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I need a python code(preferably in Tensorflow) for image random masking. I am working on a problem to mask random patch(s) for the input image and then unmask the image.
Here is the reference as an example.
https://i.stack.imgur.com/MXehy.jpg
If anyone have it, please share the code.
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Is there is any method to convert any thicked hollow obj.stl to filled obj.stl,,,,... Using python
Is any method to convert open3d mesh object from point cloud to stl file
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I'm trying to compare frames of a video and to find out the similarity of the frames.
The result should be something like a percentage
Do you have any idea guys?
Thanks
I would use ffmpeg-python to extract both videos as an image sequence and get general video stats, then use Pillow to compare each pixel for each frame individually.
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I have been using the Simple Blob Detection algorithm from the OpenCV library (for Python) for a research project. I would like to reference this particular method algorithm in my paper.
Does anyone know from where this method is from and indicate me a good to reference to cite? The openCV source code does not refer to any particular literature.
Thanks
It uses the Connected-component labeling algorithm.
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I am looking for an implementation of Continuous Wavelet Transform for Python that includes Haar Wavelet.
I would like to reproduce the experiment given by MathWorks for Matlab, at this link.
I tried with Pyscellania but I obtain completely different coefficients.
Is there a Python implementation of the CWT out there that includes the Haar Wavalet apart from Pyscellania?
Your request is clear.
Have you tried Pyscellania's normalised or standard Haar Wavelet?
Maybe you are just using the wrong one.
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Is there a way we can read the alphabets from an image, some kind of text recognition method using python.
What I have to do is, read some images to get the text written on them.
Please suggest.
See OCROPUS
EDIT:
Also after seeing the other question here (of which this question is a duplicate), pytesser is much simpler if your image only contains text and no noise (images/illustrations)