Update app.py

app.py

@@ -3,7 +3,6 @@ import numpy as np
 import torch
 from ultralytics import YOLO
 from sort import Sort
-import gradio as gr
 
 # Load YOLOv12x model
 MODEL_PATH = "yolov12x.pt"
@@ -21,7 +20,7 @@ CONFIDENCE_THRESHOLD = 0.5
 # Distance threshold to avoid duplicate counts
 DISTANCE_THRESHOLD = 50
 
-def count_unique_trucks(video_path, frame_skip_factor):
+def count_unique_trucks(video_path):
     cap = cv2.VideoCapture(video_path)
     if not cap.isOpened():
         return "Error: Unable to open video file."
@@ -31,7 +30,7 @@ def count_unique_trucks(video_path, frame_skip_factor):
 
     # Get FPS of the video
     fps = int(cap.get(cv2.CAP_PROP_FPS))
-    frame_skip = fps * frame_skip_factor  # Skip frames dynamically based on user input
+    frame_skip = fps * 7  # Skip frames every 5 seconds
 
     frame_count = 0
 
@@ -42,7 +41,7 @@ def count_unique_trucks(video_path, frame_skip_factor):
 
         frame_count += 1
         if frame_count % frame_skip != 0:
-            continue  # Skip frames based on user-defined factor
+            continue  # Skip frames to process only every 5 seconds
 
         # Run YOLOv12x inference
         results = model(frame, verbose=False)
@@ -58,7 +57,7 @@ def count_unique_trucks(video_path, frame_skip_factor):
                     x1, y1, x2, y2 = map(int, box.xyxy[0])  # Get bounding box
                     detections.append([x1, y1, x2, y2, confidence])
 
-        if detections:
+        if len(detections) > 0:
             detections = np.array(detections)
             tracked_objects = tracker.update(detections)
 
@@ -86,25 +85,25 @@ def count_unique_trucks(video_path, frame_skip_factor):
                     unique_truck_ids.add(truck_id)
 
     cap.release()
+
     return {"Total Unique Trucks": len(unique_truck_ids)}
 
 # Gradio UI function
-def analyze_video(video_file, frame_skip_factor):
-    result = count_unique_trucks(video_file, frame_skip_factor)
+def analyze_video(video_file):
+    result = count_unique_trucks(video_file)
     return "\n".join([f"{key}: {value}" for key, value in result.items()])
 
 # Define Gradio interface
+import gradio as gr
 iface = gr.Interface(
     fn=analyze_video,
-    inputs=[
-        gr.Video(label="Upload Video"),
-        gr.Slider(minimum=1, maximum=10, step=1, value=2, label="Frame Skip Factor")  # Fixed default issue
-    ],
+    inputs=gr.Video(label="Upload Video"),
     outputs=gr.Textbox(label="Analysis Result"),
     title="YOLOv12x Unique Truck Counter",
-    description="Upload a video to count unique trucks using YOLOv12x and SORT tracking. Adjust the frame skip factor to control processing speed."
+    description="Upload a video to count unique trucks using YOLOv12x and SORT tracking."
 )
 
 # Launch the Gradio app
 if __name__ == "__main__":
     iface.launch()
+