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from mediapipe.tasks.python import vision
import mediapipe as mp
import cv2
import numpy as np
import warnings
import os
import logging
# Suppress INFO and WARNING logs from MediaPipe
logging.getLogger("mediapipe").setLevel(logging.ERROR)
# Suppress INFO and WARNING logs
os.environ["GLOG_minloglevel"] = "2" # 2 means only ERROR and FATAL logs
os.environ["GLOG_logtostderr"] = "1"
# Initialize mediapipe solutions
mp_face_detection = mp.solutions.face_detection # type: ignore
mp_face_mesh = mp.solutions.face_mesh # type: ignore
def detect_faces_and_landmarks(image: np.ndarray):
"""
Detect faces and landmarks using MediaPipe Face Detection.
:param image: Input image as a numpy array.
:return: List of dictionaries with face and landmark information.
"""
with mp_face_detection.FaceDetection(
model_selection=1, min_detection_confidence=0.5
) as face_detection:
results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
face_data = []
if results.detections:
for detection in results.detections:
bboxC = detection.location_data.relative_bounding_box
h, w, c = image.shape
bbox = (
int(bboxC.xmin * w),
int(bboxC.ymin * h),
int(bboxC.width * w),
int(bboxC.height * h),
)
landmarks = detection.location_data.relative_keypoints
face_data.append({"bbox": bbox, "landmarks": landmarks})
return face_data
def mediapipe_selfie_segmentor(
image: np.ndarray, segment: list = ["face_skin", "body_skin", "hair"]
):
"""
Segment image using MediaPipe Multi-Class Selfie Segmentation.
:param image: Input image as a numpy array.
:param segment: List of segments to extract.
:return: Dictionary of segmentation masks.
"""
# Create the options that will be used for ImageSegmenter
base_options = python.BaseOptions(
model_asset_path="model_weights/selfie_multiclass_256x256.tflite"
)
options = vision.ImageSegmenterOptions(
base_options=base_options,
output_category_mask=True,
output_confidence_masks=True,
)
with vision.ImageSegmenter.create_from_options(options) as segmenter:
# Create the MediaPipe image file that will be segmented
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)
# Retrieve the masks for the segmented image
segmentation_result = segmenter.segment(mp_image)
category_mask = segmentation_result.category_mask.numpy_view()
h, w = category_mask.shape
masks = {
"face_skin_mask": np.zeros((h, w), dtype=np.uint8),
"hair_mask": np.zeros((h, w), dtype=np.uint8),
"body_skin_mask": np.zeros((h, w), dtype=np.uint8),
}
# Define class labels based on MediaPipe segmentation (example, may need adjustment)
face_skin_class = 3
hair_class = 1
body_skin_class = 2
masks["face_skin_mask"][category_mask == face_skin_class] = 255
masks["hair_mask"][category_mask == hair_class] = 255
masks["body_skin_mask"][category_mask == body_skin_class] = 255
return masks
def detect_face_landmarks(image: np.ndarray):
"""
Detect face landmarks using MediaPipe Face Mesh.
:param image: Input image as a numpy array.
:return: Dictionary with landmarks for iris, lips, eyebrows, and eyes.
"""
with mp_face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=1,
refine_landmarks=True,
min_detection_confidence=0.5,
) as face_mesh:
results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
face_landmarks = {
"left_iris": [],
"right_iris": [],
"lips": [],
"left_eyebrow": [],
"right_eyebrow": [],
"left_eye": [],
"right_eye": [],
}
if results.multi_face_landmarks:
for face_landmarks_data in results.multi_face_landmarks:
# Left iris landmarks
for i in range(468, 473): # Left iris landmarks
landmark = face_landmarks_data.landmark[i]
face_landmarks["left_iris"].append((landmark.x, landmark.y))
# Right iris landmarks
for i in range(473, 478): # Right iris landmarks
landmark = face_landmarks_data.landmark[i]
face_landmarks["right_iris"].append((landmark.x, landmark.y))
# Outer lips landmarks
for i in [
61,
146,
91,
181,
84,
17,
314,
405,
321,
375,
291,
0,
409,
270,
269,
267,
37,
39,
40,
185,
]:
landmark = face_landmarks_data.landmark[i]
face_landmarks["lips"].append((landmark.x, landmark.y))
# Left eyebrow landmarks
for i in [70, 63, 105, 66, 107]:
landmark = face_landmarks_data.landmark[i]
face_landmarks["left_eyebrow"].append((landmark.x, landmark.y))
# Right eyebrow landmarks
for i in [336, 296, 334, 293, 300]:
landmark = face_landmarks_data.landmark[i]
face_landmarks["right_eyebrow"].append((landmark.x, landmark.y))
# Left eye landmarks
for i in [
33,
246,
161,
160,
159,
158,
157,
173,
133,
155,
154,
153,
145,
144,
163,
7,
]:
landmark = face_landmarks_data.landmark[i]
face_landmarks["left_eye"].append((landmark.x, landmark.y))
# Right eye landmarks
for i in [
463,
398,
384,
385,
386,
387,
388,
466,
263,
249,
390,
373,
374,
380,
381,
382,
]:
landmark = face_landmarks_data.landmark[i]
face_landmarks["right_eye"].append((landmark.x, landmark.y))
return face_landmarks
def create_feature_masks(image: np.ndarray, landmarks: dict):
"""
Create individual masks for facial features based on landmarks.
