.py"
IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}


def resolve_project_path(value: str | Path) -> Path:
    path = Path(value).expanduser()
    if path.is_absolute():
        return path.resolve()
    return (PROJECT_ROOT / path).resolve()


def iter_images(source: Path, limit: int) -> list[Path]:
    if source.is_file():
        return [source]
    images = sorted(path for path in source.rglob("*") if path.is_file() and path.suffix.lower() in IMAGE_EXTS)
    return images[:limit] if limit > 0 else images


def load_legacy_module():
    fake_config = types.ModuleType("yolo_config")
    fake_config.MODEL_CONFIGS = {
        "YOLOv8n-seg": {},
        "YOLOv8s-seg": {},
        "YOLOv8m-seg": {},
        "YOLOv8l-seg": {},
        "YOLOv8x-seg": {},
        "YOLOv9c-seg": {},
        "YOLOv9e-seg": {},
        "YOLO11n-seg": {},
        "YOLO11s-seg": {},
        "YOLO11m-seg": {},
        "YOLO11l-seg": {},
        "YOLO11x-seg": {},
        "YOLO12-seg": {},
    }
    fake_config.TEST_IMAGE_DIR = Path(".")
    fake_config.PREDICT_BEST_MODEL_DIR = Path(".")
    fake_config.show_config_summary = lambda: None
    sys.modules["yolo_config"] = fake_config
    sys.path.insert(0, str(YOLO_DIR))
    spec = importlib.util.spec_from_file_location("seg_yolo_heatmap", LEGACY_HEATMAP)
    if spec is None or spec.loader is None:
        raise RuntimeError(f"cannot import heatmap script: {LEGACY_HEATMAP}")
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    return module


def main() -> None:
    parser = argparse.ArgumentParser(description="Generate Grad-CAM style YOLO heatmaps for uploaded/custom datasets.")
    parser.add_argument("--weights", required=True, help="Path to best.pt/last.pt or another YOLO segmentation checkpoint.")
    parser.add_argument("--source", required=True, help="Image file or image directory.")
    parser.add_argument("--project", default=str(PROJECT_ROOT / "var" / "custom_yolo_runs"))
    parser.add_argument("--name", default="custom_heatmap")
    parser.add_argument("--model-key", default="YOLO11n-seg")
    parser.add_argument("--pt-name", default="best.pt")
    parser.add_argument("--cam-method", default="GradCAM")
    parser.add_argument("--target-layers", default="model.model.model[9]")
    parser.add_argument("--limit", type=int, default=3)
    args = parser.parse_args()

    weights = resolve_project_path(args.weights)
    source = resolve_project_path(args.source)
    project = resolve_project_path(args.project)
    save_dir = project / args.name
    if not weights.exists():
        raise FileNotFoundError(f"weights not found: {weights}")
    if not source.exists():
        raise FileNotFoundError(f"source not found: {source}")

    images = iter_images(source, args.limit)
    if not images:
        raise FileNotFoundError(f"no images found in source: {source}")

    module = load_legacy_module()
    methods = list(module.CAM_METHODS.keys()) if args.cam_method == "All" else [args.cam_method]
    save_dir.mkdir(parents=True, exist_ok=True)
    for method in methods:
        for image in images:
            module.visualize_nn_comprehensive(
                model_path=str(weights),
                source_dir=str(image),
                base_save_dir=save_dir,
                pt_name=args.pt_name,
                cam_method_name=method,
                target_layer_str=args.target_layers,
                model_key=args.model_key,
            )

    outputs = sorted((save_dir / "HeartMap_Visual").rglob("*.jpg")) if (save_dir / "HeartMap_Visual").exists() else []
    metadata = {
        "weights": str(weights),
        "source": str(source),
        "save_dir": str(save_dir),
        "images": [str(image) for image in images],
        "methods": methods,
        "outputs": len(outputs),
    }
    (save_dir / "heatmap_manifest.json").write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8")
    if not outputs:
        raise RuntimeError("heatmap generation completed without image outputs")
    print(json.dumps(metadata, ensure_ascii=False))


if __name__ == "__main__":
    main()