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ff53311 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 | """MediaPipe utilities for face landmark detection and mesh visualization."""
from typing import Dict, List, Optional, Tuple
import cv2
import mediapipe as mp
import numpy as np
import torch.distributed as dist
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
# Configurable path to the MediaPipe face-landmarker model file.
# Call ``set_face_landmarker_path`` before the first ``get_detector`` call
# to override the default.
FACE_LANDMARKER_PATH: str = "./ckpts/face_landmarker_v2_with_blendshapes.task"
_detectors: Dict[int, vision.FaceLandmarker] = {}
def set_face_landmarker_path(path: str) -> None:
"""Override the face-landmarker model path (must be called before first detection)."""
global FACE_LANDMARKER_PATH
FACE_LANDMARKER_PATH = path
def get_detector() -> vision.FaceLandmarker:
"""Get or create a face detector for the current process rank."""
current_rank = dist.get_rank() if dist.is_initialized() else 0
if current_rank not in _detectors:
base_options = python.BaseOptions(model_asset_path=FACE_LANDMARKER_PATH)
options = vision.FaceLandmarkerOptions(
base_options=base_options,
output_face_blendshapes=True,
output_facial_transformation_matrixes=True,
num_faces=1,
)
_detectors[current_rank] = vision.FaceLandmarker.create_from_options(options)
return _detectors[current_rank]
def get_crop_params(
image: np.ndarray,
target_height: int,
target_width: int,
method: str = "nose"
) -> Tuple[int, int, int, int, int, int]:
"""Calculate crop parameters to fit target dimensions while centering on face.
Args:
image: Input image as numpy array.
target_height: Target height for the cropped image.
target_width: Target width for the cropped image.
method: Centering method ('nose' or 'average').
Returns:
Tuple of (new_h, new_w, left, right, top, bottom) crop parameters.
"""
if len(image.shape) != 3:
raise ValueError(f"Expected 3D image, got shape {image.shape}")
h, w, _ = image.shape
if h <= 0 or w <= 0:
raise ValueError(f"Invalid image dimensions: {h}x{w}")
if target_height <= 0 or target_width <= 0:
raise ValueError(f"Invalid target dimensions: {target_height}x{target_width}")
# Calculate scale to fit within target dimensions
scale_h = target_height / h
scale_w = target_width / w
scale = max(scale_h, scale_w)
new_h, new_w = int(h * scale), int(w * scale)
image = cv2.resize(image, (new_w, new_h))
# Initialize cropping parameters
left, right = 0, new_w
top, bottom = 0, new_h
# Detect face landmarks
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)
detector = get_detector()
detection_result = detector.detect(mp_image)
if not detection_result.face_landmarks:
# Crop to center if no face landmarks are detected
if new_w > target_width:
left = (new_w - target_width) // 2
right = left + target_width
if new_h > target_height:
top = (new_h - target_height) // 2
bottom = top + target_height
else:
# Center around face
first_landmark = detection_result.face_landmarks[0]
if method == "nose":
nose_tip = first_landmark[4]
center_x = int(nose_tip.x * new_w)
center_y = int(nose_tip.y * new_h)
elif method == "average":
x_list = [landmark.x for landmark in first_landmark]
y_list = [landmark.y for landmark in first_landmark]
center_x = int(sum(x_list) / len(x_list) * new_w)
center_y = int(sum(y_list) / len(y_list) * new_h)
else:
raise ValueError(f"Invalid method: {method}")
if new_w > target_width:
left = max(0, center_x - target_width // 2)
right = left + target_width
if right > new_w:
right = new_w
left = right - target_width
if new_h > target_height:
top = max(0, center_y - target_height // 2)
bottom = top + target_height
if bottom > new_h:
bottom = new_h
top = bottom - target_height
return new_h, new_w, left, right, top, bottom
def crop_image(
image: np.ndarray,
target_height: int = 704,
target_width: int = 480,
method: str = "nose"
) -> np.ndarray:
"""Crop image to target dimensions centered on face.
Args:
image: Input image as numpy array.
target_height: Target height for cropped image.
target_width: Target width for cropped image.
method: Centering method ('nose' or 'average').
Returns:
Cropped image as numpy array.
"""
new_h, new_w, left, right, top, bottom = get_crop_params(
image, target_height, target_width, method
)
cropped_image = cv2.resize(image, (new_w, new_h))
cropped_image = cropped_image[top:bottom, left:right]
return cropped_image
def crop_video(
frames: List[np.ndarray],
target_height: int = 704,
target_width: int = 480,
method: str = "nose"
) -> List[np.ndarray]:
"""Crop video frames to target dimensions using consistent parameters.
Args:
frames: List of video frames as numpy arrays.
target_height: Target height for cropped frames.
target_width: Target width for cropped frames.
method: Centering method ('nose' or 'average').
Returns:
List of cropped frames as numpy arrays.
"""
if not frames:
raise ValueError("Empty frames list provided")
if not all(isinstance(frame, np.ndarray) for frame in frames):
raise ValueError("All frames must be numpy arrays")
new_h, new_w, left, right, top, bottom = get_crop_params(
frames[0], target_height, target_width, method
)
cropped_frames = []
for frame in frames:
resized_frame = cv2.resize(frame, (new_w, new_h))
cropped_frame = resized_frame[top:bottom, left:right]
cropped_frames.append(cropped_frame)
return cropped_frames
def crop_reference_image(
image: np.ndarray,
target_height: int = 640,
target_width: int = 448,
crop_params: Optional[Tuple[int, int, int, int, int, int]] = None
) -> np.ndarray:
"""Crop reference image using provided or calculated crop parameters.
Args:
image: Input image as numpy array.
target_height: Target height for cropped image.
target_width: Target width for cropped image.
crop_params: Optional pre-calculated crop parameters.
Returns:
Cropped image as numpy array.
"""
if not isinstance(image, np.ndarray):
raise ValueError("Image must be a numpy array")
if crop_params is None:
crop_params = get_crop_params(image, target_height, target_width)
if len(crop_params) != 6:
raise ValueError(f"Expected 6 crop parameters, got {len(crop_params)}")
new_h, new_w, left, right, top, bottom = crop_params
resized_image = cv2.resize(image, (new_w, new_h))
cropped_image = resized_image[top:bottom, left:right]
return cropped_image
def detect_face_landmarks(numpy_image: np.ndarray) -> List:
"""Detect face landmarks from image.
Args:
numpy_image: Input image as numpy array.
Returns:
List of face landmarks.
Raises:
ValueError: If no face landmarks are detected.
"""
if not isinstance(numpy_image, np.ndarray):
raise ValueError("Input must be a numpy array")
if len(numpy_image.shape) != 3 or numpy_image.shape[2] != 3:
raise ValueError(f"Expected RGB image with shape (H, W, 3), got {numpy_image.shape}")
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=numpy_image)
detector = get_detector()
detection_result = detector.detect(mp_image)
if not detection_result.face_landmarks:
raise ValueError("No face landmarks detected in the image")
return detection_result.face_landmarks[0]
def draw_mediapipe_mesh(
annotated_image: np.ndarray,
face_landmarks: List
) -> np.ndarray:
"""Draw MediaPipe face mesh on the image.
Args:
annotated_image: Image to draw on.
face_landmarks: List of face landmarks.
Returns:
Annotated image with face mesh drawn.
"""
face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
face_landmarks_proto.landmark.extend([
landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z)
for landmark in face_landmarks
])
solutions.drawing_utils.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks_proto,
connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
landmark_drawing_spec=None,
connection_drawing_spec=mp.solutions.drawing_styles.get_default_face_mesh_tesselation_style()
)
solutions.drawing_utils.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks_proto,
connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
landmark_drawing_spec=None,
connection_drawing_spec=mp.solutions.drawing_styles.get_default_face_mesh_contours_style()
)
solutions.drawing_utils.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks_proto,
connections=mp.solutions.face_mesh.FACEMESH_IRISES,
landmark_drawing_spec=None,
connection_drawing_spec=mp.solutions.drawing_styles.get_default_face_mesh_iris_connections_style()
)
return annotated_image
def get_mediapipe_cond(image: np.ndarray) -> np.ndarray:
"""Generate face mesh conditioning image.
Args:
image: Input image as numpy array.
Returns:
White image with face mesh drawn.
Raises:
ValueError: If face detection fails.
"""
if not isinstance(image, np.ndarray):
raise ValueError("Input must be a numpy array")
# Create blank white image
annotated_image = np.ones_like(image) * 255
# Detect landmarks and draw mesh
face_landmarks = detect_face_landmarks(image)
annotated_image = draw_mediapipe_mesh(annotated_image, face_landmarks)
return annotated_image
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