一位开发者通过集成多种AI模型和自定义代码，成功自动化了35mm胶卷的完整扫描处理流程。1原本处理每卷36张照片需耗时约4小时，包括扫描、反转、编辑等环节。1该项目耗时11天、完成64次代码提交后，他实现了从扫描到元数据标签的端到端自动化。1
该项目集合了Claude AI、LaMa修复模型、Qwen3-VL视觉模型等工具，结合VueScan软件、SANE驱动、Ollama本地推理框架及Cloudflare R2存储服务。1完成的相册包含22卷胶卷共314张照片，时间跨度至2020年。1
开发过程中遭遇多个技术难题：初期尝试让Claude直接控制扫描仪几乎导致设备损坏；1灰尘检测系统误将水面反光识别为2000个灰尘点。1通过解决这些问题，开发者最终构建了一个网页相册来展示这些胶卷摄影作品。1
A developer has created an automated workflow to process 35mm film scans, dramatically reducing the time required to prepare photographs for publication.1 What previously demanded approximately four hours per roll of 36 photographs—involving scanning, reversal, editing, and metadata tagging—has been streamlined through integration of multiple AI models and specialized software.1
The project, completed over eleven days with 64 code commits, leverages Claude AI, the LaMa inpainting model for dust removal, and Qwen3-VL for visual recognition, combined with VueScan scanning software and custom automation scripts.1 The pipeline processes the complete workflow from initial scan through final metadata assignment and web publication.1 The developer has assembled a web-based photo gallery displaying 22 rolls of film containing 314 photographs spanning back to 2020.1
The automation effort encountered significant technical hurdles during development.1 An early attempt to have Claude control the scanner directly nearly caused equipment damage, while the dust detection system initially misidentified water reflections as approximately 2,000 dust particles requiring removal.1 The infrastructure incorporates Ollama for local model processing, SANE for scanner integration, and Cloudflare R2 for image storage.1
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