"""OCR cost per crop, on production-shaped crops.

The decision record claims ~18-26 ms per crop on Mac and re-measured 18.0 ms on
40 production crops. This is that measurement, so the number is reproducible
rather than remembered.

    .venv/bin/python code/bench_ocr.py
"""
import json, statistics, sys, time
sys.path.insert(0, 'code')
import cv2
from eval_identity import LocalLaneAdapter, crop_of

lane = LocalLaneAdapter('stock-lopia-clean')
labels = json.load(open('eval/labels/image6.json'))
image = cv2.imread('add/image6.jpg')

crops = []
for facing in labels['facings']:
    if facing.get('label') in (None, 'exclude'):
        continue
    crop = crop_of(image, facing['box'])
    if crop is not None and crop.size:
        crops.append(crop)
    if len(crops) >= 40:
        break

# Warm the recognizer: the first call loads the Vision model.
lane.text_lane.read(crops[0])

times = []
for crop in crops:
    started = time.perf_counter()
    lane.text_lane.read(crop)
    times.append((time.perf_counter() - started) * 1000)

times.sort()
print(f'{len(times)} crops')
print(f'  p50 {statistics.median(times):.1f} ms')
print(f'  mean {statistics.mean(times):.1f} ms')
print(f'  p95 {times[int(len(times) * 0.95)]:.1f} ms')
print(f'  min {times[0]:.1f}  max {times[-1]:.1f}')