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Convert rle to polygon segmentation asked 2 years, 8 months ago modified 1 year, 8 months ago viewed 8k times Suggestion #2 is a simple scheme to improve speed if there are lots of duplicates, and #3 is a good starting point into better compression. I used azure data labelling to prepare my semantic segmentation (preview) dataset and export it from azure into my local system

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Now i have images in images folder and labels in json file D[t] is the distance at time t (and the timespan of my data is len(d) t. I see many great solutions here but none that feels very pythonic to my eyes

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So i'm contributing with a implementation i wrote myself today for this problem

From typing import iterator, tuple from itertools import groupby def run_length_encode(data Returns run length encoded tuples for string # a memory efficient (lazy) and pythonic solution using. In this context, you should consider elias gamma coding (or some variant thereof) to efficiently encode your run lengths A reasonable first approximation for your encoding format might be

First bit = same as the first bit of the uncompressed string (to set initial polarity) remaining bits You can draw the mask on a canvas and then export the image if you need For the actual drawing you can use two approaches Decode rle into binary mask (2d matrix or flattened) and then paint pixels according to that mask draw mask directly from rle string on a virtual canvas and then rotate it by 90deg and flip horizontally here's the example.

To create a coco dataset of annotated images, you need to convert binary masks into either polygons or uncompressed run length encoding representations depending on the type of object

Int64 snappy do:0 fpo:4 sz:3040269/5671813/1.87 vc:782499 enc:bit_packed,plain,rle,plain_dictionary my question How does parquet determine what encoding type to use and what could have made parquet choose different encoding Is it something we can control using a hive / spark config? In a pylab program (which could probably be a matlab program as well) i have a numpy array of numbers representing distances