Update app.py

app.py

@@ -3,6 +3,7 @@ from transformers import ViTImageProcessor, ViTForImageClassification, AutoModel
 import torch 
 import numpy as np
 import face_recognition
+from retinaface import RetinaFace
 
 torch.backends.cudnn.benchmark = True
 
@@ -157,7 +158,10 @@ def image_inference(image):
     # Draw rectangle around the faces
     # rawfaces = postProcessed( rawfaces, image.shape[:2] )
     
-    rawfaces = face_recognition.face_locations( image, number_of_times_to_upsample = 1 , model="hog")
+    # rawfaces = face_recognition.face_locations( image, number_of_times_to_upsample = 1 , model="hog")
+    rawfaces = []
+    for name, keys in RetinaFace.detect_faces( image ).items():
+        rawfaces.append( keys['facial_area']  )
     faces = [ image[top:bottom, left:right].copy() for (top, left, bottom, right) in rawfaces ]
     faces_mean = [ x.mean() for x in faces ]
     # faces = [ image[x:w+x, y:h+y].copy() for (x, y, w, h) in rawfaces ]
@@ -192,7 +196,11 @@ def video_inference(video_path):
         except:
             break
         # rawfaces = postProcessed( rawfaces, image.shape[:2] )
-        rawfaces = face_recognition.face_locations( image, number_of_times_to_upsample = 1 , model="hog")
+        rawfaces = []
+        for name, keys in RetinaFace.detect_faces( image ).items():
+            rawfaces.append( keys['facial_area']  )
+        
+        # rawfaces = face_recognition.face_locations( image, number_of_times_to_upsample = 1 , model="hog")
         # Draw rectangle around the faces
         # https://stackoverflow.com/questions/15589517/how-to-crop-an-image-in-opencv-using-python for fliping axis 
         global_facesCo.append( rawfaces )