Comments (3)
GuoxiaWang
commented on June 17, 2024
edge_maps is the predicted .mat results, which contains two maps (the one is object boundary map and the other one is occlusion orientation map)
Here is the python save code:
edge_ori = {}
edge_ori['edge'] = edgemap
edge_ori['ori'] = orimap
sio.savemat( 'xxx.mat', {'edge_ori':edge_ori})
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buptlr
commented on June 17, 2024
Thank you! I get it!
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praful1993
commented on June 17, 2024
You have got the edge map from DooBNet. Then how you got the occlusion orientation map from edge map ???
Can you please explain me.
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Related Issues (10)
- About DOC's .h5 data HOT 4
- How to generate the oritentation label? HOT 5
- Is class_balanced_sigmoid_cross_entropy_attention_loss_layer.cu the GPU version of HED? HOT 2
- About the normalization of loss HOT 2
- About the evaluation method for occlusion orientation map ? HOT 3
- Back-propping via the Attention Loss weighting HOT 2
- Attention Loss implementation in TensorFlow HOT 1
- Offer edge_ori maps of previous methods for evaluation HOT 2
- Some small problems HOT 1
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