OPTIMA-35

Overview

OPTIMA-35 (Organizing, Processing, Tweaking Images and Modifying scanned Analogs from 35mm Film) is a Python-based project designed to provide a streamlined way to manage and edit metadata and images from analog photography. But can be used for any images.

This project is a port of my earlier work, an collection of bash script, transitioning functionality to a more modular and maintainable design.

The primary focus is on building a terminal-based user interface (TUI). Initially, the interface will utilize simple_term_menu, with plans to expand to textual for a more dynamic TUI experience in the future.

Please check if a new branch is available and read the changelog to see the progress and current features of the program. The README might sometimes lag behind.

Current Status

• While the program works and core features are available, there are currently no safety checks in place. For example, the program will write / save an image without verifying if a file with the same name already exists.
• Additionally, while EXIF data/metadata should be implemented correctly, there is a possibility of overlooked issues. In the worst case, a program might throw an error when handling EXIF data, though this has not occurred so far.

Available Features:

• Initial basic TUI functionality using simple_term_menu (planned to switch to a different interface later).
• Core features, including image resizing, metadata management, and YAML configuration.

Key Features

• Intuitive TUI for organizing and editing metadata and image properties.
• Improved modularity with classes split into separate files for flexibility and maintainability.
• Supports essential tasks like reading, editing, and saving EXIF data, as well as resizing and processing images.

Gif of program in action

Dependencies

To run OPTIMA-35, the following Python libraries are required:

• textual: For building TUI (planned future updates).
• pyyaml: To handle YAML files for configuration and settings.
• piexif: To read, modify, and write EXIF metadata.
• Pillow: For image processing.
• simple_term_menu: For building the initial TUI interface.

Installing Dependencies

You can install the dependencies using pip:

pip install textual pyyaml piexif pillow simple-term-menu

Alternatively, you can use conda or its alternatives (anaconda, mamba, micromamba):

conda install -c conda-forge textual pyyaml piexif pillow simple-term-menu

Development Approach

Compared to my previous project, FTL Save Manager, this project emphasizes:

• Enhanced Modularity: Classes and components are organized into separate files, making the codebase more maintainable and scalable.
• Improved Design Principles: Focus on creating reusable and flexible code for future expansion.
• Slower Code Pushes: Updates and code releases will be less frequent but of higher quality, ensuring stability and adherence to best practices.

Use of LLMs

In the interest of transparency, I disclose that Generative AI (GAI) large language models (LLMs), including OpenAI’s ChatGPT and Ollama models (e.g., OpenCoder and Qwen2.5-coder), have been used to assist in this project.

Areas of Assistance:

• Project discussions and planning
• Spelling and grammar corrections
• Suggestions for suitable packages and libraries
• Guidance on code structure and organization

In cases where LLMs contribute directly to code or provide substantial optimizations, such contributions will be disclosed and documented in the relevant sections of the codebase.

mradermacher gguf Q4K-M Instruct version of infly/OpenCoder-1.5B unsloth gguf Q4K_M Instruct version of both Qwen/QWEN2 1.5B and 3B

References

1. Huang, Siming, et al. OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models. 2024. PDF

2. Hui, Binyuan, et al. Qwen2.5-Coder Technical Report. arXiv preprint arXiv:2409.12186, 2024. arXiv

3. Yang, An, et al. Qwen2 Technical Report. arXiv preprint arXiv:2407.10671, 2024. arXiv