Fish Detection & Morphological Trait Analysis from Underwater Videos

Hey everyone,

I’d like to propose a computer vision project that uses underwater videos (that I have) containing several different individual fish from freshwater rivers in southern Brazil to:

- Detect fish in the videos.
- Select the best frame for each individual (e.g., the frame where the bounding box area for that fish is largest).
- Extract morphological traits from the best frame of each fish.
- Perform PCA to visualize all detected individuals in trait space.

The goal would be to rapidly evaluate functional traits across individuals to explore ecological patterns in the fish community. It would not be focused on species classification (to make the approach more replicable across environments), but results could be compared against known species composition for each location.

**Summarizing the idea:**
Combine object detection, optimal frame selection, and morphological analysis into a workflow that supports functional diversity studies from field video data.

**Possible approches:**
Object detection: YOLO
Morphological measurements: possibly using automated landmark detection traditionally used in fish morphometrics, but open to other approaches.

No files, tests, or entry points are named in the issue. Start by clarifying the available underwater videos and the requirements for fish detection, best-frame selection, trait extraction, and PCA visualization; done would be a defined workflow that supports these analyses.

Written by the indexing model from the issue text.