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How to get predicted target point from result and source point #11
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Also your paper said that "A keypoint is considered to be matched correctly if its predicted location is within a distance of α · max(h, w) of the target keypoint position". So i don't know why you code compare with source points instead of target points. |
Hi, this is just a matter of terminology. For consistency with the
ProposalFlow paper that uses inverse warping we also transform the points
from the second image (target) to the first image (source). In the paper we
explain it in the opposite way because it's more natural. But remember
"source" and "target" are just two names. Sorry for the confusion.
Le lun. 1 juil. 2019 à 19:43, binhmuc <notifications@github.com> a écrit :
… Also your paper said that "A keypoint is considered to be matched
correctly if its predicted location is within a distance of α · max(h, w)
of the target keypoint position". So i don't know why you code compare with
source points instead of target points.
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Thank for you reply :) |
Please see the explanations about inverse warping here: https://www.cs.unc.edu/~lazebnik/research/fall08/lec08_faces.pdf this should help you understand! |
so do you understand his means? I'm also confused this opinions. |
@lixiaolusunshine yes, i understood him. Clearly that, the paper said that source points to target points, but in the source code is totally inverse. |
so in his paper he got the estimated inverse affine parameters from the featuregression layer, then use this inverse mapping to warp the source image into the target image? |
@lixiaolusunshine sorry, i cannot catch up your mean. In his paper, very clear that, use GMM, find a list of parameters, from parameters => warp => loss. |
@binhmuc Link about inverse points method is broken.
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Thank for your source code !
Could you tell me how to get target point from pretrained model and the source point.
I looking for "eval_pf.py" but look like you get source point from target point...
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