20250701

analyzer.py

@@ -0,0 +1,121 @@
+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