nodes/chflame163-ComfyUI_LayerStyle_Advance
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MediapipeFacialSegment node support face_landmarker to compatible Python 3.12 and 3.13

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chflame163
2026-01-22 10:11:17 +08:00
parent 42f9dbae84
commit c31de7030c
3 changed files with 111 additions and 41 deletions
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face_landmarker/face_landmarker.task
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py/mediapipe_facial_segment.py
+110 -40
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@@ -1,41 +1,65 @@
# layerstyle advance
import os
import numpy as np
from .imagefunc import *
NODE_NAME = 'MediapipeFacialSegment'
# 获取特征点的坐标
def get_points(indices, face_landmarks, width, height):
return [(int(face_landmarks.landmark[i].x * width), int(face_landmarks.landmark[i].y * height))
for i in indices]
return [
(
int(face_landmarks.landmark[i].x * width),
int(face_landmarks.landmark[i].y * height)
)
for i in indices
]
# 绘制面部特征的多边形
def draw_feature(indices, mask, face_landmarks, width, height):
points = get_points(indices, face_landmarks, width, height)
points = np.array(points, dtype=np.int32)
cv2.fillPoly(mask, [points], 255)
class _Landmark:
__slots__ = ("x", "y")
def __init__(self, x, y):
self.x = x
self.y = y
class _FaceLandmarksWrapper:
def __init__(self, landmarks):
self.landmark = [_Landmark(lm.x, lm.y) for lm in landmarks]
def _get_face_landmarker_model_path():
base_dir = os.path.dirname(__file__) # .../ComfyUI_LayerStyle_Advance/py
plugin_dir = os.path.dirname(base_dir) # .../ComfyUI_LayerStyle_Advance
return os.path.join(
plugin_dir,
"face_landmarker",
"face_landmarker.task"
)
class FacialFeatureSegment:
def __init__(self):
pass
self._tasks_landmarker = None
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"image": ("IMAGE",), #
"image": ("IMAGE",),
"left_eye": ("BOOLEAN", {"default": True}),
"left_eyebrow": ("BOOLEAN", {"default": True}),
"right_eye": ("BOOLEAN", {"default": True}),
"right_eyebrow": ("BOOLEAN", {"default": True}),
"lips": ("BOOLEAN", {"default": True}),
"tooth": ("BOOLEAN", {"default": True}),
},
"optional": {
}
}
@@ -44,68 +68,114 @@ class FacialFeatureSegment:
FUNCTION = 'facial_feature_segment'
CATEGORY = '😺dzNodes/LayerMask'
def facial_feature_segment(self, image,
left_eye, left_eyebrow, right_eye, right_eyebrow, lips, tooth
):
def facial_feature_segment(
self,
image,
left_eye, left_eyebrow, right_eye, right_eyebrow, lips, tooth
):
import mediapipe as mp
# 定义面部特征索引
USE_SOLUTIONS = hasattr(mp, "solutions")
left_eye_indices = [33, 7, 163, 144, 145, 153, 154, 155, 133, 173, 157, 158, 159, 160, 161, 246]
right_eye_indices = [263, 249, 390, 373, 374, 380, 381, 382, 362, 398, 384, 385, 386, 387, 388, 466]
left_eyebrow_indices = [70, 63, 105, 66, 107, 55, 65, 52, 53, 46]
right_eyebrow_indices = [336, 296, 334, 293, 300, 276, 283, 282, 295, 285]
# upper_lip_indices = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95, 78]
# lower_lip_indices = [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291, 308, 415, 310, 311, 312, 13, 82, 81, 80, 191, 78]
tooth_indices = [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, 415, 310, 311, 312, 13, 82, 81, 80, 191, 78]
lips_indices = [61, 76, 62, 78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308, 324, 318, 402, 317, 14, 87, 178,
88, 95, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291, 375, 321, 405, 314, 17, 84, 181, 91, 146,
61]
lips_indices = [
61, 76, 62, 78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308,
324, 318, 402, 317, 14, 87, 178, 88, 95, 185, 40, 39, 37, 0,
267, 269, 270, 409, 291, 375, 321, 405, 314, 17, 84, 181, 91,
146, 61
]
ret_images = []
ret_masks = []
scale_factor = 4
if USE_SOLUTIONS:
