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Category: Erosion operation python

Erosion operation python

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to use scipy to do erosion and dilation of an image. However, the output is not at all what is expected. I believe that I must specify a better kernel or mask but am not really sure why I am getting a green output for erosion and completely white output for dilation.

I was using the binary erosion instead of the grey erosion array. You have two problems: as noted in the comment by theta, binary ops expect input consisting only of 0 and 1. The second issue is the nd in ndimage you supply in an array of shape nx, ny, 3. The last length-3 axis is considered to be a third spatial dimension, not the three color channels. Learn more. Image erosion and dilation with Scipy Ask Question.

erosion operation python

Asked 7 years ago. Active 6 years, 6 months ago. Viewed 14k times. Here is my basic code: import scipy from scipy import ndimage import matplotlib.

Paolo Nick Nick 8, 30 30 gold badges 87 87 silver badges bronze badges. Active Oldest Votes.Morphological operations are a set of operations that process images based on shapes. They apply a structuring element to an input image and generate an output image. The most basic morphological operations are two: Erosion and Dilation Basics of Erosion:. The second image is the eroded form of the original image and the third image is the dilated form. Uses of Erosion and Dilation:.

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Writing code in comment? Please use ide. The most basic morphological operations are two: Erosion and Dilation Basics of Erosion: Erodes away the boundaries of foreground object Used to diminish the features of an image.

Working of erosion: A kernel a matrix of odd size 3,5,7 is convolved with the image. A pixel in the original image either 1 or 0 will be considered 1 only if all the pixels under the kernel is 1, otherwise it is eroded made to zero.

Thus all the pixels near boundary will be discarded depending upon the size of kernel. So the thickness or size of the foreground object decreases or simply white region decreases in the image. Basics of dilation:. Python program to demonstrate erosion and. Taking a matrix of size 5 as the kernel. The first parameter is the original image. Load Comments.Morphological transformations are some simple operations based on the image shape. It is normally performed on binary images.

erosion operation python

It needs two inputs, one is our original image, second one is called structuring element or kernel which decides the nature of operation. Two basic morphological operators are Erosion and Dilation. Then its variant forms like Opening, Closing, Gradient etc also comes into play. We will see them one-by-one with help of following image:. The basic idea of erosion is just like soil erosion only, it erodes away the boundaries of foreground object Always try to keep foreground in white.

So what does it do? The kernel slides through the image as in 2D convolution. A pixel in the original image either 1 or 0 will be considered 1 only if all the pixels under the kernel is 1, otherwise it is eroded made to zero.

So what happends is that, all the pixels near boundary will be discarded depending upon the size of kernel. So the thickness or size of the foreground object decreases or simply white region decreases in the image.

It is useful for removing small white noises as we have seen in colorspace chapterdetach two connected objects etc. Here, as an example, I would use a 5x5 kernel with full of ones.

It is just opposite of erosion. So it increases the white region in the image or size of foreground object increases.

Image Processing Class #6 — Morphological Filter

Normally, in cases like noise removal, erosion is followed by dilation. Because, erosion removes white noises, but it also shrinks our object. So we dilate it.

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It is also useful in joining broken parts of an object. Opening is just another name of erosion followed by dilation. It is useful in removing noise, as we explained above. Here we use the function, cv2.

Closing is reverse of Opening, Dilation followed by Erosion.

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It is useful in closing small holes inside the foreground objects, or small black points on the object. It is the difference between input image and Opening of the image. Below example is done for a 9x9 kernel.Click here to download the full example code or to run this example in your browser via Binder. Morphological image processing is a collection of non-linear operations related to the shape or morphology of features in an image, such as boundaries, skeletons, etc.

In any given technique, we probe an image with a small shape or template called a structuring element, which defines the region of interest or neighborhood around a pixel. Morphological erosion sets a pixel at i, j to the minimum over all pixels in the neighborhood centered at i, j. The structuring element, selempassed to erosion is a boolean array that describes this neighborhood. Below, we use disk to create a circular structuring element, which we use for most of the following examples.

Also notice the increase in size of the two black ellipses in the center and the disappearance of the 3 light grey patches in the lower part of the image. Morphological dilation sets a pixel at i, j to the maximum over all pixels in the neighborhood centered at i, j. Dilation enlarges bright regions and shrinks dark regions. Notice how the white boundary of the image thickens, or gets dilated, as we increase the size of the disk.

erosion operation python

Also notice the decrease in size of the two black ellipses in the centre, and the thickening of the light grey circle in the center and the 3 patches in the lower part of the image. Morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots i. Since opening an image starts with an erosion operation, light regions that are smaller than the structuring element are removed.

The dilation operation that follows ensures that light regions that are larger than the structuring element retain their original size. Notice how the light and dark shapes in the center their original thickness but the 3 lighter patches in the bottom get completely eroded.

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The size dependence is highlighted by the outer white ring: The parts of the ring thinner than the structuring element were completely erased, while the thicker region at the top retains its original thickness. Morphological closing on an image is defined as a dilation followed by an erosion.

Closing can remove small dark spots i. Since closing an image starts with an dilation operation, dark regions that are smaller than the structuring element are removed. The dilation operation that follows ensures that dark regions that are larger than the structuring element retain their original size. Notice how the white ellipses at the bottom get connected because of dilation, but other dark region retain their original sizes.

