# 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

```bash
# 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

```bash
# 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 |