Closed samihormi closed 1 year ago
Hi @samihormi, thanks for sharing this, I will have a look asap! Have you tried to reproduce the results on one of the public datasets, such as Occluded-Duke? Do you have some visualization of the resulting masks to share?
Hey @VlSomers, I did reproduce the labels for Occluded-ReID, which I used as "ground-truth", to generate masks as visually close as possible to yours. I obviously don't get the exact same masks but overall, they seem to get the job down — at least visually. I put down some examples for your reference with my masks above and your masks below for comparison. Also, since I guessed my way into "reverse-engineering" your approach to get the masks — using the bpbreid and PifPaf papers — there are certainly filtering steps I have omitted, please check out the code and rectify where deemed necessary.
occluded_body_images/001/001_01.tif
whole_body_images/034/034_03.tif
occluded_body_images/131/131_01.tif
Hi @samihormi , thank you for sharing these visualizations, can you update the README file and revert all the small changes you made, to only keep the added section "### Generate human parsing labels"? I will merge your PR after that
Can you also provide more information on how you choose the segmentation mask to apply? If I'm not wrong, you just pick the first one returned by maskRCNN: how do you know it is a person and not another class?
Hi @samihormi , any update about previous questions?
Hey @VlSomers, yes I have changed the logic of the segmentation mask generation following your comment.
Initially, I was assuming that all input is "human" and I just took the first identity generated (corresponding to the highest score). Now, the code filters masks to keep only humans and takes the centre-most human(x,y) as the mask — it's still different from the paper but close enough.
Also, I tweaked the mask. extension of PifPaf for the _OccludedreID dataset from ".jpg.confidence_fields.npy" to ".tif.confidence_fields.npy". which is the correct extension
In get_labels.py: The value of "filtered_masks" could be None because 'cls!=0'
@ellzeycunha0 Could you give me the whole error log and the dataset configuration on which you tried the code.
@samihormi
Processing: 0%|▍ | 225/79927 [00:14<1:09:42, 19.06batch/s]
File "new_get_label.py", line 521, in
"zip(...)" could be None.
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