Claude cause quick

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+A computer vision + LLM pipeline that extracts rowing machine workout data from photos of Concept 2 PM5 displays. Photos go through screen detection, classification, and OCR-via-LLM to produce structured workout metrics.
+
+## Pipeline
+
+```
+photos/ → crop_to_screen.py → screen_classifier.py → extract_rowing_data.py → rowing_results.csv
+```
+
+1. **crop_to_screen.py** — Detects and perspective-corrects the LCD screen region using OpenCV edge detection, contour filtering, and morphological operations. Scores candidates by `edge_density × area × rectangularity`.
+2. **screen_classifier.py** — Binary classifier (rowing display vs. not). Two modes: a rule-based feature scorer (no training needed) and a 4-layer CNN with batch norm (requires training on `train/` data).
+3. **extract_rowing_data.py** — Sends cropped images to Claude Haiku vision API, extracts time/distance, validates against sanity bounds (pace, distance, duration), computes derived metrics (pace/500m, calories), and reads EXIF date.
+4. **optimize_crop.py** — Optuna-based hyperparameter tuner for crop_to_screen.py detection parameters (12 params). Evaluates trials by counting CNN-classified rowing displays.
+
+## Commands
+
+```bash
+# Crop screens from photos
+python crop_to_screen.py photos/ cropped/
+
+# Classify images (feature-based or CNN)
+python screen_classifier.py predict --dir cropped/
+python screen_classifier.py predict --image path/to/img.jpg --mode cnn
+
+# Train the CNN classifier
+python screen_classifier.py train --data-dir train/
+
+# Extract rowing data (requires ANTHROPIC_API_KEY)
+python extract_rowing_data.py --dir photos/
+python extract_rowing_data.py --image path/to/img.jpg
+
+# Optimize crop parameters
+python optimize_crop.py --n-trials 300 --photos-dir photos/
+```
+
+## Dependencies
+
+No requirements.txt exists. Key packages: `anthropic`, `torch`, `torchvision`, `opencv-python` (cv2), `Pillow`, `numpy`, `optuna`.
+
+## Key Details
+
+- The Claude API call in extract_rowing_data.py uses `claude-haiku-4-5-20251001` for cost efficiency on a structured extraction task.
+- Validation bounds in extract_rowing_data.py mirror those from a Go handler (`handle_rowing.go`): distance 100–100k meters, time 30s–2hrs, pace 1:20–2:30 /500m.
+- Training data lives in `train/0/` (non-rowing, 80 samples) and `train/1/` (rowing displays, 48 samples).
+- The trained CNN model is saved as `screen_classifier_model.pth`.
+- `.env` contains API keys — it is gitignored and must not be committed.