import cv2
import numpy as np
import json
from datetime import datetime


class WatermarkAnalyzer:
    def __init__(self, config_path="config/default_config.json"):
        self.watermark_regions = []
        self.selected_regions = []
        self.config_path = config_path
        self.load_config()
        self._check_opencv_version()

    def _check_opencv_version(self):
        """检查OpenCV版本，确定是否支持显著性检测"""
        self.has_saliency = hasattr(cv2, 'saliency')
        if not self.has_saliency:
            print("警告: 当前OpenCV版本不支持显著性检测，将使用替代方法")

    def load_config(self):
        try:
            with open(self.config_path, 'r') as f:
                config = json.load(f)
                self.inpaint_radius = config.get("inpaint_radius", 5)
                self.inpaint_algorithm = config.get("inpaint_algorithm", cv2.INPAINT_TELEA)
                self.dilate_size = config.get("dilate_size", 3)
                self.use_adaptive_mask = config.get("use_adaptive_mask", True)
                self.text_threshold = config.get("text_threshold", 180)
                self.min_text_area = config.get("min_text_area", 100)
                self.text_color_range = tuple(
                    tuple(c) for c in config.get("text_color_range", ((50, 50, 50), (180, 180, 180))))
                self.stamp_color_range = tuple(
                    tuple(c) for c in config.get("stamp_color_range", ((0, 0, 100), (100, 100, 255))))
                self.saliency_threshold = config.get("saliency_threshold", 0.5)
                self.edge_threshold = config.get("edge_threshold", 100)
                self.use_mser = config.get("use_mser", True)
                self.use_color_filter = config.get("use_color_filter", True)
                self.use_texture_analysis = config.get("use_texture_analysis", True)
        except Exception as e:
            print(f"加载配置失败: {e}")
            self.reset_to_defaults()

    # ... 其他方法保持不变 ...

    def _detect_by_saliency(self, img):
        """基于显著性检测水印 - 兼容旧版OpenCV"""
        if self.has_saliency:
            # 使用标准显著性检测（如果支持）
            try:
                saliency = cv2.saliency.StaticSaliencySpectralResidual_create()
                _, saliency_map = saliency.computeSaliency(img)

                # 阈值处理
                _, saliency_mask = cv2.threshold(saliency_map * 255, self.saliency_threshold * 255, 255,
                                                 cv2.THRESH_BINARY)
                saliency_mask = saliency_mask.astype(np.uint8)

                # 形态学操作
                kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
                closed = cv2.morphologyEx(saliency_mask, cv2.MORPH_CLOSE, kernel)

                # 查找轮廓
                contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

                regions = []
                for contour in contours:
                    x, y, w, h = cv2.boundingRect(contour)
                    area = w * h
                    if area > self.min_text_area * 2 and area < img.shape[0] * img.shape[1] * 0.1:
                        regions.append({
                            'type': 'saliency',
                            'position': (x, y, w, h),
                            'confidence': 0.7,
                            'contour': contour
                        })

                return regions
            except Exception as e:
                print(f"显著性检测失败: {e}")
                self.has_saliency = False  # 标记为不支持，下次使用替代方法

        # 替代方法：使用亮度变化检测
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # 高斯模糊减少噪声
        blurred = cv2.GaussianBlur(gray, (15, 15), 0)

        # 计算拉普拉斯算子检测边缘
        laplacian = cv2.Laplacian(blurred, cv2.CV2.CV_64F)
        laplacian = np.uint8(np.absolute(laplacian))

        # 阈值处理
        _, threshold = cv2.threshold(laplacian, 30, 255, cv2.THRESH_BINARY)

        # 形态学操作
        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
        closed = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, kernel)

        # 查找轮廓
        contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        regions = []
        for contour in contours:
            x, y, w, h = cv2.boundingRect(contour)
            area = w * h
            if area > self.min_text_area * 2 and area < img.shape[0] * img.shape[1] * 0.1:
                # 计算轮廓的紧致度
                perimeter = cv2.arcLength(contour, True)
                compactness = 4 * np.pi * area / (perimeter * perimeter) if perimeter > 0 else 0

                # 水印通常具有中等紧致度
                if 0.1 < compactness < 0.9:
                    regions.append({
                        'type': 'contrast',
                        'position': (x, y, w, h),
                        'confidence': 0.6,
                        'contour': contour
                    })

        return regions