#!/usr/bin/env python3
"""
Advanced matting algorithms for BackgroundFX Pro.
Implements multiple matting techniques with automatic fallback.
"""

from dataclasses import dataclass
from typing import Dict, Optional

import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from utils.logger import get_logger
from utils.hardware.device_manager import DeviceManager
from utils.config import ConfigManager  # kept for forward compatibility / config hook
from core.models import ModelFactory, ModelType  # not used directly here but kept for API consistency
from core.quality import QualityAnalyzer
from core.edge import EdgeRefinement

logger = get_logger(__name__)


@dataclass
class MattingConfig:
    """Configuration for matting operations."""
    alpha_threshold: float = 0.5
    erode_iterations: int = 2
    dilate_iterations: int = 2
    blur_radius: int = 3
    trimap_size: int = 30
    confidence_threshold: float = 0.7
    use_guided_filter: bool = True
    guided_filter_radius: int = 8
    guided_filter_eps: float = 1e-6
    use_temporal_smoothing: bool = False
    temporal_window: int = 5


class AlphaMatting:
    """Advanced alpha matting using multiple techniques."""

    def __init__(self, config: Optional[MattingConfig] = None):
        self.config = config or MattingConfig()
        self.device_manager = DeviceManager()
        self.quality_analyzer = QualityAnalyzer()
        self.edge_refinement = EdgeRefinement()

    def create_trimap(self, mask: np.ndarray, dilation_size: Optional[int] = None) -> np.ndarray:
        """
        Create trimap from a binary mask.

        Args:
            mask: Binary mask (H, W) in {0, 255} or [0,1]
            dilation_size: Size of uncertain region (pixels)

        Returns:
            Trimap with values 0 (background), 128 (unknown), 255 (foreground)
        """
        dilation_size = dilation_size or self.config.trimap_size

        # Ensure uint8 binary
        if mask.dtype != np.uint8:
            mask = (mask * 255).astype(np.uint8)
        mask = (mask > 127).astype(np.uint8) * 255

        trimap = np.copy(mask)
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dilation_size, dilation_size))

        # Dilate/erode once to form unknown band
        dilated = cv2.dilate(mask, kernel, iterations=1)
        eroded = cv2.erode(mask, kernel, iterations=1)

        # Unknown where dilation has expanded FG beyond eroded FG band
        trimap[:] = 0
        trimap[eroded == 255] = 255
        unknown = (dilated == 255) & (eroded == 0)
        trimap[unknown] = 128

        return trimap

    def guided_filter(
        self,
        image: np.ndarray,
        guide: np.ndarray,
        radius: Optional[int] = None,
        eps: Optional[float] = None,
    ) -> np.ndarray:
        """
        Apply guided filter for edge-preserving smoothing.

        Args:
            image: Input image to filter (H, W) uint8
            guide: Guide image (H, W, 3) or (H, W)
            radius: Filter radius
            eps: Regularization parameter

        Returns:
            Filtered image (H, W) uint8
        """
        radius = radius or self.config.guided_filter_radius
        eps = eps or self.config.guided_filter_eps

        if guide.ndim == 3:
            guide_gray = cv2.cvtColor(guide, cv2.COLOR_BGR2GRAY)
        else:
            guide_gray = guide

        # Convert to float32 in [0,1]
        I = guide_gray.astype(np.float32) / 255.0
        p = image.astype(np.float32) / 255.0

        # Box filter helper
        def box_filter(img, r):
            return cv2.boxFilter(img, -1, (r, r))

        mean_I = box_filter(I, radius)
        mean_p = box_filter(p, radius)
        mean_Ip = box_filter(I * p, radius)
        cov_Ip = mean_Ip - mean_I * mean_p

        mean_II = box_filter(I * I, radius)
        var_I = mean_II - mean_I * mean_I

        a = cov_Ip / (var_I + eps)
        b = mean_p - a * mean_I

        mean_a = box_filter(a, radius)
        mean_b = box_filter(b, radius)

        q = mean_a * I + mean_b
        return np.clip(q * 255.0, 0, 255).astype(np.uint8)

    def closed_form_matting(self, image: np.ndarray, trimap: np.ndarray) -> np.ndarray:
        """
        Closed-form-inspired fast matting using distance transforms + optional guided filtering.

