---
title: "Seasons time-lapse - the foundations"
url: https://daily.dev/posts/seasons-time-lapse---the-foundations-jp2gaujna
source_url: https://blog.frankel.ch/seasons-time-lapse/1
type: article
source: "A Java geek"
published: 2026-05-17T17:40:52.826Z
updated: 2026-05-17T17:43:22.666Z
tags: ["python", "ai-coding", "image-processing", "opencv"]
reading_time: 3
upvotes: 0
comments: 0
language: en
---

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# Seasons time-lapse - the foundations

**[A Java geek](https://daily.dev/sources/frankel)** · 3 min read · 0 upvotes · 0 comments

## Summary

A developer with no image processing or video experience describes building a seasons time-lapse project using an LLM coding assistant. The project is a Python pipeline covering five steps: inventory (EXIF extraction), GPS-based filtering, image alignment, frame ordering by day-of-year, and video rendering. The author explains why Python was chosen over JVM (C++ backends for OpenCV and neural inference make interpreter overhead irrelevant), and introduces practical techniques like a --sample flag for faster iteration. Alignment is flagged as the hardest step and will be covered in a follow-up post.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.frankel.ch/seasons-time-lapse/1>

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---

Tags: [#python](https://daily.dev/tags/python), [#ai-coding](https://daily.dev/tags/ai-coding), [#image-processing](https://daily.dev/tags/image-processing), [#opencv](https://daily.dev/tags/opencv)

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