File size: 4,227 Bytes
585244b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import numpy as np
FEATURE_NAMES = [
"w_frac", "h_frac", "area_frac", "log_aspect", "channel", "step_frac", "q",
"res_mean", "res_std", "res_abs_mean", "res_rms", "res_min", "res_max",
"res_p05", "res_p25", "res_p50", "res_p75", "res_p95",
"pos_frac", "neg_frac", "zeroish_frac",
"uniq_frac", "mean_abs_dx", "mean_abs_dy", "grad_energy", "lap_abs_mean",
"target_mean", "target_std", "canvas_mean", "canvas_std",
"before_mse", "before_mae",
]
FEATURE_DIM = len(FEATURE_NAMES)
# Features requiring a sort (percentiles / unique) -> excluded from the cheap path.
SORT_FEATURES = {"res_p05", "res_p25", "res_p50", "res_p75", "res_p95", "uniq_frac"}
def extract_features(target, canvas, box, step, q, image_w, image_h, patch_count, n_channels):
c, x, y, bw, bh = box
t = target[y:y + bh, x:x + bw, c].astype(np.float64)
cv = canvas[y:y + bh, x:x + bw, c].astype(np.float64)
res = t - cv
before = t - np.clip(cv, 0, 255)
flat = res.ravel()
n = max(1, flat.size)
absflat = np.abs(flat)
p05, p25, p50, p75, p95 = np.percentile(flat, [5, 25, 50, 75, 95])
dx = float(np.abs(np.diff(res, axis=1)).mean()) if bw > 1 else 0.0
dy = float(np.abs(np.diff(res, axis=0)).mean()) if bh > 1 else 0.0
lapx = float(np.abs(res[:, 2:] - 2 * res[:, 1:-1] + res[:, :-2]).mean()) if bw > 2 else 0.0
lapy = float(np.abs(res[2:] - 2 * res[1:-1] + res[:-2]).mean()) if bh > 2 else 0.0
uniq = np.unique(np.rint(flat / 2.0)).size / n
feats = np.array([
bw / image_w, bh / image_h, (bw * bh) / (image_w * image_h),
np.log((bw + 1e-6) / (bh + 1e-6)),
c / max(1, n_channels - 1), step / max(1, patch_count), q,
float(flat.mean()), float(flat.std()), float(absflat.mean()),
float(np.sqrt((flat ** 2).mean())), float(flat.min()), float(flat.max()),
p05, p25, p50, p75, p95,
float((flat > 0).mean()), float((flat < 0).mean()), float((absflat < 2).mean()),
uniq, dx, dy, dx + dy, lapx + lapy,
float(t.mean()), float(t.std()), float(cv.mean()), float(cv.std()),
float((before ** 2).mean()), float(np.abs(before).mean()),
], dtype=np.float32)
return feats
def extract_cheap(names, target, canvas, box, step, q, image_w, image_h, patch_count, n_channels):
"""Compute only the requested non-sort features (no percentile/unique)."""
c, x, y, bw, bh = box
t = target[y:y + bh, x:x + bw, c].astype(np.float64)
cv = canvas[y:y + bh, x:x + bw, c].astype(np.float64)
res = t - cv
flat = res.ravel()
absflat = np.abs(flat)
d = {
"w_frac": bw / image_w, "h_frac": bh / image_h, "area_frac": (bw * bh) / (image_w * image_h),
"log_aspect": np.log((bw + 1e-6) / (bh + 1e-6)), "channel": c / max(1, n_channels - 1),
"step_frac": step / max(1, patch_count), "q": q,
"res_mean": flat.mean(), "res_std": flat.std(), "res_abs_mean": absflat.mean(),
"res_rms": np.sqrt((flat ** 2).mean()), "res_min": flat.min(), "res_max": flat.max(),
"pos_frac": (flat > 0).mean(), "neg_frac": (flat < 0).mean(), "zeroish_frac": (absflat < 2).mean(),
"target_mean": t.mean(), "target_std": t.std(), "canvas_mean": cv.mean(), "canvas_std": cv.std(),
}
if {"mean_abs_dx", "grad_energy"} & set(names):
d["mean_abs_dx"] = float(np.abs(np.diff(res, axis=1)).mean()) if bw > 1 else 0.0
if {"mean_abs_dy", "grad_energy"} & set(names):
d["mean_abs_dy"] = float(np.abs(np.diff(res, axis=0)).mean()) if bh > 1 else 0.0
if "grad_energy" in names:
d["grad_energy"] = d["mean_abs_dx"] + d["mean_abs_dy"]
if "lap_abs_mean" in names:
lx = float(np.abs(res[:, 2:] - 2 * res[:, 1:-1] + res[:, :-2]).mean()) if bw > 2 else 0.0
ly = float(np.abs(res[2:] - 2 * res[1:-1] + res[:-2]).mean()) if bh > 2 else 0.0
d["lap_abs_mean"] = lx + ly
before = t - np.clip(cv, 0, 255)
if "before_mse" in names:
d["before_mse"] = float((before ** 2).mean())
if "before_mae" in names:
d["before_mae"] = float(np.abs(before).mean())
return np.array([d[nm] for nm in names], dtype=np.float32) |