:param image: Input image as a numpy array.
:param landmarks: Dictionary with landmarks for iris, lips, eyebrows, and eyes.
:return: Dictionary with masks for each facial feature.
"""
h, w = image.shape[:2]
masks = {
"lips_mask": np.zeros((h, w), dtype=np.uint8),
"left_eyebrow_mask": np.zeros((h, w), dtype=np.uint8),
"right_eyebrow_mask": np.zeros((h, w), dtype=np.uint8),
"left_eye_mask": np.zeros((h, w), dtype=np.uint8),
"right_eye_mask": np.zeros((h, w), dtype=np.uint8),
"left_iris_mask": np.zeros((h, w), dtype=np.uint8),
"right_iris_mask": np.zeros((h, w), dtype=np.uint8),
}
# Define the order of the points to form polygons correctly
lips_order = [
61,
146,
91,
181,
84,
17,
314,
405,
321,
375,
291,
0,
409,
270,
269,
267,
37,
39,
40,
185,
]
left_eyebrow_order = [70, 63, 105, 66, 107]
right_eyebrow_order = [336, 296, 334, 293, 300]
left_eye_order = [
33,
246,
161,
160,
159,
158,
157,
173,
133,
155,
154,
153,
145,
144,
163,
7,
]
right_eye_order = [
463,
398,
384,
385,
386,
387,
388,
466,
263,
249,
390,
373,
374,
380,
381,
382,
]
left_iris_order = [468, 469, 470, 471, 472]
right_iris_order = [473, 474, 475, 476, 477]
orders = {
"lips": lips_order,
"left_eyebrow": left_eyebrow_order,
"right_eyebrow": right_eyebrow_order,
"left_eye": left_eye_order,
"right_eye": right_eye_order,
"left_iris": left_iris_order,
"right_iris": right_iris_order,
}
for feature, order in orders.items():
points = []
for i in range(len(order)):
try:
point = (
int(landmarks[feature][i][0] * w),
int(landmarks[feature][i][1] * h),
)
points.append(point)
except KeyError:
warnings.warn(
f"Feature '{feature}' at index {i} is not present in landmarks. Skipping this point."
)
except IndexError:
warnings.warn(
f"Index {i} is out of range for feature '{feature}'. Skipping this point."
)
points = np.array(points, dtype=np.int32)
if len(points) > 0:
cv2.fillPoly(masks[f"{feature}_mask"], [points], 255)
return masks
if __name__ == "__main__":
# Test the face detection and segmentation
image = cv2.imread("inputs/vanika.png")
face_data = detect_faces_and_landmarks(image)
print(face_data)
masks = mediapipe_selfie_segmentor(image)
# write it to disk
for key, mask in masks.items():
if key == "face_skin_mask":
# create feature masks
landmarks = detect_face_landmarks(image)
feature_masks = create_feature_masks(image, landmarks)
# subtract eyes, lips and eyebrows from face skin mask
for feature, feature_mask in feature_masks.items():
if "iris_mask" in feature:
cv2.imwrite(f"outputs/{feature}.png", feature_mask)
mask = cv2.subtract(mask, feature_mask)
cv2.imwrite(f"outputs/{key}.png", mask)
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