If erosion and dilation aren't what you're looking for, then my approach would be to apply a flood fill to areas connected to the borders to create a mask of just the closed off black boxes. Then apply that mask to the original image to mask out the black boxes.
I had some extra time at work so I whipped up a quick and dirty implementation of my comment above:. HI, there is the other code which is used findContours. I don't know which one is more effictive. Asked: Tricky image segmentation in Python. RGB2Gray::operator causing segmentation fault when using Python.
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 m trying to fill holes for a chessboard for stereo application. The chessboard is at micro scale thus it is complicated to avoid dust Thus, the corners detection is impossible. Here is a function that replaces the color of each pixel with the color that majority of its neighbor pixels have.
Note: Since this function replace the color of corner pixels too, you can sue cv2. Learn more. Asked 3 years, 6 months ago.Coding Challenge #144: 2D Black Hole Visualization
Active 1 year, 9 months ago. Viewed 1k times. Kasramvd Active Oldest Votes. Kasramvd Kasramvd Thanks for your interesting results! Perhaps i could erode after your correction? I thought to a median filter but it conducts to the same problem. I think There is a way for refusing that by finding the corners and escaping the operation on them.
Yep i cant detect the corners with "cv2.In this tutorial we will learn how to fill holes in a binary image. Consider the image on the left in Figure 1. In this tutorial the circular region that contains the coin will also be referred to as the foreground. Notice that the boundary of the coin is dark and distinct from its white background. So, we use simple image thresholding to separate the boundary from the background.
In other words, we say pixels with intensities above a certain value threshold are the background and the rest are the foreground.
The center image shows this thresholded image black represents background, and white represents foreground. Unfortunately, even though the boundary has been nicely extracted it is solid whitethe interior of the coin has intensities similar to the background.
Therefore, the thresholding operation cannot distinguish it from the background. How do we fill all pixels inside the circular boundary with white? There is no imfill function in OpenCV, but we can surely write one!
The idea is rather simple. We know the pixel 0,0 is connected to the background. So we can extract the background, by simply doing a floodfill operation from pixel 0, 0. Pixels that are not affected by the floodfill operation are necessarily inside the boundary. The flood-filled image when inverted and combined with the thresholded image gives the foreground mask! There are other ways to solve the same problem. One way is to use morphological close operation. However, for morphological operations to work you will need to know the maximum size of the hole.
Another way is to use findContours to find the contours and then fill it in using drawContours. I prefer the simplicity and speed of the technique described in this post. If you liked this article, please subscribe to our newsletter to download all code and images used in this post. You will also receive a free Computer Vision Resource guide. Subscribe Now.
Skip to primary navigation Skip to main content Skip to primary sidebar Figure 1. Left : Image of a nickel. Center : Thresholded and inverted. Right : Holes filled. Figure 2. Read in the image. Threshold the input image to obtain a binary image.
Flood fill from pixel 0, 0. Notice the difference between the outputs of step 2 and step 3 is that the background in step 3 is now white.Documentation Help Center. In this syntax, a hole is a set of background pixels that cannot be reached by filling in the background from the edge of the image. In this syntax, a hole is defined as an area of dark pixels surrounded by lighter pixels.
To use this syntax, BW must be a 2-D image. Press Backspace or Delete to remove the previously selected point. Shift-click, right-click, or double-click to select a final point and start the fill operation. Press Return to finish the selection without adding a point. Linear indices identifying pixel locations, specified as a numeric vector or 2-D numeric matrix of positive integers. If locations is a p -by-1 vector, then it contains the linear indices of the starting locations.
If locations is a p -by- ndims BW matrix, then each row contains the array indices of one of the starting locations. Data Types: single double int8 int16 int32 int64 uint8 uint16 uint32 uint64 logical.
Pixel connectivity, specified as one of the values in this table.
The default connectivity is 4 for 2-D images, and 6 for 3-D images. Pixels are connected if their edges touch. The neighborhood of a pixel are the adjacent pixels in the horizontal or vertical direction. Pixels are connected if their edges or corners touch. The neighborhood of a pixel are the adjacent pixels in the horizontal, vertical, or diagonal direction.
Pixels are connected if their faces touch. The neighborhood of a pixel are the adjacent pixels in:. Pixels are connected if their faces or edges touch. Pixels are connected if their faces, edges, or corners touch. A combination of three directions, such as in-right-up or in-left-down.