        Args:
            image: RGB image (H, W, 3) uint8
            trimap: Trimap with values {0, 128, 255}

        Returns:
            Alpha matte in [0,1] float32
        """
        h, w = trimap.shape[:2]
        alpha = (trimap.astype(np.float32) / 255.0)

        is_fg = trimap == 255
        is_bg = trimap == 0
        is_unknown = trimap == 128

        if not np.any(is_unknown):
            return np.clip(alpha, 0.0, 1.0)

        dist_fg = cv2.distanceTransform(is_fg.astype(np.uint8), cv2.DIST_L2, 5)
        dist_bg = cv2.distanceTransform(is_bg.astype(np.uint8), cv2.DIST_L2, 5)

        total = dist_fg + dist_bg + 1e-10
        alpha_unknown = dist_fg / total
        alpha[is_unknown] = alpha_unknown[is_unknown]

        if self.config.use_guided_filter:
            alpha_u8 = np.clip(alpha * 255.0, 0, 255).astype(np.uint8)
            alpha_u8 = self.guided_filter(alpha_u8, image)
            alpha = alpha_u8.astype(np.float32) / 255.0

        return np.clip(alpha, 0.0, 1.0)

    def deep_matting(
        self,
        image: np.ndarray,
        mask: np.ndarray,
        model: Optional[nn.Module] = None,
    ) -> np.ndarray:
        """
        Apply deep learning-based matting refinement.

        Args:
            image: RGB image (H, W, 3) uint8
            mask: Initial mask (H, W) {0..255} or [0,1]
            model: Optional pre-trained model taking (img, mask) → alpha

        Returns:
            Refined alpha matte in [0,1] float32
        """
        device = self.device_manager.get_device()

        h, w = image.shape[:2]
        input_size = (512, 512)

        img_rs = cv2.resize(image, input_size)
        msk_rs = cv2.resize(mask, input_size)

        img_t = torch.from_numpy(img_rs.transpose(2, 0, 1)).float().unsqueeze(0) / 255.0
        msk_t = torch.from_numpy(msk_rs).float().unsqueeze(0).unsqueeze(0)
        if msk_t.max() > 1.0:
            msk_t = msk_t / 255.0

        img_t = img_t.to(device)
        msk_t = msk_t.to(device)

        with torch.no_grad():
            if model is None:
                x = torch.cat([img_t, msk_t], dim=1)
                refined = self._simple_refine_network(x)
            else:
                refined = model(img_t, msk_t)
            alpha = refined.squeeze().float().cpu().numpy()

        alpha = cv2.resize(alpha, (w, h))
        return np.clip(alpha, 0.0, 1.0)

    def _simple_refine_network(self, x: torch.Tensor) -> torch.Tensor:
        """Tiny non-learned refinement block (demo-quality)."""
        # x: [B, 4, H, W] (RGB + mask)
        mask = x[:, 3:4, :, :]

        refined = mask
        for _ in range(3):
            refined = F.avg_pool2d(refined, 3, stride=1, padding=1)
            refined = torch.sigmoid((refined - 0.5) * 10.0)

        return refined

    def morphological_refinement(self, alpha: np.ndarray) -> np.ndarray:
        """
        Apply morphological operations and boundary smoothing.

        Args:
            alpha: Alpha matte in [0,1] float32

        Returns:
            Refined alpha in [0,1] float32
        """
        alpha_u8 = np.clip(alpha * 255.0, 0, 255).astype(np.uint8)
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))

        # Close small holes in FG
        alpha_u8 = cv2.morphologyEx(
            alpha_u8, cv2.MORPH_CLOSE, kernel, iterations=self.config.erode_iterations
        )
        # Remove small specks
        alpha_u8 = cv2.morphologyEx(
            alpha_u8, cv2.MORPH_OPEN, kernel, iterations=self.config.dilate_iterations
        )

        if self.config.blur_radius > 0:
            r = self.config.blur_radius * 2 + 1
            alpha_u8 = cv2.GaussianBlur(alpha_u8, (r, r), 0)

        return alpha_u8.astype(np.float32) / 255.0

    def process(self, image: np.ndarray, mask: np.ndarray, method: str = "auto") -> Dict[str, np.ndarray]:
        """
        Process image with selected matting method.

