# -*- coding: utf-8 -*- import cv2 import numpy as np import matplotlib.pyplot as plt src = cv2.imread("09-opencv/lena.jpg") kernels = [ (u"低通滤波器", np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]]) * 0.1), (u"高通滤波器", np.array([[0.0, -1, 0], [-1, 5, -1], [0, -1, 0]])), (u"边缘检测", np.array([[-1.0, -1, -1], [-1, 8, -1], [-1, -1, -1]])) ] index = 0 fig, axes = plt.subplots(1, 3, figsize=(12, 4.3)) for ax, (name, kernel) in zip(axes, kernels): dst = cv2.filter2D(src, -1, kernel) ax.imshow(dst[:, :, ::-1]) ax.set_title(name) ax.axis('off') fig.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0) plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签 plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 plt.show()
使用不同卷积核对图像进行处理之后的效果
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转载自blog.csdn.net/qq_34000894/article/details/80426445
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