"""saliency: spectral residual (Hou & Zhang, 2007), implemented with numpy/OpenCV.

The point is the saliency-weighted centroid of the top 5 % most salient
pixels; strength is how much those pixels stand out against the frame mean.
"""

from __future__ import annotations

import threading
from typing import Any

import cv2
import numpy as np

from .. import media_io
from ..schemas import TimesRequest
from .common import Ctx, clamp01, open_input, unique_sorted

WORK_SIZE = 64


def saliency_map(frame: np.ndarray) -> np.ndarray:
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY).astype(np.float32)
    small = cv2.resize(gray, (WORK_SIZE, WORK_SIZE), interpolation=cv2.INTER_AREA)
    spectrum = np.fft.fft2(small)
    log_amp = np.log(np.abs(spectrum) + 1e-8)
    phase = np.angle(spectrum)
    residual = log_amp - cv2.blur(log_amp, (3, 3))
    sal = np.abs(np.fft.ifft2(np.exp(residual + 1j * phase))) ** 2
    sal = cv2.GaussianBlur(sal.astype(np.float32), (0, 0), 2.5)
    peak = float(sal.max())
    return sal / peak if peak > 0 else sal


def salient_point(frame: np.ndarray) -> tuple[float, float, float]:
    sal = saliency_map(frame)
    threshold = float(np.quantile(sal, 0.95))
    top = np.where(sal >= threshold, sal, 0.0)
    total = float(top.sum())
    if total <= 0:
        return 0.5, 0.5, 0.0
    ys, xs = np.mgrid[0 : sal.shape[0], 0 : sal.shape[1]]
    cx = float((xs * top).sum() / total + 0.5) / sal.shape[1]
    cy = float((ys * top).sum() / total + 0.5) / sal.shape[0]
    mean = float(sal.mean())
    top_mean = float(sal[sal >= threshold].mean())
    strength = (top_mean - mean) / top_mean if top_mean > 0 else 0.0
    return clamp01(cx), clamp01(cy), clamp01(strength)


def run(ctx: Ctx, body: TimesRequest, cancel: threading.Event) -> dict[str, Any]:
    real, info = open_input(ctx, body.path, cancel)
    ctx.registry.runtime("saliency")
    points = []
    for t_ms, frame in media_io.frames_at(real, info, unique_sorted(body.timesMs), cancel, 512):
        cx, cy, strength = salient_point(frame)
        points.append({"tMs": t_ms, "cx": cx, "cy": cy, "strength": strength})
    return {"points": points}