Tohamy House Digital Archive

A prominent interior finishing engineer sat on a rich archive of project photography that was practically useless. 3.5 years of field execution data—1,237 photos and video clips—were piled into a single directory with zero structure.

• Impossible to locate a specific project's assets (spread across 18 distinct locations) when pitching clients.
• No separation between high-quality highlights, blurry duplicates, and corrupt files.
• Strong marketing assets were entirely wasted because they were unreachable.

Instead of manual sorting, which would take dozens of labor hours, I developed a custom Python automation pipeline to parse and clean the directory:

1. Smart Inventory & Classification: Categorized files across three axes: quality (High/Medium/Low), project stage (before/during/after), and target channel (Portfolio/Social Media/Case Study).
2. Zero-Copy Restructuring: Built an entirely new file tree using Unix symbolic links (Symlinks), allowing a single file to appear in multiple categories simultaneously without consuming extra physical disk space.
3. Metadata & GPS Mapping: Conducted EXIF inspection on all images to extract camera specifications (Canon EOS R6) and GPS coordinates, mapping the geographical scope of the projects across Greater Cairo.
4. Data Cleaning & MD5 Deduplication: Identified exact duplicates via MD5 hashing and isolated short, blurry, or corrupt files, automatically reclaiming storage.

The final deliverable was a structured digital asset management system. The client received a polished PDF decision report ranking and outlining the strongest projects for immediate portfolio use, led by the "Bondok Apartment" project (46 photos + 162 videos).

"Automating the process with Python collapsed over 10 hours of tedious manual file sorting into minutes of precise execution."