去除图像中的虚假小噪声岛 - Python OpenCV [英] Remove spurious small islands of noise in an image - Python OpenCV

问题描述

我正在尝试从我的一些图像中去除背景噪音.这是未经过滤的图像.

为了过滤，我使用此代码生成了图像中应保留的内容的掩码:

 element = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))掩码 = cv2.erode(掩码，元素，迭代 = 1)掩码 = cv2.dilate(掩码，元素，迭代 = 1)掩码 = cv2.erode(掩码，元素)

使用此代码，当我从原始图像中屏蔽掉不需要的像素时，我得到的是:

如您所见，中间区域的所有小点都消失了，但是来自较密集区域的许多小点也消失了.为了减少过滤，我尝试将 getStructuringElement() 的第二个参数更改为 (1,1) 但这样做会给我第一张图像，就好像没有过滤任何东西一样.

有什么方法可以应用介于这两个极端之间的过滤器吗?

另外，谁能向我解释一下 getStructuringElement() 到底是做什么的?什么是结构元素"?它有什么作用?它的大小(第二个参数)如何影响过滤级别?

解决方案

您的许多问题源于您不确定形态学图像处理的工作原理，但我们可以消除您的疑虑.您可以将结构元素解释为要与之比较的基本形状".结构元素中的 1 对应于您要在此形状中查看的像素，而 0 是您要忽略的像素.有不同的形状，例如矩形(正如您使用 MORPH_RECT 计算出来的那样)、椭圆、圆形等.

因此，cv2.getStructuringElement 会为您返回一个结构化元素.第一个参数指定您想要的类型，第二个参数指定您想要的大小.在您的情况下，您需要一个 2 x 2 的矩形"......这确实是一个正方形，但没关系.

从更粗暴的意义上讲，您使用结构元素并从左到右和从上到下扫描图像，然后获取像素区域.每个像素邻域的中心都在您正在查看的感兴趣的像素上.每个像素邻域的大小与结构元素的大小相同.

侵蚀

对于侵蚀，您检查像素邻域中接触结构元素的所有像素.如果每个非零像素都接触到一个为1的结构元素像素，那么相对于输入的对应中心位置的输出像素为1.如果至少有一个非零像素不触及为1的结构像素，则输出为0.

就矩形结构元素而言，您需要确保结构元素中的每个像素都接触到图像中像素邻域的非零像素.如果不是，则输出为 0，否则为 1.这有效地消除了小的杂散噪声区域，并略微减少了对象的面积.

矩形越大，执行的收缩越多的大小因素.结构元素的大小是基线，其中任何小于此矩形结构元素的对象，您都可以将它们视为已过滤而不出现在输出中.基本上，选择 1 x 1 矩形结构元素与输入图像本身相同，因为该结构元素适合其中的所有像素，因为像素是图像中信息的最小表示形式.

膨胀

膨胀与腐蚀相反.如果至少有一个非零像素与结构元素中为 1 的像素相接触，则输出为 1，否则输出为 0.您可以将其视为稍微扩大对象区域并使小岛变大.

这里的大小意味着结构元素越大，对象的面积就越大，孤立的岛也越大.

<小时>

您所做的是先腐蚀，然后膨胀.这就是所谓的打开操作.此操作的目的是去除噪声的小岛，同时(尝试)保持图像中较大对象的区域.侵蚀去除了这些岛屿，而膨胀将较大的物体恢复到原来的大小.

出于某种原因，您再次出现了侵蚀，我不太明白，但没关系.

<小时>

我个人会先执行关闭操作，即先膨胀，然后是腐蚀.闭合有助于将靠近的区域组合成单个对象.因此，您会看到一些较大的区域彼此靠近，可能应该在我们做任何其他事情之前将它们连接起来.因此，我会先做一个关闭，然后做一个打开，这样我们就可以去除孤立的嘈杂区域.请注意，我将关闭结构元素的尺寸更大，因为我想确保获得附近的像素，而打开的结构元素尺寸更小，这样我就不会不想错误地删除任何较大的区域.

一旦你这样做了，我会用原始图像掩盖任何额外的信息，这样你就可以在小岛消失的同时保持较大的区域完好无损.

