segmenter | Universal segmenter based on the Universal | Natural Language Processing library
kandi X-RAY | segmenter Summary
kandi X-RAY | segmenter Summary
Universal segmenter, written by Y. Shao, Uppsala University.
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Top functions reviewed by kandi - BETA
- Compute the F1 score
- Computes the similarity between two sentences
- Check if two strings are equal
- Test a gold file
- Calculate the gold score
- Compute the accuracy of the prediction
- Count the exact match between two strings
segmenter Key Features
segmenter Examples and Code Snippets
Community Discussions
Trending Discussions on segmenter
QUESTION
The MDN documentation says that Intl.Segmenter is supported in Deno version 1.8 and above. But when trying to use it, I get an error.
...ANSWER
Answered 2022-Apr-02 at 08:36It's there, but it doesn't seem to be in the type definitions, so that's why you are receiving the compiler error. You can either use a @ts- comment directive or the --no-check CLI run argument to avoid the compiler diagnostic and continue execution of your program:
example.ts:
QUESTION
I am using Google ML Kit to do selfie segmentation (https://developers.google.com/ml-kit/vision/selfie-segmentation). However, the output am getting is exteremely poor -
Initial image:
Segmented image with overlay: Observe how the woman's hair is marked pink and the gym equipment and surrounds near her legs are marked non-pink. Even her hands are marked pink (meaning its a background).
When this is overlayed on another image, to create a background removal effect, it looks terrible
The segmentation mask returned by the ML Kit has confidence of 1.0 for all the above non-pink areas, meaning its absolutely certain that the areas non-pink are part of the person!!
Am seeing this for several images, not just this one. Infact, the performance (confidence) is pretty poor for an image segmenter.
Question is - is there a way to improve it, maybe by providing a different/better model? If I use something like the PixelLib, the segmentation is way better, albeit the performance of the library is not low latency, hence can't be run on the mobile.
Any pointers/help regardig this would be really appreciated.
...ANSWER
Answered 2022-Feb-02 at 01:04It might be too optimistic to expect a lightweight real-time CPU-based selfie model to provide accurate segmentation results for a pretty complex and in a way tricky scene (pose, black color of the background and outfit).
Official example highlights the fact complex environments will likely to be a problem.
The only "simple" way of processing your scene is to use depth estimation. Just did a quick test with a pretty complex model:
Results are too far from being usable (at least in a fully automated way). There are several other options:
- Create a custom more sport-oriented model, trained on a proper dataset
- Use a heavier model (modern phones are quite capable)
- Use some reliable pose estimation in order to make sure a particular scene is selfie-compatible
QUESTION
I converted the original u2net model weight file u2net.pth to tensorflow lite by following these instructructions, and it is converted successfully.
However I'm having trouble using it in android in tensrflow lite, I was not being able to add the image segmenter metadata to this model with tflite-support script, so I changed the model and returned only 1 output d0 (which is a combination of all i.e d1,d2,...,d7). Then metadata was added successfully and I was able to use the model, but its not giving any output and returning the same image .
So any help would be much appreciated, in letting me know where I messed up, and how can I use this use this u2net model properly in tensorflow lite with android, thanks in advance ..
...ANSWER
Answered 2021-Aug-29 at 07:31I will write a long answer here. Getting in touch with the github repo of U2Net it leaves you with the effort to examine the pre and post-processing steps so you can aply the same inside the android project.
First of all preprocessing:
In the u2net_test.py file you can see at this line that all the images are preprocessed with function ToTensorLab(flag=0). Navigating to this you see that with flag=0 the preprocessing is this:
QUESTION
I am trying to run a RUTA script with an analysis pipeline.
I add my script to the pipeline like so createEngineDescription(RutaEngine.class, RutaEngine.PARAM_MAIN_SCRIPT, "mypath/myScript.ruta)
My ruta script file contains this:
...ANSWER
Answered 2021-Aug-15 at 10:09I solved the problem. This error was being thrown simply because the script could not be found and I had to change this line from: RutaEngine.PARAM_MAIN_SCRIPT, "myscript.ruta" to: RutaEngine.PARAM_MAIN_SCRIPT, "myscript"
However, I did a few other things before this that may have contributed to the solution so I am listing them here:
- I added the ruta nature to my eclipse project
- I moved the myscript from resources to a script package
QUESTION
I'm trying to build a form in HTML/Tailwind CSS/ReactJS. I have created/styled the form fine, but I seem to be having issues where the file input is not properly being centered. It appears that the element has some inherent width, but it won't center itself within that space.
