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技术 2022年11月8日
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部分 IV
OpenCV 中的图像处理

OpenCV-Python 中文教程(搬运)目录

16 图像平滑

目标
  • 学习使用不同的低通滤波器对图像进行模糊
  • 使用自定义的滤波器对图像进行卷积(2D 卷积)

2D 卷积
  与一维信号一样,我们也可以对 2D 图像实施低通滤波(LPF),高通滤波(HPF)等。LPF 帮助我们去除噪音,模糊图像。HPF 帮助我们找到图像的边缘
OpenCV 提供的函数 cv.filter2D() 可以让我们对一幅图像进行卷积操作。下面我们将对一幅图像使用平均滤波器。下面是一个 5×5 的平均滤波器核:
      [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)
操作如下:将核放在图像的一个像素 A 上,求与核对应的图像上 25(5×5)个像素的和,在取平均数,用这个平均数替代像素 A 的值。重复以上操作直到将图像的每一个像素值都更新一边。代码如下,运行一下吧。

import cv2
import numpy as np
from matplotlib import pyplot as pltimg = cv2.imread('opencv_logo.png')kernel = np.ones((5,5),np.float32)/25
dst = cv2.filter2D(img,-1,kernel)plt.subplot(121),plt.imshow(img),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(dst),plt.title('Averaging')
plt.xticks([]), plt.yticks([])
plt.show()

结果:

    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” 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图像模糊(图像平滑)
使用低通滤波器可以达到图像模糊的目的。这对与去除噪音很有帮助。其实就是去除图像中的高频成分(比如:噪音,边界)。所以边界也会被模糊一点。(当然,也有一些模糊技术不会模糊掉边界)。OpenCV 提供了四种模糊技术。

16.1 平均
  这是由一个归一化卷积框完成的。他只是用卷积框覆盖区域所有像素的平均值来代替中心元素。可以使用函数 cv2.blur() 和 cv2.boxFilter() 来完这个任务。可以同看查看文档了解更多卷积框的细节。我们需要设定卷积框的宽和高。下面是一个 3×3 的归一化卷积框:
      [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)
注意:如果你不想使用归一化卷积框,你应该使用 cv2.boxFilter(),这时要传入参数 normalize=False。
下面与第一部分一样的一个例子:

import cv2
import numpy as np
from matplotlib import pyplot as pltimg = cv2.imread('opencv_logo.png')blur = cv2.blur(img,(5,5))plt.subplot(121),plt.imshow(img),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(blur),plt.title('Blurred')
plt.xticks([]), plt.yticks([])
plt.show()

结果:

    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” 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16.2 高斯模糊
  现在把卷积核换成高斯核(简单来说,方框不变,将原来每个方框的值是相等的,现在里面的值是符合高斯分布的,方框中心的值最大,其余方框根据距离中心元素的距离递减,构成一个高斯小山包。原来的求平均数现在变成求加权平均数,全就是方框里的值)。实现的函数是 cv2.GaussianBlur()。我们需要指定高斯核的宽和高(必须是奇数)。以及高斯函数沿 X,Y 方向的标准差。如果我们只指定了 X 方向的的标准差,Y 方向也会取相同值。如果两个标准差都是 0,那么函数会根据核函数的大小自己计算。高斯滤波可以有效的从图像中去除高斯噪音。
如果你愿意的话,你也可以使用函数 cv2.getGaussianKernel() 自己构建一个高斯核。
如果要使用高斯模糊的话,上边的代码应该写成:

#0 是指根据窗口大小( 5,5 )来计算高斯函数标准差
blur = cv2.GaussianBlur(img,(5,5),0)

结果:

    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” alt=”” />

16.3 中值模糊
  顾名思义就是用与卷积框对应像素的中值来替代中心像素的值。这个滤波器经常用来去除椒盐噪声。前面的滤波器都是用计算得到的一个新值来取代中心像素的值,而中值滤波是用中心像素周围(也可以使他本身)的值来取代他。他能有效的去除噪声。卷积核的大小也应该是一个奇数。
在这个例子中,我们给原始图像加上 50% 的噪声然后再使用中值模糊。
代码:

