Comments (2)
Hi,
There are ways to do foreground segmentation (finding pixels of the salient object in the image) using computer vision. If you are willing to click on object of interest you can use GrabCut (https://github.com/opencv/opencv/blob/master/samples/python/grabcut.py). If you want a more automated solution, you can take a look at this. This is slightly dated. So, may be look at something a little more new: this. I'll recommend using Bilinear pooling for cleaning masks. Don't worry about the .pbm extension. Once you get a binary mask as an image you can just store it as a pbm using OpenCV. Hope this helps.
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Thank you very much! I'll have a try.
from syndata-generation.
Related Issues (18)
- could you please share some example training data and folders? HOT 1
- Error when running with defaults. HOT 7
- "distractor_files" variable referenced before assignment HOT 1
- ## local variable 'attempt' referenced before assignment HOT 5
- 3D Rotation HOT 1
- Error in line 451. unbound local error HOT 2
- create_mask function is not defined HOT 3
- mask process HOT 8
- ".pbm" and "Foreground/Background segmentation:" HOT 4
- error discription
- Do you mean the masks like "NP123.pbm" in demo_data_dir/robjects_dir/honey_bunches ?
- what is the dataset Type? HOT 3
- background images HOT 2
- unable to reproduce results HOT 3
- Applicability of code in 2D images and backgrounds
- Only uses one background at a time
- Demo Data Directory Structure
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