Backend developers and product managers
Designing an automated photo curation pipeline for a mobile app that needs to select and rank user-uploaded images.
Use cases
About
The Photo Burst mind map template details a 4-station image processing pipeline for automated photo selection and ranking, covering Inputs, Stations, Output, and Form Validations with 139 nodes. Designed for developers and QA teams, this Photo Burst template maps the workflow from extracting images from S3 to delivering ranked results in XML. Key nodes include 'Robot Station' for resizing at 700x700, 'Content Station : Categorisation' for scene selection, and 'Content Station : Ranking' for ordering best photos. The Photo Burst cheat sheet also specifies validation rules for each station, such as error pop-ups when no category is selected or when the 'Other, please specify' checkbox is chosen without text. This comprehensive Photo Burst mind map serves as a blueprint for building a robust image curation system.
Terms and ConditionsWhen to use this template
QA engineers and testers
Writing test cases for a content moderation system that validates image formats and enforces selection rules.
Technical writers and solution architects
Documenting a multi-station workflow for a team building a media processing service with categorization and ranking steps.
How to use this template
Step 1
Launch and Configure Input Parameters
Open the Photo Burst template in Xmind and review the Inputs branch to define your image sources and format restrictions.
Step 2
Customize Processing Stations and Logic
Modify the Robot and Content Station nodes to align the automated resizing and categorization logic with your specific workflow.
Step 3
Define Output and Validation Rules
Update the Output branch and Form Validations section to specify the XML structure and error handling for your image pipeline.
Frequently asked questions
It outlines an automated image processing pipeline for selecting and ranking best photos from a batch, including input extraction, format validation, resizing, categorization, and ranking.
Currently only JPG is supported. Formats like .bin, .db, .MOV, .NEF, and .tif are invalid and will trigger an error.
The Robot Station uses Media-Engine to resize images to a 700x700 ratio, generating a new resized image URL for further processing.
If no best photo is found, the user can select 'Did not select any, none are good' and submit; the task will be completed.
Yes, the Form Validations section details error pop-ups for each station, which can be customized in Xmind to match your application's logic.
The template is available on Xmind marketplace; you can open and edit the .xmind file in Xmind desktop or web to adapt it to your project.
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