线性滤波与卷积的基本概念

线性滤波可以说是图像处理最基本的方法，它可以允许我们对图像进行处理，产生很多不同的效果。做法很简单。首先，我们有一个二维的滤波器矩阵（有个高大上的名字叫卷积核）和一个要处理的二维图像。然后，对于图像的每一个像素点，计算它的邻域像素和滤波器矩阵的对应元素的乘积，然后加起来，作为该像素位置的值。这样就完成了滤波过程。

import cv2import numpy#-卷积/锐化/边缘检测/模糊滤波器详解def cv_show(name, img):cv2.imshow(name, img)cv2.waitKey(0)cv2.destroyAllWindows()# 边缘检测优化方法 先对图像进行模糊处理，再转为为灰度彩色图像，再使用laplacian检测边缘。此方法可较好的避免将噪声错误地识别为边缘。def strokeEdges(src, dst, blurKsize=7, edgeKsize=5):if blurKsize >= 3:blurredSrc = cv2.medianBlur(src, blurKsize)graySrc = cv2.cvtColor(blurredSrc, cv2.COLOR_BGR2GRAY)else:graySrc = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)cv2.Laplacian(graySrc, cv2.CV_8U, graySrc, ksize=edgeKsize)# cv_show('binary',binary)normalizedInverseAlpha = (1.0 / 255) * (255 - graySrc)# cv_show('normalized',normalizedInverseAlpha)channels = cv2.split(src)for channel in channels:channel[:] = channel * normalizedInverseAlpha# cv_show('channel',channel)dst = cv2.merge(channels)return dst# 一般的卷积滤波器方法class VConvolutionFilter(object):# A filter that applies a convolution to V (or all of BGR)def __init__(self, kernel):self._kernel = kerneldef apply(self, src):# Apply the filter with a BGR or gray source /destinationdst = cv2.filter2D(src, -1, self._kernel)return dst# 特定的锐化滤波器方法class shapenFilter(VConvolutionFilter):# A shapen filter with a 1-pixel radiusdef __init__(self):kernel = numpy.array([[-1, -1, -1],[-1, 9, -1],[-1, -1, -1],])  # 若不想改变图像亮度，则权重加起来为1。若使权重和为0，则得到一个边缘检测核，把边缘转化为白色，把非边缘区域转为黑色，例如下面的FindEdgesFilter方法VConvolutionFilter.__init__(self, kernel)# 边缘检测滤波器。class FindEdgesFilter(VConvolutionFilter):# An edge-finding filter with a 1-pixel radiusdef __init__(self):kernel = numpy.array([[-1, -1, -1],[-1, 9, -1],[-1, -1, -1],])VConvolutionFilter.__init__(self, kernel)# 模糊滤波器，为了达到模糊效果，通常权重和为1，而且临近像素的权重全为正。下面实现一个简单的邻近平均滤波器:class BlurFilter(VConvolutionFilter):# A blur filter with a 2-piexl radiusdef __init__(self):kernel = numpy.array([[0.04, 0.04, 0.04, 0.04, 0.04],[0.04, 0.04, 0.04, 0.04, 0.04],[0.04, 0.04, 0.04, 0.04, 0.04],[0.04, 0.04, 0.04, 0.04, 0.04],[0.04, 0.04, 0.04, 0.04, 0.04],])VConvolutionFilter.__init__(self, kernel)# 脊状/浮雕效果滤波器class EmbossFilter(VConvolutionFilter):def __init__(self):kernel = numpy.array([[-2, -1, 0],[-1, 9, 1],[0, 1, 2],])VConvolutionFilter.__init__(self, kernel)image = cv2.imread(r'D:XX.jpg')dst = numpy.ones((image.shape[0], image.shape[1]), dtype=numpy.int8)dst = strokeEdges(image, dst)cv_show('strokeEdges', dst)shape = shapenFilter()dst = shape.apply(image)cv_show('strokeEdges', dst)FindEdgesFilter = FindEdgesFilter()dst = FindEdgesFilter.apply(image)cv_show('FindEdgesFilter', dst)BlurFilter = BlurFilter()dst = BlurFilter.apply(image)cv_show('blurfilter', dst)EmbossFilter = EmbossFilter()dst = EmbossFilter.apply(image)cv_show('EmbossFilter', dst)

参考文献

• https://blog.csdn.net/weixin_42026802/article/details/80181627