rowing_stats/README.md

Rowing Stats

Extract workout data from photos of Concept 2 PM5 rowing machine displays using computer vision and Claude's vision API.

How It Works

Photos go through a three-stage pipeline:

photos/ → crop_to_screen.py → screen_classifier.py → extract_screen_data.py → rowing_results.csv

1. Screen Detection (crop_to_screen.py) — Finds and perspective-corrects the LCD screen region using OpenCV edge detection, contour filtering, and morphological operations. Candidates are scored by edge_density × area × rectangularity.
2. Classification (screen_classifier.py) — Filters out non-rowing images. Supports a rule-based feature scorer (no training needed) and a 4-layer CNN with batch norm.
3. Data Extraction (extract_screen_data.py) — Extracts time and distance from cropped screen images using Tesseract OCR with multiple preprocessing variants (CLAHE, thresholding, scaling) and majority-vote extraction.

There is also extract_rowing_data.py, which uses Claude Haiku's vision API instead of Tesseract for data extraction. This serves as a reference/test for validating OCR accuracy but is more expensive to run due to API costs.

There is also an Optuna-based hyperparameter tuner (optimize_crop.py) for the screen detection parameters.

Setup

Dependencies

pip install anthropic torch torchvision opencv-python Pillow numpy optuna

API Key

Create a .env file with your Anthropic API key:

ANTHROPIC_API_KEY=sk-ant-...

Usage

Full pipeline

# 1. Crop screens from photos
python crop_to_screen.py photos/ cropped/

# 2. Classify — keep only rowing displays
python screen_classifier.py predict --dir cropped/

# 3. Extract workout data via Tesseract OCR
python extract_screen_data.py --dir cropped/

# 3b. (Test) Extract via Claude API — more expensive, useful for validating OCR accuracy
python extract_rowing_data.py --dir photos/

Individual commands

# Classify a single image (feature-based or CNN)
python screen_classifier.py predict --image path/to/img.jpg
python screen_classifier.py predict --image path/to/img.jpg --mode cnn

# Extract data from a single image (Tesseract OCR)
python extract_screen_data.py --image path/to/img.jpg

# Extract data from a single image (Claude API — for testing/validation)
python extract_rowing_data.py --image path/to/img.jpg

# Train the CNN classifier
python screen_classifier.py train --data-dir train/

# Optimize crop detection parameters
python optimize_crop.py --n-trials 300 --photos-dir photos/

Training Data

The CNN classifier trains on labeled images in train/:

• train/0/ — non-rowing images (negatives)
• train/1/ — rowing display images (positives)

The trained model is saved as screen_classifier_model.pth.

Validation

Extracted metrics are validated against sensible bounds:

Metric	Min	Max
Distance	100 m	100,000 m
Time	30 s	2 hrs
Pace	1:20/500m	2:30/500m