median = cv2.medianBlur(img,5)
# 运行有问题,出不了如下结果,下图为照搬的

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

16.4 双边滤波
  函数 cv2.bilateralFilter() 能在保持边界清晰的情况下有效的去除噪音。但是这种操作与其他滤波器相比会比较慢。我们已经知道高斯滤波器是求中心点邻近区域像素的高斯加权平均值。这种高斯滤波器只考虑像素之间的空间关系,而不会考虑像素值之间的关系(像素的相似度)。所以这种方法不会考虑一个像素是否位于边界。因此边界也会别模糊掉,而这正不是我们想要。双边滤波在同时使用空间高斯权重和灰度值相似性高斯权重。空间高斯函数确保只有邻近区域的像素对中心点有影响,灰度值相似性高斯函数确保只有与中心像素灰度值相近的才会被用来做模糊运算。所以这种方法会确保边界不会被模糊掉,因为边界处的灰度值变化比较大。
进行双边滤波的代码如下:

#cv2.bilateralFilter(src, d, sigmaColor, sigmaSpace)
#d – Diameter of each pixel neighborhood that is used during filtering.
# If it is non-positive, it is computed from sigmaSpace
#9 邻域直径,两个 75 分别是空间高斯函数标准差,灰度值相似性高斯函数标准差
blur = cv2.bilateralFilter(img,9,75,75)
# 运行有问题,出不了如下结果,下图为照搬的  

结果:
看见了把,上图中的纹理被模糊掉了,但是边界还在。

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17 形态学转换

目标
  • 学习不同的形态学操作,例如腐蚀,膨胀,开运算,闭运算等
  • 我们要学习的函数有:cv2.erode(),cv2.dilate(),cv2.morphologyEx()

原理
  形态学操作是根据图像形状进行的简单操作。一般情况下对二值化图像进行的操作。需要输入两个参数,一个是原始图像,第二个被称为结构化元素或核,它是用来决定操作的性质的。两个基本的形态学操作是腐蚀和膨胀。他们的变体构成了开运算,闭运算,梯度等。我们会以下图为例逐一介绍它们。

        [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.1 腐蚀
  就像土壤侵蚀一样,这个操作会把前景物体的边界腐蚀掉(但是前景仍然是白色)。这是怎么做到的呢?卷积核沿着图像滑动,如果与卷积核对应的原图像的所有像素值都是 1,那么中心元素就保持原来的像素值,否则就变为零。

  这会产生什么影响呢?根据卷积核的大小靠近前景的所有像素都会被腐蚀掉(变为 0),所以前景物体会变小,整幅图像的白色区域会减少。这对于去除白噪声很有用,也可以用来断开两个连在一块的物体等。
这里我们有一个例子,使用一个 5×5 的卷积核,其中所有的值都是以。让我们看看他是如何工作的:

import cv2
import numpy as npimg = cv2.imread('j.png',0)
kernel = np.ones((5,5),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1)

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.2 膨胀
  与腐蚀相反,与卷积核对应的原图像的像素值中只要有一个是 1,中心元素的像素值就是 1。所以这个操作会增加图像中的白色区域(前景)。一般在去噪声时先用腐蚀再用膨胀。因为腐蚀在去掉白噪声的同时,也会使前景对象变小。所以我们再对他进行膨胀。这时噪声已经被去除了,不会再回来了,但是前景还在并会增加。膨胀也可以用来连接两个分开的物体。

dilation = cv2.dilate(img,kernel,iterations = 1)

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.3 开运算
  先进性腐蚀再进行膨胀就叫做开运算。就像我们上面介绍的那样,它被用来去除噪声。这里我们用到的函数是 cv2.morphologyEx()。

opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.4 闭运算
  先膨胀再腐蚀。它经常被用来填充前景物体中的小洞,或者前景物体上的小黑点。

closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.5 形态学梯度
  其实就是一幅图像膨胀与腐蚀的差别。
结果看上去就像前景物体的轮廓。

gradient = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.6 礼帽
  原始图像与进行开运算之后得到的图像的差。下面的例子是用一个 9×9 的核进行礼帽操作的结果。

tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.7 黑帽
  进行闭运算之后得到的图像与原始图像的差。

tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

17.8 形态学操作之间的关系
  我们把以上集中形态学操作之间的关系列出来以供大家参考:

结构化元素
  在前面的例子中我们使用 Numpy 构建了结构化元素,它是正方形的。但有时我们需要构建一个椭圆形/圆形的核。为了实现这种要求,提供了 OpenCV函数 cv2.getStructuringElement()。你只需要告诉他你需要的核的形状和大小。

# Rectangular Kernel
>>> cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
array([[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]], dtype=uint8)
# Elliptical Kernel
>>> cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
array([[0, 0, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[0, 0, 1, 0, 0]], dtype=uint8)
# Cross-shaped Kernel
>>> cv2.getStructuringElement(cv2.MORPH_CROSS,(5,5))
array([[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[1, 1, 1, 1, 1],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0]], dtype=uint8)

18 图像梯度

目标
  • 图像梯度,图像边界等
  • 使用到的函数有:cv2.Sobel(),cv2.Schar(),cv2.Laplacian() 等

原理
  梯度简单来说就是求导。
  OpenCV 提供了三种不同的梯度滤波器,或者说高通滤波器:Sobel,Scharr 和 Laplacian。我们会一一介绍他们。
  Sobel,Scharr 其实就是求一阶或二阶导数。Scharr 是对 Sobel(使用小的卷积核求解求解梯度角度时)的优化。Laplacian 是求二阶导数。

18.1 Sobel 算子和 Scharr 算子
  Sobel 算子是高斯平滑与微分操作的结合体,所以它的抗噪声能力很好。你可以设定求导的方向(xorder 或 yorder)。还可以设定使用的卷积核的大小(ksize)。如果 ksize=-1,会使用 3×3 的 Scharr 滤波器,它的的效果要比 3×3 的 Sobel 滤波器好(而且速度相同,所以在使用 3×3 滤波器时应该尽量使用 Scharr 滤波器)。3×3 的 Scharr 滤波器卷积核如下:
   [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

18.2 Laplacian 算子
  拉普拉斯算子可以使用二阶导数的形式定义,可假设其离散实现类似于二阶 Sobel 导数,事实上,OpenCV 在计算拉普拉斯算子时直接调用 Sobel 算子。计算公式如下:
       [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)
拉普拉斯滤波器使用的卷积核:
      [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)
代码
  下面的代码分别使用以上三种滤波器对同一幅图进行操作。使用的卷积核都是 5×5 的。

import cv2
import numpy as np
from matplotlib import pyplot as pltimg = cv2.imread('dave.jpg',0)laplacian = cv2.Laplacian(img,cv2.CV_64F)
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
plt.title('Original'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])plt.show()

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

一个重要的事!
  在查看上面这个例子的注释时不知道你有没有注意到:当我们可以通过参数 -1 来设定输出图像的深度(数据类型)与原图像保持一致,但是我们在代码中使用的却是 cv2.CV_64F。这是为什么呢?想象一下一个从黑到白的边界的导数是整数,而一个从白到黑的边界点导数却是负数。如果原图像的深度是np.int8 时,所有的负值都会被截断变成 0,换句话说就是把把边界丢失掉。所以如果这两种边界你都想检测到,最好的的办法就是将输出的数据类型设置的更高,比如 cv2.CV_16S,cv2.CV_64F 等。取绝对值然后再把它转回到 cv2.CV_8U。下面的示例演示了输出图片的深度不同造成的不同效果。

import cv2
import numpy as np
from matplotlib import pyplot as pltimg = cv2.imread('box.png',0)# Output dtype = cv2.CV_8U
sobelx8u = cv2.Sobel(img,cv2.CV_8U,1,0,ksize=5)# Output dtype = cv2.CV_64F. Then take its absolute and convert to cv2.CV_8U
sobelx64f = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
abs_sobel64f = np.absolute(sobelx64f)
sobel_8u = np.uint8(abs_sobel64f)plt.subplot(1,3,1),plt.imshow(img,cmap = 'gray')
plt.title('Original'), plt.xticks([]), plt.yticks([])
plt.subplot(1,3,2),plt.imshow(sobelx8u,cmap = 'gray')
plt.title('Sobel CV_8U'), plt.xticks([]), plt.yticks([])
plt.subplot(1,3,3),plt.imshow(sobel_8u,cmap = 'gray')
plt.title('Sobel abs(CV_64F)'), plt.xticks([]), plt.yticks([])plt.show()

结果:

    [OpenCV-Python] OpenCV 中的图像处理 部分 IV (二)

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