不要将侵蚀后扩张或扩张后侵蚀链接起来，而是使用 在原始图像的副本上，并将不属于最终掩码的任何内容设置为 0.这与在 Python 中执行逐元素乘法相同.

#include 使用命名空间 cv；int main(int argc, char *argv[]){//读入图像Mat img = imread("spots.png", CV_LOAD_IMAGE_COLOR);//转换为黑白垫 img_bw;cvtColor(img, img_bw, COLOR_BGR2GRAY);img_bw = img_bw >5;//定义结构元素Mat se1 = getStructuringElement(MORPH_RECT, Size(5, 5));Mat se2 = getStructuringElement(MORPH_RECT, Size(2, 2));//先关闭再打开垫面罩；形态Ex(img_bw，面具，MORPH_CLOSE，se1)；形态Ex(面具，面具，MORPH_OPEN，se2)；//过滤输出Mat out = img.clone();out.setTo(Scalar(0), mask == 0);//显示图片并保存namedWindow("输出", WINDOW_NORMAL);imshow("输出"，输出);等待键(0)；destroyWindow("输出");imwrite("output.png", out);}

结果应该与您在 Python 版本中得到的结果相同.

I am trying to get rid of background noise from some of my images. This is the unfiltered image.

To filter, I used this code to generate a mask of what should remain in the image:

 element = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
 mask = cv2.erode(mask, element, iterations = 1)
 mask = cv2.dilate(mask, element, iterations = 1)
 mask = cv2.erode(mask, element)

With this code and when I mask out the unwanted pixels from the original image, what I get is:

As you can see, all the tiny dots in the middle area are gone, but a lot of those coming from the denser area are also gone. To reduce the filtering, I tried changing the second parameter of getStructuringElement() to be (1,1) but doing this gives me the first image as if nothing has been filtered.

Is there any way where I can apply some filter that is between these 2 extremes?

In addition, can anyone explain to me what exactly does getStructuringElement() do? What is a "structuring element"? What does it do and how does its size (the second parameter) affect the level of filtering?

解决方案

A lot of your questions stem from the fact that you're not sure how morphological image processing works, but we can put your doubts to rest. You can interpret the structuring element as the "base shape" to compare to. 1 in the structuring element corresponds to a pixel that you want to look at in this shape and 0 is one you want to ignore. There are different shapes, such as rectangular (as you have figured out with MORPH_RECT), ellipse, circular, etc.

As such, cv2.getStructuringElement returns a structuring element for you. The first parameter specifies the type you want and the second parameter specifies the size you want. In your case, you want a 2 x 2 "rectangle"... which is really a square, but that's fine.

In a more bastardized sense, you use the structuring element and scan from left to right and top to bottom of your image and you grab pixel neighbourhoods. Each pixel neighbourhood has its centre exactly at the pixel of interest that you're looking at. The size of each pixel neighbourhood is the same size as the structuring element.

Erosion

For an erosion, you examine all of the pixels in a pixel neighbourhood that are touching the structuring element. If every non-zero pixel is touching a structuring element pixel that is 1, then the output pixel in the corresponding centre position with respect to the input is 1. If there is at least one non-zero pixel that does not touch a structuring pixel that is 1, then the output is 0.

In terms of the rectangular structuring element, you need to make sure that every pixel in the structuring element is touching a non-zero pixel in your image for a pixel neighbourhood. If it isn't, then the output is 0, else 1. This effectively eliminates small spurious areas of noise and also decreases the area of objects slightly.

The size factors in where the larger the rectangle, the more shrinking is performed. The size of the structuring element is a baseline where any objects that are smaller than this rectangular structuring element, you can consider them as being filtered and not appearing in the output. Basically, choosing a 1 x 1 rectangular structuring element is the same as the input image itself because that structuring element fits all pixels inside it as the pixel is the smallest representation of information possible in an image.

Dilation

Dilation is the opposite of erosion. If there is at least one non-zero pixel that touches a pixel in the structuring element that is 1, then the output is 1, else the output is 0. You can think of this as slightly enlarging object areas and making small islands bigger.

The implications with size here is that the larger the structuring element, the larger the areas of the objects will be and the larger the isolated islands become.

