This is part 2 of my attempt to use an Accord.NET Decision Tree to classify Lego set themes using F#. Just as a reminder, the original data source is Rebrickable. You can find Part 1 here.

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It is time for an exploration into using a Decision Tree to classify Lego set themes using F# and Accord.NET.

First things first, the data. Rebrickable has downloadable datasets for Lego sets and pieces. I’ll use the sets.csv file as the primary dataset driver, but will grab information from sets_pieces.csv, pieces.csv, and colors.csv for feature creation. The files are not in an format appropriate for a decision tree, so some transformations will need to happen first. I don’t want the post to get too long, so this project will be broken into two components. Part 1 will be building the feature file and getting the data into the desired comsumable format, Part 2 will actually use the file to get to the end goal.

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This is a sample of how to apply edge filters to a set of images using F# and Accord.NET. The framework provides several Filters for image manipulating. Since I’m interested in edge enhancement I’ll limit my scope to those filters. In particular I’ve selected the DifferenceEdgeDetector().

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This is a quick sample using F# and Accord.NET to do face detection. The method uses the provided Haar-like feature detection. The results aren’t particularly good, but for little effort it’s an ok start. At a minimum, it does reasonably well at detecting potential regions of interest. For the test images, the best improvements were found when constraining the min/max range based on the known sizes of faces in the pictures.

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Primarily, codesuji.com is an experiment. As time progresses, it (like code) will flow in several directions. For the moment, my immediate focus is to bring practical and interesting examples of F# and functional programming to the masses (that’s you).