"""Evaluate Simam3D depth predictions against ground-truth arrays.

The evaluator is deliberately dataset-agnostic. It reports raw metrics when
the prediction is metric depth and median-scaled metrics for monocular relative
depth. It does not claim that a relative-depth score is metric reconstruction.
"""

from __future__ import annotations

import argparse
import json
from pathlib import Path

import numpy as np


def _valid_values(prediction: np.ndarray, ground_truth: np.ndarray, valid_mask=None):
    prediction = np.asarray(prediction, dtype=np.float64)
    ground_truth = np.asarray(ground_truth, dtype=np.float64)
    if prediction.shape != ground_truth.shape or prediction.ndim != 2:
        raise ValueError("prediction and ground_truth must be matching HxW arrays")
    valid = np.isfinite(prediction) & np.isfinite(ground_truth) & (ground_truth > 0) & (prediction > 0)
    if valid_mask is not None:
        mask = np.asarray(valid_mask, dtype=bool)
        if mask.shape != prediction.shape:
            raise ValueError("valid_mask must match prediction shape")
        valid &= mask
    if not valid.any():
        raise ValueError("no valid positive depth pixels")
    return prediction[valid], ground_truth[valid]


def depth_metrics(prediction: np.ndarray, ground_truth: np.ndarray, valid_mask=None) -> dict[str, float | int]:
    """Return standard depth metrics for positive, finite pixels."""
    pred, gt = _valid_values(prediction, ground_truth, valid_mask)
    error = pred - gt
    ratio = np.maximum(pred / gt, gt / pred)
    return {
        "valid_pixel_count": int(len(gt)),
        "abs_rel": float(np.mean(np.abs(error) / gt)),
        "sq_rel": float(np.mean((error ** 2) / gt)),
        "rmse": float(np.sqrt(np.mean(error ** 2))),
        "rmse_log": float(np.sqrt(np.mean((np.log(pred) - np.log(gt)) ** 2))),
        "delta1": float(np.mean(ratio < 1.25)),
        "delta2": float(np.mean(ratio < 1.25 ** 2)),
        "delta3": float(np.mean(ratio < 1.25 ** 3)),
    }


def evaluate_depth_pair(
    prediction: np.ndarray,
    ground_truth: np.ndarray,
    valid_mask=None,
    prediction_is_inverse_depth: bool = False,
) -> dict[str, object]:
    """Evaluate raw and median-scaled metrics without hiding scale ambiguity."""
    prediction = np.asarray(prediction, dtype=np.float64)
    if prediction_is_inverse_depth:
        prediction = 1.0 / np.maximum(prediction, 1e-12)
    raw = depth_metrics(prediction, ground_truth, valid_mask)
    pred_values, gt_values = _valid_values(prediction, ground_truth, valid_mask)
    scale = float(np.median(gt_values) / np.median(pred_values))
    scaled = depth_metrics(prediction * scale, ground_truth, valid_mask)
    return {
        "raw": raw,
        "median_scaled": scaled,
        "median_scale": scale,
        "interpretation": "median_scaled is appropriate for relative-depth comparison; raw requires metric scale",
    }


def main(argv: list[str] | None = None) -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("prediction", type=Path, help=".npy prediction depth array")
    parser.add_argument("ground_truth", type=Path, help=".npy ground-truth depth array")
    parser.add_argument("--inverse-depth", action="store_true", help="invert prediction before evaluation")
    args = parser.parse_args(argv)
    prediction = np.load(args.prediction)
    ground_truth = np.load(args.ground_truth)
    print(json.dumps(evaluate_depth_pair(prediction, ground_truth, prediction_is_inverse_depth=args.inverse_depth), indent=2))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())