提取图片高频信息
示例-输入：
示例-输出：
代码实现：
import cv2
import numpy as npdef edge_calc(image):src = cv2.GaussianBlur(image, (3, 3), 0)ddepth = cv2.CV_16Sgray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)grad_x = cv2.Scharr(gray, ddepth, 1, 0)grad_y = cv2.Scharr(gray, ddepth, 0, 1)abs_grad_x = cv2.convertScaleAbs(grad_x)abs_grad_y = cv2.convertScaleAbs(grad_y)grad = cv2.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0)_, th = cv2.threshold(grad, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)w, h = th.shape[1], th.shape[0]cv2.rectangle(th, (0, 0), (w-1, h-1), 255, thickness=12)kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) # 定义矩形结构元素# th = cv2.morphologyEx(th, cv2.MORPH_DILATE, kernel, iterations=1) # 膨胀运算1edge = cv2.morphologyEx(th, cv2.MORPH_CLOSE, kernel, iterations=1) # 闭运算1# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3)) # 定义矩形结构元素# closed = cv2.morphologyEx(th, cv2.MORPH_CLOSE, kernel, iterations=1) # 闭运算1return edgeif __name__ == "__main__":img = cv2.imread('paper.png')edge = edge_calc(img)cv2.imwrite("paper_mask.png", edge.astype(np.uint8))
主要过程包括：平滑、梯度计算、二值化、边框处理，以及形态学操作。
