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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()
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