Comments (1)
Hi,
In order to save disk space I use sparse format to store the localization cues.
Every column of "datafile['id_cues']" encodes a location (2nd and 3rd value) in an image, which was marked by a certain semantic class (1st value).
If you want to restore localization cues in a dense form you should do something like this:
sparse_mask = datafile['id_cues']
dense_mask = np.zeros((21, 41, 41)) # dense_mask[i] represents segm. mask for class i
dense_mask[sparse_mask[0], sparse_mask[1], sparse_mask[2]] = 1.0
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Related Issues (19)
- Upload images HOT 1
- How to Link Eigen, cannot install crf wrapper, through pip install CRF/ HOT 1
- Code to generate the localization_cues.pickle HOT 5
- Pylayer import error
- Could you provide the evaluation code? HOT 1
- generate the localization_cues.pickle
- Failed to install CRF HOT 1
- protobuf2.6.1
- Global Weight average pooling in pytorch
- Pretrained Caffe Model
- Training HOT 1
- Installation issue: libXdmcp.so.6: cannot open shared object file HOT 1
- Theano GPU ID and Caffe GPU ID must be the same HOT 2
- training error HOT 3
- Recommended caffe version? / Incompatible protobufs HOT 5
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- Train localization cues for different datasets HOT 9
- Some questions about training which could be helpful for all
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