image-segmentation | Edge detection and morphological operators

In the end it was a simple error, I just needed to change the comand line on my terminal.

Stuff like this usually happens when you have ProGuard enabled but not correctly configured. Make sure to add appropriate rules to proguard-rules.pro file to prevent it from obfuscating relevant classes.

Information about this is usually provided by the library developers. After a quick search I came up with this example. Sources seem to be documented well enough, so that it should not be a problem to find the correct settings.

Keep in mind that you probably need to add rules for more than one library.

The easiest way to do this is by using FiftyOne to iterate over your dataset in a simple Python loop, using OpenCV and Numpy to format and write the images of object instances to disk.

For example, this function will take in any collection of FiftyOne samples (either a Dataset for View) and write all object instances to disk in folders separated by class label:

First of all, you obtain an exception because OpenCV's watershed() function expects markers array to be made of 32-bit integers. Converting it forth and back will remove the errors:

From comments

It was just a matter of version with tensorflow and keras. I looked into traceback tensorflow error messages and opened it and changed import keras to from tensorflow import keras issue was resolved (Paraphrased from z2ouu).

Thank you for your feedback. The R&D team confirms that the version 3.0.0.301 is faulty. Therefore, it is recommended that you use an earlier version of the ML kit, which has been modified in the current document.

For more details, You can refer to this Docs.

You can save the plots as a .png or .jpg files and download it from the Ubuntu server. This will help you view the plots as image file in your local system.

you can save the plots using

import matplotlib. some plot function as plt do some plotting `plt.save('path to save')

An example from Matplotlib.pyplot.savefig() in Python

The task, as you have seen, is not trivial at all. OpenCV has a segmentation algorithm called "GrabCut" that tries to solve this particular problem. The algorithm is pretty good at classifying background and foreground pixels, however it needs very specific information to work. It can operate on two modes:

• 1st Mode (Mask Mode): Using a Binary Mask (same size as the original input) where 100% definite background pixels are marked, as well as 100% definite foreground pixels. You don't have to mark every pixel on the image, just a region where you are sure the algorithm will find either class of pixels.

• 2nd Mode (Foreground ROI): Using a bounding box that encloses 100% definite foreground pixels.

Now, I use the notation "100% definitive" to label those pixels you are 100% sure they correspond to either the background of foreground. The algorithm classifies the pixels in four possible classes: "Definite Background", "Probable Background", "Definite Foreground" and "Probable Foreground". It will predict both Probable Background and Probable Foreground pixels, but it needs a priori information of where to find at least "Definitive Foreground" pixels.

With that said, we can use GrabCut in its 2nd mode (Rectangle ROI) to try an segment the input image . We can try and get a first, rough, binary mask of the input. This will mark where we are sure the algorithm can find foreground pixels. We will feed this rough mask to the algorithm and check out the results. Now, the method is not easy and its automation not straightforward, there's some manual information we will set that work particularly well for this input image. I don't know the Java implementation of OpenCV, so I'm giving you the solution for Python. Hopefully you will be able to port it. This is the general outline of the algorithm:

1. Get a first rough mask of the foreground object via thresholding
2. Detect contours on the rough mask to retrieve a bounding rectangle
3. The bounding rectangle will serve as input ROI for the GrabCut algorithm
4. Set the parameters needed for the GrabCut algorithm
5. Clean the segmentation mask obtained by GrabCut
6. Use the segmentation mask to finally segment the foreground object

This is the code:

In your approach, I think if you just change labels == cluster to labels != cluster, it should work.

However, here is another way in Python/OpenCV.

Input: