Building high-perf image processing pipeline to create vernacular catalogs

Preview: Catalog for Hindi and English created on run-time.

Why an async pipeline for processing images?

A good rule of thumb is to avoid api requests which run longer than 300ms. ~ Experience

Redis Push and Pop operation on Google’s n1-standard-2.
Dashboard showing the state of workers and job

Implementation with Python and Redis

Architecture diagram in GCP.
{
"id": 1,
"style": {
"fill_color": "white",
"stroke_color": "black",
"x": 512,
"y": 900
},
"title": {
"hindi": "जोकर",
"english": "Joker"
},
"fonts": {
"hindi": "NotoSans-Bold.ttf",
"english": "NotoSans-Bold.ttf"
}
}
Image’s title showing Hindi and English script based on the above mentioned config.

Sample Project on Docker

Other use-cases for Image processing pipeline

Example of image processing in Native Ads and OTT thumbnail

Further Reading

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