| |
| """Synthetic predictions with known shift, scale and blur, made from the full-size |
| published ink preview, plus a pure-noise control. Used by tests/test_bench.py and the |
| browser end-to-end test.""" |
| import json, os, sys, math |
| import numpy as np |
| from PIL import Image |
| sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'tools')) |
| import inkbench as ib |
| ROOT = os.path.join(os.path.dirname(__file__), '..') |
|
|
| def make(seg, tx, ty, sx, sy, sigma, c=1.5, seed=0, noise=0.0): |
| idx = json.load(open(os.path.join(ROOT, 'data/bench/index.json')))['segments'][seg] |
| W, H = idx['width'], idx['height'] |
| hi = np.asarray(Image.open(os.path.join(ROOT, 'data/hi/%s.jpg' % seg)).convert('L'), float) / 255 |
| Hh, Wh = hi.shape |
| if sigma > 0: |
| hi = ib.blur(hi, sigma * Wh / W) |
| Wp, Hp = round(c * W), round(c * H) |
| u = np.arange(Wp) + 0.0; v = np.arange(Hp) + 0.0 |
| x = ((u + 0.5) * W / Wp - W / 2) * sx + W / 2 - 0.5 + tx |
| y = ((v + 0.5) * H / Hp - H / 2) * sy + H / 2 - 0.5 + ty |
| X = (x + 0.5) * Wh / W - 0.5; Y = (y + 0.5) * Hh / H - 0.5 |
| ok = (X >= 0) & (X <= Wh - 1); okY = (Y >= 0) & (Y <= Hh - 1) |
| X = np.clip(X, 0, Wh - 1.001); Y = np.clip(Y, 0, Hh - 1.001) |
| x0 = np.floor(X).astype(int); y0 = np.floor(Y).astype(int); fx = X - x0; fy = Y - y0 |
| a = hi[y0[:, None], x0[None]] * (1 - fx) + hi[y0[:, None], x0[None] + 1] * fx |
| b = hi[y0[:, None] + 1, x0[None]] * (1 - fx) + hi[y0[:, None] + 1, x0[None] + 1] * fx |
| out = a * (1 - fy[:, None]) + b * fy[:, None] |
| out = np.where(okY[:, None] & ok[None], out, 0) |
| if noise: |
| out = out + np.random.default_rng(seed).normal(0, noise, out.shape) |
| return np.clip(out, 0, 1) |
|
|
| def noise(seg, seed=1, c=1.5): |
| idx = json.load(open(os.path.join(ROOT, 'data/bench/index.json')))['segments'][seg] |
| return np.random.default_rng(seed).random((round(c * idx['height']), round(c * idx['width']))) |
|
|
| if __name__ == '__main__': |
| out = sys.argv[1]; os.makedirs(out, exist_ok=True) |
| cases = {'shift_blur': ('w046', 23.0, -17.0, 1.0, 1.0, 2.0), 'scale_shift': ('w048', -12.5, 9.25, 1.12, 1.12, 1.0)} |
| for k, (seg, *p) in cases.items(): |
| ib.save_png16(make(seg, *p), os.path.join(out, k + '.png')) |
| Image.fromarray((noise('w046') * 255).astype(np.uint8)).save(os.path.join(out, 'noise.png')) |
| json.dump(cases, open(os.path.join(out, 'cases.json'), 'w')) |
|
|
