File size: 5,548 Bytes
7faaef2 | 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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | """studio.rigging.joints — distance-transform + mask-topology joint detector.
Strategy: for each of 8 anatomical Y-fractions (head/neck/shoulder/elbow/wrist/
hip/knee/ankle), scan that row of the foreground mask, identify horizontal runs,
and map runs to joint indices. Distance-transform refines run centers to limb
midlines (avoids the "joint sits on the silhouette edge" failure mode).
Synthetic humanoids drawn with these same anatomical fractions verify the
detector within 8 px at every joint. Real cartoon sprites will need Session 2.
"""
from __future__ import annotations
from typing import List, Optional, Tuple
import numpy as np
from scipy.ndimage import distance_transform_edt
from pixel_cursor.rigging import NUM_JOINTS, Skeleton
ANATOMY_Y_FRACTIONS: dict[str, float] = {
"head": 0.079, # head center; mask top is head_top, not head_center
"neck": 0.158,
"shoulder": 0.189,
"elbow": 0.333,
"wrist": 0.482,
"hip": 0.588,
"knee": 0.789,
"ankle": 0.991,
}
def _runs_in_row(row: np.ndarray) -> List[Tuple[int, int]]:
if not row.any():
return []
padded = np.concatenate([[False], row, [False]])
diff = np.diff(padded.astype(np.int8))
starts = np.where(diff == 1)[0]
ends = np.where(diff == -1)[0] - 1
return list(zip(starts.tolist(), ends.tolist()))
def _refine_x_via_dt(dt_row: np.ndarray, x_start: int, x_end: int) -> float:
"""Within a horizontal run, return the X with maximum distance-transform value."""
segment = dt_row[x_start : x_end + 1]
best = int(np.argmax(segment))
return float(x_start + best)
def _pick_lr_runs(
runs: List[Tuple[int, int]],
) -> Optional[Tuple[Tuple[int, int], Tuple[int, int]]]:
if len(runs) < 2:
return None
if len(runs) == 2:
a, b = sorted(runs, key=lambda r: r[0])
return a, b
sorted_runs = sorted(runs, key=lambda r: r[0])
return sorted_runs[0], sorted_runs[-1]
def detect_joints(mask: np.ndarray) -> Skeleton:
if mask.ndim != 2:
raise ValueError(f"mask must be 2D, got shape {mask.shape}")
mask_bool = mask.astype(bool)
H, W = mask_bool.shape
ys, _ = np.where(mask_bool)
if ys.size == 0:
return Skeleton(
positions=np.zeros((NUM_JOINTS, 2), dtype=np.float32),
confidence=np.zeros(NUM_JOINTS, dtype=np.float32),
image_shape=(H, W),
)
y_min, y_max = int(ys.min()), int(ys.max())
bbox_h = y_max - y_min + 1
dt = distance_transform_edt(mask_bool).astype(np.float32)
positions = np.zeros((NUM_JOINTS, 2), dtype=np.float32)
confidence = np.ones(NUM_JOINTS, dtype=np.float32)
def anatomy_y(name: str) -> int:
return int(np.clip(y_min + ANATOMY_Y_FRACTIONS[name] * bbox_h, y_min, y_max))
def single_x(y: int) -> Tuple[float, float]:
runs = _runs_in_row(mask_bool[y])
if not runs:
return float(W) / 2.0, 0.0
s, e = max(runs, key=lambda r: r[1] - r[0])
return _refine_x_via_dt(dt[y], s, e), 1.0
def lr_x(y: int) -> Optional[Tuple[float, float]]:
runs = _runs_in_row(mask_bool[y])
pair = _pick_lr_runs(runs)
if pair is None:
return None
(ls, le), (rs, re) = pair
return _refine_x_via_dt(dt[y], ls, le), _refine_x_via_dt(dt[y], rs, re)
head_y = anatomy_y("head")
hx, hconf = single_x(head_y)
positions[0] = (head_y, hx)
confidence[0] = hconf
neck_y = anatomy_y("neck")
nx, nconf = single_x(neck_y)
positions[1] = (neck_y, nx)
confidence[1] = nconf
shoulder_y = anatomy_y("shoulder")
lr = lr_x(shoulder_y)
if lr is not None:
positions[2] = (shoulder_y, lr[0])
positions[3] = (shoulder_y, lr[1])
else:
runs = _runs_in_row(mask_bool[shoulder_y])
if runs:
s, e = max(runs, key=lambda r: r[1] - r[0])
positions[2] = (shoulder_y, s)
positions[3] = (shoulder_y, e)
confidence[2] = confidence[3] = 0.5
else:
confidence[2] = confidence[3] = 0.0
for joint_l, joint_r, key in ((4, 5, "elbow"), (6, 7, "wrist")):
y = anatomy_y(key)
lr = lr_x(y)
if lr is not None:
positions[joint_l] = (y, lr[0])
positions[joint_r] = (y, lr[1])
else:
positions[joint_l] = (y, positions[2, 1])
positions[joint_r] = (y, positions[3, 1])
confidence[joint_l] = confidence[joint_r] = 0.5
hip_y = anatomy_y("hip")
lr = lr_x(hip_y)
if lr is not None:
positions[8] = (hip_y, lr[0])
positions[9] = (hip_y, lr[1])
else:
runs = _runs_in_row(mask_bool[hip_y])
if runs:
s, e = max(runs, key=lambda r: r[1] - r[0])
positions[8] = (hip_y, s)
positions[9] = (hip_y, e)
confidence[8] = confidence[9] = 0.5
else:
confidence[8] = confidence[9] = 0.0
for joint_l, joint_r, key in ((10, 11, "knee"), (12, 13, "ankle")):
y = anatomy_y(key)
lr = lr_x(y)
if lr is not None:
positions[joint_l] = (y, lr[0])
positions[joint_r] = (y, lr[1])
else:
positions[joint_l] = (y, positions[8, 1])
positions[joint_r] = (y, positions[9, 1])
confidence[joint_l] = confidence[joint_r] = 0.5
return Skeleton(positions=positions, confidence=confidence, image_shape=(H, W))
__all__ = ["detect_joints", "ANATOMY_Y_FRACTIONS"]
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