For higher dimensions, imfill uses the default value conndef ndims BW ,'minimal'.
Connectivity can also be defined in a more general way for any dimension by specifying a 3-byby The 1 -valued elements define neighborhood locations relative to the center element of conn. Note that conn must be symmetric about its center element. See Specifying Custom Connectivities for more information. Data Types: double logical. Note that if you choose the generic MATLAB Host Computer target platform, imfill generates code that uses a precompiled, platform-specific shared library.
Use of a shared library preserves performance optimizations but limits the target platforms for which code can be generated.Click here to download the full example code or to run this example in your browser via Binder.
We fill holes i. Erosion expands the minimal values of the seed image until it encounters a mask image. Thus, the seed image and mask image represent the maximum and minimum possible values of the reconstructed image. Now we need to create the seed image, where the minima represent the starting points for erosion.
To fill holes, we initialize the seed image to the maximum value of the original image. Along the borders, however, we use the original values of the image.
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These border pixels will be the starting points for the erosion process. We then limit the erosion by setting the mask to the values of the original image. As shown above, eroding inward from the edges removes holes, since by definition holes are surrounded by pixels of brighter value.
Finally, we can isolate the dark regions by subtracting the reconstructed image from the original image. Alternatively, we can find bright spots in an image using morphological reconstruction by dilation. Dilation is the inverse of erosion and expands the maximal values of the seed image until it encounters a mask image.
Since this is an inverse operation, we initialize the seed image to the minimum image intensity instead of the maximum. The remainder of the process is the same. Total running time of the script: 0 minutes 0.
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Gallery generated by Sphinx-Gallery. Docs for 0. Note Click here to download the full example code or to run this example in your browser via Binder. We start with an image containing both peaks and holes: import matplotlib. Created using Bootstrap and Sphinx.At the moment I use cv::floodFill to fill holes in binary images. So, if I have the following image These images are examples produced with a painting tool.
I can't think of a possible solution, so this is a shot in the dark. PedroBatista you can recover your old user account see. How about watershed filling as explained in this tutorial? It would respect the outer boundaries and only fill up the inner circles. Other problem is that I am running on low CPU resources Using cortex A9 boards to process a stream of visual data and all the steps necessary to use watershed are just too heavy for my system capabilities.
If there are multiple holes in a blob, verify if one of them is enclosed by the others, in which case do not fill. Easy to say, less easy to do Nice comprehensive answer, a good guide for those who struggle to use these kind of operations. However, it doesn't apply to my problem, the size and shape of the holes is totally arbitrary. Pedro Batista ok the size and shape of the holes is totally arbitrary but it should be a rule, a principle, a key, a specification, something?!?
What is it? I've been thinking about that, and thought that the specification that may work is the following:. If the things you're trying to fill are consistently round as in your example, you can use findContours and use the returned hierarchy to only look at internal contours.
Check them for circularity and only fill those that are circular enough. Asked: Boundary detection-What function will be the best for this? Do all opencv functions support in-place mode for their arguments? What is the most effective way to access cv::Mat elements in a loop? Sobel derivatives in the 45 and degree direction. Saving an image with unset pixels. How to enable vectorization in OpenCV?
Filling holes in an image using OpenCV ( Python / C++ )
First time here? Check out the FAQ!HelloHow can I fill enclosed areas through openCv. Have you got contour hierarchy? This code is helpfull but if we have a big table shape with several rows and cols in image ,as you know table is a big contour and this code make all of table filled and contents of table is unreachable. Asked: Filling out elements of a cv::Mat manually.
Filling elements 2 the triangle problem. Problem whit fillConvexPoly bad fill. Detect spaces and fill with rectangle. How to fill closed contours of edged text in Python. First time here? Check out the FAQ! Hi there! Please sign in help. How to fill enclosed area. You can use fillcontour and hierarchy or search for connectedComponents with negative image. Question Tools Follow. Related questions Filling out elements of a cv::Mat manually resize an image fill the rest with color Largest enclosed area Filling elements Filling elements 2 the triangle problem Problem whit fillConvexPoly bad fill Detect spaces and fill with rectangle Fast filling of a region Fill zone with texture How to fill closed contours of edged text in Python.
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