Update app.py

app.py

@@ -6,15 +6,32 @@ from fastai.vision.all import load_learner
 import time
 import chardet
 import base64
+from deepface import DeepFace
 
 # import pathlib
 # temp = pathlib.PosixPath
 # pathlib.PosixPath = pathlib.WindowsPath
 
+backends = [
+  'opencv', 
+  'ssd', 
+  'dlib', 
+  'mtcnn', 
+  'retinaface', 
+  'mediapipe'
+]
 
 model = load_learner("gaze-recognizer-v3.pkl")
 
 def video_processing(video_file, encoded_video):
+    angry = 0
+    disgust = 0
+    fear = 0
+    happy = 0
+    sad = 0
+    surprise = 0
+    neutral = 0
+    emotion_count = 0
 
     if encoded_video != "":
     
@@ -45,6 +62,8 @@ def video_processing(video_file, encoded_video):
         
         # Convert the frame to RGB color (face_recognition uses RGB)
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        
         
 
         # Find all the faces in the frame using a pre-trained convolutional neural network.
@@ -56,10 +75,17 @@ def video_processing(video_file, encoded_video):
             for top, right, bottom, left in face_locations:
                 # cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
                 face_image = gray[top:bottom, left:right]
+                color_image = frame[top:bottom, left:right]
 
                 # Resize the face image to the desired size
                 resized_face_image = cv2.resize(face_image, (128,128))
 
+                try:
+                emotion = DeepFace.analyze(color_image,actions=['emotion'],detector_backend = backends[2],enforce_detection = False)# 2,3, 4 works
+                    total_emotion += 1
+                except Exception as e:
+                    pass
+
                 # Predict the class of the resized face image using the model
                 result = model.predict(resized_face_image)
                 print(result[0])
@@ -83,7 +109,30 @@ def video_processing(video_file, encoded_video):
     if os.path.exists("temp_video.mp4"): 
         os.remove("temp_video.mp4")
     print(gaze_percentage)
-    return str(gaze_percentage)
+
+    angry = angry / emotion_count
+    disgust = disgust / emotion_count
+    fear = fear / emotion_count
+    happy = happy / emotion_count
+    sad = sad / emotion_count
+    surprise = surprise / emotion_count
+    neutral = neutral / emotion_count
+    
+    angry = 'total anger percentage' + angry 
+    disgust = 'total disgust percentage' + disgust 
+    fear = 'total fear percentage' + fear 
+    happy = 'total happy percentage' + happy 
+    sad = 'total sad percentage' + sad 
+    surprise = 'total surprise percentage' + surprise
+    neutral = 'total neutral percentage' + neutral 
+    print(f'total anger percentage = {angry}')
+    print(f'total disgust percentage = {disgust}')
+    print(f'total fear percentage = {fear}')
+    print(f'total happy percentage = {happy}')
+    print(f'total sad percentage = {sad}')
+    print(f'total surprise percentage = {surprise}')
+    print(f'total neutral percentage = {neutral}')
+    return str(gaze_percentage,angry,disgust,fear,happy,sad,surprise,neutral)
 
 
 demo = gr.Interface(fn=video_processing,