| |
| """Example Ink Bench bundles for the leaderboard: the public ink_9um runs on every winding and two |
| baselines any ink model should beat, X-ray intensity and uniform noise (seeded), on the same grid.""" |
| import io, json, os, sys |
| import numpy as np |
| from PIL import Image |
| sys.path.insert(0, os.path.dirname(__file__)) |
| import inkbench as ib |
| root = os.path.join(os.path.dirname(__file__), '..'); out = os.path.join(root, 'docs/examples/bench') |
| refs = ib.Refs(root=root) |
| ident = {'tx': 0.0, 'ty': 0.0, 'sx': 1.0, 'sy': 1.0, 'method': 'manual'} |
| for w in [k for k, v in refs.index['segments'].items() if 'gpu' in v['layers']]: |
| s = refs.segment(w) |
| b = ib.compare(os.path.join(root, s['layers']['gpu']['file']), w, refs=refs, |
| model={'name': 'scrollprize/ink_9um (seed 42, step 75000) on the 9.362 um scan', 'url': 'https://huggingface.co/scrollprize/ink_9um'}) |
| json.dump(b, open(os.path.join(out, w + '-ink9um.json'), 'w'), indent=1) |
| b = ib.compare(os.path.join(root, s['layers']['ct']['file']), w, refs=refs, alignment=ident, |
| model={'name': 'baseline: X-ray intensity (same grid, no alignment needed)'}) |
| json.dump(b, open(os.path.join(out, w + '-ct-baseline.json'), 'w'), indent=1) |
| rng = np.random.default_rng(int(w[1:])) |
| buf = io.BytesIO(); Image.fromarray((rng.random((s['height'], s['width'])) * 255).astype(np.uint8)).save(buf, 'PNG') |
| b = ib.compare(buf.getvalue(), w, refs=refs, alignment=ident, name='noise-seed%d.png' % int(w[1:]), |
| model={'name': 'baseline: uniform noise', 'notes': 'numpy default_rng(%d).random, 8 bit' % int(w[1:])}) |
| json.dump(b, open(os.path.join(out, w + '-noise-baseline.json'), 'w'), indent=1) |
| print(w, 'done') |
|
|
