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import numpy as np
import os
import torch
from facenet_pytorch import MTCNN, InceptionResnetV1
import logging
from PIL import Image
logger = logging.getLogger(__name__)
class FacialProcessing:
def __init__(self):
os.environ['TORCH_HOME'] = '/tmp/.cache/torch'
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.mtcnn = MTCNN(keep_all=True, device=self.device)
self.resnet = InceptionResnetV1(pretrained='vggface2').eval().to(self.device)
def extract_embeddings_vgg(self, image_path):
try:
img = Image.open(image_path)
img = img.convert('RGB')
# Detect faces
boxes, _ = self.mtcnn.detect(img)
if boxes is None:
logger.warning(f"No face detected in image: {image_path}")
return None
# Get the largest face
largest_box = max(boxes, key=lambda box: (box[2] - box[0]) * (box[3] - box[1]))
face = self.mtcnn(img, return_prob=False)
if face is None:
logger.warning(f"Failed to align face in image: {image_path}")
return None
# Extract embeddings
embeddings = self.resnet(face).detach().cpu().numpy().flatten()
return embeddings.tolist()
except Exception as e:
logger.error(f"An error occurred while extracting embeddings: {e}")
return None |