---

What you're doing is an erosion first followed by a dilation. This is what is known as an opening operation. The purpose of this operation is to remove small islands of noise while (trying to) maintain the areas of the larger objects in your image. The erosion removes those islands while the dilation grows back the larger objects to their original sizes.

You follow this with an erosion again for some reason, which I can't quite understand, but that's ok.

---

What I would personally do is perform a closing operation first which is a dilation followed by an erosion. Closing helps group areas that are close together into a single object. As such, you see that there are some larger areas that are close to each other that should probably be joined before we do anything else. As such, I would do a closing first, then do an opening after so that we can remove the isolated noisy areas. Take note that I'm going to make the closing structuring element size larger as I want to make sure I get nearby pixels and the opening structuring element size smaller so that I don't want to mistakenly remove any of the larger areas.

Once you do this, I would mask out any extra information with the original image so that you leave the larger areas intact while the small islands go away.

Instead of chaining an erosion followed by a dilation, or a dilation followed by an erosion, use cv2.morphologyEx, where you can specify MORPH_OPEN and MORPH_CLOSE as the flags.

As such, I would personally do this, assuming your image is called spots.png:

import cv2
import numpy as np

img = cv2.imread('spots.png')
img_bw = 255*(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) > 5).astype('uint8')

se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
mask = cv2.morphologyEx(img_bw, cv2.MORPH_CLOSE, se1)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)

mask = np.dstack([mask, mask, mask]) / 255
out = img * mask

cv2.imshow('Output', out)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('output.png', out)

The above code is pretty self-explanatory. First, I read in the image and then I convert the image to grayscale and threshold with an intensity of 5 to create a mask of what is considered object pixels. This is a rather clean image and so anything larger than 5 seems to have worked. For the morphology routines, I need to convert the image to uint8 and scale the mask to 255. Next, we create two structuring elements - one that is a 5 x 5 rectangle for the closing operation and another that is 2 x 2 for the opening operation. I run cv2.morphologyEx twice for the opening and closing operations respectively on the thresholded image.

Once I do that, I stack the mask so that it becomes a 3D matrix and divide by 255 so that it becomes a mask of [0,1] and then we multiply this mask with the original image so that we can grab the original pixels of the image back and maintaining what is considered a true object from the mask output.

The rest is just for illustration. I show the image in a window, and I also save the image to a file called output.png, and its purpose is to show you what the image looks like in this post.

I get this:

Bear in mind that it isn't perfect, but it's much better than how you had it before. You'll have to play around with the structuring element sizes to get something that you consider as a good output, but this is certainly enough to get you started. Good luck!

---

C++ version

There have been some requests to translate the code I wrote above into the C++ version using OpenCV. I have finally gotten around to writing a C++ version of the code and this has been tested on OpenCV 3.1.0. The code for this is below. As you can see, the code is very similar to that seen in the Python version. However, I used cv::Mat::setTo on a copy of the original image and set whatever was not part of the final mask to 0. This is the same thing as performing an element-wise multiplication in Python.

#include <opencv2/opencv.hpp>

using namespace cv;

int main(int argc, char *argv[])
{
    // Read in the image
    Mat img = imread("spots.png", CV_LOAD_IMAGE_COLOR);

    // Convert to black and white
    Mat img_bw;
    cvtColor(img, img_bw, COLOR_BGR2GRAY);
    img_bw = img_bw > 5;

    // Define the structuring elements
    Mat se1 = getStructuringElement(MORPH_RECT, Size(5, 5));
    Mat se2 = getStructuringElement(MORPH_RECT, Size(2, 2));

    // Perform closing then opening
    Mat mask;
    morphologyEx(img_bw, mask, MORPH_CLOSE, se1);
    morphologyEx(mask, mask, MORPH_OPEN, se2);

    // Filter the output
    Mat out = img.clone();
    out.setTo(Scalar(0), mask == 0);

    // Show image and save
    namedWindow("Output", WINDOW_NORMAL);
    imshow("Output", out);
    waitKey(0);
    destroyWindow("Output");
    imwrite("output.png", out);
}

The results should be the same as what you get in the Python version.

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