currently, output is set both at DNNModel.set_input_output and
DNNModule.execute_model, it makes sense that the output name is
provided at model inference time so all the output info is set
at a single place.
and so DNNModel.set_input_output is renamed to DNNModel.set_input
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
different backend might need different options for a better performance,
so, add the parameter into dnn interface, as a preparation.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
currently, the model outputs the rain, and so need a subtraction
in filter c code to get the final derain result.
I've sent a PR to update the model file and accepted, see at
https://github.com/XueweiMeng/derain_filter/pull/3
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Steven Liu <lq@chinaffmpeg.org>
so, we can make a filter more general to accept different network
models, by adding a data type convertion after getting data from network.
After we add dt field into struct DNNData, it becomes the same as
DNNInputData, so merge them with one struct: DNNData.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
Add the support of dehaze filter in existing derain filter source
code. As the processing procedure in FFmpeg is the same for current
derain and dehaze, we reuse the derain filter source code. The
model training and generation scripts are in repo
https://github.com/XueweiMeng/derain_filter.git
Reviewed-by: Steven Liu <lq@onvideo.cn>
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
Remove the rain in the input image/video by applying the derain
methods based on convolutional neural networks. Training scripts
as well as scripts for model generation are provided in the
repository at https://github.com/XueweiMeng/derain_filter.git.
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
