import cv2 import numpy as np import torch import gradio as gr from ultralytics import YOLO from sort import Sort # Load YOLOv12x model MODEL_PATH = "yolov12x.pt" model = YOLO(MODEL_PATH) # COCO dataset class ID for truck TRUCK_CLASS_ID = 7 # "truck" # Initialize SORT tracker tracker = Sort() def count_unique_trucks(video_path): cap = cv2.VideoCapture(video_path) if not cap.isOpened(): return "Error: Unable to open video file." unique_truck_ids = set() frame_skip = 5 # Process every 5th frame for efficiency frame_count = 0 while True: ret, frame = cap.read() if not ret: break # End of video frame_count += 1 if frame_count % frame_skip != 0: continue # Skip frames to improve efficiency # Run YOLOv12x inference results = model(frame, verbose=False) detections = [] for result in results: for box in result.boxes: class_id = int(box.cls.item()) # Get class ID confidence = float(box.conf.item()) # Get confidence score # Track only trucks if class_id == TRUCK_CLASS_ID and confidence > 0.5: x1, y1, x2, y2 = map(int, box.xyxy[0]) # Get bounding box detections.append([x1, y1, x2, y2, confidence]) if len(detections) > 0: detections = np.array(detections) tracked_objects = tracker.update(detections) for obj in tracked_objects: truck_id = int(obj[4]) # Unique ID assigned by SORT 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()]) # Define Gradio interface 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." ) # Launch the Gradio app if __name__ == "__main__": iface.launch()