import numpy as np
from scipy.ndimage import gaussian_filter


def extract_gaze_sequence(
    saliency: np.ndarray,
    num_fixations: int = 5,
    inhibition_radius_fraction: float = 0.1,
) -> list[dict]:
    """
    Extract predicted fixation sequence using iterative peak finding
    with inhibition-of-return.
    """
    sal = saliency.copy().astype(np.float64)
    h, w = sal.shape
    inhibition_radius = int(max(h, w) * inhibition_radius_fraction)
    fixations = []

    for rank in range(1, num_fixations + 1):
        smoothed = gaussian_filter(sal, sigma=max(h, w) * 0.01)
        if smoothed.max() < 1e-10:
            break

        peak_idx = np.unravel_index(np.argmax(smoothed), smoothed.shape)
        y, x = int(peak_idx[0]), int(peak_idx[1])
        prob = float(saliency[y, x])

        fixations.append({
            "rank": rank,
            "x": x,
            "y": y,
            "x_pct": round(x / w * 100, 1),
            "y_pct": round(y / h * 100, 1),
            "probability": round(prob, 4),
        })

        # Inhibition of return
        yy, xx = np.ogrid[:h, :w]
        mask = (xx - x) ** 2 + (yy - y) ** 2 <= inhibition_radius ** 2
        sal[mask] = 0.0

    return fixations