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Howto:Processing d-tpp using Python: Difference between revisions
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Howto:Processing d-tpp using Python (view source)
Revision as of 21:13, 28 November 2017
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[[File:KSFO-28RILS.png|thumb|Screenshot showing the scraped, converted and transformed approach chart for KSFO 28R (aspect ratio doesn't matter for machine learning purposes, i.e. we can use random scaling/ratios here to come up with artificial training data).]] | [[File:KSFO-28RILS.png|thumb|Screenshot showing the scraped, converted and transformed approach chart for KSFO 28R (aspect ratio doesn't matter for machine learning purposes, i.e. we can use random scaling/ratios here to come up with artificial training data).]] | ||
[[File:Merged-charts.png|thumb|Simplified navigation charts merged into a single texture]] | |||
== Idea == | == Idea == | ||
if processing actual PDFs to "retrieve" such navigational data procedurally is ever supposed to "fly", I think it would have to be done using OpenCV runnning in a background thread (actually a bunch of threads in a separate process), i.e. using machine learning - basically, feeding it a bunch of manually-annotated PDFs, segmenting each PDF into sub-areas (horizontal/vertical profile, frequencies, identifier etc) and running neural networks. | if processing actual PDFs to "retrieve" such navigational data procedurally is ever supposed to "fly", I think it would have to be done using OpenCV runnning in a background thread (actually a bunch of threads in a separate process), i.e. using machine learning - basically, feeding it a bunch of manually-annotated PDFs, segmenting each PDF into sub-areas (horizontal/vertical profile, frequencies, identifier etc) and running neural networks. | ||