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=1,
min_detection_confidence=0.5
)
else:
from mediapipe.tasks.python import vision
from mediapipe.tasks.python import BaseOptions
if self._tasks_landmarker is None:
model_path = _get_face_landmarker_model_path()
if not os.path.exists(model_path):
raise RuntimeError(
f"[{NODE_NAME}] FaceLandmarker model not found:\n{model_path}"
)
options = vision.FaceLandmarkerOptions(
base_options=BaseOptions(model_asset_path=model_path),
output_face_blendshapes=False,
output_facial_transformation_matrixes=False,
num_faces=1
)
self._tasks_landmarker = vision.FaceLandmarker.create_from_options(options)
for i in image:
face_image = tensor2pil(i.unsqueeze(0)).convert('RGB')
width, height = face_image.size
width *= scale_factor
height *= scale_factor
cv2_image = pil2cv2(face_image)
mp_face_mesh = mp.solutions.face_mesh
fase_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, min_detection_confidence=0.5)
results = fase_mesh.process(cv2_image)
mask = np.zeros((height, width), dtype=np.uint8)
if USE_SOLUTIONS:
results = face_mesh.process(cv2_image)
faces = results.multi_face_landmarks or []
else:
mp_image = mp.Image(
image_format=mp.ImageFormat.SRGB,
data=cv2_image
)
result = self._tasks_landmarker.detect(mp_image)
faces = [
_FaceLandmarksWrapper(lms)
for lms in (result.face_landmarks or [])
]
if results.multi_face_landmarks:
for face_landmarks in results.multi_face_landmarks:
# 绘制各个面部特征
if left_eye:
draw_feature(left_eye_indices, mask, face_landmarks, width, height)
if right_eye:
draw_feature(right_eye_indices, mask, face_landmarks, width, height)
if left_eyebrow:
draw_feature(left_eyebrow_indices, mask, face_landmarks, width, height)
if right_eyebrow:
draw_feature(right_eyebrow_indices, mask, face_landmarks, width, height)
if lips:
draw_feature(lips_indices, mask, face_landmarks, width, height)
if tooth:
draw_feature(tooth_indices, mask, face_landmarks, width, height)
for face_landmarks in faces:
if left_eye:
draw_feature(left_eye_indices, mask, face_landmarks, width, height)
if right_eye:
draw_feature(right_eye_indices, mask, face_landmarks, width, height)
if left_eyebrow:
draw_feature(left_eyebrow_indices, mask, face_landmarks, width, height)
if right_eyebrow:
draw_feature(right_eyebrow_indices, mask, face_landmarks, width, height)
if lips:
draw_feature(lips_indices, mask, face_landmarks, width, height)
if tooth:
draw_feature(tooth_indices, mask, face_landmarks, width, height)
mask = cv22pil(mask).convert('L')
mask = gaussian_blur(mask, 2)
mask = mask.resize(face_image.size, Image.BILINEAR)
ret_images.append(pil2tensor(RGB2RGBA(face_image, mask)))
ret_masks.append(image2mask(mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
"LayerMask: MediapipeFacialSegment": FacialFeatureSegment
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerMask: MediapipeFacialSegment": "LayerMask: Mediapipe Facial Segment(Advance)"
}
}
pyproject.toml
+1 -1
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@@ -1,7 +1,7 @@
[project]
name = "ComfyUI_LayerStyle_Advance"
description = "The nodes detached from ComfyUI Layer Style are mainly those with complex requirements for dependency packages."
version = "2.0.34"
version = "2.0.35"
license = { text = "MIT License" }
dependencies = ["numpy", "matplotlib", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "blend_modes", "transformers", "diffusers", "loguru", "colour-science", "huggingface_hub", "segment_anything", "addict", "omegaconf", "yapf", "wget", "iopath", "mediapipe", "typer_config", "fastapi", "rich", "google-generativeai", "ultralytics", "transparent-background", "accelerate", "onnxruntime", "bitsandbytes", "peft", "protobuf", "hydra-core", "blind-watermark", "qrcode", "pyzbar", "psd-tools", "wandb", "zhipuai", "openai","google-genai", "fastapi","typer-config"]