121 lines
5.1 KiB
Python
121 lines
5.1 KiB
Python
import cv2
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import numpy as np
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import json
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from datetime import datetime
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class WatermarkAnalyzer:
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def __init__(self, config_path="config/default_config.json"):
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self.watermark_regions = []
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self.selected_regions = []
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self.config_path = config_path
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self.load_config()
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self._check_opencv_version()
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def _check_opencv_version(self):
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"""检查OpenCV版本，确定是否支持显著性检测"""
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self.has_saliency = hasattr(cv2, 'saliency')
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if not self.has_saliency:
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print("警告: 当前OpenCV版本不支持显著性检测，将使用替代方法")
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def load_config(self):
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try:
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with open(self.config_path, 'r') as f:
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config = json.load(f)
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self.inpaint_radius = config.get("inpaint_radius", 5)
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self.inpaint_algorithm = config.get("inpaint_algorithm", cv2.INPAINT_TELEA)
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self.dilate_size = config.get("dilate_size", 3)
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self.use_adaptive_mask = config.get("use_adaptive_mask", True)
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self.text_threshold = config.get("text_threshold", 180)
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self.min_text_area = config.get("min_text_area", 100)
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self.text_color_range = tuple(
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tuple(c) for c in config.get("text_color_range", ((50, 50, 50), (180, 180, 180))))
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self.stamp_color_range = tuple(
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tuple(c) for c in config.get("stamp_color_range", ((0, 0, 100), (100, 100, 255))))
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self.saliency_threshold = config.get("saliency_threshold", 0.5)
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self.edge_threshold = config.get("edge_threshold", 100)
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self.use_mser = config.get("use_mser", True)
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self.use_color_filter = config.get("use_color_filter", True)
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self.use_texture_analysis = config.get("use_texture_analysis", True)
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except Exception as e:
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print(f"加载配置失败: {e}")
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self.reset_to_defaults()
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# ... 其他方法保持不变 ...
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def _detect_by_saliency(self, img):
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"""基于显著性检测水印 - 兼容旧版OpenCV"""
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if self.has_saliency:
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# 使用标准显著性检测（如果支持）
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try:
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saliency = cv2.saliency.StaticSaliencySpectralResidual_create()
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_, saliency_map = saliency.computeSaliency(img)
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# 阈值处理
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_, saliency_mask = cv2.threshold(saliency_map * 255, self.saliency_threshold * 255, 255,
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cv2.THRESH_BINARY)
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saliency_mask = saliency_mask.astype(np.uint8)
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# 形态学操作
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
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closed = cv2.morphologyEx(saliency_mask, cv2.MORPH_CLOSE, kernel)
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# 查找轮廓
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contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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regions = []
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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area = w * h
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if area > self.min_text_area * 2 and area < img.shape[0] * img.shape[1] * 0.1:
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regions.append({
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'type': 'saliency',
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'position': (x, y, w, h),
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'confidence': 0.7,
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'contour': contour
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})
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return regions
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except Exception as e:
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print(f"显著性检测失败: {e}")
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self.has_saliency = False # 标记为不支持，下次使用替代方法
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# 替代方法：使用亮度变化检测
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 高斯模糊减少噪声
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blurred = cv2.GaussianBlur(gray, (15, 15), 0)
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# 计算拉普拉斯算子检测边缘
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laplacian = cv2.Laplacian(blurred, cv2.CV2.CV_64F)
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laplacian = np.uint8(np.absolute(laplacian))
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# 阈值处理
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_, threshold = cv2.threshold(laplacian, 30, 255, cv2.THRESH_BINARY)
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# 形态学操作
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
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closed = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, kernel)
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# 查找轮廓
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contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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regions = []
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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area = w * h
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if area > self.min_text_area * 2 and area < img.shape[0] * img.shape[1] * 0.1:
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# 计算轮廓的紧致度
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perimeter = cv2.arcLength(contour, True)
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compactness = 4 * np.pi * area / (perimeter * perimeter) if perimeter > 0 else 0
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# 水印通常具有中等紧致度
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if 0.1 < compactness < 0.9:
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regions.append({
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'type': 'contrast',
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'position': (x, y, w, h),
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'confidence': 0.6,
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'contour': contour
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})
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return regions |