import os
import cv2
import numpy as np
import torch
from torchvision import transforms

import config
from config import logger


class RVMProcessor:
    """RVM (Robust Video Matting) 抠图处理器"""

    def __init__(self):
        self.model = None
        self.available = False
        self.device = "cpu"  # 默认使用CPU，如果有GPU可以设置为"cuda"

        try:
            # 仅从本地加载，不使用网络
            local_repo = getattr(config, 'RVM_LOCAL_REPO', '')
            weights_path = getattr(config, 'RVM_WEIGHTS_PATH', '')

            if not local_repo or not os.path.isdir(local_repo):
                raise RuntimeError("RVM_LOCAL_REPO not set or invalid. Please set env RVM_LOCAL_REPO to local RobustVideoMatting repo path (with hubconf.py)")

            if not weights_path or not os.path.isfile(weights_path):
                raise RuntimeError("RVM_WEIGHTS_PATH not set or file not found. Please set env RVM_WEIGHTS_PATH to local RVM weights file path")

            logger.info(f"Loading RVM model {config.RVM_MODEL} from local repo: {local_repo}")
            # 使用本地仓库构建模型，禁用预训练以避免联网
            self.model = torch.hub.load(local_repo, config.RVM_MODEL, source='local', pretrained=False)

            # 加载本地权重
            state = torch.load(weights_path, map_location=self.device)
            if isinstance(state, dict) and 'state_dict' in state:
                state = state['state_dict']
            missing, unexpected = self.model.load_state_dict(state, strict=False)

            # 迁移到设备并设置评估模式
            self.model = self.model.to(self.device).eval()
            self.available = True
            logger.info("RVM background removal processor initialized successfully (local mode)")
            if missing:
                logger.warning(f"RVM weights missing keys: {list(missing)[:5]}... total={len(missing)}")
            if unexpected:
                logger.warning(f"RVM weights unexpected keys: {list(unexpected)[:5]}... total={len(unexpected)}")

        except Exception as e:
            logger.error(f"RVM background removal processor initialization failed: {e}")
            self.available = False

    def is_available(self) -> bool:
        """检查RVM处理器是否可用"""
        return self.available and self.model is not None

    def remove_background(self, image: np.ndarray, background_color: tuple = None) -> np.ndarray:
        """
        使用RVM移除图片背景
        :param image: 输入的OpenCV图像(BGR格式)
        :param background_color: 替换的背景颜色(BGR格式)，如果为None则保持透明背景
        :return: 处理后的图像
        """
        if not self.is_available():
            raise Exception("RVM抠图处理器不可用")

        try:
            logger.info("Starting to remove background using RVM...")

            # 保存原始图像尺寸
            original_height, original_width = image.shape[:2]

            # 将OpenCV图像(BGR)转换为RGB格式
            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

            # 转换为tensor
            src = transforms.ToTensor()(image_rgb).unsqueeze(0).to(self.device)

            # 推理
            rec = [None] * 4
            with torch.no_grad():
                fgr, pha, *rec = self.model(src, *rec, downsample_ratio=0.25)

            # 转换为numpy数组
            fgr = (fgr[0].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)  # (H,W,3)
            pha = (pha[0, 0].cpu().numpy() * 255).astype(np.uint8)                # (H,W)

            # 检查尺寸是否匹配，如果不匹配则调整
            if fgr.shape[:2] != (original_height, original_width):
                fgr = cv2.resize(fgr, (original_width, original_height))
                pha = cv2.resize(pha, (original_width, original_height))

            if background_color is not None:
                # 如果指定了背景颜色，创建纯色背景
                # 将前景图像转换为BGR格式
                fgr_bgr = cv2.cvtColor(fgr, cv2.COLOR_RGB2BGR)

                # 创建背景图像
                background = np.full((original_height, original_width, 3), background_color, dtype=np.uint8)

                # 使用alpha混合
                alpha = pha.astype(np.float32) / 255.0
                alpha = np.stack([alpha] * 3, axis=-1)

                result = (fgr_bgr * alpha + background * (1 - alpha)).astype(np.uint8)
            else:
                # 保持透明背景，转换为BGRA格式
                fgr_bgr = cv2.cvtColor(fgr, cv2.COLOR_RGB2BGR)
                rgba = np.dstack((fgr_bgr, pha))  # (H,W,4)
                result = rgba

            logger.info("RVM background removal completed")
            return result

        except Exception as e:
            logger.error(f"RVM background removal failed: {e}")
            raise Exception(f"背景移除失败: {str(e)}")

    def create_id_photo(self, image: np.ndarray, background_color: tuple = (255, 255, 255)) -> np.ndarray:
        """
        创建证件照（移除背景并添加纯色背景）
        :param image: 输入的OpenCV图像
        :param background_color: 背景颜色，默认白色(BGR格式)
        :return: 处理后的证件照
        """
        logger.info(f"Starting to create ID photo, background color: {background_color}")

        # 移除背景并添加指定颜色背景
        id_photo = self.remove_background(image, background_color)

        logger.info("ID photo creation completed")
        return id_photo