Update app.py

app.py

@@ -2,6 +2,7 @@ import cv2
 from transformers import ViTImageProcessor, ViTForImageClassification, AutoModelForImageClassification, AutoImageProcessor
 import torch 
 import numpy as np
+import face_recognition
 
 torch.backends.cudnn.benchmark = True
 
@@ -144,18 +145,21 @@ def postProcessed( rawfaces, maximunSize, minSize = 30 ):
             faces.append( (x, y, w, h) )
     return faces
 def image_inference(image):
+
     
     if sum(image.shape) == 0:
         return { 'ErrorFound': 'ImageNotFound' }
     # Convert into grayscale
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     # Detect faces
-    rawfaces = face_cascade.detectMultiScale(gray, 1.05, 5, minSize = (30, 30))
-    image = np.asarray( image )
+    # rawfaces = face_cascade.detectMultiScale(gray, 1.05, 5, minSize = (30, 30))
+    # image = np.asarray( image )
     # Draw rectangle around the faces
-    rawfaces = postProcessed( rawfaces, image.shape[:2] )
-            
-    faces = [ image[x:w+x, y:h+y].copy() for (x, y, w, h) in rawfaces ]
+    # rawfaces = postProcessed( rawfaces, image.shape[:2] )
+    
+    rawfaces = face_recognition.face_locations( image , model="cnn")
+    faces = [ image[top:bottom, left:right].copy() for (top, left, bottom, right) in rawfaces ]
+    # faces = [ image[x:w+x, y:h+y].copy() for (x, y, w, h) in rawfaces ]
     faces = [ Image.fromarray(x, mode = 'RGB') for x in faces ]
     ages, genders, beards, blurs, ethncities, masks = AnalysisFeatures( faces )
 
@@ -174,23 +178,26 @@ def video_inference(video_path):
     while(cap.isOpened()):
         _, img = cap.read()
         
-        try:
+        # try:
         # Convert into grayscale
-            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        # except:
+            # break
+        # Detect faces
+        # rawfaces = face_cascade.detectMultiScale(gray, 1.05, 6, minSize = (30, 30))
+        try:
+            image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            image = np.asarray( image )
         except:
             break
-        # Detect faces
-        rawfaces = face_cascade.detectMultiScale(gray, 1.05, 6, minSize = (30, 30))
-        image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        image = np.asarray( image )
-        
-        rawfaces = postProcessed( rawfaces, image.shape[:2] )
-        
+        # rawfaces = postProcessed( rawfaces, image.shape[:2] )
+        rawfaces = face_recognition.face_locations( image , model="cnn")
         # 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 )
-        for (x, y, w, h) in rawfaces:
-            face = image[x:w+x, y:h+y].copy()
+        for (top, left, bottom, right) in rawfaces:
+            # face = image[x:w+x, y:h+y].copy()
+            face = image[top:bottom, left:right].copy()
             global_faces.append(Image.fromarray( face , mode = 'RGB') ) 
     
     ages, genders, beards, blurs, ethncities, masks = AnalysisFeatures( global_faces )