        Args:
            image: RGB image (H, W, 3) uint8
            mask: Initial segmentation mask (H, W)
            method: 'auto' | 'trimap' | 'deep' | 'guided'

        Returns:
            dict(alpha, confidence, method_used, quality_metrics[, error])
        """
        try:
            quality_metrics = self.quality_analyzer.analyze_frame(image)

            chosen = method
            if method == "auto":
                # Heuristic selection
                blur_score = quality_metrics.get("blur_score", 0.0)
                edge_clarity = quality_metrics.get("edge_clarity", 0.0)
                if blur_score > 50:
                    chosen = "guided"
                elif edge_clarity > 0.7:
                    chosen = "trimap"
                else:
                    chosen = "deep"

            logger.info(f"Using matting method: {chosen}")

            if chosen == "trimap":
                trimap = self.create_trimap(mask)
                alpha = self.closed_form_matting(image, trimap)
            elif chosen == "deep":
                alpha = self.deep_matting(image, mask)
            elif chosen == "guided":
                alpha = mask.astype(np.float32)
                if alpha.max() > 1.0:
                    alpha = alpha / 255.0
                if self.config.use_guided_filter:
                    alpha_u8 = np.clip(alpha * 255.0, 0, 255).astype(np.uint8)
                    alpha = self.guided_filter(alpha_u8, image).astype(np.float32) / 255.0
            else:
                alpha = mask.astype(np.float32)
                if alpha.max() > 1.0:
                    alpha = alpha / 255.0

            # Morphological + edge refinement
            alpha = self.morphological_refinement(alpha)
            alpha = self.edge_refinement.refine_edges(
                image, np.clip(alpha * 255.0, 0, 255).astype(np.uint8)
            ).astype(np.float32) / 255.0

            confidence = self._calculate_confidence(alpha, quality_metrics)

            return {
                "alpha": np.clip(alpha, 0.0, 1.0),
                "confidence": float(np.clip(confidence, 0.0, 1.0)),
                "method_used": chosen,
                "quality_metrics": quality_metrics,
            }

        except Exception as e:
            logger.error(f"Matting processing failed: {e}")
            fallback = mask.astype(np.float32)
            if fallback.max() > 1.0:
                fallback = fallback / 255.0
            return {
                "alpha": np.clip(fallback, 0.0, 1.0),
                "confidence": 0.0,
                "method_used": "fallback",
                "error": str(e),
            }

    def _calculate_confidence(self, alpha: np.ndarray, quality_metrics: Dict) -> float:
        """Calculate confidence score for the matting result."""
        confidence = float(quality_metrics.get("overall_quality", 0.5))

        alpha_mean = float(np.mean(alpha))
        alpha_std = float(np.std(alpha))

        # Prefer clear separation
        if 0.3 < alpha_mean < 0.7 and alpha_std > 0.3:
            confidence *= 1.2

        edges = cv2.Canny(np.clip(alpha * 255.0, 0, 255).astype(np.uint8), 50, 150)
        edge_ratio = float(np.sum(edges > 0) / edges.size)
        if edge_ratio < 0.1:
            confidence *= 1.1

        return float(np.clip(confidence, 0.0, 1.0))


class CompositingEngine:
    """Handle alpha compositing and blending."""

    def __init__(self):
        self.logger = get_logger(f"{__name__}.CompositingEngine")

    def composite(self, foreground: np.ndarray, background: np.ndarray, alpha: np.ndarray) -> np.ndarray:
        """
        Composite foreground over background using alpha.

        Args:
            foreground: Foreground image (H, W, 3) uint8
            background: Background image (H, W, 3) uint8
            alpha: Alpha matte (H, W) or (H, W, 1) in [0..255] or [0..1]

        Returns:
            Composited image (H, W, 3) uint8
        """
        # Ensure alpha is 3-channel
        if alpha.ndim == 2:
            alpha = np.expand_dims(alpha, axis=2)
        if alpha.shape[2] == 1:
            alpha = np.repeat(alpha, 3, axis=2)

        # Normalize alpha to [0,1]
        a = alpha.astype(np.float32)
        if a.max() > 1.0:
            a = a / 255.0

        fg = foreground.astype(np.float32) / 255.0
        bg = background.astype(np.float32) / 255.0

        result = fg * a + bg * (1.0 - a)
        return np.clip(result * 255.0, 0, 255).astype(np.uint8)

    def premultiply_alpha(self, image: np.ndarray, alpha: np.ndarray) -> np.ndarray:
        """
        Premultiply RGB image by alpha channel.

        Args:
            image: (H, W, 3) uint8
            alpha: (H, W) or (H, W, 1) in [0..255] or [0..1]

        Returns:
            Premultiplied (H, W, 3) uint8
        """
        if alpha.ndim == 2:
            alpha = np.expand_dims(alpha, axis=2)
        if alpha.shape[2] == 1:
            alpha = np.repeat(alpha, 3, axis=2)

        a = alpha.astype(np.float32)
        if a.max() > 1.0:
            a = a / 255.0

        img_f = image.astype(np.float32)
        premul = img_f * a
        return np.clip(premul, 0.0, 255.0).astype(np.uint8)