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A framework for generic pattern recognition pipelines.
Add possibility to set a working directory for processing.
Maybe just stick with single channel?
Keep processing the same, just split up on every channel?
The old code relies on a template function to create numeric ranges, this would also be cool to have.
Maybe go for boost instead to lower dependencies?
Add support for different machine learning implementations / libraries (e.g. caffe) and the ability to opt them in in the build config
Currently everything is handeled by a single, large CMakelists.txt file.
Might be better if every module gets its own file.
By now options are bound to one character. Longer options would be beneficial for more parameters.
Auto generated log messages would be cool, in case you're processing an empty matrix or stuff like that.
Slower, but increased accuracy?
Configuration could be done via a json file or something similar, the file / folder to process as well as the output summary should be passed as parameters.
Only some steps take an actual mask, where as some take two separate arguments. Therefore "param" would be a more convenient name.
A class to shuffle data for input to the SGD classifier.
Each config class could have its own import method to parse config data from a JSON file.
The respective fields would be stored within a separate node given the same name as the respective pipeline step.
If values are missing default values should be used.
Better visualization of progress.
Use libProgress
Add possibility to store configs as json in order to export hard coded configs.
Provide a separate configuration for global pipeline options like output directories for descriptors, labelfiles etc.
Config classes could provide a create method which returns a new correct configured PipelineStep object.
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