I've gone ahead and created a CodePen to try and represent this issue: https://codepen.io/developerryan/pen/mdREJXo
or you can view this segment here:
...ANSWER
Answered 2021-Apr-09 at 19:27Editing the input value, in this case, is something that is usually restricted for security reasons. You can always mimic the style you want yourself though.
Written example here for your consideration:
QUESTION
I'm trying my hand at building a website from scratch to act as an online CV for networking and job/school applications. I'm very new to html and css, only started about 5 months ago. For the most part, everything has been working out just fine. The only issue is that on mobile devices the background on sections on my page are being cutoff where I would like them to trail to the end of the screen (the right side). On the desktop, it looks just fine. Any help or suggestions would be appreciated. I'm kinda at a loss on what to do.
Here is the HTML and CSS from my page:
...ANSWER
Answered 2021-Feb-26 at 23:28the root of your problem is that you have hardcoded the widths of some elements.
On mobile screens these elements are wider than the viewport or screen width, so come content extends off the right-hand side of the screen. This leads to the problem you describe as well as other issues.
This is also the reason why the problem got worse when you added the viewport meta tag
Some of the elements I noticed that have hard-coded widths that cause a problem are
QUESTION
My training images are made up of blue channels extracted from the ELAs (Error Level Analysis) of some spliced images and the labels just consist their corresponding ground truth masks.
I've have constructed a simple encoder-decoder CNN given down below to do the segmentation and have also tested it on the cell membrane segmentation task. There it performs well and creates near to ground truth images, so I guess the neural network I created is capable enough.
However, it is not working on the spliced images on CASIA1 + CASIA1GroundTruth dataset. Please help me to fix it, I have spent too many days on it trying different architectures and pre-processing on the images but no luck.
For one, it is claiming such high accuracy (98%) and low losses but the output image is so wrong. It is sort of getting the wanted mask if you look carefully but along with it there are a lot of regions splattered with white. Seems like it is not able to get the difference in the intensities of the pixels for the wanted region vs the background. Please help me fix it :(
Preparation ...ANSWER
Answered 2020-Oct-17 at 22:10Oops, I did a stupid one. In order to see what I have picked up for testing from the X array, I multiplied that array by 255 cause PIL doesn't display arrays in 0-1 range. Mistakenly, I just used the same modified variable and passed it in test/prediction.
QUESTION
I'm using the code below for segmenting the articles from an image of newspaper.
...ANSWER
Answered 2020-Oct-12 at 18:18here my pipeline. I think can be optimized.
Initialization
QUESTION
ANSWER
Answered 2020-Jul-28 at 13:23Problem
cv2.findContours uses an algorithm which has a few different 'retrieval modes'. These affect which contours are returned and how they are returned. This is documented here. These are given as the second argument to findContours. Your code uses cv2.RETR_EXTERNAL which means findContours will only return the outermost border of separate objects.
Solution
Changing this argument to cv2.RETR_LIST will give you all the contours in the image (including the one outermost border). This is the simplest solution.
E.g.
QUESTION
I have segmented and binary image of biological cells and using openCV I have extracted the areas and perimeters of the contours. I am trying to label and color with a colormap each cell according to a parameter q=perimeter/Sqrt(area) but have no idea where to even start. Essentially each cell will have a unique color according to this value.
Any help would be greatly appreciated! Here is what I have so far:
...ANSWER
Answered 2020-Jul-24 at 22:25To solve this problem, you need to collect all the q's so that you can scale them according to the observed range of q's. You can do that with a list comprehension like so:
all_the_q = [v['q'] for k, v in obj_properties.items()]
You also need to pick some colormap. I leave that as an exercise for the reader based on suggestions in the previous comments. For a quick idea, you can see a preliminary result just by scaling your q's to 8 bits of RGB.
See the complete code below. Note that index in your moment_dict is the key in your obj_properties dictionary, so the whole enumerate construct is unnecessary. I took the liberty of dropping enumerate completely. Your filtering loop picks up the correct contour index anyway. After you select your contours based on your criteria, collect all the q's and calculate their min/max/range. Then use those to scale individual q's to whatever scale you need. In my example below, I scale it to 8-bit values of the green component. You can follow that pattern for the red and blue as you wish.
Note that in this image most of the q's are in the 4.0 - 4.25 range, with a few outliers at 5.50 (plot a histogram to see that distribution). That skews the color map, so most cells will be colored with a very similar-looking color. However, I hope this helps to get you started. I suggest applying a logarithmic function to the q's in order to "spread out" the lower end of their distribution visually.
- EDIT: Replaced the primitive colormap with one from matplotlib. See https://stackoverflow.com/a/58555688/472566 for all the possible colormap choices.
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Install segmenter
You can use segmenter like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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