Update app.py

app.py

@@ -82,6 +82,7 @@ confidence_threshold = st.sidebar.slider("Confidence threshold", 0.1, 1.0, 0.4)
 min_box_area         = st.sidebar.number_input("Min box area (px²)", min_value=0, value=1600, step=100)
 min_track_hits       = st.sidebar.number_input("Min track hits (YOLO only)", min_value=1, value=5, step=1)
 shape_refine         = st.sidebar.checkbox("Reclassify tall 'car' as person", value=True)
+frame_skip = st.sidebar.slider("Process every N frame", 1, 10, 2)
 person_car_ratio     = st.sidebar.slider("Person/Car ratio (H/W)", 0.8, 2.0, 1.2, 0.05)
 
 if not selected_classes:
@@ -182,7 +183,11 @@ if uploaded_file is not None:
                 ret, frame = cap.read()
                 if not ret:
                     break
-
+                    
+                if frame_idx % frame_skip !=0:
+                    frame_idx += 1
+                    continue
+                
                 timestamp = round(frame_idx / fps, 3)
                 results   = model.track(
                     frame, persist=True, tracker="bytetrack.yaml",
@@ -312,6 +317,11 @@ if uploaded_file is not None:
                 if not ret:
                     break
 
+                if frame_idx % frame_skip != 0:
+                    frame_idx +=1
+                    continue
+
+                
                 timestamp = round(frame_idx / fps, 3)
                 img_rgb   = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
                 tensor    = TF.to_tensor(img_rgb).unsqueeze(0).to(device)