from maix import image, time
from logger_manager import logger_manager
from camera_manager import camera_manager

_USE_CV = False
try:
    import cv2
    import numpy as np

    _USE_CV = True
except ImportError:
    pass

WIDTH = 640
HEIGHT = 480
THRESHOLD = 100
RED_RATIO = 1
SEARCH_RADIUS = 60
STABLE_COUNT = 2

# Temporal smoothing
_EMA_ALPHA = 0.4
_GATE_PX = 10
_FRAME_INTERVAL_MS = 50

_prev_smoothed = None


def _red_weighted_centroid(r_ch, g_ch, b_ch, mask, x0, y0):
    y_ids, x_ids = np.where(mask)
    if len(y_ids) == 0:
        return None
    r_vals = r_ch[y_ids, x_ids].astype(np.float64)
    g_vals = g_ch[y_ids, x_ids].astype(np.float64)
    b_vals = b_ch[y_ids, x_ids].astype(np.float64)
    w = r_vals - np.maximum(g_vals, b_vals)
    w = np.clip(w, 0, None)
    w = w * w
    total_w = w.sum()
    if total_w < 1e-6:
        return None
    cx = (x_ids.astype(np.float64) * w).sum() / total_w + x0
    cy = (y_ids.astype(np.float64) * w).sum() / total_w + y0
    return (float(cx), float(cy))


def find_ellipse(img_cv, cx, cy, roi_r, th, ratio):
    x1 = max(0, cx - roi_r)
    x2 = min(WIDTH, cx + roi_r)
    y1 = max(0, cy - roi_r)
    y2 = min(HEIGHT, cy + roi_r)
    roi = img_cv[y1:y2, x1:x2]
    if roi.size == 0:
        return None
    r = roi[:, :, 0].astype(np.int32)
    g = roi[:, :, 1].astype(np.int32)
    b = roi[:, :, 2].astype(np.int32)
    mask = (r > th) & (r > g * ratio) & (r > b * ratio)
    oe = (r > 200) & (g > 200) & (b > 200) & (r >= g) & (r >= b) & ((r - g) > 10) & ((r - b) > 10)
    combined = (mask | oe).astype(np.uint8) * 255
    contours, _ = cv2.findContours(combined, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if not contours:
        return None
    largest = max(contours, key=cv2.contourArea)
    if cv2.contourArea(largest) < 5:
        return None
    cnt = largest.copy()
    for pt in cnt:
        pt[0][0] += x1
        pt[0][1] += y1
    ellipse_valid = len(cnt) >= 5
    if ellipse_valid:
        (ex, ey), (ew, eh), ang = cv2.fitEllipse(cnt)
        mask_ellipse = np.zeros((HEIGHT, WIDTH), dtype=np.uint8)
        cv2.ellipse(mask_ellipse, (int(ex), int(ey)), (int(ew / 2), int(eh / 2)), ang, 0, 360, 255, -1)
        return _red_weighted_centroid(
            img_cv[:, :, 0], img_cv[:, :, 1], img_cv[:, :, 2],
            mask_ellipse > 0, 0, 0
        )
    M = cv2.moments(cnt)
    if M["m00"] > 0:
        return (float(M["m10"] / M["m00"]), float(M["m01"] / M["m00"]))
    return None


def find_brightest_bytes(frame, cx, cy, roi_r, th, ratio):
    x1 = max(0, cx - roi_r)
    x2 = min(WIDTH, cx + roi_r)
    y1 = max(0, cy - roi_r)
    y2 = min(HEIGHT, cy + roi_r)
    data = frame.to_bytes()
    rs, gs, bs = [], [], []
    xs, ys = [], []
    step = 2
    for y in range(y1, y2, step):
        for x in range(x1, x2, step):
            idx = (y * WIDTH + x) * 3
            r = data[idx]
            g = data[idx + 1]
            b = data[idx + 2]
            if (r > th and r > g * ratio and r > b * ratio) or \
                    (r > 200 and g > 200 and b > 200 and r >= g and r >= b and (r - g) > 10 and (r - b) > 10):
                rs.append(r)
                gs.append(g)
                bs.append(b)
                xs.append(x)
                ys.append(y)
    if not rs:
        return None
    rs = np.array(rs, dtype=np.float64)
    gs = np.array(gs, dtype=np.float64)
    bs = np.array(bs, dtype=np.float64)
    xs = np.array(xs, dtype=np.float64)
    ys = np.array(ys, dtype=np.float64)
    w = rs - np.maximum(gs, bs)
    w = np.clip(w, 0, None)
    w = w * w
    total_w = w.sum()
    if total_w < 1e-6:
        return None
    cx_f = (xs * w).sum() / total_w
    cy_f = (ys * w).sum() / total_w
    return (float(cx_f), float(cy_f))


def _ema_filter(pos, alpha=_EMA_ALPHA):
    global _prev_smoothed
    if _prev_smoothed is None:
        _prev_smoothed = pos
        return pos
    sx = alpha * pos[0] + (1 - alpha) * _prev_smoothed[0]
    sy = alpha * pos[1] + (1 - alpha) * _prev_smoothed[1]
    _prev_smoothed = (sx, sy)
    return _prev_smoothed


def _gated(pos, gate_px=_GATE_PX):
    global _prev_smoothed
    if _prev_smoothed is None:
        return True
    dx = pos[0] - _prev_smoothed[0]
    dy = pos[1] - _prev_smoothed[1]
    return (dx * dx + dy * dy) <= gate_px * gate_px


def get_stable_laser_point(timeout_ms=15000, stable_count=STABLE_COUNT):
    global _prev_smoothed
    _prev_smoothed = None
    try:
        last_raw = None
        stable = 0
        start = time.ticks_ms()
        cx, cy = WIDTH // 2, HEIGHT // 2
        while True:
            if abs(time.ticks_diff(time.ticks_ms(), start)) > timeout_ms:
                _prev_smoothed = None
                return None
            frame = camera_manager.read_frame()
            if frame is None:
                time.sleep_ms(10)
                continue
            pos_bright = find_brightest_bytes(frame, cx, cy, SEARCH_RADIUS, THRESHOLD, RED_RATIO)
            pos = pos_bright
            if _USE_CV:
                img_cv = image.image2cv(frame, False, False)
                pos_ellipse = find_ellipse(img_cv, cx, cy, SEARCH_RADIUS, THRESHOLD, RED_RATIO)
                if pos_ellipse is not None:
                    pos = pos_ellipse
            if pos is not None:
                if not _gated(pos):
                    if logger_manager.logger:
                        logger_manager.logger.info(f"pos:{pos} gated,stable:{stable}")
                    time.sleep_ms(_FRAME_INTERVAL_MS)
                    continue
                filtered = _ema_filter(pos)
                if last_raw is not None:
                    dx = abs(filtered[0] - last_raw[0])
                    dy = abs(filtered[1] - last_raw[1])
                    if dx <= 2 and dy <= 2:
                        stable += 1
                    else:
                        stable = 1
                else:
                    stable = 1
                last_raw = filtered
                if logger_manager.logger:
                    logger_manager.logger.info(f"pos:{pos},filtered:{filtered},stable:{stable}")
                if stable >= stable_count:
                    result = (int(filtered[0]), int(filtered[1]))
                    _prev_smoothed = None
                    return result
            time.sleep_ms(_FRAME_INTERVAL_MS)
    finally:
        _prev_smoothed = None