Update app.py

app.py

@@ -3,6 +3,7 @@ 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"
@@ -14,13 +15,7 @@ TRUCK_CLASS_ID = 7  # "truck"
 # Initialize SORT tracker
 tracker = Sort()
 
-# Minimum confidence threshold for detection
-CONFIDENCE_THRESHOLD = 0.5
-
-# Distance threshold to avoid duplicate counts
-DISTANCE_THRESHOLD = 50
-
-def count_unique_trucks(video_path):
+def count_unique_trucks(video_path, video_type, confidence_threshold, distance_threshold, frame_skip_seconds):
     cap = cv2.VideoCapture(video_path)
     if not cap.isOpened():
         return "Error: Unable to open video file."
@@ -30,7 +25,7 @@ def count_unique_trucks(video_path):
 
     # Get FPS of the video
     fps = int(cap.get(cv2.CAP_PROP_FPS))
-    frame_skip = fps * 2  # Skip frames every 5 seconds
+    frame_skip = max(1, int(fps * frame_skip_seconds))  # Dynamic frame skipping
 
     frame_count = 0
 
@@ -41,7 +36,7 @@ def count_unique_trucks(video_path):
 
         frame_count += 1
         if frame_count % frame_skip != 0:
-            continue  # Skip frames to process only every 5 seconds
+            continue  # Skip frames dynamically
 
         # Run YOLOv12x inference
         results = model(frame, verbose=False)
@@ -53,7 +48,7 @@ def count_unique_trucks(video_path):
                 confidence = float(box.conf.item())  # Get confidence score
 
                 # Track only trucks
-                if class_id == TRUCK_CLASS_ID and confidence > CONFIDENCE_THRESHOLD:
+                if class_id == TRUCK_CLASS_ID and confidence > confidence_threshold:
                     x1, y1, x2, y2 = map(int, box.xyxy[0])  # Get bounding box
                     detections.append([x1, y1, x2, y2, confidence])
 
@@ -72,7 +67,7 @@ def count_unique_trucks(video_path):
                     last_position = truck_history[truck_id]["position"]
                     distance = np.linalg.norm(np.array(truck_center) - np.array(last_position))
 
-                    if distance > DISTANCE_THRESHOLD:
+                    if distance > distance_threshold:
                         # If the truck moved significantly, count as new
                         unique_truck_ids.add(truck_id)
 
@@ -85,24 +80,27 @@ def count_unique_trucks(video_path):
                     unique_truck_ids.add(truck_id)
 
     cap.release()
-
     return {"Total Unique Trucks": len(unique_truck_ids)}
 
 # Gradio UI function
-def analyze_video(video_file):
-    result = count_unique_trucks(video_file)
-    return "\n".join([f"{key}: {value}" for key, value in result.items()])
+def analyze_video(video_file, video_type, confidence, distance, frame_skip):
+    return count_unique_trucks(video_file, video_type, confidence, distance, frame_skip)
 
 # Define Gradio interface
-import gradio as gr
 iface = gr.Interface(
     fn=analyze_video,
-    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."
+    inputs=[
+        gr.Video(label="Upload Video"),
+        gr.Radio(["drone", "fixed"], label="Video Type"),
+        gr.Slider(0.3, 0.9, 0.5, label="Confidence Threshold"),
+        gr.Slider(10, 100, 50, label="Distance Threshold"),
+        gr.Slider(1, 10, 2, label="Frame Skip (Seconds)"),
+    ],
+    outputs=gr.JSON(label="Analysis Result"),
+    title="YOLOv12x Dynamic Truck Counter",
+    description="Upload a video, adjust parameters, and analyze unique trucks."
 )
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()