Since the previous dev log, I have been focused on getting Blur Stuff ready for deployment. This ended up being much more painful than I expected.
The biggest part was dealing with dependency issues. The project worked locally, but moving everything to a production environment exposed a bunch of problems with Python versions, PaddlePaddle, PaddleOCR, PaddleX, PyYAML, spaCy, OpenCV and other dependencies. I spent a lot of time testing different configurations, fixing dependency conflicts, debugging the production container, and figuring out why things that seemed to work okay locally were breaking during deployment.
The biggest headache was PaddleOCR. I had to deal with missing system libraries, model downloading, Paddle initialization issues and eventually a oneDNN runtime error that was causing OCR requests to return 503s. After a lot of debugging and testing different configurations, I eventually got the OCR pipeline working properly in production. That was probably the one of the most frustrating parts of the entire project, but it is finally done.
I also switched the spaCy model from en_core_web_lg to en_core_web_sm. The larger model was unnecessarily heavy for what Blur Stuff needs and was making deployments take way longer. Switching to the smaller model made deployment faster and also improved performance without having a noticeable impact on the PII detection quality. After successful deployment I finished the README and added the final assets, screenshots, logo and documentation. I added the MIT license, favicon, deployment related changes and generally cleaned up the entire repository.
At this point, I think Blur Stuff is ready to ship. There are definitely still improvements I could make, especially improving performance, making video anonymization more reliable and efficient, and polishing various parts of the application but I don’t want to spend more time trying to make everything perfect. The current deployment is also running on Railway’s free plan, so there are some resource and memory limitations since Blur Stuff runs several machine learning models in the same process. For now, I’m okay with that rather than spending more time optimizing around the limitations of a free deployment.
For now, I’m calling it finished and shipping it since the core functionality is working and the project is deployed. I’ll definitely come back to Blur Stuff later to improve it, but I think it’s time to move on to something new.