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89d633f 6aad5c7 89d633f 6aad5c7 4ed47f2 6aad5c7 32f036f 6aad5c7 32f036f 6aad5c7 32f036f 6aad5c7 32f036f 6aad5c7 4ed47f2 32f036f 4ed47f2 32f036f 4ed47f2 32f036f 4ed47f2 6aad5c7 4ed47f2 6aad5c7 89d633f 6aad5c7 4ed47f2 6aad5c7 4ed47f2 6aad5c7 32f036f 6aad5c7 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | """Surgical mask generation with morphological dilation and Gaussian feathering.
Procedural masks (not SAM2) — deterministic, no model dependency.
Feathered boundaries prevent visible seams in ControlNet inpainting.
Supports clinical edge cases (vitiligo preservation, keloid softening).
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import cv2
import numpy as np
from landmarkdiff.landmarks import FaceLandmarks
if TYPE_CHECKING:
from landmarkdiff.clinical import ClinicalFlags
# Procedure-specific mask parameters
MASK_CONFIG: dict[str, dict] = {
"rhinoplasty": {
"landmark_indices": [
1, 2, 4, 5, 6, 19, 94, 141, 168, 195, 197, 236, 240,
274, 275, 278, 279, 294, 326, 327, 360, 363, 370, 456, 460,
],
"dilation_px": 30,
"feather_sigma": 15.0,
},
"blepharoplasty": {
"landmark_indices": [
33, 7, 163, 144, 145, 153, 154, 155, 157, 158, 159, 160, 161, 246,
362, 382, 381, 380, 374, 373, 390, 249, 263, 466, 388, 387, 386,
385, 384, 398,
],
"dilation_px": 15,
"feather_sigma": 10.0,
},
"rhytidectomy": {
"landmark_indices": [
10, 21, 54, 58, 67, 93, 103, 109, 127, 132, 136, 150, 162, 172,
176, 187, 207, 213, 234, 284, 297, 323, 332, 338, 356, 361, 365,
379, 389, 397, 400, 427, 454,
],
"dilation_px": 40,
"feather_sigma": 20.0,
},
"orthognathic": {
"landmark_indices": [
0, 17, 18, 36, 37, 39, 40, 57, 61, 78, 80, 81, 82, 84, 87, 88,
91, 95, 146, 167, 169, 170, 175, 181, 191, 200, 201, 202, 204,
208, 211, 212, 214,
],
"dilation_px": 35,
"feather_sigma": 18.0,
},
"brow_lift": {
"landmark_indices": [
10, 21, 46, 52, 53, 54, 55, 63, 65, 66, 67, 68, 69, 70, 71,
103, 104, 105, 107, 108, 109, 151, 282, 283, 284, 285, 293,
295, 296, 297, 298, 299, 300, 301, 332, 333, 334, 336, 337, 338,
],
"dilation_px": 25,
"feather_sigma": 15.0,
},
"mentoplasty": {
"landmark_indices": [
0, 17, 18, 57, 83, 84, 85, 86, 87, 146, 167, 169, 170, 175,
181, 191, 199, 200, 201, 202, 204, 208, 211, 212, 214,
],
"dilation_px": 30,
"feather_sigma": 16.0,
},
}
def generate_surgical_mask(
face: FaceLandmarks,
procedure: str,
width: int | None = None,
height: int | None = None,
clinical_flags: ClinicalFlags | None = None,
image: np.ndarray | None = None,
) -> np.ndarray:
"""Generate a feathered surgical mask for a procedure.
Pipeline:
1. Create convex hull from procedure-specific landmarks
2. Morphological dilation by N pixels
3. Gaussian feathering for smooth alpha gradient
4. Add Perlin-style noise at boundary to prevent visible seams
Args:
face: Extracted facial landmarks.
procedure: Procedure name.
width: Mask width.
height: Mask height.
Returns:
Float32 mask array [0.0-1.0] with feathered boundaries.
"""
if procedure not in MASK_CONFIG:
raise ValueError(f"Unknown procedure: {procedure}. Choose from {list(MASK_CONFIG)}")
config = MASK_CONFIG[procedure]
w = width or face.image_width
h = height or face.image_height
# Get pixel coordinates of procedure landmarks
coords = face.landmarks[:, :2].copy()
coords[:, 0] *= w
coords[:, 1] *= h
pts = coords[config["landmark_indices"]].astype(np.int32)
# Create binary mask from convex hull
binary = np.zeros((h, w), dtype=np.uint8)
hull = cv2.convexHull(pts)
cv2.fillConvexPoly(binary, hull, 255)
# Morphological dilation
dilation = config["dilation_px"]
kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE,
(2 * dilation + 1, 2 * dilation + 1),
)
dilated = cv2.dilate(binary, kernel)
# Add slight boundary noise to prevent clean-edge seams
# (Spec: Perlin noise 2-4px on boundary before feathering)
boundary = cv2.subtract(
cv2.dilate(dilated, np.ones((5, 5), np.uint8)),
cv2.erode(dilated, np.ones((5, 5), np.uint8)),
)
noise = np.random.default_rng().integers(0, 4, size=(h, w), dtype=np.uint8)
noise_boundary = cv2.bitwise_and(boundary, noise.astype(np.uint8) * 64)
dilated = cv2.add(dilated, noise_boundary)
dilated = np.clip(dilated, 0, 255).astype(np.uint8)
# Gaussian feathering
sigma = config["feather_sigma"]
ksize = int(6 * sigma) | 1 # ensure odd
feathered = cv2.GaussianBlur(
dilated.astype(np.float32) / 255.0,
(ksize, ksize),
sigma,
)
mask = np.clip(feathered, 0.0, 1.0)
# Clinical edge case adjustments
if clinical_flags is not None:
# Vitiligo: reduce mask over depigmented patches to preserve them
if clinical_flags.vitiligo and image is not None:
from landmarkdiff.clinical import adjust_mask_for_vitiligo, detect_vitiligo_patches
patches = detect_vitiligo_patches(image, face)
mask = adjust_mask_for_vitiligo(mask, patches)
# Keloid: soften transitions in keloid-prone regions
if clinical_flags.keloid_prone and clinical_flags.keloid_regions:
from landmarkdiff.clinical import adjust_mask_for_keloid, get_keloid_exclusion_mask
keloid_mask = get_keloid_exclusion_mask(
face, clinical_flags.keloid_regions, w, h,
)
mask = adjust_mask_for_keloid(mask, keloid_mask)
return mask
def mask_to_3channel(mask: np.ndarray) -> np.ndarray:
"""Convert single-channel mask to 3-channel for compositing."""
return np.stack([mask, mask, mask], axis=-1)
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