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gt_im_s's Introduction

Description

Implementation of segmentation methods integrated as described in [1].

References

[1] A. Chakraborty, J. S. Duncan. Game-Theoretic Integration for Image Segmentation. IEEE Transactions on pattern analysis and machine intelligence, 1999.

[2] X. Wang, H. Wang. β€œAn extended ICM algorithm for range image segmentation.” ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004. nr 1 2004 p. 98-102

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gt_im_s's Issues

Example of usage as a python library needed

Hey! The project seems to be superb and really impactful! I want to use this library in my own project. I'd love to have it imported into my project as a normal python library

  1. Is it possible to just load a static config and use it with an image to get an output mask? Something like this:
import gt_im_s

gts = gt_im_s.load_config("default_config.yaml")
mask = gts.generate_mask(image)

Where the default_config.yaml file contains all the needed configs to run the mask generator, the input image is a numpy.ndarray with shape for example (100, 150, 3) which are the image dimensions and the three RGB canals, and the output mask is a similarly (100,150) shaped numpy.ndarray which contains 0 or 1 (or eventually 255) values that correspond to the mask itself. If so, what the default_config.yaml should contain to work correctly on for example this image?
image


  1. I also want to ask if there is a possibility to use this library as mentioned above to remove hair from the image. So it's something like this:
import gt_im_s

gts = gt_im_s.load_config("default_config.yaml")
image_without_hair = gts.remove_hair(image)

Where all the things are the same as previously and the image_without_hair is a numpy.ndarray with the (100, 150, 3) shape and the same structure as the input image

I'd love to have it working like this. If not, how can I make this project work smoothly and similarly to the two flows mentioned above?

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