Update app.py

app.py

@@ -59,35 +59,7 @@ def getTranscription(path):
     return transcription[0]
 
 def process_frame(frame):
-    # 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.
-        face_locations = face_recognition.face_locations(gray)
-        #face_locations = face_recognition.face_locations(gray, number_of_times_to_upsample=0, model="cnn")
-
-        if len(face_locations) > 0:
-            # Show the original frame with face rectangles drawn around the faces
-            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
-                    emotion_count += 1
-                    print(emotion)
-                except Exception as e:
-                    emotion = 0
-                    pass
-
-                # Predict the class of the resized face image using the model
-                result = model.predict(resized_face_image)
-                print(result[0])
-                return result[0], emotion
+    
 
 
 def video_processing(video_file, encoded_video):
@@ -134,26 +106,47 @@ def video_processing(video_file, encoded_video):
         if not ret:
             break
 
+        # 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.
+        face_locations = face_recognition.face_locations(gray)
+        #face_locations = face_recognition.face_locations(gray, number_of_times_to_upsample=0, model="cnn")
+
+        if len(face_locations) > 0:
+            # Show the original frame with face rectangles drawn around the faces
+            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]
 
-        result, emotion = process_frame(frame)
-        print(emotion)
-        if result:
-            if result == 'on_camera':
-                on_camera += 1
-            elif result == 'off_camera':
-                off_camera += 1
-            total += 1
-
-        if emotion != 0:
-            # print(emotion[0]['emotion'])
-            
-            angry += emotion[0]['emotion']['angry']
-            disgust += emotion[0]['emotion']['disgust']
-            fear += emotion[0]['emotion']['fear']
-            happy += emotion[0]['emotion']['happy']
-            sad += emotion[0]['emotion']['sad']
-            surprise += emotion[0]['emotion']['surprise']
-            neutral += emotion[0]['emotion']['neutral']
+                # 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
+                    emotion_count += 1
+                    print(emotion)
+                except Exception as e:
+                    emotion = 0
+                    pass
+
+                # Predict the class of the resized face image using the model
+                result = model.predict(resized_face_image)
+                print(result[0])
+                if result[0] == 'on_camera':
+                    on_camera += 1
+                elif result[0] == 'off_camera':
+                    off_camera += 1
+                total += 1
+                
+                angry += emotion[0]['emotion']['angry']
+                disgust += emotion[0]['emotion']['disgust']
+                fear += emotion[0]['emotion']['fear']
+                happy += emotion[0]['emotion']['happy']
+                sad += emotion[0]['emotion']['sad']
+                surprise += emotion[0]['emotion']['surprise']
+                neutral += emotion[0]['emotion']['neutral']
 
     try:
         # your processing code here