Update face_analysis.py

face_analysis.py

@@ -24,7 +24,7 @@ def cluster_faces(embeddings):
         return np.zeros(len(embeddings), dtype=int)
 
     X = np.stack(embeddings)
-    dbscan = DBSCAN(eps=0.2, min_samples=5, metric='cosine')
+    dbscan = DBSCAN(eps=0.3, min_samples=5, metric='cosine')
     clusters = dbscan.fit_predict(X)
 
     if np.all(clusters == -1):
@@ -40,29 +40,3 @@ def organize_faces_by_person(embeddings_by_frame, clusters, aligned_faces_folder
         src = os.path.join(aligned_faces_folder, f"frame_{frame_num}_face.jpg")
         dst = os.path.join(person_folder, f"frame_{frame_num}_face.jpg")
         shutil.copy(src, dst)
-
-
-def draw_facial_landmarks(image, landmarks):
-    mp_face_mesh = mp.solutions.face_mesh
-    mp_drawing = mp.solutions.drawing_utils
-    
-    # Adjust these values to make the drawing thinner and less obtrusive
-    connection_drawing_spec = mp_drawing.DrawingSpec(color=(255, 0, 0), thickness=1)
-    
-    if landmarks:
-        # Create a blank image with the same dimensions as the input image
-        connections_layer = np.zeros_like(image)
-        
-        # Draw connections on the blank image
-        mp_drawing.draw_landmarks(
-            image=connections_layer,
-            landmark_list=landmarks,
-            connections=mp_face_mesh.FACEMESH_TESSELATION,
-            landmark_drawing_spec=None,  # Set to None to avoid drawing points
-            connection_drawing_spec=connection_drawing_spec)
-        
-        # Blend the connections layer with the original image
-        alpha = 0.25
-        image = cv2.addWeighted(image, 1, connections_layer, alpha, 0)
-    
-    return image