computer-vision

Computer-vision study & research repo, driven by the Aegis Nexus curriculum. The single source of truth for what to study and why is CURRICULUM.md (vendored verbatim from the Aegis Nexus Course Catalog — [[wikilinks]] in it resolve inside Aegis Nexus, not this repo). This README and all course docs derive from it.

Where this sits in the curriculum

This repo currently covers Course #1 — Python for CV with OpenCV & Deep Learning (Portilla), the June 2026 main course. Its artifact target per the catalog is a real-time camera/video pipeline + first detector demo with structured detection logs.

Forward gate: OpenCV #1 → PyTorch #2 (July) → YOLOv12 #6. See opencv-deep-learning-course/ROADMAP.md.

Layout

computer-vision/
├── CURRICULUM.md                  # authoritative curriculum (Aegis Nexus catalog)
├── requirements.txt               # Python deps (OpenCV, NumPy, matplotlib, scikit-learn, Jupyter)
└── opencv-deep-learning-course/   # Course #1 — Portilla OpenCV
    ├── README.md                  # course section index
    ├── PROGRESS.md                # live status + resume cursor + session log
    ├── ROADMAP.md                 # how to finish the course + what's next
    ├── notes/                     # concept notes / artifacts
    └── 01..09-*/                  # one folder per course section

Convention for future courses: PyTorch (#2) and YOLOv12 (#6) will land as sibling folders next to opencv-deep-learning-course/ when they start — they are documented here, not pre-created.

Current progress (snapshot)

See opencv-deep-learning-course/PROGRESS.md for the live table. Resume cursor: 06-object-detection/face_detection.ipynb.

Running

pip install -r requirements.txt
jupyter lab          # open any 0X-*/section notebook