removed l2 norm

app.py

@@ -26,40 +26,67 @@ def main():
     video_folders = setup()
     for folder_info in video_folders:
         foldername, video_path, image_paths = folder_info
-        print(1)
         run(image_paths, foldername, video_path)
     saveToJson()
 
 def processImage(image, idx, foldername, video_path):
     try:
 
+        # Initialize variables to accumulate sums
+        sums = {
+            'angry': 0,
+            'disgust': 0,
+            'fear': 0,
+            'happy': 0,
+            'sad': 0,
+            'surprise': 0,
+            'neutral': 0
+        }
+
         anaylse_obj = DeepFace.analyze(
             img_path=image,
             actions=['emotion'],
             enforce_detection=True,
-            detector_backend=backends[0]
+            detector_backend=backends[3]
         )
 
+        print(anaylse_obj)
+
         emotions = anaylse_obj[0]['emotion']
 
-        raw_emotions = [value for value in emotions.values()]  
+        num_of_faces = len(anaylse_obj)
+
+        # Get average emotion
+        for face in anaylse_obj:
+            emotions = face['emotion']
+            for emotion in sums:
+                sums[emotion] += emotions.get(emotion, 0)
+
+        averages = {emotion: sums[emotion] / num_of_faces for emotion in sums}
+
+        print(f"averages: {averages}")
+
+        raw_emotions = [value for value in averages.values()]  
+
+        normalized_emotions = [value / 100 for value in raw_emotions]
 
-        normalised_emotions = l2_normalize(np.array(raw_emotions))
+        #normalised_emotions = l2_normalize(np.array(raw_emotions))
 
-        normalised_emotions = normalised_emotions.tolist()
+        # normalised_emotions = normalised_emotions.tolist()
 
         entry = {
             "folder": foldername,
             "frame": image,  # Extract the filename from the path
             "video": video_path,
-            "vector": normalised_emotions
+            "faces": num_of_faces,
+            "vector": normalized_emotions
         }
         
         # Add the entry to the JSON data list
         embeddings.append(entry)
     except:
         pass
-        print("Could not locate face in image. Skipping.")
+        #print("Could not locate face in image. Skipping.")
 
 
 def setup():