Also notice how the crack we added is mostly removed.

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This operation returns the bright spots of the image that are smaller than the structuring element. As you can see, the pixel wide white square is highlighted since it is smaller than the structuring element.

This operation returns the dark spots of the image that are smaller than the structuring element. As you can see, the pixel wide black square is highlighted since it is smaller than the structuring element. As you should have noticed, many of these operations are simply the reverse of another operation. This duality can be summarized as follows:.

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Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton.

It is important to note that this is performed on binary images only. As the name suggests, this technique is used to thin the image to 1-pixel wide skeleton by applying thinning successively. Again note that this is also performed on binary images. If we add a small grain to the image, we can see how the convex hull adapts to enclose that grain:.

MathWorks tutorial on morphological processing. Total running time of the script: 0 minutes 1.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I would like to know if there are any examples or cases where Opening and Closing Morphology operations on an single image produce the same results.

As an example, let's say we have an image Xand we have done opening operation to produce Y. Similarly, we have done a closing operation on the original X to produce the same Y. I would like to know if there are examples for these type of images X. Yes there are. As one small example, if you had a binary image where it consists of a bunch of squares that are disconnected and distinct. Provided that you specify a structuring element that is square, and choosing the structuring element so that it is smaller than the smallest square in the image, then doing either operation will give you the same results.

If you did an opening on this image and a closing on this image, you will produce the same results. Remember, an opening is an erosion followed by a dilation where a closing is a dilation followed by an erosion.

Erosion

In terms of analyzing the shapes, erosion slightly shrinks the area of the image while dilation slightly enlarges it. By doing an erosion followed by a dilation openingyou're shrinking the object and then growing it again.

This will bring the image back to where it was before, provided that you choose the structuring element like what we talked about before. Similarly, if you did an dilation followed by an erosion closingyou're growing the object and then shrinking it again, also bringing the image back to where it was before If you were to choose a structuring element where it is larger than the smallest object, doing an opening will remove this object from the image, and so you won't get the original image back.

Also, you need to make sure that the objects are well far away from each other, and that the size of the structuring element does not overlap any of the objects as you slide over and do the morphology operations.

cv2.erode() function in OpenCV – Python

The reason why is because if you were to do a closing, you would join these two objects together and so that won't get you the same results either! The reason why I had to create the array as uint8 in numpy is because when we want to show this image, I'm going to use OpenCV and it requires that the image be at least a uint8 type.

Now, let's choose a 5 x 5 square structuring element, and let's perform a closing and an opening with this image. We will display the results in a single figure going from left to right:. It certainly looks the same!The neighborhood connectivity.

The integer represents the maximum number of orthogonal steps to reach a neighbor. In 2D, it is 1 for a 4-neighborhood and 2 for a 8-neighborhood. Default value is 1. Parent image representing the max tree of the inverted image. The value of each pixel is the index of its parent in the ravelled array.

See Note for further details. The ordered pixel indices referring to the ravelled array. The pixels are ordered such that every pixel is preceded by its parent except for the root which has no parent. Vincent L.

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Soille, P. Salembier, P. Najman, L. Building the component tree in quasi-linear time. Carlinet, E. We create an image quadratic function with a minimum in the center and 4 additional local minima.

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Parent image representing the max tree of the image. We create an image quadratic function with a maximum in the center and 4 additional local maxima. This is the 3D equivalent of a disk.

A pixel is within the neighborhood if the Euclidean distance between it and the origin is no greater than radius.

erosion operation python

This function returns the same result as greyscale closing but performs faster for binary images. The morphological closing on an image is defined as a dilation followed by an erosion. Closing can remove small dark spots i.This article is about basic image processing. If you are new in this field, you can read my first post by clicking on the link below.

Median filter makes image structure change a lot. A figure below shows the result of applying median filter to a binary image. The small structures, single line, and dot, are removed and small size holes are filled. So in this chapter, I will introduce an idea which overcomes this problem. The idea of the morphological filter are shrink and let grow process.

The morphological operation of the binary image is described first and will talk in the following outline. The structuring elements contain only value 0 and 1.

And the hot spot of the filter is the dark shade element. The binary image is described as sets of two-dimensional coordinate point.

Some operations of point set are similar to the operation in others image. For inverting binary image is complement operation and combining two binary image use union operator.

Shifting binary image I by some coordinate vector d by adding vector d to point p. Or reflection of binary image I by multiply -1 to point p.

Properties of dilation and erosion. Note that: in erosion is in contrast to dilation, not have commutative property. In addition, erosion and dilation are duelsfor a dilation of the foreground can be accomplished by an erosion of background and subsequent of the result in two different properties but work similarity.

In morphological process, dilation and erosion work together in composite operation. There are common way to represent the order of these two operations, opening and closing. Opening denotes an erosion followed by dilation and closing work in opposite way. The opening and closing also are dual in sense that opening the foreground is equal to closing the background. Morphological Filter can also apply to gray-scale image, but in the different definition. I will describe in following outline.

In gray-scale morphology, structuring elements are defined as real-value 2D functions instead of point sets. The value in H can be negative or zero value. But it contrast to linear convolution, zero elements are used to compute the result.


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