使用积分图像进行自适应阈值二值化

import cv2 as cv import numpy as np def adaptive_threshold(gray: np.ndarray, whs: int = None, sub_threshold: float = 0.15): """ 基于积分图像的自适应阈值二值化算法 :param gray: 输入的灰度图像 :param whs: 滑动窗口的半宽高（默认为图像宽度的1/10） :param sub_threshold: 自定义子阈值，需要通过实验对比确定 :return: 二值化图像 """ # 计算积分图像 integral_image = cv.integral(gray, cv.CV_32F) # 创建滑动窗口 height = gray.shape[0] width = gray.shape[1] window_half_size = int(width / 10) if whs is None else whs binary = np.zeros((height, width), dtype=np.uint8) # 开始二值化 for j in range(height): for i in range(width): # 定义滑动窗口的角点 x1 = i - window_half_size x2 = i + window_half_size y1 = j - window_half_size y2 = j + window_half_size # 检查滑动窗口的边界 x1 = 0 if x1 < 0 else x1 y1 = 0 if y1 < 0 else y1 x2 = width - 1 if x2 > width else x2 y2 = height - 1 if y2 > height else y2 count = (x2 - x1) * (y2 - y1) # \mbox{I}_W=\mbox{I}(x_2,y_2)-\mbox{I}(x_1-1,y_2)-\mbox{I}(x_2,y_1-1)+\mbox{I}(x_1-1,y_1-1) summed = integral_image[y2, x2] - integral_image[y1, x2] - integral_image[y2, x1] + integral_image[y1, x1] binary[j, i] = 0 if int(gray[j, i] * count) < int(summed * (1.0 - sub_threshold)) else 255 return binary