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Intelligent Flying Machines

I ran this pipeline on the two images provided, and also created two modified images to experiment on: a rotated version of the low resolution image, and a flipped version of the low resolution image with two additional labels placed onto the box.

The pipeline returns the annotated images; the label is normalized and outlined in green.

High res Low res Low res, rotated Low res, modified

Scripts and functions -- contains the main functions for finding and annotating labels -- a script to input parameters and run the functions in -- a script to input parameters and run the functions in -- contains functions for displaying trackbars and updates the window with parameter changes -- a script to run; example images were input here


These scripts and functions have been tested on OpenCV 3.2.0, OpenCV 3.1.0, Python 3.5.2, and Python 3.6.1.


Currently, and do output the locations, orientations, perimeter, and area of the labels, but I need to return them from, preferably in an object or list format for any additional scripts. Also, there is currently no calculation for confidence in the label prediction; my current idea for implementation is to use template matching to calculate correlation between all the labels. This will work for an image with many labels, but won't be useful when there is only one label in the image. I could also find if there exists barcodes/text lines in the labels by looking where they should be and see if darker values appear, but this seems like it won't produce a robust metric.

Other stuff

You can find more about me on my website,

Thanks to ferret-guy for putting this repo online so that I could clone it just hours after IFM removed the repo!


CV challenge for Intelligent Flying Machines



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