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import cv2
import mediapipe as mp
import numpy as np
from rembg import remove
from PIL import Image
class FaceSegmenter:
def __init__(self, threshold=0.5):
self.threshold = threshold
# Initialize face detection
self.face_detection = mp.solutions.face_detection.FaceDetection(
model_selection=1, # 1 for general use, 0 for close-up faces
min_detection_confidence=0.5
)
# Initialize selfie segmentation (for background removal)
self.selfie_segmentation = mp.solutions.selfie_segmentation.SelfieSegmentation(
model_selection=1 # 1 for general use, 0 for close-up faces
)
def segment_face(self, image_path):
# Load the image
image = cv2.imread(image_path)
if image is None:
raise ValueError("Image not found or unable to load.")
# Convert to RGB (MediaPipe requires RGB input)
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Step 1: Detect the face
face_results = self.face_detection.process(rgb_image)
if not face_results.detections:
# Use rembg to remove the background
with open(image_path, "rb") as input_file:
input_image = input_file.read()
output_image = remove(input_image)
# Convert the output image to a numpy array
output_image = np.array(Image.open(io.BytesIO(output_image)))
# Convert RGBA to RGB (remove alpha channel)
if output_image.shape[2] == 4:
output_image = cv2.cvtColor(output_image, cv2.COLOR_RGBA2RGB)
return output_image
# Get the bounding box of the first detected face
detection = face_results.detections[0]
bboxC = detection.location_data.relative_bounding_box
h, w, _ = image.shape
x, y, width, height = int(bboxC.xmin * w), int(bboxC.ymin * h), \
int(bboxC.width * w), int(bboxC.height * h)
# Step 2: Segment the foreground (selfie segmentation)
segmentation_results = self.selfie_segmentation.process(rgb_image)
if segmentation_results.segmentation_mask is None:
raise ValueError("Segmentation failed.")
# Create a binary mask
mask = (segmentation_results.segmentation_mask > self.threshold).astype(np.uint8)
# Step 3: Crop the face using the bounding box
face_mask = np.zeros_like(mask)
face_mask[y:y+height, x:x+width] = mask[y:y+height, x:x+width]
# Apply the mask to the original image
segmented_face = cv2.bitwise_and(image, image, mask=face_mask)
return segmented_face
def save_segmented_face(self, image_path, output_path):
segmented_face = self.segment_face(image_path)
cv2.imwrite(output_path, segmented_face)
def show_segmented_face(self, image_path):
segmented_face = self.segment_face(image_path)
cv2.imshow("Segmented Face", segmented_face)
cv2.waitKey(0)
cv2.destroyAllWindows() |