Hey everyone, been lurking here for a bit but had to share a project that just saved our team *countless* hours. You know me—always knee-deep in migrating data between CRMs like Salesforce, HubSpot, and Zoho. Well, I applied that same "automate the pain away" RevOps mindset to our burgeoning Midjourney image library, and it's been a game-changer.
We've generated thousands of images for various marketing campaigns and concepts. The problem? Our internal database was a mess of vague filenames like `mj_v7_forest_01.png`. Finding anything specific was pure guesswork. We needed proper captions and searchable tags, but doing it manually for each image was a non-starter.
So, I built a Python script that uses the Google Cloud Vision API to analyze each generated image. It automatically writes a descriptive caption and generates a set of relevant tags. It then writes all this metadata into a CSV and even renames the image file with a consistent structure. The whole process runs on a schedule, watching our output folder.
Here’s a quick look at the core logic flow:
* **Ingestion:** Script picks up new images from our designated Midjourney download folder.
* **Analysis:** Sends each image to Cloud Vision for label and object detection. We're specifically using the `LABEL_DETECTION` and `WEB_DETECTION` features.
* **Processing:** Parses the JSON response to build a natural language caption (prioritizing strongest labels) and a list of tags.
* **Output:** Creates/updates a master spreadsheet with columns for Filename, New Filename, Auto-Caption, Tags, and Date. Renames the file to something like `campaign_concept_autumn_forest_cyberpunk_20250415.jpg`.
The results are shockingly good! For a fantasy cityscape, it gave us tags like "futuristic," "spires," "neon lights," "aerial view." It's not perfect—sometimes it misinterprets abstract styles—but it's about 90% accurate, which is enough to make our library searchable. It’s like finally having a clean, segmented database after a messy CRM migration. The relief is real!
Next, I'm thinking of feeding these captions back into Midjourney as prompts for variations. Has anyone else tried building pipelines around their generated assets? I'd love to compare notes, especially on handling batch processing for large libraries.
Hopefully last migration,
Interesting. Have you considered tying the output to a ticketing system? Like automatically creating a ticket in your help desk if the captioning process fails for a batch, so it doesn't just go silent.
That's a great point - integrating with the ticketing system is a smart escalation path for failures. It turns a one-off script into a monitored process.
My only caveat would be to think about threshold alerts to avoid ticket spam. Maybe it only creates a ticket if more than 10% of a batch fails, or if the same error repeats three times. Otherwise, a simple Slack notification to the marketing ops channel might suffice for smaller hiccups.
Have you set up something similar? I'm curious about the trigger logic people use.
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Oh, this is fantastic! I've been down a similar road, but migrating product image metadata between our old on-prem DAM and a new cloud one. The "automate the pain away" mindset is everything.
Using Google Cloud Vision for generated images is clever - I found its label detection can get a bit... surrealist with AI art. Sometimes it tags a fantasy creature as "reptile, dragon, mythology" and other times it just confidently says "animal, wildlife, bird" for the same thing. Did you have to add any post-processing logic to filter or standardize the tags, or do you just take the API's output as-is?
Also, totally feel you on the filenames - `mj_v7_forest_01.png` tells you nothing! Renaming with a consistent structure was a lifesaver for us too. Did you embed the generated metadata back into the image files' EXIF data, or is it living separately in that CSV? We started with CSV but then lost track of which file belonged to which record after a server move 😬
Backup first.
You're hitting on the exact reason I don't trust these "magic box" APIs for anything beyond a rough first pass. The inconsistent tagging for the same subject is a feature, not a bug - it reveals how brittle the underlying model is when faced with non-photographic content. Trusting that output as-is just replaces one messy taxonomy with another, albeit a more granular one.
Embedding in EXIF sounds good in theory until you realize most DAMs and CMSs strip that data on upload or transformation, or simply can't read it at scale. A CSV is a single point of failure, as you found out. The only sane way is a separate, versioned metadata store keyed to a hash of the image file itself, not its mutable filename. But then you're building a whole system, not just a script.
Buyer beware.
Yeah, the surrealist tags from Vision are definitely a thing! I didn't do much post-processing, just took the top 5 labels. For AI art, maybe the inconsistency is okay? It still gives you more search hooks than `mj_v7_whatever.png`.
We kept the metadata in a separate JSON file keyed to the new filename. I was scared of messing with EXIF and having a CSV get lost, like you said. How did you solve that after your server move? Did you switch to a database?