Update services/facial_processing.py

services/facial_processing.py

@@ -2,7 +2,6 @@ import numpy as np
 import os
 import torch
 from facenet_pytorch import MTCNN, InceptionResnetV1
-import cv2
 import logging
 
 logger = logging.getLogger(__name__)
@@ -10,8 +9,6 @@ logger = logging.getLogger(__name__)
 
 class FacialProcessing:
     def __init__(self):
-        self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-        self.model = cv2.dnn.readNetFromTorch('openface.nn4.small2.v1.t7')
         # Set the cache directory to a writable location
         os.environ['TORCH_HOME'] = '/tmp/.cache/torch'
 
@@ -21,33 +18,6 @@ class FacialProcessing:
         self.resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
 
 
-    def extract_embeddings(self, image_path):
-        try:
-            image = cv2.imread(image_path)
-            if image is None:
-                logger.error(f"Failed to load image: {image_path}")
-                return None
-            
-            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-            faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)
-            
-            if len(faces) == 0:
-                logger.warning(f"No face detected in image: {image_path}")
-                return None
-            
-            (x, y, w, h) = faces[0]
-            face = image[y:y+h, x:x+w]
-            
-            faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255, (96, 96), (0, 0, 0), swapRB=True, crop=False)
-            self.model.setInput(faceBlob)
-            vec = self.model.forward()
-            
-            return vec.flatten().tolist()
-        except Exception as e:
-            logger.error(f"An error occurred while extracting embeddings: {e}")
-            return None
-
-
     def extract_embeddings_vgg(self, image):
         try:
             # Preprocess the image